Toward Dose Prediction at Point of Design

Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model that provides a preliminary estimate of the dose for a given exposure target based on chemical structure. First, we construct machine learning models to estimate rat pharmacokinetics, which are subsequently allometrically scaled to estimate human pharmacokinetics. Second, we establish a 10 nM free concentration target for early HDP where potency data are not yet available. Finally, we assess the uncertainty associated with each model and propagate these into the final estimate, providing us with actionable guidance on the level of accuracy of these estimates. We find that this strategy can reduce preparation of compounds with poor properties relative to an unstructured approach, but extensive experimental testing remains required.

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Publication Details

Journal
Journal of Medicinal Chemistry
Published
2024-12-12
DOI
https://doi.org/10.1021/acs.jmedchem.4c02385
Citations
6
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
4.25
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article

Toward Dose Prediction at Point of Design

Renée L. DesJarlais, James P. Edwards, Zhe Wu, An Van den Bergh et al.
6 citations
Journal of Medicinal Chemistry
Statistical Methods in Clinical Trials
4.25
article

Toward Dose Prediction at Point of Design

Renée L. DesJarlais, James P. Edwards, Zhe Wu, An Van den Bergh, Hugo Ceulemans, David Polidori, Justin S. Cisar, Dries Van Rompaey, Mazen Ahmad, Kaushik Mitra, Marian C. Bryan, Jörg K. Wegner, Jeremy R. Ash, Siladitya Ray Chaudhuri
article en
6 citations

Abstract

Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model that provides a preliminary estimate of the dose for a given exposure target based on chemical structure. First, we construct machine learning models to estimate rat pharmacokinetics, which are subsequently allometrically scaled to estimate human pharmacokinetics. Second, we establish a 10 nM free concentration target for early HDP where potency data are not yet available. Finally, we assess the uncertainty associated with each model and propagate these into the final estimate, providing us with actionable guidance on the level of accuracy of these estimates. We find that this strategy can reduce preparation of compounds with poor properties relative to an unstructured approach, but extensive experimental testing remains required.

Journal of Medicinal ChemistryVol. 67(24)
Johnson & Johnson (United States) (US), Janssen (Belgium) (BE), In Silico Toxicology (Switzerland) (CH), In Silico Biosciences (United States) (US), Johnson & Johnson (Israel) (IL), PharmacoGenetics (China) (CN), Janssen (United States) (US)
Openalex Percentile: Top 5%
Statistical Methods in Clinical Trials
4.25
DOISourceOpenAlex
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