Agrahari Vivek, Choonara Yahya E, Mosharraf Mitra, Patel Sravan Kumar, Zhang Fan
CONRAD, Eastern Virginia Medical School, Old Dominion University, Norfolk, VA, 23507, USA.
Wits Advanced Drug Delivery Platform Research Unit, Department of Pharmacy and Pharmacology, School of Therapeutic Science, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Pharm Res. 2024 Dec;41(12):2289-2297. doi: 10.1007/s11095-024-03798-9. Epub 2024 Dec 2.
The unique potential of nanomedicine to address challenging health issues is rapidly advancing the field, leading to the generation of more effective products. However, these complex systems often pose several challenges with respect to their design for specific functionality, scalable manufacturing, characterization, quality control, and clinical translation. In this regard, the application of artificial intelligence (AI) and machine learning (ML) approaches can enable faster and more accurate data assessment, identifying trends and predicting outcomes, leading to efficient nanomedicine product development. This perspective paper discusses the potential of AI and ML in nanomedicine product development with a focus on their applications in discovery, assessment, manufacturing, and clinical trials. The potential limitations of AI and ML approaches in nanomedicine product development are also covered.
纳米医学在解决具有挑战性的健康问题方面的独特潜力正在迅速推动该领域的发展，从而催生更有效的产品。然而，这些复杂系统在针对特定功能进行设计、可扩展制造、表征、质量控制和临床转化方面常常带来若干挑战。在这方面，人工智能（AI）和机器学习（ML）方法的应用能够实现更快、更准确的数据评估，识别趋势并预测结果，从而实现高效的纳米医学产品开发。这篇观点论文讨论了AI和ML在纳米医学产品开发中的潜力，重点关注它们在发现、评估、制造和临床试验中的应用。文中还涵盖了AI和ML方法在纳米医学产品开发中的潜在局限性。
