Designing clinical trials for patients who are not average

Thomas E Yankeelov; David A Hormuth 2nd; Ernesto A B F Lima; Guillermo Lorenzo; Chengyue Wu; Lois C Okereke; Gaiane M Rauch; Aradhana M Venkatesan; Caroline Chung

doi:10.1016/j.isci.2023.108589

Designing clinical trials for patients who are not average

iScience. 2023 Nov 29;27(1):108589. doi: 10.1016/j.isci.2023.108589. eCollection 2024 Jan 19.

Authors

Thomas E Yankeelov^{1

2

3

4

5

6}, David A Hormuth 2nd^{4

5}, Ernesto A B F Lima^{4

7}, Guillermo Lorenzo^{4

8}, Chengyue Wu⁴, Lois C Okereke⁴, Gaiane M Rauch⁹, Aradhana M Venkatesan⁹, Caroline Chung¹⁰

Affiliations

¹ Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
² Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA.
³ Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA.
⁴ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
⁵ Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
⁶ Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
⁷ Texas Advanced Computer Center, The University of Texas at Austin, Austin, TX 78712, USA.
⁸ Department of Civil Engineering and Architecture, University of Pavia, 27100 Pavia, Italy.
⁹ Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
¹⁰ Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.

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.

Publication types

Review

Abstract

Publication types

Grants and funding