A platform for mechanistic modelling of subcutaneous administration with the effect of immunogenicity within the Open Systems Pharmacology framework
Moriah Pellowe (1), Johanna Eriksson (1), Marylore Chenel (1), Erik Sjögren (1)
(1) Pharmetheus AB, Sweden
Background:
Mechanistic models for subcutaneous (SC) administration in combination with physiologically-based pharmacokinetic (PBPK) modelling are valuable in that they allow for prediction of bioavailability, translation from preclinical to clinical, and extrapolation to special populations. For biologics, there is always the potential for an immunological response, e.g., anti-drug antibodies (ADAs), which can affect the pharmacokinetics, bioavailability, and efficacy of a drug. Consequently, these two aspects coincide in the contexts of injectable biologics and of vaccines. Modelling both the mechanistic absorption and the body’s immune response allows for a more informed prediction of the pharmacokinetics of a drug following SC administration.
Objectives:
- Implement a previously established mechanistic model framework (soon to be published) for subcutaneous administration within the Open Systems Pharmacology framework
- Interface the subcutaneous administration model with the whole body model available in PK-Sim and MoBi
- Validate the model with data collected from a literature review of subcutaneous administration
- Implement a previously established model [2,3] of immunogenicity within the Open Systems Pharmacology framework
- Interface the immunogenicity model with the subcutaneous model and the whole body model available in PK-Sim and MoBi
Methods:
The software MoBi [1] was used to implement a SC administration framework (soon to be published) within the Open Systems Pharmacology framework [1]. Data from a literature review was then used for model validation. An intravenous (IV) model was developed in the software PK-Sim [1] for the molecule of interest based on plasma concentration-time profiles following IV administration. Once the IV model was established, information regarding metabolism and distribution as well as formulation characteristics were used to simulate plasma concentration-time profiles following SC administration. These results were then compared to observed data to validate the model’s predictions.
For the immunogenicity module, the software MoBi [1] was again used to implement the model within the Open Systems Pharmacology framework. The implementation was then validated by comparing to the original publication’s simulation results for adalimumab [2,3]. After model validation, the immunogenicity and SC models were used to predict the immunogenic response following an injection of a vaccine and of a biopharmaceutical compound.
Results:
The model predictions for the SC model were within two-fold error of the observed data. The model predictions for the immunogenicity model were in line with the simulations of the original publication [2,3]. Thus, the implementation of the two models were individually validated. Sensitivity analysis was completed on the individual modules to identify the most significant factors with respect to the compound and/or ADAs. For the SC model, the key parameters were proportion of the drug in the depot, dose volume, and extracellular fluid fraction. For the immunogenicity model, bioavailability and absorption rate were the key factors. This confirms the importance of modelling drug administration. For AUC of the ADAs, the key factors were T cell differentiation into memory T cells, T cell capacity to stimulate B cells, proliferation rate of activated T cells, and initial number of naïve T cells. For Cmax, bioavailability and absorption rate were additionally identified. For half-life, the key factor was death rate of long-lived plasma cells.
This modelling exercise demonstrated how the model platform could aid drug development through multi-layered analysis including drug absorption, bioavailability, and exposure as well as immunogenic response.
Conclusion:
We present an open-source platform for modelling SC administration with the effects of immunogenicity in the Open Systems Pharmacology framework [1]. These individual models were previously established (SC model soon to be published and immunogenicity model from the literature [2,3]) and implemented using the software tool MoBi [1]. These implementations were then validated using the original publication for the immunogenicity model [3] and using data collected from the literature for the SC model. These individual models together with the whole body model available in the software tool PK-Sim [1] are then used to make predictions about the immunogenic response to SC administration in the context of vaccines and biopharmaceutics.
References:
[1] Lippert, J., Burghaus, R., Edginton, A., Frechen, S., Karlsson, M., Kovar, A., ... & Teutonico, D. (2019). Open systems pharmacology community—an open access, open source, open science approach to modeling and simulation in pharmaceutical sciences. CPT: pharmacometrics & systems pharmacology, 8(12), 878.
[2] Chen, X., Hickling, T. P., & Vicini, P. (2014). A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 1—theoretical model. CPT: pharmacometrics & systems pharmacology, 3(9), 1-9.
[3] Chen, X., Hickling, T. P., & Vicini, P. (2014). A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 2—model applications. CPT: pharmacometrics & systems pharmacology, 3(9), 1-10.
