药物发现
药效学
药品
计算生物学
药理学
生化工程
风险分析(工程)
虚拟筛选
结果(博弈论)
化学
管理科学
计算机科学
药代动力学
医学
工程类
生物
经济
数理经济学
生物化学
作者
Emile P. Chen,Robert W. Bondi,Carolyn Zhang,Daniel Price,Ming‐Hsun Ho,Kira A. Armacost,Michael P. DeMartino
标识
DOI:10.1021/acs.jmedchem.2c00330
摘要
Many critical decisions faced in early discovery require a thorough understanding of the dynamic behavior of pharmacological pathways following target engagement. From fundamental decisions on the optimal target to pursue and the ultimate drug product profile (combination of modality, potency, and compound properties) expected to elicit the desired clinical outcome to tactical program decisions such as what chemical series to pursue, what chemical properties require optimization, and what compounds to synthesize and progress, all demand detailed consideration of pharmacodynamics. Model-based target pharmacology assessment (mTPA) is a computational approach centered around large-scale virtual exploration of pharmacokinetic and pharmacodynamic models built early in discovery to guide these decisions. The present work summarizes several examples (use cases) from programs at GlaxoSmithKline that demonstrate the utility of mTPA throughout the drug discovery lifecycle.
科研通智能强力驱动
Strongly Powered by AbleSci AI