MDIC IVD RWE Framework for Public Comment
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Artificial Intelligence: the Key to Unlocking Novel Real-World Data?
Artificial Intelligence: The Key to Unlocking Novel Real-World Data? While Artificial intelligence stands to make significant contributions to clinical research due to its unparalleled ability to translate unstructured data into real-world evidence (RWE), significant challenges remain in achieving regulatory-grade evidence. By Michele Cleary 16 | March/April 2019 Value & Outcomes Spotlight Artificial intelligence (AI) is revolutionizing healthcare services. From improving disease detection to supporting treatment decision making, AI has become ubiquitous in care delivery. Now AI is poised to transform the drug and device development process, helping researchers refine the approval process and significantly cutting both the time and the expense needed to bring products to market. While AI has long been used to facilitate recruitment of study subjects, optimize study design, and support patient adherence to study protocols, AI’s greatest contribution to clinical research may still be on the horizon— unlocking the data richness that lies within the mountains of novel real- world data (RWD) sources. This article explores how AI may improve clinical research through its ability to better translate RWD into real-world evidence (RWE), thus providing more valid evidence of clinical benefits and risks. Dan Riskin, MD, of Verantos, Rich Glinklich, MD of OM1, and Sebastian Schneeweiss, MD of Aetion all shared their valuable insights into how AI is transforming clinical research. THE SEARCH FOR REGULATORY-GRADE DATA With innovations in digital data, HEOR researchers are facing explosive growth in novel RWD sources. But as researchers move from traditional RWD sources (eg, registries and claims data) to these novel data sources, unstructured data present a significant opportunity and challenge. -
Optimizing Product Lifecycle Management with Real World
1/17/2019 Optimizing Product Lifecycle Management With Real-World Evidence :: Medtech Insight This copy is for your personal, non-commercial use. For high-quality copies or electronic reprints for distribution to colleagues or customers, please call +44 (0) 20 3377 3183 Printed By Optimizing Product Lifecycle Management With Real- World Evidence 27 Dec 2018 SPONSORED Executive Summary Thought Leadership In Association With IQVIA An increasingly data-rich healthcare sector presents both opportunities and challenges for the MedTech industry, just as it does for the health systems and patients served by MedTech products. Used intelligently and appropriately, the vast quantities of real-world data emanating from multiple healthcare sources, such as electronic medical records (EMRs), claims databases, products and disease registries, provide the raw material for real-world evidence (RWE) that can inform strategy and decision- making throughout the MedTech-product lifecycle. An increasingly data-rich healthcare sector presents both opportunities and challenges for the MedTech industry, just as it does for the health systems and patients served by MedTech products. Used intelligently and appropriately, the vast quantities of real-world data emanating from multiple healthcare sources, such as electronic medical records (EMRs), claims databases, products and disease registries, provide the raw material for real-world evidence (RWE) that can inform strategy and decision-making throughout the MedTech-product lifecycle. “What we’re seeing in the industry -
Using Real World Data in Pharmacoeconomic Evaluations
Using Real World Data in Pharmacoeconomic Evaluations: Challenges, Opportunities, and Approaches Andreas M. Pleil, PhD Senior Director Worldwide Medical and Outcomes Research Pfizer, Inc., La Jolla, CA Slide:2 Disclaimer and Acknowledgements The opinions expressed by the presenter are his and may not reflect the opinions or position of Pfizer, Inc., it’s Board or Management Many thanks to Lou Garrison for allowing me to steal many of these slides shamelessly Slide:3 Today’s Agenda Overview of the Landscape Where do data come from Report from the Task Force What was their charge Who did the charging What did they conclude Discussion points Is the RWTF sufficient Are there gaps in our knowledge What should we do next Applications from your world Group Project Slide:4 Where do “Data” Come From? Pre-clinical studies Provides a first assessment of the expected safety and efficacy of a compound using proven animal models Early Phase Clinical trials IND Safety focus and the beginnings of efficacy, dose ranging, and tolerability Pivotal Clinical trials Demonstrate safety and efficacy in well controlled (generally masked) randomized studies sufficient for market authorization NDA Filed Phase IIIB Expanded trials in different use situations NDA Approved or populations Phase IV Post marketing safety or “new” indications Real World Data Evaluations of safety, effectiveness and outcomes in “routine” clinical practice Slide:5 Who uses “Data”? Companies making internal decision regarding drug development Regulators responsible -
