Pre-Clinical and Clinical Pharmacokinetic/Pharmacodynamic Evaluations Of

Pre-clinical and Clinical Pharmacokinetic/Pharmacodynamic Evaluations of

Cyclin-dependent Kinase Inhibitors as Chemotherapeutic Agents

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By Yuan Zhao, M.S.

Graduate Program in Pharmaceutical Sciences

The Ohio State University

2015

Dissertation Committee

Mitch A. Phelps, Ph.D, Advisor

Jack C. Yalowich, Ph.D

Amy J. Johnson, Ph.D

Thomas D. Schmittgen, Ph.D

Yuan Zhao

2015

Abstract

Chronic lymphocytic leukemia (CLL) is the most prevalent adult leukemia in the western world. CLL is incurable with current therapies, and new strategies are needed. Cyclin-dependent kinases play fundamental roles in cell cycle control and gene transcription and have become promising drug targets. A selective and potent cyclin-dependent kinase inhibitor (CDKI), dinaciclib, has been discovered and has shown significant clinical efficacy in relapsed and refractory CLL patients, including those who carry high risk genetic abnormalities. However, the toxicity of tumor lysis syndrome (TLS) has developed in CLL patients in a phase 1 study and limited further dose escalation of dinaciclib. TLS is a constellation of metabolic events due to rapid tumor cell death and lysis. It is characterized by hyperuricemia, hyperkalemia, hyperphosphatemia and secondary hypocalcemia, and causes numerous complications. This potentially fatal toxicity was demonstrated to correlate with the glucuronide metabolites of flavopiridol, the first generation CDKI. Glucuronidation is also a major metabolic pathway of dinaciclib in humans. In our Phase 1b/2 clinical study, our objective was to investigate the correlations between dinaciclib and dinaciclib-glucuronide plasma levels and the incidence of TLS or its biochemical markers. In addition, dinaciclib is a P-

ii glycoprotein (P-gp) substrate based on in vitro study. We aimed to evaluate the effect of P-gp on dinaciclib disposition and potential drug-drug interaction (DDI) between dinaciclib and lenalidomide via P-gp for the potential clinical interest in combing these two drugs to treat CLL patients.

A sensitive liquid chromatography-tandem mass spectrometry method has been developed and validated for quantification of dinaciclib and dinaciclib-glucuronide in human plasma samples (Chapter 2). This assay was modified for mouse plasma and applied to study the role of P-gp in the disposition of dinaciclib. P-gp knockout (KO) and matching wild type (WT) mice were used to evaluate the in vivo contribution of P-gp to dinaciclib disposition and the potential interaction of dinaciclib with another clinically relevant experimental therapy in CLL, lenalidomide, which is also a P-gp substrate. Our results demonstrated that dinaciclib AUC decreased in P-gp KO mice relative to P-gp WT mice, and there was an apparent DDI between dinaciclib and lenalidomide. Further study revealed this interaction may have been due primarily to protein binding displacement with a minor contribution from via P-gp (Chapter3).

To investigate the potential association of dinaciclib-glucuronide with the occurrence of TLS, we evaluated dinaciclib and dinaciclib-glucuronide pharmacokinetics in CLL patients in a phase II study evaluating the combination

iii of ofatumumab and dinaciclib, which was hypothesized to yield reduced prevalence of dinaciclib-induced TLS. Population pharmacokinetic/ pharmacodynamic analysis of the patient data was performed using nonlinear mixed effects modeling. A combined four-compartment model adequately described the PK properties of both parent drug and its glucuronide metabolite.

Linear relationships were identified between dinaciclib-glucuronide maximal concentration or area under the concentration-time curve and the maximal changes in phosphate, which is a biochemical marker of TLS. However, given that only one patient experienced clinical TLS within a study combining ofatumumab and dinaciclib, no relationship was observed between drug/metabolite levels and TLS incidence (Chapter 4).

TG02, an oral CDKI, is under development for acute leukemia (AL), multiple myeloma (MM) and CLL. Drug accumulations were observed with patients who developed grade 3 fatigue, which was the dose limiting toxicity in AL and MM patients. Maximum tolerated dose was 70 mg with daily dose for 28 days, and

150 mg with daily dose for 5 days for two weeks followed by two-week rest. In addition, great population PK variability also displayed within these disease cohorts. A modeling and simulation approach was used to characterize TG02 PK and design a new dose regimen. Twice weekly dosing for four weeks was

iv proposed given the lack of observed drug accumulation based on simulated data, and this dose regimen was applied in an ongoing phase 1 clinical trial. Thus far grade 1/2 fatigue has been mainly observed, and there was only one case of grade 3 TLS which persisted less than 24 hours. The dose regimen has increased drug tolerability and enabled drug escalation up to 300 mg thus far

(Chapter 5). Collectively, these studies contribute to the development of CDKIs for the treatment of CLL.

Dedication

This dissertation is dedicated to my dear mother, boyfriend and other family

members, and in memory of my beloved father.

Acknowledgements

Now it is almost the end of another milestone in my life, the journey of pursuing a

Doctor of Philosophy degree in Pharmaceutics. I could never have made it this far without the great help from numerous people, and I want to say ―thank you‖ from the bottom of my heart to everyone who has supported me.

Most importantly, I would like to extend my great gratitude to my advisor, Dr.

Mitch Phelps, who has been highly supportive of me for so many years. His generous guidance, understanding and encouragement have led me to this field and have helped to shape me who I am today.

My great gratitude also goes to my committee members, Dr. Jack Yalowich, Dr.

Amy Johnson and Dr. Schmittgen, who are very knowledgeable mentors and who always provide excellent suggestions and instructions for my research work.

I highly enjoyed the brainstorming with them.

I also want to thank everyone who has directly or indirectly contributed to my research, including all the patients who I don’t even know, clinical staff involved in the clinical trials and all of our collaborators: Dr. Jeffrey Jones, Dr. John Byrd at OSU, and Ms. Tracy Parrot, Ms. Mary Syto from Tragara, who are always supportive. Sincere thanks are also given to Dr. Yonghua Ling and Dr. Jiang

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Wang at the PhASR facility. I would also like to acknowledge people here at

College of Pharmacy, Ms. Betsy Bulgrin, Ms. Mary Kivel, and Mr. Casey Hoerig for their support as well as my lab members: Dr. Xiaoxia Yang, Dr. Xiaohua Zhu,

Dr. Dolly Rozewski, Dr. Lei He, Yu Kyoung Cho, and Yao Jiang.

My special thanks go to Dr. Neeraj Gupta and Dr. Karthik Venkatakrishnan at

Takeda-Millennium who provided me the internship opportunity. I enjoyed working there and spent my best summer at Cambridge since I came to America.

I want to give my sincere gratitude to all of my friends: Dr. Youna Zhao, Dr.

Yicheng Mao, Dr. Yue Gao, Dr. Jia Ji, Dr. Jing Li, Dr. Haiyan Peng, Dr. Jie Li, Dr.

Ming Poi, Dr. Junan Li, Hongshan Lai, Wenquan Jiao, Liguang Mao, Jing Wang and Di Bei. Thanks for being my friends with whom I can share happiness and sorrow.

Finally, I want to give the deepest appreciation to my mother and other relatives including grandma, uncles, aunts, cousins and Mr. Seungsoo Lee. Thanks for being patient with me while I spent a long time at schooling.

Thanks to you all!

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Vita

1984………….Born in Taiyuan, Shanxi, China

2007…………. B.S. Pharmacy, Sichuan University, China

2009………… M.S. Forensic Science, Towson University, US

2009 to present………… Division of Pharmaceutics and Pharmaceutical

Chemistry, The Ohio State University, US

Publications

Zhao Y, Ling Y, Johnson AJ, Phelps MA. A LC/MS/MS method for simultaneous quantification of dinaciclib and its metabolite dinaciclib-glucuronide in human plasma (submitted)

Maddocks K, Wei L, Rozewski D, Jiang Y, Zhao Y, Adusumilli M, Peirceall WE, Doykan C, Cardone MH, Jones JA, Flynn J, Andritsos LA, Grever MR, Byrd JC, Johnson AJ, Phelps MA, Blum KA. Reduced occurrence of tumor flare with flavopiridol followed by combined flavopiridol and lenalidomide in patients with relapsed B-cell chronic lymphocytic leukemia. American Journal of Hematology, 2015 Jan 12

Gupta N, Zhao Y, Hui A, Lee D, Venkatakrishnan K. Switching from body surface area-based to fixed dosing for the investigational proteasome inhibitor ixazomib: a population pharmacokinetic analysis. British Journal of Clinical Pharmacology, 2014 Nov 6

Wang W, Wang CM, Gou S, Chen ZH*, Zhao Y*. Pharmacokinetics and pharmacodynamics of oral and intravenous cefetamet in dogs. European Journal of Drug Metabolism and Pharmacokinetics, 2014 Jul 13 (*Co-corresponding author)

Mani R, Mao Y, Frissora FW, Chiang L, Wang J, Zhao Y, Wu Y, Yu B, Yan R, Mo X, Yu L, Flynn J, Jones J, Andritsos L, Baskar S, Rader C, Phelps MA, Chen CS, Lee RJ, Byrd JC, Lee LJ, Muthusamy N. Tumor antigen ROR1 targeted drug delivery mediated selective leukemic but not normal B cell cytotoxicity in chronic lymphocytic leukemia. Leukemia, 2014 Jun 20

Mao Y, Wang J, Zhao Y, Chen C, Lee RJ, Byrd JC, Lee LJ, Muthusamy N, Phelps MA. Quantification of OSU-2S, a novel derivative of FTY720, in mouse plasma by liquid chromatography-tandem mass spectrometry. Journal of Pharmaceutical and Biomedical Analysis 2014 May 23; 98C:160-165

Mao Y, Wang J, Zhao Y, Wu Y, Kwak KJ, Chen CS, Byrd JC, Lee RJ, Phelps MA, Lee LJ, Muthusamy N. A novel liposomal formulation of FTY720 (Fingolimod) for promising enhanced targeted delivery. Nanomedicine 2014 Feb; 10(2):393- 400

Ji J, Mould DR, Blum KA, Ruppert AS, Poi M, Zhao Y, Johnson AJ, Byrd JC, Grever MR, Phelps MA. A Pharmacokinetic/Pharmacodynamic Model of Tumor Lysis Syndrome in Chronic Lymphocytic Leukemia Patients Treated with Flavopiridol. Clinical Cancer Research, 2013 Mar 1;19(5):1269-80.

Stephens DM, Ruppert AS, Maddocks K, Andritsos L, Baiocchi R, Jones J, Johnson AJ, Smith LL, Zhao Y, Ling Y, Li J, Phelps MA, Grever MR, Byrd JC, Flynn JM. Cyclophosphamide, alvocidib (flavopiridol), and rituximab, a novel feasible chemoimmunotherapy regimen for patients with high-risk chronic lymphocytic leukemia. Leukemia Research, 37 (2013) 1195–1199

Yang CY, Wang J, Zhao Y, Shen L, Jiang X, Xie ZG, Liang N, Zhang L, Chen ZH. Anti-diabetic effects of Panax notoginseng saponins and its major anti- hyperglycemic components. Journal of Ethnopharmacology, 130 (2010) 231–236

Chen ZH, Li J, Liu J, Zhao Y, Zhang P, Zhang MX, Zhang L. Saponins isolated from the root of Panax Notoginseng showed significant anti-diabetic effects in KK-Ay mice. The American Journal of Chinese Medicine, (2008) Vol.36, No. 5, 939–951

Fields of Study

Major Field: Pharmaceutical Sciences

Specialized in: Pharmacokinetics, pharmacodynamics and pharmacometrics

Minor Field: Statistics

List of Tables

Table 2.1 Dinaciclib within-day (n=6) and between-day (n=18) accuracy and precision ...... 40 Table 2.2 Dinaciclib standard curve linearity, accuracy and precision (n=5 runs) ...... 41 Table 2.3 Dinaciclib-glucuronide within-day (n=6) and between-day (n=12) accuracy and precision ...... 42 Table 2.4 Dinaciclib-glucuronide standard curve linearity, accuracy and precision (n=5 runs) ...... 43 Table 3.1 PK parameter estimates of dinaciclib in WT and KO mice ...... 50 Table 3.2 Dinaciclib AUC changes after combination with lenalidomide in WT and P-gp KO mice ...... 53 Table 3.3 Lenalidomide AUC changes after combination with dinaciclib in WT and P-gp KO mice ...... 54 Table 3.4 Protein binding of dinaciclib ...... 57 Table 3.5 Free drug fraction of dinaciclib ...... 58 Table 4.1 Summary of patient demographics ...... 69 Table 4.2 Final population PK model parameter estimates ...... 74 Table 5.1 Dose administration of TG02 in phase 1 clinical trials ...... 90 Table 5.2 Summary of patient demographics ...... 93 Table 5.3 AUC of TG02 at 10 different dose levels ...... 95 Table 5.4 TG02 Cmax at 10 different dose levels...... 95

Table 5.5 Gender and food control effect in TG02 dose normalized AUC0-24 ... 101 Table 5.6 Compartmental PK parameter estimates for simulation...... 104

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Table 5.7 Accumulation ratio of mean Cmax and Ctrough from 1000 simulations . 106 Table 5.8 Population pharmacokinetic base model parameters ...... 110 Table 5.9 Covariate forward selection process ...... 113 Table 5.10 Final population PK model parameters ...... 114

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List of Figures

Figure 1.1 Therapeutic agents and their targets in chronic lymphocytic leukemia 6 Figure 1.2 Uric acid production, elimination and treatment ...... 12 Figure 1.3 Chemical structure of dinaciclib ...... 19 Figure 1.4 Chemical structure of TG02 ...... 23 Figure 2.1 The chemical structure, full-scan product ion spectra, and hypothesized fragment structure of IS (A), dinaciclib (B) and dinaciclib- glucuronide (C)...... 34 Figure 2.2 Chromatograms of blank human plasma. Mass chromatograms were generated from analysis of blank plasma using the three transition channels for IS (A), dinaciclib (B) and dinaciclib-glucuronide (C)...... 37 Figure 2.3 Representative chromatograms of blank human plasma spiked with 1000 ng/ml IS (A), dinaciclib at LLOQ of 1ng/ml (B), and dinaciclib-glucuronide at LLOQ of 2 ng/ml (C)...... 38 Figure 2.4 PK profiles of dinaciclib and dinaciclib-glucuronide in CLL patients (n=5)...... 39 Figure 3.1 PK profiles of dinaciclib in WT and KO mice ...... 51 Figure 4.1 Pharmacokinetic plots of dinaciclib (A) and dinaciclib-glucuronide after the initiation of dinaciclib intravenous infusion...... 70 Figure 4.2 The pharmacokinetic model of dinaciclib and its glucuronide metabolite ...... 72 Figure 4.3 Goodness-of-fit plots for dinaciclib (A) and dinaciclib-glucuronide (B) ...... 75 Figure 4.4 VPC of dinaciclib and dinaciclib-glucuronide from 1000 simulations . 77

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Figure 4.5 Changes in phosphate (A), LDH (B), uric acid (C) and potassium (D) after first infusion of dinaciclib on cycle 2 day 2 ...... 80 Figure 4.6 Evaluation of the correlation between maximum changes in phosphate (before and after the first dose of dinaciclib) and dinaciclib Cmax (A), AUC (B), dinaciclib-glucuronide Cmax (C) and AUC (D); and the correlation between maximum changes in LDH and those PK parameters ...... 81

Figure 5.1 TG02 AUC0-24 (A) and AUCinf (B) over 10 different dose levels ...... 96

Figure 5.2 Food effect on TG02 dose normalized AUC0-24 ...... 99

Figure 5.3 Gender effect on TG02 dose normalized AUC0-24 ...... 100 Figure 5.4 Mean simulated TG02 concentration under twice weekly dosing at 7 doses up to 300 mg ...... 105 Figure 5.5 Observed TG02 plasma concentrations with the twice weekly dosing regimen compared to simulations...... 107 Figure 5.6 The pharmacokinetic model of TG02...... 109 Figure 5.7 The diagnostic plots of TG02 PK base model ...... 111 Figure 5.8 The diagnostic plots of final PK model of TG02 ...... 115 Figure 5.9 VPC of TG02 without food control ...... 117 Figure 5.10 VPC of TG02 with food control ...... 118

Table of Contents

Abstract ...... ii Dedication ...... vi Acknowledgements ...... vii Vita ...... ix List of Tables ...... xii List of Figures ...... xiv Table of Contents ...... xvi CHAPTER 1, INTRODUCTION ...... 1 1.1 Cyclin-dependent kinases and Cyclin-dependent kinase inhibitiors (CDKIs) ...... 1 1.1.1 Cyclins and cyclin-dependent kinases (CDKs) ...... 1 1.1.2 Cyclin-dependent kinase inhibition under development ...... 2 1.2 The use of CDKIs in chronic lymphocytic leukemia ...... 3 1.2.1 CLL and current treatments and developments ...... 3 1.2.3 Tumor lysis syndrome ...... 8 1.3 A potent and selective CDKI: dinaciclib ...... 13 1.3.1 Basic information of dinaciclib ...... 13 1.3.2 Mechanisms and pre-clinical studies of dinaciclib ...... 13 1.3.3 Current clinical development status of dinaciclib ...... 14 1.3.4 Pharmacokinetic features of dinaciclib ...... 17 1.4 TG02 ...... 20 1.4.1 Development of TG02 ...... 20 1.4.2 Pharmacokinetic characteristics of TG02 ...... 21 CHAPTER 2, DEVELOPMENT OF A SENSITIVE LC/MS/MS ASSAY FOR DINACICLIB AND DINACICLIB-G IN HUMAN PLASMA ...... 24 xvi

