# Towards new approach methodologies for biological therapeutics: a novel model-informed metric to assess immunogenicity risk

## TL;DR

This paper introduces a new model to predict how anti-drug antibodies affect the effectiveness of biological therapies.

## Contribution

A novel model-informed metric using quantitative systems pharmacology to assess immunogenicity risk and its impact on drug concentration.

## Key findings

- The model accurately predicted ADA impact on drug concentration in ten out of thirteen cases.
- ADA to drug concentration ratio is a strong predictor of clinically relevant immunogenicity effects.

## Abstract

Immunogenicity poses a significant challenge in biotherapeutics development due to the formation of anti-drug antibodies (ADA), which can alter drug pharmacokinetics (PK) and reduce efficacy. However, ADA presence does not always correlate with a clinically relevant reduction in efficacy, or in some cases can be managed by adjusting dosing regimens. Current preclinical strategies focus on predicting the propensity for ADA development, but do not assess the liability for ADA to impact PK. Quantitative systems pharmacology (QSP) models integrate knowledge of biological mechanisms with physiological and drug-specific parameters to predict ADA dynamics and their effect on PK. This study describes recent progress in using QSP models to predict the incidence of immunogenicity and the impact of ADA on PK. We report continued challenges in accurately predicting ADA incidence from available data from experimental and computational methods used in immunogenicity risk assessment. However, across 13 monoclonal antibodies and fusion proteins, the model accurately predicted ADA impact on drug concentration in ten cases, Furthermore, the ADA to drug concentration ratio was identified as a strong predictor of clinically relevant immunogenicity and drug exposure impact.