Marcel Mohr (1), Rami Lissilaa (2), Andreas Hohlbaum (3), Friedemann Schmidt (1)
(1) Sanofi, R&D Preclinical Safety, Industriepark Höchst, Frankfurt/Main, Germany, (2) Sanofi, R&D Checkpoint Immunology, 13 Quai Jules Guesde, Vitry-Sur-Seine, France, (3) Sanofi, R&D Preclinical Safety, Technologiepark Zwijnaarde, Gent, Belgium
Objectives: Biological modalities, such as antibody-based biologics, have revolutionized the treatment of many neoplastic and inflammatory diseases over the last years. Although biologics are often less prone to off-target interactions than synthetic immunosuppressive drugs, infections remain to be a key safety consideration. The immunosuppressive modulation of targeted immune system components allows infectious organisms a greater chance to spread or reactivate and could lead to clinical symptoms in treated patients. Understanding how the modulated immune system combats infection is crucial to assess and predict potential mechanism of action related complications of therapeutic biologics.
Methods: The immune response to infections is a complex biological process, involving multiple cell types, pathways, and anatomical compartments, and it is difficult to interpret solely at the level of in vitro assays. Additionally, mathematical models and computer simulations have emerged to study immune responses to infections, e.g., in case of Influenza A Virus (IAV). We adapted a mathematical model [1], originally parametrized from published experimental findings on IAV infection, to study the effect of a preclinical drug candidate on an IAV-specific immune response as compared to clinical or marketed competitor drugs with overlapping mechanism of action. In our present use-case, the drugs target autoreactive T cells in autoimmune diseases via inhibition of co-stimulatory T cell receptors leading to a decreased proliferation of effector CD4+ and CD8+ T cells. For each drug, in vitro data derived from mixed lymphocyte reaction assays were used to quantify the concentration-dependent inhibition of T cell proliferation and were integrated into the model. The drug-specific immune responses to IAV infection were simulated for two scenarios, i.e., primary IAV infection occurring during the treatment, and secondary IAV infection occurring during the treatment while IAV memory had been established pre-treatment. Two in silico biomarkers of the altered immune responses were generated and compared, i.e., concentration-dependent changes in the time to viral clearance and antibody levels up to 30 days.
Results: The drug-dependent modulation of immune responses to IAV infection were consistent for primary and secondary biomarker responses. Furthermore, modulation of the immune response by the preclinical candidate could be ranked between those of two already marketed drugs. This allowed to exclude the burden of an overt risk associated with the preclinical drug candidate in comparison with drugs on the market, which have an established clinical safety profile.
Conclusions: This in silico study is generalizable, since the underlying systems immunology model is neither tailored to a specific modality, nor to the precise mode of action of the new biological or chemical entity. Instead, drug-specific actions on the immune system are represented by in vitro data integrated into the mechanistic description of model species. Furthermore, the study serves as a blueprint for the quantitative preclinical assessment of various common infections, e.g., reactivation of Tuberculosis, and their associated clinical risks.
References:
[1] Lee et al. Simulation and Prediction of the Adaptive Immune Response to Influenza A Virus Infection. Journal of Virology, 7151-7165, 2009
Reference: PAGE 32 (2024) Abstr 10785 [www.page-meeting.org/?abstract=10785]
Poster: Drug/Disease Modelling - Safety