ADMET Profiling in Drug Discovery and Development: Perspectives of In Silico, In Vitro and Integrated Approaches

Title:ADMET Profiling in Drug Discovery and Development: Perspectives of In Silico, In Vitro and Integrated Approaches

Volume: 22 Issue: 7

Author(s): Nour El-Huda Daoud, Pobitra Borah*, Pran Kishore Deb, Katharigatta N. Venugopala*, Wafa Hourani, Muhammed Alzweiri, Sanaa K. Bardaweel and Vinod Tiwari

Affiliation:

• School of Pharmacy, Graphic Era Hill University, Dehradun-248002, Uttarakhand,India

• Department of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982,Saudi Arabia

Keywords: In silico, in vitro, ADMET profiling, drug discovery, predictive tools, molecular descriptors, computational tools.

Abstract: In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The earlystage ADMET profiling has brought a new dimension to lead drug development. Although several high-throughput in vitro models are available for ADMET profiling, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate, and therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance the predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.

Cite this article as:

Daoud El-Huda Nour , Borah Pobitra*, Deb Kishore Pran , Venugopala N. Katharigatta *, Hourani Wafa , Alzweiri Muhammed , Bardaweel K. Sanaa and Tiwari Vinod, ADMET Profiling in Drug Discovery and Development: Perspectives of In Silico, In Vitro and Integrated Approaches, Current Drug Metabolism 2021; 22 (7) . https://dx.doi.org/10.2174/1389200222666210705122913