广告
药理学
药代动力学
生物信息学
人口
临床试验
化学
药效学
医学
药品
分布(数学)
生物制药
产品(数学)
生化工程
药物开发
生物利用度
计算生物学
业务
生物
内科学
环境卫生
基因
生物化学
作者
Bappaditya Chatterjee,Pinaki Sengupta,Rakesh K. Tekade
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2021-01-01
卷期号:: 195-277
被引量:2
标识
DOI:10.1016/b978-0-12-814425-1.00010-3
摘要
Advancement in combinatorial chemistry in recent decades has synthesized a large number of potential candidates for therapeutic use. High-throughput screening technology helps select the most suitable candidate to proceed from preclinical to clinical stage. However, attrition of drug candidates at the late stage of the clinical trial is not rare. Such attrition increases drug development costs and adversely affects the demand and expectation for new therapeutic agents. Therefore decision-making step for “go or no-go” in every stage of preclinical and clinical development is of utmost importance. Pharmacokinetic (PK) characterization of the drug candidate under development is one of the essential steps that help the decision-making process. In general, PK characterization in a preclinical study assesses the drug’s absorption, distribution, metabolism and elimination (ADME) to predict its bioavailability and determine human dose. Apart from ADME assessment, drug–drug interaction, food–drug interaction, and dose modification in special population groups are evaluated by PK characterization in the clinical phase. Evaluation of the drug’s safety and efficacy is another objective of the different drug development stages, strongly correlated with PK characterization. Nowadays, PK/pharmacodynamics-based modeling, population-based PK, and physiological-based PK modeling are well adopted by industries and regulatory bodies to predict different PK parameters using an integrated experimental data approach in silico modeling. This chapter holds an extensive account of PK characterization at different drug development stages, starting from preclinical to phase III clinical trials. Several aspects of PK-based modeling have also been discussed in this chapter. The reader can get a complete understanding of the PK feature’s characterization and its application throughout a drug development process.
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