Biomarker Analysis in Clinical Trials with R

Nusrat Rabbee

• Publisher: Chapman and Hall/CRC
• Year: 2020
• Print ISBN: 9781032242453
• eBook ISBN: 9780429766794
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry, much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating, understanding and synthesizing biomarker data. -From the Foreword, Jared Christensen, Vice President, Biostatistics Early Clinical Development, Pfizer, Inc.

Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic, predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R, the book helps students, researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers.

Features:

• Analysis of pharmacodynamic biomarkers for lending evidence target modulation.

• Design and analysis of trials with a predictive biomarker.

• Framework for analyzing surrogate biomarkers.

• Methods for combining multiple biomarkers to predict treatment response.

• Offers a biomarker statistical analysis plan.

• R code, data and models are given for each part: including regression models for survival and longitudinal data, as well as statistical learning models, such as graphical models and penalized regression models.

Section I

Pharmacodynamic Biomarkers

1

Introduction

Biomarker development is a very active area in drug development for their utility in screening, diagnosing, monitoring disease, as well as for predicting treatment modulated clinical outcome. Of these, pharmacodynamic (PD) biomarkers are used from early phase human pharmacology stage studies to late phase studies to obtain pharmacological information of drugs interacting with the physical systems under study (e.g., heart, kidneys, central nervous system). Pharmacological effects of drugs on cells, organs, and systems are measured in animals in preclinical studies and in humans in controlled clinical experiments in the drug development process. The goal is to study drug characteristics for assessing target engagement and confirming mechanism of action (MoA). In precision medicine (especially in oncology), the MoA confirmation in humans is a key step in advancing the drug toward clinical development from preclinical development.

Biomarkers can be biological properties or molecules that can be detected and measured in parts of the body like the blood or tissue. Biomarkers can be specific cells, molecules, or genes, gene products, enzymes, or hormones. Complex organ functions or general characteristic changes in biological structures can also serve as biomarkers [1]. In early phase clinical trials, the drug manufacturer is focused on developing MoA assays for the drug. There is extensive literature about the development of MoA assays, as well as on developing other PD biomarkers, which measure further downstream molecular, biochemical, and physiological changes. Let us take an example of the latter type of PD biomarkers, specifically blood-based biomarkers. (i) In oncology, in order to study the pharmacologic effect of the drug on cancer tissues, an invasive biopsy of the actual tumor is usually needed. Current progress of science permits us to measure blood-based biomarkers of circulating tumor cells as alternative biomarkers of antitumor activity. (ii) In brain disorders, like Alzheimer’s disease, blood biomarkers are less prevalent, since brain disorders may not have peripheral manifestation. However, blood and cerebrospinal fluid (CSF)-based biomarkers are presently an active area of research and development in neurology. (iii) In cardiovascular disease, hemoglobin A1c may be used as a PD/response biomarker when evaluating patients with diabetes to assess response to antihyperglycemic agents.

Several PD biomarkers over several phases of clinical development may be needed to constitute a complete picture of the drug’s mechanism in the body from minutes to hours and days of drug administration [2]. The effort of developing assays and measuring the relevant biomarkers help drug development in the following ways:

• validating target engagement

• selection of optimal dose

• identifying potential early efficacy and/or safety

• potential identification of patient subgroup likely to respond from treatment with the drug

• help design combination therapies

Pfizer has defined three pillars of survival of a drug through clinical development in order to progress to phase III [2]. In order for the drug candidate to show...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Foreword
• Preface
• Acknowledgments
• Author
• Section I Pharmacodynamic Biomarkers
• Section II Predictive Biomarkers
• Section III Surrogate Endpoints
• Section IV Combining Multiple Biomarkers
• Section V Biomarker Statistical Analysis Plan
• Index