Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery

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• 作者：Travis T. Wager ; Xinjun Hou ; Patrick R. Verhoest ; Anabella Villalobos
• 刊名：ACS Chemical Neuroscience
• 出版年：2016
• 出版时间：June 15, 2016
• 年：2016
• 卷：7
• 期：6
• 页码：767-775
• 全文大小：714K
• 年卷期：0
• ISSN：1948-7193

文摘

Significant progress has been made in prospectively designing molecules using the central nervous system multiparameter optimization (CNS MPO) desirability tool, as evidenced by the analysis reported herein of a second wave of drug candidates that originated after the development and implementation of this tool. This simple-to-use design algorithm has expanded design space for CNS candidates and has further demonstrated the advantages of utilizing a flexible, multiparameter approach in drug discovery rather than individual parameters and hard cutoffs of physicochemical properties. The CNS MPO tool has helped to increase the percentage of compounds nominated for clinical development that exhibit alignment of ADME attributes, cross the blood–brain barrier, and reside in lower-risk safety space (low ClogP and high TPSA). The use of this tool has played a role in reducing the number of compounds submitted to exploratory toxicity studies and increasing the survival of our drug candidates through regulatory toxicology into First in Human studies. Overall, the CNS MPO algorithm has helped to improve the prioritization of design ideas and the quality of the compounds nominated for clinical development.