Clinical Trial Optimization for Hidradenitis Suppurativa

The project covered data aggregation, AI model training, predictive analysis, and deployment of interactive planning dashboards.The platform was designed to support continuous scenario modeling for future studies and integrate with the client’s existing trial planning infrastructure.

The client is a global pharmaceutical company managing multiple dermatology clinical programs.Their goal was to improve the design and success rate of hidradenitis suppurativa trials by identifying baseline patient variables most strongly associated with positive outcomes.

Clinical outcomes across HS studies showed significant variability.Research teams lacked a structured way to determine which patient characteristics correlated with treatment efficacy, making trial design heavily dependent on manual review and expert interpretation.

During implementation, it became clear that valuable predictive signals existed across historical datasets but were not systematically analyzed.Traditional statistical review methods were unable to efficiently evaluate multidimensional interactions between patient characteristics and outcomes.

Nextigent AI deployed the Trial Optimization Intelligence Platform to analyze baseline and outcome data from previous HS studies.Machine learning models were used for feature selection, predictive scoring, and scenario simulation. The system identified which patient variables most strongly correlated with successful endpoints and generated recommendations for inclusion and exclusion criteria.Interactive dashboards enabled researchers to visualize correlations, compare trial scenarios, and model future study outcomes in real time.The platform was deployed within a secure cloud environment integrated with the client’s clinical planning tools.

The client plans to expand the platform into additional inflammatory and immunology indications.Future enhancements include adaptive trial simulations and integration with real-world evidence datasets.