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Selective Optimization of Side Activities (SOSA) as an Efficient Approach for Generation of New Leads from Old Drugs
Author(s) -
Preeti P. Mehta,
Yogita Ozarde,
Ranjit Gadhave,
Arti Swami
Publication year - 2021
Publication title -
journal of pharmaceutical research international
Language(s) - English
Resource type - Journals
ISSN - 2456-9119
DOI - 10.9734/jpri/2021/v33i42b32447
Subject(s) - drug , drug discovery , computational biology , side effect (computer science) , combinatorial chemistry , computer science , chemistry , pharmacology , biology , biochemistry , programming language
The selective optimization of side activities (SOSA) approach appears to be a promising strategy for lead generation. In this approach old drugs are used to generate new hits or leads. The objective of SOSA is to prepare analogues of the hit molecule in order to transform the observed “side activity” into the main effect and to strongly reduce or abolish the initial pharmacological activity. The idea of taking a molecule with a primary activity in humans and then enhancing a secondary effect through structural changes describes the most common implementation of SOSA. An advantage to starting a drug discovery program with molecules that have already been tested in humans is that those molecules have already satisfied many safety criteria. Such molecules also likely have favourable pharmacokinetic profiles. In the present review different successful examples of SOSA switches are summarized. We hope that the present review will be useful for scientists working in the area of drug design and discovery.

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