Andrew Rusinko | PubMed Health

PubMed2003

A novel and selective 5-HT2 receptor agonist with ocular hypotensive activity: (S)-(+)-1-(2-aminopropyl)-8,9-dihydropyrano[3,2-e]indole.

Serotonin 5-HT2 receptor agonists have recently been shown to be effective in lowering intraocular pressure in nonhuman primates and represent a potential new class of antiglaucoma agents. As part of an effort to identify new selective agonists at this receptor, we have found that (S)-(+)-1-(2-aminopropyl)-8,9-dihydropyrano[3,2-e]indole (AL-37350A, 11) has high affinity and selectivity (>1000-fold) for the 5-HT(2) receptor relative to other 5-HT receptors. More specifically, 11 is a potent agonist at the 5-HT2A receptor (EC50 = 28.6 nM, E(max) = 103%) that is comparable to serotonin. Evaluation of 11 in conscious ocular hypertensive cynomolgus monkeys showed this compound to be efficacious in reducing intraocular pressure (13.1 mmHg, -37%). Thus, 11 is a potent full agonist with selectivity for the 5-HT2 receptor and is anticipated to serve as a useful tool in exploring the role of the 5-HT2 receptor and its effector system in controlling intraocular pressure.

Administration, Topical↗

PubMed2002

Optimization of focused chemical libraries using recursive partitioning.

A number of methods currently exist for designing chemical libraries. General or universal libraries use a measurement of chemical diversity in their design and seek to cover as much of chemical space as possible in order to maximize the likelihood of discovering a novel lead class of active compounds. Focused chemical libraries are then synthesized to expand on this particular class and thoroughly explore the space about it. Rarely, however, is relevant biological data tightly incorporated in the design of focused libraries. Recursive partitioning is a statistical technique that is used to quickly build SAR models from high-throughput screening data sets and associated chemical descriptors. Using these models in a virtual screening mode significantly increases the probability of finding other active compounds. The predicted activity can be also be used as the fitness function for a genetic algorithm that is designed to select monomer subsets having a higher probability of being active. This dramatically reduces the number of compounds that need to be synthesized in focused libraries thus saving considerable time, effort and expense. This paper describes how recursive partitioning models are used to optimize the design of focused chemical libraries.

Chemistry, Pharmaceutical↗