Brunner Dani, Nestler Eric, Leahy Emer
Psychogenics, 4 Skyline Drive, Hawthorne, NY 10532, USA.
Drug Discov Today. 2002 Sep 15;7(18 Suppl):S107-12. doi: 10.1016/s1359-6446(02)02423-6.
One of the current major bottlenecks in drug discovery is in vivo testing of candidate drugs in behavioral paradigms in normal or genetically altered mice. This testing is essential in discovering gene function and predicting potential efficacy of CNS drugs in humans. New efforts in the biotech community aim to alleviate this bottleneck by developing higher-throughput systems of behavioral, neurological and physiological analyses. Together with large pharmacological databases, equipped with state-of-the-art bioinformatic and/or data-mining algorithms, these systems will provide rapid and accurate indices of the therapeutic potential of novel drugs. By providing a substantial increase in the speed of behavioral testing, new high-throughput systems will facilitate current behavioral research with faster, more reliable approaches. Furthermore, screening whole drug-libraries and comparing the profiles of novel compounds to those of known compounds will facilitate the discovery of novel drugs. Target validation will also become more efficient with the fast characterization of novel mutant mice.
药物研发当前的主要瓶颈之一是在正常或基因改造小鼠的行为范式中对候选药物进行体内测试。这种测试对于发现基因功能和预测中枢神经系统药物在人类中的潜在疗效至关重要。生物技术领域的新努力旨在通过开发更高通量的行为、神经和生理分析系统来缓解这一瓶颈。这些系统与配备了最先进生物信息学和/或数据挖掘算法的大型药理学数据库相结合,将提供新药治疗潜力的快速准确指标。通过大幅提高行为测试的速度,新的高通量系统将以更快、更可靠的方法促进当前的行为研究。此外,筛选整个药物库并将新化合物的谱与已知化合物的谱进行比较将有助于发现新药。随着新型突变小鼠的快速表征,靶点验证也将变得更加高效。