Xiong Yuan, Samtani Mahesh N, Ouellet Daniele
Johnson and Johnson, Raritan, NJ, USA.
Johnson and Johnson, Spring House, PA, USA.
Adv Drug Deliv Rev. 2025 Feb;217:115503. doi: 10.1016/j.addr.2024.115503. Epub 2024 Dec 19.
The last two decades have witnessed profound changes in how advanced computational tools can help leverage tons of data to improve our knowledge, and ultimately reduce cost and increase productivity in drug development. Pharmacometrics has demonstrated its impact through model-informed drug development (MIDD) approaches. It is now an indispensable component throughout the whole continuum of drug discovery, development, regulatory review, and approval. Today, applications of pharmacometrics are common in designing better trials and accelerating evidence-based decisions. Newly emerging technologies, especially those from data and computer sciences, are being integrated with existing computational tools used in the pharmaceutical industry at a remarkably fast pace. The new challenges faced by the pharmacometrics community are not what or how to contribute, but which optimal MIDD strategy should be adopted to maximize its value in the decision-making process. While we are embracing new innovative approaches and tools, this article discusses how a variety of existing modeling tools, with differentiated advantages and focus, can work in concert to inform drug development.
在过去二十年中，先进的计算工具在利用大量数据以增进我们的知识，并最终降低成本和提高药物研发生产力方面所带来的深刻变化有目共睹。药物计量学已通过模型驱动药物研发（MIDD）方法展现出其影响力。如今，它在药物发现、开发、监管审评及批准的整个连续过程中都是不可或缺的组成部分。当下，药物计量学在设计更优试验和加速基于证据的决策方面应用广泛。新兴技术，尤其是来自数据和计算机科学领域的技术，正以极快的速度与制药行业现有的计算工具相结合。药物计量学领域面临的新挑战并非贡献什么或如何贡献，而是应采用哪种最佳的MIDD策略，以在决策过程中最大化其价值。在我们接纳新的创新方法和工具之际，本文探讨了各种具有不同优势和重点的现有建模工具如何协同作用，为药物研发提供信息。
