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Particle Size Distribution Equivalency as Novel Predictors for Bioequivalence
Pratak Ngeacharernkul1, Stephen D Stamatis1,2, Lee E Kirsch3
1Division of Pharmaceutics, The University of Iowa, Iowa City, Iowa, USA.
AAPS Pharmscitech
|August 18, 2018
Summary
Particle size distribution (PSD) similarity metrics and drug release predictions can holistically evaluate bioavailability. Combining PSD overlap and f2 release profile comparators accurately predicts bioequivalence for subcutaneous drug emulsions.
Area of Science:
- Pharmaceutical Sciences
- Pharmacokinetics
- Drug Delivery
Background:
- Particle size distribution (PSD) significantly impacts drug product performance.
- Predicting in vitro-in vivo correlations for subcutaneous emulsions remains challenging.
- Evaluating bioequivalence (BE) often relies on complex clinical studies.
Purpose of the Study:
- To develop a rational approach for assessing in vitro comparators to predict bioequivalence of subcutaneous drug emulsions.
- To evaluate the utility of PSD similarity metrics and drug release profiles for predicting bioequivalence.
- To integrate these predictors into physiologically based pharmacokinetic (PBPK) models.
Main Methods:
- Developed in vitro comparators: PSD characterization (overlap metrics, ratios) and release profiles (f2, fractional time ratios).
- Constructed a subcutaneous PBPK disposition model to simulate drug concentrations.
- Utilized Monte Carlo simulations (10,440 pairs) and logistic regression to correlate in vitro data with bioequivalence outcomes.
Main Results:
- The f2 method alone was the best single predictor of bioequivalence.
- Combinations of f2 with PSD overlap comparators (OVL, PROB) improved bioequivalence prediction to approximately 90%.
- Simulation procedures demonstrated a process for developing reliable in vitro BE predictors.
Conclusions:
- In vitro PSD and release profile comparators can effectively predict bioequivalence for subcutaneous emulsions.
- Combining multiple in vitro metrics enhances prediction accuracy.
- This approach offers a more holistic and potentially streamlined method for evaluating drug product performance.
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