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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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The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
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Predicting oral disintegrating tablet formulations by neural network techniques.

Run Han1, Yilong Yang1,2, Xiaoshan Li2

  • 1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau 999078, China.

Asian Journal of Pharmaceutical Sciences
|February 28, 2020
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Summary

Deep neural networks (DNNs) improve oral disintegrating tablet (ODT) formulation prediction, reducing development time. This AI approach offers a more efficient alternative to traditional trial-and-error methods in pharmaceutical research.

Keywords:
Artificial neural networkDeep neural networkDeep-learningFormulation predictionOral disintegrating tablets

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Drug Delivery Systems

Background:

  • Oral disintegrating tablets (ODTs) offer rapid dissolution for improved patient compliance, particularly in geriatric and pediatric populations.
  • Current ODT formulation development relies heavily on expert experience and iterative laboratory testing, leading to inefficiencies and prolonged timelines.
  • Predictive modeling using artificial intelligence presents a promising avenue to optimize ODT formulation design and streamline the development process.

Purpose of the Study:

  • To develop and compare artificial neural network (ANN) and deep neural network (DNN) models for predicting ODT formulation characteristics using direct compression.
  • To evaluate the predictive accuracy of ANN and DNN models on training, validation, and testing datasets.
  • To establish a novel AI-driven approach for ODT formulation prediction, applicable even with limited data.

Main Methods:

  • Data encompassing 145 ODT formulations were curated from Web of Science.
  • Datasets were partitioned into training (105), validation (20), and testing (20) sets for model evaluation.
  • ANN and DNN models were trained and compared for their ability to predict ODT disintegration time.

Main Results:

  • The DNN model achieved higher prediction accuracy on the testing set (80.00%) compared to the ANN model (75.00%).
  • Both ANN and DNN models demonstrated strong performance on the training and validation sets, with DNN slightly outperforming ANN on the validation set (85.00% vs. 80.00%).
  • The study highlights the successful application of DNNs with an improved dataset selection algorithm for formulation prediction in small data scenarios.

Conclusions:

  • Deep neural networks offer superior predictive performance for ODT formulations compared to traditional artificial neural networks.
  • The proposed AI-based predictive approach can guide critical quality control parameters and optimize research and process development for ODTs.
  • Implementing this DNN model can significantly reduce drug product development timelines, minimize material consumption, and enhance the robustness of ODT formulations.