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• W3136305460 startingPage "3185" @default.
• W3136305460 abstract "The optimal pharmacokinetic (PK) required for a drug candidate to elicit efficacy is highly dependent on the targeted pharmacology, a relationship that is often not well characterized during early phases of drug discovery. Generic assumptions around PK and potency risk misguiding screening and compound design toward nonoptimal absorption, distribution, metabolism, and excretion (ADME) or molecular properties and ultimately may increase attrition as well as hit-to-lead and lead optimization timelines. The present work introduces model-based target pharmacology assessment (mTPA), a computational approach combining physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, sensitivity analysis, and machine learning (ML) to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology. Examples using frequently encountered PK/PD relationships are presented to illustrate its application, and the utility and benefits of deploying such an approach to guide early discovery efforts are discussed." @default.
• W3136305460 created "2021-03-29" @default.
• W3136305460 creator A5030906295 @default.
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• W3136305460 date "2021-03-15" @default.
• W3136305460 modified "2023-09-25" @default.
• W3136305460 title "Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery" @default.
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• W3136305460 doi "https://doi.org/10.1021/acs.jmedchem.0c02033" @default.
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• W3136305460 hasPublicationYear "2021" @default.
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