T a O L I U Tao.Liu@Umaryland.Edu PROFESSIONAL GOAL Utilize My

Learn and Apply Paradigm to Optimize Pharmacotherapy in Neonatal Abstinence Syndrome Using Pharmacometrics

Item Type dissertation

Authors Liu, Tao

Publication Date 2017

Abstract Every one hour a baby with neonatal abstinence syndrome (NAS) is born in the United States. NAS is a clinical syndrome of opiate withdrawal in infants exposed to drugs either prenatally in the form of maternal use (non-iatrogenic), or postnatally in ...

Keywords dosing optimization; modified Finnegan score; Morphine-- therapeutic use; Neonatal Abstinence Syndrome--drug therapy

Download date 04/10/2021 08:18:25

Link to Item http://hdl.handle.net/10713/7395 T A O L I U [email protected]

PROFESSIONAL GOAL

Utilize my knowledge and skills to improve drug development and clinical therapeutics.

PROFESSIONAL EXPERIENCE

06/2016 – Summer Intern

08/2016 DMPK, Novartis Institute of Biomedical Research Supervisors: Drs. Handan He, Tycho Heimbach and Wen Lin Support study design and dose selection in both clinical and pre-clinical oncology drug development Evaluate physiological based pharmacokinetic model in hepatic and renal impairment patients Give weekly Phoenix WinNonlin/NLME training

02/2013 – ORISE Fellow

04/2015 Office of Clinical Pharmacology, Center for Drug Evaluation and Research, Food and Drug Administration Supervisors: Drs. Angela Men and Atul Bhattaram Extrapolation of Efficacy for the Treatment of Partial Seizures from Adults to Pediatrics in Adjunctive Therapy

04/2008 – Biostatistician and Pharmacokineticist

08/2012 Phase I Unit, Clinical Pharmacology Research Center, Peking Union Medical College Hospital Supervisors: Drs. Pei Hu and Ji Jiang Participate in more than 60 phase I clinical trials and take charge of pharmacokinetics and biostatistics analysis. Major responsibilities include: PK/PD Analysis: Compartmental and Non-Compartmental Analysis (WinNonlin) , Population PK / PD Research Statistical Analysis: Study Design, Sample Size and Power Calculation, Randomization, Bioequivalence, Dose Proportionality, etc. Management: Team leader in data management and biostatistics team, System Manager of Watson LIMS, System Manager of Promasys

EDUCATION

2012 – 2017 Doctor of Philosophy in Pharmaceutical Sciences

Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore Advisor: Jogarao V.S. Gobburu

2005 – 2009 Bachelor of Science in Information and Computing Science

Department of Mathematics, School of Science, Beijing Jiaotong University

SKILLS

Drug Extensive experience in design and analysis of Phase I clinical trials, including FIH, SAD/MAD, food effect, dose Development proportionality, bioequivalence Experience in Clinical Pharmacology, dose finding and & Regulatory registration trials Experience in working as a research fellow at the FDA to Experience perform systematic analysis to support the policy of full extrapolation of efficacy in children for the treatment of partial onset seizures

Technical- Advanced knowledge of pharmacokinetics and pharmacodynamics PKPD Extensive hands-on experience in modeling and simulation Experience in time-to-event and categorical data analysis

Technical- Extensive experience in Phoenix NLME, WinNonlin, NONMEM, Trial Simulator, SimCYP, PFIM Software Experience in Visual C++, Visual Basic, Matlab, Mathematica Extensive hands on experience with R and SAS statistical software Data Management Systems: Watson LIMS and Promasys

Soft Skills Proficiency in successful communication and negotiation with interdisciplinary team including clinicians, statisticians and clinical pharmacologists Demonstrated ability to work independently and as a part of team

Leadership skills: Initiated, managed and executed projects with regular interaction with the team members

HIGHLIGHTED PROJECTS

Dosing To improve clinical outcome in neonates with abstinence syndrome Optimization Developed population pharmacokinetic model in neonates after for oral administration of morphine solution Developed exposure response model between morphine Therapeutics exposure and modified Finnegan score Evaluated different dosing strategies in neonates with abstinence syndrome

Regulatory Extrapolate efficacy from adults to children(≥ 4 years) for anti-epileptic drugs (AEDs) Policy For a new AED approved in adults, FDA will waive efficacy Development trial in pediatric patients Conducted systematic analysis on the largest database including 26 registration trials of approved AEDs indicated for partial onset seizures. Conducted exposure-response analysis to demonstrate that pediatric patients need similar exposures as adults for efficacy. Collaborative project between UMB, US FDA and PEACE (Pediatric Epilepsy Academic Consortium for Extrapolation). Effectively communicated and negotiated with a cross disciplinary team of clinicians, clinical pharmacologists and statisticians from FDA, academia and industry, to influence decisions in a timely manner.

Pediatric Drug Evaluate Allometric Scaling Approach in Pediatrics

Development Systematically review the predictability of allometric scaling in pediatrics ≥2 years and support the application of allometric scaling in pediatric drug development

Drug-Drug Support waiving clinical DDI studies

Interaction Develop buprenorphine PBPK model after subcutaneous administration and predict DDI effect with strong CYP3A4 Prediction inhibitors and inducers.

Phase II dose Dose selection to balance clinical efficacy and safety selection Dose of 200mg BID in AML patients recommended in Phase II

Develop population PK/PD model for sorafenib in AML patients Simulate different dosing regimens to support dosing adjustment to maintain efficacy and improve safety

PUBLICATIONS

1. Benjamin Laliberte, Brent N. Reed, Sandeep Devabhakthuni, Kristin Watson, Vijay Ivaturi, Tao Liu, Stephen S. Gottlieb. Observation of Patients Transitioned to an Oral Loop Diuretic Prior to Discharge and Risk of Readmission for Acute Decompensated Heart Failure. Journal of cardiac failure 07/2017;, DOI:10.1016/j.cardfail.2017.06.008 2. Vijay Ivaturi, Christopher C. Dvorak, Danna Chan, Tao Liu, Morton J. Cowan, Justin Wahlstrom, Melisa Stricherz, Cathryn Jennissen, Paul J. Orchard, Jakub Tolar, Sung Yun Pai, Liusheng Huang, Francesca Aweeka, Janel Long-Boyle. Pharmacokietics and model-based dosing to optimize fludarabine therapy in pediatric hematopoietic cell transplant recipients. Biology of Blood and Marrow Transplantation 07/2017;, DOI:10.1016/j.bbmt.2017.06.021 3. Tao Liu, Aabha Jain, Michael Fung, Christopher Vinnard, Vijay Ivaturi. Valacyclovir as Initial Treatment for Acute Retinal Necrosis: A Pharmacokinetic Modeling and Simulation Study. Current Eye Research 03/2017(30)., DOI:10.1080/02713683.2016.1231323. 4. Tao Liu, Parima Ghafoori, Jogarao V.S. Gobburu: Allometry Is a Reasonable Choice in Pediatric Drug Development. The Journal of Clinical Pharmacology 09/2016; DOI:10.1002/jcph.831 5. Martin J. Edelman, Rena Lapidus, Josephine Feliciano, Miroslav Styblo, Jan H. Beumer, Tao Liu, Jogarao Gobburu: Phase I and pharmacokinetic evaluation of the anti-telomerase agent KML-001 with cisplatin in advanced solid tumors. Cancer Chemotherapy and Pharmacology 09/2016; 78(5)., DOI:10.1007/s00280- 016-3148-x

6. Xia Chen, Qian Zhao, Jianyan Zhang, Tao Liu, Ji Jiang, Pei Hu: An open-label, multiple-dose study to assess the pharmacokinetics and tolerability of sitagliptin/metformin fixed-dose combination (FDC) tablet in healthy Chinese adult subjects. International journal of clinical pharmacology and therapeutics 07/2016; 54(9)., DOI:10.5414/CP202646 7. Christine E. Shulenberger, Anthony Jiang, Sandeep Devabhakthuni, Vijay Ivaturi, Tao Liu, Brent N. Reed: Efficacy and Safety of Intravenous Chlorothiazide versus Oral Metolazone in Patients with Acute Decompensated Heart Failure and Loop Diuretic Resistance. Pharmacotherapy 07/2016;, DOI:10.1002/phar.1798 8. Hongzhong Liu, Hongyun Wang, Tao Liu, Ji Jiang, Xia Chen, Feng Gao, Pei Hu: Pharmacokinetic and Pharmacodynamic Properties of Cinacalcet (KRN1493) in Chinese Healthy Volunteers: A Randomized, Open-Label, Single Ascending–Dose and Multiple-Dose, Parallel-Group Study. Clinical Therapeutics 01/2016; DOI:10.1016/j.clinthera.2015.12.015 9. Tao Liu, Tamorah Lewis, Estelle Gauda, Jogarao Gobburu, Vijay Ivaturi: Mechanistic Population Pharmacokinetics of Morphine in Neonates with Abstinence Syndrome after Oral Administration of Diluted Tincture of Opium. The Journal of Clinical Pharmacology 12/2015; DOI:10.1002/jcph.696 10. Xin Zheng, Tao Liu, Xia Chen, Xiaoyan Chen, Ji Jiang, Pei Hu: Investigation of a potential pharmacokinetic interaction between perindopril arginine and amlodipine when administered as a single perindopril/amlodipine fixed-dose combination tablet in healthy Chinese male volunteers. International journal of clinical pharmacology and therapeutics 09/2015; 54(1). DOI:10.5414/CP202250 11. Hongzhong Liu, Ji Jiang, Hongyun Wang, Xia Chen, Tao Liu, Haijun Cao, Jonathan Palmer, Anita Gu, Pei Hu: A Single and Multiple Dose Study to Investigate the Pharmacokinetics of a Prolonged Release Formulation of Ropinirole in Healthy Chinese Subjects. Clinical Pharmacology in Drug Development 03/2014; 3(2). DOI:10.1002/cpdd.28 12. Xia Chen, Pei Hu, Ji Jiang, Tao Liu, Wen Zhong, Hongzhong Liu, Qian Zhao: Pharmacokinetic and Pharmacodynamic Profiles of a Fixed-Dose Combination of Olmesartan Medoxomil and Amlodipine in Healthy Chinese Males and Females. Clinical Drug Investigation 11/2012; 32(12). DOI: 10.1007/s40261-012-0026-0 13. Sheng Feng, Ji Jiang, Pei Hu, Jian-Yan Zhang, Tao Liu, Qian Zhao, Bi-Lu Li: A phase I study on pharmacokinetics and pharmacodynamics of higenamine in healthy Chinese subjects. Acta Pharmacologica Sinica 10/2012; 33(11). DOI:10.1038/aps.2012.114 14. Xia Chen, Ji Jiang, Tao Liu, Hongzhong Liu, Wen Zhong, Pei Hu: Pharmacokinetics of single and repeated oral doses prucalopride in healthy Chinese volunteers. International journal of clinical pharmacology and therapeutics 09/2012; 50(11):797-804. DOI: 10.5414/CP201769

SUBMITTED MANUSCRIPTS

1. Alyssa Mezochow, Kiran Thakur, Isaac Zentner, Tao Liu, Vijay Ivaturi, Christopher Vinnard. Attainment of Target Rifampin Concentrations in Cerebrospinal Fluid during Treatment of Tuberculosis Meningitis. (Submitted to Antimicrobial Agents and Chemotherapy) 2. Tao Liu and Jogarao V.S. Gobburu. A physiologically-based pharmacokinetic modeling approach to predict drug-drug interactions of buprenorphine after subcutaneous administration of CAM2038 with perpetrators of CYP3A4 (to be submitted to the Journal of Pharmaceutical Sciences) 3. Xin Zheng, Tao Liu, Ji Jiang and Pei Hu. Bioequivalence study of the fixed dose combination of perindopril and indapamide administered as two salts of perindopril after single dose oral administration in healthy Chinese male volunteers. (to be submitted to International journal of clinical pharmacology and therapeutics)

MANUSCRIPTS UNDER PREPARATION

1. Tao Liu, Vijay Ivaturi, Jogarao Gobburu, Tamorah Lewis, Jason Moore, Estelle Gauda, Walter Kraft. Dosing optimization for oral morphine therapy in neonatal abstinence syndrome (Part I): a mechanistic –based exposure-response relationship 2. Tao Liu, Vijay Ivaturi, Jogarao Gobburu, Tamorah Lewis, Jason Moore, Estelle Gauda, Walter Kraft. Dosing optimization for oral morphine therapy in neonatal abstinence syndrome (Part II): evaluation and optimization of different dosing strategies 3. Tao Liu, Jogarao Gobburu, Vijay Ivaturi. Integrating Current Understanding in Morphine Pharmacokinetics in Humans: A Pharmacokinetic Modeling Based Review of Morphine. (to be submitted to Clinical Pharmacokinetics) 4. Tao Liu, Tamorah Lewis, Jason Moore, Walter Kraft, Estelle Gauda, David Sartori, Jogarao Gobburu, Vijay Ivaturi. Uridine diphosphate glucuronic acid is a potential rate-limiting step in the metabolism of morphine during the first week of life. (to be submitted to Clinical Pharmacology and Therapeutics) 5. Tao Liu, Vijay Ivaturi, Philip Sabato, Jacqueline M. Greer, John J. Wright, B. Douglas Smith, Keith W. Pratz, Michelle A. Rudek. Pharmacometrics approach guided sorafenib dose recommendation in acute myeloid leukemia. (to be submitted to Clinical Cancer Research) 6. A. Men, S. Mehrotra, A. Bhattaram, K. Krudys, M. Bewernitz, R. Uppoor, M. Mehta, T. Liu, P. Sheridan, N. Hershkowitz, E. Bastings, B. Dunn. Full extrapolation of efficacy from adults to children of anti-epileptic drugs (AEDs) indicated for the treatment of partial onset seizures (POS): A Regulatory Perspective (to be submitted to Clinical Pharmacology & Therapeutics). 7. Tycho Heimbach, Wen Lin, Fan Wu, Jin Zhang, Tao Liu, Jing-He Yan, Kamal

Hamed, Stefania Beato, JP Jain, Ganga Sunkara, Heidi Einolf, Venkat Jarugula, Handan He. Pediatric PBPK Strategies & Tools During Drug Development (to be submitted to Journal of Pharmaceutical Sciences) 8. Tao Liu, Jogarao Gobburu, F. Randy Sallee, Angus Mclean and Bev Incledon. HLD200, a novel, delayed- and modified-release formulation of methylphenidate: the effect of food on its bioavailability and the determination of pharmacokinetic dose proportionality in healthy volunteers (to be submitted to Clinical Pharmacology & Therapeutics) 9. Qian Zhao, Ji Jiang, Tao Liu and Pei Hu. Single and multiple dose pharmacokinetics of a fixed-dose combination tablet of extended release niacin/laropiprant (MK-0524A) in healthy Chinese (to be submitted to Xenobiotica) 10. Xia Chen, Xin Zheng, Qian Zhao, Ji Jiang, Dalvin Ni, Tom Neff, Peony Yu, James Chou, Mitch Brenner, Frank Valone, Tao Liu, Wen Zhong, Pei Hu. Pharmacokinetics and pharmacodynamics of a single-dose and multiple-dose regimen of roxadustat (FG-4592), a novel hypoxia-inducible factor prolyl hydroxylase inhibitor, in Phase I, randomized, double-blinded, placebo-controlled studies in healthy, Chinese volunteers. 11. Hongzhong Liu, Tao Liu, Ji Jiang, Xia Chen, Pei Hu. Single and repeated dose pharmacokinetic profiles of roflumilast and roflumilast N-oxide in a healthy Chinese population

POSTERS AND ABSTRACTS

1. Martin J. Edelman, Rena Lapidus, Josephine Feliciano, Myroslav Styblo, Tao Liu, Joga Gobburu. Phase I and pharmacokinetic trial of the antitemolerase agent KML001 (KML) and cisplatin (CDDP) in advanced solid tumors. 2013 ASCO Annual Meeting J Clin Oncol 31, 2013 (suppl; abstr 2515). May 31st to Jun. 4th, 2013 2. Lewis, Tamorah; Liu, Tao; Gauda, Estelle; Ezell, Tarrah; Sartori, David; Ivaturi, Vijay. Population Pharmacokinetics of Enteral Morphine to Aid Dosing Stragegy in Neonatal Abstinence Syndrome. 2014 ASCPT Annual Meeting. Mar. 20th, 2014 3. Mathangi Gopalakrishnan, Tao Liu, Joga Gobburu, Mitchell David Creinin. Levonorgestrel Release Rates With LNG20, a New Levonorgestrel Intrauterine System 4. Tao Liu, Philip Sabato, Vijay Ivaturi, Jacqueline M. Greer, Keith W. Pratz, B. Douglas Smith, Michelle A. Rudek. Population Pharmacokinetic of Sorafenib in Acute Myelogenous/B-type Leukemia Patients. 2015 American Conference of Pharmacometrics. Oct. 6th, 2015 5. Anthony Jiang, Christine Puschak, Sandeep Devabhakthuni, Vijay Ivaturi, Tao Liu, Brent N. Reed Metolazone versus Chlorothiazide in Patients with Acute Decompensated Heart Failure and Loop Diuretic Resistance 2015 American Conference of Clinical Pharmacy 6. Tao Liu, Parima Ghafoori, Jogarao Gobburu. Allometry is an efficient approach

to project dosing for pediatric trials. 2016 ASCPT Annual Meeting Mar. 10th, 2016 7. Tao Liu, Philip Sabato, Vijay Ivaturi, Jacqueline M. Greer, Keith W. Pratz, B. Douglas Smith, Michelle A. Rudek. Population Pharmacokinetic and Pharmacodynamic of Sorafenib in Acute Myelogenous/B-type Leukemia Patients. 2016 American Conference of Pharmacometrics. Oct. 25th, 2016 8. Alyssa Mezochow, Tao Liu, Kiran Thakur, Vijay Ivaturi, Christopher Vinnard: Rifampin Target Attainment During Treatment of Tuberculosis Meningitis: The Role of Pharmacogenetics. Open Forum Infectious Diseases 12/2016; 3(suppl_1)., DOI:10.1093/ofid/ofw172.1539. IDWeek Oct. 27th, 2016 9. A. Men, S. Mehrotra, A. Bhattaram, K. Krudys, M. Bewernitz, R. Uppoor, M. Mehta, T. Liu, P. Sheridan, N. Hershkowitz, E. Bastings, B. Dunn. Full extrapolation of efficacy from adults to children of anti- epileptic drugs indicated for the treatment of partial onset seizures: A scientific and regulatory perspective. American Epilepsy Society, Annual Meeting 2016. 10. Tao Liu, Jogarao Gobburu, F. Randy Sallee, Angus Mclean and Bev Incledon. Dose Proportionality and Effect of Food on the Pharmacokinetics of HLD200, a Delayed-Release and Extended-Release Methylphenidate Formulation, in Healthy Adults. American Professional society of ADHD and Related Disorders Annual Meeting 2017 11. Benjamin Laliberte, Brent N. Reed, Sandeep Devabhakthuni, Kristin Watson, Vijay Ivaturi, Tao Liu, Stephen S. Gottlieb. Duration of Oral Loop Diuretics and 30-Day Readmission in Patients With Acute Decompensated Heart Failure. American College of Cardiology's 66th Annual Scientific Session. Mar 17th, 2017 12. Randy Sallee, Tao Liu, Jogarao Gobburu, F. Angus Mclean and Bev Incledon. Dose Proportionality and Effect of Food on the Pharmacokinetics of HLD200, a Delayed-Release and Extended-Release Methylphenidate Formulation, in Healthy Adults. 6th World Congress on ADHD 2017

HONOR

 FDA Leveraging and Collaboration Award received as a member of “Extrapolation of AED Efficacy Working Group” for exemplary collaborative review of research work to extrapolate the efficacy of anti-epilepsy drugs from adults to pediatrics to minimize unnecessary clinical trials in the pediatric population, September 2016.  Third Place in National Competition, CSEE (The Chinese Society for Electrical Engineering) Cup National Mathematical Contest in Modeling 2005  Second Place in National Competition, HEP (Higher Education Press) Cup National Mathematical Contest in Modeling 2007  Second Prize, BJTU Mathematical Contest in Modeling 2007  Third Prize (Successful Participation), COMAP (the Consortium for Mathematics and Its Applications):The Mathematical Contest in Modeling (MCM) 2007  Third Prize (Successful Participation), COMAP (the Consortium for Mathematics and Its Applications):The Mathematical Contest in Modeling (MCM) 2008

ORAL PRESENTATIONS

Feb. 10th, 2017 Topic: Pharmacometrics guided sorafenib dose recommendation in acute myeloid leukemia “Research in Progress” for the Division of Clinical Pharmacology/Department of Medicine at Johns Hopkins University Nov. 16th, 2016 Topic: Mechanistic Population Pharmacokinetics of Morphine in Neonates with Abstinence Syndrome after Oral Administration of Diluted Tincture of Opium ACCP Virtual Journal Club Nov. 6th, 2011 Topic: PK/PD study design in Phase I Research: Case studies. The 3rd International Symposium of Quantitative Pharmacology in Drug Development and Regulation

RELEVANT COURSEWORK

Basic, Intermediate, and Advanced Population PK/PD Modeling and Simulation, Pharmacology and Therapeutics, Statistics for Pharmacometrics I (continuous data), Statistics for Pharmacometrics II (categorical data), Communications & Negotiations, Pharmacometric decision making, R for pharmacometrics and Comparative effectiveness research & Pharmacoeconomics.

TEACHING EXPERIENCE

Teaching Assistant 2013-2015 Statistics for Pharmacometrics I and II 2014-2017 Dose-Response Trials 2014-2016 Intermediate PKPD 2012-2013 Pharmaceutics, Compounding and Abilities Lab 5 PK/PD Workshops Aug. 13th -14th, 2014 Population PK-PD and R workshop,

Hands on: population pharmacokinetic modeling Apr. 3rd -5th, 2016 Phoenix NLME workshop at Alnylam, Boston,

Course instructor for Phoenix NLME training for conducting population PK, PK/PD analysis and allometric scaling to Clinical Pharmacology group at Alnylam. Apr. 13th -16th, 2016 Phoenix NLME workshop at Pfizer, Lajolla,

Course instructor for Phoenix NLME training for conducting population PK, PK/PD analysis, time-to-event analysis and categorical data modeling to Clinical Pharmacology group at Pfizer. Phase I Clinical Trial Workshops Sep.13th, 2010 2010 Pharmacokinetics and Bioequivalence Research Training Course (Sponsored by State Food and Drug Administration, SFDA)

Topic: Calculation issue in pharmacokinetics research, as well as demonstration and evaluation of pharmacokinetics analysis software May 12th, 2010 2010 Advanced Training Course on Phase I Clinical Pharmacology Research

Topic: Statistical application and demonstration in Phase I Clinical Trials Apr. 23rd, 2011 2011 Advanced Training Course on Phase I Clinical Pharmacology Research

Topic 1: Statistical application and demonstration in Phase I Clinical Trials Topic 2: Introduction to Population Pharmacokinetics and Pharmacodynamics Aug 17th, 2011 Clinical Trial Design and Trial Simulator Workshop

Topic: Clinical Trial Simulation and Disease progression

SPECIALIZED TRAINING/CERTIFICATION

Population Pharmacokinetic and Pharmacodynamic Sep. 15th, 2009 Pharsight and Cloud Scientific Phoenix WinNonlin 6.0 Basic Pharmacokinetics Analysis Training Course Sep. 8th to 11th, 2010 NONMEM Intensive Training Course (Given by Nicholas H.G. Holford and Stephen B. Duffull) May 24th, 25th, 2010 The First Tri-iBiotech Pharmacokinetics, Pharmacodynamics Analysis and Software Application Training Course (Sponsored by Pharsight and Tri-iBiotech) Nov. 8th to 9th, 2010 First PopPK Software Phoenix NLME Training Course Aug. 17th, 2011 Clinical Trial Design and Trial Simulator Seminar Jun. 22nd to 23rd, 2015 Advanced PHX Modeling Workshop Baltimore, MD –by Serge Guzy August 2013 Advanced PHX Modeling Workshop Baltimore, MD –by Serge Guzy

Physiologically Based Pharmacokinetic Feb. 20th to 24th , 2012 SimCYP Hands-On Workshop on Model-Based Drug Development: Incorporating Population Variability into Mechanistic Prediction of PK and Modelling of PK-PD Mar. 17th to 20th, 2015 Simcyp Focused Workshop: DDI and Pediatrics Mar. 7th, 2016 A Systems Pharmacology Workshop by Bayer using PK- Sim® and MoBi® Mar. 14th to 17th, 2016 Simcyp Focused Workshop: Best Practice and Discovery

Abstract

Title of Dissertation: Learn and Apply Paradigm to Optimize Pharmacotherapy in

Neonatal Abstinence Syndrome Using Pharmacometrics

Tao Liu, Doctor of Philosophy, 2017

Dissertation Directed by: Dr. Jogarao Gobburu, Professor, Executive Director, Center for

Translational Medicine, Schools of Pharmacy & Medicine

Every one hour a baby with neonatal abstinence syndrome (NAS) is born in the United

States. NAS is a clinical syndrome of opiate withdrawal in infants exposed to drugs either prenatally in the form of maternal use (non-iatrogenic), or postnatally in the form of medical therapy (iatrogenic). The syndrome is comprised of a combination of central nervous system, digestive system and autonomic system abnormalities that result from uninhibited excitatory neurotransmitter release from the neurons. Between 2000 and

2009, a 3-fold increase in the use of opiate drugs among pregnant women led to an increase in NAS and associated higher health care costs. Currently, morphine is the first line pharmacotherapy for NAS. Pharmaceutical companies have no incentive to invest in therapy optimization for NAS, and the current dosing strategies vary from hospital to hospital. This research is based on a virtual consortium between the Center for

Translational Medicine at the University of Maryland Baltimore, Johns Hopkins Medical

Institute and Thomas Jefferson University Hospital. The purpose of this research is to optimize the morphine dosing strategy in NAS patients who require pharmacotherapy by using a pharmacometrics approach with the ‘learn and apply’ philosophy. First, a comprehensive morphine pharmacokinetic model that accounts for prognostic factors, such as body size and age, was developed in neonates. The results suggested a uridine

diphosphate glucuronic acid dependent morphine clearance during the first week of life.