Generating Real-World Evidence by Strengthening Real-World Data Sources
Generating Real-World Evidence by Strengthening Real-World Data Sources Using “real-world evidence” to bring new “Every day, health care professionals are updating patients’ treatments to patients as part of the 21st electronic health records with data on clinical outcomes Century Cures Act (the “Cures Act”) is a key resulting from medical interventions used in routine clinical priority for the Department of Health and practice. As our experience with new medical products Human Services (HHS). Specifically, the Cures expands, our knowledge about how to best maximize their Act places focus on the use of real-world data benefits and minimize potential risks sharpens with each data to support regulatory decision-making, including point we gather. Every clinical use of a product produces data the approval of new indications for existing that can help better inform us about its safety and efficacy.” drugs in order to make drug development faster jacqueline corrigan-curay, md, jd and more efficient. As such, the Cures Act director of the office of medical policy in fda’s center for drug evaluation and research has tasked the Food and Drug Administration (FDA) to develop a framework and guidance for evaluating real-world evidence in the context of drug regulation.1 Under the FDA’s framework, real-world evidence (RWE) is generated by different study designs or analyses, including but not limited to, randomized trials like large simple trials, pragmatic trials, and observational studies. RWE is the clinical evidence about the use, potential benefits, and potential risks of a medical product based on an analysis of real-world data (RWD). -
Use of in Vivo and in Vitro Models to Guide Dose Selection for Neonatal Infections
Use of In Vivo and In Vitro Models to Guide Dose Selection for Neonatal Infections Professor William Hope Antimicrobial Pharmacodynamics & Therapeutics University of Liverpool September 2016 Disclosures • William Hope has received research funding from Pfizer, Gilead, Astellas, AiCuris, Amplyx, Spero Therapeutics and F2G, and acted as a consultant and/or given talks for Pfizer, Basilea, Astellas, F2G, Nordic Pharma, Medicines Company, Amplyx, Mayne Pharma, Spero Therapeutics, Auspherix, Cardeas and Pulmocide. First of all BIOMARKER OUTCOME OF CLINICAL DOSE INTEREST/IMPORTANCE •Decline in Log10CFU/mL •Linked to an outcome of clinical interest •Survival 7 6 5 4 CFU/g) CNS 10 3 Effect (log 2 1 0 0.01 0.1 1 10 100 1000 Total doseDose 5FC (mg/kg) PHARMACOKINETICS PHARMACODYNAMICS Concept of Expensive Failure COST Derisking White Powder Preclinical Program Clinical Program Concept from Trevor Mundell & Gates Foundation And this… Normal Therapeutics Drug A, Dose A1 versus Drug B, Dose B1 Pharmacodynamics is the Bedrock of ALL Therapeutics…it sits here without being seen One of the reasons simple scaling does not work is the fact pharmacodynamics are different Which means the conditions that govern exposure response relationships in neonates need to be carefully considered …Ignore these at your peril Summary of Preclinical Pharmacodynamic Studies for Neonates** • Hope el al JID 2008 – Micafungin for neonates – Primary question of HCME – Rabbit model with hematogenous dissemination • Warn et al AAC 2010 – Anidulafungin for neonates – Primary question -
In Vitro–In Vivo Correlation (IVIVC): a Strategic Tool in Drug Development
alenc uiv e & eq B io io B a f v o a Sakore and Chakraborty, J Bioequiv Availab 2011, S3 i l l a a b n r i l i u DOI: 10.4172/jbb.S3-001 t y o J Journal of Bioequivalence & Bioavailability ISSN: 0975-0851 Review Article OpenOpen Access Access In Vitro–In Vivo Correlation (IVIVC): A Strategic Tool in Drug Development Somnath Sakore* and Bhaswat Chakraborty Cadila Pharmaceuticals Ltd, Research & Development, 1389, Trasad Road, Dholka, Ahmedabad 387810, Gujarat, India Abstract In Vitro–In Vivo Correlation (IVIVC) plays a key role in pharmaceutical development of dosage forms. This tool hastens the drug development process and leads to improve the product quality. It is an integral part of the immediate release as well as modified release dosage forms development process. IVIVC is a tool used in quality control for scale up and post-approval changes e.g. to improve formulations or to change production processes & ultimately to reduce the number of human studies during development of new pharmaceuticals and also to support the biowaivers. This article provides the information on the various guidances, evaluation, validation, BCS application in IVIVC, levels of IVIVC, applications of