Nonlinear Mixed Effects Modeling in Systems Pharmacology

Bonate, Peter L.; Desai, Amit; Rizwan, Ahsan; Li, Zheng; Tannenbaum, Stacey

doi:10.1007/978-3-319-44534-2_12

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…where only individual baseline disease severity is varied (Supplementary Information S2.2). To better explain this approach, an analogy to mixed effect modeling 88 is useful: in the reference virtual population, severity would be a random effect while all biomarkers would be mixed (with a severity-dependent mean and a random component). In the simplified severity-only population, all biomarkers' fixed effects are kept but the random components are discarded.…”

Section: /43mentioning

confidence: 99%

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

Go,

Arsène,

Faddeenkov

et al. 2024

Journal of Allergy and Clinical Immunology

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Section: /43mentioning

confidence: 99%

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

Go,

Arsène,

Faddeenkov

et al. 2024

Journal of Allergy and Clinical Immunology

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“…The QSP model may thus be used to characterize (over time, in terms of relative levels, and for various sets of prevailing TME conditions) fundamental mechanisms of anti-tumor immune responses for dosing schemes of choice, in mono- and combination therapy settings. Another distinctive feature of the proposed framework is the application of a non-linear mixed effect modeling (NLME) methodology ( 23 ), enabling evaluation of between-animal and between-study variabilities (BAV, BSV) in the observed tumor size dynamics. Such a model includes a statistical component, capturing variations in immune system parameters across the animals, driving differences in treatment efficacy, which is essential in the analysis of immunotherapeutic interventions, where a wide range of responses are often observed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a Systems Pharmacology Model

Voronova¹,

Peskov

Kosinsky³

et al. 2021

Front. Immunol.

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BackgroundAdenosine receptor type 2 (A2AR) inhibitor, AZD4635, has been shown to reduce immunosuppressive adenosine effects within the tumor microenvironment (TME) and to enhance the efficacy of checkpoint inhibitors across various syngeneic models. This study aims at investigating anti-tumor activity of AZD4635 alone and in combination with an anti-PD-L1-specific antibody (anti-PD-L1 mAb) across various TME conditions and at identifying, via mathematical quantitative modeling, a therapeutic combination strategy to further improve treatment efficacy.MethodsThe model is represented by a set of ordinary differential equations capturing: 1) antigen-dependent T cell migration into the tumor, with subsequent proliferation and differentiation into effector T cells (Teff), leading to tumor cell lysis; 2) downregulation of processes mediated by A2AR or PD-L1, as well as other immunosuppressive mechanisms; 3) A2AR and PD-L1 inhibition by, respectively, AZD4635 and anti-PD-L1 mAb. Tumor size dynamics data from CT26, MC38, and MCA205 syngeneic mice treated with vehicle, anti-PD-L1 mAb, AZD4635, or their combination were used to inform model parameters. Between-animal and between-study variabilities (BAV, BSV) in treatment efficacy were quantified using a non-linear mixed-effects methodology.ResultsThe model reproduced individual and cohort trends in tumor size dynamics for all considered treatment regimens and experiments. BSV and BAV were explained by variability in T cell-to-immunosuppressive cell (ISC) ratio; BSV was additionally driven by differences in intratumoral adenosine content across the syngeneic models. Model sensitivity analysis and model-based preclinical study simulations revealed therapeutic options enabling a potential increase in AZD4635-driven efficacy; e.g., adoptive cell transfer or treatments affecting adenosine-independent immunosuppressive pathways.ConclusionsThe proposed integrative modeling framework quantitatively characterized the mechanistic activity of AZD4635 and its potential added efficacy in therapy combinations, across various immune conditions prevailing in the TME. Such a model may enable further investigations, via simulations, of mechanisms of tumor resistance to treatment and of AZD4635 combination optimization strategies.

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A quantitative systems pharmacology workflow towards optimal design and biomarker stratification of atopic dermatitis clinical trials

Go¹,

Arsène²,

Faddeenkov³

et al. 2023

Preprint

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Background: The development of atopic dermatitis (AD) drugs is confronted by many disease phenotypes and trial design options, which are hard to explore experimentally. Objective: Optimize AD trial design using simulations. Methods: We constructed a quantitative systems pharmacology (QSP) model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution is a) derived from known relationships between AD biomarkers and disease severity and b) calibrated using disease severity evolution under SoC regimens. Results: We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example, a) the choice of endpoint is more important than the choice of dosing-regimen and b) patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis reveals that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. Conclusion: This AD QSP workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases so as to optimize several trial protocol parameters and biomarker-stratification and therefore holds promise to become a powerful model-informed drug development tool.

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Nonlinear Mixed Effects Modeling in Systems Pharmacology

Cited by 3 publications

References 41 publications

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a Systems Pharmacology Model

A quantitative systems pharmacology workflow towards optimal design and biomarker stratification of atopic dermatitis clinical trials

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