Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
GNS Healthcare, Somerville, MA, USA.
Biochim Biophys Acta Rev Cancer. 2021 Aug;1876(1):188572. doi: 10.1016/j.bbcan.2021.188572. Epub 2021 May 31.
Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have made it possible to simulate the efficacy of cancer drug protocols in diverse patient populations, which could inform and improve clinical trial design. Here, we review the current and potential use of in silico trials and causal AI to increase the efficacy and safety of traditional clinical trials. We conclude that in silico trials using causal AI approaches can simulate control and efficacy arms, inform patient recruitment and regimen titrations, and better enable subgroup analyses critical for precision medicine.
肿瘤学中的药物制剂在早期到晚期临床试验中淘汰率很高。最近计算方法的进步，特别是因果人工智能，以及丰富的临床基因组数据库的可用性，使得在不同的患者群体中模拟癌症药物方案的疗效成为可能，这可以为临床试验设计提供信息并加以改进。在这里，我们回顾了使用计算机试验和因果人工智能来提高传统临床试验的疗效和安全性的当前和潜在用途。我们的结论是，使用因果人工智能方法的计算机试验可以模拟对照和疗效臂，为患者招募和方案滴定提供信息，并更好地支持对于精准医学至关重要的亚组分析。
