A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab

Toroghi, Masood Khaksar; Bosley, Jim; Powell, Lynn M.; Zhang, Yi; Yang, Feng; Xia, Pu; Davis, John D.; Al‐Huniti, Nidal

doi:10.1002/psp4.12694

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…In these situations, mathematical modeling could provide a relevant link between the available nonclinical data and understanding the experimental treatments available for many human diseases. 5 , 9 , 10 , 11 Extrapolation of available information from animal studies to predict outcomes in humans require a framework of dynamic mathematical models and a strong link between models and data such that the model‐predicted disease dynamics information can be translated to treatment in humans. 5 , 9 , 11 , 12 , 13 , 14 Additionally, biomarkers that are of clinical utility 15 may be used to represent disease progression in modeling.…”

Section: Introductionmentioning

confidence: 99%

A drug‐disease model for predicting survival in an Ebola outbreak

Toroghi

Al‐Huniti

Davis

et al. 2022

Clinical Translational Sci

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REGN‐EB3 (Inmazeb) is a cocktail of three human monoclonal antibodies approved for treatment of Ebola infection. This paper describes development of a mathematical model linking REGN‐EB3’s inhibition of Ebola virus to survival in a non‐human primate (NHP) model, and translational scaling to predict survival in humans. Pharmacokinetic/pharmacodynamic data from single‐ and multiple‐dose REGN‐EB3 studies in infected rhesus macaques were incorporated. Using discrete indirect response models, the antiviral mechanism of action was used as a forcing function to drive the reversal of key Ebola disease hallmarks over time, for example, liver and kidney damage (elevated alanine [ALT] and aspartate aminotransferases [AST], blood urea nitrogen [BUN], and creatinine), and hemorrhage (decreased platelet count). A composite disease characteristic function was introduced to describe disease severity and integrated with the ordinary differential equations estimating the time course of clinical biomarkers. Model simulation results appropriately represented the concentration‐dependence of the magnitude and time course of Ebola infection (viral and pathophysiological), including time course of viral load, ALT and AST elevations, platelet count, creatinine, and BUN. The model estimated the observed survival rate in rhesus macaques and the dose of REGN‐EB3 required for saturation of the pharmacodynamic effects of viral inhibition, reversal of Ebola pathophysiology, and survival. The model also predicted survival in clinical trials with appropriate scaling to humans. This mathematical investigation demonstrates that drug‐disease modeling can be an important translational tool to integrate preclinical data from an NHP model recapitulating disease progression to guide future translation of preclinical data to clinical study design.

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