Translational Research and Antibody Discovery in Atopic Dermatitis
In healthcare and immunology research, improving treatment outcomes often requires understanding both therapeutic mechanisms and patient variability. This case study shows how Langate helped a biotechnology company accelerate multispecific antibody discovery and uncover biological insights behind treatment non-response in atopic dermatitis using AI-assisted translational research platforms.
Summary
Langate implemented two AI-powered research platforms to support target discovery, biomarker analysis, and the generation of translational insights for atopic dermatitis programs.
Quick Facts
- Timeline: 9 months combined
- Team: 10+ specialists
- Output: 7 high-confidence IL-18 co-targets identified
- Additional output: 5 non-responder-associated targets discovered
- Research cycles reduced from months to weeks
- Significant acceleration of translational and therapeutic discovery workflows
Project Scope
The project combined two connected initiatives within the client’s immunology research program:
- multispecific antibody target discovery
- clinical insight generation for treatment non-responders
The engagement included AI model deployment, translational data analysis, dashboard development, and integration with internal research systems.
Tech Stack
- Python
- PyTorch
- TensorFlow
- Vertex AI
- Spotfire
Data sources: OpenTargets, Human Protein Atlas, STRING, GEO, ClinVar, TCGA, PubMed
The Client
The client is a biotechnology company focused on the development of immunotherapies and treatments for inflammatory diseases. Their objective was to improve therapeutic discovery in atopic dermatitis by identifying synergistic antibody targets and uncovering mechanisms behind Dupixent treatment resistance.
Business Requirements
- Accelerate discovery of multispecific therapeutic approaches
- Improve understanding of treatment non-response mechanisms
- Reduce manual effort in translational research analysis
- Support evidence-based hypothesis generation
- Enable scalable and repeatable research workflows
The Problem
The client’s research teams faced increasing complexity in both target discovery and translational analysis. Traditional workflows could not efficiently evaluate large-scale biological interactions or uncover clinically relevant patterns in non-responder datasets. As a result, research timelines were extended, and important biological signals risked being overlooked.
The Challenges
-
Managing complex multi-omics and clinical datasets - Identifying clinically meaningful biological correlations
- Integrating AI-generated outputs into scientific workflows
- Supporting collaboration between translational and therapeutic research teams
- Maintaining data security and compliance standards
Problems Revealed
The project revealed that translational insights and therapeutic discovery processes were disconnected. While substantial clinical and biological data existed, there was no unified framework capable of linking non-response mechanisms with new therapeutic opportunities in a scalable way.
The Solution
Achievements
-
7 high-potential co-targets identified for multispecific antibody development
-
5 targets associated with Dupixent non-response discovered
-
Significant reduction in translational research timelines
-
Faster generation of therapeutic hypotheses
-
Improved collaboration between clinical, translational, and R&D teams
The combined solution enabled the client to move from fragmented exploratory analysis toward a more structured, AI-assisted discovery process.
Future Plans
-
The client is expanding these AI-driven workflows into additional immunology and dermatology programs.
-
Langate continues to support the development of predictive translational analytics and next-generation therapeutic discovery capabilities.
Ready to Accelerate Discovery?
Send a message to Langate sales
