V A Shriya, Nayak Usha Y, Sathyanarayana Muddukrishna Badamane, Chaudhari Bhim Bahadur, Bhat Krishnamurthy
Department of Pharmaceutical Quality Assurance, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
AAPS PharmSciTech. 2025 Apr 17;26(5):106. doi: 10.1208/s12249-025-03093-9.
BCS class II candidates pose challenges in drug development due to their low solubility and permeability. Researchers have explored various techniques; co-amorphous and solid dispersion are major approaches to enhance in-vitro drug solubility and dissolution. However, in-vivo oral bioavailability remains challenging. Physiologically based pharmacokinetic (PBPK) modeling with a detailed understanding of drug absorption, distribution, metabolism, and excretion (ADME) using a mechanistic approach is emerging. This review summarizes the fundamentals of the PBPK, dissolution-absorption models, parameterization of oral absorption for BCS class II drugs, and provides information about newly emerging artificial intelligence/machine learning (AI/ML) linked PBPK approaches with their advantages, disadvantages, challenges and areas of further exploration. Additionally, the fully integrated workflow for formulation design for investigational new drugs (INDs) and virtual bioequivalence for generic molecules falling under BCS-II are discussed.
BCS II类候选药物因其低溶解度和低渗透性，在药物研发中面临挑战。研究人员探索了各种技术；共无定形和固体分散体是提高体外药物溶解度和溶出度的主要方法。然而，体内口服生物利用度仍然具有挑战性。基于生理学的药代动力学（PBPK）建模正在兴起，该建模使用机制方法详细了解药物的吸收、分布、代谢和排泄（ADME）。本综述总结了PBPK的基本原理、溶出-吸收模型、BCS II类药物口服吸收的参数化，并提供了有关新出现的与人工智能/机器学习（AI/ML）相关的PBPK方法的信息，包括其优点、缺点、挑战和进一步探索的领域。此外，还讨论了用于研究性新药（IND）制剂设计和属于BCS-II类的仿制药虚拟生物等效性的完全集成工作流程。
