Objectivization of virus titration method in GMP-regulated environment using artificial intelligence-based...

The implementation of artificial intelligence-based systems (AI) into highly regulated industries faces significant regulatory and logistical challenges. The lack of established practices and guidelines, risk aversion attitude, perception of AI technology as insufficiently tested and unreliable are among the most important factors preventing wider adoption of AI solutions into pharmaceutical industry. Here we demonstrate by an example development and validation of neural network-based computer vision systems for binary classification of results from virus titration assays often used in research and production of vaccines and antivirals. The systems were shown to effectively classify images of cells from the titration assays to negative (non-infected) or positive (virus infected) with an accuracy of over 99%. The methods have been validated according to GMP principles and related guidelines. Regulatory and logistical challenges of implementation of AI-based solutions into GMP-compliant systems are discussed. Although the implementation of AI-systems into GMP-regulated environment brings specific challenges related to introduction of novel technologies, future benefits for early adopters could outweigh the initial efforts.