2.1 Introduction ...... 24 2.2 Materials and methods ...... 26 2.2.1 Materials ...... 26 2.2.2 Standard and quality control (QC) solutions of dinaciclib and IS .... 26 2.2.3 Extraction and quantification of dinaciclib-glucuronide...... 27 2.2.4 Plasma sample preparation ...... 28 2.2.5 Liquid chromatography-tandem mass spectrometry (LC-MS) ...... 28 2.2.6 Assay validation ...... 29 2.2.7 Pharmacokinetic study ...... 30 2.3 Results ...... 30 2.3.1 Mass spectrometry and chromatography ...... 30 2.3.2 Selectivity and sensitivity ...... 31 2.3.3 Linearity, accuracy and precision ...... 31 2.3.4 Recovery, matrix effect and stability ...... 32 2.3.5 Application of the assay in a PK study ...... 32 2.4 Conclusions and discussions ...... 33 CHAPTER 3, IN VITRO AND IN VIVO EVALUATION OF P-GLYCOPROTEIN IMPACT ON DINACICLIB PHARMACOKINETICS ...... 44 3.1 Introduction ...... 44 3.2 Materials and methods ...... 46 3.2.1 Chemicals and materials ...... 46 3.2.2 Animals and study design ...... 46 3.2.3 Pharmacokinetic sample assessment ...... 47 3.2.4 Protein binding assays ...... 48 3.2.5 Data analysis ...... 48 3.3 Results ...... 49

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3.3.1 Pharmacokinetics of dinaciclib in P-gp knockout mice and wild type mice 49 3.3.2 Pharmacokinetics of dinaciclib and lenalidomide in combinational therapy in knockout mice model ...... 52 3.3.3 Protein binding measurement ...... 55 3.4 Conclusions ...... 55 CHAPTER 4, POPULATION PHARMACOKINETIC/PHARMACODYNAMIC MODELING OF DINACICLIB AND ITS GLUCURONIDE METABOLITE IN CLL PATIENTS ...... 59 4.1 Introduction ...... 59 4.2 Methods ...... 61 4.2.1 Subjects ...... 61 4.2.2 Study design and data collection ...... 62 4.2.3 Supportive care and TLS management ...... 63 4.2.4 Population pharmacokinetic modeling ...... 64 4.2.5 Model evaluation ...... 67 4.2.6 Pharmacokinetic/pharmacodynamic (PK/PD) correlation analysis.. 67 4.3 Results ...... 68 4.3.1 Patient summary ...... 68 4.3.2 Patient raw PK and TLS data ...... 68 4.3.3 Structural basic PK model ...... 71 4.3.4 Evaluation of covariates and final model determination ...... 73 4.3.5 Model evaluation and visual predictive check ...... 76 4.3.6 Pharmacokinetic/pharmacodynamic correlations ...... 78 CHAPTER 5, POPULATION PHARMACOKINETIC MODELDING AND SIMULATION OF TG02 ...... 83 5.1 Introduction ...... 83

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5.2 Methods ...... 85 5.2.1 Subjects and clinical trial design ...... 85 5.2.2 Pharmacokinetic samples and data clean ...... 86 5.2.3 Pharmacokinetic analysis ...... 86 5.2.4 Population PK modeling ...... 87 5.2.5 Model evaluation and simulation ...... 89 5.3 Results ...... 92 5.3.1 Patient summary ...... 92 5.3.2 Dose linearity analysis ...... 94 5.3.3 Covariate exploration ...... 97 5.3.3.1 Food effect ...... 97 5.3.3.2 Gender effect ...... 97 5.3.4 Stage 1 population pharmacokinetic model for new dose regimen design 102 5.3.5 Stage 2 of population pharmacokinetic model ...... 108 5.3.5.1 Pharmacokinetic base model ...... 108 5.3.5.2 Covariate testing and final PK model ...... 112 5.3.6 Model evaluation and visual predictive check (VPC) ...... 116 5.4 Conclusions ...... 116 CHAPTER 6, SUMMARY AND FUTURE PERSPECTIVES ...... 119 References ...... 124

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CHAPTER 1, INTRODUCTION

1.1 Cyclin-dependent kinases and Cyclin-dependent kinase inhibitiors (CDKIs)

1.1.1 Cyclins and cyclin-dependent kinases (CDKs)

CDKs, a group of 20 serine-threonine kinases, are key regulators in cell cycle control (CDK 1, 2, 4 and 6), transcription initiation and elongation (CDK 7 and 9), and neuronal function (CDK5) [1]. Structurally, CDKs consist of a catalytic effector subunit and a regulatory subunit where they bind with cyclins to form complexes. Among CDK members, 13 have been identified with cyclin partners, whereas binding partners of the other 7 remain unknown [2, 3]. CDK/cyclin complex functions are negatively regulated by two groups of endogenous inhibitors, the Ink4 and Cip/Kip families [4].

The cell cycle has five phases, namely G0, G1, S, G2 and M phase. During G1 to

S transition, cyclin D-CDK4/6 and cyclin E-CDK2 can phosphorylate the retinoblastoma (Rb) protein, which then disassociates from E2F transcription factors and allows for transcription for S phase [5]. CDK2 also assembles with cyclin A and controls S phase. CDK1 couples with cyclin A and B, subsequently facilitates the G2/M phase transition and participates in M phase. CDK7, 8 and 9 and their coupling cyclins target the carboxyl-terminal domain (CTD) of RNA

1 polymerase II and thus regulate gene transcription [6]. CDK7-cyclin can phosphorylate RNA polymerase II on serine 5 and initiate transcription; while

CDK9-cyclin T is involved in transcriptional elongation by phosphorylating the serine 2 site of CTD.

Deregulated cell cycle has been a hallmark of cancers, and CDKs have been shown to be associated with cancers. For example, CDK1 and CDK2 activities can be used for breast cancer prognosis [7]. CDK1 expression can serve as a diagnostic indicator for esophageal adenocarcinoma [8]. Aberrant expressions of

CDK4 and CDK6 have been found in oral cancer [9]. Overall, CDKs are promising and appealing targets for cancer therapy, and the inhibition of CDKs can lead to the termination of DNA synthesis and apoptosis induction.

1.1.2 Cyclin-dependent kinase inhibition under development

Various CDKIs have been broadly investigated for multiple malignancies.

Flavopiridol is a semi-synthetic flavonoid and a pan CDKI targeting CDK1, 2, 4/6,

7 and 9. It was the first CDKI to enter human clinical trials. Flavopiridol has been extensively studied in both solid and hematological malignancies. The success of flavopiridol has been mainly in relapsed chronic lymphocytic leukemia (CLL), and limited activity has been observed in solid tumors like non-small-cell lung cancer

(NSCLC), colorectal cancer and prostate cancer [10-13]. Roscovitine (also known as Seliciclib) is an oral CDK1, 2, 5 and 7 inhibitor and under phase II evaluation for NSCLC [14]. SNS-032, a CDK2, 7, and 9 inhibitor, has been tested in patients with CLL and multiple myeloma (MM) [15]. Another promising 2 molecule is palbociclib (PD-0332991), an oral selective CDKI against CDK4 and

CDK6. It has been investigated as monotherapy or combinational treatment in solid tumors, lymphoma, breast cancer, glioblstoma, and MM [16]. A recent phase II study has shown that palbociclib is associated with a significant improvement in progression-free survival when combined with letrozole in women with advanced ER-positive and HER2-negative breast cancer, and phase

3 trial has been initiated [17]. Apart from these molecules, there are still many

CDKIs under different stages of development, either by pharmaceutical companies or academic institutes [14]. Overall, CDKIs are a new class of attractive and active drugs for cancer treatment.

1.2 The use of CDKIs in chronic lymphocytic leukemia

1.2.1 CLL and current treatments and developments

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western hemisphere, and more than 15,000 new cases have emerged annually in recent years [18-22]. CLL is characterized by abnormal B cell lymphocytes accumulation in bone marrow and bloodstream, and it is considered as a stage of small lymphocytic lymphoma (SLL) with different manifestations, which is primarily nodal [23]. CLL is most prevalent in elderly males and rare in teenagers and children (National Cancer Institute). The diagnosis of CLL is mainly based on a blood test with at least 5x109 B lymphocytes/L and abnormal lymphocyte morphology, but is also assisted by other tests, such as immunophenotype determination, molecular cytogenetics, serum markers and marrow examination 3

[18, 24, 25]. CLL is possible to be detected in a routine blood test without any symptoms, and treatments for early disease have been shown to not be advantageous over treatment at later stages when symptoms emerge [26, 27].

Therefore, the current treatment strategy for CLL is to monitor asymptomatic early-stage disease and provide therapy when CLL progresses and develops symptoms [18, 24, 25]. The treatment, depending on the disease stages, includes purine analogs (e.g.fludarabine), alkylating agents (e.g. chlorambucil), monoclonal antibodies (e.g. rituximab), and small investigational molecules

(e.g.flavopiridol, lenalidomide), or a combination of chemotherapy with antibodies

[28-31]. Generally, the combination of fludarabine, cyclophosphamide and rituximab (known as FCR) is considered as the gold standard for treatment.

Meta-analysis of CLL first-line chemotherapies has shown that different treatments provide benefits in prolonging survival depending on patients’ ages and fitness conditions [32]. However, relapse occurs in CLL patients after first- line treatments [33, 34]. In addition, cytogenetic abnormalities, such as del(17p13) and del(11q22), often render the disease resistant to standard therapies [35, 36], which calls for new effective therapies.

In the past decades, substantial efforts have been invested in developing advanced therapies for CLL and great achievements have been made, as shown in Figure 1.1. Following the discovery of rituximab, second- and third-generation antibodies targeting CD20 have been developed. Obinutuzumab, a humanized type II antibody, improved response significantly in CLL patients in combination

4 with chlorambucil compared to rituximab-chlorambucil combination with similar tolerance, and received FDA approval in 2013 [37]. The advantage of another anti-CD20 antibody ofatumumab is that it can be used either as monotherapy or in combination with other agents for CLL. In 2014, ofatumumab was approved for

CLL patients refractory to fludarabine and alemtuzumab, which is the first monoclonal anti-CD20 antibody for refractory CLL [38, 39]. In the same year, a highly tolerated oral Bruton agammaglobulinemia tyrosine kinase (BTK) inhibitor ibrutinib, and phosphoinositide 3-kinase (PI3K) inhibitor idelalisib with appealing effects in patients with p53 dysfunctional disease, both targeting B-cell receptor signaling pathway, were granted accelerated approval by FDA for the treatment of relapsed and refractory CLL [40-44].

Though many options are available, CLL is still considered incurable. For example, combination therapy, as introduced above, is a very attractive and commonly selected strategy, but it might not be appropriate for elderly and unfit patients. While ibrutinib has shown clinical significant responses, ibrutinib resistance has been developed in CLL patients already, with mechanism elucidated by Woyach and colleagues [45-47]. Disease with cytogenetic abnormalities is still a hurdle and challenge for CLL treatment. Other molecules under investigation include the immune modulatory drug lenalidomide [48, 49], the anti-apoptotic protein BCL-2 agonist ABT-199 [50, 51], and cyclin-dependent kinase inhibitors, which will be discussed in further details in section 1.2.2.

Figure 1.1 Therapeutic agents and their targets in chronic lymphocytic leukemia

BCL2, B-cell lymphoma 2; BCR, B-cell receptor; BTK, Bruton’s tyrosine kinase; NFkB, nuclear factor kappa B; PI3K, phosphoinositide 3-kinase; PKC, protein kinase C; PLC, phospholipase C. Modified from Tausch et al [51].

1.2.2 The early development of CDKIs in CLL

Flavopiridol is a pan CDKI and has been comprehensively studied in CLL patients. In early trials of flavopiridol in CLL disease treatment with a 24-hour, 72- hour or 1-hour infusion dosing regimen, the clinical results were disappointing [52,

53]. With the help of pharmacokinetic modeling, a new dose schedule was designed: weekly 30-minute loading dose followed by a 4-hour infusion for 4 weeks. This was applied in a phase I trial, and though patients’ pretreatment disease had relapsed and become refractory to standard therapies, the clinical efficacy of this dose regimen was remarkable in this 52-patient population.

Approximately half of the patients achieved an objective response, and median progression-free survival was 12 months in this first demonstration of flavopiridol single-agent activity [54]. The dose limiting toxicity (DLT) was hyper acute tumor lysis syndrome (TLS) leading to one death in this trial, while diarrhea was the most common grade 3 toxicity previously. In a later phase II trial with this dose strategy, promising clinical effects of flavopiridol were confirmed in relapsed CLL patients, even those with high risk, genetic abnormalities [55]. However, TLS hindered further dose escalation of flavopiridol. Another CDKI, SNS-032, was studied in a phase I trial with CLL and MM patients, showing limited clinical results. TLS was determined as the DLT for CLL patients, while no DLT for MM patients [15]. In general, TLS has been the obstacle and limitation of CDKIs in the treatment for CLL, and better CDKIs with greater efficacy and safer profile are in need.

1.2.3 Tumor lysis syndrome

Tumor lysis syndrome refers to a constellation of metabolic abnormalities characterized by hyperkalemia, hyperphosphatemia, hyperuricemia, hypocalcemia or azatemia, consequentially after the release of intracellular contents to the blood stream due to rapid tumor cell death and lysis [56, 57]. It is a potentially life-threatening emergency typically occurring after chemotherapeutic treatment of hematological malignancies, but it is also seen in many types of solid tumors, such as small cell and non-small cell lung cancer, breast, gynecological, urological, gastrointestinal, neurological and skin cancers

[58, 59]. TLS is commonly induced by chemotherapy but also by other therapies like radiation treatment [60] and hormone treatment with dexamethasone [61]. In addition, TLS can occur spontaneously in both hematologic and solid malignancies, especially in bulky and rapidly growing tumors. Among all the malignancies, acute leukemia, B-cell acute lymphoblastic leukemia and Burkitt's lymphoma are classified as high-risk diseases, while solid tumors, multiple myeloma and CLL are considered as low-risk diseases for TLS [62]. According to

Cairo-Bishop classification, TLS is defined as laboratory and clinical TLS. If two of the values among uric acid, potassium, phosphorus and calcium, have 25% corresponding change from baseline in the time range of 3 days before and 7 days after drug treatment, laboratory TLS is determined and diagnosed. Clinical

TLS is defined when clinical symptoms have developed, including seizure, cardiac arrhythmia and sudden death, or serum creatinine has increased beyond

1.5 times the upper limit of normal level in addition to the presence of laboratory 8

TLS [63]. Based on lab values and clinical manifestations, Cairo-Bishop criteria also provide a grading system of TLS severity, from Grade 0 to 5.

Clinically, TLS can lead to various complications such as cardiac arrhythmias,

seizures, tetany, acute kidney injury (AKI) and even death, due to hyperkalemia,

hyperphosphatemia, hyperuricemia, or hypocalcemia. With respect to AKI, the

major causes are hyperuricemia and hyperphosphatemia. Uric acid, metabolized

by xanthine oxidases from intracellular nucleic acids (as shown in Figure 1.2),

can crystallize and precipitate in renal tubules. Moreover, human beings lack

urate oxidase, which converts uric acid to allantoin, a much more soluble product.

Therefore, uric acid crystals accumulate in the renal tubules, resulting in AKI. In

turn, AKI would exacerbate TLS by attenuating kidney function to eliminate

overwhelming potassium, phosphate or uric acid [64]. To treat the complication of

hyperuricemia, allopurinol was discovered to inhibit the xanthine oxidase and

decrease the production of uric acid. However, it does not ameliorate the already

existing hyperuricemic conditions, limiting its use to prophylaxis. Additionally,

allopurinol can sometimes cause hypersensitivity syndromes [65]. In the past

decade, rasburicase, a recombinant urate oxidase, has gained wide acceptance

for TLS treatment. The limitation of rasburicase is its high costs and that it is not

suitable for glucose-6-phosphate dehydrogenase deficient patients [66]. In

addition to uric acid, calcium phosphate salt can also block renal tubules and

further contribute to AKI [58, 64]. Urinary alkalinization with bicarbonate can

improve the hyperuricemia condition but will worsen the precipitation of calcium

9 phosphate, which is not recommended. The current consensus on TLS therapy is that the prevention and identification of high risk patients for avoiding serious adverse events is preferred compared to treatment after TLS arises. Risk factors for TLS include patient age, male gender, type and volume of cancer, high white blood cell count, lactate dehydrogenase, renal dysfunction and elevated baseline uric acid/potassium/phosphate levels [67, 68]. The prophylaxis and treatment plans are case dependent. The common methods include large fluid intake of 3 L per day to increase glomerular filtration rate, hypouricemic agents to control uric acid levels, phosphate binder or potassium binder therapy and even hemodialysis in severe situations [69].

As discussed above, CLL is generally considered as a low-risk disease for development of TLS. However, there are increasing incidences, both with standard therapies and also with the discovery of new drugs with enhanced activity. For example, TLS has been observed in CLL patients in multiple case reports with bendamustine [70, 71], ibrutinib [72], fludarabine/cyclophosphamide

[73], ABT-199 [74], lenalidomide [75], and rituximab [76]. TLS has been observed with the use of various CDKIs and has been defined as DLT, and concluded to be a class effect. In a phase I clinical trial of SNS-032 mentioned above, 4 out of

6 CLL patients at 75 mg/m2 and 2 out 3 CLL patients at 100 mg/m2 experienced

TLS, which was determined as DLT [15]. Similarly, the DLT of TLS was also associated with flavopiridol [54], and risk factors were identified as female gender, increased white blood cell count and β2-microglobulin, bulky tumor size and

10 decreased albumin [77]. In addition, a strong correlation has been reported between occurrence of TLS and the flavopiridol-glucuronide maximal concentration (Cmax) [78], and a population pharmacokinetic/pharmacodynamic model has been developed to predict the incidence of TLS using the flavopiridol- glucuronide exposure [79]. In the clinical investigation of dinaciclib in relapsed and refractory CLL patients, 5 TLS cases were observed in a 33-patient population and define the maximum tolerated dose (MAD) of dinaciclib [80].

Flavopiridol is converted into flavopiridol-7-glucuronide and flavopiridol-5- glucuronide metabolites by UDP-glucuronosyltransferases UGT1A1 and

UGT1A9 [81]. It is known that dinaciclib also undergoes phase II metabolism, being converted to glucuronide conjugates by UGTs. Therefore, it is worthwhile to study the relationship between glucuronide metabolites of dinaciclib and TLS.

Figure 1.2 Uric acid production, elimination and treatment

Modified from Wilson and Berns [82]

1.3 A potent and selective CDKI: dinaciclib

1.3.1 Basic information of dinaciclib

Dinaciclib (SCH 727965), was discovered by studying the structure-activity relationship of a series of compounds which share the pyrazolo[1,5-a]pyrimidine core [83]. Based on the nomenclature by the International Union of Pure and

Applied Chemistry, the chemical name of dinaciclib is (S)-3-(((3-Ethyl-5-(2-(2- hydroxyethyl)piperidin-1-yl)pyrazolo[1,5-a]pyrimidin-7-yl)amino)methyl)pyridine, as shown in Figure 1.3. Dinaciclib has a pKa of 5.1, octanol/water partition coefficient (log P) of 2.0, and a melting point of 165 to 168ºC. The formulation of dinaciclib has been developed as a sterile solution in citrate buffer at pH 3.0 to

4.2 for IV administration, which is stable for greater than 4 years stored at 4ºC and away from light [84].

1.3.2 Mechanisms and pre-clinical studies of dinaciclib

Dinaciclib is a potent and selective CDKI, which inhibits CDK1, CDK2, CDK5 and

CDK9 with low nanomolar range of 50% inhibitory concentration (IC50 of less than 5 nM). It has equivalent potency to flavopiridol against CDK 1 and CDK 9, but is greater than 10-fold more potent against CDK2 and CDK5. Dinaciclib has a superior in vivo therapeutic index (TI, calculated as the ratio of maximum tolerated dose and minimum effective dose) of 10 compared to other CDKIs, like

SNS-032 (TI of 2) and flavopiridol (TI~1) [85]. In vitro studies have shown that dinaciclib has antitumor activity in over 100 tumor cell lines, representing both

13 adult and pediatric solid tumors and hematological malignancies [85-87]. In various xenograft models of pancreatic cancer, ovarian cancer and lung cancer, dinaciclib delayed tumor growth in a dose-dependent manner [85, 88]. The

Gorlick group evaluated dinaciclib monotherapy in 43 xenograft models (7 acute lymphoblastic leukemia xenografts and 36 solid tumor xenografts covering kidney/rhabdoid tumors, scarcomas, neuroblastoma, glioma and brain tumors), and reported that more than half of the models had improved survival, with objective response only in ALL xenografts, providing evidence for the preferable use of dinaciclib in blood cancers [86].

The major mechanisms of the anti-tumor activities of dinaciclib are introduced as follows. First, dinaciclib can block thymidine DNA incorporation (IC50=4 nM in vitro), and thus inhibit DNA synthesis. Second, it induces apoptosis by the inhibition of serine 807/811 on the Rb tumor suppressor gene and PARP cleavage [85, 88, 89]. Third, dinaciclib activates the mitochondrial pathway of apoptosis, supported by the cleavage in caspase 3 and 9 but not caspase 8 [90].

In addition, dinaciclib can cause the decrease in the Mcl-1 protein level, which can serve as a predictor for dinaciclib induced apoptosis in solid tumors [89, 91].

It is controversial whether dinaciclib induced apoptosis is dependent of p53 or not

[90, 92]. Other mechanisms might include abrogating the microenvironmental cytokine protection [93], inhibiting the unfolded protein response [94], and interacting with bromodomains via the acetyl-lysine recognition site [95].

1.3.3 Current clinical development status of dinaciclib 14

In the first clinical trial of dinaciclib in humans, 48 patients with solid tumors were recruited including colorectal cancer, non-small cell lung cancer, ovarian cancer, breast cancer and melanoma (descending order in number of subjects). Weekly dose of dinaciclib (for three weeks and a week rest within a 28-day cycle) was given as a 2-hour intravenous infusion, in a dose-escalation design from 0.33 to

14.0 mg/m2. The maximum tolerated dose (MAD) was determined at 14 mg/m2 with this dose schedule, and the associated DLTs were orthostatic hypotension or hyperuricemia. Ten subjects had stable disease with more than 4 cycles of therapy [96]. The other adopted dosing regimen was a higher single dose on day

1 in the 21-day cycle, and 50 mg/m2 was the MTD. In previously treated non- small cell lung cancer patients, diniciclib did not show antitumor activities as monotherapy at 50 mg/m2 dose, though it was well tolerated [97]. In a phase II clinical trial comparing dinaciclib and capecitabine in advanced breast cancer patients, one patient treated with 50 mg/m2 dinaciclib had confirmed partial response (PR) while the other had unconfirmed PR. The single agent activity of dinaciclib is considered poor with breast cancer with an overall response rate

(ORR) of 8% [98]. In general, common adverse events in solid tumors include hematologic toxicities like neutropenia, leukopenia, increased aspartate aminotransferase, gastrointestinal toxicities like diarrhea nausea, vomiting, and fatigue and appetite loss, without observations of TLS (though one case of hyperuricemia occurred as DLT in the first human trial).

Dinaciclib has also been actively investigated in hematological malignancies. In relapsed multiple myeloma, confirmed minor or better responses have been achieved in 19% patients with diniciclib at doses of 30, 40, and 50 mg/m2, with a median of 3.5-month progression-free survival [99]. Similar to solid tumors, the common adverse events in this trial were leukopenia, thrombocytopenia, alopecia, gastrointestinal toxicities and fatigue. In patients with acute leukemia

(including acute myeloid leukemia and acute lymphoid leukemia), no patients achieved remissions. In addition to the above-mentioned toxicities, TLS was observed in this population [100]. The authors also suggested that prolonged drug exposure might improve clinical outcomes in acute leukemias supported by in vitro studies in primary leukemia cells.

Several clinical trials have been developed to pursue dinaciclib for the treatment of CLL. A phase I trial, conducted by our collaborator, Dr. Joseph Flynn, demonstrated dinaciclib monotherapy was well tolerated and elicited improved response rates compared to other diseases (15 of 33 patients showed response), including moderate responses in patients with del(17p13.1) [80]. There were five cases of TLS starting at a weekly dose of 14 mg/m2 [80]. In another trial with relapsed and refractory CLL patients, dinaciclib was given in the combination with rituximab. Five patients were evaluated due to trial termination, among whom, four had stable disease and one had complete response, without the observations of TLS. Though this data was very limited, it showed that the combination of anti-CD20 antibody and dinaciclib might be an advantageous

16 strategy for treating refractory CLL patients with improved efficacy and safety profile [101]. In our phase 1b/2 clinical study, the treatment of combinational dinaciclib and ofatumumab was evaluated in patients with the relapsed and refractory chronic lymphocytic leukemia/small lymphocytic leukemia/B-cell prolymphocytic leukemia (CLL/SLL/B-PLL).

1.3.4 Pharmacokinetic features of dinaciclib

Pre-clinical pharmacokinetic (PK) studies were performed in mice, rats, dogs and monkeys. The half-life (t1/2) ranged from 0.3 to 0.8 hours and clearance (CL) from

28 to 96 ml/min/kg [84]. In humans, the t1/2 was about 3 hours and CL was 15

L/h/m2 [102]. The distribution of dinaciclib in brain tissue is low, with undetectable levels one hour after IV bolus of 5mg/kg. In the permeability assay with Caco-2 cells, dinaciclib had an efflux ratio of 15, suggesting that it is a substrate of P- glycoprotein (P-gp). The protein binding of dinaciclib to human plasma was determined 87% in vitro, with a range of 69% to 80% in mouse, rat, dog and monkey plasma [84]. The metabolism of dinaciclib included glucuronidation in dogs, oxidation in rats, and both in humans. Ketoconazole, a CYP3A4/5 inhibitor, suppressed the formation of oxidative metabolites. In the further evaluation, oxidative metabolites were only found after incubation of CYP3A4 enzymes, but not CYP1A2, CYP2C9, CYP2C19, CYP2D6 [84]. Thus, dinaciclib is considered as a CYP3A4/5 substrate [102]. A pharmacokinetic study in oncology patients was performed to evaluate the clinical drug-drug-interaction (DDI) between dinaciclib and aprepitant, which is a weak-to-moderate inhibitor and inducer of 17

CYP3A4 [102]. In this 2-period crossover study, the same patients received dinaciclib only in one cycle and dinaciclib with aprepitant in the other. Each individual had similar dinaciclib exposure, demonstrated by similar Cmax and area under the concentration-time curve (AUC) values. The ratio of geometric means of dinaciclib Cmax under co-administration and single dinaciclib administration was 1.06 (90% confidence interval (CI) of 0.89—1.26); and that of dinaciclib AUC was 1.11 (90% CI of 0.93—1.32). This study suggested that there was no concern of significant clinical DDI when administering dinaciclib and aprepitant together to patients. In addition, dinaciclib was found to be an inhibitor of CYP1A2 and CYP3A4/5 at clinically relevant PK concentrations. The CYP induction could not be assessed at relevant concentrations due to cytotoxicity. In the intact and bile duct-cannulated rats and dogs, the major elimination pathway of dinaciclib was biliary excretion (greater than 60%) while renal excretion was minor (about 10%) [84].

Figure 1.3 Chemical structure of dinaciclib

1.4 TG02

1.4.1 Development of TG02

TG02, a small molecule macrocycle (structure as shown Figure 1.4 Chemical structure of TG02was discovered from structure-based design and obtained after high-throughput screen to target an array of kinases, including CDKs, Janus kinase 2 (JAK2), and Fms-like tyrosine kinase-3 (FLT3), aiming for a superior efficacy by multi-kinase inhibition [103, 104]. In a test with a panel of 63 kinases,

TG02 was found to have low nanomolar range IC50 for CDK1, 2, 3, 5, 7 and 9 (3-

37 nM), JAK2 (19 nM), FLT3 (19 nM), and MAPK family member ERK5 (43 nM)

[104]. Further, TG02 was tested in cell lines of both solid tumor and hematological malignancies, and was showed to have better inhibitory effects with the liquid tumor panel compared to the solid tumor panel. Meanwhile, when compared to the CDK inhibitor SNS-032 (not a JAK2 or FLT3 inhibitor), TG02 had a lower IC50 potentially suggesting the superior activity may be due to simultaneous inhibition of other kinases. In two AML animal models (MV4-11 and

HL60), TG02 treatment had inhibited tumor growth and led to an improved survival compared to vehicle groups [104]. In another study, TG02 delayed tumor growth in 2 human MM plasmacytomaxenograft, both bortezomib-sensitive

MM1S and bortezomib-resistant OPM2 models. In addition, the combinations of

TG02 and lenalidomide or bortezomib improved the antitumor effects compared to the single agent treatment in the xenograft model, demonstrating that TG02 can potentiate other anti-myeloma drugs [105]. The only solid tumor mouse

20 model evaluated with TG02 treatment so far was one with triple negative breast cancer, which showed significant tumor growth inhibition compared to a vehicle group [106].

The mechanisms of TG02 include inhibition of cell cycle, induction of apoptosis, and the loss of mitochondrial membrane potential. TG02 was tested in a series of

12 MM cell lines and freshly isolated cells from 8 myeloma patients up to 1000 nmol/L and showed promising antimyeloma activities [105]. TG02 decreased

CDK1 and CDK2 levels, as well as cyclin B which binds to CDK1, and subsequently accumulated cells at G2/M phases in the life cycle. By inhibiting

CDK7 and CDK9, TG02 can interrupt the transcription initiation and elongation.

In the MM cell lines, TG02 triggered apoptosis by both intrinsic and extrinsic pathways. It also caused mitochondrial membrane change, resulting in cytochrome C release into the cytosol, and it downregulated XIAP and Mcl-1 protein levels [105]. In AML cell lines, TG02 led to decreased level of cdc6 by phosphorylating CDK2 and induced G1 arrest to inhibit DNA synthesis. In addition, TG02 also suppressed RNA polymerase II, even in dormant leukemia cells, and decreased both mRNA and protein levels of Mcl-1 and XIAP, leading to apoptosis [104, 107, 108].

1.4.2 Pharmacokinetic characteristics of TG02

TG02 has a high protein binding, greater than 99%, in all species tested including mouse, dog and human plasma [109, 110]. It also showed high permeability in

Caco-2 cell assays. TG02 is mainly metabolized by CYP1A2 and CYP3A4, and the major metabolite in humans is the N-demethylated product. Of the major CYP enzymes, TG02 inhibits only CYP2D6 (IC50=1 µM), and it does not induce

CYP3A4 or CYP1A. The oral bioavailability of TG02 is 24% in mice, 4% in rat, and 37% in dog [110]. Mice pharmacokinetic analysis showed that TG02 concentration in the tumor tissues remained above cellular IC50 for more than 8 hours, suggesting that TG02 may have advantageous target tissue distribution

[104]. The favorable PK properties and potential advantages of oral dosing have led to TG02 further clinical investigations.

Figure 1.4 Chemical structure of TG02

CHAPTER 2, DEVELOPMENT OF A SENSITIVE LC/MS/MS ASSAY FOR

DINACICLIB AND DINACICLIB-G IN HUMAN PLASMA

2.1 Introduction

Dinaciclib is a selective and potent cyclin-dependent kinase inhibitor (CDKI) which has been shown to inhibit CDK1, 2, 5 and 9 at nanomolar concentrations

[83]. Cyclin-dependent kinases (CDKs) play critical roles in the cell cycle and have abnormal expression in cancer cells, which renders CDKs as rational targets for cancer therapy. Dinaciclib has shown superior activity and has a higher therapeutic index compared to flavopiridol, the first CDKI entering clinical trials [85]. Dinaciclib activates the mitochondrial pathway to induce apoptosis, independent of p53 [90, 92]. It has been demonstrated to have promising antitumor activities in cell lines from both solid tumor and hematologic malignancies and in xenografted mouse models [85, 88]. Dinaciclib has been investigated in clinical trials for multiple indications, including non-small cell lung cancer, breast cancer, relapsed multiple myeloma, acute leukemias, and chronic lymphocytic leukemia (CLL) [23, 97, 98, 100, 102, 111].

Among the various diseases evaluated, we are interested in the development of dinaciclib for the treatment of CLL. The dose limiting toxicity of tumor lysis syndrome (TLS) had been observed with dinaciclib treatment. TLS refers to a 24 series of metabolic abnormalities characterized by hyperkalemia, hyperphosphatemia, hyperuricemia, hypocalcemia, consequentially after the release of intracellular contents to the blood stream due to death and lysis of massive cancer cells [56, 112]. It is a life-threatening emergency typically occurring after the chemotherapeutic treatment of hematologic malignancies, but it is also seen in many types of solid tumors [59, 113]. Clinically, TLS can lead to various complications such as cardiac arrhythmias, seizures, tetany, acute kidney injury (AKI) and even death, due to hyperkalemia, hyperphosphatemia, hyperuricemia, or hypocalcemia. Risk factors for TLS include patient age, male gender, type and volume of cancer, high white blood cell count, lactate dehydrogenase, renal dysfunctions and baseline uric acid/potassium/phosphate levels [67, 68]. Current consensus is that the successful management of TLS includes identifying patients at high risk and providing prophylaxis. According to previous studies of flavopiridol in CLL patients, there was an association between flavopiridol glucuronide metabolite PK and the toxicity of tumor lysis syndrome

[78, 79]. In a previous phase 1 clinical trial of dinaciclib in CLL patients, tumor lysis syndrome was the dose limiting toxicity [114]. Current data suggests TLS may be a class effect CDKIs. Therefore, it is necessary to monitor both parent drug and glucuronide metabolite of dinaciclib in the current phase 1b/2 trial of dinaciclib in refractory and relapsed CLL patients.

To date, full validation of a dinaciclib and dinaciclib-glucuronide assay has not been presented in the public literature. Herein, we present a sensitive liquid

25 chromatography-tandem spectroscopy (LC-MS/MS) method for dinaciclib and its metabolite dinaciclib-glucuronide in human plasma. This method has been used to simultaneously quantify the parent drug and metabolite in CLL patients from the phase 1b/2 clinical trials to characterize their PK profiles.

2.2 Materials and methods

2.2.1 Materials

Dinaciclib, (S)-3-(((3-Ethyl-5-(2-(2-hydroxyethyl)piperidin-1-yl)pyrazolo[1,5- a]pyrimidin-7-yl)amino)methyl)pyridine 1-oxide, was obtained from the National

Cancer Institute-Cancer Therapy Evaluation Program. The internal standard (IS)

4-chloro-2-[2-(4-methoxyphenyl)pyrozolo[1,5-A]pyrimidin-7-yl]phenol, beta- glucuronidase, octanol and formic acid was purchased from Sigma-Aldrich (St.

Louis, MO). HPLC-grade methanol and acetonitrile were purchased from Fisher

Scientific (Waltham, MA). Human plasma was obtained from the local American

Red Cross (Columbus, OH). Solid phase extraction C18 cartridges were purchased from Applied Separations (Allentown, PA).

2.2.2 Standard and quality control (QC) solutions of dinaciclib and IS

A high concentration solution (50 mg/ml) of dinaciclib was obtained by dissolving the powder in DMSO. Stock solutions (1 mg/ml) of dinaciclib and IS were prepared in methanol and ethanol respectively and stored as 10 μL aliquots at -

80 ºC. A series of standard working solutions and QC solutions of dinaciclib and

IS (10 µg/ml) were freshly prepared for each run. The calibration curve of

26 dinaciclib ranged from 1 to 1000 ng/ml, with concentrations at 1, 2, 5, 10, 20, 50,

100, 200, 500, and 1000 ng/ml. QCs were produced at 3, 30, and 300 ng/ml.

2.2.3 Extraction and quantification of dinaciclib-glucuronide

Dinaciclib-glucuronide was extracted from patient urine collected 0-4 hours after drug administration. 20 mL urine and 20 mL octanol were vortex-mixed for 30 sec and stabilized for phase separation. After repeating three times, the aqueous phase was collected and loaded on a solid phase extraction C18 cartridge

(Octadecyl/18, 20g, 60ml), which was pre-balanced with 100 mL water and 100 mL methanol. After washing with 100 mL of 20% methanol, dinaciclib- glucuronide was eluted with 50 mL 45% methanol. The eluted solution was centrifuged for 10 minutes at 14,000 rpm before drying with a vacuum system.

The dried residue was reconstituted in water followed by a 10-min centrifugation step at 14,000 rpm to prepare a stock solution of dinaciclib-glucuronide. The concentration of this solution was determined by converting metabolite to product using a beta-glucuronidase enzyme reaction. Briefly, triplicates of 10 μL of 10000 fold and 5000 fold diluted dinaciclib-glucuronide stock solutions were incubated with 5000 U beta-glucuronidase at 37 °C for 2 hours. The incubation was then processed and quantified for dinaciclib using procedures described below. A series of standard working solutions and QC of dinaciclib-glucuronide (10×) were freshly made for each run. The calibration curve of dinaciclib-glucuronide ranged from 2 to 500 ng/ml, with QCs at 3, 30, and 300 ng/ml.

2.2.4 Plasma sample preparation

Standard curve samples were made by spiking 10 μL of dinaciclib or dinaciclib- glucuronide 10× standard working solutions and 10 μL of IS solution (10 μg/mL) into 100 μL human plasma. Patient PK samples were prepared by spiking 10 μL

IS solution (10 μg/mL) into 100 μL patient plasma. To both 1 mL methanol were added for protein precipitation. After vortexing for 20s and centrifugation at

18,000×g for 10 min at 4 °C, the supernatant was transferred and collected in a glass tube for evaporation under a gentle stream of nitrogen. The residue was reconstituted in 100 μL 50% methanol and transferred into microcentrifuge tubes for another 10 min centrifugation at 18,000×g at 4 °C. A portion (20 μL) was injected for LC-MS/MS analysis.

2.2.5 Liquid chromatography-tandem mass spectrometry (LC-MS)

The LC–MS system comprised a Thermo Fisher Accela U-HPLC and a TSQ

Quantum Discovery triple quadrupole mass spectrometer (Thermo Fisher

Scientific Corporation, San Jose, CA) equipped with an electrospray ionization source. Sample separation was performed on a reversed-phase Thermo

BetaBasic-C8 column (5 μm, 2.1 × 50 mm) with a BetaBasic C8 guard column.

Mobile phases consisted of water (A) and acetonitrile (B), each with 0.1% formic acid. The 12 min gradient was applied at a constant flow rate of 200 μL/min:

0 min, 15% B; 5 min, 85% B; 8.5 min, 15% B. Xcalibur LCquan 2.7. was employed for system control and data processing.

The ion transition channels for selected reaction monitoring (SRM) were m/z

397.25 →335.06 and 397.25→321.01 for dinaciclib, 573.32→397.26 and

573.32→335.27 for dinaciclib-glucuronide, and 352.07→165.00 for IS, respectively. Parameters optimized under a direct syringe infusion of analytes and IS were as follows: spray voltage, 4900 V; sheath gas pressure, 25 mTorr; aux gas pressure, 5 mTorr; capillary temperature, 320 °C. Tube lens offsets were

108, 118 and 110 for dinaciclib, dinaciclib-glucuronide and IS respectively. Scan time was 0.02 s. Both Q1 and Q3 peak widths were 0.2 full widths at half- maximum m/z.

2.2.6 Assay validation

The assay performance was assessed using the FDA Guidance for Industry

2013 updated document ―Bioanalytical Method Validation‖ to determine the selectivity, accuracy, precision, reproducibility and stability. To evaluate assay selectivity, blank human plasma and plasma spiked with analytes at lower limits of quantification (LLOQ) were compared to identify any potential endogenous interference. Assay accuracy, precision and reproducibility were evaluated for within- and between-run validation, in which a calibration curve was established between 1-1000 ng/ml for dinaciclib and 2-500 ng/ml for dinaciclib-glucuronide.

Three QC levels were selected to represent low, medium and high concentrations. By the FDA guidance, the between- and within-day accuracy and precision (expressed as coefficient of variation %CV) determined by six replicates of QC samples prepared at 3, 30 and 300 ng/ml should have nominal 29 variability within ±15%. The LLOQ, however, may have an acceptable nominal variability within ±20%. The freeze-thaw stability was performed after three freeze-thaw cycles at -80 °C. Short-term and long-term stability samples were analyzed after 4 hours at room temperature and 7 months after storage at -80 °C.

The autosampler stability was evaluated after 12 hours. Recovery was determined by the peak area ratios of pre-spiked QC plasma samples compared to those of neat solutions. Matrix effect was evaluated by comparing peak area ratios of analytes in blank plasma extracts spiked with dinaciclib or dinaciclib- glucuronide to those of neat solution. All recovery, matrix effect and stability were reported from replicated QC samples at 3, 30 and 300 ng/ml (n=4).

2.2.7 Pharmacokinetic study

This analytical method was used to quantify the dinaciclib and dinaciclib- glucuronide in plasma samples from patients enrolled on clinical trial

NCT01515176. Dinaciclib was given as a 2-hour infusion at 7 mg/m2 on day 2 and escalated to 10 mg/m2 on day 8 and 14 mg/m2 on day 15. PK plasma samples were collected at 1, 2, 2.25, 2.5, 4, 6, and between 8-24 hours after infusion started on day 2 and 15 respectively, and stored at -80 °C until analysis.

2.3 Results

2.3.1 Mass spectrometry and chromatography

The chemical structure, full-scan product ion spectra and hypothesized fragment pathway of dinaciclib, dinaciclib-glucuronide and IS are shown in Figure 2.1. The predominant product ions at m/z 335.3 and 321.4 were chosen for the SRM 30 transition channel for dinaciclib, m/z 397.2 for dinaciclib-glucuronide and m/z

164.98 for IS.

The Thermo BetaBasic-C8 column provided satisfactory chromatographic separation of analytes and IS after protein precipitation. Formic acid was added to the mobile phase to enhance ionization and peak shape. The gradient elution was used to achieve optimal separated peaks among parent drug, metabolite, and IS, with retention times (RT) at 3.39, 3.86, and 6.86 min respectively.

2.3.2 Selectivity and sensitivity

Representative chromatograms (in blank human plasma) for three analytes are displayed in Figure 2.2, while plasma spiked with the LLOQs of dinaciclib and dinaciclib-glucuronide, and plasma spiked with IS are shown in Figure 2.3. No interference peak was observed at the retention times of the analytes, which indicated sufficient selectivity for this assay. The LLOQ was 1 ng/ml for dinaciclib and 2 ng/ml for dinaciclib-glucuronide.

2.3.3 Linearity, accuracy and precision

The calibration curve exhibits acceptable linearity across the lower concentration range of 1-50 ng/ml and medium to high concentration range of 50-1000 ng/mL for dinaciclib. The accuracy and precision were calculated based on three batches of plasma samples, in which all three QC levels were measured within

±15% of nominal values (Table 2.1). Assay accuracy and precision were determined from standard curves in three individual runs, as shown in Table 2.2. 31

Dinaciclib-glucuronide had acceptable linearity from 2 to 500 ng/ml, which was sufficient to cover the concentration levels from clinical plasma samples. The within-day and between-day accuracy and precision are presented in Tables 2.3 and 2.4.

2.3.4 Recovery, matrix effect and stability

The recovery evaluated at three QC levels ranged 74-89% for dinaciclib, and 77-

90% for dinaciclib-glucuronide. The matrix effect was 94-110 % and 89-114 % for the parent drug and metabolite, respectively.

The 4-hour room temperature short-term stability of dinaciclib was 99-114%, and

7-month long-term stability at -80 ºC was 89-99%, which covers the storage time period of our clinical patient samples. Dinaciclib-glucuronide was also stable after short-term (98-109%) and long-term storage (107-114%). In addition, dinaciclib was shown to be stable in the autosampler tray after 12 hours (96-102%) and after three freeze-thaw cycles (87-97%).

2.3.5 Application of the assay in a PK study

The PK profiles of dinaciclib and dinaciclib-glucuronide in plasma samples from 5

CLL patients are shown in Figure 2.4. Non-compartmental analysis was performed to obtain PK parameters from the data. The maximum observed concentration (Cmax) ranged 190 to 504 ng/ml for dinaciclib and 36 to 126 ng/ml for dinaciclib glucuronide; area under the curve (AUC) ranged 400 to 1227 hr*ng/ml for dinaciclib and 118 to 381 hr*ng/ml for glucuronide metabolite. 32

2.4 Conclusions and discussions

This was the first study to provide a sensitive LC-MS/MS assay for simultaneous quantification of dinaciclib and dinaciclib-glucuronide in human plasma. This assay was linear over the range of 1-1000 ng/ml for the parent drug and 2-500 ng/ml for the glucuronide metabolite. Both dinaciclib and dinaciclib-glucuronide were stable for as long as 7 months in human plasma, which allowed for a satisfactory analysis timeframe for clinical samples. This assay has been successfully applied to the study of dinaciclib pharmacokinetics in refractory CLL patients.

In our study, we used extracted glucuronide metabolite as a standard to quantify plasma levels. Our initial efforts showed that the glucuronide metabolite was most abundant in the 0-4 hour urine, less in 4-8 hour urine collection, and the least in 8-24 hour urine samples. Therefore, extraction was mainly performed with 0-4 hour urine samples. Solid phase extraction with octanol could effectively separate the parent drug and metabolites. In order to further remove the impurities, several steps of high speed centrifugation were applied. Though the beta-glucuronidase assay could accurately quantify the glucuronide stock solution, the purity of the powder was still a limitation. Future study might consider synthesize the glucuronide metabolite to obtain a purer powder.

(Continued)

Figure 2.1 The chemical structure, full-scan product ion spectra, and hypothesized fragment structure of IS (A), dinaciclib (B) and dinaciclib- glucuronide (C).

Table 2.1: Continued

(Continued)

Table 2.1: Continued

RT: 0.00 - 12.00 A 11.71 NL: 1.61E3 100 TIC F: + c ESI 0.07 sid=10.00 SRM ms2 80 0.32 6.82 352.070 [164.900-165.100] MS matrixblank 0.49 60 8.82 9.23 0.59 1.21 11.20 9.41 10.58 40 5.58 8.34 8.13 9.58 2.66 10.06 1.31 5.00 5.86 7.62 20 3.76 4.31 4.58 1.45 0 10.51 NL: 3.10E1 100 TIC F: + c ESI B 3.28 sid=10.00 SRM ms2 80 397.250 [320.910-321.110, 334.960-335.160] MS 60 3.87 matrixblank 11.37 4.17 7.21 40 4.48 7.96 1.32 2.66 6.83 0.53 1.87 4.59 6.52 8.48 8.93 9.62

RelativeAbundance 6.07 20

0 4.49 NL: 9.35E2 100 TIC F: + c ESI C sid=10.00 SRM ms2 80 573.320 [335.170-335.370, 397.160-397.360] MS 60 matrixblank

5.42 40

5.80 20 4.63 6.28 0.53 1.29 1.88 2.60 3.49 4.22 7.18 7.28 8.49 9.24 10.48 11.27 0 0 1 2 3 4 5 6 7 8 9 10 11 122 Time (min) Figure 2.2 Chromatograms of blank human plasma. Mass chromatograms were generated from analysis of blank plasma using the three transition channels for IS (A), dinaciclib (B) and dinaciclib- glucuronide (C).

RT: 0.00 - 12.0012.01 6.826.86 NL: 2.62E63.29E6 100 TIC F: + c ESI A sid=10.00 SRM ms2 90 90 352.070 [164.900-165.100][164.900-165.100] 80 MS QC1-3STD3 70 60

RelativeAbundance RelativeAbundance 30

10 7.067.557.51 RT: 0.00 -0.11 12.010.630.90 1.661.73 2.282.622.62 3.664.033.974.58 4.695.31 5.895.93 6.516.55 8.107.968.34 9.659.829.9610.6110.75 11.82 0 3.86 4.49 6.86 NL: 7.79E33.29E69.43E2 100 TIC F: + c ESI sid=10.00 SRM ms2 90 90 B 397.250352.070573.320 [320.910-321.110,[164.900-165.100][335.170-335.370, 80 334.960-335.160]MS397.160-397.360] STD3 5.35 MS QC1-3STD3 70 60 3.39 50

RelativeAbundance 30 20 5.84 10 5.04 3.60 5.90 7.59 7.49 7.55 8.18 0.460.630.830.77 1.731.742.252.572.622.633.253.593.66 3.974.31 4.695.03 5.936.246.55 7.27 7.58 8.10 8.348.798.59 9.449.8210.1110.4110.6111.2811.3011.8211.90 0 4.49 NL: 9.43E2 100 0 1 2 3 4 5 6 7 8 9 10 11 12 TIC F: + c ESI Time (min) sid=10.00 SRM ms2 90 C 573.320 [335.170-335.370, 80 397.160-397.360] 5.35 MS STD3 70

60 3.39 50

20 5.84

10 5.04 3.60 5.90 7.49 7.59 0.46 0.77 1.74 2.57 3.25 8.18 8.59 10.11 11.28 11.90 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Time (min) Figure 2.3 Representative chromatograms of blank human plasma spiked with 1000 ng/ml IS (RT at 6.86 min) (A), dinaciclib (RT at 3.86 min) at LLOQ of 1ng/ml (B), and dinaciclib-glucuronide (RT at 3.39 min) at LLOQ of 2 ng/ml (C).

1000

01-01 01-02 01-04 02-01 100 02-02

Dinaciclib (ng/ml)

1 0 2 4 6 8 310 312 314 316 318 320

100

Dinaciclib-G (ng/ml)

1 0 2 4 6 8 310 312 314 316 318 320

Time (hrs)

Figure 2.4 PK profiles of dinaciclib and dinaciclib-glucuronide in CLL patients (n=5). 39

Table 2.1 Dinaciclib within-day (n=6) and between-day (n=18) accuracy and precision

Within-day Between-day Nominal Mean Mean Conc. % % conc. % accuracy conc. % accuracy (ng/ml) CV CV (ng/ml) (ng/ml) 3 2.75 91.7 6.0 2.97 99.0 13.1 30 28.3 94.3 2.9 27.9 93.0 6.3 300 292.9 97.6 2.9 313.3 94.5 12.3

Accuracy values are mean concentrations; % accuracy was determined as mean concentration/nominal concentration × 100%; CV% was determined as standard deviation/mean × 100%.

