The importance of the immune system (IS) in tuberculosis (TB) drug development is often underestimated because of the intricate nature of experiments and the specialized knowledge needed. In vitro and animal studies fall short in replicating the intricate reactions of the human IS to drugs and infections. In this study, we present our initial efforts in employing an in silico approach to comprehend how an individual’s IS impacts the efficacy of therapy, particularly in managing mycobacterium tuberculosis (Mtb) infection and minimizing the risk of relapse. We employed a well-established agent-based IS simulator called C-IMMSIM. We conducted simulations to investigate the long-term outcomes of TB disease in a virtual cohort infected with Mtb over a 50-year period. Our simulations revealed that individuals with competent IS showed a high success rate in containing Mtb infection. Furthermore, to better understand the dynamic interactions between Mtb and the IS, we deliberately introduced specific IS deficiencies, thus successfully inducing short-term relapses and mortality. These results confirm the model’s ability to elucidate the mechanisms underlying the interactions between Mtb and the IS.

Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.