Second, an exposure-response (ER) relationship between morphine plasma concentrations and modified Finnegan scores was built, and the model prediction was evaluated for the primary and secondary clinical outcomes, such time-on-treatment and total morphine dose. Lastly, different morphine dosing strategies were simulated based on the ER relationship and then optimized dosing strategies were proposed. The proposed dosing strategies will minimize suffering due to the withdrawal symptoms and ultimately lead to an earlier hospital discharge.

Learn and Apply Paradigm to Optimize Pharmacotherapy in Neonatal Abstinence Syndrome Using Pharmacometrics

by Tao Liu

Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, Baltimore in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2017

Acknowledgement

It has been a great experience for me during the past five years. I would like to take this opportunity to thank all those people who supported me, helped me and worked with me.

First of all, I would like to thank my advisor Dr. Jogarao Gobburu. You are a great mentor I have never met before. I feel very lucky to have you as my supervisor. Five years ago, Bob told me ‘My recommendation is to write to Joga

([email protected]) and ask. They have great projects at FDA and he is a very good teacher. Your career in that area will be set if you can do that.’ Now, I know he didn’t lie!

Thank you to Dr. Vijay Ivaturi. Thank you for helping me a lot on my research, thank you for all the discussions we had during the past five years, it is a great learning experience.

I would also like to thank my dissertation committee members: Dr. James Polli, Dr.

Hazem Hassan, Dr. Ivaturi Vijay, and Dr. Walter Kraft for their precious time and great feedback and recommendations to my research.

Thank you to Drs. Tamorah Lewis, Estelle Gauda, Jason Moore and Walter Kraft for conducting the clinical trials in neonatal abstinence syndrome, sharing the data with me, helping me understand the therapy for NAS, and providing constructive feedback to our research!

Thank you to all our current and previous partners at Center for Translational Medicine,

University of Maryland Baltimore, including Mathangi, Jeff, Shailly, Mukul, Devin, Jing,

iii

Eliford, Elyes, Hechuan, Shamir, Neha and Betrize. I spent a happy and memorable life with you guys for five years in Baltimore!

Thank you, Drs. Pei Hu and Robert Powell. Thank you for introducing me to this wonderful world of pharmacometrics, otherwise I would have never imagined I can work in the pharmaceutical industry as a math undergraduate.

In the end, I would like to thank my dad, Chengshun Liu, and my mom, Lianping Yu.

The Master said, "While his parents are alive, the son may not go abroad to a distance. If he does go abroad, he must have a fixed place to which he goes." (Confucian Analects)

Thank you for your love, understanding and support.

Table of Contents

Acknowledgement ...... iii

List of Tables ...... vi

List of Figures ...... viii

List of Abbreviations ...... xiii

Chapter 1 Introduction ...... 1

Chapter 2 Integrated model to describe morphine pharmacokinetics in humans ...... 14

Chapter 3 Allometry is a reasonable choice in pediatric drug development ...... 47

Chapter 4 Mechanistic population pharmacokinetics of morphine in neonates with abstinence syndrome after oral administration of diluted tincture of opium ...... 66

Chapter 5 Uridine diphosphate glucuronic acid is a potential rate-limiting step in the metabolism of morphine during the first week of life ...... 92

Chapter 6 Mechanistic–based exposure-response relationship in neonatal abstinence syndrome………………………………………………………………………………..113

Chapter 7 Dosing optimization for oral morphine therapy in neonatal abstinence syndrome………………………………………………………………………………..160

References ...... 184

List of Tables

Table 1-1 Projected benefit (patients with effectiveness failures) and risk of nephrotoxicity for CsA tapering dosing ...... 5

Table 1-2 Examples of current morphine dosing strategies in clinical practice ...... 9

Table 2-1 Parameter estimates in morphine parent metabolite model ...... 23

Table 3-S1 Detailed Pediatric Clearance Extrapolation Information……………………62

Table 4-1 Summary of Previously Published Morphine IV Models…………………….72

Table 4-2 Population PK parameter estimates for the final model and corresponding bootstrap estimates ...... 77

Table 5-1 Patient characteristics at baseline in each clinical trial ...... 102

Table 5-2 Final parameter estimates ...... 106

Table 6-1 Summary of study drug formulation and dosing strategies ...... 118

Table 6-2 Demographics and Baseline characteristic ...... 119

Table 6-3 Model Parameter Estimates ...... 128

Table 6-4 Body weight model parameter estimates ...... 129

Table 6-5 Comparison between the observed and model predicted median time-on- treatment (median (95% confidence interval)) ...... 133

Table 6-6 Comparison between the observed and model predicted time-on-treatment, total morphine dose, time to the max dose and time of MFS > 8 in the three arms.(Data are presented as median (95 percentile interval)) ...... 135

Table 6-S1 Modified Finnegan Score………………………………………………….140

Table 7-1 Relationship between morphine dose and inhibition effect ...... 164

Table 7-2 Dosing strategy 1 ...... 165

Table 7-3 Dosing strategy 2 ...... 166

Table 7-4 Dosing scenarios 1: Strategy 1 with 4 hours dosing interval ...... 167

Table 7-5 Scenario 2: Strategy 1 with body weight normalized dose and 4 hour dosing168

Table 7-6 Scenarios 3: Strategy 2 with fixed dose ...... 169

Table 7-7 Dosing Scenarios 4 to 9: Strategy 1 with optimized titration dose ...... 171

Table 7-8 Dosing Scenarios 10 to 13: Strategy 1 with optimized titration dose ...... 172

Table 7-9 Simulation results for Strategy 1 and Scenario 1 ...... 174

Table 7-10 Simulation results for Scenario 1 and 2 ...... 175

Table 7-11 Simulation results for Strategy 2 and Scenario 3 ...... 176

Table 7-12 Simulation results for Strategy 1 and Scenario 4 to 9 ...... 178

Table 7-13 Simulation results for Strategy 2 and Scenario 10 to 12 ...... 180

vii

List of Figures

Figure 1-1 (a) Cyclosporine–probability of failure (i.e., organ rejection, death, etc) and change in creatinine clearance relationships at various everolimus concentrations. (b)

Exposure–response relationships for the azathioprine arm...... 4

Figure 2-1 Structural PK model of morphine and it two major metabolites M3G and M6G after intravenous and oral administration of morphine...... 19

Figure 2-2 Summary of morphine disposition in human ...... 25

Figure 2-S1 Observed and model predicted plasma concentration of M3G after IV bolus of M3G…………………………………………………………………………………. 32

Figure 2-S2 Observed and model predicted plasma concentration of M6G after IV bolus plus IV infusion of M6G…………………………………………………………………33

Figure 2-S3 Observed and model predicted plasma concentration of morphine and M6G after IV bolus plus IV infusion of morphine…………………………………………….34

Figure 2-S4 Observed and model predicted plasma concentration of morphine, M3G and

M6G after IV bolus of morphine………………………………………………………...35

Figure 2-S5 Observed and model predicted plasma concentration of morphine and morphine-glucuronides (M3G + M6G) and the amount recovered in urine of morphine and morphine-glucuronides (M3G + M6G) after IV administration of morphine………36

Figure 2-S6 Observed and model predicted plasma concentration of morphine and morphine-glucuronides (M3G + M6G) and amount recovered in urine of morphine and morphine-glucuronides (M3G + M6G) after oral administration of morphine…………37

Figure 2-S7 Observed and model predicted plasma concentration of morphine, M3G and

M6G after oral administration of morphine……………………………………………..38

viii

Figure 3-1 Pediatric PK Extrapolation Strategy ...... 50

Figure 3-2 Evaluation of allometric scaling predicted pediatric clearance a) by administration routes b) by elimination routes c) by age groups ...... 56

Figure 3-3 Evaluation of allometric scaling predicted pediatric clearance versus bodyweight and comparison with Clark’s rule predicted pediatric clearance...... 57

Figure 4-1 Maturation of extracellular body water as a percentage of body weight. The red solid dots represent digitized data from previous work. The solid blue line represents model fit ...... 75

Figure 4-2 External evaluation of morphine structural models a) after IV bolus administration of morphine 0.14 mg/kg plus infusion of 0.05mg/kg/hour for 4hours in 20 healthy adults with mean age 24.6 years and mean body weight 74.2kg17. b) after IV bolus administration 10mg/70kg in 6 healthy volunteers with mean body weight 71.4kg and mean age 25.8 years16...... 83

Figure 4-3 External evaluation of morphine structural model after intravenous bolus administration a) in infants undergoing elective surgery (mean age 21 months)19. b) in children with leukemia undergoing therapeutic lumbar puncture (median age 5.5 years and median weight 20.0 kg)21 c) in neonates (N=10, mean age 1.1 days and mean weight

3.5kg)18 d) in neonates (N=10, mean age 29 days and mean weight 3.9kg)18 e) in infants

(N=7, mean age 112 days and 6.2kg)18 f) in infants (N=5, mean age 0.3 years and mean weight 5.3kg)20 g) in children (N=5, mean age 3.7 years and mean weight 16.3kg)20 h) in children (N=4, mean age 6.4 years and mean weight 22.3kg)20 ...... 84

Figure 4-4 a) Population predicted plasma concentration versus observed plasma concentration. b) Individual predicted plasma concentration versus observed plasma concentration ...... 85

Figure 4-5 Comparison of post hoc PK profiles with observed concentration in four representative neonates ...... 86

Figure 4-6 a) Histogram of normalized prediction distribution error. The blue curve is the normal distribution with corresponding mean (-0.169) and standard deviation (1.03). b)

Normalized prediction distribution error versus time after dose. c) Normalized prediction distribution error versus population prediction. The 3 dashed lines represent NPDE

(CWRES) = -2, 0, and 2, separately...... 87

Figure 5-1 Final Structural Morphine PK Model after Oral administration of Morphine

Sulfate Solution in Neonates with Abstinence Syndrome...... 98

Figure 5-2 DTO model external evaluation: CWRES vs PNA...... 104

Figure 5-3 Model with PNA as prognostic factor: CWRES versus PNA...... 105

Figure 5-4 Individual predicted morphine concentration-time profile in four representative subjects for model external evaluation (red solid dots: observed morphine concentration; blue line: DTO model predicted morphine concentration; green line: current model predicted morphine concentration) ...... 107

Figure 6-1 Structural Model...... 121

Figure 6-2 Simulation workflow for MFS and dosing history...... 125

Figure 6-3 Comparison between observed and model fitted MFS-time profiles in representative patients...... 128

Figure 6-5 Comparison between the model predicted and observed time-on-treatment in

JHU clinical trial DTO-only arm...... 131

Figure 6-6 Comparison between the observed and model predicted time-on-treatment in

JHU clinical trial DTO plus clonidine arm...... 132

Figure 6-7 Comparison between the observed and model predicted time-on-treatment in

BBORN clinical trial morphine arm...... 133

Figure 6-8 Distribution of the simulated 25%, 50% and 75% percentile of time-on- treatment in the JHU clinical trial DTO arm with the corresponding observed percentiles

(solid red line)...... 134

Figure 6-9 Distribution of the simulated 25%, 50% and 75% percentile of time-on- treatment in the JHU clinical trial DTO plus clonidine arm with the corresponding observed percentiles (solid red line)...... 134

Figure 6-10 Distribution of the simulated 25%, 50% and 75% percentile of time-on- treatment in the BBORN clinical trial morphine arm with the corresponding observed percentiles (solid red line)...... 134

Figure S6-1 Observed and model predicted MFS time profiles………………………..141

Figure 7-1 Comparison of Time-on-treatment between Strategy 1 and Scenario 1 ...... 173

Figure 7-2 Comparison of Time-on-treatment between Scenario 1 and 2 ...... 175

Figure 7-3 Comparison of Time-on-treatment between Strategy 2 and Scenario 3 ...... 176

Figure 7-4 Time-on-treatment plot for Strategy 1 and Scenario 5 to 9 ...... 179

Figure 7-5 Time-on-treatment plot for Strategy 2 and Scenario 10 to 12 ...... 181

xii

List of Abbreviations

AC adenylate cyclase

ADME absorption, distribution, metabolism and excretion

AS allometric scaling

AUC area under the curve

BBORN blinded buprenorphine or neonatal morphine solution

BDDCS biopharmaceutical drug disposition classification system

BPCA Best Pharmaceuticals for Children Act

BSA body surface area

BSV between subject variability cAMP cyclic adenosine monophosphate

CDC Centers for Disease Control and Prevention

CLr renal clearance

CNS central nervous system

CWRES conditional weighted residuals

CYP cytochromes P450

DTO diluted tincture of opium

ER exposure-response

F bioavailability

FDA US Food and Drug Administration

FOCE-I first order conditional estimation method with interaction

GABA γ-Aminobutyric acid

GPCR G-protein coupled receptor

xiii

IM intramuscular

IPRED individual prediction

IV intravenous

JHU Johns Hopkins University

M3G morphine-3-glucuronide

M6G morphine-6-glucuronide

MFS modified Finnegan score

MOR mu opioid receptor

NAS neonatal abstinence syndrome

NDA new drug application

NE norepinephrine

NICU neonatal intensive care unit

NPDE normalized prediction distribution error

OCT organic cation transporter

OFV objective function value

PBPK physiologically based pharmacokinetic

PD pharmacodynamics

PK pharmacokinetics

PMA postmenstrual age

PML phoenix modeling language

PNA postnatal age

PREA Pediatric Research Equity Act

PRED population prediction

SC subcutaneous

xiv

TAD time after dose

TJU Thomas Jefferson University

UDPGA UDP glucuronic acid

UGT uridine 5'-diphospho-glucuronosyltransferase

Vss steady state volume of distribution

WSV within subject variability

WT body weight

Chapter 1 Introduction

Pharmacometrics and Learn-Apply paradigm

Pharmacometrics is the science that deals with quantifying drug, disease and trial characteristics to aid in drug development, regulatory decisions and therapeutic decisions.

1 Recently, the commissioner of US Food and Drug Administration (FDA) Scott Gottlieb,

M.D addressed the use of modeling and simulation in drug development and regulatory review (https://blogs.fda.gov/fdavoice/index.php/2017/07/how-fda-plans-to-help- consumers-capitalize-on-advances-in-science/).

Nonlinear mixed-effects modeling approach combines both fixed effect in the mathematical model (structural model) and random effects in the statistical model for data analysis. A mathematical model is used to describe the understanding of pharmacokinetics, pharmacodynamics, disease progress, etc. A statistical model is used to describe the variability in the observed data, including both between subject variability and within subject variability.

Clinical trial simulation is a powerful and useful tool to optimize trial design and dosing strategy. Not only can simulate pharmacokinetic profiles in patient population and match the exposure level of the drug, but the clinical trial simulation can also help with more complicated situations, such as situations that the dose should be titrated based on response and multiple drugs are given at the same time.

Learn & apply paradigm is being utilized as an effective way to leverage pharmacometrics methods to enhance drug development, regulatory decisions and clinical practice.2 ‘Learn’ refers to transforming information (e.g. clinical trial data) to

1 knowledge while ‘apply’ is the process of utilizing this knowledge to make informed decisions such as dose selection, evidence of effectiveness, go-no go decisions, etc.

Case Example: Everolimus

Everolimus is a mTOR inhibitor immunosuppressant manufactured by Novartis. In 2010, everolimus was approved by FDA indicated for the prophylaxis of organ rejection in adult patients, known as ZORTRESS. However, the original New Drug Application

(NDA) 021628 for everolimus was not approved by FDA, and further clinical trial with a lower dosing regimen was recommended at that time based on a modeling and simulation outcome.

Learn

In the NDA 021628, higher dosing regimens of 0.75mg b.i.d and 1.5mg b.i.d in combination with cyclosporine were selected in the pivotal clinical trial. Based on the observed exposure-response relationship, the higher exposure of everolimus and cyclosporine showed both lower rate of failure (better efficacy) in Figure 1-1a and more renal toxicity in Figure 1-1b.

Apply

A modeling and simulation work was performed to optimize the dosing regimens of everolimus and cyclosporine for a higher benefit-risk ratio. The new dosing regimens recommended a concentration controlled everolimus dosing with a lower dose of cyclosporine (Table 1-1).

Impact

In 2010, based on the new dosing regimens for everolimus and cyclosporine, the new pivotal clinical trial showed a similar efficacy as before but with less renal toxicity.

Thereafter, the NDA submission to FDA was approved with the concentration controlled everolimus dosing regimen starting at 0.75mg b.i.d.

Figure 1-1 (a) Cyclosporine–probability of failure (i.e., organ rejection, death, etc) and change in creatinine clearance relationships at various everolimus concentrations. (b) Exposure–response relationships for the azathioprine arm.

Table 1-1 Projected benefit (patients with effectiveness failures) and risk of nephrotoxicity for CsA tapering dosing

Neonatal abstinence syndrome and current challenge in clinical practice

Neonatal Abstinence Syndrome (NAS) is a clinical syndrome of opiate withdrawal in infants exposed to drugs either prenatally in the form of maternal use (non-iatrogenic), or postnatally in the form of medical therapy (iatrogenic). The syndrome is comprised of a combination of central nervous system, digestive system and autonomic system abnormalities that result from uninhibited excitatory neurotransmitter release from the neurons that have been chronically exposed to opiate once the opiate is taken away.

NAS is an important public health issue in the United States. Every one hour there’s a baby born with NAS. Between 2000 and 2009, a 3-fold increase in the use of opiate drugs among pregnant women led to an increase in NAS and associated with higher health care costs.3 The annual incidence of antepartum maternal opiate use increased significantly from 1.19 to 5.63 per 1000 hospital births, and the incidence of NAS increased significantly from 1.20 to 3.39 per 1000 hospital births3. The majority of these neonates with NAS were covered by Medicaid.4 Mean hospital charges for newborns with NAS increased from US$39,400 in 2000 to $53,400 in 2009.3 Though the length of stay in

NAS remained relatively constant at 16 days, it is much longer than other newborns hospital stay of 3 days.3

Neonates at risk of NAS are monitored closely with physiologic scoring systems to rate the severity of withdrawal and determine when non-pharmacologic therapies have failed and pharmacologic therapy is warranted. For neonates with mild withdrawal symptom, non-pharmacotherapy, such as swaddling, rocking, pacifiers, reducing external stimuli, maintaining temperature stability, is routine care in NICU for the management of

6 withdrawal symptoms. However, those neonates who cannot adjust themselves to the normal activities with non-pharmacotherapy should receive opiates, such as morphine, buprenorphine, and other drugs, such as clonidine and phenobarbital, for pharmacotherapy.

Finnegan Score, which was developed in 1975, and further modified in 1986, has been used as the standard tool to evaluate the severity of withdrawal symptoms and determine when non-pharmacologic therapies have failed and pharmacologic therapy is warranted.

The main symptoms are classified by pathophysiology into three aspects: central nervous system, digestive system and autonomic system. After evaluating each symptom during a given period of time, a total score of all the sub scores will be used to guide the next dosing amount of medication.

Currently, morphine is the first line pharmacotherapy for NAS. Diluted tincture of opium, in which morphine is the active ingredient, is also widely used in clinical practice.

Though the clinical outcome in terms of days on treatment is similar between morphine sulfate solution and diluted tincture of opium (DTO), the alcohol in the DTO raised the concern on central nervous system effect. As a result, DTO is not recommended as first line pharmacotherapy in NAS nowadays.

Briefly, the dosing strategy could be divided into three phases: symptom control, stabilization and detoxification. During the symptom control phase, an initial dose will be given to the patient, and the dose could be titrated up to a certain level to control the withdrawal symptoms. After the symptoms are controlled, patients are normally stabilized for 48 to 72 hours (stabilization phase) before detoxification. During the

7 detoxification phase, the morphine dose is normally decreased every 24 hours. A couple of different dosing strategies are given as an example in Table 1-2 for the illustration of different dosing strategies.

Methadone, which is a full MOR agonist, is also investigated in NAS and used in the clinical practice for the treatment of neonatal withdrawal symptoms5. However, considering the long half-life of methadone in neonates, methadone is not recommended in the treatment of NAS6. Buprenorphine, a partial MOR agonist, has been evaluated recently as first-line pharmacotherapy for NAS as well, more importantly showed superiority to morphine in the primary endpoint, time-on-treatment.7,8 Potentially, buprenorphine could be another option for physicians when treating NAS.

Clonidine, an alpha-2 receptor agonist, has been investigated as both add-on therapy to morphine9,10 as well as monotherapy in NAS.11 Phenobarbital, which is a GABA receptor agonist, is commonly used as second-line pharmacotherapy in the treatment of NAS, or as first-line pharmacotherapy for those whose mom was not on opiates.6,9

Despite the huge public health issue in NAS and the wide application of morphine as the first line pharmacotherapy, pharmaceutical companies have no incentive to invest in morphine dose selection for NAS, and the current dosing strategies are various from hospital to hospital.12 This research is based on a virtual consortium between the Center for Translational Medicine at the University of Maryland Baltimore, Johns Hopkins

Medical Institute and Thomas Jefferson University Hospital.

Table 1-2 Examples of current morphine dosing strategies in clinical practice

Overview of Thesis

The purpose of this research is to optimize the morphine dosing strategy in NAS patients who require pharmacotherapy by using pharmacometrics approach with the philosophy of learn and apply.

In Chapter 2, we reviewed the current understanding of morphine pharmacokinetics in adults.

The pharmacokinetics (PK) of morphine has been extensively investigated. Though different publications have focused on the various aspects of morphine PK, none of them have quantitatively interpreted morphine PK across different publications. To summarize the current understanding of morphine PK in humans quantitatively, a parent-metabolite compartmental PK modeling approach was used to summarize the current understanding of morphine PK in humans. Plasma concentration-time profiles and accumulative urine recovery time profiles of morphine, morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G) were digitized from the previous publications to develop the parent-metabolite PK model. The parent- metabolite PK model successfully described the plasma concentration-time profiles and accumulative urine recovery of morphine as well as its two major metabolites, M3G and M6G, after intravenous and oral administration of morphine. This research separated out the first pass effect on morphine metabolism after oral administration, and for the first time quantitatively described it. By integrating these results with two mass balance studies of morphine, a clear picture of morphine absorption and disposition was given. Though the results are mainly based on data collected from healthy volunteers or patients whose disease does not expect to impact on morphine PK, the parent-metabolite model set a framework to further evaluate morphine PK in special populations, such as pediatrics and patients with renal impairment.

In Chapter 3, we evaluated the predictability of allometric scaling (AS) approach in extrapolation adults PK to pediatrics 2 years and above. We systematically evaluated drugs that had pediatrics label information updated from 1998 to 2015. Only intravenous (IV) or oral administration drugs with available PK information in both pediatrics and adults from FDA approved labels were included. The AS approach was used to extrapolate adult clearance to pediatrics. The relative difference between the observed and the AS approach predicted clearance were summarized and used to evaluate the predictive power of the AS approach. Totally, 36 drugs eliminated by a metabolic pathway and 10 drugs by the renal pathway after IV or oral administration were included. Regardless of the administration route, elimination pathway and age group, the AS approach can predict clearance in pediatrics within a two-fold difference; 18 of the included drugs were predicted within 25% difference and 31 drugs within 50% difference. The AS approach can adequately design PK study in pediatrics 2 years and older.

