Matthias von Herrath, Yulia Manenkova and Damien Bresson (Principal Investigator)
Years of intensive research on immune-based therapies for type 1 diabetes (T1D) have revealed difficulties in 1) finding optimal therapeutic regimens to achieve maximal efficacy in animal models for T1D and 2) translating the efficacy of those promising compounds to the clinic. This is particularly true in the case of (auto)antigen-specific immunization where the protein or peptide must be administered at the correct dose and time appropriate to the route of administration in order to result in tolerance induction (in many cases the activation of adaptive, islet-antigen specific regulatory T cells [Tregs]). Furthermore, once a clinical trial has been initiated there is no reliable biomarker developed to date that successfully predicts therapeutic outcomes.
To address these issues and guide research for the development and mechanistic evaluation of immune based therapies in T1D, Entelos Inc., a life sciences company, has developed the T1D PhysioLab® platform, a predictive in silico model of diabetes progression in the (non-obese diabetic) NOD mouse. This platform was developed in collaboration with the American Diabetes Association to address key scientific questions related to the disease onset and progression in the NOD mouse. In this research, Entelos' biosimulation capabilities will be used to accelerate the path to clinical translation through a priori in silico testing of candidate experimental protocols in "virtual" NOD mice. In particular, simulations will be used to optimize experimental design and identify assumptions that are critical for the predicted therapeutic efficacy, which may be experimentally investigated to reveal key mechanisms of action. This close collaboration between ‘wet-lab' and ‘virtual lab' investigators enables more rational experiment design. Additionally, new experimental data can be used to refine the in silico model and subsequent model predictions, yielding a highly efficient iterative process. This study will aid the design of human combination therapies (CT) consisting of i.v. injected anti-CD3 and mucosally administered insulin. Rational translation of this approach to human therapy will require precise testing and knowledge of the optimal dosing regimen. Defining this experimentally is a daunting task, requiring many years of wet‑lab study in NOD mice. Therefore, we decided to use the Entelos platform to optimize dose and timing of mucosal insulin/anti-CD3 combination therapy for maximal efficacy in treating newly-diabetic NOD mice, while improving the mechanistic understanding of these strategies. Optimally this CT will utilize the lowest anti-CD3 doses possible to avoid systemic side-effects when translated into humans. Finally, mechanistic studies will be performed to define potential peripheral biomarkers to assess treatment efficacy.