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，以及先导优化过程中小分子药物任何潜在的药物相互作用 启动人体临床试验。目前 DMPK 测试是使用动物模型（通常是大鼠）进行的。 然而，这些研究存在以下几个原因：1）基于动物的 DMPK 研究费用昂贵且 在先导化合物优化过程中造成巨大的财务负担；排名前 20 的制药公司支出 > 1.2 美元 每年 10 亿美元用于 PK 测试，每年工业使用超过 100 万只动物； 2）它们很耗时 并延迟重要数据的收集。例如，每种化合物的合成都需要放大 在开始大鼠研究之前，数量从 µg 到 mg。 3）而高等动物，如狗、猪， 和非人类灵长类动物，具有更大的人类相关性，它们比啮齿动物研究更昂贵， 需要合成更多的活性药物成分 (API)； 4）动物的能力 预测人类结果的模型存在争议。尽管最近已经尝试在体外鉴定 方法，这些方法在可靠性、长期分析能力、再现性、反映差等方面都受到限制 体内肝脏运输、清除和代谢的过程。因此，医药行业需要更好的 DMPK 测试方法比目前的体外和测试更具预测性、更便宜且更快 体内方法。成功开发这样的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。 为了解决这一未满足的需求，我们提出了一个集成的（体外和计算机）肝脏清除平台， 融合了人类肝脏组织芯片和翻译软件。该技术可预测人体肝脏 两周内即可准确确定清除参数，无需 API 放大。 Javelin是唯一的组织 追求这种药物代谢和处置技术，这需要独特的设计功能 DMPK 研究无法使用专为毒理学设计的微生理系统来满足 药理学应用。直接进入第二阶段 SBIR 提案的目的是改进我们的技术 通过优化芯片设计以增强可用性、评估药物清除机制以及 评估我们技术的临床转化。这将通过以下目标来实现： 1) 系统 设计优化； 2) Javelin 上已知药物清除机制的评估和表征 肝间隙芯片； 3) 评估体外到体内的翻译以预测临床结果。成功的 我们的组织芯片灌注系统的应用将减少对其他临床前模型来估计药物的需求 PK 结果，从而降低与药物开发相关的风险和成本。事实上，我们的早期证据 表明我们可以以 10 倍的成本和 10 倍的成本更准确地预测临床结果 15% 比老鼠研究快几倍。