In Chapter 4, we developed a morphine PK model in neonates after oral administration of morphine. Conducting and analyzing clinical trials in vulnerable neonates are extremely challenging. The aim of this analysis is to develop a morphine population PK model using data collected during a randomized control trial in NAS. A 3-compartment morphine structural PK model after IV administration from previously published work was utilized as prior, while allometric scaling method with physiological consideration was used to extrapolate PK profile from adults to pediatrics. Absorption rate constant and bioavailability were estimated in neonates with abstinence syndrome after oral administration of DTO. Goodness-of fit plots along with normalized prediction distribution error and bootstrap method were performed for model evaluation. We successfully extrapolated the PK profile from adults to pediatrics after IV administration. The estimated first-order absorption rate constant and bioavailability were 0.751

11 hour-1 and 48.5%, respectively. Model evaluations showed that the model could accurately and precisely describe the observed data. The population pharmacokinetic model we derived for morphine after oral administration of DTO is reasonable and acceptable; therefore, it can be used to describe the PK and guide future studies. The integration of the previous population PK knowledge as prior information successfully overcomes the logistic and practical issue in vulnerable neonate population.

In Chapter 5, we further extended the morphine PK in neonates after oral administration from

Chapter 4 and explained the lower systemic clearance and higher bioavailability during the first week of life. Neonates experience dramatic changes in the disposition of drugs after birth due to maturation in enzyme activities and adjustment to environmental changes that provide neonatologist with challenges in therapeutic decision making. By integrating knowledge of time course of bilirubin, morphine and other drugs metabolized by glucuronidation pathways from previous studies, we propose that the lower concentration of uridine diphosphate glucuronic acid during the first week of life is the rate-limiting step in the metabolism of morphine and other glucuronidation activities. This result can be extended to all the drugs metabolized by UGT pathways in neonates and therefore has an important clinical implication for the use of drugs in this population.

In Chapter 6, we established an exposure-response (ER) relationship between morphine concentration and MFS in NAS based on the current understanding of the pathophysiology of

NAS and pharmacology of morphine. The ER relationship incorporating with the current dosing strategies was used to reproduce the observed time-on-treatment and total morphine dose in the clinical trials. This ER relationship could be further applied in the evaluation of different dosing strategies in NAS and optimization of pharmacotherapy.

In Chapter 7, we simulated different dosing scenarios to evaluate difference aspects in the morphine dosing strategies. Based on the exposure response relationship in Chapter 6 and the simulation results, optimized dosing strategies were proposed for further clinical practice. The simulation results showed 1) the 3 hours dosing interval isn’t different from the 4 hours dosing interval in terms of clinical outcome when the same daily dose is given 2) body weight based dosing strategies are not better than the fixed dose based dosing strategies 3) the optimized dosing up-titration and down-titration dosing strategies showed clinical meaningful improvement compared with the original dosing strategies.

Chapter 2 Integrated model to describe morphine pharmacokinetics in humans

Abstract

OBJECTIVE: The pharmacokinetics (PK) of morphine has been extensively investigated.

Though different publications have focused on the various aspects of morphine PK, none of them have quantitatively interpreted morphine PK across different publications. The objective of this research is to summarize the current understanding of morphine PK in humans quantitatively.

METHODS: In this research, a parent-metabolite compartmental PK modeling approach was used to summarize the current understanding of morphine PK in humans. Plasma concentration- time profiles and accumulative urine recovery time profiles of morphine, morphine-3- glucurondine (M3G) and morphine-6-glucuronide (M6G) were digitized from the previous publications to develop the parent-metabolite PK model. RESULTS: The parent-metabolite PK model successfully described the plasma concentration-time profiles and accumulative urine recovery of morphine as well as its two major metabolites, M3G and M6G, after intravenous and oral administration of morphine. CONCLUSION: This research separated out the first pass effect on morphine metabolism after oral administration, and for the first time quantitatively described it. By integrating these results with two mass balance studies of morphine, a clear picture of morphine absorption and disposition was given. Though the results are mainly based on data collected from healthy volunteers or patients whose disease does not expect to impact on morphine PK, the parent-metabolite model set a framework to further evaluate morphine PK in special populations, such as pediatrics and patients with renal impairment.

Key words: morphine, pharmacokinetics, metabolite, first-pass effect

Introduction

Morphine is an ancient drug which is isolated from opium poppy back to early 1800s.

Nowadays, morphine products are widely used in medical care. Morphine is indicated in the management of pain severe enough to require an opioid analgesic and for which alternative treatments are inadequate. However, morphine is widely off-label used for many reasons, such as pain in pediatric population, shortness of breath or hastening death. Under so many trade names, morphine can be administered intravenously, intramuscularly, intrathecally, subcutaneously, rectally, and orally. Morphine is also prepared by pharmacist extemporaneously in clinical practice. Diluted oral morphine solution and diluted tincture of opium are prepared for the treatment of neonatal abstinence syndrome.17 Morphine oral solution may not be available in some countries in the world, so it might be prepared by pharmacist for adult uses as well.18

Though the PK characteristics of morphine have been extensively evaluated and reviewed,19 different research papers reported different PK properties of morphine. For example, the ratio of the two major metabolites, morphine-3-glucuronide (M3G) to morphine-6-glucuronide (M6G), was reported to be 5:1as well as 9:1.20 Lack of an integrated PK assessment makes it cumbersome for further researchers to readily access the comprehensive PK and most importantly project the likely outcome under various scenarios before experimenting.

Morphine is a biopharmaceutical drug disposition classification system (BDDCS) class I drug.21

The absorption of morphine is rapid with a Tmax of 0.75 hours after oral administration of morphine solution. 22

Morphine is extensively distributed in human body but mainly in extracellular water with a Vss of 2.1 to 4 L/kg. The blood-plasma ratio is about 1.08, indicating an equal distribution between red blood cells and plasma.23 The plasma protein binding is about 26%.24

Morphine is extensively metabolized via glucuronidation by Phase II metabolism enzyme UDP- glucuronosyl transferase-2B7 (UGT 2B7) into M3G and M6G.25 In-vitro studies have shown that other UGT enzymes, such as UGT 1A3 and 1A8, also play a minor role in the formation of morphine glucuronides.26,27 Previous clinical trials showed that morphine is eliminated in the form of other metabolites, such as hydromorphone and normorphine in humans, by Phase I metabolism. Several CYP enzymes, such as CYP 2C19 and 3A4, were involved in the formation of morphine N-demethylation in vitro.28

Renal clearance of morphine was reported to be around 10% of total plasma clearance in adults

[Label]. The comparison between healthy volunteers and patients with end-stage renal failure showed that the magnitude of renal elimination pathway has limited influence on morphine clearance.29,30 However, the excretion of M3G and M6G is dramatically determined by renal function and highly correlated with creatinine clearance.30–32 Accumulation of M6G in renal failure patients has raised the attention in clinical practice as M6G is a potent active metabolite.

Though many clinical trials have been conducted to investigate morphine pharmacokinetics in different aspects, and review publications have also been published to summarize the existing data using descriptive statistics, none of those publications have interpreted and integrated different clinical trials together from a modeling perspective. In this study, a parent-metabolite

PK model was developed based on previous publications in adults to quantify the absorption,

16 distribution, metabolism and excretion (ADME) of morphine and its metabolites after oral and

IV administration in humans.

Methods

Data Collection

Publications with full concentration time profiles after administration of morphine (IV and oral),

M3G (IV) or M6G (IV) were included in this analysis. Plasma concentration-time profiles and accumulative urine recovery of morphine, M3G, and M6G in different clinical trials were digitized from the previously published studies. All these PK studies were conducted using rich sampling design. Data were digitized from these publications using GetData Graph Digitizer

(version 2.24.0.25). The dosage administered and the amount of drug recovered in urine were adjusted by their molecular weight to the unit of nmol, and the concentrations of morphine, M3G and M6G were adjusted to the unit of nmol/L. The summary of clinical trials and the corresponding demographics are given in Table S1 (supplementary material).

Parent-metabolite PK model

In this research, we developed a parent-metabolite model of morphine to quantitatively describe the concentration-time profiles and accumulative urine recovery of morphine, M3G and M6G after IV or oral administration of morphine, or IV administration of M3G or M6G. The structural

PK model of morphine, M3G and M6G are given in Figure 2-1. The detailed description of the structural model is given in the supplementary material with the phoenix modeling language

(PML) code.

Figure 2-1 Structural PK model of morphine and it two major metabolites M3G and M6G after intravenous and oral administration of morphine. Disposition of morphine, M3G and M6G

The three-compartment PK model of morphine after IV administration of morphine was adopted from a previous publication in healthy adults and the PK parameters of morphine disposition, including the volume of distribution and clearance, were fixed at the previously published values.33

Similarly, the two-compartment structural PK model of M6G after IV administration of M6G was also adopted from a previous publication, and the PK parameters of M6G disposition were also fixed at the previously reported values.33

However, no previously published M3G PK model after IV administration of M3G was available. Therefore, a two-compartment PK model was used to describe the concentration-time profile after IV administration of M3G based on the shape of the concentration-time profile, and the corresponding PK parameters were estimated based on the digitized PK profiles.

The fraction of morphine excreted in urine (fe) was estimated using the accumulative urine recovery of morphine data, while the fraction of M3G and M6G excreted in urine was assumed and fixed as 100%.

Morphine metabolism and first-pass effect after oral administration

The metabolic fraction from morphine to M3G, fmM3G, was estimated based on the M3G concentration-time profile after IV administration of morphine and after IV administration of

M3G. Similarly, the metabolic fraction from morphine to M6G, fmM6G, was also estimated.

After oral administration, M3G and M6G were formed from both systemic clearance and first pass effect. Therefore, a first pass metabolic pathway was added for M3G and M6G separately.

Absorption rate constants (K3 and K6) and fractions of first pass metabolism (F3 and F6) were estimated to account for the formation of M3G and M6G as a result of the first pass effect. The ratio of F3 to F6 was fixed at the ratio of fmM3G to fmM6G, because they were reasonably close when estimated independently and expected to be identical from a PK perspective. A parameter named FFP was estimated to account for the first pass metabolism to morphine glucuronides, including both M3G and M6G, and F3 and F6 were finally estimated as secondary PK parameters using the following equations:

3 3 = ∙ 3 + 6

6 6 = ∙ 3 + 6

In one clinical trial, the bioassay method did not distinguish the two glucuronide metabolites,

M3G and M6G, and the total plasma concentration and urine recovery of these two metabolites

20 were reported. Therefore, the sum of plasma concentration of M3G and M6G was used to fit the reported glucuronide concentration in this clinical trial, and the sum of urine recovery for M3G and M6G was used to fit the glucuronide recovery in urine.

The plasma concentrations and the accumulative urine recovery of morphine, M3G and M6G in different clinical trials were estimated simultaneously. The morphine PK model was developed and evaluated using Phoenix 6.4 (Certara, MO, USA). A naïve pooled method was used to fit all the digitized data simultaneously. No adjustment to sample size in each clinical trial was made in the model development process.

Results

Parent-metabolite PK model

The parent-metabolite model we proposed simultaneously described the morphine, M3G and

M6G plasma concentration-time profiles and the accumulative urine recovery in the previously published clinical trials very well. The PK profiles for the observed and predicted data are given in the supplementary material Figure 2-S1 to Figure 2-S7 for the visual inspection of the model predictions.

The estimated PK parameters with the corresponding 95% confidence interval for morphine,

M3G and M6G, are given in Table 1. Though only digitized mean data was used to develop the parent-metabolite model, the parameter estimates showed a good precision for all the PK parameters estimated with a maximum relative standard error of 20.8% (data are shown as 95% confidence interval in Table 2-1). The three-compartment PK model of morphine and the two- compartment PK model of M6G were fixed at previously published values, and the digitized morphine and M6G IV data from the previous publications was used as an external evaluation for these two compartmental models. The external evaluation supported the further application of these two models in the parent-metabolite PK model.

Table 2-1 Parameter estimates in morphine parent metabolite model

PK Parameters Units Estimates 2.5% 97.5% Absorption F 30.6% 28.0% 33.1% Ka 1/hr 0.679 0.579 0.778 FFP 45.7% 42.6% 48.9% F3 38.1% F6 7.7% Ka3 1/hr 0.798 0.531 1.06 Ka6 1/hr 0.732 0.433 1.03

Morphine Disposition V# L 17.8 Cl# L/hr 75.3 V2# L 87.3 Cl2# L/hr 136 V3# L 199 Cl3# L/hr 19.5 felm 9.3% 8.8% 9.9%

M6G Disposition VM6G$ L 9.5 ClM6G$ L/hr 9.9 VM6GP$ L 7.1 QM6G$ L/hr 5.8 fmM6G 11.0% 10.2% 11.8%

M3G Disposition VM3G L 12.4 10.5 14.2 ClM3G L/hr 6.56 6.06 7.06 VM3GP L 31.2 25.0 37.3 QM3G L/hr 6.74 4.22 9.26 fmM3G 54.6% 50.9% 58.4% #Fixed at [x] and $Fixed at [y]

F, morphine bioavailability; Ka, morphine first order absorption rate constant; FFP, fraction metabolized to morphine glucuronide during first pass;F3, fraction metabolized to M3G during first pass; F6, fraction metabolized to M6G during first pass; Ka3, M3G first order absorption

23 rate constant; Ka6, M6G first order absorption rate constant; V, morphine central volume of distribution; Cl, morphine clearance; V2, morphine peripheral volume of distribution; Cl2, morphine inter-compartment clearance; V3, morphine peripheral volume of distribution; Cl3, morphine inter-compartment clearance; felm, morphine fraction eliminated as parent drug in urine; VM6G, M6G central volume of distribution; ClM6G, M6G clearance;VM6GP, M6G peripheral volume of distribution;QM6G, M6G inter-compartment clearance; fmM6G, morphine metabolic fraction to M6G; VM3G, M3G central volume of distribution; ClM3G, M3G clearance;VM3GP, M3G peripheral volume of distribution;QM3G, M3G inter-compartment clearance; fmM3G, morphine metabolic

The ratio of M3G and M6G formation were identical during the first pass effect and systemic clearance, and therefore fixed at the same ratio in the parent-metabolite model. The estimated metabolic ratio (fmM3G:fmM6G = 54.6%:11.0%) is about 5:1. Also, the ratio of the secondary parameters, F3:F6, was also 5:1.

Based on the PK parameter estimates and the previously published data, the disposition of morphine after IV and oral administration were summarized in Figure 2. The radio-activity of

10% recovered in feces after IM administration of morphine was also presented in Figure 2. The amount recovered in the feces after oral administration remains unknown. Therefore a dashed line is given from the oral dose to feces in Figure 2-2.

Figure 2-2 Summary of morphine disposition in human

Discussion

PK of morphine was extensively investigated in humans. In this research, we developed a parent- metabolite PK model. Under most circumstances, the first pass effect and systematic clearance of the parent drug in a parent-metabolite model are combined together and modeled as a nominal systematic clearance only. To the authors’ knowledge, this is the first time that first pass effect is separated from systematic clearance after oral administration of morphine in humans. In this study, we quantitatively described the PK of morphine and its two major metabolites, M3G and

M6G, using a parent-metabolite model, and interpreted the previously published clinical trial data using the same model. Moreover, the structural model we proposed can be used as a framework for further investigation of morphine PK, such as extrapolation to pediatrics or other special populations.

In this research, we only focused on the PK data after IV and oral administration of morphine.

Other administration routes, including intramuscular (IM), subcutaneous (SC) and rectal, are also available on the market for patients who require morphine therapy. The previously published radiolabeled morphine PK study showed that the PK profile and mass balance after IM administration and SC administration are very close to IV administration. Therefore, the bioavailability of morphine after IM and SC administration could be considered to be at 100% and absorption profile after IM and SC administration could not make the PK profile quite different from IV administration. Moreover, the 10% radioactivity in feces after IM administration could be considered as the radioactivity after IV administration.

Recently, a couple of morphine PK models were published to describe the concentration-time profiles of morphine and its two major metabolites, M3G and M6G in adults or pediatrics after

26 oral administration. However, the first pass effect was estimated as part of the systemic clearance of morphine after oral administration of morphine34. The metabolite concentration-time profiles after IV administration of the metabolite are usually unavailable for most drugs. The concentration-time profile after IV administration of M3G and M6G provided the plausibility to estimate the absolute volume of distribution and clearance for these two metabolites. As a result, the fraction of M3G and M6G formed during first pass effect, and systematic clearance after oral administration could be distinguished from each other.

After oral administration of morphine sulfate solution, the estimated bioavailability of 30.6% is similar to the one indicated in another PK study, where the urine recovery of morphine were

2.6% and 8.2% after oral and IV administration and equivalent to a bioavailability of 31.7% ( =

2.6% / 8.2%)35. In a previously conducted morphine PK study in humans, the hepatic extraction ratio (EH) of 65% was determined. Therefore, the fraction absorbed (Fa) after oral administration of morphine sulfate solution and the fraction of morphine escape the metabolism in gut wall (Fg) would be 87.4% (= 30.6% / (1 – 65%)). Moreover, based on the interpretation of mass balance data after oral administration with our modeling result, Fa should be more than 95%.

Originally, the fraction of M3G and M6G formation during first pass effect (F3 and F6) were estimated independently. However, the result showed that the ratio of F3 over F6 was very close to the ratio of fmM3G over fmM6G. From a PK perspective, these two ratios should be identical, because M3G and M6G are mainly formed in the liver through UGT pathways during both first pass effect and system clearance. Therefore, the PK parameter FFP was estimated, and F3 and F6 were estimated as secondary parameters depending on FFP, fmM3G and fmM6G, as defined in the methods section.

CYPs28 and UGTs26,27 in vitro studies showed that morphine undergoes both Phase I and Phase II metabolism.

The fraction metabolized to M3G and M6G are 55% (= 30.6%×54.6%+38.1%) and 11% (=

30.6%×11%+7.7%) after oral administration of morphine, which is identical to the fractions of

54.6% and 11% after IV administration. Therefore, the fraction metabolized to the two major metabolites, M3G and M6G, accounted for 55% and 11% of the dose regardless of the administration route. This ratio of 55% to 11% after both IV and oral administration is close to previous findings of urine recovery of M3G and M6G35.

The formation of normorphine was by CYP enzymes and mainly through the 3A4 pathway.

Based on the two radiolabeled morphine PK studies, 5% of the administered radioactivity was recovered in the air and was considered as the result of normorphine formation36,37. This proportion of 5% was further confirmed in the urine recovery of normorphine and normorphine- glucuronide38.

The metabolism of morphine to other minor metabolites, such as hydromorphone, morphine-3- sulfate, morphine-6-sulfate, have been detected in plasma and urine39,40. However, the concentration-time profiles and metabolic fractions for these minor metabolites remain unclear.

The previously reported morphine renal clearance in humans with normal renal function was

2.3mL/min/kg35, which is 9 L/hr after adjusted by the average bodyweight of 65kg. The renal clearance of 160.4 ± 132.5(mean ± standard deviation) mL/min (approximately 9.6L/hr) in cancer patients with normal renal function and the relationship between renal clearance of morphine and creatinine clearance in intensive-care patients both further supported the renal clearance of 9 L/hr in adults with normal renal function31,32. Similarly, the renal clearance (CLr)

28 estimated as 9.3% × 75.3 L/hr = 7 L/hr is approximately consistent with these studies. The estimated fraction of renal clearance of 9.3% of the total clearance in healthy volunteers is similar to the observed 10% reported in the FDA-approved label. After oral administration, the model predicted urine recovery of morphine is F × fe = 30.6% × 9.3% = 2.8%, which is consistent with the observed urine recovery after oral administration from previous clinical trials35.

The relationship between morphine, M3G and M6G renal clearance with creatinine clearance has been published before. By incorporating our model with the creatinine clearance, the influence of renal function on morphine, M3G and M6G disposition could be evaluated.

Based on the mass balance study and our model results, a morphine ADME paradigm was generated (Figure 2-2). The radioactivity recovered in urine, feces and air were 80%, 10%, and

5%, respectively. The 5% radioactivity recovered in the air represented CO2 produced during the normorphine formation, which is finally eliminated in urine as normorphine or normorphine- glucuronide. Therefore, the amount of morphine or in the form of metabolites recovered in urine and feces were 85% and 10%, respectively. The metabolic fraction to M3G, M6G, normorphine were 55%, 10% and 5%, respectively. Renal clearance of morphine in urine samples accounts

10% of the total clearance. For the organs responsible for morphine eliminations, hepatic metabolism and excretion account for 60%, and kidney excretion accounts for 10%. Extrahepatic metabolism accounts for the rest of 30% of morphine clearance. The extrahepatic metabolism to

M3G and M6G was supported by another study in hepatic transplant patients41. Similarly, zidovudine, another UGT 2B7 substrate, was also metabolized in both liver and kidney42.

Considering the expression of UGT2B7 also occurs in the kidneys, M3G and M6G are formed in both the liver and the kidneys.

Enterohepatic recirculation has been reported in humans for morphine disposition. In a recently published M6G oral administration PK study, morphine, M3G and M6G plasma concentration- time profiles were determined, suggesting that M6G could be hydrolyzed to morphine in the small intestine and then absorbed as morphine43. However, the biliary elimination of morphine and the two metabolites were not quantitatively described in the parent-metabolite model. By visualizing the concentration-time profiles of morphine and the two major metabolites, no consistent EHC was observed. Around all the studies included in the modeling, EHC only was only observed for M6G after oral administration of morphine in one clinical trial. Therefore, the

PK profile of M6G after 6 hours when the re-absorption of M6G happened was not included.

The non-observed EHC in many studies might be a result of sampling design, food intake or both. Considering the inconsistency of EHC and the relatively small magnitude of this process and its limited impact on morphine PK, EHC was no modeled.

The parent-metabolite PK model we developed in this research could be further used to evaluate the PK characteristics as a starting point in special populations, such as renal impairment patients, where the clearance, especially for M3G and M6G, is compromised. In a recently published morphine PK model in pediatrics, we successfully extrapolated the morphine PK model to neonates after IV administration of morphine using allometric scaling, and estimated the absorption rate constant and bioavailability.44 With the morphine parent-metabolite PK model we developed, reasonable assumptions could be made to further inform other morphine

PK studies.

Evaluating all the aspects of PK in one clinical trial is impossible, but different clinical trials may focus on the various aspects of PK to answer different questions. Therefore, integrating and interpreting knowledge from different clinical trials are necessary. In this research of morphine

PK, we developed a morphine-metabolite PK model and quantitatively summarized the current understanding of morphine PK under one framework. The results showed consistency across different clinical trials, and the model elucidated valuable information when different clinical trials were combined.

Supplementary material:

Figure 2-S1 Observed and model predicted plasma concentration of M3G after IV bolus of

M3G

Figure 2-S2 Observed and model predicted plasma concentration of M6G after IV bolus plus IV infusion of M6G

Figure 2-S3 Observed and model predicted plasma concentration of morphine and M6G after IV bolus plus IV infusion of morphine

Figure 2-S4 Observed and model predicted plasma concentration of morphine, M3G and

M6G after IV bolus of morphine

Figure 2-S7 Observed and model predicted plasma concentration of morphine, M3G and

M6G after oral administration of morphine.

