title: Application of a stochastic modeling to assess the evolution of tuberculous and non-tuberculous mycobacterial infection in patients treated with tumor necrosis factor inhibitors.
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.
authors: Dipartimento di Fisica, Università di Parma, Parma, Italy. elena.agliari@fis.unipr.it
authors: Elena
authors: E
authors: Agliari
authors: Lorenzo
authors: L
authors: Asti
authors: Adriano
authors: A
authors: Barra
authors: Rossana
authors: R
authors: Scrivo
authors: Guido
authors: G
authors: Valesini
authors: Robert S
authors: RS
authors: Wallis
mesh_terms: D000995:Antitubercular Agents / Q000494:pharmacology* / Q000627:therapeutic use; D018450:Disease Progression; D005240:Feasibility Studies; D006801:Humans; D055985:Latent Tuberculosis / Q000188:drug therapy; D008390:Markov Chains; D009010:Monte Carlo Method; D012008:Recurrence; D018570:Risk Assessment; D014397:Tuberculosis, Pulmonary / Q000188:drug therapy*; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors*
chemical_list: D000995:Antitubercular Agents; D014409:Tumor Necrosis Factor-alpha
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