Aymara Sancho-Araiz (1,2), Sara Zalba (1,2), MarÃa J.Garrido (1,2), Pedro Berraondo (2,3,4), Brian Topp (5), Dinesh de Alwis (5), Zinnia P Parra-Guillen (1,2), Victor Mangas-Sanjuan (6,7), Iñaki F. Trocóniz (1,2)
(1) Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain, (2) Navarra Institute for Health Research (IdiSNA), Pamplona, Spain, (3) Program of Immunology and Immunotherapy, CIMA Universidad de Navarra, Pamplona, Spain, (4) Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain, (5) Merck & Co., Inc, Kenilworth, NJ, USA, (6) Department of Pharmacy Technology and Parasitology, Faculty of Pharmacy, University of Valencia, Valencia, Spain, (7) Interuniversity Institute of Recognition Research Molecular and Technological Development. Valencia, Spain.
Objectives: Immune checkpoint inhibitors administered as single agent have demonstrated clinical efficacy in certain tumor types [1,2]. However, when treating non-immunogenic or cold tumors, different combination strategies are needed to induce an optimal antitumor immune response [3]. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of different immunotherapy combinations in cold tumors.
Methods: Mice were inoculated with TC-1/A9 non-inflamed tumors and treated with all possible combinations of an antigen (E7 long peptide), a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (α-PD1). Tumor size and drug effects were modeled using Monolix following a middle-out strategy in which the model structure was constrained to the known immune system mechanisms. The dynamics of immune cells, their role on tumor response, and the mechanisms of the different immuno-oncology treatments were included in the model: (i) growth of unperturbed tumors, (ii) antigen presenting cells stimulation by antigens triggering the activation and proliferation of naïve CD8+ T cells [4], (iii) exacerbation and maintenance by a toll-like receptor the process activated by the antigens [5], (iv) antitumor cytotoxic response induced by activated CD8+ T cells [6], and (v) the presence of tumor resistance mechanisms used to evade CD8+ T cells-mediated death such as the recruitment of immune suppressor cells (e.g., Treg) and expression of the PD-L1 ligand leading to CD8+ T cell exhaustion, which can be at least partly blocked by immune checkpoints inhibitors [4,7,8].
Results: Tumor growth in the absence of treatment was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm3 and a doubling time of 3.6 days. Contrary to the lack of response observed when treatments were administered as single agents, combinations including the antigen were able to induce antitumor response. The effects of PIC and α-PD1 were included taking into account their mechanism of action and were best described with a linear model. Unimodal distributions of random effects were used to account for the response heterogeneity observed within the groups. The magnitude of inter-animal variability associated to rate constants of PIC and α-PD1 exceeds 100% resembling the variability in the tumor size profiles in all the treatment groups in which a certain degree of response was achieved. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that although the inhibition of the resistance mechanisms is less efficient with respect to antitumor effects than CD8+ T lymphocytes proliferation and expansion, both are strongly related to the clinical outcome.
Conclusions: A semi-mechanistic model for immunotherapeutic combinations in cold tumors was developed. The analysis suggests that activation of antigen-presenting cells is needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. This type of models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.
References:
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[3] Bonaventura P, Shekarian T, Alcazer V, Valladeau-Guilemond J, Valsesia-Wittmann S, Amigorena S, et al. Cold tumors: A therapeutic challenge for immunotherapy. Vol. 10, Frontiers in Immunology. Frontiers Media S.A.; 2019. p. 168.
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[5] Seya T, Akazawa T, Tsujita T, Matsumoto M. Role of Toll-like Receptors in Adjuvant-AugmentedImmune Therapies. 2006;3(1):31–8.
[6] Maimela NR, Liu S, Zhang Y. Fates of CD8+ T cells in Tumor Microenvironment. Vol. 17, Computational and Structural Biotechnology Journal. Elsevier B.V.; 2019. p. 1–13;
[7] Ma H, Wang H, Sové RJ, Wang J, Giragossian C, Popel AS. Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model. J Immunother Cancer. 2020 Aug 27;8(2).
[8] Jafarnejad M, Gong C, Gabrielson E, Bartelink IH, Vicini P, Wang B, et al. A Computational Model of Neoadjuvant PD-1 Inhibition in Non-Small Cell Lung Cancer. AAPS J. 2019 Sep 1;21(5).
Reference: PAGE 29 (2021) Abstr 9629 [www.page-meeting.org/?abstract=9629]
Poster: Drug/Disease Modelling - Oncology