A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons

Michael, Christian T.; Almohri, Sayed Ahmad; Linderman, Jennifer J.; Kirschner, Denise E.

doi:10.3389/fsysb.2023.1283341

Cited by 4 publications

(1 citation statement)

References 81 publications

(129 reference statements)

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“…This collection of VPs is called a virtual population (Vpop) [8]. VCTs have been utilized across a number of medical conditions, including cancer [9–13], infectious diseases [14, 15], cardiovascular disease [16–18], rheumatoid arthritis [19], and diabetes [17, 18, 20]. These studies and others demonstrate the value of VCTs for rational protocol design that accounts for inter-patient heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

Gevertz,

Wares

2024

Preprint

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A systematic efficacy analysis of tuberculosis treatment with BPaL‐containing regimens using a multiscale modeling approach

Budak,

Via,

Weiner

et al. 2024

CPT Pharmacom & Syst Pharma

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Tuberculosis (TB) is a life‐threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB‐related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well‐studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.

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Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

Nanda,

Budak,

Michael

et al. 2024

Modeling and Simulation in Science, Engineering and Technology

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A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons

Cited by 4 publications

References 81 publications

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

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

A systematic efficacy analysis of tuberculosis treatment with BPaL‐containing regimens using a multiscale modeling approach

Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

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