Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB

Abstract

Background: In 2018, about 10 million people were found infected by tuberculosis, with approximately 1.2 million deaths worldwide. Despite these numbers have been relatively stable in recent years, tuberculosis is still considered one of the top 10 deadliest diseases worldwide. Over the years, Mycobacterium tuberculosis has developed a form of resistance to first-line tuberculosis treatments, specifically to isoniazid, leading to multi-drug-resistant tuberculosis. In this context, the EU and Indian DBT funded project STriTuVaD-In Silico Trial for Tuberculosis Vaccine Development-is supporting the identification of new interventional strategies against tuberculosis thanks to the use of Universal Immune System Simulator (UISS), a computational framework capable of predicting the immunity induced by specific drugs such as therapeutic vaccines and antibiotics.

Results: Here, we present how UISS accurately simulates tuberculosis dynamics and its interaction within the immune system, and how it predicts the efficacy of the combined action of isoniazid and RUTI vaccine in a specific digital population cohort. Specifically, we simulated two groups of 100 digital patients. The first group was treated with isoniazid only, while the second one was treated with the combination of RUTI vaccine and isoniazid, according to the dosage strategy described in the clinical trial design. UISS-TB shows to be in good agreement with clinical trial results suggesting that RUTI vaccine may favor a partial recover of infected lung tissue.

Conclusions: In silico trials innovations represent a powerful pipeline for the prediction of the effects of specific therapeutic strategies and related clinical outcomes. Here, we present a further step in UISS framework implementation. Specifically, we found that the simulated mechanism of action of RUTI and INH are in good alignment with the results coming from past clinical phase IIa trials.

Keywords: Computational modeling; Immunity; In silico trials; Isoniazid; RUTI; Therapeutic strategies; Tuberculosis.

Fig. 1

Web Graphic User Interface of UISS-TB. This figure depicts the GUI of UISS that allows the run of the simulations. The "Simulation's Parameters" zone, on the left side of the figure represents the vector of features for the personalization of digital patients. The "Your Simulations" box, on the right side of the figure, depicts the list of all the simulations launched by the user. The simulations are classified in "running" or in "completed" status

Fig. 2

Outcome of digital patients treated with INH. Green line shows the average trend of the considered cellular entities. The orange shaded area represents their standard deviation (SD + /−). a Depicts the dynamics of AM before and after the administration of INH; the antibiotic, administered accordingly to the clinical trial protocol, reveals a not negligible biological restore of the damaged AM. b The dynamics of CD8 T cells. d The dynamics of TH17 cells responding to bacterial infection. c and e show flat curves because INH is not supposed to stimulate immune response. Simulation time has been set to 365 days (1 years) and digital patients have been challenged with MTB at day 15

Fig. 3

Outcome of digital patients treated with INH and RUTI vaccine. Green line shows the average trend of cell populations, while the orange shaded area represents the standard deviation. One month after the end of antibiotic treatment, RUTI vaccine was administered accordingly to the clinical trial design. a Depicts the dynamics of AM before and after the administration of INH and after the administration of RUTI. The combination of INH with RUTI allows a better recovery of infected AM population when compared to the one without RUTI injection. Substantial increase in levels of TC, Th1, Th17 and IFNG is observed (b–e). For all the biological scenarios, simulation time has been set to 365 days (1 years) and digital patient have been challenged with MTB at day 15

Fig. 4

Outcome of digital patients treated with the second RUTI vaccine administration. Green line shows the average trend of cell populations, while the orange shaded area represents the standard deviation. One month after the end of antibiotic treatment, RUTI vaccine was administered accordingly to the clinical trial design followed by a second injection of RUTI (28 days after the first one). a The dynamics of AM that is comparable to the scenario observed after only one vaccine administration. Substantial increase in levels of TC, Th1, Th17 and IFNG is observed (b–e) compared to dynamics obtained with only one vaccine administration. For all the biological scenarios, simulation time has been set to 365 days (1 years) and digital patient have been challenged with MTB at day 15

Fig. 5

UISS in silico predictions with different timing of a second RUTI vaccine administration. Green line shows the average trend of cell populations, while the orange shaded area represents the standard deviation. In comparison to the scenarios observed in Fig. 4, a negligible difference in the overall immune response driven by CD4⁺ Th1 cells and CD8⁺ T cells is observed