Open AccessTranslational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin‐concentrating Hormone Receptor 1 Antagonist
Author(s) -
Gennemark P,
Trägårdh M,
Lindén D,
Ploj K,
Johansson A,
Turnbull A,
Carlsson B,
Antonsson M
Publication year - 2017
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12199
Subject(s) - parametric statistics , computer science , parametric model , melanin concentrating hormone , computational biology , scaling , computation , translation (biology) , machine learning , biological system , artificial intelligence , biology , receptor , medicine , mathematics , statistics , algorithm , biochemistry , geometry , neuropeptide , messenger rna , gene
In this study, we present the translational modeling used in the discovery of AZD1979, a melanin‐concentrating hormone receptor 1 (MCHr1) antagonist aimed for treatment of obesity. The model quantitatively connects the relevant biomarkers and thereby closes the scaling path from rodent to man, as well as from dose to effect level. The complexity of individual modeling steps depends on the quality and quantity of data as well as the prior information; from semimechanistic body‐composition models to standard linear regression. Key predictions are obtained by standard forward simulation (e.g., predicting effect from exposure), as well as non‐parametric input estimation (e.g., predicting energy intake from longitudinal body‐weight data), across species. The work illustrates how modeling integrates data from several species, fills critical gaps between biomarkers, and supports experimental design and human dose‐prediction. We believe this approach can be of general interest for translation in the obesity field, and might inspire translational reasoning more broadly.