Development and validation of a best practices framework for PBPK analysis for biopharmaceutic applications in support of model-informed biowaivers of fed state BE studies for BCS class II drugs

开发和验证生物制药应用 PBPK 分析的最佳实践框架，以支持 BCS II 类药物的联邦州 BE 研究的模型知情生物豁免

• 批准号: 10372315
• 负责人: Rodrigo Cristofoletti
• 金额: $ 30万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372315
• 关键词: Development; validation; best; practices; framework

PROJECT SUMMARY Lower-cost generic drugs generated $313 billion in savings to the US healthcare system in 2019 and approximately $2.2 trillion in the past decade. Therefore, generic drugs play a pivotal role in the sustainability of the US health system. A generic drug is approved on the basis of sufficient demonstration of sameness to the corresponding brand-name drug. Critical evidence for approval is bioequivalence (BE). Even though well established, pharmacokinetic-based BE studies are both costly and lengthy. In the case of orally administered drug products, co-administration with food may influence oral absorption, thus BE should be demonstrated under both fasting and fed conditions if the reference listed drug labeling states the product can be taken with or without meals. This requirement poses an additional financial burden on generic manufacturers, which will ultimately be passed along to patients. Furthermore, the regulatory requirement of two in vivo BE studies slows down the generic drug product development process and may increase review cycles and attrition rates due to variability that is related to the drug itself rather than the formulation. Quantitative methods and modeling have been widely used in the realm of new drug discovery and development to inform more efficient and cost-effective development programs. Physiologically-based pharmacokinetic (PBPK) modeling is one of the key tools under the overarching umbrella of quantitative models. PBPK models are an effective tool to integrate information about the product characteristics, the physiology of the individual subject, and the variability among subjects within a population to simulate the absorption and subsequent systemic disposition of the drug without conducting in vivo PK studies. PBPK models have shown promise in supporting generic drug development and regulatory decision-making, since, under a model-integrated evidence perspective, it enables the leveraging of all prior knowledge generated to support the regulatory approval of the respective brand-name drug product. We will develop a best practices framework for integrating drug and drug product data together with gastrointestinal physiology in PBPK models tailored to oral drug administration to predict food-formulation interactions and promote biowaivers of fed state BE studies for poorly soluble and highly permeable drugs. This will be done at both the individual and population levels. We have excellent in vitro testing and PBPK modeling capabilities, along with unique PK and luminal drug concentration data sets. Once established, this framework can also be applied to address emerging issues in generic drug development and assessment, such as BE study protocol changes necessitated, for example, by pandemic situations and hypochlorhydria-formulation interactions.

项目摘要 2019年，成本较低的仿制药为美国医疗保健系统节省了3130亿美元， 在过去的十年里，大约有2.2万亿美元。因此，仿制药在可持续性方面发挥着关键作用。 美国卫生系统。仿制药的批准是基于充分证明与 对应的品牌药品。批准的关键证据是生物等效性（BE）。尽管 已建立的基于药代动力学的BE研究既昂贵又冗长。在口服给药的情况下， 与食物联合给药可能影响口服吸收，因此应在以下条件下证明BE 空腹和进食条件下，如果参考列出的药物标签说明产品可以与或不与 吃饭。这一要求给仿制药制造商带来了额外的财务负担，最终将 沿着传递给病人。此外，两项体内BE研究的监管要求减缓了 仿制药产品开发过程，并可能因可变性而增加审查周期和损耗率 这与药物本身有关，而不是配方。 定量方法和模型已广泛应用于新药发现和开发领域 为更有效和更具成本效益的发展计划提供信息。生理药代动力学 （PBPK）建模是定量模型总体框架下的关键工具之一。PBPK模型 是一个有效的工具，整合有关产品特性，个人生理学， 受试者，以及人群中受试者之间的变异性，以模拟吸收和后续 在不进行体内PK研究的情况下进行药物的全身分布。PBPK模型在以下方面显示出了希望： 支持仿制药开发和监管决策，因为在一个模型下， 从这一角度来看，它能够利用生成的所有先验知识来支持 各自的品牌药品。 我们将制定一个最佳实践框架，将药品和药品产品数据与 用于口服给药的PBPK模型中的胃肠道生理学，以预测食品配方 相互作用和促进进食状态生物等效性研究的生物等效性豁免的难溶性和高渗透性药物。这 将在个人和群体两个层面上进行。我们有出色的体外测试和PBPK建模 沿着独特的PK和管腔药物浓度数据集。这个框架一旦建立， 也可用于解决仿制药开发和评估中出现的问题，例如BE研究 例如，由于大流行情况和次氯酸盐制剂而需要对方案进行修改 交互.