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Current Drug Targets

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ISSN (Online): 1873-5592

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Review Article

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology

Author(s): Amit Sahu, Sunny Rathee, Shivani Saraf and Sanjay K. Jain*

Volume 25, Issue 6, 2024

Published on: 11 January, 2024

Page: [416 - 430] Pages: 15

DOI: 10.2174/0113894501281290231221053939

Price: $65

Abstract

Background: Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.

Objectives: The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.

Methods: This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.

Results: Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.

Conclusion: The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

Keywords: Material handling, tablets, artificial intelligence, machine learning, tablet technology, pharmaceutical sciences.

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