IVIVC in mapping, novel drug delivery systems and prediction of IVIVC from the dissolution profile characteristics of product. Keywords: IVIVC definitions; Predictions; BCS classification; IVIVC definitions IVIVC Levels; Applications, Guidance United state pharmacopoeia (USP) definition of IVIVC Abbreviations: IVIVC: In Vitro In Vivo correlation; FDA: Food and Drug Administration; AUC: Area Under Curve; MDT vitro: The establishment of a rational relationship between a biological Mean in vitro Dissolution Time; MRT: Mean Residence Time; BCS: property, or a parameter derived from a biological property produced Biopharmaceutical Classification System by a dosage form, and a physicochemical property or characteristic of the same dosage form [2]. -
Using Real-World Evidence to Accelerate Safe and Effective Cures Advancing Medical Innovation for a Healthier America June 2016 Leadership Senator William H
Using Real-World Evidence to Accelerate Safe and Effective Cures Advancing Medical Innovation for a Healthier America June 2016 Leadership Senator William H. Frist, MD Former U.S. Senate Majority Leader Chair, FDA: Advancing Medical Innovation Bipartisan Policy Center Representative Bart Gordon Former Member, U.S. House of Representatives Chair, FDA: Advancing Medical Innovation Bipartisan Policy Center Advisory Committee Marc M. Boutin, JD Chief Executive Officer National Health Council Mark McClellan, MD, PhD Director, Robert J. Margolis Center for Health Policy Duke University Patrick Soon-Shiong, MD Chairman and Chief Executive Officer Institute for Advanced Health Andrew von Eschenbach, MD President Samaritan Health Initiatives 1 Sta G. William Hoagland Ann Gordon Senior Vice President Writer Bipartisan Policy Center Michael Ibara, PharmD Janet M. Marchibroda Independent Consultant Director, Health Innovation Initiative and Executive Director, CEO Council on Health and Innovation Bipartisan Policy Center Tim Swope Senior Policy Analyst Bipartisan Policy Center Sam Watters Administrative Assistant Bipartisan Policy Center 2 FDA: ADVANCING MEDICAL INNOVATION EFFORT The Bipartisan Policy Center’s initiative, FDA: Advancing Medical Innovation, is developing viable policy options to advance medical innovation and reduce the time and cost associated with the discovery, development, and delivery of safe and effective drugs and devices for patients in the United States. Key areas of focus include the following: Improving the medical product development process; Increasing regulatory clarity; Strengthening the Food and Drug Administration’s (FDA) ability to carry out its mission; Using information technology to improve health and health care; and Increasing investment in medical products to address unmet and public health needs. -
Korea Sukyeong Kim, Phd Senior Research Fellow, International Cooperation Advisor Evidence-Based Healthcare Research Division
Real World Evidence and Local Evidence Generation: How Should it Be Approached in Asia Pacific? - Korea Sukyeong Kim, PhD Senior Research Fellow, International Cooperation Advisor Evidence-based Healthcare Research Division Real World Data & Real World Evidence • Real World Data o The data relating to patient health status and/ or the delivery of health care routinely collected from a variety of sources • Electronic health records (EHRs) • Claims and billing activities • Product and disease registries • Patient-related activities in out-patient or in-home use settings • Health-monitoring devices • Real World Evidence o The clinical evidence regarding the usage and potential benefits or risks of medical product derived from analysis of Real World Data FDA. Real World Evidence. https://www.fda.gov/ScienceResearch/SpecialTopics/RealWorldEvidence/default.htm 2 1 NHI system and Real World Data • National Health Insurance System o Operating based on Electronic Data Interchange and web-base claims submission 3 Real World Data • Medical Record o Electronic Medical Record in Hospitals and Clinics • Facilitated by electronic National Health Insurance Claims Review and Assessment system building • Tertiary hospitals have been leading Electronic Medical Record and hospital Information and Communication System • Medium and small hospitals and clinics adopted EMR system around 97% in 2014 4 2 Real World Data • National Health Insurance Information System o Electronic NHI Claims Review and Assessment System • Based on web-based claims submission 5 Real -
Opportunities and Gaps in Real-World Evidence for Medical Devices 1201 Pennsylvania Ave