Table 2.2 Dinaciclib standard curve linearity, accuracy and precision (n=5 runs)

Nominal conc. Mean conc. Accuracy (%) Precision (%) (ng/ml) (ng/ml) 1 1.0 99.7 5.5 2 1.9 97.4 11.5 5 5.1 102.1 6.2 10 10.0 100.0 5.0 20 19.5 97.6 4.3 50 49.4 98.8 9.5 100 102.0 102.0 5.2 200 196.1 98.0 4.9 500 499.0 99.8 7.8 1000 1003.3 100.3 3.1

Table 2.3 Dinaciclib-glucuronide within-day (n=6) and between-day (n=12) accuracy and precision

Within-day Between-day Nominal Mean Mean Conc. % % conc. % CV conc. % CV (ng/ml) accuracy accuracy (ng/ml) (ng/ml) 3 3.18 105.9 8.8 3.22 107.4 13.7 30 29.1 97.0 8.5 29.2 97.5 6.4 300 309.4 103.1 8.1 313.3 104.4 6.2

Table 2.4 Dinaciclib-glucuronide standard curve linearity, accuracy and precision (n=5 runs)

Nominal conc. Mean conc. Accuracy (%) Precision (%) (ng/ml) (ng/ml) 2 2.0 100.7 12.0 5 4.8 95.3 6.4 10 10.0 99.6 4.8 20 19.7 98.6 10.4 50 49.3 98.6 7.5 100 104.7 104.7 5.9 200 207.0 103.5 3.5 500 489.5 97.9 2.2

CHAPTER 3, IN VITRO AND IN VIVO EVALUATION OF P-GLYCOPROTEIN

IMPACT ON DINACICLIB PHARMACOKINETICS

3.1 Introduction

Dinaciclib is a selective inhibitor of cyclin-dependent kinase 1, 2, 5 and 9, and it can inhibit DNA synthesis and induce apoptosis. Dinaciclib has been demonstrated with impressive anti-tumor activities in multiple pre-clinical cancer models and is currently under clinical investigations for different indications.

Dinaciclib has a relatively short half-life (about 3.3 hours in human) and is mainly metabolized by CYP3A4/5. In vitro transport studies have shown that dinaciclib has an efflux ratio of 15 on Caco-2 cells and is a substrate of P-glycoprotein (P- gp) [84].

P-gp is an efflux transporter and also known as multidrug resistance protein 1

(MDR1), encoded by gene ATP-binding cassette sub-family B member 1 (ABCB1)

[115]. It is widely distributed and expressed in various tissues, including intestine, liver, kidney and the blood-brain-barrier, etc. [116-118]. P-gp functions to pump foreign compounds out of cells and protect organs from xenobiotic exposure. P- gp has a broad range of substrates, including cardiac glycosides, corticosteroids, immunosuppressants, antimicrobial agents, opioids, and anticancer agents.

Tumor cells expressing P-gp have lower intracellular chemotherapeutic drug concentrations compared to those without P-gp. Therefore, P-gp overexpression is an important mechanism responsible for multi-drug resistance in cancer therapy. In addition, studies demonstrate that P-gp is also involved in clinical drug-drug-interactions (DDI) [119-121]. For example, ranolazine can cause a 40-

60% increase in digoxin AUC by inhibiting P-gp activity [122].

In our study, we were interested in the potential DDI between dinaciclib and lenalidomide. Lenalidomide is an immunomodulatory agent, which has been approved by FDA for multiple myeloma. Currently it is under clinical evaluation for the treatment of CLL, and the mild to life-threatening toxicity of tumor flare has been associated with lenalidomide dose [123]. A recent phase I study has shown that the combination of CDKI flavopiridol and lenalidomide has increased drug safety in CLL patients [124]. Dinaciclib is a more selective CDKI and has a larger therapeutic index compared to flavopiridol, thus there was potential interest to combine dinaciclib with lenalidomide and examine their combinatory effects in treating CLL patients. Lenalidomide is a P-gp substrate and has been shown to have an apparent clinical DDI with CCI-779 via P-gp [125]. Dinaciclib is also a P- gp substrate based on the preclinical study, therefore there is a potential dinaciclib-lenalidomide interaction.

The goals of our studies were to evaluate the impact of P-gp on dinaciclib PK profile in vivo and the potential drug-drug-interactions between dinaciclib and lenalidomide via P-gp in animal models. We used a P-gp knockout mouse model 45

(mdr1a/b-/-) for our study, which has been widely used to evaluate the role of P- gp on drug disposition in vivo [126, 127].

3.2 Materials and methods

3.2.1 Chemicals and materials

Dinaciclib was obtained from the National Cancer Institute-Cancer Therapy

Evaluation Program. Lenalidomide was extracted from commercial capsules. The internal standard (IS) 4-chloro-2-[2-(4-methoxyphenyl)pyrozolo[1,5-A]pyrimidin-7- yl]phenol, formic acid and mouse plasma was purchased from Sigma-Aldrich (St.

Louis, MO). HPLC-grade methanol and acetonitrile were purchased from Fisher

Scientific (Waltham, MA). Dosing solutions were prepared by adding dinaciclib and lenalidomide to the appropriate volume of saline solution.

EDTA tubes were purchased from BD Company (Franklin Lakes, New Jersey).

The dialysis membrane with a 12-14 KDa molecular weight cutoff was bought from HTDialysis (Gales Ferry, CT).

3.2.2 Animals and study design

Mdr1a/b-/- knockout mouse breeding pairs were purchased from Taconic. The mouse colony was bred in accordance with agreements contracted through

Taconic. Mixed gender mice 6 to 10 weeks of age were used for this study. Age matched background Friend leukemia virus B-type (FVB) wild type mice of both gender were purchased from Harlan and acclimated to the facility for at least 48

46 hours prior to study initiation. Mice were given ad libitum access to soft food diets of RecoveryGel® and placed on wire flooring cage inserts without bedding for 3 days prior to the drug administration. Food was removed 30 minutes before the dark cycle on the third night so mice were fasted for this study. The study design was approved and performed in compliance with Institutional Animal Care and

Use Committee guidelines.

Mice were assigned randomly to treatment groups and time points with a uniform representation of gender. Dinaciclib saline solution was dosed intravenously (i.v.) in FVB wild type (WT) and Mdr1a/b -/- knockout (KO) mice at 5mg/kg to evaluate the impact of P-gp on dinaciclib PK. Dinaciclib was also given to mice in combination with i.v. lenalidomide at 0.5mg/kg in a single dose solution to evaluate the potential DDI of this clinically relevant combination. Single dose of lenalidomide was given to mice at 0.5 mg/kg. Five mice were sacrificed at 5, 10,

20, 30, 45, minutes, 1, 1.5, 2.5, 4 and 6 hours after dosing, respectively, thus there were 50 mice in each group. Mouse blood was obtained by cardiac puncture and collected in EDTA-tubes. After spinning for 3 minutes, plasma was transferred to a new tube and stored at -80ºC until analysis.

3.2.3 Pharmacokinetic sample assessment

Standard curve samples were made by spiking 10 μL of 10× standard working solutions and 10 μL of IS solution (10 μg/mL) into 100 μL mouse plasma. Mice

PK samples were prepared by spiking 10 μL IS solution (10 μg/mL) into 100 μL mouse plasma. To both 1 mL of methanol was added for protein precipitation. 47

After vortexing for 20s and centrifugation at 18,000×g for 10 min at 4 °C, the supernatant was transferred and collected in a glass tube for evaporation under a gentle stream of nitrogen. The residue was reconstituted in 100 μL 50% methanol and transferred into microcentrifuge tubes for another 10 min centrifugation at 18,000×g at 4 °C. A portion (20 μL) was injected for LC-MS/MS analysis.

The tested compounds were separated by a C8 column and detected under positive mode. A gradient elution was used with 100% water with 0.1% formic acid (FA) and 100% acetonitrile with 0.1 FA as mobile phase A and B, respectively. The 12 min gradient was applied at a constant flow rate of

200 μL/min: 0 min, 15% B; 5 min, 85% B; 8.5 min, 15% B.

3.2.4 Protein binding assays

Protein binding of dinaciclib and lenalidomide in human plasma was assessed using a 96-well micro-equilibrium dialysis device (HTDialysis, Gales Ferry, CT).

The plasma and PBS compartments were separated by a dialysis membrane of

12-14 KDa molecular weight cut-off, with a volume of 100 µl on each side. Two groups of samples included those: with dinaciclib only and those with the combination of dinaciclib and 2 μM of lenalidomide. Four pharmacologically attainable concentrations of dinaciclib were tested at 50, 100, 500 and 5000 ng/ml.

3.2.5 Data analysis

The mouse plasma drug concentration data was analyzed by WinNonlin Phoenix

(version 6.3) to fit a non-compartment model to obtain all PK parameters [128].

Model type was chosen as ―Plasma (200-202)‖, with dose option of ―IV bolus‖, and linear trapezoidal linear interpolation method was used.

3.3 Results

3.3.1 Pharmacokinetics of dinaciclib in P-gp knockout mice and wild type mice

After 5 mg/kg dinaciclib was dosed into mice as an intravenous bolus via tail vein, mice were sacrificed 5, 10, 20, 30, 45, minutes, 1, 1.5, 2.5, 4 and 6 hours after dosing, respectively. The PK profiles of dinaciclib in two groups of mice were present in Figure 3.1. As shown in the plot, the plasma drug concentrations were higher in FVB mice in the first hour, and the elimination phases were parallel in these two groups. There were five mice at each time point, and the mean drug concentration from these five mice was used for non-compartmental analysis.

The major PK parameters have been summarized in Table 3.1. Dinaciclib had a relatively short half-life (t1/2), which was about half an hour in mice with FVB background. The area under the plasma concentration-time curve (AUC) of dinaciclib was 163.9 min*ug/ml in the FVB mice and 117.6 min*ug/ml in KO mice.

As dinaciclib is a substrate of P-gp, we expected to observe a higher AUC in KO mice compared to WT. However, we observed an approximate 40% increased plasma dinaciclib AUC in WT compared to KO mice.

Table 3.1 PK parameter estimates of dinaciclib in WT and KO mice

Parameters Units Wide type P-gp KO KO/WT Cmax ug/ml 7.8 5.4 0.69 AUC min*ug/ml 163.9 117.6 0.72 t1/2 min 36.9 35.0 0.95 V L/kg 1.6 2.1 1.31 CL ml/min/kg 30.5 42.5 1.39

Cmax, maximum concentration; AUC, area under the concentration-time curve; t1/2, half-life; V, volume of distribution; CL, clearance.

iv dose of 5mg/kg dinaciclib

10000 P-gp KO mice FVB mice

1000

100

Dinaciclib Conc (ng/ml) 0.1 0 100 200 300 400 Time (min)

Figure 3.1 PK profiles of dinaciclib in WT and KO mice

3.3.2 Pharmacokinetics of dinaciclib and lenalidomide in combinational therapy

in knockout mice model

In order to evaluate the potential DDI in the combination of dinaciclib and lenalidomide, 5 mg/kg dinaciclib and 0.5 mg/kg lenalidomide were given via tail vein injection. The DDI was assessed in two ways: the impact of lenalidomide on dinaciclib’s exposure, and the influence of dinaciclib on lenalidomide’s disposition.

As shown in Table 3.2, in FVB WT mice, the addition of lenalidomide led to a decrease of 38.7% in the AUC of dinaciclib. Similarly in P-gp KO mice, the combination with lenalidomide caused 40.5% decline in dinaciclib AUC.

Regarding the effects of dinaciclib on lenalidomide PK, there were increases of

58.6% and 32.2% in lenalidomide AUC in FVB WT and P-gp KO mice, respectively, as summarized in Table 3.3. Based on FDA guidance, the AUC range for determining bioequivalence within a new formulation of an approved drug is within 80-125% of that on the market [129]. In our current study, the changes in AUC exceeded this range, and we would therefore conclude DDI occurred in the combinational treatment of intravenous dinaciclib and lenalidomide. However, the fact that PK changed in the same direction and with similar magnitudes in both wild type and knockout mice suggested the DDI resulted from interactions at sites other than P-gp.

Table 3.2 Dinaciclib AUC changes after combination with lenalidomide in WT and P-gp KO mice

Mice Dose Dinaciclib AUC (min*ug/ml) Change

Dinaciclib 163.9 FVB -38.7% Dinaciclib + lenalidomide 100.5

Dinaciclib 117.6 P-gp KO -40.5% Dinaciclib + lenalidomide 47.6

Table 3.3 Lenalidomide AUC changes after combination with dinaciclib in WT and P-gp KO mice

Lenalidomide AUC Change Mice Dose (min*ug/ml)

Lenalidomide 16.2

FVB Dinaciclib + 25.7 +58.6% lenalidomide

Lenalidomide 20.2 P-gp Dinaciclib + +32.2% KO 26.7 lenalidomide

3.3.3 Protein binding measurement

In order to investigate the cause of DDI between dinaciclib and lenalidomide, in vitro protein binding assays were performed. The dinaciclib protein binding is 87% in human plasma, and in the range of 69-80% in other tested species, including mouse, rat, dog and monkey. We hypothesized that the DDI was due to the displacement of dinaciclib from plasma proteins. Evaluations of binding to mouse plasma were carried out at 4 different pharmacologically relevant concentrations of dinaciclib: 50, 100, 500 and 5000 ng/ml. In the combination group, 2 µM of lenalidomide was added. Mean protein binding percentage was calculated from 6 duplicates. As shown in Table 3.4, the protein binding of dinaciclib in mouse plasma ranged from 81 to 91% at the evaluated concentrations. In vivo, there is an equilibrium between free drugs and plasma bound drugs, and the free fraction is believed to exert the pharmacological effects and to be distributed, metabolized and excreted. The data shown in Table 3.5 shows the free drug fraction of dinaciclib increased with the addition of lenalidomide, which suggests lenalidomide could displace dinaciclib from plasma binding. Presumably, this could then lead to increased drug elimination, which would consequently decrease dinaciclib AUC in plasma. This result was consistent with the findings in our animal studies, and it provides one possible explanation for the decreased plasma AUC observed in both WT and P-gp KO mice.

3.4 Conclusions

The P-gp transporter did not contribute to in vivo dinaciclib disposition, as demonstrated in the mouse model. Drug-drug-interaction was observed between dinaciclib and lenalidomide in mice. In vitro study suggested that the DDI might be attributed to protein binding displacement.

Table 3.4 Protein binding of dinaciclib

Protein binding (%) Dinaciclib Concentration Dinaciclib only Combo group

50 ng/ml 86.0 ± 8.7 80.8 ± 4.5

100 ng/ml 90.7 ± 3.4 88.7 ± 3.7

500 ng/ml 89.5 ± 3.2 88.8 ± 4.7

5000 ng/ml 86.5 ± 5.4 83.0 ± 2.6

Data was expressed as Mean ± Standard deviation.

Table 3.5 Mean Free drug fraction of dinaciclib

Free drug fraction (%)

Dinaciclib Concentration Dinaciclib Changes (%) Dinaciclib only +Lenalidomide

50 ng/ml 14.0 19.2 37.1 100 ng/ml 9.3 11.3 21.5 500 ng/ml 10.5 11.2 7 5000 ng/ml 13.5 17.0 26.0

CHAPTER 4, POPULATION PHARMACOKINETIC/PHARMACODYNAMIC

MODELING OF DINACICLIB AND ITS GLUCURONIDE METABOLITE IN CLL

PATIENTS

4.1 Introduction

Chronic lymphocytic leukemia (CLL) is the most prevalent adult leukemia in the western world, and more than 10,000 new cases occur annually. The median survival of patients with advanced disease is 18 months to 3 years, and genetic abnormalities such as del(17p13) and del(11q22) bring additional challenges in the treatment of CLL. The first line therapy for CLL is the combination of cytotoxic agents like fludarabine, cyclophosphamide and anti-CD20 antibody rituximab

(termed FCR). Though new agents have been approved in the past several years including ibrutinib, idelalisib and obinutuzumab, CLL still remains incurable and calls for new treatment strategies.

Cyclin-dependent kinases (CDKs) and their cyclin partners are critical regulators in cell cycle and transcription processes, and aberrations in the functions of

CDKs and cyclins are common in cancer. Therefore CDKs have been interesting targets for different malignancies, and many CDK inhibitors (CDKIs) have been developed. Flavopiridol is the best studied CDKI thus far, and it has shown

59 significant clinical efficacy in CLL patients compared to other disease cohorts.

Dinaciclib is a second-generation CDKI and selectively and potently inhibits

CDK1, 2, 5 and 9 with 1 to 4 nM of IC50. It has been shown to inhibit DNA synthesis and decrease levels of the pro-survival protein, Mcl-1, thus inducing apoptosis. Compared to flavopiridol whose therapeutic index is approximately 1, dinaciclib has a much more favorable TI of 10. Hence, dinaciclib has attracted great attention because of its promising efficacy and safety properties. Currently, dinaciclib has been evaluated at different stages of clinical trials in non-small cell lung cancer, breast cancer, multiple myeloma etc. In the phase I trial with CLL patients, dinaciclib is well tolerated and 45% of patients responded including those with high-risk genetic features. However, a common characteristic between flavopiridol and dinaciclib in treating CLL patients is that the toxicity of TLS has been developed for both drugs.

TLS is a series of metabolic events taking place after tumor cells lyse and is featured by hyperkalemia, hyperphosphatemia, hyperuricemia and secondary hypocalcemia. The risk factors for TLS include bulky tumor size, abnormal kidney functions, high white blood cell count, and dehydration. The successful management of TLS relies on the identification of patients at risk and the maintenance of electrolytes within the normal range. In this phase 1b/2 study with relapsed and refractory CLL patients, dinaciclib was administered in the combination with ofatumumab. Ofatumumab (ARZERRATM), an anti-CD20 monoclonal antibody, has been approved for the indication of subjects with CLL

60 refractory to fludarabine and alemtuzumab. The rationale for this combination is that giving ofatumumab before dinaciclib can reduce the tumor burden in lymph node, blood and bone marrow, and thus lower the risk of TLS after effective cytoreduction.

