Generation of digital patients for the simulation of tuberculosis with UISS-TB

Abstract

Background 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. Results 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. Conclusions 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.

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

“…There are also models able to simulate pharmacokinetics in TB on a population level (33,34), but none of these produce data relevant to the endpoint of the clinical trial considered in this study. UISS-TB is a bespoke ABM capable of simulating cohorts of TB in silico patients treated with RUTI (12,24), which has been through ASME V&V 40-2018 (23); to the extent of our knowledge, currently there is no alternative. We illustrate our approach within an augmented Phase II clinical trial of a co-adjuvant vaccine for treating patients with TB.…”

“…This ABM produces in silico data from a number of biological entities and chemical species (e.g., cytokines) for an individual virtual patient, identified and characterised through an initial vector of 22 features. In order to create cohorts of virtual patients, we use the novel approach from Juárez et al ( 24 ), tailored for UISS-TB. In short, these features can be sampled, either at once or sequentially, and based on the joint distribution of the population characteristics, each virtual patient is then simulated using UISS-TB and the endpoint of the clinical trial recorded.…”

Bone mineral density modeling via random field: normality, stationarity, sex and age dependence

“…The study of BMD and shape variations as random fields, potentially cross-correlated, will form the objective of subsequent studies. Although the random field model of BMD inferred from CT of patients with no bone disease seems a leap towards creating digital twins of bone [36,37], a most beneficial approach would be to estimate random fields respecting pathological changes in bone structure and predicting the related risk of bone fractures [10,8,9].…”

“…Representing BMD and bone shape using random fields can be considered as a step towards creating a digital twin of bone [39, 40]. However, the next key step is to include osteoporotic changes and analyze their effect on the random field of BMD.…”

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

“…In the past, UISS has been successfully applied to a large number of immune system disease modelling scenarios. 69 , 70 , 71 , 72 , 73 , 74 In preliminary studies, 75 , 76 , 77 , 78 it has been shown that the resulting simulator (UISS‐TB) could be used to simulate the relevant individual human physiology and physiopathology in patients affected by Mycobacterium tuberculosis (MTB) and to test in silico the efficacy of new vaccines against tuberculosis. (Figure 2 ) Moreover, UISS shows the capability of simulating the intrinsic immune system behaviour against MTB infection (eliciting or not eliciting the complete clearance of the infection or, eventually, allowing the chronic establishment of MTB reservoir inside the host due to both MTB characteristics and genetic features of the host).…”