Semi-Mechanistic Pharmacokinetic-Pharmacodynamic Model of Camostat Mesylate-Predicted Efficacy against SARS-CoV-2 in COVID-19.

The SARS-CoV-2 coronavirus, which causes COVID-19, uses a viral surface spike protein for host cell entry and the human cell-surface transmembrane serine protease, TMPRSS2, to process the spike protein. Camostat mesylate, an orally available and clinically used serine protease inhibitor, inhibits TMPRSS2, supporting clinical trials to investigate its use in COVID-19. A one-compartment pharmacokinetic (PK)/pharmacodynamic (PD) model for camostat and the active metabolite FOY-251 was developed, incorporating TMPRSS2 reversible covalent inhibition by FOY-251, and empirical equations linking TMPRSS2 inhibition of SARS-CoV-2 cell entry.

Individualized treatment strategies for hyperuricemia informed by a semi-mechanistic exposure-response model of uric acid dynamics.

To provide insight into pharmacological treatment of hyperuricemia we developed a semi-mechanistic, dynamical model of uric acid (UA) disposition in human. Our model represents the hyperuricemic state in terms of production of UA (rate, PUA), its renal filtration (glomerular filtration rate, GFR) and proximal tubular reabsorption (fractional excretion coefficient, FE). Model parameters were estimated using data from 9 Phase I studies of xanthine oxidase inhibitors (XOI) allopurinol and febuxostat and a novel uricosuric, the selective UA reabsorption inhibitor lesinurad, approved for use in combination with a XOI.

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

Background: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.

Methods: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response.