Asset Lifecycle Value Management

Identifying high-value PsA patients along disease progression to support portfolio value-based positioning

Characterizing high-value (high costs / high response rates) patients across disease stages by modeling disease progression onto structural damage under care pathways scenarii to maximize medical benefit and portfolio value.

Peripheral and axial manifestations of psoriatic arthritis (PsA) can lead to irreversible structural damage and chronic disability. Multiple treatments are available with different mechanisms of action, different potency, efficacy and safety.

It is medically and economically critical to optimize and personalize the treatment sequences so to delay efficiently structural damages while containing cost of care.


Identify and characterize patients with higher unmet need or high-value patients to optimize positioning and guide medical practice

To explore predictors of radiographic progression and to characterize differences in response in relation to rates of structural damage progression.

To characterize high burden patients across disease stages and care pathways.

To demonstrate value of “precision care” adapting treatment sequencing and switching times vs patients’ profiles.

Methods & solution

Modeling structural damage and prescription scenarii to maximize medical benefits

Characterize “high burden” patients in real-world using AI/ML and pharmacoeconomic evaluations.

Analyze data from two large Phase-3 trials of a new PsA drug to profile patients in terms of progression rate.

Model and simulate prescription patterns scenarios to maximize medical benefits over patient lifetime.

Combine advanced AI/ML with predictive modeling of structural damage.

Apply a longitudinal Bayesian mixture model with random effects to account for the variability in the repeated radiographic assessments.


High-cost patients and robust predictors of radiographic progression were characterized, and the added benefit of precision care could be simulated

High-cost patients profiles were identified and characterized based on disease and care historical data.

Profiles of patients with unmet needs and high response rates were characterized.

Higher baseline inflammation and higher body weight were identified as significant predictors of radiographic progression (multivariate model).

Model-estimated structural damage progression in an average patient treated with new drug was slower compared to SoC.


Earlier switch to the sponsored drug was shown to be more beneficial and cost-effective in high cost and in fast progressors

  • Optimal positioning could be identified for multiple assets with higher cost but higher medical value and efficiency on fast progressors and/or high burden patients.

Reference: Secukinumab’s effect on structural damage progression in psoriatic arthritis: longitudinal mixture modelling of FUTURE-1 and FUTURE-5 –