Computational Model Illuminates Certolizumab Pegol's Impact in Psoriasis Treatment

In a recent study published in the Frontiers in Immunology, researchers utilized quantitative systems pharmacology (QSP) to model the effects of certolizumab pegol (CZP) within a virtual population (vPop) consisting of individuals with moderate to severe psoriasis.

The study aimed to demonstrate the potential of a computational model to create a diverse vPop and predict the variable responses to CZP based on individual patient profiles.

A multifaceted approach incorporated an algorithm to generate the vPop based on real-world data, physiologically-based pharmacokinetic (PBPK) models for CZP dosing, and systems biology-based models to unravel the drug's mechanisms of action (MoA).

By combining these approaches, the researchers successfully established a vPop and PBPK model that closely matched existing data. Their QSP and systems biology models replicated known clinical and molecular effects, offering predictions that aligned with real-world clinical efficacy outcomes. Notably, the research unveiled distinct patient clusters based on their predicted response to CZP, offering insights into why the drug's efficacy can vary across different subpopulations.

Moreover, the study revealed various mechanistic pathways, or MoA solutions, which remained consistent regardless of the dosing regimen applied.

“Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as [a] modelling strategy to explore drug response variability,” concluded the study authors. “This might shed light on the differences in drug efficacy in diverse subpopulations, especially useful in complex diseases such as psoriasis, through the generation of mechanistically based hypotheses.”

Reference
Coto-Segura P, Segú-Vergés C, Martorell A, et al. A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis. Front Immunol. 2023;14:1212981.. doi:10.3389/fimmu.2023.1212981