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Cellular Heterogeneity in Drug Uptake Amplifies Pharmacodynamic Variability: A Stochastic PK-PD Analysis
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DOI：10.1002/psp4.70282.png)
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Abstract
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Traditional pharmacokinetic-pharmacodynamic models assume cellular homogeneity, yet clinical observations reveal substantial response variability even among patients with similar plasma exposure. We hypothesized that cellular heterogeneity in drug transporter expression, coupled with nonlinear dose–response relationships, can amplify microscopic cellular variability into population-level outcome variability. Using cobimetinib as an exemplar, we developed a proof-of-principle multiscale stochastic framework that couples deterministic systemic pharmacokinetics with cellular-level stochastic differential equations. In this framework, transporter expression was modeled as log-normally distributed across cells, generating heterogeneity in intracellular drug concentrations despite identical plasma exposure. Simulations showed that cellular heterogeneity can broaden the distribution of extinction times and produce population-level outcomes that differ from those predicted by homogeneous or mean-field formulations. Under the intermittent 21/7 regimen, extinction times were cycle-structured and, in the extended simulations, were better described by a three-component mixture than by a unimodal model, indicating schedule-associated survival cohorts rather than a universal multimodal law. Across the simulations, treatment failure probability increased with population size while the amplification factor remained approximately constant, consistent with an intensive single-cell property. Sensitivity analyses indicated that the coefficient of variation (CV) of transporter expression was a key determinant of outcome variability across the explored parameter space. These findings support the hypothesis that non-genetic heterogeneity in drug uptake can contribute to variability in treatment response and apparent resistance. More broadly, this proof-of-principle framework highlights the value of stochastic cell-level modeling for studying therapeutic response distributions when cellular heterogeneity and nonlinear pharmacodynamics are expected to play important roles.
Keywords:
cellular heterogeneity
drug transporter variability
pharmacokinetic-pharmacodynamic modeling
stochastic differential equations
treatment resistance
variance amplification
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