Supplementary material: PML code and model description

test(){

covariate(WT)

###IV administration of morphine, M3G and M6G

dosepoint(A1, idosevar = A1Dose, infdosevar = A1InfDose, infratevar = A1InfRate)

dosepoint(AM3, idosevar = AM3Dose, infdosevar = AM3InfDose, infratevar =

AM3InfRate)

dosepoint(AM6, idosevar = AM6Dose, infdosevar = AM6InfDose, infratevar =

AM6InfRate)

###Oral administration of Morphine

dosepoint(Aa, bioavail = (F))

dosepoint(Aa3, bioavail = (F3))

dosepoint(Aa6, bioavail = (F6))

F3=FG*(FMM3/(FMM3+FM6)) #fraction metabolized to M3G during first pass

F6=FG*(FM6/(FMM3+FM6)) #fraction metabolized to M6G during first pass

###Three compartment PK model of morphine

deriv(Aa = - Ka * Aa)

deriv(A1 = Ka * Aa - (Cl * C)- (Cl2 * (C - C2))- (Cl3 * (C - C3)))

deriv(A2 = (Cl2 * (C - C2)))

deriv(A3 = (Cl3 * (C - C3)))

urinecpt(UM = (felm*Cl * C))

###Two compartment M6G PK Model

deriv(Aa6 = -Ka6 * Aa6)

deriv(AM6 = Ka6 * Aa6 + FM6 * Cl * C - ClM6 * CM6 - QM6 * (CM6 - CM6P))

deriv(AM6P = QM6 * (CM6 - CM6P))

urinecpt(UM6G = ClM6 * CM6)

###Two compartment M3G PK Model

deriv(Aa3 = -Ka3 * Aa3)

deriv(AM3 = Ka3 * Aa3 + FMM3 * Cl * C - ClM3 * CM3 - QM3 * (CM3 - CM3P))

deriv(AM3P = QM3 * (CM3 - CM3P))

urinecpt(UM3G = ClM3 * CM3)

###

C = A1 / V

C2 = A2 / V2

C3 = A3 / V3

CM6 = AM6 / VM6

CM6P = AM6P / VM6P

CM3 = AM3 / VM3

CM3P = AM3P / VM3P

CMG = CM3 + CM6

UMG = UM3G + UM6G

###Observation and Residual Error

error(CMEps = 0.262361)

observe(CSObs = C * (1 + CMEps))

error(CM6GEps = 0.24412)

observe(C6Obs = CM6 * (1 + CM6GEps))

error(CM3GEps = 0.311671)

observe(C3Obs = CM3 * (1 + CM3GEps))

error(CMGEps = 0.390125)

observe(CMGObs = CMG * (1 + CMGEps))

error(UMEps = 0.109028)

observe(UMObs = UM * (1 + UMEps))

error(UMGEps = 0.0441667)

observe(UMGObs = UMG * (1 + UMGEps))

###Parameterization and covariate model

#morphine bioavailability

stparm(F = tvF)

#morphine first order absorption rate constant

stparm(Ka = tvKa)

#fraction metabolized to morphine glucuronide during first pass

42 stparm(FG = tvFG)

#M3G first order absorption rate constant stparm(Ka3 = tvKa3)

#M6G first order absorption rate constant stparm(Ka6 = tvKa6)

#morphine central volume of distribution stparm(V = tvV * (WT/70))

#morphine clearance stparm(Cl = tvCl * (WT/70)**0.75)

#morphine peripheral volume of distribution stparm(V2 = tvV2 * (WT/70))

#morphine inter-compartment clearance stparm(Cl2 = tvCl2 * (WT/70)**0.75)

#morphine peripheral volume of distribution stparm(V3 = tvV3 * (WT/70))

#morphine inter-compartment clearance stparm(Cl3 = tvCl3 * (WT/70)**0.75)

#morphine fraction eliminated as parent drug in urine stparm(felm = tvfelm)

#M6G central volume of distribution stparm(VM6 = tvVM6 * (WT/70))

#M6G clearance stparm(ClM6 = tvClM6 * (WT/70)**0.75)

#morphine metabolic fraction to M6G stparm(FM6 = tvFM6)

#M6G peripheral volume of distribution stparm(VM6P = tvVM6P * (WT/70))

#M6G inter-compartment clearance stparm(QM6 = tvQM6 * (WT/70)**0.75)

#M3G central volume of distribution stparm(VM3 = tvVM3 * (WT/70))

#M3G clearance stparm(ClM3 = tvClM3 * (WT/70)**0.75)

#morphine metabolic fraction to M3G

stparm(FMM3 = tvFMM3)

#M3G peripheral volume of distribution

stparm(VM3P = tvVM3P * (WT/70))

#M3G inter-compartment clearance

stparm(QM3 = tvQM3 * (WT/70)**0.75)

###Typical values (THETAs)

fixef(tvF = c(0, 0.305748, 1))

fixef(tvKa = c(0, 0.678644, ))

fixef(tvFG = c(0, 0.457306, ))

#fixef(tvF3 = c(0, 0.3, 1))

fixef(tvKa3 = c(0, 0.797904, ))

#fixef(tvF6 = c(0, 0.3, 1))

fixef(tvKa6 = c(0, 0.732269, ))

fixef(tvV (freeze) = c(, 17.8, ))

fixef(tvCl (freeze) = c(, 75.3, ))

fixef(tvV2 (freeze) = c(, 87.3, ))

fixef(tvCl2 (freeze) = c(, 136, ))

fixef(tvV3 (freeze) = c(, 199, ))

fixef(tvCl3 (freeze) = c(, 19.5, ))

fixef(tvfelm = c(0, 0.0934833, 1))

fixef(tvVM6 (freeze) = c(, 9.5, ))

fixef(tvClM6 (freeze) = c(, 9.9, ))

fixef(tvVM6P (freeze) = c(, 7.1, ))

fixef(tvQM6 (freeze) = c(, 5.8, ))

fixef(tvFM6 = c(0, 0.110065,1))

fixef(tvVM3 = c(, 12.3719, ))

fixef(tvClM3 = c(, 6.55624, ))

fixef(tvVM3P = c(, 31.1607, ))

fixef(tvQM3 = c(, 6.73794, ))

fixef(tvFMM3 = c(0, 0.546482,1))

}

Chapter 3 Allometry is a reasonable choice in pediatric drug development

Abstract

Objective: Pharmacokinetics (PK) plays a key role in bridging drug efficacy and safety from adults to pediatrics. The principal purpose of projecting dosing in pediatrics is to guide trial design, not to waive the study per se. This research was designed to evaluate whether the allometric scaling (AS) approach is a satisfactory method to design PK study in pediatric patients

2 years and older. Methods: We systematically evaluated drugs that had pediatrics label information updated from 1998 to 2015. Only intravenous (IV) or oral administration drugs with available PK information in both pediatrics and adults from FDA approved labels were included.

The AS approach was used to extrapolate adult clearance to pediatrics. The relative difference between the observed and the AS approach predicted clearance were summarized and used to evaluate the predictive power of the AS approach. Results: Totally, 36 drugs eliminated by a metabolic pathway and 10 drugs by the renal pathway after intravenous (IV) or oral administration were included. Regardless of the administration route, elimination pathway and age group, the AS approach can predict clearance in pediatrics within a two-fold difference; 18 of the included drugs were predicted within 25% difference and 31 drugs within 50% difference.

Conclusion: The AS approach can adequately design PK study in pediatrics 2 years and older.

Keywords pharmacokinetics, pediatrics, allometric scaling, drug development

Introduction

Goal of Understanding Pediatrics PK from a Drug Development Perspective

Under the Best Pharmaceuticals for Children Act (BPCA) amendment of 2007 and the Pediatric

Research Equity Act (PREA) amendment of 2007, pharmaceutical companies began to conduct more clinical trials in pediatrics to support pediatrics dosing decision. The overall goal of pediatrics studies is to obtain regulatory approval in pediatrics and support labeling. Under most of the circumstances, adult information is available before designing clinical trials in pediatrics.

However, the strategy to obtain approval in pediatrics from regulatory agencies and thereafter provide information for pediatric labeling could be different. In the recently published FDA guidance for pediatric studies45, three bridging pathways are proposed from adult to pediatric drug development: 1) PK only approach;2) PK and pharmacodynamics (PD) approach; 3) PK and Efficacy approach.

The collection of PD and efficacy data varies according to different situations and bridging pathways, but PK and safety data are required in pediatrics regardless of which of the three bridging pathways are used. Sometimes, conducting PK study in pediatrics and collecting PK samples is impossible or limited for logistical reasons. In this manuscript, we focus on those cases where PK is expected. The goal of pediatric PK study from a drug development perspective is to improve pediatric dosing regimens with a solid understanding of PK characteristics in pediatrics. PK is influenced by the physiological maturation in pediatrics46, therefore, the evaluation of PK in pediatrics is normally conducted throughout the entire pediatric age. The role of PK modeling in pediatric drug development is to guide PK study design and analyze the data from clinical trials to support decision making. Conducting clinical

48 trials in pediatrics is not a problem in most cases and analyzing the data is fairly straightforward.

To use modeling in pediatric PK study design, three aspects should be included: 1) scaling adult dose to pediatrics; 2) choosing sampling time points; 3) determining sample size. The last two aspects were discussed and published by Wang et al47. In this manuscript, we focus on the first aspect: scaling adult dose to pediatrics.

The strategy to investigate PK in pediatrics depends highly on the availability of prior PK information in adults and the feasibility of conducting a PK study in pediatrics (Figure 3-1). If prior PK information in adults is available and conducting a PK study in pediatrics is feasible, the purpose of pre-study evaluation of pediatrics PK study design is to leverage existing knowledge to optimize the utility of plasma samples collected from those vulnerable pediatric patients, not for proposing the dose that should be used in pediatric patients and thereby waiving the pediatric PK study. Therefore, a reasonably close prediction (e.g. within 2 fold difference) in pediatric PK parameters can sufficiently guide the study design in pediatrics. However, if conducting a PK study in pediatrics is not feasible, which is not true in most cases, projecting the

PK profile in pediatrics is an alternative solution. Further, if no adult PK information is available but understanding the pediatrics PK profile is required, a more complicated approach could be a possible solution, e.g. using in-vitro or pre-clinical PK study to build up a physiologically based pharmacokinetic (PBPK) model for the prediction of PK profile in pediatrics.

If prior PK information in adults is available

If conducting PK study in If In-vitro or pre-clinical PK Pediatrics is feasible studies are available

Extrapolate from animal Use allometric scaling to Use allometric scaling to studies or use more design PK study in project PK in pediatrics complicated approach, pediatrics such as PBPK

* allometric scaling approach was tested for pediatrics > 2 years and for small molecules only.

Figure 3-1 Pediatric PK Extrapolation Strategy Allometric scaling (AS) has been used to extrapolate adult PK to pediatrics for more than 20 years. The mechanism basis of AS has been widely discussed in many publications48,49 and the predictability of AS in adolescents has been evaluated50. In order to extrapolate to neonates and infants, application of AS with maturation effect of age in population pharmacokinetic modeling for specific drugs, such as morphine44,51 and clonidine52,53, has been well established. Also, AS has been successfully applied in the dosing regimen determination in pediatric patients, with drugs such as busulfan54.

PBPK modeling and simulation has been widely used in drug development and regulatory review55. Also, application of PBPK in pediatric drug development has become more popular in the past few years and has accounted for a major application in PBPK. According to previously published information, pediatric PBPK submission to FDA accounted for 18% of 33 total PBPK submissions in 201256. The extrapolation into pediatrics using the PBPK approach has been applied and published, e.g. acetaminophen57, docetaxel58, sirolimus59, sotalol60, tramadol61 and etc.

In this research, we focused on the common situation in drug development when prior PK information in adults is available and conduction of PK study in pediatrics is feasible. In order to evaluate the performance of AS in pediatric study design, we reviewed the predictability of AS in all the small molecular drugs approved by the FDA from 1998 to 2015 and collected the evidence to support the application of the AS approach in extrapolation from adults to pediatrics as young as 2 years.

Methods

Approved drugs with label information updated for pediatrics from 1998 to 2015 were extracted from the New Pediatric Labeling Information Database

(http://www.accessdata.fda.gov/scripts/sda/sdNavigation.cfm?sd=labelingdatabase). Orally or intravenously (IV) administered small molecular drugs were included. Biologics, vaccines, inhalation and small molecular drugs administered subcutaneously were not included.

Pediatrics clearance was extrapolated from adults first and then compared with observed clearance in pediatrics. In order to extrapolate clearance from adults to pediatrics and compare with the observed clearance in pediatrics, four variables were collected from label information: observed clearance in adults (CLadults) and pediatrics (CLpediatrics) and the corresponding body weight in adults (WTadult) and pediatrics (WTpediatrics).

First, WTadults was assumed as 70kg in all the studies for all the drugs included.

Second, WTpediatrics was derived according to the four following situations: 1) if mean or median body weight was reported, it was directly used. 2) if the minimum and maximum body weight was reported, the average of minimum and maximum was used. 3) if mean/median age was reported without bodyweight, bodyweight was derived from the CDC growth chart62 according to the reported mean/median age. 4) if minimum and maximum age were given without body weight, the average of minimum and maximum age was used to derive bodyweight from the

CDC growth chart62. If multiple groups of pediatric patients were reported, their clearances were predicted based on each body weight or age group provided.

Third, observed clearance in pediatrics and adults were derived according to the five following situations: 1) if the clearance was reported, it was used directly in the comparison with extrapolation to pediatrics. 2) if the bodyweight adjusted clearance was reported, clearance was

52 calculated using the bodyweight derived above times bodyweight adjusted clearance. 3) if clearance was not reported but dose and corresponding area under the curve (AUC) were provided, clearance was calculated by dose over AUC. 4) if dose was given without clearance and AUC but with a statement of similar exposure was reached between pediatrics and adults,

AUC was assumed exactly the same between adults and pediatrics, then the observed clearance in adults was assumed to be 1 and the relative observed clearance was calculated by dose in pediatrics over dose in adults as follow:

= ⦁

5) If dose normalized AUC (AUC/Dose) was reported, then clearance was calculated as reciprocal of this dose normalized AUC (CL = Dose/AUC). 6) if BSA adjusted clearance was reported, clearance was calculated by BSA adjusted clearance times BSA where 1.73 cm2 was assumed for adults and pediatrics BSA were calculated using Boyd formula63 based on their average body weight.

All the demographic and pharmacokinetic information were collected from labels and review documents on the FDA website: Drugs@ FDA

(http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm). According to the Food and

Drug Administration guidance45, age group (e.g. children and adolescents) was determined for each drug based on the reported age, or justified based on body weight when age information was not reported.

The Allometric scaling approach was used to extrapolate adult clearance into pediatrics using the following equation:

. = ⦁( )

In order to evaluate the predictability of AS across all the drugs simultaneously, a prediction- observation ratio that reflects the relative difference between the AS approach predicted clearance and the observed clearance in pediatrics was defined as follows:

= prediction-observation ratio was summarized according to different administration route (IV and oral) and age group (children and adolescents).

The use of bodyweight adjusted clearance to extrapolate to pediatrics (i.e. linear extrapolation using per kg scaling, known as Clark’s rule) is also a commonly applied approach. In comparison with allometric scaling predicted clearance, the Clark’s rule predicted pediatric clearance was calculated as follows:

= ⦁( ) and the same prediction observation ratio was also calculated for the Clark’s rule predicted pediatric clearance as mentioned above for allometric scaling.

Results

558 records were found from the New Pediatric Labeling Information Database (the last record was accessed on March 28, 2015). 162 records were updated under BPCA only and 270 records were updated under PREA only. 76 records were updated under both BPCA and PREA. 134 drugs were found from these 558 records after combining multiple records for the same drug.

After exclusion of biologics, vaccines and drugs given other than by oral or IV administration route, totally 46 drugs with PK information in both adults and pediatrics from FDA approved labels were included. 12 drugs were administered intravenously and the absolute clearance was reported while 34 drugs were administered orally and the apparent clearance was reported. 36 drugs were mainly eliminated by a hepatic pathway and 10 drugs by the renal pathway. Drugs metabolized by phase I enzyme (such as CYP 3A4), phase II enzyme (such as UGT 1A1) or eliminated from biliary pathway were all included in the hepatic pathway. Totally, 91 pediatric predictions were calculated for these 46 drugs (34 in adolescents for 32 drugs and 57 in children for 37 drugs). The detailed prediction is given in Table S1 (supplementary materials).

Predicted clearance in pediatrics and the corresponding prediction-observation ratio were calculated according to the method section. Based on the worst prediction-observation ratio calculated for each drug, The AS approach can predict clearance in pediatrics within a two-fold difference (50% to 200%) regardless of the administration route (Figure 3-2a), elimination pathway (Figure 3-2b) and age group (Figure 3-2c); 18 of the included drugs were predicted within 25% difference and 31 drugs within 50% difference. No body weight (Figure 3-3) or age dependent prediction-observation ratio was observed.

Figure 3-2 Evaluation of allometric scaling predicted pediatric clearance a) by administration routes b) by elimination routes c) by age groups

Figure 3-3 Evaluation of allometric scaling predicted pediatric clearance versus bodyweight and comparison with Clark’s rule predicted pediatric clearance. In comparison with allometric scaling approach, the Clark’s rule predicted clearances in pediatrics were also calculated in the 46 drugs. The comparison between AS approach predicted clearance in pediatrics with Clark’s rule is provided in Figure 3. Negligible difference was observed above 40kg and Clark’s rule under-predicted clearance in below 40kg.

Discussion

In a recently published review paper, existing evidence from other publications was collected to compare AS with PBPK in the prediction of clearance in pediatrics64. In our research, we systematically evaluated the predictability of AS in 46 small molecular drugs approved by the

FDA and provided further evidence to support the application of AS in extrapolation from adults to pediatrics down to 2 years to guide the PK study design in pediatrics.

Only small molecular drugs given orally or intravenously were included. Biologics, vaccines and small molecular drugs given via other routes (e.g. subcutaneous) accounted for only 22 drugs out of the 134 drugs found in the database. Especially, pharmacokinetics of vaccines is not evaluated in most of the cases. Under predicted clearance was reported in pediatrics 6 years and older by combining the AS approach with the virtual population, especially for drugs orally administered65. However, when incorporating observed body weight or the CDC chart derived bodyweight into the AS approach, we didn’t find meaningful bias in AS predicted pediatric clearance regardless of the administration route in our study.

Bodyweight with an exponent of 0.75 has been shown as a promising predictor of liver size66.

This empirical relationship provides an additional physiological basis in the application of allometric scaling in the prediction of pediatrics hepatic clearance. Moreover, ontogeny of enzymes activity showed that the maturation of most human metabolism enzymes reaches 80% of the adult level by 2 years67–70. Therefore, maturation effect as a function of age has a minor influence on hepatic function for most drugs, and the relative change of body weight itself with an exponent of 0.75 is a physiologically reasonable predictor of hepatic function. Similarly, 97% of the adult level is reached in renal function by 2 years after adjusted for body weight with an

58 exponent of 0.7571. Moreover, the lack of body weight or age dependent prediction-observation ratio in our research further support that the maturation is close to the adult level in pediatrics 2 years and older. Regardless of the elimination pathway, bodyweight itself is a reliable predictor of hepatic and renal function.

Sometimes, the BSA-adjusted clearance in the prediction of pediatric clearance is used by physicians. As demonstrated in the previous publication, the BSA-adjusted clearance has a negligible difference compared with the AS approach49,72. BSA has also been shown to be proportional to liver volume73. In fact, BSA and body weight are mathematically related.

Therefore, it is expected that the predictability of the BSA adjusted clearance is similar to AS.

In most cases, clearance is concentration-independent. That is, Km>>C, CLadult=Vmax/(Km+C) approximates to CLadult=Vmax/Km, where Vmax is the maximum rate of elimination and Km is the

Michaelis-Menten constant that represents the concentration at half the Vmax. Therefore, AS is actually scaling Vmax as Km is a constant. When clearance is saturated in adults, the clearance is concentration dependent CLadult=Vmax/(Km+C). In this situation, Vmax, which reflects the maximum capacity of drug elimination, should be scaled down to pediatrics as

0.75 0.75 0.75 Vmax·(WT/70) , and CLpediatrics = Vmax·(WT/70) /(Km+C) = CLadult·(WT/70) at same concentration. Allometric scaling can also be successfully applied when non-linear clearance happens as similar concentration range74.

Bodyweight adjusted clearance is another widely used form of clearance. However, bodyweight

∗ adjusted clearance in pediatrics () tends to be higher than bodyweight adjusted

∗ clearance in adults ().

∗ . 1 = = ⦁( ) ⦁

. ∗ . = ⦁( ) = ⦁( )

∗ ∗ where is adjusted from by a fraction of the ratio of bodyweight in adults and pediatrics with exponent of 0.25. Therefore, assuming the same bodyweight adjusted clearance between pediatrics and adults is acceptable only when the pediatric bodyweight is close to adults.

As we found in Figure 3, the linear extrapolation using per kg scaling (Clark’s rule) under predicted pediatric clearance in below 40kg. This finding, on the other hand, suggests that allometry provides a better option for pediatric subjects.

In cases where matching concentration at a particular time point instead of AUC is important, the extrapolation of volume of distribution in pediatrics can also play an important role in the dose selection for pediatrics PK study design. For example, the maximum tolerated dose is always used in chemotherapy and matching adult Cmax in pediatrics is important to avoid higher adverse event rate. Similarly, for some of the antibiotics the efficacy is sensitive to the trough concentration Cmin.. Unfortunately, volume of distribution is the most ambiguous PK parameter reported. Often researchers report volume of distribution of the terminal phase (so-called ‘Vz’) which has no physiological bearing75. Therefore, the systemic evaluation of the extrapolation for volume of distribution in pediatrics using AS approach was not performed. However, the extrapolation of volume of distribution in pediatrics should be considered when matching concentration at a particular time (e.g, Cmax or Cmin) is required.

Many publications applied PBPK in pediatrics and compared PBPK with the AS approach. In the recently published docetaxel PBPK research58, the PBPK approach extrapolated PK profile in

60 pediatrics showed similar predictive performance compared to AS approach. Similarly, the acetaminophen PBPK model57 predicted pediatric clearance also overlapped with estimated clearance using the AS approach. However, application of PBPK in pediatric drug development is costly. Unlike AS that can be easily calculated using a smartphone (or a simple calculator),

PBPK requires complex software which requires special training. Moreover, in vitro and in vivo data is required to build up and validate PBPK model. Where pediatrics studies are to be conducted, PBPK for projecting pediatric doses does not offer additional benefit over the simple allometry, as shown by our research. However, PBPK plays a vital role when in vivo studies in cannot be conducted (e.g. pregnancy).

The derived pediatric bodyweight and assumed 70kg adult body weight may not be able to replicate the average body weight in the corresponding clinical trials. In some of the cases, the reference bodyweight (typically the average) for the clearance was not reported. In such cases the reference bodyweight was selected based on the CDC growth charts62 for the prediction of clearance in pediatrics. Therefore, part of the difference between the reported and AS predicted average clearance was likely due to the difference in the reference bodyweights. Similarly, all the assumptions we made for the imputation of missing information, such as the derived BSA for pediatrics and the assumed equal AUC between pediatrics and adults in some of the cases, contributed to the difference between the observed and predicted pediatric clearance.

Consequently, the AS predictions might seem more discordant than they might be. Nevertheless, the projected clearances are within 2-fold of the reported clearances, and are suitable for the purpose of designing future pediatric trials.

In conclusion, the AS approach is sufficient to design PK study in pediatrics 2 years and older for small molecular drugs in pediatric drug development.

Table 3-S1 Detailed Pediatric Clearance Extrapolation Information

Chapter 4 Mechanistic population pharmacokinetics of morphine in neonates with

abstinence syndrome after oral administration of diluted tincture of opium

Abstract

Objective: Conducting and analyzing clinical trials in vulnerable neonates are extremely challenging. The aim of this analysis is to develop a morphine population pharmacokinetics (PK) model using data collected during a randomized control trial in neonates with Abstinence

Syndrome (NAS). Methods: A 3-compartment morphine structural PK model after intravenous

(IV) administration from previously published work was utilized as prior, while allometric scaling method with physiological consideration was used to extrapolate PK profile from adults to pediatrics. Absorption rate constant and bioavailability were estimated in neonates with abstinence syndrome after oral administration of diluted tincture of opium (DTO). Goodness-of fit plots along with normalized prediction distribution error and bootstrap method were performed for model evaluation. Results: We successfully extrapolated the PK profile from adults to pediatrics after IV administration. The estimated first-order absorption rate constant and bioavailability were 0.751 hour-1 and 48.5%, respectively. Model evaluations showed that the model can accurately and precisely describe the observed data. The population pharmacokinetic model we derived for morphine after oral administration of DTO is reasonable and acceptable; therefore, it can be used to describe the PK and guide future studies. Conclusion: The integration of the previous population PK knowledge as prior information successfully overcomes the logistic and practical issue in vulnerable neonate population.

Keywords morphine, diluted tincture of opium, bioavailability, population pharmacokinetic model, neonate, neonatal abstinence syndrome

Introduction

Neonatal Abstinence Syndrome (NAS) is a clinical syndrome of opiate withdrawal in neonates exposed to drugs prenatally via chronic maternal opiate use. The syndrome is comprised of a combination of central nervous system, digestive system and autonomic system abnormalities after birth that results from uninhibited excitatory neurotransmitter release from the neurons that have been chronically exposed to opiates in utero. The main symptoms include tremors, hyperactive reflexes, disturbed sleep, poor feeding and failure to thrive. Neonates at risk of NAS are monitored closely with physiologic scoring systems76 to rate severity of withdrawal and determine when non-pharmacologic therapies have failed and pharmacologic therapy is warranted. Morphine is the standard first line pharmacotherapy in Neonatal Abstinence

Syndrome (NAS)77. The current population pharmacokinetic (PK) analysis was based on data collected in a randomized controlled trial of adjunct therapy with clonidine vs placebo in prenatally exposed neonates treated with diluted tincture of opium (DTO) for NAS10.