In previous studies, TLS has been shown to be associated with the level of glucuronide metabolites of flavopiridol. Since glucuronidation is a major metabolic pathway of dinaciclib, as it is for flavopiridol, we hypothesized that there would be a correlation between dinaciclib-glucuronide plasma levels and

TLS in dinaciclib-treated CLL. We therefore monitored pharmacokinetics of both parent drug and glucuronide metabolite of dinaciclib in this study. Our objectives were to characterize the PK of dinaciclib and dinaciclib-glucuronide and to evaluate the potential relationship between dinaciclib-glucuronide exposure and

TLS incidences or modulation of TLS biochemical markers.

4.2 Methods

4.2.1 Subjects

This analysis was based on a phase 1b/2 clinical trial of dinaciclib and ofatumumab in relapsed or refractory chronic lymphocytic leukemia/small lymphocytic leukemia/B-cell prolymphocytic leukemia (CLL/SLL/B-PLL), conducted at The James Cancer Hospital at The Ohio State University

(Columbus, OH). By definition, patients over 18 years old who were confirmed with CLL/SLL/B-PLL according to 2008 World Health Organization diagnostic

61 criteria and who had received at least one prior therapy were eligible for this study. Patients were excluded if they had chemotherapy or radiotherapy within 4 weeks of enrollment. Dinaciclib is known to be metabolized by CYP3A4, therefore patients whose current medications included potent CYP3A4 inducers or inhibitors were carefully reviewed for eligibility and any identified medication was stopped at least 2 weeks prior to enrollment. One exception was aprepitant with the evidence from Merck that there was no drug-drug interaction between aprepitant and dinaciclib [102]. This study was approved by the Institutional

Review Boards of The Ohio State University, and written informed consent was provided by all patients.

4.2.2 Study design and data collection

In this study patients received a combination treatment of ofatumumab and dinaciclib to determine the tolerable dose of this combination therapy and characterized the toxicity and overall response rate in the defined diseases of

CLL/SLL/B-PLL. Ofatumumab was administered intravenously (IV) once weekly with 300 mg on cycle 1 day 1, and then 2000 mg from cycle 1 day 8 for 7 more weeks throughout cycle 2, followed by 4 monthly doses till cycle 7; dinaciclib was administered by a 2-hour infusion on cycle 2 days 2, 8 and 15 with doses escalating from 7 to 14 mg/m2 and then on days 1, 8 and 15 of cycles 3-7. A standard 3 by 3 phase 1b study design was used for the dose assessment, fulfilling the phase 1b part of this trial. If it is not tolerated for the dose level of 7 mg/m2 on cycle 2 day 2 with escalation to 10 mg/m2 from cycle 2 day 8 and 62 afterwards, dinaciclib dose will be de-escalated and kept as 7 mg/m2 throughout the treatment cycles. The high level of dinaciclib is 7 mg/m2 on cycle 2 day 2, 10 mg/m2 on cycle 2 day 8 and escalated to 14 mg/m2 on cycle 2 day 15 and continuing thereafter. Plasma PK samples were collected pre-dose and at various post-dose time points (1, 2, 2.25, 2.5, 3, 4, 6 hours and anytime between

8 and 18 hours) on both day 2 and day 15 during cycle 2 after the first and third doses of dinaciclib. In order to extract dinaciclib metabolites, patient urine was collected during three time intervals after dinaciclib infusion started on cycle 2 day 2: 0-4, 4-8 and 8-24 hours. Plasma concentrations of dinaciclib and dinaciclib-glucuronide were measured using a validated liquid chromatography- tandem mass spectrometry method as described in Chapter 2. In addition, TLS lab markers, including blood potassium, serum phosphate, uric acid and LDH, were monitored from 3 hours prior to and up to approximately 24 hours after the dinaciclib infusion on cycle 2 day 2.

4.2.3 Supportive care and TLS management

To minimize the occurrence of TLS, substantial prophylaxis and supportive care was applied. Daily 300 mg allopurinol was given to patients orally 2 days before therapy started and continued for the duration of participation on this trial. On cycle 2 day 2 when the first dinaciclib infusion was applied, patients were given

4.5 mg intravenous rasburicase 2 hours prior and 1334 mg oral calcium acetate

12 hours and 2 hours prior to dinaciclib. Following ofatumumab infusion, a more than 10 hours IV hydration with 0.45% sodium chloride sterile solution was given 63 to subjects before initiation of and during dinaciclib infusion. After dinaciclib infusion stopped, IV hydration continued for at least another 10 hours. In addition, patients also received 30 g of kayexalate at least half an hour prior to dinaciclib if their baseline serum potassium level exceeded 4 mmol/L. On subsequent dinaciclib dosing days, 1334 mg calcium acetate was still given orally 12 and 2 hours before each dinaciclib dose, and the rasburicase was administered as needed per discretion of the treating physician. IV hydration was provided for at least one 1 hour prior to initiation of, during, and continuing for at least 2 hours after the completion of dinaciclib infusion.

4.2.4 Population pharmacokinetic modeling

Population pharmacokinetic analysis of dinaciclib and dinaciclib-glucuronide were conducted using a non-linear, mixed-effects modeling approach with NONMEM® software, version 7.2 (ICON Development Solutions, Dublin, Ireland) compiled with the Intel® Fortran compiler version 12 (Intel Corporation, CA, USA).

NONMEM outputs and graphical diagnostics were handled using R (version

2.15.0). A log transformation of both sides (LTBS) approach was used to fit dinaciclib and dinaciclib-glucuronide PK data simultaneously, unless otherwise stated. Throughout the modeling process, the first-order conditional estimation method with interaction option (FOCE INTER) was used to carry out all the estimations.

A sequential approach has been widely used for simultaneously modeling parent drug and metabolites [130-134]. A PK base model was developed first for dinaciclib and then, once the parent drug model was established, dinaciclib- glucuronide was included in the base model to fit simultaneously. One-, two-, and three-compartment models were tested for both parent drug and metabolite, respectively. A logit transformation was applied to constrain the parameter, Fm, between (0, 1) which estimated the fraction of parent drug converted to metabolite. Between-subject variability (BSV) in PK parameters was evaluated as exponential terms in the model. By design, patients received an escalated dose on day 15 if the low dose was tolerated, and between-occasion variability (BOV) was tested on PK parameters using an exponential error model. Residual variability was described with an additive error model, which was translated to a proportional error in linear scale since LTNS was used. Both visual check and

Akaike Information Criteria (AIC) were used to compare goodness-of-fit of models.

Covariates were tested on different PK parameters, including age, sex, body surface area (BSA), creatinine clearance (CRCL), albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and bilirubin. BSA was calculated using equation (1):

BSA =0.007184*Weight (kg)0.425* Height (cm)0.725 (1)

CRCL was estimated from serum creatinine concentration using the Cockcroft and Gault equation (2):

( ) ( ) CRCL= ( ) (2) ( )

The continuous covariates were introduced into the base model individually as a power function normalized to the population median shown in equation (3):

θ2 TVPi = θ1*(COVi/COVpop) (3)

where TVPi is the typical value of a PK parameter (P) for an individual i with a

COVi value of the covariate, θ1 is the typical value for an individual with a covariate value equaling the population median, and θ2 is the exponent of the power function. The effects of a categorical covariate on PK parameters were

COVi assessed as a dichotomous variable in: TVPi = θ1*θ2 , where COVi is the dichotomous covariate for individual i, θ1 is the typical parameter value for an individual whose dichotomous covariate takes the value of 0, and θ2 is the fractional change in the parameters if the value of the covariate is 1. The likelihood ratio test was used to compare nested models and guide the covariate selection. The significance levels were set at p=0.05 for forward addition of covariates (ΔOFV = 3.84) and p=0.01 for backward elimination of covariates

(ΔOFV = 6.64). Evaluation of diagnostic plots, plausibility and precision of parameter estimation, biological or clinical relevance were also considered during the covariate selection process. The final model was evaluated by goodness-of-

66 fit plots, standard errors and shrinkage of the estimates, validity of model assumptions, and visual predictive check (VPC) generated from 1000 simulations.

4.2.5 Model evaluation

The final model was evaluated by examination of standard errors, shrinkage and correlations of fixed and random effect parameters. Histogram and quantile- quantile plots were generated to check the model normality assumption.

Goodness-of-fit plots were used to assess model appropriateness. One thousand

(1000) simulations of 7 mg/m2 for the first dose and 14 mg/m2 for the third dose were performed to generate visual predictive check (VPC) plots which were used for prediction evaluation.

4.2.6 Pharmacokinetic/pharmacodynamic (PK/PD) correlation analysis

Based on the protocol, on cycle 2 day 2, blood samples were collected before the start of dinaciclib administration and 1, 2, 3, 6, 8, 12 and approximately 24 hours after dinaciclib infusion. Whole blood potassium and serum phosphate, uric acid and LDH levels were determined. Percentage changes of each biomarker were calculated as: maximum concentration (within 24 hrs after dinaciclib infusion)/ baseline value* 100%. Baseline levels were the measurements within 24 hrs prior to dinaciclib administration. If there was more than one measurement, then the average of those values were used as baseline. Correlations between each individual’s PK parameters (including AUC and Cmax) and PK biomarker

67 changes were evaluated by plotting and calculating Pearson correlation coefficients.

4.3 Results

4.3.1 Patient summary

There were 31 patients with available PK data from this phase 1b/2 clinical trial and a total of 1011 non-BLQ (below the limit of quantification) plasma observations included in this pharmacokinetic analysis. This population was male dominant (68%) with a median age of 61 years. One patient had missing ALT and albumin values. The patient demographics are summarized in Table 4.1.

4.3.2 Patient raw PK and TLS data

Real time PK of dinaciclib and dinaciclib-glucuronide on cycle 2 day 2 and day 15 are plotted in Figure 4.1. The maximum concentration (Cmax) was reached within an hour after dinaciclib infusion started. Among patients Cmax ranged approximately 10-fold for both dinaciclib and dinaciclib glucuronide.

There was only one incident of TLS observed in this patient cohort, which occurred on cycle 3 day 1. This particular patient had rapidly proliferative disease and was refractory to ibrutinib and IPI-145 prior to dinaciclib. In this case, she had high risk for TLS from any potentially effective therapy. In addition, her infusion was interrupted and resumed, and TLS developed on cycle 3 day 1 eventually.

Table 4.1 Summary of patient demographics

Continuous covariates Median Range

Age, years 61 35—80

Body surface area, m2 1.9 1.5—2.4

Creatinine clearance, mL/min 98 47—209

Alanine aminotransferase, U/L 15 8—79

Aspartate aminotransferase, U/L 21 11—51

Albumin, g/dL 3.3 2.4—4.1

Bilirubin, mg/dL 0.5 0.3—1.1

Categorical covariates N (%)

Sex (male/female) 21 (68)/10 (32)

Race (Caucasians/African Americans) 26 (84)/5 (16)

Note: one missing albumin and ALT values

1000

100

Dinaciclib Conc. (ng/ml) Conc. Dinaciclib

0.1 -2 0 2 4 6 8 10 300 400 500 Time (hrs) B

100

Dinaciclib-glucuronide Conc. (ng/ml) Conc. Dinaciclib-glucuronide 0 2 4 6 8 10 300 400 500

Time (hrs)

Figure 4.1 Pharmacokinetic plots of dinaciclib (A) and dinaciclib- glucuronide after the initiation of dinaciclib intravenous infusion.

4.3.3 Structural basic PK model

Initially, only data of dinaciclib was used to build a PK base model for the parent drug, and a two-compartment model was determined. Subsequently, dinaciclib- glucuronide was included in the base model to fit simultaneously. Fm, the fraction of conversion from dinaciclib to dinaciclib-glucuronide, is defined as

K13/(K10+K13), where K13 is the rate constant from dinaciclib central compartment to dinaciclib-glucuronide central compartment, and K10 is the elimination rate constant of dinaciclib, as shown in Figure 4.2. According to the analysis of metabolites in human urine from the investigator’s brochure [84], the glucuronide metabolite is at least 20% of the dinaciclib dose, thus we fixed Fm at

0.2. The model of dinaciclib-glucuronide used a two-compartment structure, as shown in Figure 4.2.

This was a dose-escalation study in which patients received an initial dose of 7 mg/m2 on cycle 2 day 2 and 14 mg/m2 on day 15. By adding BOV on the clearance (CL) parameter of dinaciclib, the objective function value was decrease by 102. LTBS was used in this model, and additive residual error models were used, which is a proportional error after transforming back to linear scale.

Therefore, the base model of dinaciclib and dinaciclib-glucuronide contained a total of four compartments and a BOV term on dinaciclib CL, with additive residual error terms on both parent drug and metabolite.

Figure 4.2 The pharmacokinetic model of dinaciclib and its glucuronide metabolite

Dina, dinaciclib; Dina-G, dinaciclib-glucuronide

4.3.4 Evaluation of covariates and final model determination

Multiple covariates were tested on PK parameters based on their physiological relevance, including gender, BSA, creatinine clearance, ALT, AST, total bilirubin, and albumin. The forward selection process yielded three statistically significant covariates using the likelihood ratio test at significance level of 0.05: BSA on dinaciclib CL, BSA on dinaciclib central volume of distribution (V1), and albumin on dinaciclib CL. Subsequently, a full model with all three covariates was generated, with which backward selection was started. Covariates were dropped from the full model one by one unless the p-value was less than 0.01

(corresponding to an OFV increase of 6.64). The effects on the model performance by adding covariates are also taken into account. For examples,

BSA on CL is a significant covariate but the SE of ETA1 (BSA of dinaciclib CL) is

112% in that model, which was not chosen. This process yielded only one covariate which was albumin on CL. Therefore, our final population PK model included combined two-compartment models for dinaciclib and its glucuronide metabolite, with an inclusion of BOV and albumin covariate on dinaciclib clearance. All parameter estimates are summarized in Table 4.2. The basic goodness-of-fit plots of final PK model (Figure 4.3) suggest that the model is appropriate for characterizing both of the parent drug and its metabolite.

Table 4.2 Final population PK model parameter estimates

Parameter Estimate (% SE) %BSV (% SE) % Shrinkage %BOV (% SE) Parent Drug--Dinaciclib CL, L/h 27.8 (8.2) 18 (84.6) 31 23 (33.8) V1, L 11.1 (17.6) 53 (42.6) 21 Q, L/h 10.7 (17.2) -- -- V2, L 13.2 (11.7) 22 (58.9) 18 Albumin on CL 1.34 (44.6) -- Additive Residual error 0.38 (40.5) -- 9.5

Fm 0.2* 71 (32.5) 5.7 CLm 17.4 (11.6) 22 (78.9) 31 V3 15.3 (10.4) -- --

74 Qm 5.72 (17.7) 52 (46.7) 24

V4 23.3 (17.3) -- -- Additive Residual error 0.26 (15.4) -- 8.3 *Fm was fixed at 0.2. BSV, between-subject variability; BOV, between-occasion variability; CL, CLM, central clearance; Q, Qm, inter-compartmental clearance terms; V1, V3, central volume of distribution for dinaciclib and dinaciclib glucuronide, respectively; V2, V4, peripheral volume of distribution for

dinaciclib and dinaciclib glucuronide, respectively; SE, standard error of estimate.

Figure 4.3 Goodness-of-fit plots for dinaciclib (A) and dinaciclib- glucuronide (B)

4.3.5 Model evaluation and visual predictive check

The final PK model was used for simulation, with 7 mg/m2 as the first dose and

14 mg/m2 as the third dose of dinaciclib infusion. VPC is a useful tool to check model performance and is widely used [135, 136]. VPC plots for dinaciclib and the metabolite dinaciclib-glucuronide are displayed in Figure 4.4. From those, 95% simulation intervals cover most observations, indicating the current model is valid.

Figure 4.4 VPC of dinaciclib and dinaciclib-glucuronide from 1000 simulations

4.3.6 Pharmacokinetic/pharmacodynamic correlations

On cycle 2 day 2, blood samples were collected and potassium, uric acid, phosphate and LDH levels were monitored before the start of dinaciclib administration, and 1, 2, 3, 6, 8, 12 and approximately 24 hours after dinaciclib infusion. Each measurement has been normalized to baseline level and presented in Figure 4.5. Phosphate levels started to increase after time 0, which was the starting time of dinaciclib infusion, as displayed in Figure 4.5, panel A. In panel B, there was an overall increasing trend in LDH levels among patients.

However, uric acid and potassium levels decreased compared to baseline due to the prophylaxis administered to prevent TLS. Therefore, any relationships between drugs levels and responses of these TLS biomarkers are confounded with the prophylactic therapy, and instead the correlation between PK parameters (including Cmax and AUC of dinaciclib and dinaciclib-glucuronide) and maximal changes in phosphate and LDH were explored, as shown in Figure

4.6. There was no relationship between dinaciclib PK parameters and phosphate changes. However, there appeared to be a linear relationship between dinaciclib- glucuronide Cmax (correlation coefficient ρ=0.5, p-value <0.01) and AUC

(correlation coefficient ρ=0.6, p-value <0.01). No correlations were found among drug PK and maximal changes in LDH.

