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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
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Drug delivery methods like oral inhalation, nasal sprays, transdermal patches, eye drops, intravitreal injection,  and rectal administration provide localized effects with reduced toxicity.
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The selection of a drug's delivery route depends upon its physicochemical properties, including lipid or water solubility and ionization, as well as the therapeutic requirement, such as immediate or sustained effect. These routes can be divided into three primary categories: enteral, parenteral, and topical.
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Updated: Aug 22, 2025

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Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms.

Junhuang Jiang1, Xiangyu Ma2, Defang Ouyang3

  • 1Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA.

Pharmaceutics
|November 11, 2022
PubMed
Summary

Artificial Intelligence (AI) accelerates drug formulation by optimizing solid dosage forms. This review details AI algorithms and data strategies for efficient pharmaceutical development, improving product quality and reducing development time.

Keywords:
artificial intelligencedeep learningmachine learningsolid dosage formulation

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Area of Science:

  • Pharmaceutical Sciences
  • Drug Delivery Systems
  • Computational Chemistry

Background:

  • Traditional drug formulation relies on inefficient trial-and-error methods, which are time-consuming and resource-intensive.
  • Solid dosage forms (tablets, capsules, powders) are prevalent but influenced by numerous critical material attributes and processing parameters.
  • Artificial Intelligence (AI) offers a novel, data-driven approach to overcome formulation challenges.

Purpose of the Study:

  • To provide a comprehensive overview of AI applications in solid dosage form development.
  • To guide researchers on database generation, data processing, and AI algorithm selection for pharmaceutical formulation.
  • To highlight the potential of AI, including deep learning-based image analytics, in predicting and optimizing drug product properties.

Main Methods:

  • Review of current literature on AI in pharmaceutical sciences and regulatory guidance.
  • Exploration of database creation strategies for solid dosage formulations.
  • Analysis of data preparation techniques and comparison of various AI algorithms (e.g., machine learning, deep learning).

Main Results:

  • AI enables a more efficient and predictive approach to drug formulation compared to conventional methods.
  • Established methodologies for data handling and AI model implementation in pharmaceutical development.
  • Demonstrated success of AI in understanding and predicting critical quality attributes of solid dosage forms.

Conclusions:

  • AI integration significantly enhances the efficiency and success rate of drug product development for solid dosage forms.
  • AI tools empower scientists to better predict formulation performance, leading to faster and more reliable product realization.
  • The review provides a roadmap for leveraging AI and deep learning in pharmaceutical research and development.