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Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

The Structure–property Relationships of Clinically Approved Protein Kinase Inhibitors

Author(s): Kihang Choi

Volume 30, Issue 22, 2023

Published on: 24 October, 2022

Page: [2518 - 2541] Pages: 24

DOI: 10.2174/0929867329666220822123552

Price: $65

Abstract

Background: Protein kinase inhibitors have become one of the most successful classes of small-molecule drugs during the last decades. In modern drug discovery, considering ‘drug-like’ physicochemical and pharmacokinetic properties as early as possible in drug design is widely acknowledged as an important strategy to reduce drug attrition rates.

Methods: In this review, clinically approved 25 protein kinase inhibitors and their key analogues reported in medicinal chemistry literature were compared for their biological, physicochemical, and pharmacokinetic properties. Although there is no common trajectory to follow through complex drug discovery campaigns, knowledge of the structure– activity relationship obtained from the successful lead optimization studies might be extended to other drug design efforts.

Results: Among more than 70 protein kinase inhibitors clinically approved around the world, the structure–activity relationships of 25 inhibitors and their key analogues are compiled from medicinal chemistry literature, in which detailed results from the ‘lead-tocandidate’ stage are available with associated property data. For the other inhibitors, such information has not been disclosed in the literature, or the available data is limited and not sufficient to provide clear structural analysis.

Conclusion: The structure–property relationships summarized for 25 inhibitors and their analogues illustrate general guidelines for lead optimization and candidate selection, and this information could be extended for better property-based drug design in the future.

Keywords: Structure–property relationship, protein kinase inhibitors, solubilizing groups, lead optimization, candidate selection, drug discovery.

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