Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D.

Area of Science:

• Computational neuroscience
• Pharmacology
• Drug discovery and development

Background:

• Central nervous system (CNS) drug discovery success rates are critically low, necessitating novel approaches.
• Historical drug discovery evolved from clinical observation to phenotypic and then target-driven strategies, with limited success in increasing the probability of drug approval.
• Current reliance on preclinical animal models often lacks robust translational power for complex CNS disorders.

Purpose of the Study:

• To propose a new paradigm for CNS drug discovery leveraging Quantitative Systems Pharmacology (QSP).
• To utilize QSP as a knowledge-generation engine for rational polypharmacy.
• To enhance the predictive validity of preclinical models and mitigate clinical trial variability.

Main Methods:

• Developed a mechanism-based computer model of biological processes (QSP) integrating preclinical, neuroimaging, and clinical data.
• Modeled the effects of pathology and therapeutic interventions on neuronal circuit activity related to clinical phenotypes.
• Validated the model by correlating emergent neuronal circuit properties with historical datasets; employed reverse engineering and AI for target profiling and small molecule identification.

Main Results:

• The QSP platform integrates diverse knowledge into actionable simulations.
• The approach facilitates the discovery of rational polypharmacy target profiles.
• Small molecules are identified using AI and computational modeling, with subsequent testing in cellular systems and animal models.

Conclusions:

• QSP offers a powerful paradigm for rational polypharmacy in CNS drug discovery.
• The QSP platform can significantly improve the success rate of CNS R&D programs.
• This approach has the potential to reduce both costs and the number of animals used in drug development.