The active ingredient of DTO is morphine, with every 1 ml of DTO containing 0.04 mg morphine equivalent. There are many studies investigating the pharmacology of parenteral morphine in neonates, but there are currently no studies of the PK of enteral morphine in neonates. Several publications describing the population PK of morphine in pediactrics51,78–81 have been published. Bouwmeester78 and Anand79 used the maturation model to describe the physiological development of clearance and volume of distribution. Knibbe80 developed another model to account for the maturation and development of clearance and volume of distribution in pediatrics. Holford51 evaluated the models above and updated their previous model published in

200879. While Krekel82 provide another review of Bouwmeester’s and Knibbe’s models, Wang et al81 published another morphine PK model in adults and pediatrics using an empirical exponent

68 on the commonly used allometric scaling. However, all of these models focused on intravenous administration of morphine.

The general lack of PK knowledge about enteral DTO or morphine in neonates leaves the clinician to empiric dosing of the opiate and often leads to undertreatment of the symptoms while the dose is titrated to maximal effect and NAS is brought under symptomatic control. The extended titration phase as well as weaning off phase result in increased duration of hospital stay.

The aim of this analysis is to elucidate the PK properties of enteral morphine in neonates using prior knowledge on pharmacology of morphine, concepts of pediatric physiological maturation as well as data collected from the large randomized controlled trial mentioned above. The current work will provide an opportunity to assess exposures at different doses and dosing regimens of enteral morphine in neonates with NAS.

Methods

Clinical Trial Design

The clinical trial was approved by the Johns Hopkins institutional review board

(ClinicalTrials.gov: NCT00510016). Informed consent was obtained from the parents of each neonate. The objective of the clinical trial was to determine if oral clonidine as an adjunct therapy to DTO would reduce the duration of opioid detoxification in neonatal abstinence syndrome resulting from in utero methadone and/or heroine exposure. Modified Finnegan

Scores76,83 (MFS, range from 0 to 40) were used as a symptom evaluation tool for the guidance of pharmacotherapy. After receiving informed consent, a total of 80 patients who required pharmacologic therapy as indicated by MFS threshold were randomized equally to either the

DTO only group or the DTO plus clonidine group. Morphine PK samples were collected in the

DTO arm only. The treatment regimen involves 3 phases. During the first phase, all neonates were started on 0.2 mL of DTO (0.08 mg morphine equivalent) orally every 4 hours. Then, DTO was incrementally escalated to 0.3, 0.4, and 0.5 mL (0.12, 0.16 and 0.2 mg morphine equivalent) every 4 hours, then to 0.5, 0.7, and 0.9 mL (0.2, 0.28 and 0.36 mg morphine equivalent) every 3 hours until withdrawal symptoms were controlled (defined as average MFS in 24 hours period

<9). In the second phase, when symptoms were controlled, neonates were continued on the DTO dose that controlled symptoms for at least another 48 hours (stabilization). Finally in the third phase, the neonates entered the medication weaning phase. DTO was deescalated by increments of 0.05 mL per dose every 24 hours as long as MFS remained in the target range. Further details of the trial methods and the clinical results were published in 200910.

During the clinical trial, 2 to 3 plasma samples were collected per neonate based on clinical convenience. No nominal sampling time points were designed in the protocol. Morphine was isolated by solid phase extraction and plasma concentration was analyzed by LC/MS/MS method utilizing deuterated internal standard at the NMS Labs (Willow Grove, PA).

Population PK Model

Our strategy for building a PK model for neonates was to borrow strength from prior models.

There are five prior IV models reported with potential difference. Owing to the sparse nature of data, the development of the PK model involved 2 major steps: 1) We evaluated the suitability of the prior models to extend to neonates; and 2) a population PK model after oral administration of

DTO/morphine was built based on the best structural IV model in the previous step and plasma data collected from the current clinical trial.

Prior IV Model Evaluation

Extrapolation of Structural PK Model after Intravenous Administration into Pediatrics

Four of the five IV morphine PK models represented in Table 4-1 were built in pediatrics or pediatrics plus adults to predict PK profiles over the entire lifespan4,5,7,8. However, Lotsch’s model33 was built only in healthy adults after IV administration with rich sampling as opposed to sparse sampling in the other four studies.. Therefore, Lotsch’s model was extrapolated to pediatrics using an allometric scaling approach in 3 steps before we evaluated the rest of the IV morphine models in adults and pediatrics.

Table 4-1 Summary of Previously Published Morphine IV Models

Publication Demographics PK Model Parameterization Bouwmeester78 Number of 1- tvCL= subjects:184 Compartm . CLstd⦁ ⦁ Liter Number of ent Model samples: 1856 hour tvV1 = V1std⦁ ⦁ 1 + Mean age: 195 () (range: 0-1070) ⦁ βvol⦁e Liter days Mean body weight: 5.9 (range: 1.9-16.8) kg Holford51 Number of 2- tvCL= subjects:875 Compartm . CLstd⦁ ⦁ Liter Number of ent Model samples: 1598 hour tvV1 = V1std⦁ ⦁ 1 + PNA: 0.27 (SD: () 0.26, ⦁ βvol⦁e Liter range: 0–2.84) . weeks, tvQ2 = Q2std⦁ Liter/hour PMA: 27.35 (SD: 2.31, tvV2 = V2std⦁ ⦁ 1 + range: 23–32) () ⦁ weeks βvol⦁e Liter Body weight: 1.04 (SD: 0.35, range: 0.42– 2.44) kg

with data from Bouwmeester78

Table 4-1 Continued

Publication Demographics PK Model Parameterization Knibbe80 Number of 2- tvCL= CLstd⦁(Wt). Liter/hour subjects:248 Compartm tvV1 = V1std⦁(Wt)Liter Number of ent Model tvQ2 = Q2std Liter/hour samples: 792 tvV2 = V2std⦁(Wt)Liter

Median body weight: 3.58kg (25–75 percentile: 2.2– 7.0); Median PNA: 33 (25–75 percentile: 0.95– 203) days Median PMA: 41.9 (25–75 percentile: 35.6– 62.6) weeks 81 Wang Number of 2- tvCL= CLstd⦁ Liter/hour subjects: 475 Compartm . Number of ent Model k⦁Wt k = k − . . samples: 9494 k + Wt tvV1 = V1std⦁ Liter Body weight range: 0.56-85 tvQ2 = Q2std⦁ Liter/hour kg tvV2 = V2std⦁ Liter Age range: 0.1 days-36 years Lotsch Number of 3- (No body weight, PMA and PNA was subjects:8 Compartm used for parameterization) Number of ent Model samples: 152

Mean PNA: 26.4 years Mean body weight: 70.5 kg PMA: post menstrual age PNA: post-natal age

First, we used the 3-compartment morphine PK model by Lotsch that is based on rich sampling after IV administration in healthy adults as the base model.33 Second, body weight based allometric scaling was added onto the volumes of distribution (V1, V2 and V3), the systematic clearance (CL), and the inter-compartment clearance (Q2 and Q3) with an exponent of 1, 0.75 and 0.75 respectively. 48 Lastly, the age effect of maturation on relative change in clearance and central volume of distribution after adjustment for body weight were also modeled. The maturation of drug metabolizing enzymes starts prenatally – in the case of morphine, mainly

UGT 2B785. Hence, we modeled the relative maturation of morphine clearance as a function of post-menstrual age (PMA) as in a previous publication 51:

⑴ =

HillCL is the Hill coefficient for clearance that defines the steepness of the maturation curve, and

CLmat50 is the PMA at which clearance was 50% of the mature adult value. Morphine is mainly distributed in extracellular water, which is represented by the central volume of distribution in the 3-compartment model. The physiological maturation of extracellular water as a percentage of body weight was modeled from previous work86 and used to adjust the maturation of morphine central volume of distribution in neonates. Since the maturation of extracellular water is due to postnatal adjustment to environment and activities, the extracellular water maturation curve was described by an exponential model as a function of postnatal age (PNA) as shown in Figure 4-1.

Figure 4-1 Maturation of extracellular body water as a percentage of body weight. The red solid dots represent digitized data from previous work. The solid blue line represents model fit The exponential maturation function is given below:

() ⦁ = + ⦁ ⑵

The final model with the appropriate covariates for typical value of clearance (tvCL), central compartment volume of distribution (tvV1), inter-compartment clearance (tvQ2, tvQ3) and peripheral compartment volume of distributions (tvV2, tvV3) are shown as follows:

. ⑶ tvCL= ⦁ ⦁ /

() ⦁ tv = ⦁ ⦁ + ⦁ ⑷

. tvQ = ⦁ / ⑸

tv = ⦁ ⑹

. tv = ⦁ / ⑺

= ⦁ ⑻ tvCL and tvV were predicted from the equations above using the covariates PMA, PNA and Wt

(body weight in kilograms). The rest of the parameters in the equation, described below, were fixed to the values obtained from the previous publication as seen in Table 4-2. CLstd and V1std are the clearance and the volume of distribution for the body weight of 70 kg respectively. Tvol is the maturation half-life of the PNA age-related changes of V1, and βvol is a parameter estimating the fractional difference from V1std at birth. Q2std, Q3std are the inter-compartment clearance for body weight of 70kg and V2std, V3std are the corresponding volume of distributions for the body weight of 70 kg. Since the relative change of other body composition such as intracellular water and body fat is negligible, it will not dramatically influence V2 and

V3. Also, no maturation effect was considered for inter-compartment clearances Q2 and Q3.

Thus, no maturation function was added onto V2, V3 and Q2, Q3.

Table 4-2 Population PK parameter estimates for the final model and corresponding bootstrap estimates

External Evaluation of Morphine Pharmacokinetics Model after IV administration in Adults and

Pediatrics

Five IV morphine PK models as listed in Table 4-1 were evaluated (including Bouwmeester

200478, Knibbe 200980, Holford 201251, Wang 201381 and the one we developed above based on

Lotsch 200233) in adults and pediatric populations based on population predictions using digitized mean concentration time profiles from previously published morphine PK studies in healthy adults87,88 , neonates, infants and children after surgery89–91 and leukemia patients in children92(individual data). These concentration time profiles were digitized from the publications using GetData Graph Digitizer (version 2.24.0.25). Predictions of these concentration time profiles were based on the IV administration models mentioned above, as well as study design and demographics given in the publications, including administration route

(bolus/infusion), dose, postnatal age, postmenstrual age and body weight. The comparison of those observed and predicted PK profiles after administration of IV morphine in pediatric patients were used as model external evaluation.

Oral PK Model in Neonates with Abstinence Syndrome

Based on the external evaluation of the IV morphine models in adults and pediatrics, the aforementioned structural model we developed based on Lotsch’s adults IV morphine model best predicted the external source of data; therefore, it was employed as the starting point for estimating the oral PK parameters. A first-order absorption rate constant (Ka) and bioavailability

(F) were added to model we developed to account for oral administration of morphine. The final

3-compartment PK model with first order oral administration of morphine is as follows:

= −⦁ ⑼

= ⦁ − ⦁ − ⦁ ∙ + ⦁ − ⦁ + ⦁ ⑽

= ⦁ − ⦁ ⑾

= ⦁ − ⦁ ⑿ where, Aa is the amount of morphine in the absorption compartment. A1 is the amount in the central compartment. A2 and A3 are the amount in the 2 corresponding peripheral compartments. Bioavailability (F) and absorption rate constant (Ka) were estimated by fixing all the parameters from the previously built IV morphine administration model.

Statistical Model

Between Subject Variability

Between Subject Variability (BSV) was modeled assuming a log-normal distribution:

, = ⦁ ⒀

where, Pi is the individual PK parameter for patient i, such as CLi, tvP is the typical value of that

PK parameter such as tvCL, ηp,i is the corresponding between subject variability for patient i

2 which is assumed to follow normal distribution with mean 0 and variance of ωp . BSV was estimated from the data in this study.

Within Subject Variability

Within Subject Variability (WSV) was modeled using a proportional residual error model as follows:

, = ,⦁( + ,) ⒁

where Observed Concentrationi,j and Ci,j are the observed and individual predicted morphine concentration in the central compartment for patient i at time j, respectively, εi,j is corresponding proportional error term.

Internal Evaluation of PopPK Model in Neonates

Model evaluation was based on various goodness‐of‐fit indicators, including comparisons based on the minimum objective function value (OFV), visual inspection of diagnostic scatter plots, and evaluation of estimates of population fixed and random effect parameters.

Normalized Prediction Distribution Error93 (NPDE) analysis was performed in Phoenix and

NPDE R package94 (version 2) in R 3.1.2. Two hundred replications of simulation were generated for each observation in the original dataset using the final model in Phoenix. NPDE vs time after dose (TAD) were used to determine whether trends were present.

A nonparametric bootstrap method using Phoenix was used to evaluate the precision of parameter estimation in the final model95,84. 200 replications were generated by resampling from the observed morphine concentration dataset and PK parameters were estimated for each replicate separately. The median and corresponding 95% percentile interval (2.5th and 97.5th percentiles) obtained from the 200 sets of parameter estimations were compared to the estimation from FOCE-I.

All the modeling was performed using Phoenix NLME 1.4 (Certara, LP, St. Louis, MO 63101,

USA). The first order conditional estimation method with interaction (FOCE-I) was used in the

80 population modeling process. All the plots were generated using Phoenix or R 3.1.2 (R

Foundation for Statistical Computing, Vienna, Austria).

Results

Eighty eight blood samples from 34 neonates were used to build the population PK model after oral administration of DTO. Six neonates (17.6%) were preterm (defined as gestational age less than 34 weeks) and the rest (82.4%) were full-term. Twenty one neonates (61.8%) were African-

American and the rest (38.2%) were Caucasian. At the beginning of treatment, the average body weight was 2.9±0.4 kg (mean ± SD); post-menstrual age (PMA) was 39.1±2.1 weeks and postnatal age (PNA) was 2.0±0.9 days.

Population PK Model

Prior IV Model Evaluation

A 3-compartment structural model with allometric scaling approach and physiologic considerations successfully extrapolated the IV morphine PK profile from adults to pediatrics.

PK parameters from a healthy adult IV model, including CLstd, Q2std, Q3std, Vstd, V2std and

V3std, are provided in Table 4-2. The maturation effect on clearance and volume were modeled as a function of PMA and PNA respectively. As previously published51, the time to reach 50% maturation of clearance relative to adults was 58.3 weeks. Similarly, the time to reach 50% maturation of volume relative to adults was 9.65 weeks and the relative difference at birth compared to the adult value was 0.614.

The comparison of the observed and predicted PK profiles after IV morphine administration in healthy adult and pediatric patients is shown in Figure 2 and 3 separately. There is a high consistency between our model (indicated as Lotsch 2002 in Figure 4-2 and Liu 2016 in Figure

4-3) predictions and the observations in both adult and pediatric populations. Though most of the previously published morphine models can predict the observed concentrations on similar level

as shown in Figure 2 and 3, comparisons with them have shown that our model and Wang’s

model predict not only the levels but the shape of the concentration time profile as well.

Figure 4-2 External evaluation of morphine structural models a) after IV bolus administration of morphine 0.14 mg/kg plus infusion of 0.05mg/kg/hour for 4hours in 20 healthy adults with mean age 24.6 years and mean body weight 74.2kg88. b) after IV bolus administration 10mg/70kg in 6 healthy volunteers with mean body weight 71.4kg and mean age 25.8 years87.

Figure 4-3 External evaluation of morphine structural model after intravenous bolus administration a) in infants undergoing elective surgery (mean age 21 months)90. b) in children with leukemia undergoing therapeutic lumbar puncture (median age 5.5 years and median weight 20.0 kg)92 c) in neonates (N=10, mean age 1.1 days and mean weight 3.5kg)89 d) in neonates (N=10, mean age 29 days and mean weight 3.9kg)89 e) in infants (N=7, mean age 112 days and 6.2kg)89 f) in infants (N=5, mean age 0.3 years and mean weight 5.3kg)91 g) in children (N=5, mean age 3.7 years and mean weight 16.3kg)91 h) in 91 children (N=4, mean age 6.4 years and mean weight 22.3kg) Oral PK Model in Neonates with Abstinence Syndrome

The structural PK parameters of the IV model being fixed, the first order absorption rate constant was estimated to be 0.751 hour-1 and the bioavailability was 48.5% in neonates with abstinence syndrome. Between subject variability (BSV) could only be estimated on clearance. The estimated proportional residual error was 44.9%. The model parameter estimates are shown in

Table 4-2.

Internal Evaluation of PopPK Model in Neonates

Goodness-of-fit plots including population prediction of concentrations (PRED) versus observation and individual prediction of concentration (IPRED) versus observation are shown in

Figure 4. No obvious model misspecification and bias were found from these two diagnostic plots. Bayesian individual concentration time profiles during the entire study were simulated based on post hoc PK parameter estimates in four representative neonates. The comparison between the simulated PK profiles and the observed concentrations in representative neonates are given in Figure 4-5.

Figure 4-4 a) Population predicted plasma concentration versus observed plasma concentration. b) Individual predicted plasma concentration versus observed plasma concentration

Figure 4-5 Comparison of post hoc PK profiles with observed concentration in four representative neonates The histogram plot of NPDE along with corresponding normal distribution curve is shown in

Figure 4-6a. No trends were visible in the NPDE versus TAD and PRED plot as shown in Figure

4-6b and c. Evaluation of the NPDE distribution shows that the model adequately describes the observed data.

Figure 4-6 a) Histogram of normalized prediction distribution error. The blue curve is the normal distribution with corresponding mean (-0.169) and standard deviation (1.03). b) Normalized prediction distribution error versus time after dose. c) Normalized prediction distribution error versus population prediction. The 3 dashed lines represent NPDE (CWRES) = -2, 0, and 2, separately Bootstrapping was performed as described in the methods. From the original dataset of 34 patients, 200 replicates were generated. Based on the re-estimation of the PK parameters in the

200 replications, the bootstrap median and 95% percentile interval are shown in Table 4-2. All the point estimates from FOCE-I are comparable to the bootstrap median and fall within the 95% bootstrap percentile interval. The comparison between FOCE-I estimation and bootstrap estimation shows that the model is stable and can precisely describe the observed data.

Discussion

Neonates are one of the most challenging populations for researchers to conduct clinical trials with, and thus they remain therapeutic orphans in that the PK and PD of many drugs are understudied. Due to lack of robust PK understanding, the dosing and clinical therapeutic effect are not optimized. The situation is even more dire for clinical indications not included in FDA approved drug labels, such as treatment of NAS. The current clinical trial provides a unique opportunity to characterize the PK knowledge of orally dosed morphine in patients with NAS.

Prior PK knowledge of morphine, physiologic knowledge and sparse data from the current trial were successfully integrated to describe the PK of enteral morphine in neonates with NAS.

Rich sampling in vulnerable neonate population is very challenging, especially for observational studies. There are both logistic and resource limitations such as obtaining consent from parents and availability of clinical staff for collecting rich invasive data that are not part of standard treatment. However, it is these observational studies which render us with valuable information to improve therapeutics in the future. The well-known pioneering work of population pharmacokinetics of phenobarbital in neonates96 is a good example of deriving meaningful PK information using observational sparse sampling data. A reasonable estimate of bioavailability and other PK parameters is extremely useful for optimizing therapy. If we don’t possess rich prior knowledge, analyzing such observational data is impossible. In the case of morphine, we have excellent prior knowledge regarding its PK. Further, not analyzing and reporting such data especially from vulnerable patients poses ethical issues. We elected to perform exhaustive research to make the best of the available data to steer future research in this area.

Due to the sparse sampling feature in previously published IV morphine PK models, it is very challenging to capture the shape of the profile. Moreover, all the observed PK profiles in rich sampling pediatric studies have shown a multi-phase disposition. Therefore, it is expected that

Bouwmeester’s 1-compartment PK model cannot describe the shape of the curve well as seen by the black line in Figure 2 and 3. It is to be noted that we ignored a bilirubin effect originally present in Bouwmeester’s model, as the data that we were predicting into did not provide any information on bilirubin. The inclusion of this bilirubin effect, that decreased the prediction of morphine clearance, would have increased the magnitude of over prediction of morphine concentration that was already present.

Meanwhile, Knibbe’s model which was developed based on data from pediatrics younger than 3 years, cannot provide good prediction in adult population as it has a 1.44 exponent on body weight. The exponent is mostly expected to be around 0.75 in children, adolescents and adults48.

Though Wang’s model predict well in both pediatric and adult population, it lacks physiological and pharmacological interpretation on its parameters due to forcing the exponent on body weight as a function of body weight itself. Our model on the other hand inherits the highly informed structural PK model derived from rich sampling after morphine IV administration in adults as compared to the previously published morphine PK models in pediatrics. The use of allometric scaling approach with age maturation models facilitated a better extrapolation from adult to pediatrics. In external evaluation of the structural IV model, the predictions are in good agreement with the observed PK profile in both adults and pediatrics (Figure 2 and 3). The external evaluation results strongly suggest the high reliability of the structural IV model and provided the confidence to employ this IV model as the starting point for estimating the oral PK parameters in neonates. The adult PK model, knowledge in pharmacology and physiology

89 attribute to our understanding of oral morphine PK in neonates, even with our sparse sampling design.

The previously published population PK model of morphine from which we borrowed the maturation curve of clearance51 as prior in this study found that clearance is different between preterm and full term neonates. They interpreted this difference as the result of mechanical ventilation, and not an inherent physiologic difference between preterm and full term neonates or maturation in PMA. Although 6 of 34 neonates were preterm in our study, they were not adjusted by the ventilation scaling factor used in the Holford publication for two main reasons. First, the preterm neonates in our clinical trial were very close in age to full term neonates given the average PMA was 34.5 weeks (compared to 27.35 weeks in their publication). Second, preterm neonates in our study did not receive mechanical ventilation after delivery.

The estimated oral bioavailability of 46.3% in neonates is higher than the observed rectal bioavailability of 35% after hydrogel administration (4 to 43 months) and the observed rectal bioavailability after the administration of morphine solution (5 to 31 months) of 27% in infants and children90. Also, the estimated oral bioavailability in neonates is higher than the oral bioavailability of 23.9% in healthy adults22. The higher bioavailability in neonates and the decreasing trend in bioavailability from neonates to adults fits well with our understanding of the physiology which dictates morphine disposition. There is lower expression of UGT2B7 per unit of hepatocyte in neonates; this is the major enzyme that is responsible for the metabolism of morphine and it is accounted for by the age effect in the clearance model. Additionally, the smaller liver size is accounted for by body weight in the clearance model, and both of these physiologic differences result in lower metabolic function and morphine clearance. This lower

90 systemic metabolic function leads to lower systemic clearance and first-pass effect after oral administration resulting in higher bioavailability.

Recently, allelic variability in OCT1 was proven partly responsible for the racial difference observed in morphine clearance between Caucasians and African-Americans. This study used a population approach to estimate pharmacologic parameters after the administration of IV morphine in children97,98. However, in our study, genotype of OCT1 was not collected and the race covariate did not influence the clearance based on the visual inspection of post hoc PK parameter estimates.

Conclusion

Overall, the population PK model of morphine after oral administration of DTO is reasonable and acceptable, and can be used to guide future studies by simulating exposure under different dosing regimens among various neonates with NAS. The integration of the previous population

PK knowledge as prior information served to confirm the previous model and reaffirm the physiologic basis of extrapolation to neonates. Furthermore, this approach is an efficient way to combine multiple prior studies with sparse neonatal data in order to make big decisions with little data. We feel that this approach is especially valuable in this vulnerable population.

Chapter 5 Uridine diphosphate glucuronic acid is a potential rate-limiting step in the

metabolism of morphine during the first week of life

Abstract

Neonates experience dramatic changes in the disposition of drugs after birth due to maturation in enzyme activities and adjustment to environmental changes that provide neonatologist with challenges in therapeutic decision making. In this research, we established postnatal age, postmenstrual age and body weight as physiologically reasonable predictors of morphine clearance in neonates. By integrating knowledge of time course of bilirubin, morphine and other drugs metabolized by glucuronidation pathways from previously published studies, we propose that the lower concentration of uridine diphosphate glucuronic acid during the first week of life is the rate-limiting step in the metabolism of morphine and other glucuronidation activities. This result might be extended to all the drugs metabolized by UGT pathways in neonates and therefore has an important clinical implication for the use of drugs in this population.

