Rats can predict aversiveness of Active Pharmaceutical Ingredients
Jessica Soto1, Alexander Keeley2, Alison V Keating2
1Department of Pharmaceutics, UCL School of Pharmacy, 29/39 Brunswick Square, WC1N 1AX London, United Kingdom; Novartis Pharma AG, Basel, Switzerland.
Summary
The rat brief-access taste aversion (BATA) model effectively predicts human taste responses for active pharmaceutical ingredients (APIs). This reliable screening tool aids in developing palatable medications, especially for pediatric patients.
Area of Science:
- Pharmacology
- Drug Development
- Sensory Science
Background:
- Patient acceptability and compliance with medicines, particularly for pediatric populations, are significantly influenced by taste.
- Effective taste-masking strategies for active pharmaceutical ingredients (APIs) are essential for developing palatable drug formulations.
- Optimized taste assessment methods are urgently needed throughout the drug development pipeline.
Purpose of the Study:
- To evaluate the rat brief-access taste aversion (BATA) model as a predictive screening tool for API aversiveness.
- To compare the rat BATA model's taste assessment with human taste panel responses for various APIs.
- To establish the model's utility in predicting human taste tolerability.
Main Methods:
- Investigated the taste intensity of nine marketed APIs with varying bitterness levels using the rat BATA model.
- Conducted parallel human taste panels to assess the same APIs across overlapping concentration ranges.
- Determined drug concentrations eliciting half the maximal response (IC50 for rats, EC50 for humans).
Main Results:
- A strong correlation (R² = 0.896) was observed between rat IC50 and human EC50 values for API aversiveness.
- The rat BATA model demonstrated a reliable quantitative assessment of API aversiveness, correlating well with human perception.
- Ranking of aversiveness was consistent for high and medium intensity compounds, with some divergence for weakly aversive substances.
Conclusions:
- The rat BATA model is a rapid, reliable, and predictive tool for assessing API aversiveness and forecasting human taste responses.
- The model facilitates the classification of poor taste intensity in APIs, aiding in the development of acceptable pharmaceutical formulations.
- This approach supports early-stage drug development by identifying and addressing taste challenges efficiently.
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