A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.

ABSTRACT:

Background

The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The choice of excipients in tablet formulations plays a critical role in ensuring product quality, highlighting its importance in tablet creation. The traditional trial-and-error approach to this process is both expensive and time-intensive. To tackle these obstacles, we introduce a fresh approach leveraging machine learning and deep learning methods to automate and enhance pre-formulation drug design.

Methods

We collected a comprehensive dataset of 1983 formulations, including excipient names, quantities, active ingredient details, an

SUBMITTER: Momeni M 

PROVIDER: S-EPMC10967102 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature