Open AccessComparing Model Performance in Characterizing the PK/PD of the Anti‐Myostatin Antibody Domagrozumab
Author(s) -
Tiwari Abhinav,
Bhattacharya Indranil,
Chan Phylinda L.S.,
Harnisch Lutz
Publication year - 2020
Publication title -
clinical and translational science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.303
H-Index - 44
eISSN - 1752-8062
pISSN - 1752-8054
DOI - 10.1111/cts.12693
Subject(s) - selection (genetic algorithm) , model selection , nonlinear model , growth model , econometrics , biological system , chemistry , mathematics , computer science , nonlinear system , physics , statistics , biology , artificial intelligence , mathematical economics , quantum mechanics
Modeling and simulation provides quantitative information on target coverage for dose selection. Optimal model selection often relies on fit criteria and is not necessarily mechanistically driven. One such case is discussed where healthy volunteer data of an anti‐myostatin monoclonal antibody domagrozumab were used to develop different target‐mediated drug disposition models; a quasi‐steady state (QSS) rapid binding approximation model, a Michaelis−Menten (MM)‐binding kinetics (MM‐BK) model, and an MM‐indirect response (MM‐IDR) model. Whereas the MM‐BK model was identified as optimal in fitting the data, with all parameters estimated with high precision, the QSS model also converged but was not able to capture the nonlinear decline. Although the least mechanistic model, MM‐IDR, had the lowest objective function value, the MM‐BK model was further developed as it provided a reasonable fit and allowed simulations regarding growth differentiation factor‐8 target coverage for phase II dose selection with sufficient certainty to allow for testing of the underlying mechanistic assumptions.