Keywords morphine, uridine diphosphate glucuronic acid, pharmacokinetics, neonates

Introduction

Uridine diphosphate glucuronosyltransferases (UDP- glucuronosyltransferase, UGTs) plays a major role in drug disposition through the glucuronidation pathways in human and accounts for the metabolism of 1 in every 10 drugs in the 200 most prescribed medicines in the United

States.99 Also, UGTs are also responsible for the elimination of endogenous substances, such as bilirubin and bile acids. Bilirubin (indirect bilirubin) is exclusively metabolized by UGT1A1 in human into bilirubin mono- and di-glucuronide (direct bilirubin).100 The lack of UGT1A1 activity is responsible for several diseases, e.g. neonatal jaundice or hyperbilirubinemia.101 The age-dependent expression of different UGTs has been characterized in human liver microsomes.68,102

Morphine is mainly metabolized by UGT2B7 and has been used for neonatal pain control and sedation for decades.85 In addition to pain control and sedation, morphine is one of the first line opiates for the treatment of neonatal opiate withdrawal syndrome, known as Neonatal

Abstinence Syndrome (NAS). Neonates who were exposed to opiates in utero are at risk for the development of NAS when the exposure to opiates is abruptly stopped at the time of delivery. In a national survey of the management of in utero acquired NAS, morphine sulfate was by far the most commonly used first-line agent for both opiate and polysubstance withdrawal. Despite common first-line use, this survey also found that there were 23 different treatment regimens reported from 211 neonatal units, with doses ranging from 10-400 µg/kg administered 2-8 hourly.103

To date, extensive effort has been put into the understanding of morphine disposition in pediatrics, especially neonates. Physiologically meaningful predictors, such as body weight

(WT) and postmenstrual age (PMA), have been established to describe the PK characteristics of morphine in pediatrics.51 Other relevant predictors, such as bilirubin concentration, were also reported.78 In an earlier publication, we developed a population pharmacokinetic model of morphine (hereby referred to as DTO model) after oral administration of diluted tincture of opium (DTO), in which morphine is the active ingredient.44

To further understand the pharmacokinetics (PK) of enteral morphine in neonates, two clinical trials in late preterm and full-term neonates treated with enteral morphine for in utero acquired

NAS were included here. The external evaluation of the DTO model using data from these two clinical trials in NAS showed PNA dependent bias in model prediction that warranted further investigation and improvement to the DTO model. The objective of this research was to thus expand upon the published DTO model to further improve the understanding of morphine PK in neonates.

Methods

Clinical Trials

JHU-Morphine Trial

Between the years 2012-2014, all women who delivered babies at risk for NAS due to in utero methadone or heroin exposure were approached for consent at a large multi-site academic hospital system. This clinical trial was approved by the Johns Hopkins Medical Institution IRB.

Once parental consent was obtained, neonates were monitored closely for NAS and were only enrolled in the study if they required opiate therapy. Once started on enteral morphine therapy, the daily times and doses of administration were collected and entered into an electronic database. In addition to in utero acquired opiate withdrawal, neonates with ICU-acquired NAS as a result of weaning of medical opiates who were treated with enteral morphine were also eligible for study entry. The dosing strategy was similar to the previously published clinical trial, which is the basis for the DTO model10. Briefly, all the patients started oral morphine sulfate solution at

80ug every 4 hours. The dosing adjustment was made based on the evaluation of withdrawal symptoms by the modified Finnegan score. The dose was up-titrated by 40ug increments to

200ug every 4 hours or 360ug every 3 hours if necessary. Upon stabilization, morphine dose was then de-escalated by increments of 20ug per dose every 24 hours. Neonates underwent heel stick puncture for capillary samples up to four times over the entire study period for collection of drug and metabolite concentrations based on clinical practice convenience and the samples were refrigerated for less than 24 hours. They were then centrifuged to isolate the plasma and the plasma samples were frozen at -80 °C until batch analysis via HPLC-MS/MS techniques as described in a separate publication.104

TJU-Morphine Trial / The Blinded Buprenorphine OR Neonatal morphine solution (BBORN) trial7(ClinicalTrials.gov Identifier: NCT01452789).

Between the years 2011-2016, neonates exposed to opiates in utero with more than 37 weeks of gestational age were eligible to enroll in the clinical trial if they demonstrated adequate signs and symptoms of NAS and required treatment. Morphine sulfate solution was one of the therapeutic options in this clinical trial. For neonates on morphine sulfate solution, their initial dose was 0.07 mg/kg every 4 hours and the maximum daily dose was 1.25 mg/kg/day. Morphine dose was up- titrated by 20% and weaned off by 20% based on the withdrawal symptoms. The cessation dose was 0.025 mg/kg every 4 hours. Sparse sampling for the evaluation of pharmacokinetics was pre- defined, including peak and trough concentration during the first week and second week as well as after dose cessation. In light of the sparse sampling regimen, some allowance for variation from this schedule was anticipated to reflect feeding and sleeping schedules for the neonates.

Capillary blood samples were drawn by heel stick with a goal volume of 0.4 ml blood into a lithium heparin tube. The method used for analysis of morphine was modified from the previously published method.105 Since pediatric sample has a limited volume 100 µL aliquot was used. The calibration curve was adjusted to 1 to 250 ng/mL, with quality control sample concentrations at 3, 20 and 200 ng/mL. The internal standard concentration was 20 ng/mL. 10 mM ammonium carbonate buffer with pH 9.3 was used for solid phase extraction. A shorter

HPLC column (YMC ODS AQ 5 µm 2x100 mm) was adopted. The method was cross validated to a Thermo Scientific – TSQ Quantum instrument with 0.1% formic acid in Milli-Q water(A) and methanol(B) as mobile phase, running isocratic with 92% A and 8% B for 6.5 minutes.

Reconstitution volume was 75 µL in 100% A. The clinical trial protocol was approved by the

Thomas Jefferson University Institutional Review Board. Informed consent was obtained from the parent for each neonate included.

DTO Model External Evaluation

DTO model hereby refers to the previously published morphine population pharmacokinetic model after oral administration of DTO, in which morphine is the active moiety.44 Briefly, a three compartment PK disposition model of morphine based on rich sampling after intravenous administration in healthy adults was first adopted as the structural model for morphine.33 Second, body weight-based allometric scaling was added to the central (V) and peripheral (V2 and V3) volume of distributions using an exponent of 1 and on systematic clearance (CL) and inter- compartmental clearance (Q2 and Q3) using an exponent of 0.75. Third, developmental ontogeny and its effect on PK characteristic maturation were modeled based on physiological consideration and literature knowledge. External evaluation of the DTO model was performed using the morphine concentration data collected in JHU-Morphine study and TJU-Morphine study. In this external evaluation, FOCE-I method was chosen to fit the data but with a number of iterations set to 0 to derive individual PK parameters.

Modified Morphine PK Model in NAS

The DTO model was adapted further to fit the combined data from all the three studies (DTO,

JHU, TJU).

An sigmoid Emax model was added to the DTO structural model to describe the PNA-dependent disposition of morphine after oral administration and its corresponding first pass effect.

Figure 5-1 Final Structural Morphine PK Model after Oral administration of Morphine Sulfate Solution in Neonates with Abstinence Syndrome. The final structural pharmacokinetic model (Figure 5-1) is as follows:

= −Ka⦁Aa (1)

= Ka⦁Aa − ⦁A1⦁UDPGA − ⦁A1 + ⦁A2 − ⦁A1 + ⦁A3 (2)

= ⦁A1 − ⦁A2 (3)

= ⦁A1 − ⦁A3 (4)

UDPGA = 1 − fraction⦁(1 − ) (5)

F = 1 − E⦁UDPGA (6) where Aa is the amount of morphine in the absorption compartment, A1 is the amount in the central compartment, A2 and A3 are the amounts in the peripheral compartments, and Q2 and

Q3 are the inter-compartmental clearance. UDPGA (UDP glucuronic acid) is a PNA-dependent variable, and theoretically ranges from 1 – fraction to 1, where UDPGA50 is the time to 50% of the UDPGA maturation and the parameter Gam is the sigmoid shape factor. When there’s no

UDPGA in the system, UDPGA = 0; when there’s enough UDPGA for the glucuronidation activity which makes the enzyme expression level the rate limiting step, UDPGA = 1. At the time of birth (t = 0 hour), the UDPGA = 1 – fraction. E is the extraction ratio. Based on our assumption, UDPGA level has the impact on intrinsic clearance of morphine and results in a change in both systematic clearance and bioavailability. Therefore, the UDPGA effect was directly added to the systematic clearance and bioavailability at the same time. More importantly, considering the glucuronidation activity is the conjugation of morphine and

UDPGA, the clearance of morphine is described as a second order reaction, and the elimination

rate constant of Kel (= ) is no longer a first order rate constant anymore.

The parameterization for the final model with the appropriate covariates for typical values of clearance (tvCL), the central compartment for the volume of distribution (tvV), inter- compartmental clearance (tvQ2, tvQ3) and peripheral compartment volume of distribution (tvV2, tvV3) are adopted from the previous publication and shown as follows:

. tvCL = CLstd⦁ ⦁ Liter/hour (7)

() ⦁ tvV = Vstd⦁ ⦁ 1 + βvol⦁e Liter (8)

. tvQ2 = Q2std⦁ Liter/hour (9)

. tvQ3 = Q3std⦁ Liter/hour (10)

tvV2 = V2std⦁ Liter (11)

tvV3 = V3std⦁ Liter (12) tvCL and tvV were predicted from the equations above using the covariates PMA, PNA and WT in kilograms. The rest of the parameters in the model, described below, were fixed to the values obtained from the previous publication as seen in Table 2. CLstd and Vstd are the clearance and the volume of distribution for the WT of 70 kg respectively. Tvol is the maturation half-life of the PNA age-related changes of V, and βvol is a parameter estimating the fractional difference from Vstd at birth. Q2std, Q3std are the inter-compartment clearance for WT of 70kg and V2std,

V3std are the corresponding volume of distributions for the WT of 70 kg.

Statistical Model

Between Subject Variability

Between Subject Variability (BSV) was modeled assuming a log-normal distribution:

, = ⦁ (13)

where, Pi is the individual PK parameter for patient i, such as CLi, tvP is the typical value of that

PK parameter such as tvCL, ηp,i is the corresponding between subject variability for patient i

2 which is assumed to follow a normal distribution with mean 0 and variance of ωp . BSV was estimated from the data in all three trials.

Within-Subject Variability

Within-Subject Variability (WSV) was modeled using a proportional residual error model as follows:

100

, = ,⦁( + ,) (14)

Model Evaluation

Model evaluation was based on various goodness‐of‐fit indicators, including visual inspection of diagnostic scatter plots, and plausibility and precision of estimates of population fixed and random effect parameters. Especially, the CWRES versus PNA plot was used to evaluate the improvement in the model predictions.

The nonparametric bootstrap method was used to evaluate the precision of parameter estimation and model stability. Two hundred replications were generated by re-sampling from the observed morphine concentration dataset and PK parameters were estimated for each of the replicated datasets separately. The median and corresponding 95% percentile interval (2.5th and 97.5th percentiles) obtained from the 200 sets of parameter estimates were compared to the estimates obtained from First Order Condition Estimation with Interaction (FOCE-I).

Population pharmacokinetic model analysis was performed using Phoenix NLME 7 (Certara,

L.P., St. Louis, MO 63101 USA). The FOCE-I method was applied in the modeling process. All the plots were generated using Phoenix.

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Results

Eighteen prospectively enrolled full-term neonates from Johns Hopkins University (JHU)-

Morphine study were included. The 18 neonates included three with ICU-acquired NAS and 15 with in utero acquired NAS. These 18 infants provided 51 plasma samples on which the parent drug morphine and the two major metabolites, morphine-3-glucuronide (M3G) and morphine-6- glucuronide (M6G), were measured. On average, 2 to 4 plasma samples were collected in each neonate.

Twenty-nine prospectively enrolled full-term neonates from Thomas Jefferson University (TJU)-

Morphine study (BBORN) were also included.7 One out of the 30 neonates in the original clinical trial was excluded due to incomplete information. All the 29 neonates included are acquired NAS in utero, and together provided 209 plasma samples on which parent drug morphine concentrations were measured. Seven samples were collected on average from each neonate with a minimum of 4 and a maximum of 16 samples.

Data from the 34 neonates with 88 morphine plasma samples after oral administration of DTO from our previously published clinical study in neonates were also included in the model development.10,44 This was included to maximize the information content for model development.106

In this research, we totally included 81 neonates from three clinical trials conducted at JHU and

TJU. The descriptive statistics of their demographics and baseline characteristics are given in

Table 5-1.

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Table 5-1 Patient characteristics at baseline in each clinical trial

JHU-DTO JHU-Morphine TJU-Morphine

(N = 34) (N = 18) (N = 29)

PNA (day) 2.0 ± 0.90 2.71 ± 4.21 1.06 ± 2.49

PMA (week) 39.1 ± 2.1 38.2 ± 1.62 39.5 ± 1.10

WT (kg) 2.9 ± 0.4 2.79 ± 0.60 2.98 ± 0.40

PNA: postnatal age; PMA: postmenstrual age; WT: bodyweight; data are presented as mean ± standard deviation DTO Model External Evaluation

The JHU and TJU clinical trials in NAS were used to evaluate the DTO model externally. All the

model diagnostic plots were acceptable except for the plot of conditional weighted residuals

(CWRES) versus postnatal age (PNA)107. The CWRES versus PNA diagnostic plots are given in

Figure 5-2 by each clinical trial including the one from the original DTO trial. The CWRES

versus PNA plot strongly suggested that the morphine plasma concentrations were under-

predicted during the first week of life and over predicted after the first week of life in the DTO

model, though this phenomenon was not obvious in the DTO model diagnostic plots.

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Figure 5-2 DTO model external evaluation: CWRES vs PNA Modified Morphine PK Model in NAS

For the purpose of elucidating the useful information from those neonates and solving the problem of the PNA-dependent bias in the DTO model external evaluation, the PNA instead of time after the first morphine dose, which is used in the previous DTO model, was used as the independent variable (time) in the model development. Therefore, the dosing time and sampling time are represented as time after birth in hours.

A PNA-dependent variable, which represents the level of UDP glucuronic acid (UDPGA), was successfully added to the DTO model to influence both the bioavailability (in equation 6) and systematic clearance (in equation 2) of morphine as shown in Figure 5-1. The UDPGA level was set to 1 when the enzyme expression level becomes the rate-limiting step at a later time point of life, and the lower baseline level of UDPGA was estimated as a fraction number (1 - fraction) to account for the insufficient UDPGA level in the early of life. As PNA was used as the independent variable, this baseline level of UDPGA represents the UDPGA level at birth. Due to the lack of morphine concentration data after intravenous administration in this research, we cannot independently evaluate the UDPGA effect on systemic CL. Hence, UDGPA was assumed

104 to have the same magnitude of impact on both F and CL. This makes mechanistic sense, as the pre-systemic clearance mechanism is as same as systemic.

The final parameter estimates with bootstrap evaluation are given in Table 2. Especially, the plots of CWRES versus PNA are shown in Figure 5-3. The PNA-dependent modeling bias was not found in any of the three clinical trials using the current model, and the improvement in the current model is very obvious in comparison with DTO model prediction in Figure 5-2. A comparison of concentration-time profiles between the previously published DTO model with the current model is given in Figure 5-4.

Figure 5-3 Model with PNA as prognostic factor: CWRES versus PNA

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Table 5-2 Final parameter estimates

FOCE-I Bootstrap Estimate Median BSV BSV 95%CI 95%PI 0.779 0.754 Ka (1/hr) (0.587, 0.971) (0.304, 1.10) 0.800 0.801 E (0.768, 0.832) (0.758, 0.853) a V (L) 17.8 17.8 37.4% 38.4% CL (L/hr) a 75.3 75.3 (23.4%, (29.5%, 45.6%)b 45.5%)b a V2 (L) 87.3 87.3 a Q2 (L/hr) 136 136 a V3 (L) 199 199 a Q3 (L/hr) 19.5 19.5 a CLmat50 (week) 58.3 58.3 a βVol 0.614 0.614 a Tvol (year) 0.185 0.185 4.10 3.91 UDPGA (day) 50 (2.61, 5.59) (1.96, 5.96) 0.481 0.516 fraction (0.297, 0.664) (0.317, 0.912) 2.54 2.54 Gam (1.37, 3.70) (1.37, 5.83) Proportional 0.427 0.417 residual errors (0.386, 0.469) (0.364, 0.465)

FOCE-I: First Order Conditional Estimate with Interaction. BSV: Between-Subject Variability. UPDGA: uridine diphosphate glucuronic acid. CI: confidence interval. PI: percentile interval. aParameters were fixed as in the previous publication44. bThe confidence interval of BSV on CL was calculated based on the BSV on CL and the corresponding SE assuming t-distribution.

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107

Discussion

In this research, we identified a PNA-dependent morphine metabolism in neonates with the previously recognized WT and PMA effect by externally evaluating the previously published morphine PK model in neonates.44

A consistent lower glucuronidation activity during the first week of life has been reported. In another previously published morphine PK model in neonates and infants, a lower clearance during the first 10 days of life was reported after adjusting for WT effect on clearance.80

Similarly, propofol is mainly metabolized by UGT1A9, and the propofol clearance was found to be lower during the first 10 days of life after adjusting for WT and PMA effect on clearance.108,109 Moreover, an increase of 103% in clearance has been reported in neonates after 7 days of life for zidovudine, which is also mainly metabolized by UGT2B7.110,111

Enzyme maturation is always believed to be correlated with PMA.49 However, the consistent lower glucuronidation activity during the first week of life across different UGT families and different substrates suggested a common phenomenon in neonatal glucuronidation activity. The reason of lower glucuronidation activity doesn’t seem to be a result of enzyme maturation, especially when the lower glucuronidation activity was reported after the adjustment for WT and

PMA.

UDPGA concentration-dependent glucuronidation activity in human hepatocytes has been reported based on an in vitro study.112 An in vivo study also showed that fasting condition could reduce the UDPGA content in rats, and thereafter resulted in a decreased hepatic bilirubin glucuronidation rate.113 Also, the lack of UDPGA was shown to be responsible for the hyperbilirubinemia in neonates during the first week of life regardless of whether the UGT1A1 is

108 induced by phenobarbital or not.114 Especially, the depletion of hepatic UDPGA by UGT substrates suggested that UGT substrates, including bilirubin, can competitively inhibit each other’s metabolism by conjugation with UDPGA.115

UDPGA is made from UDP-glucose by UDP-glucose 6-dehydrogenase, and the UDP-glucose is synthesized by uridyl transferase from a-glucose-1-phosphate and uridine triphosphate.116,117 The increasing plasma glucose level during the first week of life contributes to the increased glucuronidation activities by increasing the formation of UDPGA.118 Therefore, the formation of

UDPGA could be a function of PNA, and leading to a PNA-dependent morphine metabolism in the first few week of life. The mechanism of glucuronidation in neonates could be summarized in the following formula:

UGTs (PMA) Substrate + UDPGA (PNA) Substrate-glucuronide + UDP where the concentration of UDPGA is a function of PNA and the UGT activity is a function of

PMA. During the first week of life, the lower concentration of UDPGA is the rate limiting step and dominates the glucuronidation activities. Afterwards, substantial UDPGA is available at the site of action and the UGTs level, which is dependent on PMA, is the rate limiting step.

As an endogenous entity, bilirubin is part of the hepatic function evaluation in Child-Pugh.119

Bilirubin is mainly metabolized by UGT1A1.120 After birth, the concentration of bilirubin increases until 3 to 4 days of life and then decreases. The individual bilirubin concentration-time profiles from the 29 neonates in BBORN study with the corresponding LOESS regression are given in Figure 5. A similar pattern of clearance changes in bilirubin could be found, which is consistent with our findings in the analysis and the previously reported clearance of morphine

109 and propofol in neonates. In a previous publication, Smits et al. raised a question on the relationship between hyperbilirubinemia and propofol clearance.121 However, hyperbilirubinemia failed to show significant effect on propofol clearance. In addition, Bouwmeester et al. found that bilirubin concentration is a predictor on morphine clearance in the formation of M3G, but did not provide a physiological reason.78

110

Figure 5-5 Observed individual indirect bilirubin concentration (open circle with the grey line) versus postnatal age in the 29 neonates from TJU trial with LOESS regression (solid black line) By putting both in vitro and in vivo evidences together, we are able to show the previously reported bilirubin effect on morphine clearance by Bouwmeester et al. is not only as a general covariate for hepatic function but more mechanistically as a result of lower UDPGA concentration. In the correlation with bilirubin, the clearance of morphine and propofol may not be a function of the bilirubin concentration or hyperbilirubinemia, but the change of bilirubin elimination rate.

111

PMA and PNA are always considered correlated with each other. However, the PNA-dependent clearance in neonates cannot be interpreted as a result of PMA. In this study, the gestation age ranges from 32 to 40 weeks. With the 2 to 4 weeks therapeutic time in these NAS patients, gestational age plays a major role in PMA, and the PMA for those neonates with abstinence syndrome is not highly correlated with PNA. The bias trend observed in the model external evaluation is not correlated with PMA, and no model improvement was found when PMA was further included as a covariate on clearance.

The bioavailability of 54% estimated in the previous DTO model was an average of the increased bioavailability and decreased clearance during the first week of life.44 Due to the inconsistent length of hospital stay, the sparse sampling of plasma concentrations was mainly collected during the first two weeks of life. As a result, a twofold higher bioavailability was reported mainly driven by the majority of plasma samples collected during the first week of life, and the PNA-dependent metabolism of morphine was not covered.

In this research, we proposed a new explanation for the PNA-dependent lower glucuronidation activity in the first week of life, and linked the relationship between bilirubin and the clearance of morphine by the UDPGA level. The conclusion was extended beyond the morphine clearance to all the glucuronidation activity in the first week of life. Based on the findings in this research, drugs mainly metabolized through UGT pathways may potentially experience a much lower clearance during the first week of life regardless of their PMA and WT, and a dosing adjustment may be required.

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Chapter 6 Mechanistic–based exposure-response relationship in neonatal abstinence

syndrome

Abstract

Neonatal abstinence syndrome (NAS) is a clinical syndrome of opiates withdrawal. Modified

Finnegan score (MFS) is a commonly used clinical tool for the evaluation of the withdrawal symptoms. Morphine is one of the first-line pharmacotherapy in NAS. Due to the rapid change in physiological development after birth and the lack of reliable baseline predictors for the severity of NAS, no dosing strategy could be recommended for the treatment of NAS, and physicians have to rely on the empirical dosing strategies to treat neonates with abstinence syndrome, which makes the pharmacotherapy extremely challenging. In this research, we established an exposure- response (ER) relationship between morphine concentration and MFS in NAS based on the current understanding of the pathophysiology of NAS and pharmacology of morphine. The ER relationship incorporating with the current dosing strategies was used to reproduce the observed time-on-treatment, total morphine dose, time to the max morphine dose and time of MFS above

8 in the clinical trials. This ER relationship could be further applied in the evaluation of different dosing strategies in NAS and optimization of pharmacotherapy.

Keywords neonatal abstinence syndrome, morphine, exposure-response relationship, modified Finnegan score

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Introduction

Neonatal abstinence syndrome (NAS) is a clinical syndrome of opiate withdrawal.77 In utero acquired NAS is a result of the cutoff source of opiates from their mom due to delivery. Pregnant women who addict to opiates are normally administered high dose methadone during their pregnancy for maintenance therapy. Opiates, such as methadone, can distribute through the placenta into the fetus122. Therefore, the fetus could be exposed to opiates for several months before delivery. During the long term exposure to opiates, the activated opiates receptor inhibits the G-protein coupled receptor (GPCR), resulting in a reduced activity of adenylate cyclase

(AC), lower cyclic adenosine monophosphate (cAMP) level and Ca+ channel activity.123–125 This intracellular signal transduction can finally lead to a reduced release of norepinephrine (NE) into the synaptic cleft. NE is an agonist of α2-adrenoceptor. On the surface of pre-synapse, the expression of α2-adrenoceptor is responsible for the self-regulation of NE release. The intracellular signal transduction process for NE on α2-adrenoceptor is similar to opiates on the opiates receptor. Therefore, the lower NE concentration in the synaptic cleft will self-regulate its release through the α2-adrenoceptor to reach the equilibrium again. Since the source of opiates was cut off after the delivery, the inhibition effect of opiates was diminished, which results in an over-activated AC activity and higher cAMP level. Thereafter, the neonates over-released NE into the synaptic cleft, so-called “norepinephrine storm”. The over-released NE is believed to be responsible for the withdrawal symptoms in NAS. The major clinical signs and symptoms of

NAS are related to the increased levels of CNS excitation and include a group of readily observable behavioral characteristics, ranging from mild tremors to major seizures.

