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Custom Software • AI • Data Engineering • Bioinformatics

Custom Software • AI • Data Engineering • BioinformaticsCustom Software • AI • Data Engineering • BioinformaticsCustom Software • AI • Data Engineering • Bioinformatics
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Custom Software • AI • Data Engineering • Bioinformatics

Custom Software • AI • Data Engineering • BioinformaticsCustom Software • AI • Data Engineering • BioinformaticsCustom Software • AI • Data Engineering • Bioinformatics
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AI-Driven Platform for Bioproduction Optimization

Overview

Developed a GAMP-compliant, web-based platform tailored to bioproduction workflows, integrating multi-omics data with phenotypic, imaging, environmental, media analytics, and metadata automatically. The platform leverages AI-driven classification models to uncover correlations and establish custom scoring systems aligned with unique bioproduction needs, streamlining assay development and analysis for complex biological systems. 


Key Features

  • Automated integration of multi-omics with phenotypic and imaging data
  • Advanced assay development and analysis for complex cell culture systems
  • ISO PAT–compliant classification framework supporting validated models for accurate scoring and decision-making
  • Cell culture health monitoring across pluripotency, lineage differentiation, and species-specific markers
  • Detection and minimization of genetic drift for enhanced cell line stability
  • QA/QC modules for contamination detection and mutation verification (adventitious agents, cross-contamination)
  • Comprehensive oncogene expression profiling for safety and functionality
  • Predictive allergenicity analysis leveraging metabolic data
  • Biochemical, molecular, and compositional comparative analyses
  • Integration-ready workflows compatible with external software systems
     

Add-ons

  • Proprietary machine learning models validated within the ISO PAT framework to predict cell behavior with high precision
  • Scale-up optimization to increase production efficiency and consistency
  • Scale-down strategies to identify representative assay parameters, reducing cost and time
  • Synthetic data generation to accelerate assay development while minimizing expenses
     

Results

  • Improved assay reliability and scalability
  • Increased production throughput with optimized cell culture conditions
  • Better decision-making with AI-driven analytics aligned to PAT principles
  • Reduced costs and iteration times via predictive modeling and synthetic data
  • Achieved full compliance with GAMP and ISO quality standards
     

Deployment
The platform was deployed on the client’s secure internal infrastructure, ensuring data privacy, IP protection, and compliance with pharma IT standards

Client: Mid-size pharmaceutical company (Bioprocess Development R&D Area) 

Timeline: 12 months (core platform deployment); 5 months (add-ons)

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