Murali Mohan Babu, Ni Luh Putu Nurshanti, Harry Martha Wijaya, Raymond R. Tjandrawinata

Harnessing Artificial Intelligence in Generic Formulation Development and Life Cycle Management - A Comprehensive Review

Introduction

Harnessing artificial intelligence in generic formulation development and life cycle management - a comprehensive review. Discover AI's transformative role in generic drug development and lifecycle management. Enhance efficiency, precision, and cost-effectiveness for improved drug affordability & accessibility.

Abstract

Artificial intelligence (AI) is revolutionizing the pharmaceutical industry by enhancing efficiency, precision, and cost-effectiveness in drug development. This study explores the application of AI in the lifecycle management of generic drugs, focusing on key stages such as active pharmaceutical ingredient (API) synthesis, excipient selection, pre-formulation studies, bioequivalence testing, and regulatory compliance. By leveraging machine learning algorithms, AI facilitates predictive modeling, risk assessment, and optimization of drug formulation processes, reducing time-to-market and improving scalability. Despite significant advancements, challenges such as data quality, algorithm transparency, and infrastructure limitations persist, particularly in resource-constrained settings. This review highlights case studies and emerging technologies that address these challenges, providing actionable insights for pharmaceutical stakeholders. The study also discusses AI's potential to streamline supply chain logistics, enhance accessibility, and ensure regulatory adherence. By integrating AI across all stages of generic drug development, this research underscores its transformative potential in improving drug affordability, accessibility, and patient outcomes globally.

Review

This review article, "Harnessing Artificial Intelligence in Generic Formulation Development and Life Cycle Management," promises a timely and comprehensive examination of a critical area within the pharmaceutical industry. The abstract clearly articulates the study's scope, detailing the revolutionary impact of AI in enhancing efficiency, precision, and cost-effectiveness in generic drug development. By focusing on key stages such as API synthesis, excipient selection, pre-formulation studies, bioequivalence testing, and regulatory compliance, the article positions itself to provide a holistic view of AI's integration across the entire lifecycle of generic drugs. A significant strength of the proposed review lies in its emphasis on the tangible benefits AI brings, including predictive modeling, risk assessment, and optimization of formulation processes, which collectively contribute to reducing time-to-market and improving scalability. The abstract highlights the potential for "actionable insights" derived from case studies and emerging technologies, alongside discussions on streamlining supply chain logistics and enhancing drug accessibility. This forward-looking perspective, coupled with the explicit aim to improve drug affordability, accessibility, and patient outcomes globally, underscores the profound practical and societal relevance of this research. While outlining a compelling vision, the abstract also judiciously acknowledges the significant challenges that persist, particularly concerning data quality, algorithm transparency, and infrastructure limitations, especially in resource-constrained settings. The success of this "comprehensive review" will hinge on the depth with which it not only identifies these hurdles but also critically evaluates and presents robust, practical solutions and emerging technologies that genuinely address them. A thorough analysis in these areas will be crucial for providing a truly balanced perspective and offering concrete guidance for pharmaceutical stakeholders navigating the complexities of AI adoption.

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