In silico methods for drug repurposing and pharmacology.
In silico methods for drug repurposing and pharmacology.
复制标题
DOI：
10.1002/wsbm.1337
复制
发表时间：
2016-05
期刊：
影响因子：
--
通讯作者：
Dudley JT
中科院分区：
文献类型：
--
作者：
Hodos RA;Kidd BA;Shameer K;Readhead BP;Dudley JT
生物、化学和临床领域的数据正在以不断增长的速度积累，并有可能以新的方式加速和告知药物开发。现在的挑战和机遇在于开发分析工具，将这些往往复杂和异构的数据转化为可测试的假设和可操作的见解。这是计算药理学的目标，它使用计算机技术来更好地理解和预测药物如何影响生物系统，从而改善临床使用，避免不必要的副作用，并指导更好的治疗方法的选择和开发。计算药理学的一个令人兴奋的应用是药物再利用-为现有药物寻找新的用途。这一策略已经产生了许多有前途的候选药物，有可能提高药物开发过程的效率，并接触到以前未满足需求的患者群体，如罕见疾病患者。虽然目前的计算药理学和药物再利用技术通常只关注单一的数据模式，如基因表达或药物-靶点相互作用，但我们合理地认为，矩阵因子分解等方法可以整合不同数据类型内的数据，有可能提高预测性能，并提供更全面的药物药理作用。
Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.