Neonates with abstinence syndrome normally experience various withdrawal symptoms. The withdrawal symptoms could be mainly classified into three categories: central nervous system

114 disturbance, gastrointestinal tract disturbance and autonomic/metabolic system disturbance. The central nervous system disturbance includes: inconsolability, high-pitched crying, hyperactive reflex, increased muscle tone, disturbed and undisturbed tremors and seizures. The gastrointestinal disturbance includes: poor feeding, excessive sucking, vomiting and loose or watery stools. The autonomic and metabolic disturbance includes: sweating, nasal stuffiness, sneezing, fever, tachypnea and frequent yawning.

Modified Finnegan score (MFS) is a commonly used clinical tool for the evaluation of withdrawal symptoms in NAS.76,83 For each withdrawal symptom mentioned above, an empirically based score is assigned to the symptom. Those having the least pathological significance were arbitrarily given a score of "1", such as sweating and nasal stuffiness; those with the greatest potential for clinically adverse effect were scored as "8", such as seizures, while the others were given intermediate point values (Table S1). A total score of all those items will be calculated every 3 to 4 hours and used to guide the pharmacotherapy.

Morphine is a mu opiate receptor agonist and one of the first line pharmacotherapy for NAS. The goal of pharmacotherapy, which focuses only on the short-term clinical outcome, is to reduce the time-on-treatment by controlling withdrawal symptom and restoring normal newborn activities as soon as possible. Currently, different morphine dosing strategies were applied in the clinic to treat NAS. Generally, the pharmacotherapy could be divided into three steps: symptom control, stabilization and restoration of normal activities. During the symptom control phase, a starting dose of enteral morphine was given to the neonates, for example 80 μg every 4 hours. Then, based on the clinical evaluation of the response to the current dosing regimen, a higher dose of enteral morphine might be given if the symptoms could not be controlled, for example 120ug every 4 hours. Once the withdrawal symptoms were controlled, the neonates entered stabilization

115 phase. In the stabilization phase, the neonates were stabilized at the dose level that controlled their withdrawal symptoms for 48 hours. Afterwards, physicians could start to wean off the enteral morphine dose gradually to restore the normal activity for the neonates. However, the starting dose, the dose titration step, the cessation dose and the criteria for dose titration could be different from hospital to hospital. Since NAS is a self-limiting disease, the purpose of morphine administration was not to directly treat the neonates but to control their withdrawal symptoms by inhibiting the release of NE. On the other hand, the over-released NE contributed to the restoration of normal activity directly. Therefore, the pharmacotherapy is a balance between the control of withdrawal symptoms and control of self-regulation in the pre-synapse.

Clonidine is a α2-adrenoceptor agonist, which has the similar pharmacological effect as NE and shares similar underlying signal transduction pathway as morphine. The previously published clinical trial has shown that adding clonidine onto morphine in the treatment of NAS can decrease the time-on-treatment.10 Moreover, clonidine might be a favorable alternative to morphine as a monotherapy for NAS.11 On the other hand, clonidine is introduced into NAS management relatively new and we have limited experience on how to use clonidine efficiently.

To date, the current dosing strategy has never been optimized and the ER relationship remains unknown. The dosing strategies could vary dramatically in every aspect, including the threshold to start pharmacotherapy and increase the morphine dose, the starting dose of morphine, the incremental amount of morphine, the stabilization time, the weaning-off amount of morphine every time, the cessation dose of morphine, etc.12 The purpose of this research is to characterize the relationship between the morphine exposure and MFS to support the further evaluation and optimization of dosing strategies in NAS.

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Method

Clinical trials

Neonates who were treated with morphine sulfate solution or diluted tincture of opium (DTO) from the two previously published randomized control clinical trials, adjunctive clonidine therapy (JHU) trial and Blinded Buprenorphine or Neonatal Morphine Solution (BBORN) trial, were included in this exposure response analysis.7,10

The JHU trial is a double-blinded, placebo controlled trial including two arms, morphine only arm and morphine plus clonidine arm. Both two arms in this clinical trial were included in this exposure-response analysis. During the JHU clinical trial, neonates under the risk of NAS were started on diluted tincture of opium (DTO) when two consecutive modified Finnegan score

(MFS) were above 9. Neonates who were randomized to morphine plus clonidine arm were also given oral clonidine dose of 6 μg/kg per day. The DTO starting dose is 80 μg q4h. The dose was further titrated up to 120 μg q4h, 160 μg q4h, 200 μg q4h, 240 μg q4h, 240 μg q3h, 280 μg q3h,

320 μg q3h and 360 μg q3h if two consecutive MFS was above 9 after the previous lower dose was given. The neonates were given at the dose that can control their withdrawal symptoms for

48 hours, and then the morphine dose was weaned off every 24 hours by 20 μg. Detailed dosing strategy and the clinical trial outcome could be found at Alexander G. Agthe et al10.

BBORN is a double-blinded controlled trial including two arms, buprenorphine arm and morphine arm. Only the morphine arm was included in this exposure response analysis. During the BBORN clinical trial, neonates under the risk of NAS were started on morphine sulfate solution oral therapy when the sum of three consecutive scores is more than 24. The starting dose is 70 μg/kg q4h. The dose was increased every 24 hours by 20% if the sum of the three

117 consecutive score is more than 24. No dosing interval adjustment was made in this clinical trial.

After stabilization for 48 hours without meeting the criteria to increase the morphine dose, morphine dose was weaned off every 24 hours by 10% until the dose was less than 25 μg/kg q4h.

The detailed dosing strategy and the clinical trial outcome could be found at Walter Kraft et al7.

The comparison of the two morphine dosing strategies is given in Table 6-1, and a summary of demographics and baseline characteristics is given in Table 6-2. One of the neonates in the BBORN morphine arm was excluded from this analysis due to incomplete information.

Table 6-1 Summary of study drug formulation and dosing strategies

JHU BBORN

Active drug solution Diluted tincture of opium Morphine sulfate solution

(0.4mg/mL) (0.4mg/mL)

Initial dose 80 μg q4h 70 μg/kg q4h

Maximum dose 360 μg q3h 200 μg/kg q4h

Up-titration rate 40 μg# 20%

Weaning rate 20 μg 10%

Cessation Dose 20 μg q4h 25 μg/kg q4h

# switch dosing interval to 240 μg every 3 hours without increasing the dose after 240 μg every 4 hours

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Table 6-2 Demographics and Baseline characteristic

Clinical Trial JHU BBORN Overall Arm DTO Clonidine + DTO Morphine Sample Size 40 40 29 109 Gestational age 38.5±2.22 38.3±1.94 39.4±1.13 38.7±1.91 (weeks) Birth weight (g) 3049±394 2864±365 2993±402 2961±389 Gender: Male% 45% 47.5% 62.1% 50.5% Female% 55% 52.5% 37.9% 49.5% Maternal Barbitals 2.5% 0% 0% 0.9% Maternal Cocaine 27.5% 22.5% 17.2% 22.9% Maternal Opiate 37.5% 37.5% 17.2% 32.1% Maternal Methadone 87.5% 90% 93.1% 89.9% Maternal Methadone 76.2±26.0 78.9±40.1 128.3±64.4 91.7±49.7 Dose (mg) (N = 33) (N = 34) (N = 26) (N = 93) Race: White% 32.5% 52.5% 79.3% 52.3% Black% 67.5% 47.5% 13.8% 45.9% Others% 0% 0% 6.9% 1.8% Breastfeeding Yes% 0% 0% 27.6% 7.3% Delivery type: Vaginal% 82.5% 65% 82.8% 76.2% Cesarean% 17.5% 35% 17.2% 23.8%

Morphine Pharmacokinetics

A sequential PKPD approach was applied to build up the relationship between morphine plasma concentration and MFS in the ER analysis. The previously developed morphine PK model

(Chapter 5) in neonates was used to derive the morphine concentrations (Cmorphine) based on the

119 posthoc estimates in each neonate. For those neonates who were not included in the morphine population PK model development (6 neonates from morphine only arm, all the 40 neonates in the morphine plus clonidine arm in the JHU trial), the typical values for all the PK parameters based on their demographics were used.

Exposure-Response relationship

Based on the understanding of pathophysiology and pharmacology mentioned in the introduction section, a mechanism-based ER relationship was developed. The structural model is given in

Figure 6-1. In the structural model, the time course of MFS was described by two component, symptom control and disease remission.

120

Figure 6-1 Structural Model Disease Remission

The disease remission is described by an indirect response model following zero-order formation and first-order degradation.

= · · − ∙ Equation 1

where Ktol is the turnover rate, Kremis is the disease remission rate, and the M level at birth was set to 1. The M, which represents the underling disease status, such as over activated adenylate cyclase, has a positive effect on the formation of MFS.

Symptom Control

121

The impact of morphine plasma concentration on the control of withdrawal symptoms was described by an inhibitory indirect response model of MFS. Here, we assumed that the withdrawal symptoms in terms of MFS were a measurement of NE concentration in the synaptic cleft. Therefore, the inhibitory effect of morphine on the synthesis of MFS is actually representing the inhibitory effect of morphine on the release of NE from the pre-synapse.

+ + · , = ∙ ∙ ⎛1 − ⎞ + 1 + + · ⎝ , ⎠

− ∙

Equation 2

where Kformation and Kdegradation are the corresponding formation rate and degradation rate constant,

, ∙ and the MFS at birth was initiated as , . The clonidine effect on symptom control was controlled by a binary time-varying covaraite isClonidine, where isClonidine = 1 when patients were on clonidine therapy and isClonidine = 0 when patients were not on clonidine; the clonidine effect was estimated by a constant value (delta) due to the small range of clonidine concentration after 1ug/kg in the JHU clinical trial. Considering the opiate concentration (mainly methadone) left in the newborn's system circulation at the time of delivery, a morphine equivalent concentration Cmorphine-equivalent was estimated at the birth time

(Cmorphine-equivalent0) and assumed following the first-order elimination with an elimination rate constant fixed at ln2/26 hours, where 26 hours is the previously reported methadone half-life in neonates.

122

For all the pharmacokinetics and pharmacodynamics models described above, time after birth in hours was used as the independent component in the dynamic systems.

Statistical Model

Between Subject Variability

Between Subject Variability (BSV) was modeled assuming a lognormal distribution:

, = ⦁

where, Pi is the individual parameter for patient i, such as Kdegradationi, tvP is the typical value of that parameter such as tvKdegradation, ηp,i is the corresponding between subject variability for

2 patient i which is assumed to follow a normal distribution with mean 0 and variance of ωp .

Within-Subject Variability

Within-Subject Variability (WSV) was modeled using a proportional residual error model as follows:

, = , + ,

where Observed MFSi,j and MFSi,j are the observed and individual predicted morphine concentration in the central compartment for patient i at time j, respectively, εi,j is corresponding proportional error term.

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Prospective predictors

Demographics and Baseline characteristics including gestational age, birth body weight, gender, race, maternal methadone dose, maternal and neonatal urinary drug test result (barbital, cocaine, opiates), were all evaluated as covariates for the ER relationship parameters.

Model Evaluation

Goodness-of-fit plots were generated for the visual inspection of overall model fit and the model fits in each patient.

Moreover, from a clinical point of view, the primary endpoint of interest is the time-on- treatment. Therefore, the model evaluation was extended to the time-on-treatment by simulating the longitudinal MFS with the morphine dosing strategies implemented in the JHU and BBORN clinical trials. The prediction of dosing history is mainly based on two components: dose- exposure-response relationship and dosing strategy. The simulated MFS and dosing records in

1000 patients were generated for each arm based on the ER model and the dosing strategy accordingly (Figure 6-2). In the simulation, the individual demographics, including body weight, gestational age, and individual PK parameters for both morphine and clonidine were simulated based on the typical value and BSV. The individual parameters related to the ER relationship, such as IC50 and Kdegradation, were re-sampled from the model posthoc estimates.

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Figure 6-2 Simulation workflow for MFS and dosing history. For example, a new patient was resampled from the posthoc estimates for the 102 NAS patients in the corresponding clinical trial. Based on the dose-exposure-response model, MFS was simulated every 4 hours after birth until the patient meets the criteria to start pharmacotherapy.

Then, based on the dosing strategy, a dose of morphine was determined and given to the patient.

After 4 hours, a new MFS was simulated based on the previous dose that was given. And then the next morphine dose was derived based on the dosing strategy again. This process was implemented until the patients meet the criteria for dosing cessation. After all, the time-on- treatment could be determined from the simulated dosing records for this patient. At the same time, the total dose of morphine used during the treatment, the time from the first dose the max morphine dose in this patient, and the time that this patient suffered from the withdrawal symptoms (MFS > 8) were also calculated.

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For the purpose of describing the body weight change in the simulation, neonatal body weight was modeled as a function of PMA and PNA based on the observed data in the two clinical trials.

= + · ( − 40) + · Equation 3 where WT is the body weight, BWT is the typical body weight at birth for a newborn with 40 weeks of gestation age (GAGE), dBWTdPMA is the gestation age effect on birth weight and

GRW is the body weight growth rate per day of life. Between-subject variabilities were added on

BWT and GWR assuming lognormal distribution. Goodness-of-fit plots and visual prediction check (1000 replicates) were used to evaluate the body weight model.

The Kaplan-Meier plot with 95% confidence interval was generated and overlaid with the observed one in each clinical trial for model evaluation. And the 25%, 50% and 75% time-on- treatment were compared between the observed data and the simulated. Moreover, a descriptive statistical summary of the simulated total dose of morphine used during the treatment, the time from the first dose the max morphine dose in this patient, and the time that this patient suffered from the withdrawal symptoms (MFS > 8) in each arm were compared with the corresponding observations in the clinical trials.

The exposure-response analysis and the body weight model were performed using Phoenix

NLME 7 (Certara, L.P., St. Louis, MO 63101 USA). The FOCE-I method was applied in the modeling process. The simulation for time-on-treatment was performed in R 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria). All the plots were generated using Phoenix or R

3.4.1.

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Result

Clinical trials

Totally, 109 patients with 13,300 MFSs from the two clinical trials were included, all 40 neonates in the DTO only arm, all the 40 neonates in the clonidine plus DTO arm, and 29 out of

30 neonates in the morphine sulfate solution arm, where one out of the 30 neonates in BBORN trial was not included due to incomplete information. Seven out of the 29 neonates from BBORN trial who were on phenobarbital as second-line therapy in the morphine sulfate solution arm were included only before phenobarbital was given. For the 80 neonates in the DTO clinical trial, none of them were on any other drugs that may interfere with the morphine and clonidine pharmacodynamic effect, such as phenobarbital and methadone. Eight out of the 29 neonates in

BBORN study was on breastfeeding, while the rest 21 neonates out of 29 neonates in BBORN study and all the neonates in JHU trial were on bottle feeding.

Exposure-Response relationship

The model parameter estimates for the ER relationship are given in Table 6-3. The comparison between observed and model fitted MFS-time profiles in six representative patients are given in

Figure 6-3, two from the morphine arm in the BBORN trial, two from the DTO only arm in the

JHU trial, and the other two from the DTO plus clonidine arm in the JHU trial. The comparisons between observed and model fitted MFS-time profiles in all the neonates are given in Figure S1.

The estimated individual MFS-time profiles successfully described the observed ones in these three arms from the two clinical trials, despite different dosing strategy and combination with clonidine.

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Table 6-3 Model Parameter Estimates

Parameters (units) Parameter Estimates Between Subject Variability Ke (hr-1) 0.0267

Cmorphine-equivalent0 (μg/L) 30.9 74.1% -1 Kformation (hr ) 1.46 93.6% -1 Kdegradation (hr ) 0.0499 95.2% -1 Ktol (hr ) 0.0166 144% -1 Kremis (hr ) 0.00400 71.4%

IC50,morphine (μg/L) 6.30 108% delta 0.305 Additive residual error 2.43

Figure 6-3 Comparison between observed and model fitted MFS-time profiles in representative patients.

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Prospective predictors

No baseline characteristics and demographics were identified in the covariate analysis.

Model Evaluation

The body weight model successfully described the observed body weight in 109 neonates after birth until they finished the pharmacotherapy for NAS. The model parameter estimates are given in Table 6-4 and the model diagnostic plots are given in Figure 6-4.

Table 6-4 Body weight model parameter estimates

Parameters Parameter Estimates Between Subject Variability (90% CI) (90% CI) BWT (g) 2809 12.1% (2730, 2887) (10.7%, 30.0%) dGAGEdBWT (g/day) 11 (6, 16) GRW (g/day) 23 44.0% (20, 27) (13.4%, 54.5%) Additive residual error (g) 110 (89, 127)

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Figure 6-4 Left: Goodness-fit-plot. Right: visual predictive check plot for the body weight model. The open gray circles are observed body weight in 109 neonates, and the three dash lines are the model predicted 2.5%, 50% and 97.5% percentiles During the simulation, 2 and 4 out of the 1000 simulated patients did not reach the MFS threshold to start pharmacotherapy in the DTO and DTO plus clonidine arm respectively, and therefore not included in the analysis. In the simulation for morphine arm in BBORN clinical trial, 52 patients did not reach the MFS threshold to start pharmacotherapy. Therefore, these 52 patients were not included in the analysis. The comparison between observed and simulated

Kaplan-Meier plot for each arm in the two clinical trials are given in Figure 6-5, Figure 6-6 and

Figure 6-7. The median with the corresponding 95% confidence interval for the time-on- treatment are given in Table 6-5, and the distribution of 25%, 50%, and 75% percentile of the time-on-treatment are given in Figure 6-8, Figure 6-9 and Figure 6-10. Also, the total morphine dose, time to the max morphine dose and the time of MFS above 8 were summarized in the simulation and compared with the observed ones in the clinical trials (

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Table 6-6). The ER relationship incorporating with the corresponding dosing strategy provided a good reproduction of the observed treatment-on-time, as well as other aspects in the clinical trial.

Figure 6-5 Comparison between the model predicted and observed time-on-treatment in JHU clinical trial DTO-only arm.

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Figure 6-6 Comparison between the observed and model predicted time-on-treatment in JHU clinical trial DTO plus clonidine arm.

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Figure 6-7 Comparison between the observed and model predicted time-on-treatment in BBORN clinical trial morphine arm. Table 6-5 Comparison between the observed and model predicted median time-on- treatment (median (95% confidence interval))

Trial and Arm Observed Simulated

N Median (95% CI) N Median (95%CI)

JHU DTO 40 14.7 (7.88, 19.8) 998 16.2 (15.5, 17.5)

JHU Clonidine 40 12.0 (10.0, 17.9) 996 12.8 (11.7, 14.3)

BBORN Morphine 29 28.0 (24.0, 39.0) 943 30.5 (29.5, 31.3)

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Figure 6-8 Distribution of the simulated 25%, 50% and 75% percentile of time-on- treatment in the JHU clinical trial DTO arm with the corresponding observed percentiles (solid red line).

Figure 6-10 Distribution of the simulated 25%, 50% and 75% percentile of time-on- treatment in the BBORN clinical trial morphine arm with the corresponding observed percentiles (solid red line).

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Trial and Arm Observed Simulated

JHU DTO N = 40 N = 998

Total morphine dose (mg) 8.1(1.52, 91.9) 11.5 (1.68, 78.6)

Time to Max Dose (hr) 44 (0, 469) 52 (0, 332)

Time MFS > 8 (hr) 88(12, 477) 112 (8, 626)

JHU Clonidine N = 40 N = 996

Total morphine dose (mg) 4.6(1.66, 31.7) 6.94 (1.68, 44.5)

Time to Max Dose (hr) 24(0, 237) 36 (0, 289)

Time MFS > 8 (hr) 78(12, 265) 84 (12, 303)

BBORN Morphine N = 29 N = 948

Total morphine dose (mg) 29.4 (12.1, 114) 32.5 (8.45, 136)

Time to Max Dose (hr) 87.5 (0, 249) 68 (0, 394)

Time MFS > 8 (hr) 88 (25.6, 354) 120 (24, 414)

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Discussion

The management of NAS remains challenging in NICU. Intrinsic and extrinsic factors are all potentially contributing to the diverse situation in NAS. In this research, we developed an ER relationship between morphine plasma concentration and MFS. Understanding the ER relationship in NAS is a critical step in the dosing optimization.

In this research, we dealt with patients who required pharmacotherapy and followed current clinical practice for the treatment of NAS. Though the two clinical trials that we included in this research are both randomized control trials, none of them evaluated different morphine dosing strategies but followed one pre-defined dosing strategy based on their clinical practice.

Moreover, no placebo data was available to directly elucidate the disease remission characteristics because all the patients recruited in the clinical trials required pharmacotherapy.

On the other hand, putting NAS patients on placebo without any background therapy is equivalent to putting them under the risk of death, which is an ethical issue. Therefore, the calculation of disease remission is based on the response in MFS to the longitudinal changes in morphine dose and exposure.

Considering the development of NAS as a result of the exposure to opiates, such as methadone, morphine can potentially make the NAS severity even worse if unnecessary doses of morphine was administered to the patients. However, the neonates recruited in the two clinical trials and thereafter in this ER analysis are all iatrogenic NAS patients. As a result of the natural characteristics of the clinical trial, the development of the NAS could not be controlled and even not observed in any of the patients before they were delivered. The further development of NAS, if there any, is extremely hard to identify given that they already showed withdrawal symptoms.

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Therefore, the current ER relationship doesn’t consider the development of NAS and is not able to describe it, though the self-regulation of NAS and the development of NAS share the same underlying physiological basis as mentioned in the introduction section.

The most straightforward predictor for NAS disease severity would be the opiate dose their mom took during their pregnancy. Unfortunately, the relationship between the methadone dose and the severity of NAS remains unknown. Different studies showed different relationships between the methadone doses with the outcome in NAS, either positive or non-positive relationship between methadone doses with the clinical outcome in NAS.126–129 Surprisingly, one observatory study even showed the length of hospital stay is negatively correlated with the mother’s methadone exposure, which is in the opposite way of our expectation.130 The inconsistent relationships make the prediction of NAS severity based on methadone dose controversial. In our ER analysis, we cannot correlate the posthoc parameter estimates with the maternal methadone dose, which suggests that the disease severity is not sensitive to the current prescription dose of methadone.

Longitudinal dosing information or other information such as core blood concentration and genetic information should be collected for further understanding of the development of NAS.

Actually, the prediction of baseline disease severity is more complicated than the methadone dose and genetic polymorphisms. Pregnant women who addicted to opiates may not take methadone or not only methadone during their pregnancy. Some of them may also take other drugs, such as morphine, codeine, heroin, benzodiazepine, and more commonly cocaine.

However, the information of the time and the amount of legal and illegal drugs that were taken by the moms are always incomplete, and the exposure to multi-substances in those neonates can even make the situation more complicated and worse. Normally, the only available source of this information is from the urinary test for the moms or babies, which is qualitative not quantitative.

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Therefore, the patient characteristics at baseline are diverse or unknown, and the estimates for individual baseline characteristics based on their responses to the therapy are crucial to describe the different time course of MFS in each patient.

In the JHU clinical trial, DTO, in which morphine is the active ingredient, was used, while morphine sulfate solution was used in the BBORN clinical trial. Considering the potential CNS effect of alcohol in the DTO formulation, the current guidance recommends morphine sulfate solution as the first-line pharmacotherapy.6 However, DTO is still widely used in the clinical practice. In this ER analysis, we didn’t find any meaningful difference between the DTO and morphine sulfate solution on the IC50,morphine, which is consistent with the previously published result.13,131

Due to limit number of patients was administered phenobarbital as second line treatment, MFS data in these patients were not included in this analysis, and therefore the phenobarbital effect on

MFS was not evaluated. Similarly, only 8 out of the 109 patients in the two clinical trials were fed with breast milk. The effect of breastfeeding on NAS outcome requires further evaluation, though no obvious difference was found in this ER analysis as well as the clinical trial.

The disease remission, as a result of self-regulation, is potentially dependent on NE concentration at the site of action, which is also expected to be correlated with MFS. In this research, however, the rate of disease remission is described by a turnover rate constant, not dependent on NE concentration or MFS due to identification issues.

Though the ER relationship was not directly included the time-on-treatment as an endpoint, the model evaluation was conducted based on time-on-treatment which were generated under three different dosing strategies. The consistent results between the observed and simulated time-on-

138 treatment and total morphine dose showed a high quality of the ER relationship and gave us more confidence in applying this ER relationship to predict clinical outcome under other dosing strategies. In this way, further research on the dosing optimization for morphine in NAS could be performed.

