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Review Article

The Structure-property Relationships of Clinically Approved Protease Inhibitors

[ Vol. 31 , Issue. 12 ]


Kihang Choi*   Pages 1441 - 1463 ( 23 )


Background: Proteases play important roles in the regulation of many physiological processes, and protease inhibitors have become one of the important drug classes. Especially because the development of protease inhibitors often starts from a substrate- based peptidomimetic strategy, many of the initial lead compounds suffer from pharmacokinetic liabilities.

Objective: To reduce drug attrition rates, drug metabolism and pharmacokinetics studies are fully integrated into modern drug discovery research, and the structure-property relationship illustrates how the modification of the chemical structure influences the pharmacokinetic and toxicological properties of drug compounds. Understanding the structure- property relationships of clinically approved protease inhibitor drugs and their analogues could provide useful information on the lead-to-candidate optimization strategies.

Methods: About 70 inhibitors against human or pathogenic viral proteases have been approved until the end of 2021. In this review, 17 inhibitors are chosen for the structure- property relationship analysis because detailed pharmacological and/or physicochemical data have been disclosed in the medicinal chemistry literature for these inhibitors and their close analogues.

Results: The compiled data are analyzed primarily focusing on the pharmacokinetic or toxicological deficiencies found in lead compounds and the structural modification strategies used to generate candidate compounds.

Conclusion: The structure-property relationships hereby summarized how the overall druglike properties could be successfully improved by modifying the structure of protease inhibitors. These specific examples are expected to serve as useful references and guidance for developing new protease inhibitor drugs in the future.


Structure-property relationship, protease inhibitors, peptidomimetics, lead optimization, candidate selection, drug discovery.


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