1. Phenotypic approaches for CNS drugs.
- Author
-
Sharma, Raahul, Oyagawa, Caitlin R.M., Abbasi, Hamid, Dragunow, Michael, and Conole, Daniel
- Subjects
- *
CHEMICAL libraries , *DRUG discovery , *HIGH throughput screening (Drug development) , *CENTRAL nervous system , *ARTIFICIAL intelligence - Abstract
Discovering new drugs to treat central nervous system (CNS) disease involves establishing high-throughput screening (HTS) formats for central phenotypes such as neuroinflammation, oxidative stress, pathological proteins, hyperexcitability, and neuroplasticity. A range of complex and patient-based models exist to discover new drugs for these phenotypes; however, certain trade-offs between clinical relevance and scalability need to be carefully considered. While most CNS drug discovery studies have employed chemogenomic-based compound libraries, new fragment-based approaches are emerging that offer distinct advantages. The design of the screening cascade for hit-to-lead studies is often key to the success of CNS phenotypic drug discovery, with experimental and artificial intelligence (AI) target identification approaches now playing an important role in this process. Central nervous system (CNS) drug development is plagued by high clinical failure rate. Phenotypic assays promote clinical translation of drugs by reducing complex brain diseases to measurable, clinically valid phenotypes. We critique recent platforms integrating patient-derived brain cells, which most accurately recapitulate CNS disease phenotypes, with higher throughput models, including immortalized cells, to balance validity and scalability. These platforms were screened with conventional commercial chemogenomic compound libraries. We explore emerging library curation strategies to improve hit rate and quality, and screening novel fragment libraries as alternatives, for more tractable drug target deconvolution. The clinically relevant models used in these platforms could harbor important, unidentified drug targets, so we review evolving agnostic target deconvolution approaches, including chemical proteomics and artificial intelligence (AI), which aid in phenotypic screening hit mechanism elucidation, thereby facilitating rational hit-to-drug optimization. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF