Since many drug development projects fail during clinical trials due to poor ADME properties, it is a wise practice to introduce ADME tests at the early stage of drug discovery. Various experimental and computational methods have been developed to obtain ADME properties in an economical manner in terms of time and cost. As in vitro and in vivo experimental data on ADME have accumulated, the accuracy of in silico models in ADME increases and thus, many in silico models are now widely used in drug discovery. Because of the demands from drug discovery researchers, the development of in silico models in ADME has become more active. In this chapter, the definitions of ADME endpoints are summarized, and in silico models related to ADME are introduced for each endpoint. Part I discusses the prediction models of the physicochemical properties of compounds, which influence much of the pharmacokinetics of pharmaceuticals. The prediction models of physical properties are developed based mainly on thermodynamics and are knowledge based, especially QSAR (quantitative structure activity relationship) methods. Part II covers the prediction models of the endpoints in ADME which include both in vitro and in vivo assay results. Most models are QSAR based and various kinds of descriptors (topology, 1D, 2D, and 3D descriptors) are used. Part III reviews physiologically based pharmacokinetic (PBPK) models.
|Title of host publication||Handbook of Computational Chemistry|
|Publisher||Springer International Publishing|
|Number of pages||37|
|Publication status||Published - 2017 Jan 1|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2017.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)