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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
Designing clinical trials for patients who are not average
Thomas E Yankeelov1,2,3,4,5,6, David A Hormuth4,5, Ernesto A B F Lima4,7
1Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, 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.
Insights
Personalized clinical trials can optimize cancer treatment by using mathematical models and digital twins to simulate interventions for individual patients. This computational framework reframes treatment as a personal trial, improving therapeutic outcomes.
Area of Science:
- Computational biology
- Oncology
- Mathematical modeling
Background:
- Cancer's inherent heterogeneity limits the effectiveness of standard clinical trials, as treatments benefit only a subset of patients.
- Optimizing therapeutic interventions for individual cancer patients requires a personalized approach.
Purpose of the Study:
- To introduce a computational framework for conducting personalized clinical trials.
- To leverage mathematical techniques for optimizing individual cancer treatment.
Main Methods:
- Utilizing patient-specific mathematical models for accurate response prediction.
- Employing digital twins to simulate intervention effects.
- Applying optimal control theory to digital twins for outcome optimization.
- Implementing data assimilation for continuous prediction refinement.
Main Results:
- The framework integrates mathematical modeling, digital twins, optimal control, and data assimilation.
- Each technique's readiness level and potential use cases in personalized trials are discussed.
Conclusions:
- This computational framework offers a pathway to personalized cancer therapy.
- Reframing treatment as a personalized trial holds promise for improving patient outcomes in oncology.
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