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Anne Marie Clark, Christopher Baddeley, Matthew McBride; Chemical Landscape Analysis in Drug Discovery: Ophthalmic Fluoroquinolones. Invest. Ophthalmol. Vis. Sci. 2019;60(9):253.
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© ARVO (1962-2015); The Authors (2016-present)
For a successful drug discovery program, it important to maximize the chance of success using multiple parameters including efficacy, safety, ability to formulate and likely patentability. We performed an in-silico analysis of the existing chemical space around fluoroquinolones, used the resulting landscape to create candidates, then filtered and ranked the resulting potential drug candidates using physicochemical properties, ADME properties and toxicity prediction.
Data from CAS Registry, MARPAT and CAPlus on five ophthalmic fluoroquinolones plus additional information from other source were used to create the landscape. The physicochemical properties, ADME properties and toxicity predictions were developed using standard algorithms. The resulting compounds analyzed from both an R&D and IP perspective to select example compounds.
Several compounds with describable properties and likely patentability selected, and compared to existing drugs.
Chemical landscape analysis combines data from multiple sources in order to increase the likelihood of success in drug discovery.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
Patent landscape around fluoroquinolones.
Chemical Space Analysis methodology.
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