体外毒理学
体内
药物发现
高通量筛选
计算生物学
高含量筛选
体外
药物开发
毒性
药品
药理学
生物
细胞
生物信息学
化学
生物技术
生物化学
有机化学
作者
Anna Astashkina,Brenda Mann,David W. Grainger
标识
DOI:10.1016/j.pharmthera.2012.01.001
摘要
Drug candidate and toxicity screening processes currently rely on results from early-stage in vitro cell-based assays expected to faithfully represent essential aspects of in vivo pharmacology and toxicology. Several in vitro designs are optimized for high throughput to benefit screening efficiencies, allowing the entire libraries of potential pharmacologically relevant or possible toxin molecules to be screened for different types of cell signals relevant to tissue damage or to therapeutic goals. Creative approaches to multiplexed cell-based assay designs that select specific cell types, signaling pathways and reporters are routine. However, substantial percentages of new chemical and biological entities (NCEs/NBEs) that fail late-stage human drug testing, or receive regulatory "black box" warnings, or that are removed from the market for safety reasons after regulatory approvals all provide strong evidence that in vitro cell-based assays and subsequent preclinical in vivo studies do not yet provide sufficient pharmacological and toxicity data or reliable predictive capacity for understanding drug candidate performance in vivo. Without a reliable translational assay tool kit for pharmacology and toxicology, the drug development process is costly and inefficient in taking initial in vitro cell-based screens to in vivo testing and subsequent clinical approvals. Commonly employed methods of in vitro testing, including dissociated, organotypic, organ/explant, and 3-D cultures, are reviewed here with specific focus on retaining cell and molecular interactions and physiological parameters that determine cell phenotypes and their corresponding responses to bioactive agents. Distinct advantages and performance challenges for these models pertinent to cell-based assay and their predictive capabilities required for accurate correlations to in vivo mechanisms of drug toxicity are compared.
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