2021 - Online - In the cloud

PAGE 2021: Methodology - New Modelling Approaches
Vincent Hurez

Development of a systemic lupus erythematosus (SLE) QSP model linking systemic biomarkers to cutaneous clinical score (CLASI) outcomes.

Vincent Hurez (1), Krishnakant Dasika (1), Katherine Kudrycki (1), Robert Sheehan (1), Christina Friedrich (1), Mike Reed (1), Glenn Gauderat (2), Audrey Aussy (2), Loubna Chadli (2), Florian Chassereau (2), Sylvain Fouliard (2), Sandra Hubert (2), Emiko Desvaux (2), Perrine Soret (2), Alexia Blesius (2), Philippe Moingeon (2)

(1) Rosa and Co. LLC, San Carlos, CA, USA;(2) Servier, Suresnes, France

Introduction: Evaluation of novel therapies or interventions for regulatory approval depends on the measurement of clinically relevant and often semi-quantitative or subjective outcome measures during clinical trials. To support predictions of the clinical efficacy of new drugs, clinical protocol optimization, or competitive differentiation compared to existing therapies, quantitative systems pharmacology (QSP) models must find a way to integrate biological components to predict the clinical endpoints of interest to the clinical team. The objective assessment of disease activity and clinical response in SLE is particularly challenging even for the clinicians designing new clinical trials due to the variety of clinical manifestations and the choice between multiple clinical scores. Here, we are using QSP modeling to investigate the link between pathological pathways and the various components of SLE clinical scores and support the rational design of new trials with predictions of relevant clinical SLE endpoints.


  • Establish a link between biological pathways involved in SLE pathophysiology represented in the QSP model and components of the clinical SLE activity scores
  • Calibrate the change in the SLE disease activity score components using published response to standard of care (SOC) therapies
  • Qualify the overall SLE disease activity endpoints and compared the response to new SLE drugs with existing SOC therapies

Methods: Together with Servier, Rosa developed an SLE PhysioPD Research Platform, a QSP model that mechanistically represents SLE pathophysiology with emphasis on skin manifestations. The mechanistic QSP model includes keratinocytes, dendritic cells, T and B lymphocytes and macrophages in the skin and the lymph nodes. Relevant cytokines and chemokines regulate cellular processes in the Platform. The model also integrate novel and SOC drug PK in blood, skin and lymph nodes. Clinical endpoints simulated focus on the Cutaneous LE Disease Area and Severity Index (CLASI) score with calibration of the CLASI score components based on published response to SOC therapies in the treated and the placebo groups. The SLE Platform was qualified according to the Rosa Model Qualification Method [1].

Results: Baseline SLE condition were calibrated to match the estimated cell numbers and mediators concentration in blood, lymph node and skin for a representative moderate to severe SLE patients using published data. Changes in tissue biomarkers upon treatment, e.g., keratinocyte activity, pro-inflammatory mediators, and skin immune cell infiltration, were used to establish a link between the various cellular and mediator outcomes of the Platform and the disease manifestations that compose the CLASI score. The CLASI score subcomponents were modeled by (1) identifying the critical components of the SLE activity score, (2) formulating a biological rationale for associating specific biomarkers with each score component (see Table 1), and (3) calibrating the proposed function using clinical data from existing therapies. Simulations of SOC therapies in the Platform showed that the CLASI score response matched published clinical trial data and the model was further used to predict the clinical efficacy of novel interventions.

Conclusions: QSP models are valuable tools to integrate existing mechanistic and clinical data and generate plausible predictions of disease scores in response to novel interventions. Integrating clinically relevant disease activity scores facilitates the adoption of QSP modeling as an integral part of the drug development process by demonstrating the predictive value of quantitative tools to clinical teams.

Table 1. CLASI score subcomponents and their mapping to QSP model species.

CLASI score component

QSP model species

Baseline value
from ref [2]

Clinical range


Pro-inflammatory mediators
Activated vasculature




Skin corneocytes
Keratinocyte activity




Correlated with disease activity



Recent Hair Loss

Correlated with disease activity




Hair follicle damage

Effector T cells

Pro-inflammatory mediators




Calculated skin pigmentation index




Fixed value (no change with therapy)




[1] Friedrich, C.M., A model qualification method for mechanistic physiological QSP models to support model-informed drug development. CPT Pharmacometrics Syst Pharmacol, 2016. 5(2): p. 43-53.
[2] AlE'ed, A., et al., Validation of the Cutaneous Lupus Erythematosus Disease Area and Severity Index and pSkindex27 for use in childhood-onset systemic lupus erythematosus. Lupus Sci Med, 2018. 5(1): p. e000275.

Reference: PAGE 29 (2021) Abstr 9867 [www.page-meeting.org/?abstract=9867]
Poster: Methodology - New Modelling Approaches