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Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression

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The incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the NHANES database. Biomarker dynamics were modeled using discrete stochastic vector autoregression (VAR) equations. BN were used to derive the topological order and connectivity of a data driven QSP model structure for inter-dependence of biomarkers across the lifespan. The strength and directionality of the connections in the QSP models were evaluated using bootstrapping. VAR models based on QSP model structures from BN were assessed for modeling biomarker system dynamics. BN-restricted VAR models of order 1 were identified as parsimonious and effective for characterizing biomarker system dynamics in the MB, MB-L and CVB datasets. Simulation of annual and triennial data for each biomarker provided good fits and predictions of the training and test datasets, respectively. The novel strategy harnesses machine learning to construct QSP model structures for inter-dependence of biomarkers. Stochastic modeling with the QSP models was effective for predicting the age-varying dynamics of disease-relevant biomarkers over the lifespan.

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Funding for the Ramanathan laboratory from MS190096 from Department of Defense Congressionally Directed Medical Research Programs, USAMRDC, Multiple Sclerosis Research Program and from NIGMS 131800 (PI: William Jusko) is gratefully acknowledged.

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Authors and Affiliations



MM—performed research, analyzed data, wrote manuscript. RHB—data interpretation, Bayesian networks, manuscript preparation. ML—stochastic modeling, manuscript preparation. MR—study concept and design, data analysis and interpretation, manuscript preparation.

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Correspondence to Murali Ramanathan.

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Conflict of interest:

Mason McComb has no conflicts to disclose. Rachael Hageman Blair received funding from Department of Defense. Martin Lysy has no conflicts to disclose. Murali Ramanathan received research funding from the National Science Foundation, Department of Defense and Otsuka Pharmaceuticals and the National Institutes of Health. These are unrelated to the research presented in this report.

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McComb, M., Blair, R.H., Lysy, M. et al. Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression. J Pharmacokinet Pharmacodyn 49, 65–79 (2022).

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