Hepatic Clearance Chip for Pharmacokinetics

用于药代动力学的肝脏清除芯片

• 批准号: 10761027
• 负责人: Murat Cirit
• 金额: $ 100.96万
• 依托单位: JAVELIN BIOTECH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-30 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10761027
• 关键词: 3-Dimensional; Acceleration; Adoption; African American; Albumins; Algorithms; Animal Model; Animals; Architecture; Asian; Biliary; Biological; Biological Markers; Canis familiaris; Caucasians; Cells; Clinical; Clinical Trials; Coculture Techniques; Collection; Complex; Computer software; Consumption; Data; Data Analyses; Development; Differential Equation; Diffusion; Drug Combinations; Drug Industry; Drug Interactions; Drug Kinetics; Ensure; Enzymes; Equipment; Ethnic Origin; Evaluation; Excretory function; Family suidae; Fee-for-Service Plans; Female; Financial Hardship; Hepatic; Hepatocyte; Hispanic; Human; In Vitro; Industry; Lead; Liver; Mass Spectrum Analysis; Measurement; Measures; Mediating; Metabolic; Metabolic Clearance Rate; Metabolism; Methods; Modeling; Outcome; Patients; Perfusion; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology and Toxicology; Phase; Physiological; Physiology; Population; Pre-Clinical Model; Productivity; Property; Qualifying; Rattus; Readiness; Reproducibility; Reproducibility of Results; Risk; Risk Reduction; Rodent; Safety; Sampling; Service delivery model; Small Business Innovation Research Grant; Suspensions; System; Technology; Testing; Time; Tissue Engineering; Tissue Microarray; Tissues; Training; Translating; Translations; Urea; candidate selection; clinical predictors; clinical translation; cloud based; commercialization; cost; cost effective; design; drug candidate; drug clearance; drug development; drug discovery; drug disposition; drug metabolism; improved; in silico; in vivo; insight; lead optimization; liver function; male; manufacturing scale-up; microphysiology system; nonhuman primate; novel strategies; predict clinical outcome; research and development; scale up; small molecule; tool; uptake; usability

Drug metabolism and pharmacokinetic testing (DMPK) is crucial for understanding the clearance mechanisms, clearance rate, and any potential drug-drug interactions of small molecule drugs during lead optimization prior to initiating in-human clinical trials. DMPK testing is currently performed using animal models, usually rats. However, these studies are problematic for several reasons: 1) Animal-based DMPK studies are expensive and create a large financial burden during lead optimization; the top 20 pharmaceutical companies spend >$1.2 billion each year on PK testing, with over 1 million animals used by industry per year; 2) They are time-consuming and delay the collection of important data. For example, the synthesis of each compound needs to be scaled up from µg to mg quantities before rat studies can be initiated. 3) While higher animal species, such as dogs, pigs, and non-human primates, have greater human relevance, they are more expensive than rodent studies and require synthesis of even greater quantities of active pharmaceutical ingredient (API); and 4) The ability of animal models to predict human outcomes is controversial. Although recent attempts have been made to identify in vitro approaches, these are all limited in terms of reliability, long-term analysis ability, reproducibility, poor reflection of in vivo hepatic transport, clearance, and metabolism. Thus, the pharmaceutical industry needs better approaches for DMPK testing that are more predictive, cheaper, and faster than currently available in vitro and in vivo methods. Successful development of such a system will improve safety and help reduce the ~90% of drug candidates that currently fail in clinical trials. In solution to this unmet need, we propose an integrated (in vitro and in silico) hepatic clearance platform that merges a human liver tissue chip and translational software. This technology can predict human hepatic clearance parameters accurately in 2 weeks without the need of API scale-up. Javelin is the only organization pursuing this technology for drug metabolism & disposition, which requires design features that are unique to DMPK studies and that cannot be met using microphysiological systems designed for toxicology and pharmacology applications. The purpose of this Direct to Phase II SBIR proposal is improve our technology ready for launch by optimizing the chip design to enhance usability, evaluate drug clearance mechanisms, and assess the clinical translation of our technology. This will be achieved through the following Aims: 1) System design optimization; 2) Evaluation and characterization of known drug clearance mechanisms on the Javelin hepatic clearance chip; and 3) Assessment of in vitro to in vivo translation to predict clinical outcomes. Successful application of our tissue chip perfusion system will reduce the need for other preclinical models to estimate drug PK outcomes, thereby reducing the risk and cost associated with drug development. Indeed, our early evidence indicates that we can predict clinical outcomes 15% more accurately and in a manner 10-times cheaper and 10- times faster than rat studies.

药物代谢和药代动力学测试（DMPK）对于了解清除机制至关重要， 清除率，以及在铅优化期间小分子药物的任何潜在药物相互作用 开始人类内临床试验。目前使用动物模型（通常是大鼠）进行DMPK测试。 但是，这些研究是有问题的，原因有几个：1）基于动物的DMPK研究昂贵，并且 在潜在客户优化期间创造大量的财务燃烧；前20家制药公司花费> 1.2美元 每年在PK测试中进行十亿美元，每年使用超过100万只动物； 2）他们很耗时 并延迟收集重要数据。例如，需要对每种化合物的合成进行缩放 在开始大鼠研究之前，从µg到MG数量。 3）虽然较高的动物物种，例如狗，猪， 和非人类的素数具有更大的人类相关性，它们比啮齿动物研究更昂贵， 需要合成更多数量的活性药物成分（API）； 4）动物的能力 预测人类结果的模型是有争议的。尽管最近尝试了识别体外的尝试 方法，这些都受到可靠性，长期分析能力，繁殖，反射不佳的限制 体内肝运输，清除和代谢。那就是制药行业需要更好的 DMPK测试的方法比当前在体外可用的更具预测性，更便宜和更快 体内方法。成功开发这种系统将提高安全性并有助于减少约90％ 目前在临床试验中失败的候选药物。 为了解决这种未满足的需求，我们提出了一个集成的（体外和硅）肝清除平台，该平台 合并人肝组织芯片和翻译软件。这项技术可以预测人类肝 清除参数在2周内准确，而无需API扩大。 Javalin是唯一的组织 为药物代谢和处置寻求这项技术，这需要设计特征 DMPK研究，使用用于毒理学和毒理学和 药理学应用。直接向第二阶段SBIR提案的目的改善了我们的技术 通过优化芯片设计来提高可用性，评估药物清除机制和 评估我们技术的临床翻译。这将通过以下目的实现：1）系统 设计优化； 2）评估和表征Javalin上已知的药物清除机制 肝炎清除芯片； 3）评估体外翻译以预测临床结果。成功的 我们组织芯片灌注系统的应用将减少其他临床前模型估计药物的需求 PK结果，从而降低了与药物开发相关的风险和成本。确实，我们的早期证据 表明我们可以准确地预测15％的临床结果，并以一种便宜和10倍的方式预测临床结果。 比大鼠研究快的时间。