4.4 Conclusions

A combined four-compartment model can fully characterize the PK profiles of both dinaciclib and dinaciclib-glucuronide. There appeared to have a linear relationship between maximal changes in phosphate and dinaciclib-glucuronide

Cmax and AUC. However, there was no link between dinaciclib-glucuronide plasma levels and the incidence of tumor lysis syndrome.

220 300 200

180 A 250 B 160 200 140

120 150 100

80 100 60

40 50 20

0 0

LDH % (normalized to baseline) (normalized % LDH

Phosphate % (normalized to baseline) to (normalized % Phosphate -20 -10 0 10 20 30 -20 -10 0 10 20 30 Time (hrs) Time (hrs)

200 160 180

160 C 140 D 80

140

120 120

100 100 80

60 80 40

20 60 0

40 Uric Acid % (normalized to baseline) to (normalized % Acid Uric -20 -10 0 10 20 30 Potassium % (normalized to baseline) to (normalized % Potassium -20 -10 0 10 20 30 Time (hrs) Time (hrs) Figure 4.5 Changes in phosphate (A), LDH (B), uric acid (C) and potassium (D) after first infusion of dinaciclib on cycle 2 day 2

120 120

100 A 100 B

80 80

60 60

40 40

20 20

Delta Phosphate % Phosphate Delta 0 0

-20 -20 0 200 400 600 800 1000 0 200 400 600 800 10001200140016001800

Dinaciclib Cmax (ng/ml) Dinaciclib AUC (hr*ng/ml)

120 120 C D 100 100

80 80

60 60

40 40

20 20

% Phosphate Delta 0 0

-20 -20 0 20 40 60 80 100 120 140 160 0 100 200 300 400 500 600 700 Dinaciclib-G Cmax (ng/ml) Dinaciclib-G AUC (hr*ng/ml)

Figure 4.6 Evaluation of the correlation between maximum changes in phosphate (before and after the first dose of dinaciclib) and dinaciclib Cmax (A), AUC (B), dinaciclib- glucuronide Cmax (C) and AUC (D); and the correlation between maximum changes in LDH and those PK parameters

(Continued)

Figure 4.6: Continued

200 200

E F 150 150

100 100

50 50

Delta LDH % LDH Delta 0 0

-50 -50 0 200 400 600 800 1000 0 200 400 600 800 10001200140016001800 Dinaciclib Cmax (ng/ml) Dinaciclib AUC (hr*ng/ml)

200 200 H 150 G 150

100 100

50 50

Delta LDH % LDH Delta 0 0

-50 -50 0 20 40 60 80 100 120 140 160 0 100 200 300 400 500 600 700 Dinaciclib-G Cmax (ng/ml) Dinaciclib-G AUC (hr*ng/ml)

CHAPTER 5, POPULATION PHARMACOKINETIC MODELDING AND

SIMULATION OF TG02

5.1 Introduction

TG02 is a small molecule macrocycle, inhibiting CDK1, 2, 7, and 9, JAK2 and

FLT-3 at low nanomolar range IC50 [104]. TG02 has been shown to have promising anti-tumor effects in both solid tumor and blood tumor cell lines, and better inhibitory effects in a hematological malignancy panel compared to solid tumor cells. Meanwhile, when compared to SNS-032, a CDK inhibitor but not

JAK2 or FLT3 inhibitor, TG02 exhibited a lower IC50, suggesting that TG02 has superior activities by simultaneously inhibiting other kinases. In both multiple myeloma and acute leukemia animal models, TG02 treatment led to the delay in tumor growth and the increase of survival length [105, 107]. The mechanisms of

TG02’s anti-tumor activity include inhibition of cell cycle, induction of apoptosis, and loss of mitochondrial membrane potential. TG02 can decrease CDK1 and

CDK2 levels, and subsequently accumulate cells at G2/M phases in the life cycle.

CDK7 can phosphorylate RNA polymerase II on serine 5, while CDK9 phosphorylates serine 2. By targeting CDK7/9 and inhibiting the phosphorylation of RNA polymerase II, TG02 interrupts the transcription initiation and elongation, leading to the decrease in XIAP and Mcl-1 protein levels.

TG02 also has advantageous pharmaceutical and pharmacokinetic properties allowing for further development. First, in animal studies, it has been demonstrated that tumor had good drug distribution where TG02 concentration remained above cellular IC50 over 8 hours. Second, the oral bioavailability of

TG02 is 24% in mice and 37% in dogs [110], enabling oral administration. Last,

TG02 is stable at room temperature, and has been manufactured as a citrate salt to improve its aqueous solubility. To sum up, the remarkable inhibitory effects of

TG02 in hematological malignancies and its favorable pharmacokinetic properties have brought TG02 for further clinical investigations.

In early phase of our study, TG02 was under clinical development for treatment of acute leukemia (AL) and multiple myeloma (MM) in phase 1 clinical trials. In the 28-day treatment cycle, TG02 was given daily for 28 days, or daily for 5 day for two consecutive weeks and two-week rest, with dose escalation from 10 to

150 mg. Under these dosing schedules, grade 3 fatigue occurred at 70 mg dose in AL patient and 150 mg in MM patient, and was defined as dose limiting toxicity

(DLT). This adverse event was believed to be related to drug accumulations in patients. Meanwhile, great variability was identified among the raw PK profiles from patient to patient. The maximal concentration of TG02 exhibited 5 to 8 fold differences among patients at the same dose level. Therefore, our objectives were to design a new dose regimen for TG02 to prevent drug accumulation and previously defined DLT of grade 3 fatigue; and to investigate the reasons and

84 sources for the great PK variability observed in patient population. Population PK modeling and simulation was used to achieve these objectives.

5.2 Methods

5.2.1 Subjects and clinical trial design

Subjects included in this study were patients who have been enrolled in several phase 1 clinical trials with acute leukemia (AL, including acute myelogenous leukemia (AML) and acute lymphocytic leukemia (ALL)), multiple myeloma (MM) and small lymphocytic lymphoma/chronic lymphocytic leukemia (SLL/CLL)

(www.clinicaltrials.gov). These trials were conducted at multiple sites and signed informed consent was obtained from all patients. Review boards at all participating centers approved the study protocols and protocol amendments.

There were four cohorts, including single-agent therapy of TG02 in acute leukemia, multiple myeloma and CLL patients, respectively, and one cohort of multiple myeloma patients with combinational therapy of carfilzomib. To date, there have been 104 patients enrolled with PK samples obtained. Dose escalation was conducted in these phase 1 clinical trials to determine the maximal tolerated dose, and dose schedules were explained in the Table 5.1.

There were 10 dose levels: 10, 20, 50, 70, 100, 150, 200, 250, and 300 mg. In

AL patients, three dose regimens were applied, once daily for 28 days, once daily for 5 days for 2 weeks and twice weekly for 3 weeks in a cycle of 28 days. In single agent MM patients, three dose schedules were once daily for 28 days, three times a week for 3 weeks and twice weekly for 3 weeks. In MM with 85 carfilzomib and CLL patients, only twice weekly for 3 weeks schedule was used.

In the earlier trials with single agent TG02 in AL and MM patients, food was not controlled at all which could potentially influence drug absorption and result in variability among population PK. Thus in the two latter trials with MM patients in which TG02 was given in combination with carfilzomib and CLL patients, TG02 capsules were given while food intake was controlled, meaning at least 1 hour before or 2 hours after a meal.

5.2.2 Pharmacokinetic samples and data clean

Patient plasma samples were obtained before dosing and 0.25, 0.5, 1, 2, 4, 6, 8 hours after the first dose, and day 2, 3, 8, 9, 11, 12, 15, 17 or 22 pre-dose depending on their dosing regimens. Plasma drug concentrations were determined by LC-MS/MS. Since the actual dose time was not recorded, thus the first dosing time was extrapolated from the 15 min PK samples by calculating 15 minutes earlier. All other consecutive doses used the same clock time as the first one unless this given dose time was earlier than a specific day x pre-dose PK sample, in which case the dosing time was calculated as pre-dose PK sample time plus 15 minutes. Patients 1053, 7096, and 9091 had wrong ―PK collection date‖ and were corrected accordingly.

5.2.3 Pharmacokinetic analysis

The 24-hour PK data, which had a complete PK profile, was used for non- compartmental analysis in Phoenix Winnonlin 64-bit version. Model type was

86 chosen as ―Plasma (200-202)‖, with extravascular dose, and linear trapezoidal linear interpolation method was used. Obtained PK parameters were used for further analysis, including dose dependency evaluation and covariate analysis.

Due to the fact that day 2 pre-dose PK still had substantial drug level and extrapolated AUC could contribute a decent portion to the AUCinf (AUC infinity), and the fact that 19 patients had missing PK valued because of non-decreasing elimination phase, AUC0-24 (area under the curve from 0 to 24 hour time point) was also calculated for analysis.

5.2.4 Population PK modeling

Population PK models of TG02 were built in non-linear, mixed-effect modeling using NONMEM® software version 7.2 (ICON Development Solutions, Dublin,

Ireland) compiled with the Intel® Fortran compiler version 12 (Intel Corporation,

CA, USA). NONMEM outputs and graphical diagnostics were handled using R

(version 2.15.0). Throughout the modeling process, the first-order method (FO) and first-order conditional estimation with interaction option (FOCE INTER) were compared while carrying out the estimations. One and two-compartment models were tested and one-compartment model was demonstrated more appropriate.

Furthermore, one-compartment model with lag time and with two absorption depots were tested. A logit transformation was applied to constrain the parameter

F1 between (0, 1) which estimated the portion of the dose into absorption depot 1.

The between-subject variability (BSV) in PK parameters was included as an exponential term in the model. Residual variability was tested in different ways, 87 such as an additive error model, a proportional error model or a combination of these two. Both visual check and Akaike Information Criteria (AIC) were used to compare goodness-of-fit of models.

Covariates were tested on different PK parameters, including age, sex, food control, and body surface area. The continuous covariates were introduced into the base model individually as a power function normalized to the population

θ2 median: TVPi = θ1*(COVi/COVpop) , where TVPi is the typical value of a PK parameter (P) for an individual i with a COVi value of the covariate, θ1 is the typical value for an individual with a covariate value equaling population median, and θ2 is the exponent of the power function. The effects of a categorical covariate on PK parameters were assessed as a dichotomous variable in: TVPi =

COVi θ1*θ2 , where COViis the dichotomous covariate for individual i,θ1 is the typical parameter value for an individual whose dichotomous covariate takes the value of 0 and θ2 is the fractional change in the parameters if the value of covariate is

1. Likelihood ratio test was used to compare nested models and guide the covariate selection. The significance levels were set at p=0.05 for forward addition of covariates (ΔOFV = 3.84) and p=0.01 for backward elimination of covariates (ΔOFV = 6.63). Evaluation of diagnostic plots, plausibility and precision of parameter estimation, biological or clinical relevance were also considered during covariate selection process. The final model was evaluated by goodness-of-fit plots, standard errors and shrinkage of the estimates, validity of

88 model assumptions, and visual predictive check (VPC) generated from 1000 simulations.

5.2.5 Model evaluation and simulation

Final model was evaluated in multiple aspects. Examination was done for reasonable estimation, standard errors, shrinkage and correlations of fixed and random effect parameters. Histogram and quantile-quantile plots were generated to check the model normality assumption. Goodness-of-fit plots were used to assess model appropriateness. Visual predictive check (VPC) was obtained from

1000 simulations and used to prediction evaluation. In addition, 1000 simulations at different levels with newly proposed twice weekly for four weeks dosing schedule were performed to evaluate drug accumulation by calculating accumulation ratios.

Table 5.1 Dose administration of TG02 in phase 1 clinical trials

TG02 Patients Diseases TG02 Citrate Dose Administration Dose (mg)

10 4 20 4 Once daily 30 3 (Dosing Days 1-28) 50 8 70 8 30 3 Once daily for 5 days for 2 weeks 50 3 Acute Leukemia then a 2-week rest 70 4 (N=55) (Dosing Days 1-5 and 8-12) 100 5 150 7

Twice a week for 3 weeks then a 1-week rest 150 6 (Dosing Days 1, 4, 8, 11, 15, 18)

Once daily 50 3 (Dosing Days 1-28) 70 3

Three times a week for 3 weeks 100 3 Multiple Myeloma then a 1-week rest 150 6 (N=18) (Dosing Days 1, 3, 5, 8, 10, 12, 15, 17, 19)

200 Twice a week for 3 weeks then a 1-week rest 3 (Dosing Days 1, 4, 8, 11, 15, 18)

(Continued)

Table 5.1: Continued

150 3 Multiple Myeloma Twice a week for 3 weeks then a 200 3 with Carfilzomib 1-week rest 250 5 (N=15) (Dosing Days 1, 4, 8, 11, 15, 18) 300 4

Chronic Twice a week for 3 weeks then a 70 5 Lymphocytic 1-week rest Leukemia 100 7 (Dosing Days 1, 4, 8, 11, 15, 18) (N=16) 150 4

5.3 Results

5.3.1 Patient summary

There were 104 patients from different phase 1 clinical trials and a total of 3934 non-BLQ (below the limit of quantification) TG02 plasma observations included in this pharmacokinetic analysis. This population was male dominant (63%) and had a median age of 65 years old. A wide range of body surface area from 0.9 to

2.6 m2 has been identified (17 patients (16%) missed weight, and thus body surface area and creatinine clearance). 30% patients from combinational treatment MM group and CLL cohort had food control by no food intake one hour prior and two hours after TG02 treatment. A series of lab test values were collected and used for covariate analysis. Among those, 4 patients (4%) had missing ALT; 13 patients (12.5%) had missing AST; 2 patients (2%) had missing blood urea nitrogen, while 3 patients (3%) had missing albumin, bilirubin, and total protein. The patient demographics were summarized in Table 5.2.

Table 5.2 Summary of patient demographics

Continuous covariates Median Range

Age, years 65.0 21—87

Body surface area, m2 1.9 0.9—2.6

Creatinine clearance, mL/min 79.5 32—192

Alanine aminotransferase, U/L 20.5 7—133

Aspartate aminotransferase, U/L 24.0 13—104

Albumin, g/dL 3.6 2—4.8

Bilirubin, mg/dL 0.7 0.2—1.8

Blood urea nitrogen, mg/dL 17.0 6—42

Total protein, g/dL 6.6 4.8—10.5

Categorical covariates N (%)

Sex (male/female) 66 (63)/38 (37)

Food (controlled/non-controlled) 31 (30)/73 (70)

N, number of subjects. The percentages in the population were presented in the parenthesis. (17 pts missed weight, and thus body surface area and creatinine clearance; 4 pts missed ALT; 13 pts missed AST; 3 pts missed albumin, bilirubin, and total protein; 2 pts missed blood urea nitrogen)

5.3.2 Dose linearity analysis

TG02 was given to patients at 10 different dose levels, and AUC0-24 was obtained.

From Table 5.2 and Figure 5.1, we can see that AUC0-24did not increase proportionally to doses from 10 to 300 mg. Similarly, the maximal concentration

(Cmax) of TG02 also displayed a non-linear change with dose at the clinical relevant dose range of 10 to 300 mg, as shown in Table 5.3. Some patients had high day 2 pre-dose PK levels (>5 µg/ml) and the extrapolated AUC could contribute more than 50% to the total AUCinf. It is possible that the higher dose groups had higher extrapolation percentage and much larger AUCinf than lower dose groups. In order to reevaluate the influence of AUC extrapolation on the analysis, dose dependence was assessed with AUCinf, as shown in Figure 5.1B.

There was a lack of linear relationship of AUCinf and dose from 10 to 300 mg, indicating of non-linear PK in this dose range.

Table 5.3 AUC of TG02 at 10 different dose levels

AUC0-24 (hr*ug/mL) Doses 10 20 30 50 70 100 150 200 250 300 (mg)

N 4 5 5 14 20 15 26 6 5 4

Mean 6.3 9.4 6.3 22.2 25.6 28.9 43.7 44.8 37.2 42.7

SD 2.6 6.4 3.4 11.7 18.7 26.6 32.8 43.7 14.7 14.3

N, number of subjects; SD, standard deviation.

95 6 6 6 6 6 6 6 6 6

Table 5.4 TG02 Cmax at 10 different dose levels

Cmax (ug/mL)

N 4 5 5 14 20 15 26 6 5 4

Mean 0.45 0.86 0.64 2.03 1.92 2.15 3.69 3.25 3.39 3.08

SD 0.09 0.45 0.16 0.85 1.11 1.32 1.96 1.72 1.57 0.80

N, number of subjects; SD, standard deviation.

