Speaker
\nBiography
\nAbstract
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Ayon Mukherjee\, Eli Lilly
Ayon is the Head of Biostatistics\ , India at Eli Lilly and is managing a group of 40 Biostatisticians in mul tiple therapeutic areas. He is an expert in the field of adaptive designs in clinical trials and has been actively contributing to the field of Cova riate-Adjusted Response Adaptive Randomization for survival trials and Dos e Optimization methods in early phase oncology through multiple cross indu stry collaborations. He is a Chartered Statistician from the Royal Statist ical Society and in also involved in supervising multiple PhD students in the field of Adaptive Randomization and Dose Optimization Trials.
\nProj ect Optimus and Model Assisted Methods to Find an Optimum Dose
\n Model-assisted methods are extensively used in in dose-esca lation studies\, providing a framework that achieves efficiency in selecti ng the target dose level while also being very simple to implement in prac tice. The BOIN design has been traditionally used to target the maximum to lerated dose level accurately while maintaining the ease of implementation as compared to the traditional 3+3 method. With the advent of novel thera peutics such as immunotherapies and targeted therapies\, the assumption of monotonic increase of efficacy with dose level is no longer valid and the FDA\, through the Project Optimus initiative\, encourages to use a dose e scalation design method that considers not only dose limiting toxicities b ut the overall clinical data\, such as efficacy measures to determine the optimal biologic dose (OBD). This discussion introduces the concept of Pro ject Optimus with real life trial examples and discusses the various model -assisted design options available to find an OBD. In particular\, we will introduce the BOIN12 and the U-BOIN model-assisted designs present a real -life trial example to demonstrate the benefit of using such optimization approach as compared to the traditional MTD based dose escalation approach . The discussion would conclude by providing some future directions of res earch that are being discussed in the fraternity in this area.\n
\n Gina D'Angel o\, AstraZeneca
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\nDr. Gina D&rsquo\;Angelo is a Director at AstraZeneca Oncology Statistical Innovation\, and offers he r guidance and statistical expertise on early-late clinical trials. She ha s over 20 years of academic and industry experience on preclinical and cli nical trials with an emphasis on dose-finding\, biomarkers\, and study des ign. 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-ed iting a book on statistical and design consideration for biomarkers in cli nical trials. Her experience spans from discovery to late phase trials\, with small to high-dimensional data\, across various therapeutic areas. \n
The initiation of dose optimization has resulted in a paradigm shift in oncology clinical trials to determine the optimal biolo gical 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 p aradigm using U-MET (utility-based dose optimization approach for multiple -dose randomized trial designs) to facilitate a more informed dosing decis ion will be described. There will be discussion on the decision framework that can compare doses via a utility-based approach in a hypothesis testin g framework\, where a Bayesian inference is evaluated to compare the utili ty scores across doses and identify the OBD. Considerations for multiple e ndpoints will be accounted for in a decision framework and utility-based a pproach to compare multiple doses\; and will be demonstrated using U-MET a nd 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 demonstra ted for the approaches.
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\n Michael Sweeting\, AstraZeneca
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\nDr. Michael Sweeting is the Director of Statistical Innovatio n in Oncology Biometrics at AstraZeneca\, with over two decades of extensi ve research and management experience in Biostatistics. His expertise span s early and late-phase trial design\, real-world evidence (RWE)\, observat ional data analysis\, Health Technology Assessment (HTA)\, simulation\, an d software development. Michael is dedicated to advancing innovative stati stical research and leadership within drug development and reimbursement a nd is committed to creating tools and training materials to facilitate the implementation of new statistical methods.\n Before joining As traZeneca\, 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 cardiovasc ular research programs. He has published in both substantive and methodolo gical areas of research\, with an h-index of 64\, over 250 peer-reviewed p ublications\, and more than 15\,000 citations.\n
\nDose-Opti
mization using BOIN12: Efficient Implementation with R&rsquo\;s &lsquo\;Es
calation&rsquo\; Package\n
\n Mic
hael Sweeting\, Daniel Slade\, Dan Jackson\, Kristian Brock
\n
\n 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 t
rials. Its appeal lies in its simplicity and objective decision-making pro
cess\, which enhances the safety and efficacy of dose-finding studies. BOI
N12 extends the BOIN approach to use both binary toxicity and efficacy dat
a to find the optimal biologic dose (OBD) that optimizes a risk-benefit tr
ade-off.\n The "escalation" package in R provides a comprehensi
ve suite of dose-finding tools for researchers and has recently been exten
ded to include the BOIN12 design. In this presentation\, we demonstrate th
e implementation of BOIN12 within the escalation package\, highlighting ke
y features such as ease of customization\, robust and efficient simulation
capabilities\, and support for a range of dose-escalation designs beyond
BOIN12. A new &ldquo\;potential outcomes&rdquo\; simulation approach empow
ers users to conduct thorough simulations efficiently\, enabling head-to-h
ead comparisons of multiple designs and decision-making strategies in a st
reamlined manner.\n We will demonstrate these capabilities by p
erforming a comparative analysis of two distinct variants of the BOIN12 de
sign through simulation\, showcasing the practical advantages and insights
afforded by the escalation package.\n
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\n Anaï\;s Andrillon\, Saryga
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\nAnaï\;s Andrillon is a Statistical Methodologist at Sa ryga\, a company dedicated to support innovation in statistics and decisio n-making in healthcare. Jointly with her colleagues\, she assists pharmace utical companies\, and biotechnology companies on developing and using adv anced statistical methodologies to optimize drug development plans and cli nical trials. With an active collaboration with academia\, Saryga also con tributes to the research and the publication of novel approaches. Anaï \;s holds a PhD in Biostatistics from Université\; Paris Cité\ ; on adaptive dose-finding designs in oncology.
\nU-DESPE: a Bayesian Ut ility-based methodology for dosing regimen optimization in early-phase onc ology trials based on Dose-Exposure\, Safety\, Pharmacodynamics\, Efficacy
\nWith the development of novel therapies\, t he maximum tolerated dose paradigm that ``more is better'' does not necess arily hold anymore. In this context\, doses and schedules of novel therapi es may be inadequately characterized and oncology drug dose-finding approa ches should be revised. This is increasingly recognized by health authorit ies\, notably through the Optimus project. We developed a Bayesian dose-fi nding design\, called U-DESPE\, which allows to either determine the optim al dosing regimen at the end of the dose-escalation phase\, or use of dedi cated cohorts for randomizing patients to candidate optimal dosing regimen s after that safe dosing regimens have been found. U-DESPE design relies o n a dose-exposure model built from pharmacokinetic data using non-linear m ixed-effect modelling approaches. Then three models are built to assess th e relationships between exposure and the probability of selected relevant endpoints on safety\, efficacy\, and pharmacodynamics. These models are th en combined to predict the different endpoints for every candidate dosing regimens. Finally\, a utility function is proposed to quantify the trade-o ff between these endpoints and to determine the optimal dosing regimen. We applied the proposed method on a clinical trial case study and performed an extensive \;simulation study to evaluate the operating characterist ics of the method.
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Webinar Chair \;
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Biography \n | \n
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Pavel is an MRC Investigator (Programme Leader Track) and NIHR Fellow working on the development and implementation of adaptive designs in clini cal trials. He provides statistical support in a number of academic clinic al trials and consults pharmaceutical companies on the development of nove l adaptive designs and supports their implementations in real trials. \ n | \n
