Screening methods for influenza antiviral drug discovery

药物发现 计算生物学 抗病毒药物 生物 医学 药品 病毒学 生物信息学 药理学
作者
Colm Atkins,Carrie W. Evans,E. Lucile White,James W. Noah
出处
期刊:Expert Opinion on Drug Discovery [Taylor & Francis]
卷期号:7 (5): 429-438 被引量:16
标识
DOI:10.1517/17460441.2012.674510
摘要

Introduction: Influenza antiviral high-throughput screens have been extensive, and yet no approved influenza antivirals have been identified through high-throughput screening. This underscores the idea that development of successful screens should focus on the exploitation of the underrepresented viral targets and novel, therapeutic host targets. Areas covered: The authors review conventional screening applications and emerging technologies with the potential to enhance influenza antiviral discovery. Real-world examples from the authors' work in biocontained environments are also provided. Future innovations are discussed, including the use of targeted libraries, multiplexed assays, proximity-based endpoint methods, non-laboratory-adapted virus strains, and primary cells, for immediate physiological relevance and translational applications. Expert opinion: The lack of successful anti-influenza drug discovery using high-throughput screening should not deter future efforts. Increased understanding of the functions of viral targets and host–pathogen interactions has broadened the target reservoir. Future screening efforts should focus on identifying new drugs against unexploited viral and host targets using currently developed assays, and on the development of novel, innovative assays to discover new drugs with novel mechanisms. Innovative screens must be designed to identify compounds that specifically inhibit protein–protein or protein–RNA interactions or other virus/host factor interactions that are crucial for viral replication. Finally, the use of recent viral isolates, increased biocontainment (for highly-pathogenic strains), primary cell lines, and targeted compound libraries must converge in efficient high-throughput primary screens to generate high-content, physiologically-relevant data on compounds with robust antiviral activity.

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