Designing Clinical Trials for Patients Who Are Not Average

Authors

Thomas E Yankeelov
David A Hormuth
Ernesto A B F Lima
Guillermo Lorenzo
Chengyue Wu
Lois C Okereke
Gaiane M Rauch
Aradhana M Venkatesan
Caroline Chung

Publication Date

1-19-2024

Journal

iScience

Abstract

The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.

Keywords

Health sciences

Comments

PMID: 38169893