President Somru BioScience USA Inc Charlottetown, Prince Edward Island, Canada
This presentation explores the critical role of Artificial Intelligence (AI) in bioanalysis, addressing challenges and opportunities in method development, regulatory compliance, and troubleshooting. While AI has transformed drug discovery and manufacturing, its adoption in bioanalysis remains limited due to fragmented data ecosystems, legacy data architecture, and regulatory complexities.
We will highlight three case studies showcasing AI-driven innovations: 1. Rapid Method Development AI-powered predictive analytics can accelerate method development by analyzing historical assay performance and suggesting optimal conditions. A case study will illustrate how machine learning algorithms enhanced ligand binding assay (LBA) development, reducing development time by 40% while improving assay robustness. 2. Regulatory Compliance AI-driven automation enhances compliance by ensuring real-time quality control and adherence to regulatory guidelines. We will discuss how an AI-assisted bioanalytical validation process streamlined documentation, minimized deviations, and improved data integrity, aligning with FDA and EMA guidelines. 3. Method Troubleshooting AI enables root cause analysis and troubleshooting in complex bioanalytical workflows. A case study will demonstrate how pattern recognition algorithms detected inconsistencies in LBA data, identifying analyst variability as a key issue and preventing batch failures. By implementing AI-driven solutions, bioanalytical laboratories can increase efficiency, reduce errors, and improve regulatory readiness, ultimately leading to faster and more reliable drug development.
Learning Objectives:
Upon completion, participants will be able to Understand the Key Challenges in AI Adoption for Bioanalysis. Explore how these challenges impact method development, validation, and regulatory compliance in the bioanalysis..
Upon completion, participants will be able to analyze real-world case studies showcasing how AI can accelerate ligand binding assay (LBA) development, improve regulatory compliance, and enhance troubleshooting in bioanalysis
Apply AI Strategies to Enhance Efficiency and Regulatory Readiness. Understand how AI can support compliance with FDA, EMA, and other regulatory frameworks, ensuring data integrity and reducing errors in bioanalysis