Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery

Area of Science:

• Medicinal Chemistry
• Neuroscience
• Drug Discovery

Background:

• Aging significantly impacts the central nervous system (CNS), leading to diseases like Alzheimer's and Parkinson's, posing immense treatment costs.
• Blood-brain barrier (BBB) penetration is a critical challenge in CNS drug discovery, while also being a potential liability for non-CNS targets.
• Declining success rates and high costs are causing pharmaceutical companies to reduce CNS drug discovery efforts.

Purpose of the Study:

• To analyze the physicochemical and structural differences between CNS and non-CNS oral drugs.
• To provide guidelines for designing high-quality CNS compounds and aid lead optimization.
• To develop a classification tree for differentiating CNS from non-CNS oral drugs.

Main Methods:

• Analysis of physicochemical properties and chemical structures of 317 CNS and 626 non-CNS oral drugs.
• Utilized chemoinformatics approaches for graphical analysis of multiple properties.
• Developed a classification tree to distinguish CNS from non-CNS oral drugs.

Main Results:

• Identified an ideal property profile for CNS drug lead selection and optimization.
• Provided a list of substructural units beneficial for CNS drug design.
• Established specific guidelines for high-quality CNS drug design, including topological polar surface area, nitrogen content, and molecular volume.

Conclusions:

• The study offers actionable guidelines for optimizing CNS drug discovery and development.
• Understanding physicochemical differences aids in designing drugs for both CNS and non-CNS targets.
• The proposed guidelines can improve the success rate of CNS drug discovery efforts.