Speaker

Biography

Abstract

Ayon Mukherjee, Eli Lilly

Ayon is the Head of Biostatistics, India at Eli Lilly and is managing a group of 40 Biostatisticians in multiple therapeutic areas. He is an expert in the field of adaptive designs in clinical trials and has been actively contributing to the field of Covariate-Adjusted Response Adaptive Randomization for survival trials and Dose Optimization methods in early phase oncology through multiple cross industry collaborations. He is a Chartered Statistician from the Royal Statistical Society and in also involved in supervising multiple PhD students in the field of Adaptive Randomization and Dose Optimization Trials.

Project Optimus and Model Assisted Methods to Find an Optimum Dose

Dr. Gina D’Angelo is a Director at AstraZeneca Oncology Statistical Innovation, and offers her guidance and statistical expertise on early-late clinical trials. She has over 20 years of academic and industry experience on preclinical and clinical trials with an emphasis on dose-finding, biomarkers, and study design. She received a PhD in Biostatistics from University of Pittsburgh and was faculty in the Biostatistics Division at Washington University in St. Louis. She is co-authoring a book chapter on dose optimization and co-editing a book on statistical and design consideration for biomarkers in clinical trials. Her experience spans from discovery to late phase trials, with small to high-dimensional data, across various therapeutic areas.

A decision framework for dose comparisons in dose optimization

The initiation of dose optimization has resulted in a paradigm shift in oncology clinical trials to determine the optimal biological dose (OBD). Early phase trials with randomized doses can facilitate additional investigation of the identified OBD in a targeted population by incorporating safety, efficacy, and biomarker data. A dose comparison paradigm using U-MET (utility-based dose optimization approach for multiple-dose randomized trial designs) to facilitate a more informed dosing decision will be described. There will be discussion on the decision framework that can compare doses via a utility-based approach in a hypothesis testing framework, where a Bayesian inference is evaluated to compare the utility scores across doses and identify the OBD. Considerations for multiple endpoints will be accounted for in a decision framework and utility-based approach to compare multiple doses; and will be demonstrated using U-MET and a clinical utility index-based (CUI-MET) approach. U-MET uses a utility to account for multiple endpoints jointly (e.g., toxicity-efficacy trade-off), while the CUI-MET does this marginally. Examples will be demonstrated for the approaches.

Dr. Michael Sweeting is the Director of Statistical Innovation in Oncology Biometrics at AstraZeneca, with over two decades of extensive research and management experience in Biostatistics. His expertise spans early and late-phase trial design, real-world evidence (RWE), observational data analysis, Health Technology Assessment (HTA), simulation, and software development. Michael is dedicated to advancing innovative statistical research and leadership within drug development and reimbursement and is committed to creating tools and training materials to facilitate the implementation of new statistical methods. Before joining AstraZeneca, Michael served as an Associate Professor at the University of Leicester and a Senior Researcher at the University of Cambridge. There, he was the lead statistician on several high-profile cancer and cardiovascular research programs. He has published in both substantive and methodological areas of research, with an h-index of 64, over 250 peer-reviewed publications, and more than 15,000 citations.

Dose-Optimization using BOIN12: Efficient Implementation with R’s ‘Escalation’ Package
Michael Sweeting, Daniel Slade, Dan Jackson, Kristian Brock

The Bayesian Optimal Interval (BOIN) design is a popular approach in dose-escalation studies, offering a flexible and robust framework for determining the maximum tolerated dose in clinical trials. Its appeal lies in its simplicity and objective decision-making process, which enhances the safety and efficacy of dose-finding studies. BOIN12 extends the BOIN approach to use both binary toxicity and efficacy data to find the optimal biologic dose (OBD) that optimizes a risk-benefit trade-off. The "escalation" package in R provides a comprehensive suite of dose-finding tools for researchers and has recently been extended to include the BOIN12 design. In this presentation, we demonstrate the implementation of BOIN12 within the escalation package, highlighting key features such as ease of customization, robust and efficient simulation capabilities, and support for a range of dose-escalation designs beyond BOIN12. A new “potential outcomes” simulation approach empowers users to conduct thorough simulations efficiently, enabling head-to-head comparisons of multiple designs and decision-making strategies in a streamlined manner. We will demonstrate these capabilities by performing a comparative analysis of two distinct variants of the BOIN12 design through simulation, showcasing the practical advantages and insights afforded by the escalation package.

TBC

U-DESPA: a Utility-based Bayesian approach for dosage optimization relying on Dose-Exposure- Safety/Pharmacodynamics/anti-tumor activity relationship modeling for oncology clinical trials

With the development of novel therapies such as Molecularly Targeted Agents (MTAs) and immunotherapies, the MTD paradigm that "more is better" does not necessarily hold anymore. In this context, doses and schedules of novel therapies may be inadequately characterized and oncology drug dose-finding approaches should be revised. In January 2023, in the frame of the interdisciplinary Project Optimus, FDA issued a draft guidance requiring new strategies of dosage optimization prior to initiating registration trials in oncology. In this guidance, the dosage optimization is proposed to rely on a quantitative assessment of the relationship between dosage and relevant endpoints. We developed a Bayesian dose-finding design allowing to 1. Directly determine the optimal dosage at the end of the dose escalation phase, or 2. Use of dedicated dose finding cohorts randomizing patients to candidate optimal dosages after safe dosages have been found. This Bayesian dose finding design relies on a dose-exposure model built from pharmacokinetic data using non-linear mixed effect modeling approaches. Three models are also built to assess the relationships between exposure and the probability of different relevant endpoints on safety, pharmacodynamics and anti-tumor activity. These models are then combined to predict the different endpoints for every candidate dosages. A utility function is finally proposed to quantify the trade-off between these three endpoints and to determine the optimal dosage. We perform an extensive simulation study to evaluate the operating characteristics of the method. Based on these outcomes, this approach is planned to be applied on a dose finding clinical trial to support decision on the dosage to be further used for late-stage development.

Chair

Biography

Dr. Pavel Mozgunov, MRC Biostatistics Unit

Pavel is an MRC Investigator (Programme Leader Track) and NIHR Fellow working on the development and implementation of adaptive designs in clinical trials. He provides statistical support in a number of academic clinical trials and consults pharmaceutical companies on the development of novel adaptive designs and supports their implementations in real trials.