Applications of Machine Learning in Solid Oral Dosage Form Development.

Area of Science:

• Pharmaceutical Sciences
• Computer Science
• Chemical Engineering

Background:

• Machine learning (ML) has seen increasing application in pharmaceutical research over the last three decades.
• Both academic and industrial sectors are utilizing ML for preformulation, formulation, and process development studies.
• The integration of ML offers potential for optimizing drug product development.

Purpose of the Study:

• To comprehensively review the application of machine learning in solid oral dosage form development.
• To provide expert perspectives on the optimal utilization of ML in pharmaceutical projects.
• To guide researchers on the strategic implementation of ML for enhanced drug development outcomes.

Main Methods:

• Review of literature and industry practices concerning ML in solid oral dosage form development.
• Analysis of key components for successful ML implementation: defining objectives, data generation, model development, and validation.
• Synthesis of expert insights for practical application of ML in pharmaceutical science.

Main Results:

• ML is extensively applied in preformulation, formulation design, and process development for solid oral dosage forms.
• Key factors for successful ML application include high-quality data, clear objectives, robust model training, and validation.
• Established ML models can be effectively used for predicting outcomes with new samples.

Conclusions:

• Machine learning is a powerful tool with a growing role in solid oral dosage form development.
• Strategic and informed application of ML is crucial for maximizing its benefits in pharmaceutical research.
• The future of drug development is expected to heavily involve advanced machine learning techniques.