Application of a stochastic modeling to evaluate tuberculosis onset in patients treated with tumor necrosis factor inhibitors

TL;DR

This paper develops a stochastic Markov chain model to predict tuberculosis reactivation risk in patients treated with tumor necrosis factor inhibitors, validated by simulations and experimental data, with implications for infection monitoring.

Contribution

It provides an exact analytical solution for TB reactivation dynamics during treatment, extending the model to non-tuberculous mycobacteria, and offers a general framework for drug-related adverse event research.

Findings

Excellent agreement between model and experimental data

Reactivation plays a minor role in non-tuberculous mycobacteria

Monitoring is crucial for tuberculosis, less so for non-tuberculous infections

Abstract

In this manuscript we apply stochastic modeling to investigate the risk of reactivation of latent mycobacterial infections in patients undergoing treatment with tumor necrosis factor inhibitors. First, we review the perspective proposed by one of the authors in a previous work and which consists in predicting the occurrence of reactivation of latent tuberculosis infection or newly acquired tuberculosis during treatment; this is based on variational procedures on a simple set of parameters (e.g. rate of reactivation of a latent infection). Then, we develop a full analytical study of this approach through a Markov chain analysis and we find an exact solution for the temporal evolution of the number of cases of tuberculosis infection (re)activation. The analytical solution is compared with Monte Carlo simulations and with experimental data, showing overall excellent agreement. The generality of this theoretical framework allows to investigate also the case of non-tuberculous mycobacteria infections; in particular, we show that reactivation in that context plays a minor role. This may suggest that, while the screening for tuberculous is necessary prior to initiating biologics, when considering non-tuberculous mycobacteria only a watchful monitoring during the treatment is recommended. The framework outlined in this paper is quite general and could be extremely promising in further researches on drug-related adverse events.