Automated design of ligands to polypharmacological profiles

作者：Besnard, Jeremy; Ruda, Gian Filippo; Setola, Vincent; Abecassis, Keren; Rodriguiz, Ramona M.; Huang, Xi-Ping; Norval, Suzanne; Sassano, Maria F.; Shin, Antony I.; Webster, Lauren A.; Simeons, Frederick R. C.; Stojanovski, Laste; Prat, Annik; Seidah, Nabil G.; Constam, Daniel B.; Bickerton, G. Richard; Read, Kevin D.; Wetsel, William C.; Gilbert, Ian H.; Roth, Bryan L.; Hopkins, Andrew L.^*

来源：Nature, 2012, 492(7428): 215-+.

DOI：10.1038/nature11691

• DE-NOVO DESIGN
• DRUG-DISCOVERY
• SMALL-MOLECULE
• IDENTIFICATION
• OPTIMIZATION
• PHARMACOLOGY
• ANTAGONISTS
• INHIBITORS
• PREDICTION
• PROGRAM

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

• 出版日期2012-12-13