Artificial Intelligence-Driven Optimization of Dissolution Testing Parameters for Oral Drug Dosage Forms Screening

Abstract

Dissolution testing is an essential quality control procedure used in pharmaceutical industries to evaluate how a drug is released from oral dosage forms such as tablets and capsules. Traditional methods for optimizing dissolution parameters often require multiple laboratory experiments and significant time. Recently, artificial intelligence (AI) and machine learning (ML) techniques have been introduced to improve this process. AI models can analyse large pharmaceutical datasets and accurately predict drug release behavior. These predictive models help optimize important dissolution parameters such as agitation speed, dissolution medium composition, temperature, and sampling time. By applying AI in dissolution testing, pharmaceutical scientists can reduce experimental workload, improve prediction accuracy, and accelerate formulation development.