Microfluidic Liver Array for Drug Metabolite Profiling

描述（由申请人提供）：CellASIC开发了一种新型微流控技术，将32个生物反应器集成在标准96孔板上，用于人原代肝细胞的多路灌注培养，具有长期活力和肝脏酶功能。该1期SBIR将利用该技术开发和验证肝脏特异性药物代谢物分析的体外筛选平台。模型化合物将用于评估代谢活性，通过LC-MS/MS与我们在BD Biosciences的合作者进行分析。一旦验证完成，公司定位于迅速将产品推向市场。在过去的十年中，利用原代人肝细胞进行药物代谢谱分析变得越来越重要，因为人们已经认识到药物代谢与药物安全性密切相关。FDA最近发布了关于药物相互作用测试以及药物代谢物的药物安全性测试的指南。此外，在美国和欧洲，减少动物试验和开发改进的体外技术的运动也很强烈。然而，最先进的体外药物代谢模型只能预测大约50%的体内代谢物，部分原因是原代肝细胞在培养中迅速降解并失去其肝酶功能。

DESCRIPTION (provided by applicant): CellASIC has developed a novel microfluidic technology to integrate 32 bioreactors on a standard 96-well plate for multiplexed perfusion culture of primary human hepatocytes with demonstrated long-term viability and liver-enzymatic function. This Phase 1 SBIR will utilize this technology to develop and validate an in vitro screening platform for liver-specific drug metabolite profiling. Model compounds will be used to assess metabolic activities, with analysis via LC-MS/MS with our collaborators at BD Biosciences. Once the validation is complete, the company positioned to quickly make the product available to the marketplace. Drug metabolite profiling using primary human hepatocytes has gained more importance in the past decade as it has become recognized that drug metabolism is closely related to drug safety. The FDA has recently issued guidance on drug-drug interaction tests as well as drug safety testing of drug metabolites. Additionally, there is a strong movement in the US as well as in Europe to reduce animal trials and develop improved in vitro technologies. However, the state-of-the-art in vitro drug metabolism models only predict around 50% of the in vivo metabolites, partly because primary hepatocytes rapidly degrade in culture and lose their liver-enzymatic functions. PUBLIC HEALTH RELEVANCE: CellASIC is developing a microfluidic liver array (MLA) system that will allow biopharmaceutical companies to more accurately predict the adverse effects of new drug compounds on human liver prior to clinical and animal studies. Key benefits include safer drugs in the clinic, reduced cost per data point, more clinically relevant data at an earlier stage, reduced reliance on animal testing, and improved understanding of toxicity mechanisms.