US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Testing and Research, 10903 New Hampshire Ave., Silver Spring, Maryland, 20993, USA.
Bioneer: FARMA, Department of Pharmacy, University of Copenhagen, 2100, Copenhagen, Denmark.
AAPS J. 2020 Jul 27;22(5):97. doi: 10.1208/s12248-020-00470-z.
Decision-making in drug development benefits from an integrated systems approach, where the stakeholders identify and address the critical questions for the system through carefully designed and performed studies. Biopharmaceutics Risk Assessment Roadmap (BioRAM) is such a systems approach for application of systems thinking to patient focused and timely decision-making, suitable for all stages of drug discovery and development. We described the BioRAM therapy-driven drug delivery framework, strategic roadmap, and integrated risk assessment instrument (BioRAM Scoring Grid) in previous publications (J Pharm Sci 103:3377-97, 2014; J Pharm Sci 105:3243-55, 2016). Integration of systems thinking with pharmaceutical development, manufacturing, and clinical sciences and health care is unique to BioRAM where the developed strategy identifies the system and enables risk characterization and balancing for the entire system. Successful decision-making process in BioRAM starts with the Blueprint (BP) meetings. Through shared understanding of the system, the program strategy is developed and captured in the program BP. Here, we provide three semi-hypothetical examples for illustrating risk-based decision-making in high and moderate risk settings. In the high-risk setting, which is a rare disease area, two completely alternate development approaches are considered (gene therapy and small molecule). The two moderate-risk examples represent varied knowledge levels and drivers for the programs. In one moderate-risk example, knowledge leveraging opportunities are drawn from the manufacturing knowledge and clinical performance of a similar drug substance. In the other example, knowledge on acute tolerance patterns for a similar mechanistic pathway is utilized for identifying markers to inform the drug release profile from the dosage form with the necessary "flexibility" for dosing. All examples illustrate implementation of the BioRAM strategy for leveraging knowledge and decision-making to optimize the clinical performance of drug products for patient benefit.
药物开发中的决策得益于一种综合系统方法,利益相关者通过精心设计和执行的研究来确定并解决系统的关键问题。生物药剂学风险评估路线图(BioRAM)就是这样一种系统方法,它将系统思维应用于以患者为中心和及时的决策中,适用于药物发现和开发的所有阶段。我们在之前的出版物中描述了 BioRAM 治疗驱动的药物输送框架、战略路线图和综合风险评估工具(BioRAM 评分网格)(J Pharm Sci 103:3377-97, 2014; J Pharm Sci 105:3243-55, 2016)。将系统思维与制药开发、制造和临床科学以及医疗保健相结合是 BioRAM 的独特之处,其中开发的策略确定了系统,并能够对整个系统进行风险特征描述和平衡。BioRAM 中的成功决策过程始于蓝图(BP)会议。通过对系统的共同理解,制定了项目策略,并在项目 BP 中进行了捕获。在这里,我们提供了三个半假设的例子来说明高风险和中风险环境下基于风险的决策。在高风险环境中,这是一个罕见的疾病领域,考虑了两种完全不同的开发方法(基因治疗和小分子)。两个中度风险的例子代表了不同的知识水平和项目驱动力。在一个中度风险的例子中,从制造知识和类似药物的临床性能中利用了知识利用机会。在另一个例子中,利用类似机制途径的急性耐受模式知识来识别标记,以告知从剂型释放药物的特征,从而为剂量调整提供必要的“灵活性”。所有的例子都说明了 BioRAM 策略的实施,以利用知识和决策来优化药物产品的临床性能,从而使患者受益。