ArticleCPT: pharmacometrics & systems pharmacology2017
Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin-concentrating Hormone Receptor 1 Antagonist.
Article in CPT: pharmacometrics & systems pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Applications of oxetanes in drug discovery and medicinal chemistry.European journal of medicinal chemistry · 2023Review
- A long-acting GDF15 analog causes robust, sustained weight loss and reduction of food intake in an obese nonhuman primate model.Clinical and translational science · 2023Article
- A CreER mouse to study melanin concentrating hormone signaling in the developing brain.Genesis (New York, N.Y. : 2000) · 2018Article
- Modeling energy intake and body weight effects of a long-acting amylin analogue.Journal of pharmacokinetics and pharmacodynamics · 2018Article
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Authors and funding
8 authors.
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Abstract
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.
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