(M1230-09-59) Artificial Intelligence Based Particle Characterization for Predictive Insight of Drug Product Quality and Performance

Purpose: The presence of particulate matter and particles, particle size, morphology and distribution are critical material attributes that impact the quality, safety and efficacy of drug products. Over the years many techniques have been used for particle characterization and visualization. The use of artificial intelligence (AI) predictive modeling of particulate matter for product performance is now being investigated.

Methods: Images of raw materials, product formulations and process blends are collected and analysed through complex algorithms of machine learning and artificial intelligence. Particles are subsequently characterized and classified to generate important statistics and properties, that are useful during formulation development and manufacturing.

Results: AI/ML models derived from particle properties improve product knowledge, process understanding and manufacturing capability. The options of having the system on cloud platform, allows Pharmaceutical Manufacturers to integrate particle analysis from various global locations and all stakeholders into a single global database to yield more rapid and deeper insights into particles and product attributes.

Conclusion: AI/ML based cloud platforms are anticipated to revolutionize particle characterization and predictive analytics of important attributes of drug performance like dissolution profiling, blend uniformity and aggregation propensity etc.