Robert J. Margolis, MD Center for Health Policy Duke Opportunities and Gaps in Real-World Evidence for Medical Devices 1201 Pennsylvania Ave. NW Suite 500 ● Washington, DC 20004 April 26, 2017 Meeting Summary Purpose Medical devices have substantially improved our ability to manage and treat a wide variety of conditions. Given the extent of their use, it is important that there be an effective system for monitoring medical device performance and the associated patient outcomes. Although significant steps have been takento enable evaluation and safety surveillance of medical products, critical gaps remain in capturingreal-world data(RWD) to evaluate medical devices before and afterFDA approval. As the collection and use of RWD advances, real-world evidence (RWE) will be able to incorporate data captured throughout the total medical device lifecycle, informing and improving the next iteration of devices. 1 The recently launched National Evaluation System for health Technology CoordinatingCenter(NESTcc) is compilingalandscape analysis report to facilitate conversation and encourage the increased and improved use of RWD with stakeholders across the medical device ecosystem. The analysis will build on the work ofFDA,thePlanning Board, the Registry Taskforce, and many stakeholders and experts. The long-term goal is forthelandscape analysis to become a living document and a NESTcc-maintained resource that encourages communication and collaboration. This workshop convened a broad range of experts and stakeholders to provide input on the analysis, including highlighting some of the current uses of RWD/RWEand identifying where gaps still remain.2 For overadecade, there have been increasing concerns that the post-market surveillance system in the United States was not fully meeting the demands of a constantly evolving medical device ecosystem. -
Meta-Analysis of Randomized Trials of Ivermectin to Treat SARS-Cov-2 Infection
Meta-analysis of randomized trials of ivermectin to treat SARS-CoV-2 infection Andrew Hill ( [email protected] ) University of Liverpool Ahmed Abdulamir College of Medicine, Alnahrain University, Baghdad, Iraq Sabeena Ahmed International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh, Asma Asghar Department of Medicine, Combined Military Hospital, Lahore, Pakistan Olufemi Emmanuel Babalola Bingham University / Lagos University, Nigeria Rabia Basri Fatima Memorial Hospital, Lahore, Pakistan Carlos Chaccour Barcelona Institute for Global Health, Clinica Universidad de Navarra, Spain Aijaz Zeeshan Khan Chachar Fatima Memorial Hospital, Lahore, Pakistan Abu Tauib Mohammed Chowdhury Xi'an Jiaotong University Medical College First Aliated Hospital, Shaannxi, China Ahmed Elgazzar Faculty of Medicine, Benha University, Benha, Egypt Leah Ellis Faculty of Medicine, Imperial College, London, UK Jonathan Falconer Department of Infectious Diseases, Chelsea and Westminster Hospital, London, UK Anna Garratt Department of Infectious Diseases, University Hospital of Wales, Cardiff and Vale University Health Board, UK Basma Hany Faculty of Medicine, Benha University, Benha, Egypt Hashim A Hashim Alkarkh Hospital, Alateya, Baghdad, Iraq Wasim Ul Haque Department of Nephrology, BIRDEM General Hospital, Dhaka, Bangladesh Arshad Hayat Department of Medicine, Combined Military Hospital, Lahore, Pakistan Shuixiang He Xi'an Jiaotong University Medical College First Aliated Hospital, Shaanxi, China Ramin Jamshidian Jundishapur University School -
In Vitro Diagnostic Medical Devices
In vitro diagnostic medical devices SUMMARY In vitro diagnostic medical devices are tests used on biological samples to determine the status of a person's health. The industry employs about 75 000 people in Europe, and generates some €11 billion in revenue per year. In September 2012, the European Commission (EC) published a proposal for a new regulation on in vitro diagnostic medical devices, as part of a larger legislative package on medical devices. The proposed legislation aims at enhancing safety, traceability and transparency without inhibiting innovation. In April 2014, the European Parliament (EP) amended the legislative proposals to strengthen the rights of patients and consumers and take better into account the needs of small and medium-sized enterprises (SMEs). Some stakeholders consider that a provision for mandatory genetic counselling interferes with the practice of medicine in Member States and violates the subsidiarity principle. Device manufacturers warn that the proposed three-year transition period may be too tight. In this briefing: Introduction EU legislation Commission proposal for a new Regulation European Parliament Expert analysis and stakeholder positions Further reading EPRS In vitro diagnostic medical devices Glossary Companion diagnostic: (in vitro) medical device which provides information that is essential for the safe and effective use of a corresponding drug or biological product. In vitro diagnostic medical device: test performed outside the human body on biological samples to detect diseases, conditions, or infections. Notified body: (private-sector) organisation appointed by an EU Member State to assess whether a product meets certain standards, for example those set out in the Medical Devices Directive.