期刊
JOURNAL OF MEDICINAL CHEMISTRY
卷 64, 期 6, 页码 3185-3196出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jmedchem.0c02033
关键词
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The optimal pharmacokinetic required for a drug candidate is highly dependent on the targeted pharmacology, which is often not well characterized in early stages of drug discovery. Model-based target pharmacology assessment combines various computational methods to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology.
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.
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