# A Semi-Mechanistic Mathematical Model of Immune Tolerance Induction to Support Preclinical Studies of Human Monoclonal Antibodies in Rats

## TL;DR

This paper introduces a mathematical model to predict immune tolerance in rats when testing human monoclonal antibodies, helping to reduce immune responses in preclinical studies.

## Contribution

A semi-mechanistic model integrating pharmacokinetics and immunogenicity data to simulate immune tolerance in preclinical antibody studies.

## Key findings

- The model accurately described anti-drug antibody (ADA) formation in rats across treatment groups.
- The model captured the effect of tacrolimus/sirolimus on suppressing ADA formation and inducing immune tolerance.
- The approach provides a framework for simulating immune tolerance scenarios in monoclonal antibody development.

## Abstract

Background/Objectives: The administration of human monoclonal antibodies (mAb) in preclinical pharmacokinetics and toxicology studies often triggers an immune response, leading to the formation of anti-drug antibodies (ADA). To mitigate this effect, we have recently performed and reported on studies using short-term immunosuppressive regimens to induce prolonged immune tolerance towards a human mAb, erenumab, in rats. Here, we report on the development of a semi-mechanistic modeling approach that quantitatively integrates pharmacokinetic and immunogenicity assessments from immune tolerance induction studies to provide a framework for the simulation-based evaluation of different immune induction scenarios for the maintenance of prolonged immune tolerance towards human mAbs. Methods: The integrated pharmacokinetic/pharmacodynamic (PK/PD) modeling approach combined a semi-mechanistic model of the adaptive immune system to predict ADA formation kinetics with a population pharmacokinetic model to assess the impact of the time course of the ADA magnitude on the PK of erenumab in rats. Model-derived erenumab concentration–time profiles served as input for a quantitative system pharmacology-style semi-mechanistic model of the adaptive immune system to conceptualize the ADA response as a function of the kinetics of CD4+ T helper cells and T regulatory cells. Results: The model adequately described the observed ADA magnitude–time profiles in all treatment groups and reasonably simulated the kinetics of selected immune cells responsible for ADA formation. It also successfully captured the impact of tacrolimus/sirolimus immunomodulation on ADA formation, demonstrating that the regimen effectively suppressed ADA formations and induced immune tolerance. Conclusions: This work demonstrates the utility of modeling approaches to integrate pharmacokinetic and immunogenicity assessment data for the prospective planning of long-term toxicology studies to support the preclinical development of mAbs.