silico clinical trial
Artificial Intelligence for In Silico Clinical Trials: A Review
Another closely related research area is synthetic patient data generation. This research area seeks to simulate patient records, usually for specific disease conditions. For example, one can predict future patient EHR data using current patient baseline metrics or historical EHR data. Early works on patient record generation methods used rule-based methods [lombardo2008ta, buczak2010data, mclachlan2016using]. However, the rule-based methods cannot provide realistic and complex patient data to support general machine learning (ML) tasks.
Artificial Intelligence for In Silico Clinical Trials: A Review
Wang, Zifeng, Gao, Chufan, Glass, Lucas M., Sun, Jimeng
A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications.
Optimal personalised treatment computation through in silico clinical trials on patient digital twins
Sinisi, Stefano, Alimguzhin, Vadim, Mancini, Toni, Tronci, Enrico, Mari, Federico, Leeners, Brigitte
In Silico Clinical Trials (ISCT), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.