Differences in reactivation of tuberculosis induced from anti-TNF treatments are based on bioavailability in granulomatous tissue

Abstract

The immune response to Mycobacterium tuberculosis (Mtb) infection is complex. Experimental evidence has revealed that tumor necrosis factor (TNF) plays a major role in host defense against Mtb in both active and latent phases of infection. TNF-neutralizing drugs used to treat inflammatory disorders have been reported to increase the risk of tuberculosis (TB), in accordance with animal studies. The present study takes a computational approach toward characterizing the role of TNF in protection against the tubercle bacillus in both active and latent infection. We extend our previous mathematical models to investigate the roles and production of soluble (sTNF) and transmembrane TNF (tmTNF). We analyze effects of anti-TNF therapy in virtual clinical trials (VCTs) by simulating two of the most commonly used therapies, anti-TNF antibody and TNF receptor fusion, predicting mechanisms that explain observed differences in TB reactivation rates. The major findings from this study are that bioavailability of TNF following anti-TNF therapy is the primary factor for causing reactivation of latent infection and that sTNF--even at very low levels--is essential for control of infection. Using a mathematical model, it is possible to distinguish mechanisms of action of the anti-TNF treatments and gain insights into the role of TNF in TB control and pathology. Our study suggests that a TNF-modulating agent could be developed that could balance the requirement for reduction of inflammation with the necessity to maintain resistance to infection and microbial diseases. Alternatively, the dose and timing of anti-TNF therapy could be modified. Anti-TNF therapy will likely lead to numerous incidents of primary TB if used in areas where exposure is likely.

Figure 1. Mathematical Model Simulation of a Latent State

Shown are intracellular and extracellular bacterial loads (A), CD4+ and CD8+ T cells (B,C) (linear scale), cytokines (D,E) (linear scale), and macrophages (F). The volumetric unit for cell and bacteria populations is number per cm³ of a granulomatous tissue. The unit of measure for cytokine concentrations is pg/mL of granuloma homogenate. BE, extracellular bacteria; BI, intracellular bacteria; MA, activated Mφ; MI, infected Mφ; MR, resident Mφ.

Figure 2. Mathematical Model Simulation of an Active TB State

Shown are intracellular and extracellular bacterial loads (A), CD4+ and CD8+ T cells (B,C) (linear scale), cytokines (D) (linear scale) and (E) (linear-log scale), and macrophages (F) (linear scale). See Figure 1 for measure units and abbreviations. The main differences in parameter value choices used to distinguish active TB from latency in this simulation are the following: decreased lymphocyte TNF-dependent recruitment, increased macrophage TNF-dependent and independent recruitment, decreased CTL killing (k₅₂), and increased extracellular bacteria growth rate (α₂₀).

Figure 3. Comparing the Roles of sTNF and tmTNF

(A) Mathematical model simulations of total bacterial load corresponding to different proportions σ (percent sTNF versus tmTNF); all the other parameters are fixed to parameters yielding a latent state (see Table S6). (B) Simulated depletion of variable levels of sTNF. Until day 500, the system is in latency and σ = 0.95. Then at day 500, the depletion of sTNF is performed. Different values of σ are shown, where σ is the percent cleaved TNF (sTNF). Total bacterial loads are shown corresponding to different percentages of sTNF after day 500.

Figure 4. Macrophage Dynamics

Descriptive diagram of macrophage dynamics implemented in the mathematical model in Equations 1–3.

Figure 5. CD4+ T Cell Dynamics

Descriptive diagram of lymphocyte dynamics implemented in the mathematical model in Equations 4–6.

Figure 6. Bacteria Dynamics

Descriptive diagram of bacteria dynamics implemented in the mathematical model in Equations 15–16.

Figure 7. Simulations of Total TNF Deletion and Depletion

Mathematical model simulations of bacterial loads during TNF deletion (TNF^−/-) and depletion (TNF depl). The y-axis represents total bacterial load. Latency is our wild-type control (see Figure 1). Note, σ = 0.95 for these simulations.