Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping

Derippe, Thibaud; Fouliard, Sylvain; Declèves, Xavier; Mager, Donald E.

doi:10.1007/s10928-022-09826-8

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…It should be noted that the predicted effectiveness depends on our mechanistic hypotheses as well as limited data availability. In addition, generating realistic virtual patients 39 , 40 , 41 and accelerating virtual patient generation 42 are active fields of study. At the current stage, we assigned random values independently for each parameter based on their estimated distributions (Table S1 ), partly due to the lack of correlation information from the literature.…”

Section: Resultsmentioning

confidence: 99%

Quantitative systems pharmacology modeling of macrophage‐targeted therapy combined with PD‐L1 inhibition in advanced NSCLC

Wang,

Arulraj,

Anbari

et al. 2024

Clinical Translational Sci

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Immune checkpoint inhibitors remained the standard‐of‐care treatment for advanced non‐small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti‐PD‐(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage‐targeted therapies in combination with PD‐L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti‐CD47 treatment is not an optimal option of second‐ and later‐line treatment for advanced NSCLC resistant to PD‐(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti‐PD‐(L)1 therapy, especially in patients who are likely to respond to anti‐PD‐(L)1 monotherapy and those with a high level of tumor‐associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early‐stage clinical trial data.

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Section: Resultsmentioning

confidence: 99%

Quantitative systems pharmacology modeling of macrophage‐targeted therapy combined with PD‐L1 inhibition in advanced NSCLC

Wang,

Arulraj,

Anbari

et al. 2024

Clinical Translational Sci

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“…For instance, Meyers et al show how a surrogate machine learning model can be trained on the full mathematical model and used to rapidly pre-screen for parametrizations that result in plausible patients [29]. Though, this and other surrogate modeling approaches still must contend with the computational time to develop the surrogate model, not to mention possible inaccuracies in the surrogate model’s representation of the true model [32]. In Derippe et al [32], an approach is developed to improve the computational efficiency of VP development (particularly the acceptance/rejectance step) under the assumption of monotonicity of a subset of model parameters with respect to the model output.…”

Section: Discussionmentioning

confidence: 99%

“…Though, this and other surrogate modeling approaches still must contend with the computational time to develop the surrogate model, not to mention possible inaccuracies in the surrogate model's representation of the true model [32]. In Derippe et al [32], an approach is developed to improve the computational efficiency of VP development (particularly the acceptance/rejectance step) under the assumption of monotonicity of a subset of model parameters with respect to the model output. Beyond the computation costs of working with more complex models, they also generate large amounts of data that can be quite challenging to rigorously analyze.…”

Section: Discussionmentioning

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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“…An algorithm (PaSM, Parameter Space Mapping) was used for accelerating the generation of QSP modeling results (see Supplemental Materials ). 41 This approach requires the identification of so‐called monotonic parameters, and six model parameters (all except BAX and BAK initial values) fulfilled that criterion.…”

Section: Methodsmentioning

confidence: 99%

Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3‐mimetics using virtual tumors calibrated with cell viability assays

Derippe,

Fouliard,

Decleves

et al. 2024

CPT Pharmacom & Syst Pharma

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Both primary and acquired resistance mechanisms that involve intra‐tumoral cell heterogeneity limit the use of BH3‐mimetics to trigger tumor cell apoptosis. This article proposes a new quantitative systems pharmacology (QSP)‐based methodology in which cell viability assays are used to calibrate virtual tumors (VTs) made of virtual cells whose fate is determined by simulations from an apoptosis QSP model. VTs representing SU‐DHL‐4 and KARPAS‐422 cell lines were calibrated using in vitro data involving venetoclax (anti‐BCL2), A‐1155463 (anti‐BCLXL), and/or A‐1210477 (anti‐MCL1). The calibrated VTs provide insights into the combination of several BH3‐mimetics, such as the distinction between cells eliminated by at least one of the drugs (monotherapies) from the cells eliminated by a pharmacological combination only. Calibrated VTs can also be used as initial conditions in an agent‐based model (ABM) framework, and a minimal ABM was developed to bridge in vitro SU‐DHL‐4 cell viability results to tumor growth inhibition experiments in mice.

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Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping

Cited by 5 publications

References 28 publications

Quantitative systems pharmacology modeling of macrophage‐targeted therapy combined with PD‐L1 inhibition in advanced NSCLC

Quantitative systems pharmacology modeling of macrophage‐targeted therapy combined with PD‐L1 inhibition in advanced NSCLC

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

Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3‐mimetics using virtual tumors calibrated with cell viability assays

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