Research and Development, Servier Laboratories, 50 Rue Carnot, 92150 Suresnes, France; French Academy of Pharmacy, 4 Avenue de l'Observatoire, 75006 Paris, France.
Trends Pharmacol Sci. 2023 Jul;44(7):411-424. doi: 10.1016/j.tips.2023.04.005. Epub 2023 May 31.
Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.
人工智能 (AI) 预测模型正在被用于推动精准医学方法来治疗复杂的慢性疾病，如自身免疫和自身炎症性疾病 (AIIDs)。在过去的几年中，通过使用组学技术对系统性红斑狼疮 (SLE)、原发性干燥综合征 (pSS) 和类风湿关节炎 (RA) 患者进行分子谱分析，并将数据与 AI 相结合，已经产生了这些疾病的首个模型。这些进展证实了一种涉及多个促炎途径的复杂病理生理学，并为不同 AIIDs 之间存在共享的分子失调提供了证据。我将讨论如何使用模型对患者进行分层，评估病理生理学中的因果关系，在计算机上设计候选药物，并预测虚拟患者中的药物疗效。通过将个体患者特征与数百万种候选药物的预测特性相关联，这些模型可以通过更个性化的治疗来改善 AIIDs 的管理。