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Table 6-S1 Modified Finnegan Score

Scored Elements

Signs and Symptoms Score Crying: Excessive high pitched 2 Crying: Continuous high pitched 3 Sleeps < 1 hours after feeding 3 Sleeps < 2 hours after feeding 2 Sleeps < 3 hours after feeding 1 Hyperactive Moro Reflex 1 Markedly Hyperactive Moro Reflex 2 Mild Tremors: Disturbed 1 Moderate-Severe Tremors: Disturbed 2 Mild tremors: Undisturbed 1 Moderate-Severe Tremors: Undisturbed 2 Increased Muscle Tone 1-2 Excoriation (Indicate specific area): 1-2 Generalized Seizure (or convulsion) 8 Fever > 37.3 C (99.2 F) 1 Frequent Yawning (4 or more successive times) 1 Sweating 1 Nasal Stuffiness 1 Sneezing (4 or more successive times) 1 Tachypnea (Respiratory Rate >60/mm) 2 Poor feeding 2 Vomiting (or regurgitation) 2 Loose Stools 2 Failure to thrive (Current weight > 10% below birth weight 90% BWT=______2 (record weight in score box 1 x day) Excessive Irritability 1-3 Total Score

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Figure 6-S1 Observed and model predicted MFS time profiles

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Figure 6-S1 Continued

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Figure 6-S1 Continued

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Figure 6-S1 Continued

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Chapter 7 Dosing optimization for oral morphine therapy in neonatal abstinence

syndrome

Abstract

Morphine is the first-line pharmacotherapy in the treatment of neonatal abstinence syndrome

(NAS). However, no morphine dosing strategy could be recommended, and the currently applied morphine dosing strategies are different from hospital to hospital, which results in different morphine dose ranges, exposure levels and time-on-treatment. The purpose of the pharmacotherapy is to reduce the total hospital stay, which is dominated by time-on-treatment.

Currently, there’s no head-to-head comparison between different morphine dosing strategies to optimize the use of morphine in NAS. For the purpose of optimizing morphine dosing strategy for NAS, we simulated different dosing strategy scenarios based on the validated morphine dose- exposure-response (MFS) relationship in Chapter 6.

Keywords neonatal abstinence syndrome, modified Finnegan score, morphine, optimization

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Introduction

Neonatal abstinence syndrome is a self-limiting disease which is characterized by the opiates withdrawal symptoms in neonates after birth. Morphine is the current first-line pharmacotherapy for NAS. However, the empirically-based dosing strategies are various hospital to hospital without optimization12.

Currently, different dosing strategies are applied in NAS pharmacotherapy based on clinical experience, and the dosing strategies could be different in many aspects, including the initial dose, the up-titration dose, the maximum dose, the down-titration dose, and the cessation dose.

Though those dosing strategies could manage the patients with NAS, the differences between those dosing strategies raised the question that is there a better dosing strategy that we can use to reduce the treatment-on-time with an acceptable control of the withdrawal symptoms?

NAS is a life-threatening disease. Therefore, it is unethical to have a placebo arm in NAS clinical trials for the evaluation of disease remission. On the other hand, morphine can cause respiratory depression, and an unnecessarily high exposure to morphine could further develop the addiction and make the withdrawal symptoms even worse. Therefore, high dose of morphine should be given with caution, and explorer different dosing strategies without understanding the exposure response (ER) relationship is risky. For all these considerations, running different dosing strategies in clinical trials to optimize the morphine dosing strategy could be very challenging.

In this situation, modeling and simulation could be helpful in determining the optimized dosing strategy. The purpose of this simulation research is to optimize the morphine monotherapy dosing strategy to reduce the time-on-treatment with the controlled withdrawal symptoms.

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Methods

Maximum Dose Selection

The major safety concern in patients receiving morphine therapy is respiratory depression. Given the current experience from the two published clinical trials,7,10 those neonates did not develop respiratory depression with morphine dose range up to 360μg q3h ( about 240μg/kg q4h).

Therefore, an equivalent morphine dose of 720μg is safe, and the dose below this level could be applied in clinical practice.

Endpoints

The ER relationship was used to simulate MFS and morphine dosing history by incorporating with different dosing strategies. One thousand patients were simulated for each dosing scenario.

The primary endpoint is time-on-treatment in days. At the same time, other clinical endpoints, including:

1) total morphine dose in mg,

2) time to the maximum morphine dose in hours, and

3) total time of uncontrolled withdrawal symptoms (MFS above 8) in hours, were also derived from the simulation for the purpose of comparison between different dosing strategy scenarios. Descriptive statistics including median and 95% percentile interval were summarized for each endpoint (95% confidence interval for time-on-treatment).

Exposure-response relationship derived titration doses

Based on the IC50 from the ER relationship in Chapter 6, the inhibition effect at each dose level was calculated to help with dose selection (Table7-1). A typical patient demographics of

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40weeks postmenstrual age and 3kg body weight was assumed in the calculation. The maximum morphine dose of 720μg is able to reach an 80% inhibition level. The dose required to reach a

90% inhibition level is about 1500μg q4h, which is beyond the observed dose level in the clinical trials. Considering the 108% between subject variability on IC50 estimated in Chapter 6, the inhibition effect was evaluated at the 25%, 50% and 75% percentiles (3.0μg/L, 6.3μg/L, and

13.1μg/L) of IC50 to make sure most of the patient could have a reasonable change in the inhibition effect when the morphine dose is titrated.

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Table 7-1 Relationship between morphine dose and inhibition effect

% Inhibited

Dose CL Cavg 25% 50% 75% (μg q4h) (L/hr) (μg/L) percentile percentile percentile IC50 IC50 IC50 20 7.27 0.7 19% 10% 5% 32 7.27 1.1 27% 15% 8% 45 7.27 1.5 34% 20% 11% 60 7.27 2.1 41% 25% 14% 80 7.27 2.8 48% 30% 17% 100 7.27 3.4 53% 35% 21% 120 7.27 4.1 58% 40% 24% 150 7.27 5.2 63% 45% 28% 180 7.27 6.2 67% 50% 32% 220 7.27 7.6 72% 55% 37% 280 7.27 9.6 76% 60% 42% 340 7.27 11.7 80% 65% 47% 420 7.27 14.5 83% 70% 52% 540 7.27 18.6 86% 75% 59% 720 7.27 24.8 89% 80% 65%

Different Dosing Scenarios

To answer different questions in the dosing strategy, the following aspects were evaluated in different dosing scenarios:

1) Dosing interval: 3h vs 4h

2) Fixed-dose versus body weight adjusted dose

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3) Dosing optimization

When the dosing strategy was optimized, different aspects, including 1) initial dose, 2) up- titration dose, 3) maximum dose, 4) down-titration dose and 5) cessation dose, were considered.

The two dosing strategies used in the previously published clinical trials were used as a control group in the simulation, and the summary of these two dosing strategies are given in Table 7-2 and Table 7-3.

Dosing strategy 1:

Neonates under the risk of NAS were started on morphine sulfate solution oral therapy when the two consecutive scores are more than 8. The starting dose is 80μg q4h. The dose is increased by

40μg if two consecutive scores are more than 8. Dosing interval is changed from q4h to q3h after

240μg q4h. After stabilization for 48 hours without meeting the criteria to increase the morphine dose, morphine dose is weaned off every 24hours by 20μg until the dose was less than 20μg q4h.

Table 7-2 Dosing strategy 1

Initial Dose Titration Dose Weaning Dose Cessation Dose

2MFS > 8 2MFS > 8 (until 2MFS > 11)

80ug q4h + 40ug - 20ug per 24 hours 20ug

240ug q4h to

240ug q3h

until 360ug q3h

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Dosing strategy 2:

Neonates under the risk of NAS were started on morphine sulfate solution oral therapy when the sum of three consecutive scores is more than 24. The starting dose is 70μg/kg q4h. The dose is increased by 20% if the sum of the consecutive score is more than 24. No dosing interval adjustment is made in dosing strategy 2. After stabilization for 48 hours without meeting the criteria to increase the morphine dose, morphine dose is weaned off every 24hours by 10% until the dose was less than 25μg/kg q4h.

Table 7-3 Dosing strategy 2

Initial Dose Titration Dose Weaning Dose Cessation Dose

Sum of 3MFS > 24 Sum of 3MFS > 24

1MFS > 12 1MFS > 12

70μg/kg q4h + 20% -10% 25μg/kg

up to 209μg/kg per 24hours

As a clinical judgment, the thresholds to start the pharmacotherapy and to increase and decrease the dose are different between Strategy 1 and Strategy 2. For Strategy 1, the dose should be increased as long as there are two consecutive MFS above 8 during the up-titration and above 12 during the down-titration. For Strategy 2, the dose should be increased when the sum of three consecutive MFS above 24 or a single score above 12. The thresholds are similar for up-titration, but different for down-titration. Considering the MFS thresholds are different, the dosing strategies are optimized for Strategy 1 and 2 separately.

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During the simulation of different dosing strategy scenarios, we changed one aspect in either

Strategy 1 or Strategy 2 and then compared the outcome with the original strategy. For most of the time, we started with the strategy 1, because during the clinical trials, the strategy 1 showed a much shorter time-on-treatment. More importantly, because the thresholds to initiate the pharmacotherapy or re-titrate up the dose are based on clinical judgment, we didn’t try any other threshold but followed the original practice.

The detailed information for the dosing strategies in the simulation is given below:

1) Dosing interval: 3h vs 4h

In the following dosing scenario, the q3h morphine dosing regimen in Strategy 1 was

changed to q4h by keeping the same daily dose (Table 7-4). For example, the dosing

regimen of 240μg q3h was changed to 320μg q4h, and the dosing regimen of 360μg q3h was

changed to 480μg q4h. The results were compared with Strategy 1.

Table 7-4 Dosing scenarios 1: Strategy 1 with 4 hours dosing interval

Initial Dose Titration Dose Weaning Dose Cessation Dose

2MFS > 8 2MFS > 8 (until 2MFS > 11)

80μg q4h + 40μg -20μg per 24 hours 20μg

up to 480μg q4h

Scenario 1: The initial morphine dose is 80μg q4h. The dose was further titrated up to 120μg

q4h, 160μg q4h, 200μg q4h, 240μg q4h, 320q4h, 380μg q4h, 430μg q4h and 480μg q4h if

two consecutive MFS was above 8 after the previous dose was given. The neonates were

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administered the dose that can control their withdrawal symptoms for 48 hours for

stabilization, and then the morphine dose was weaned off every 24hours by 20μg.

2) Fixed-dose versus body weight adjusted dose

In the following dosing scenario, the fixed dose dosing regimens in Scenario 1 were changed

to body weight based dosing strategy by assuming a body weight of 3kg. For example, the

dosing regimen of 240μg q4h and 360μg q4h were changed to 80μg/kg q4h and 120μg/kg

q4h. The results were compared with Scenario 1.

Scenario 2 (Table 7-5): The initial morphine dose is 26.7 μg/kg q4h. The dose was further

titrated up to 40 μg/kg q4h, 53.3 μg/kg q4h, 66.7 μg/kg q4h, 80 μg/kg q4h, 106.7 μg/kg q4h,

124.4 μg/kg q4h, 142.2 μg/kg q4h and 160 μg/kg q4h if two consecutive MFS was above 8

after the previous lower dose was given. The neonates were given at the dose that can control

their withdrawal symptoms for 48 hours, and then the morphine dose was weaned off every

24 hours by 10μg/kg.

Table 7-5 Scenario 2: Strategy 1 with body weight normalized dose and 4 hour dosing

Initial Dose Titration Dose Weaning Dose Cessation Dose

2MFS > 8 2MFS > 8 (until 2MFS > 11)

26.7μg/kg + 13.3 μg/kg - 8μg/kg per 24 hours 8μg/kg

q4h until 80μg/kg

In the following dosing scenario, the body weight based dosing regimens in the dosing

strategy 2 were changed to a fixed dose based dosing strategy by assuming a body weight of

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3kg. For example, the dosing regimen of 70μg/kg q4h was changed to 210μg q4h. The results

were compared with dosing strategy 2.

Scenario 3 (Table 7-6): The initial dose is 210μg q4h. The dose is increased by 20% if the

sum of the consecutive score is more than 24. After stabilization for 48hours without meeting

the criteria to increase the morphine dose, morphine dose is weaned off every 24hours by

10% until the dose is less than 75μg q4h.

Table 7-6 Scenarios 3: Strategy 2 with fixed dose

Initial Dose Titration Dose Weaning Dose Cessation Dose

3MFS > 24 3MFS > 24 Mean of 3MFS < 8

210μg q4h + 20% -10% 75μg q4h

up to 600μg q4h per 24hours

3) Dose optimization

The thresholds in Strategy 1 and 2 for dose up-titration and down-titration are mainly based

on clinical judgment, and the evaluation for these thresholds is out of the scope of this

research. Therefore, the dosing strategies are optimized for Strategy 1 and 2 separately

without changing the corresponding thresholds for dose titration.

In the following dosing scenarios, the incremental dose and decremental dose was optimized

based on the ER relationship to make sure any dose increment could result in meaningful

pharmacological differences, and any dose decrement could compensate the change in

disease remission. Based on the ER relationship in Chapter 6 and

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Table 7-1, new dose up-titration strategies were proposed (Table 7-7 and Table 7-8) to make sure every dose increase can add at least 10% inhibition effect on the withdrawal symptoms, and every dose decrease can reduce the inhibition by at least 5%. Considering the disease

-1 remission rate (Kremis) in Chapter 6 was estimated as 0.004 hr , which is equivalent to 0.096 day-1, the dose down-titration was optimized to reduce the inhibition effect by 5%, 10% or

20% every 24 hours. Therefore, the dose optimization was started with +10% inhibition effect every time for up-titration and -10% inhibition effect every 24 hours for down- titration.

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Table 7-7 Dosing Scenarios 4 to 9: Strategy 1 with optimized titration dose

Scenarios Initial Dose Titration Dose Weaning Dose Cessation Dose 2MFS > 8 2MFS > 8 (until 2MFS > 11) Scenario 4: 20μg q4h 45μg 720μg 20μg 20μg to 20μg 80μg 420μg (10% up to 120μg 280μg 10% down) 180μg 180μg 280μg 120μg 420μg 80μg 720μg 45μg 20μg Scenario 5: 80μg q4h 120μg 720μg 80μg 80μg to 80μg 180μg 420μg (10% up to 280μg 280μg 10% down) 420μg 180μg 720μg 120μg 80μg Scenario 6: 80μg q4h 120μg 720μg 45μg 10% up to 180μg 280μg 20% and 280μg 120μg 420μg 45μg 720μg Scenario 7: 120μg q4h 280μg 720μg 120μg 20% up and 720μg 280μg 20% down 120μg Scenario 8: 120μg q4h 280μg 720ug 120ug 20% up and 720μg 420ug 10% down 280ug 180ug 120ug Scenario 9: 80μg q4h 120μg 720μg 20μg 10% up and 180μg 540μg 5% down 280μg 420μg 420μg 340μg 720μg 280μg 220μg 180μg 150μg 120μg 100μg 80μg 60μg 45μg 32μg 20μg

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Table 7-8 Dosing Scenarios 10 to 13: Strategy 1 with optimized titration dose

Scenarios Initial Dose Titration Dose Weaning Dose Cessation Dose

Sum of Sum of 3MFS > per 24 hours

3MFS > 24 24

Scenario 10 210μg q4h + 20% -20% 75μg q4h

up to 600μg

Scenario 11 120μg q4h 280μg 720μg 120μg

720μg 280μg

120μg

Scenario 12 120μg q4h 280μg 720μg 80μg

720μg 420μg

280μg

180μg

120μg

80μg

The body weight was generated using the same model in Chapter 6 and patients were re-sampled from the posthoc estimates in Chapter 6 as well. All the simulations and plots were generated in

R 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria).

172

Results

1) Scenario 1: 3 hours dosing interval vs 4 hours dosing interval

The simulation results for Scenario 1 with the result of Strategy 1 are given in Figure 7-1 and

Table 7-9. Dosing intervals of every 3hours and every 4hours do not have obvious impact on the primary endpoint, time-on-treatment, as well as all the other endpoints.

Figure 7-1 Comparison of Time-on-treatment between Strategy 1 and Scenario 1

173

Table 7-9 Simulation results for Strategy 1 and Scenario 1

Strategy 1 Scenario 1

N = 998 N = 996

Time-on-treatment 16.2 (15.5, 17.5) 17.1 (15.7, 18.3)

Total morphine dose (mg) 11.5 (1.68, 78.6) 11.8 (1.68, 82.7)

Time to Max Dose (hr) 52 (0, 332) 56 (0, 365)

Time MFS > 8 (hr) 112 (8, 626) 108 (8, 631)

2) Scenario 2 and 3: body weight based vs fixed dose

The simulation for Scenario 2 and 3 with the result of Scenario 1 and Strategy 2 are given in

Figure 7-2 and Figure 7-3 and Table 7-10 and Table 7-11. Body weight based dosing strategy cannot make meaningful improvement for all the clinical endpoints when compared with fixed dose based dosing strategies in Strategy 1 and Scenario 3.

174

Figure 7-2 Comparison of Time-on-treatment between Scenario 1 and 2 Table 7-10 Simulation results for Scenario 1 and 2

Scenario 1 Scenario 2

N = 996 N = 996

Time-on-treatment (day) 17.1 (15.7, 18.3) 19.8 (19.0, 20.7)

Total morphine dose (mg) 11.8 (1.68, 82.7) 10.2 (0.54, 53.9)

Time to Max Dose (hr) 56 (0, 365) 76 (0, 281)

Time MFS > 8 (hr) 108 (8, 631) 144 (16, 798)

175

Figure 7-3 Comparison of Time-on-treatment between Strategy 2 and Scenario 3 Table 7-11 Simulation results for Strategy 2 and Scenario 3

Strategy 2 Scenario 3

N = 948 N = 943

Time-on-treatment (day) 30.3 (29.2, 31.2) 31.3 (30.3, 32.3)

Total morphine dose (mg) 32.5 (8.54, 158) 35.9 (8.75, 171)

Time to Max Dose (hr) 62 (0, 456) 144 (0, 408)

Time MFS > 8 (hr) 120 (24, 448) 116 (24, 443)

3) Dose Optimization

176

The simulation for Scenario 4 to 9 with the result of Strategy 1 are given in Figure 7-4 and Table

7-12. Around all the 6 proposed dosing strategies for Strategy 1 optimization, Scenario 7 performed best with the shortest time-on-treatment, time to the max dose and time of MFS above

8, though the total morphine dose used in scenario 7 was a little bit higher than Scenario 4, 5, 6 and 9. Compared to the original Strategy 1, Scenario 7 can save an average of 5.5 days on time- on-treatment with 44 hour faster to reach the max dose, and reduce the time patients suffer from the withdrawal symptoms by 36 hours on average.

The simulation for Scenario 10 to 12 with the result of Strategy 2 are given in Figure 7-5 and

Table 7-13. Around all the 3 proposed dosing strategies for Strategy 2 optimization, Scenario 11 performed best with the lowest time-on-treatment, time to the max dose, time of MFS above 8 and total morphine dose. Compared to the original Strategy 2, Scenario 11 can save an average of 3.2 days on time-on-treatment with 50 hour faster to reach the max dose.

177

Table 7-12 Simulation results for Strategy 1 and Scenario 4 to 9

Strategy 1 Scenario 4 Scenario 5 (20μg 20μg) (80μg 80μg) (10%↑ 10%↓) (10%↑ 10%↓) N = 998 N = 997 N = 994

Time-on-treatment (day) 16.2 (15.5, 17.5) 13.3 (12.0, 14.7) 12.1 (11.2, 13.0)

Total morphine dose (mg) 11.5 (1.68, 78.6) 9.34 (0.24, 67.4) 10.7 (0.96, 63.1)

Time to Max Dose (hr) 52 (0, 332) 56 (0, 252) 44 (0, 244)

Time MFS > 8 (hr) 112 (8, 626) 132 (15.6, 654) 92 (8, 528)

Table 7-12 Continued

Scenario 6 Scenario 7 Scenario 8 (10%↑ 20%↓) (20%↑ 20%↓) (20%↑ 10%↓) N = 994 N = 992 N = 992

Time-on-treatment 11.7 (10.8, 12.5) 10.7 (9.83, 11.3) 12.5 (11.8, 13.0) (day) Total morphine dose 10.1 (0.96, 63.8) 11.0 (1.44, 55.9) 12.6 (1.92, 53.5) (mg) Time to Max Dose (hr) 48 (0, 224) 8 (0, 124) 8 (0, 132)

Time MFS > 8 (hr) 104 (8, 541) 76 (8, 628) 76 (4, 582)

178

Table 7-12 Continued

Scenario 9 (10%↑ 5%↓) N = 994

Time-on-treatment (day) 15.8 (15.3 16.3)

Total morphine dose 10.8 (1.90, 69.0) (mg) Time to Max Dose (hr) 36 (0, 272)

Time MFS > 8 (hr) 112 (8, 642)

Figure 7-4 Time-on-treatment plot for Strategy 1 and Scenario 5 to 9

179

Table 7-13 Simulation results for Strategy 2 and Scenario 10 to 12

Strategy 2 Scenario 10 Scenario 11 (20%↑ 20%↓) N = 948 N = 945 N = 934

Time-on-treatment 30.3 (29.2, 31.2) 28.7 (28, 29.5) 26.5 (25.8, 27.2) (day) Total morphine dose 32.5 (8.54, 158) 32.4 (10.2, 143) 26.0 (7.24, 103) (mg) Time to Max Dose 62 (0, 456) 164 (0, 432) 12 (12, 207) (hr) Time MFS > 8 (hr) 120 (24, 448) 124 (20, 452) 140 (28, 963)

Table 7-13 Continued

Scenario 12 (20%↑ 10%↓) N = 947

Time-on-treatment 27.3 (26.7, 28.2) (day) Total morphine dose 26.8 (8.04, 101) (mg) Time to Max Dose (hr) 12 (12, 189)

Time MFS > 8 (hr) 144 (28, 910)

180

Figure 7-5 Time-on-treatment plot for Strategy 2 and Scenario 10 to 12

181

Discussion

In this simulation study, the dosing interval and the time for MFS evaluation were conducted exactly every 3 or 4 hours. However, during clinical practice, the time of dosing and MFS evaluation might be influenced by the time when neonates wake up and the time for breast feeding or bottle feeding. In the dosing strategy 1, the dosing interval was switched to every 3 hours at 240μg from every 4 hours at 240μg. When comparing the dosing interval of every 3 hours and every 4 hours by keeping the same daily dose, we did not find any meaningful difference between these two dosing intervals. Theoretically, more frequent dosing with same daily dose is associated with a smaller fluctuation ratio, but same average concentration at steady state (Cavg). In the management of NAS, as we showed in the ER relationship, the rate limiting steps are the disease remission and symptom relief, not the change in morphine concentration.

Therefore, by changing the dosing interval from 4 hours to 3 hours can only make negligible difference in the management of withdrawal symptoms. Especially, when taking into the consideration of real practice settings, the dosing interval could also be influence by the wake-up time and the feeding time. Therefore, the 3hours and 4hours dosing intervals could not make a meaningful difference in practice, and if necessary, the dosing interval could be flexible from 3 to 4 hours with the same daily dose.

In scenario 2 and 3, the body weight based morphine dosing strategy didn’t show obvious benefit compared with the corresponding fixed dosing strategy. The major reason for the lack of contribution from body weight based dosing is due to the dose titration used in the dosing strategies. When the dose is titrated, it should always titrate to a dose that meets its therapeutic requirement. Therefore, the impact of body weight can be covered by the dose titration, and body

182 weight based dosing strategy could not make obvious difference to the fixed dose based dosing strategies.

Change in body weight is actually one of the items for the evaluation in MFS. If the body weight at the time of evaluation (normally on the day of evaluation) was less than 90% of the body weight at birth, the baby is considered as failure-to-thrive and a score of 2 will be added to the total MFS. Therefore, body weight and MFS are correlated. However, a body weight that is independent of MFS was used in the simulation for the description of morphine disposition change after birth, not for the purpose of withdrawal symptom evaluation. The description of body weight as a result of NAS could only be done by describing the body weight change with the morphine exposure. In the ER model, the total MFS score was used instead of individual scores. Therefore, the ER model has the limitation to link body weight changes with MFS. Given the validation in many aspects from the ER model evaluation, the lack of correlation between body weight and MFS does not weaken the prediction in MFS. And the validated empirical body weight model is enough for the description of morphine disposition.

In this simulation research, we use morphine as the first line pharmacotherapy. Second line treatment: such as clonidine and phenobarbital, should be considered when the maximum morphine dose failed to control the withdrawal symptoms. The optimization of add-on treatment to morphine or when morphine was used in combination with second line pharmacotherapies requires further investigation.

183

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