Incorporating Placebo Response in Quantitative Systems Pharmacology Models

Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients. Thus, there is room for new approaches to improve response to therapy, including designing better trials e.g., by identifying subpopulations that can benefit from specific classes of therapy and enabling reverse translation by analyzing completed clinical trials. We have developed an open-source, mechanistic multi-scale model of RA, which captures the interactions of key immune cells and mediators in an inflamed joint. The model consists of a treatment-naive Virtual Population (Vpop) that responds appropriately (i.e. as reported in clinical trials) to standard-of-care treatment options-Methotrexate (MTX) and Adalimumab (ADA, anti-TNF-α) and an MTX inadequate responder sub-population that responds appropriately to Tocilizumab (TCZ, anti-IL-6R) therapy. The clinical read-outs of interest are the American College of Rheumatology score (ACR score) and Disease Activity Score (DAS28-CRP), which is modeled to be dependent on the physiological variables in the model. Further, we have validated the Vpop by predicting the therapy response of TCZ on ADA Non-responders. This paper aims to share our approach, equations, and code to enable community evaluation and greater adoption of mechanistic models in drug development for autoimmune diseases.

Current practices for QSP model assessment: an IQ consortium survey

Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.

Model-based meta-analysis to optimise S. aureus-targeted therapies for atopic dermatitis

Several clinical trials of Staphylococcus aureus (S. aureus)‒targeted therapies for atopic dermatitis (AD) have shown conflicting results about whether they improve AD severity scores. This study performs a model-based meta-analysis to investigate the possible causes of these conflicting results and suggests how to improve the efficacies of S. aureus‒targeted therapies. We developed a mathematical model that describes systems-level AD pathogenesis involving dynamic interactions between S. aureus and coagulase-negative Staphylococcus (CoNS). Our model simulation reproduced the clinically observed detrimental effects of the application of S. hominis A9 and flucloxacillin on AD severity and showed that these effects disappeared if the bactericidal activity against CoNS was removed. A hypothetical (modeled) eradication of S. aureus by 3.0 log₁₀ colony-forming unit per cm² without killing CoNS achieved Eczema Area and Severity Index 75 comparable with that of dupilumab. This efficacy was potentiated if dupilumab was administered in conjunction with S. aureus eradication (Eczema Area and Severity Index 75 at week 16) (S. aureus eradication: 66.7%, dupilumab 61.6% and combination 87.8%). The improved efficacy was also seen for virtual dupilumab poor responders. Our model simulation suggests that killing CoNS worsens AD severity and that S. aureus‒specific eradication without killing CoNS could be effective for patients with AD, including dupilumab poor responders. This study will contribute to designing promising S. aureus‒targeted therapy.