School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH, UK.
Computer Science Institute, DiSIT, University of Eastern Piedmont, Alessandria, Italy.
BMC Bioinformatics. 2020 Dec 14;21(Suppl 17):449. doi: 10.1186/s12859-020-03776-z.
The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial.
One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject.
We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.
由 Horizon 2020 资助的 STriTuVaD 项目旨在通过一项 IIb 期临床试验来测试针对结核病的最先进的治疗性疫苗之一。作为该计划的一部分，我们开发了一种针对目标人群特征生成计算机模拟患者的策略，然后可以将其与增强临床试验中的体内数据结合使用。
使用虚拟患者的最具挑战性任务之一是开发一种方法来再现目标人群的生物学多样性，即提供一种用于生成数字患者库的适当策略。这是通过以随机方式创建初始免疫系统库以及通过确定特征向量来实现的，该特征向量结合了生物和病理生理参数，将数字患者个性化以再现主体的生理学和病理生理学。
我们提出了一种从联合特征总体分布中进行抽样的顺序方法，以便创建具有某些特定特征的虚拟患者队列，类似于目标临床试验的招募过程，然后可以将其用于增强来自物理试验的信息，以帮助缩小其规模和缩短试验周期。
