The 3rd Stat4Onc Annual Symposium

Emerging new approaches in early-phase oncology drug development

Speaker Biography and Abstract

Richard Patt, M.D.

Bio:

Dr. Patt is co-founder and principal in RadMD, and is a board-certified radiologist with > 25 year’s experience customizing imaging endpoints for oncology trials and training and managing performance of an independent reader group that has performed reviews for over 500 oncology trials. He has served as head of MRI, Georgetown University Medical Center, and Director, Imaging Clinical Development at Berlex Labs. He has also co-founded The Blinded Reader and Investigator Training Institute (BRITI) which focuses on web-based training of research sites on imaging efficacy criteria for hundreds of research sites, site readers, and trial personnel globally. He has special interest in advanced reader training and performance management methods, and utilizing imaging to better define drug mechanism of action in early phase trials.

Title:

How Imaging AI, Radiomics, and Live Central Image Reviews are Transforming Early Oncology Development

Abstract:

Historically blinded independent central review was generally utilized in later phase trials as surrogate endpoints of efficacy. It was the results of site image interpretations, however, that was used for early signal detection and go no-go decision making. The added complexity of imaging immune-oncology agents, combined with new methods of evaluating images for early efficacy signals (radiomics and artificial intelligence (AI) resulted in greater demands on image reviewers. This presentation will provide an overview of how centralized image review is changing early phase oncology development.

Chang-Heok Soh, Ph.D.

Bio:

Chang-Heok has more than 16 years of experience in medical research and biotech/pharmaceutical industry, spanning early- to late-stage drug development. She is currently Head of Early Oncology Statistics at AbbVie, providing leadership to the early-stage oncology statistics groups at AbbVie’s sites in California and Illinois.

Prior to joining AbbVie, she was Director of Biostatistics at Alnylam Pharmaceuticals in Cambridge, Massachusetts, helping to advance RNA interference therapies for multiple rare diseases. Before Alnylam, Chang-Heok worked at Genentech/Roche for 10 years, providing statistical leadership on key oncology and Alzheimer’s disease programs, including successful regulatory filings. Before expanding her career in biotech/pharma industry, Chang-Heok was involved in pediatrics AIDS research in research institute setting and diverse disease areas in hospital setting. Her career has spanned geographic locations in USA, Europe and Asia, including 2.5 years in Switzerland representing Genentech/Roche Biostatistics group in internal and cross-industry collaborations in Alzheimer’s disease.

Chang-Heok received her master’s and Ph.D. degrees in Biostatistics from Harvard University.

Title:

Bayesian Interim Monitoring for Faster Decision-Making in Early Oncology Trials

Abstract:

Traditional approaches for performing interim analysis of early phase oncology trials typically involve examining the data at specified sample size(s). There is often no formal mechanism if the actual timing of the interim analysis deviates from plan. For example, such situations may arise in practice when there is a need to perform an interim analysis before the specified sample size due to slow accrual on the clinical trial.

A Bayesian interim monitoring approach allows more flexibility in the timing of interim analysis and could accelerate decision-making in early-stage oncology trials. Simulations show high level of concordance between the decision made at interim analysis and the decision that would have been made should the trial continue to its planned end.

Revathi Ananthakrishnan

Bio:

Revathi Ananthakrishnan works as a Biostatistician at Celgene on designing, analyzing and interpreting immuno-oncology trials.

She has a broad interdisciplinary background of math, statistics, physics and biology and is interested in various aspects of Oncology clinical trials. She has worked on several early phase Oncology trials as well as trials for regulatory submission for solid tumors as well as blood cancers.

Title:

Extensions of the TEQR and mTPI designs including non-monotone efficacy in addition to toxicity in dose selection

Abstract:

With the emergence of immunotherapy and other novel therapies, the traditional assumption that the efficacy of the study drug increases monotonically with dose levels is not always true. Therefore, dose-finding methods evaluating only toxicity data may not be adequate. Hence, this talk will cover three new early phase designs that consider efficacy in addition to safety in dose selection. The first two designs are the extended TEQR and mTPI designs – in these designs, the optimal dose for safety and efficacy is determined by applying isotonic regression to the observed toxicity and efficacy rates, once the early phase trial is completed. The third design is the 2D TEQR design, the frequentist counterpart of an existing Bayesian design called the TEPI (Toxicity Efficacy Probability Interval) design. We conduct simulation studies to investigate the operating characteristics of the proposed designs for various underlying DLT and response rates and compare them to existing designs. We found that the extended mTPI design selects the optimal dose for safety and efficacy more accurately than the other designs considered for most of the scenarios considered. Although for the same sample size and cohort size, the frequentist 2D TEQR design is less accurate than the Bayesian TEPI design in selecting the optimal dose, the accuracy of optimal dose selection of the 2D TEQR design can be increased, in many cases, with a moderate increase in cohort size.