Vidiyala Nithin, Sunkishala Pavani, Parupathi Prashanth, Nyavanandi Dinesh
Small Molecule Drug Product Development, Cerevel Therapeutics, Cambridge, Massachusetts, 02141, USA.
Process Validation, PCI Pharma Services, Bedford, New Hampshire, 03110, USA.
AAPS PharmSciTech. 2025 May 14;26(5):133. doi: 10.1208/s12249-025-03134-3.
In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10 years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.
在当今世界,随着患者数量的不断增加,对药物的需求也在迅速增长。然而,目前的药物研发实践耗时且需要制药行业投入大量资金。目前,研发一种药物大约需要8至10年时间和30亿美元的投资。制药行业和监管机构正在不断采用新技术来提高药物研发过程的效率。然而,几十年来制药行业一直未能加快药物研发进程。大流行(新冠疫情)给制药行业和监管机构上了昂贵的一课,表明需要通过加快药物研发进程来做好应急准备。在过去几年里,制药行业一直在与人工智能(AI)公司合作,开发可在药物研发过程的各个阶段实施的算法和模型,以提高效率并大幅缩短研发时间线。近年来,经人工智能筛选的候选药物已进入人体临床试验,这显示出制药公司和监管机构的兴趣。在药物研发过程中对人工智能进行端到端整合将有利于制药行业预测药代动力学和药效学特征、毒性、加速临床试验、研究设计、对受试者进行虚拟监测、优化制造工艺、分析和实时监测产品质量以及做好监管准备。这篇综述文章详细讨论了人工智能在制药药物研发过程的各个方面所起的作用、其局限性、监管情况以及未来展望。