High-throughput screening in embryonic stem cell-derived neurons identifies potentiators of alpha-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate-type glutamate receptors.

Stem cell biology offers advantages to investigators seeking to identify new therapeutic molecules. Specifically, stem cells are genetically stable, scalable for molecular screening, and function in cellular assays for drug efficacy and safety. A key hurdle for drug discoverers of central nervous system disease is a lack of high quality neuronal cells. In the central nervous system, alpha-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate (AMPA) subtype glutamate receptors mediate the vast majority of excitatory neurotransmissions. Embryonic stem (ES) cell protocols were developed to differentiate into neuronal subtypes that express AMPA receptors and were pharmacologically responsive to standard compounds for AMPA potentiation. Therefore, we hypothesized that stem cell-derived neurons should be predictive in high-throughput screens (HTSs). Here, we describe a murine ES cell-based HTS of a 2.4 x 10(6) compound library, the identification of novel chemical "hits" for AMPA potentiation, structure function relationship of compounds and receptors, and validation of chemical leads in secondary assays using human ES cell-derived neurons. This reporting of murine ES cell derivatives being formatted to deliver HTS of greater than 10(6) compounds for a specific drug target conclusively demonstrates a new application for stem cells in drug discovery. In the future new molecular entities may be screened directly in human ES or induced pluripotent stem cell derivatives.

Analysis of drug-induced effect patterns to link structure and side effects of medicines.

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.