A quantitative systems pharmacology model for certolizumab pegol treatment in moderate-to-severe psoriasis

Abstract

Background: Psoriasis is a chronic immune-mediated inflammatory systemic disease with skin manifestations characterized by erythematous, scaly, itchy and/or painful plaques resulting from hyperproliferation of keratinocytes. Certolizumab pegol [CZP], a PEGylated antigen binding fragment of a humanized monoclonal antibody against TNF-alpha, is approved for the treatment of moderate-to-severe plaque psoriasis. Patients with psoriasis present clinical and molecular variability, affecting response to treatment. Herein, we utilized an in silico approach to model the effects of CZP in a virtual population (vPop) with moderate-to-severe psoriasis. Our proof-of-concept study aims to assess the performance of our model in generating a vPop and defining CZP response variability based on patient profiles.

Methods: We built a quantitative systems pharmacology (QSP) model of a clinical trial-like vPop with moderate-to-severe psoriasis treated with two dosing schemes of CZP (200 mg and 400 mg, both every two weeks for 16 weeks, starting with a loading dose of CZP 400 mg at weeks 0, 2, and 4). We applied different modelling approaches: (i) an algorithm to generate vPop according to reference population values and comorbidity frequencies in real-world populations; (ii) physiologically based pharmacokinetic (PBPK) models of CZP dosing schemes in each virtual patient; and (iii) systems biology-based models of the mechanism of action (MoA) of the drug.

Results: The combination of our different modelling approaches yielded a vPop distribution and a PBPK model that aligned with existing literature. Our systems biology and QSP models reproduced known biological and clinical activity, presenting outcomes correlating with clinical efficacy measures. We identified distinct clusters of virtual patients based on their psoriasis-related protein predicted activity when treated with CZP, which could help unravel differences in drug efficacy in diverse subpopulations. Moreover, our models revealed clusters of MoA solutions irrespective of the dosing regimen employed.

Conclusion: Our study provided patient specific QSP models that reproduced clinical and molecular efficacy features, supporting the use of computational methods as modelling strategy to explore drug response variability. 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.

Keywords: anti-TNF; certolizumab pegol; mathematical modelling; mechanism of action; psoriasis; virtual population.

Figure 1

In silico clinical trial protocol overview. The protocol was divided into three main stages: Phase I, study design and information compilation; Phase II, mathematical modelling; and Phase III, data analysis to obtain molecular insights on the drug’s mechanisms of action. PBPK, Physiologically based pharmacokinetic; QSP, Quantitative systems pharmacology.

Figure 2

(A) Study branch scheme, (B) Comorbidity frequency distribution and (C) BMI distribution by sex. CZP, Certolizumab pegol; NAFLD, Non-alcoholic fatty liver disease; PsA, Psoriatic arthritis; BMI, Body mass index.

Figure 3

(A) PBPK model representation. (B-D) Comparison of CZP concentration in blood between literature’s Cmax values and extrapolated datapoints and the simulated curve using the PBPK model, generated from a standard adult patient, for: (B) 200 mg single dose of CZP (67); (C) 400mg single dose of CZP (67); and (D) 3 x 400 mg loading doses + 4 x 200mg doses (95). CZP, Certolizumab pegol; PBPK, Physiologically based pharmacokinetic.

Figure 4

Clustering analysis results. (A) Two-dimension representation of the best clustering setting result using all 1,000 patient models after MDS, and (B) the branch incidence of each obtained cluster. MDS, Multidimensional scaling.

Figure 5

Comparison of mechanisms of action enrichment analysis. (A) Network representation of the enrichment analysis results of the most differentially modulated proteins in each cluster (C1, C2, and C3) with respect to the population mean and their relationship. Details on the least active processes in (B) cluster 1 and (C) cluster 2.