Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their Data into Machine...

Area of Science:

• Pharmaceutical Sciences
• Drug Delivery
• Sensory Science

Background:

• Taste-masking is crucial for pediatric drug formulations to improve adherence.
• Ethical and practical challenges exist in using children for taste evaluation.
• Young adults (18-40) exhibit similar taste sensitivity to children, making them suitable substitutes.

Purpose of the Study:

• To review the utility of human taste panels and artificial neural networks (ANNs) in taste-masking pediatric drug formulations.
• To explore the potential of young adults as substitutes for children in taste evaluations.
• To assess the role of ANNs in optimizing taste-masking strategies.

Main Methods:

• Utilizing human taste panels, including screening for bitterness detection thresholds and employing scoring systems.
• Employing young adults (18-40) as taste panellists.
• Leveraging artificial neural networks (ANNs) and machine learning classifiers for taste-masking optimization and bitterness prediction.

Main Results:

• Young adults can serve as effective proxies for pediatric taste sensitivity.
• ANNs demonstrate potential in modeling complex taste-masking variables and predicting bitterness intensity.
• A scoring system and acceptance value can enhance formulation evaluation during development.

Conclusions:

• Human taste panels and ANNs offer complementary approaches to address taste-masking challenges in pediatric medicines.
• Further research is needed to identify specific taste-masking techniques for various drug molecules.
• Refining machine learning algorithms with human acceptability data can significantly advance pediatric formulation development.