DDI-on-a-chip: an optimized liver microphysiological system and microenvironment for complex drug-drug interaction studies

项目摘要 药物-药物相互作用(DDIS)，由同时给药引起(即 多药联用)，会导致药物不良反应(ADRs)。多药房相关DDIS的流行情况 在老年人中是~80%。DDI相关的不良反应可以引起深远的临床影响，要么通过减少 治疗效果或增加药物的毒性。在美国，ADR很常见，导致 每年住院人数的1.1%，估计每年相关费用在30-180之间，费用很高 十亿美元。此外，大约50%的因安全原因从美国市场召回的药品 1999至2003年间与DDIS有关。这对于复杂的DDI尤其重要，它包括 药物代谢酶的代谢-转运体相互作用、时间依赖和混合抑制/诱导 (DMES)和转运体，以及基于代谢产物的抑制/诱导。因为没有DDIS就无法预防 综合药物药代动力学(PK)数据，以指导根据DDI风险进行用药调整， 在药物开发过程中需要精心计划的临床前和临床DDI研究。但是，当前传入 体外临床前肝脏PK模型，包括微生理系统(MPSS)，受到重要功能的影响 限制因素，如胎儿表型的表达，药物代谢酶的低表达水平和 转运蛋白和快速表型去分化(即短期培养系统)。这项建议的技术 将特别适用于具有复杂DDI敏感性的药物，包括时间依赖地诱导DMES和 传送者。 标枪的总体战略是开发针对药物新陈代谢进行优化的微生理系统(MPSS 和药代动力学(DMPK)研究，包括与我们的定量系统结合使用的DDIS 药理学(QSP)模型，以生成更具预测性的临床前药物数据。这个SBIR第一阶段项目将 建立最佳的细胞和促肝因子微环境，以长期、生理性地推动 肝脏DMES和转运蛋白的相关表达和活性。内源性促肝因子将是 选择它们是因为它们有能力维持肝细胞健康，并驱动转录因子信号网络 调节DME和转运蛋白基因的表达。亲肝因子将用高密度脂蛋白进行筛选。 吞吐量分数因子分析方法及其在标枪聚碳酸酯中的优化(即，不含PDMS 最大限度地减少非特异性药物吸附)、微流体、循环MPS，专为DMPK研究而设计。由此产生的 中等补充剂，“DMPK最佳”将是第一个化学定义的，不含异种蛋白的补充剂 为DMPK-DDI研究提供持续的DME和转运蛋白基因表达和活性。标枪的DMPK- DDI平台将作为低成本、商业可用的产品提供给制药公司 有兴趣生成全面、准确、基于人的PK数据，以更好地为他们的药物开发提供信息 第一个人体(FIH)试验的过程和设计。

Project Summary Drug-drug interactions (DDIs), caused by the administration of multiple drugs simultaneously (i.e polypharmacy), can lead to adverse drug reactions (ADRs). The prevalence of polypharmacy-related DDIs among older adults is ~80%. DDI-related ADRs can cause profound clinical effects, either by reducing therapeutic efficacy or increasing the toxicity of drugs. In the United States (US), ADRs are common, causing 1.1% of annual hospital admissions, and expensive with associated annual costs estimated between 30-180 billion dollars. Moreover, approximately 50% of the drugs withdrawn for safety reasons from the US market between 1999 and 2003 were associated with DDIs. This is especially relevant for complex DDIs which include metabolism-transporter interplay, time-dependent and mixed inhibition/induction of drug-metabolizing enzymes (DMEs) and transporters, and metabolite-based inhibition/induction. As DDIs cannot be prevented without comprehensive drug pharmacokinetic (PK) data to guide medication adjustments according to DDI risks, there is a need for carefully planned preclinical and clinical DDI studies during drug development. However, current in vitro preclinical liver PK models, including microphysiological systems (MPSs), suffer from important functional limitations such as expression of a fetal phenotype, low expression levels of drug-metabolizing enzymes and transporters, and rapid phenotypic dedifferentiation (i.e., short-term culture systems). This proposed technology will be especially applicable for drugs with complex DDI liability including time-dependent induction of DMEs and transporters. Javelin’s overall strategy is to develop microphysiological systems (MPSs) optimized for drug metabolism and pharmacokinetics (DMPK) studies including DDIs to be used in combination with our quantitative systems pharmacology (QSP) models to generate more predictive preclinical drug data. This SBIR phase I project will establish the optimal cellular and hepatotropic factor microenvironment to drive long-term, physiologically- relevant expression, and activity of liver DMEs and transporters. The endogenous hepatotropic factors will be selected for their ability to maintain liver cell health and drive the transcription factor signaling networks that regulate DME and transporter gene expression. The hepatotropic factors will be screened using a high- throughput fractional factorial analysis approach and optimized in Javelin’s polycarbonate (i.e., PDMS-free to minimize nonspecific drug adsorption), millifluidic, recirculating MPS engineered for DMPK studies. The resulting medium supplement, “DMPK-optimal” will be the first chemically-defined, xenoprotein-free supplement designed to drive sustained DME and transporter gene expression and activity for DMPK-DDI studies. Javelin’s DMPK- DDI platform will be provided as a low-cost, commercial-available product to pharmaceutical companies interested in generating comprehensive, accurate, human-based PK data to better inform their drug development process and design of first in-human (FIH) trials.