A 100

AUC0-24 (hr*ug/ml) AUC0-24 0

0 50 100 150 200 250 300 350

Dose (mg)

B 300

250

200

150

100

AUCinf (hr*ug/ml)AUCinf

-50

-100 0 50 100 150 200 250 300 350

Dose (mg)

Figure 5.1 TG02 AUC0-24 (A) and AUCinf (B) over 10 different dose levels

5.3.3 Covariate exploration

5.3.3.1 Food effect

In the previous trials with single agent TG02 in AL and MM patients, food was not controlled at all which could possibly influence drug absorption and result in variability among population PK. Thus in the two latter trials with MM patients in which TG02 was given in combination with carfilzomib and CLL patients, TG02 capsules were given while food intake was controlled. The non-parametric Mann-

Whitney U test has shown that the difference in the median values between the two groups is greater than would be expected by chance; there is a statistically significant difference (p-value <0.001). The controlled group has a lower AUC compared to non-controlled group, as shown in Figure 5.2.

5.3.3.2 Gender effect

There were 66 male patients and 38 female patients included in the trials.

Gender was evaluated to see if it affected the dose normalized AUC in the population. The non-parametric Mann-Whitney U test has shown that the difference in the median values between the two groups is greater than would be expected by chance; there is a statistically significant difference with p- value=0.03, as shown in Figure 5.3.

In order to furthur investigate if the food control and gender factor were confounded, patients were stratified into 4 groups based on their gender and if they had food fasting or not, and group medians were calculated as shown in

Table 5.5. The group medians were tested to be not all the same (p-value

<0.001). However, when comparing every two groups at a family error rate of 5%, there were no statistical difference between female and male in fasted group, and neither was the non-fasted group. Therefore, food control factor was considered more important when analyzing the PK parameters.

Figure 5.2 Food effect on TG02 dose normalized AUC0-24

The dash lines within the boxes are at the median. The boxes are bounded by the 25th and 75th percentiles of the distribution, and the whiskers represent 1.5 times the inter-quartile range with outliers marked as triangles.

Figure 5.3 Gender effect on TG02 dose normalized AUC0-24

100

Table 5.5 Gender and food control effect in TG02 dose normalized AUC0-24

Female Male

AUC/dose (hr*ug/ml/mg)

N 28 45 Non-controlled Median 0.41 0.28

N 10 21 Controlled Median 0.19 0.15

101

5.3.4 Stage 1 population pharmacokinetic model for new dose regimen design

In 2012, TG02 was administered to patients daily for 28 days or daily for 5 days for 2 weeks in a 28-day cycle, and grade 3 fatigue was the DLT. Drug accumulation was observed in patients, and a new dose regimen was needed.

Thus, we developed a population PK model for TG02 based on 55 patients and used this to design a new dose regimen. A one-compartment model with lag-time in absorption was chosen, and no covariates evaluated were included in the model. The population mean of clearance (CL) was estimated as 2.0 L/h, and inter-individual variability (IIV) was large on all PK parameters, as shown in Table

5.6, which was consistent with the large variability observed in patient PK profiles.

One thousand (1000) simulations of the newly proposed dose regimen of twice weekly for 4 weeks from doses 50 to 300 mg were carried out using this model with fixed residual errors. The simulation means were plotted in Figure 5.4, and no accumulation was observed up to 4 weeks. Accumulation ratios (AR) were calculated as day 22 Cmax (in week 4)/day 1 Cmax, and day 24 Ctrough (in week 4)/day 3 Ctrough. As shown in Table 5.7, AR ranged from 1.09 to 1.45, which indicated no or minimal acculation with this new dose regimen. Later on, a twice weekly dose schedule was applied in clinical trials, and PK profiles from 24 patients were obtained. As shown in Figure 5.5, the raw data from these 24 patients (dosed of 70 to 200 mg) fell within the region of the 95% simulation interval at 150 mg dose. In addition, fatigue was reduced to grade 1/2 with this regimen, and only one case of grade 3 fatigue was observed, which persisted

102 less than 24 hours. These data suggested the twice weekly dose schedule was successful in avoiding drug accumulation and preventing DLT of grade 3 fatigue.

103

Table 5.6 Compartmental PK parameter estimates for simulation

104

50 mg 6 70 mg 100 mg 150 mg 200 mg 4 250 mg 300 mg

TG02 conc. (ug/ml) conc. TG02 0

-2 0 100 200 300 400 500 600 700

Time (hrs)

Figure 5.4 Mean simulated TG02 concentration under twice weekly dosing at 7 doses up to 300 mg

105

Table 5.7 Accumulation ratio of mean Cmax and Ctrough from 1000 simulations

Dose Cmax Cmax,ss AR Ctrough Ctrough,ss AR

50 1.26 1.37 1.09 0.09 0.12 1.38

70 1.80 1.96 1.09 0.12 0.17 1.39

100 2.65 2.88 1.09 0.19 0.25 1.34

150 3.93 4.21 1.07 0.25 0.34 1.34

200 5.05 5.51 1.09 0.37 0.54 1.45

250 6.43 7.05 1.10 0.47 0.65 1.39

300 7.86 8.60 1.09 0.58 0.81 1.38

AR, Accumulation ratio; Cmax,ss, steady state Cmax in week 4.

106

TG02 Concentration (ug/ml) 0 100 200 300 400 500

Time (Hr)

Figure 5.5 Observed TG02 plasma concentrations with the twice weekly dosing regimen compared to simulations.

Grey area represents the 95% simulation interval at twice weekly dose of 150 mg TG02. Dots represent observations from patients who received TG02 using the twice weekly dosing schedule (N=24).

107

5.3.5 Stage 2 of population pharmacokinetic model

5.3.5.1 Pharmacokinetic base model

In 2014, with a larger dataset of 104 patients under 4 different dose schedules and 4 disease cohorts, the PK base model was built as a one-compartment model with two different absorption sites of different absorption rate constants

(Ka). The diagram for this model is shown in Figure 5.6. This model successfully converged with the FOCE method, and standard errors (SE) of all PK parameters were acceptable, as shown in Table 5.8. BSV on CL, Ka1, and Ka2 were 85%,

83%, and 62%.

Diagnostic plots are shown in Figure 5.7. The individual predictions were more consistent with observations compared to population predictions after including

BSV terms. The upper panel of the plot demonstrats a linear association between observed and predicted data, which suggests a lack of bias in the model. In the lower panel, the conditional weighted residuals (CWRES) were evenly distributed along population predictions and different time points, suggesting that there were no major bias in the structural or residual error models.

108

F1*Dose Fraction 1 (%) Ka1 Plasma Central F2*Dose Fraction 2 (%) Ka2

K10

Figure 5.6 The pharmacokinetic model of TG02

109

Table 5.8 Population pharmacokinetic base model parameters

Parameter Estimate (% SE) %BSV (% SE) % Shrinkage

CL, L/h 2.61 (9.5) 83.3 (17.6) 8.9

V, L 8.50 (15.3) 41.1 (29.3) 43.2

Ka1, h-1 0.38 (14.6) 84.6 (18.5) 24.9

Ka2, h-1 0.03 (12.1) 55.6 (33.0) 52.8

Fr 0.31 (17.5) -- --

Additive Residual error 0.50 (15.0) -- 10.7

BSV, between-subject variability; CL, central clearance; V, central volume of distribution; Ka1, Ka2, absorption rate constants for two absorption sites; Fr, fraction of dose into absorption site 1; SE, standard error.

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Figure 5.7 The diagnostic plots of TG02 PK base model

CWRES, conditional weighted residuals. The black line is a line with a slope of 1 in the upper panel and 0 in the lower panel. The red lines represent the LOWESS (locally weighted scatterplot smoothing) line.

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5.3.5.2 Covariate testing and final PK model

Different covariates were tested on different PK parameters based on physiological relevance, including gender, food control, dose levels, BSA, creatinine clearance, ALT, AST, total bilirubin, albumin, total protein and blood urea nitrogen (BUN). Forward selection process yielded five statistically significant covariates based on likelihood ratio test at significance level of 0.05: dose level on CL, food control on absorption rate constant Ka1 and Ka2, and

BSA on volume of distribution (V), as shown in Table 5.9. Late on, a full model with all five covariates was generated, with which back forward selection was started. Covariates were dropped from the full model one by one unless the p- value was less than 0.01 (OFV increased6.64) and then that covariate would be kept in the final model. This process yielded only one covariate which was food control on Ka1. Therefore, our final population PK model was a one-compartment model with two absorption depots and included food control as a covariate on parameter of absorption rate constant. All the parameter estimates were summarized in Table 5.10. The Figure 5.8 of diagnostic plots of final PK model demonstrated that the model is appropriate for the population and each individual and a lack of bias in the structural and residual error models.

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Table 5.9 Covariate forward selection process

MODEL MINI COV OBJ P-value

DOSE -V 1 1 167.9 <0.05 FOOD-KA1 1 1 170.1 <0.05 FOOD-KA2 1 1 171.3 <0.05 BSA-V 1 1 172.4 <0.05 ALB-CL 1 1 173.7 >0.05 TP-V 1 1 175.0 >0.05 DOSE-CL 1 0 175.3 >0.05 TP-CL 1 1 175.7 >0.05 ALB-V 1 1 175.8 >0.05 BUN-CL 1 1 175.8 >0.05 SEX-CL 1 1 176.2 >0.05 CRCL-CL 1 1 176.9 >0.05 AST-CL 1 0 178.0 >0.05 ALT-CL 1 0 178.7 >0.05 BSA-CL 1 1 179.1 >0.05 BASE MODEL 1 1 179.6 . BILI-CL 1 0 179.6 >0.05 FOOD-FR 1 0 185.3 >0.05 DOSE-KA1 1 0 219.1 >0.05

Models were ranked based on their objective function values from small to large. MINI, minimization process; COV, covariance step; OBJ, objective function value; CL, clearance; V, volume of distribution; BSA, body surface area; ALB, albumin; TP-total protein; BUN, blood urea nitrogen; CRCL, creatinine clearance.

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Table 5.10 Final population PK model parameters

Parameter Estimate (% SE) %BSV (% SE) % Shrinkage

CL, L/h 2.59 (9.5) 82.2 (17.0) 8.9

V, L 7.92 (15.7) 37.0 (29.3) 46.0

Ka1, h-1 0.44 (13.5) 80.6 (19.9) 24.7

Ka2, h-1 0.03 (11.9) 54.5 (35.0) 53.7

Fr 0.31 (16.4) -- --

Food control on Ka1* -0.51 (21.9)

Additive Residual error 0.50 (14.9) -- 10.5

*By adding food control factor on Ka1, BSV of Ka1 decreased by 4%.

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Figure 5.8 The diagnostic plots of final PK model of TG02

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5.3.6 Model evaluation and visual predictive check (VPC)

The final PK model was used for simulation stratified by food control, with 24- hour PK after a single dose of TG02. In the current patient population, dose levels ranged from 10 to 200 mg for those without control of food intake (non- controlled), and from 70 to 300 mg for patients who fasted 1 hour prior to and two hours after dosing. Thus, the simulations were performed at doses of 150 mg for the non-controlled group and 300 mg for the fasted group. VPC plots stratified by food control are displayed in Figure 5.9 and 5.10, respectively. From those, the

95% simulation intervals include most of the observed data.

5.4 Conclusions

TG02 PK can be characterized by a one-compartment model with two absorption depots. Food control had an impact on TG02 PK. Model simulations suggested twice weekly dosing would yield low/no accumulation. Observed data from 24 patients treated on the new regimen supported our simulation. Most importantly, this new regimen increased drug tolerability and enabled administration of higher dose level up to 300 mg.

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TG02 Concentration (ug/ml) 0 10 20 30

Time (Hr)

Figure 5.9 VPC of TG02 without food control

Thin solid red lines represent the 95% simulation interval after a single dose of 150mg TG02 for 24 hours, while the dense red line indicates the median of simulated concentrations. Black dots were observations from patients who did not have food controlled with different TG02 doses of 10 to 200 mg.

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TG02 Concentration (ug/ml) 0 10 20 30

Time (Hr)

Figure 5.10 VPC of TG02 with food control

Thin solid red lines represent the 95% simulation interval after a single dose of 300mg TG02 for 24 hours, while dense red line indicates the median of simulated concentrations. Black dots were observations from patients who underwent food control with different TG02 dose from 70 to 300 mg.

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CHAPTER 6, SUMMARY AND FUTURE PERSPECTIVES

The research work presented in this dissertation has been focused on understanding the pharmacokinetic behavior, population variability, sources of variability, and PK/PD relationships of CDKIs to support improved drug safety.

In our clinical study of dinaciclib, drug efficacy and safety were evaluated under the combination treatment of dinaciclib and ofatumumab, and we were particularly interested in the relationship between the glucuronide metabolites plasma levels and the occurrence of TLS. In this trial, numerous efforts were invested to prevent TLS, including prophylaxis with rasbiricase, allopurinol, calcium acetate and kayaxalate, and frequent monitoring of potassium, phosphate, uric acid, creatinine, LDH levels. Importantly, these strategies, along with the ofatumumab pretreatment and other supportive care, contributed to the successful prevention of TLS for all but one patient in this trial. These aggressive measures for preventing TLS, however, made it difficult to characterize any relationships that may have existed between dinaciclib/dinaciclib-glucuronide PK and TLS. Nonetheless, we did observe a linear relationship between maximal changes in phosphate and dinaciclib-glucuronide Cmax and AUC. While this may indicate dinaciclib is in fact correlated with TLS biomarkers, the limited clinical

119 grading of TLS in this study prohibited us from evaluating TLS association with dinaciclib or dinaciclib-glucuronide PK. Most importantly, dinaciclib was well tolerated with the combination of ofatumumab. In addition, with the clinical supportive care and prophylaxis, TLS could be effectively prevented and managed. In the past, hyper-acute TLS had been a major clinical concern for

CDKIs in CLL patients due to the potential for severe conditions where dialysis was necessary. This toxicity had a significant negative impact on the fate of flavopiridol, the first CDKI to reach the clinic, which was sold by its owner, Sanofi

Aventis, despite the exciting and significant clinical efficacy in relapsed and refractory CLL, and AML patients. The future of dinaciclib is also uncertain.

Previously owned by Schering-Plough (SCH-72765) now owned by Merck when it acquired Schering-Plough, dinaciclib is effectively on hold as no new clinical trials are being planned (www.clinicaltrials.gov).

Despite the problems encountered with TLS, CDKIs represent a class of drugs with a novel mechanism of action that has demonstrated good activity in refractory CLL. There are still many challenges in CLL treatment, such as an older patient population and some who are unfit for other therapies, including therapies that are not effective in genetically high risk disease. There are still limitations with other drugs, and CDKIs provide another option for clinicians in treating CLL. For example, ibrutinib has demonstrated remarkable activity and received accelerated approval from the FDA in 2014, but patients do develop resistance and relapse with ibrutinib therapy. CDKIs will likely be useful within a

120 subset of ibrutinib refractory patients. The clinical data from the trial combining ofatumumab and dinaciclib demonstrates this particular CDKI can be administered safely to produce durable responses in a refractory patient population.

In terms of sources for the PK variability of dinaciclib, our model has identified albumin as a covariate. In addition, evaluations of pharmacogenetic effects of metabolic enzymes were performed (results were not included in this dissertation). Dinaciclib is primarily metabolized by CYP3A4 and CYP3A5, and single nucleotide polymorphisms (SNPs) in these two genes among this patient population were assessed, CYP3A4*22 and CYP3A5*3. One patient carried the

CYP3A4*22 SNP in our study, and this low frequency is consistent with the low prevalence of this SNP in the general population. Within the first 30 patients, there appeared to be a trend in dinaciclib AUC between CYP3A5*3 carriers and non-carriers (p=0.056), which suggested this may be a useful SNP to further characterize in clinical studies of dinaciclib.

TG02, an oral CDKI, has displayed high PK variability among patients. Our analysis has revealed that food control contributed to TG02 PK variability. Apart from the factor of food control, large unexplained variability still remained. In addition, there was variability in the time to reach Cmax (Tmax), which ranged 1 to 6 hours after oral administration. Therefore, it is possible that other factors involved in drug absorption contribute mainly to the PK variability. It is unknown which part of the GI tract the TG02 absorption mainly occurs. TG02 has a pH- 121 dependent solubility, and if stomach absorption is important for this drug, TG02

PK behaviors could be altered if there are stomach pH changes due to disease status, other co-medicines, food intake, etc. Also, the extent of the first pass effect has not been studied for TG02. The bioavailability of TG02 is 24% in mice and 37% in dogs, but it is unknown in humans. It is not understood which process contributes primarily to this relative low bioavailability, extensive first pass metabolism, poor absorption, or others. TG02 is mainly metabolized by

CYP1A2 and CYP3A4. If TG02 has a large first pass effect, the differences in enzyme activities from patients might explain the population PK variability to some extent. Many factors can have effects on CYP functions, such as enzyme inhibition or induction by DDI, genetic polymorphisms, etc. Overall, a better understanding of the absorption, distribution, metabolism and elimination of this drug will help to further improve dose regimen design and continue to reduce variability in drug exposure and potentially in clinical outcomes.

The plasma protein binding of TG02 is greater than 99%, meaning the free drug fraction is less than 1%. If patient plasma protein level is decreased due to disease status, the fraction unbound could be easily changed by several folds. In addition, the quality of plasma proteins may cause decreased binding sites for drugs, leading to higher free drug fraction. Further, co-administered medications might induce protein binding displacement and free drug concentrations will possibly increase several folds. Overall, the differences in individual TG02

122 protein binding might contribute to the population PK variability. Currently, assessment of protein binding in patient clinical plasma samples is ongoing.

123

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