A practical guide for the generation of model-based virtual clinical trials

Craig, Morgan; Gevertz, Jana L.; Kareva, Irina; Wilkie, Kathleen P.

doi:10.3389/fsysb.2023.1174647

Cited by 11 publications

(15 citation statements)

References 62 publications

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“…It is an opportune time to discuss what mathematical biologists and modellers, whether they apply their efforts towards (pre-)clinical and public health problems or beyond, can do to better respond to and include DEI in their models. Technical aspects could include departing from seeking a single parameterization to considering a range of parameterizations for a model to encompass observed diversity (Craig et al 2023 ), modifications of model structure to better represent differences between individuals/groups of individuals and using parameter estimation techniques that can account for heterogeneity (e.g., nonlinear mixed effects models, etc.). There is also a need to continue to develop methodologies that incorporate complexity in the form of between-subject variability (Nande et al 2021 ; Brady-Nicholls et al 2021 ; Ojwang et al 2024 ).…”

Section: Challenges For the Field Of Mathematical Biology: Where Do W...mentioning

confidence: 99%

Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models

Sheen,

Curtin,

Finley

et al. 2024

Bull Math Biol

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Mathematical modelling applied to preclinical, clinical, and public health research is critical for our understanding of a multitude of biological principles. Biology is fundamentally heterogeneous, and mathematical modelling must meet the challenge of variability head on to ensure the principles of diversity, equity, and inclusion (DEI) are integrated into quantitative analyses. Here we provide a follow-up perspective on the DEI plenary session held at the 2023 Society for Mathematical Biology Annual Meeting to discuss key issues for the increased integration of DEI in mathematical modelling in biology.

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Section: Challenges For the Field Of Mathematical Biology: Where Do W...mentioning

confidence: 99%

Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models

Sheen,

Curtin,

Finley

et al. 2024

Bull Math Biol

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“…A recent review paper [21] has introduced a step-by-step best practice guide for conducting a model-based virtual clinical trial. The process that the authors' outlined can be cyclical, where knowledge gained at one step may necessitate returning to an earlier step.…”

Section: Introductionmentioning

confidence: 99%

“…Though, we will present the steps in linear order here. A VCT begins with defining the question of interest for the study; the authors call this VCT Step 0 [21]. For instance, one may be interested in quantifying how the percent of responders changes as a function of drug dose (see Figure 3 in Craig et al [21]).…”

Section: Introductionmentioning

confidence: 99%

“…A VCT begins with defining the question of interest for the study; the authors call this VCT Step 0 [21]. For instance, one may be interested in quantifying how the percent of responders changes as a function of drug dose (see Figure 3 in Craig et al [21]). Other potential questions of interest include the impact of the number of doses, the spacing between doses, combining therapies, etc.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes

Gevertz,

Wares

2024

Preprint

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Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.

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“…In response, in silico models can integrate data from various studies to help predict patient responses, optimize therapeutic regimens, and virtually test a huge number of scenarios to inform clinical trial designs. 34 Thus, the integration of mathematical and computational models is cost effective 35 , 36 and helps to reduce attrition along the drug development pipeline. Such models have been used in e.g., glioblastoma, 37 , 38 prostate cancer, 39 and ovarian cancer, 40 aiding to verify therapeutic effectiveness and illustrating the adaptability and usefulness of in silico models.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling predicts pathways to successful implementation of combination TRAIL-producing oncolytic virus and PAC-1 to treat granulosa cell tumors of the ovary

Le Sauteur-Robitaille,

Crosley,

Hitt

et al. 2023

Cancer Biology & Therapy

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The development of new cancer therapies requires multiple rounds of validation from in vitro and in vivo experiments before they can be considered for clinical trials. Mathematical models assist in this preclinical phase by combining experimental data with human parameters to provide guidance about potential therapeutic regimens to bring forward into trials. However, granulosa cell tumors of the ovary lack a relevant mouse model, complexifying preclinical drug development for this rare tumor. To bridge this gap, we established a mathematical model as a framework to explore the potential of using a tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-producing oncolytic vaccinia virus in combination with the chemotherapeutic agent first procaspase activating compound (PAC-1). We have previously shown that TRAIL and PAC-1 act synergistically on granulosa tumor cells. In line with our previous results, our current model predicts that, although it is unable to stop the tumor from growing in its current form, combination oral PAC-1 with oncolytic virus (OV) provides the best result compared to monotherapies. Encouragingly, our results suggest that increases to the OV infection rate can lead to the success of this combination therapy within a year. The model developed here can continue to be improved as more data become available, allowing for regimen-tailoring via virtual clinical trials, ultimately shepherding effective regimens into trials.

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A practical guide for the generation of model-based virtual clinical trials

Cited by 11 publications

References 62 publications

Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models

Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models

Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes

Mathematical modeling predicts pathways to successful implementation of combination TRAIL-producing oncolytic virus and PAC-1 to treat granulosa cell tumors of the ovary

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