Raman Karthik, Kumar Rukmini, Musante Cynthia J, Madhavan Subha
Centre for Integrative Biology and Systems mEdicine (IBSE), Wadhwani School of Data Science and AI, Indian Institute of Technology (IIT) Madras, Chennai, India.
Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai, India.
Clin Transl Sci. 2025 Jan;18(1):e70124. doi: 10.1111/cts.70124.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high-quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI-enhanced MIDD methods. In conclusion, integrating model-driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data-driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.
制药行业一直在努力改进药物研发流程，以降低成本、提高效率并改善患者的治疗效果。模型引导药物研发（MIDD）利用数学模型来模拟药物吸收、分布、代谢和排泄以及药代动力学和药效学中涉及的复杂过程。人工智能（AI）涵盖机器学习、深度学习和生成式AI等技术，提供了强大的工具和算法，可从大数据中高效识别有意义的模式、相关性和药物-靶点相互作用，从而实现更准确的预测和新假设的产生。MIDD与AI的结合使制药研究人员能够通过虚拟试验优化候选药物的选择、给药方案和治疗策略，以帮助降低候选药物的风险。然而，必须谨慎应对一些挑战，包括相关的、有标签的高质量数据集的可用性、数据隐私问题、模型可解释性和算法偏差。模型架构、数据格式和验证过程的标准化对于确保可靠且可重复的结果至关重要。此外，监管机构已经认识到需要调整其指南，以评估来自AI增强的MIDD方法的建议。总之，将模型驱动的药物研发与AI相结合为制药创新提供了一种变革性的范式。通过整合计算模型的预测能力和AI的数据驱动见解，这些方法之间的协同作用有可能加速药物发现、优化治疗策略，并开创个性化医疗的新时代，使患者、研究人员和整个制药行业受益。
