Predicting adme properties of chemicals

Hyun Kil Shin, Young Mook Kang, Kyoung Tai No

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish
Title of host publicationHandbook of Computational Chemistry
PublisherSpringer International Publishing
Pages2265-2301
Number of pages37
ISBN (Electronic)9783319272825
ISBN (Print)9783319272818
DOIs
Publication statusPublished - 2017 Jan 1

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Computer Simulation
physicochemical properties
Drug Discovery
Quantitative Structure-Activity Relationship
Prediction Model
Quantitative Structure-activity Relationship
Pharmacokinetics
drugs
endpoints
Descriptors
quantitative structure-activity relationships
Thermodynamics
Model
Pharmaceutical Preparations
pharmacokinetics
prediction
Research Personnel
Pharmaceuticals
Clinical Trials
Knowledge-based

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Computer Science(all)
  • Engineering(all)
  • Mathematics(all)
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Shin, H. K., Kang, Y. M., & No, K. T. (2017). Predicting adme properties of chemicals. In Handbook of Computational Chemistry (pp. 2265-2301). Springer International Publishing. https://doi.org/10.1007/978-3-319-27282-5_59
Shin, Hyun Kil ; Kang, Young Mook ; No, Kyoung Tai. / Predicting adme properties of chemicals. Handbook of Computational Chemistry. Springer International Publishing, 2017. pp. 2265-2301
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Shin, HK, Kang, YM & No, KT 2017, Predicting adme properties of chemicals. in Handbook of Computational Chemistry. Springer International Publishing, pp. 2265-2301. https://doi.org/10.1007/978-3-319-27282-5_59

Predicting adme properties of chemicals. / Shin, Hyun Kil; Kang, Young Mook; No, Kyoung Tai.

Handbook of Computational Chemistry. Springer International Publishing, 2017. p. 2265-2301.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Shin HK, Kang YM, No KT. Predicting adme properties of chemicals. In Handbook of Computational Chemistry. Springer International Publishing. 2017. p. 2265-2301 https://doi.org/10.1007/978-3-319-27282-5_59