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Long-Term Amorphous Drug Stability Predictions Using Easily Calculated, Predicted, and Measured Parameters
Katarzyna Nurzyńska1, Jonathan Booth2, Clive J Roberts1
1School of Pharmacy and Centre for Biomolecular Sciences, The University of Nottingham , Nottingham NG7 2RD, United Kingdom.
Predicting amorphous drug stability is crucial for formulation. This study developed statistical models using key physicochemical properties to forecast long-term amorphous stability, aiding faster drug development.
Area of Science:
- Pharmaceutical Sciences
- Physical Chemistry
- Materials Science
Background:
- Amorphous solid forms of drugs offer advantages in solubility and bioavailability, especially for poorly water-soluble compounds.
- However, amorphous drugs can be physically unstable, prone to crystallization over time, impacting drug product performance.
- Predicting and ensuring amorphous stability is critical for successful drug formulation and development.
Purpose of the Study:
- To develop a predictive model for the amorphous stability of drugs, focusing on poorly water-soluble compounds.
- To identify key molecular, thermodynamic, and kinetic parameters influencing the long-term physical stability of amorphous drug forms.
- To create statistical models that can support rapid decision-making in pharmaceutical formulation.
Main Methods:
- Selection of 25 neutral, poorly soluble compounds with diverse properties from marketed drugs.
- Assessment of physical stability via polarized light microscopy over 6 months, monitoring crystallization of amorphous films.
- Development of multiple linear regression models using calculated/predicted and measured physicochemical parameters.
Main Results:
- Inclusion of measured parameters significantly improved the predictive ability of the statistical models.
- The best model achieved 82% prediction accuracy, incorporating parameters like melting temperature, glass transition temperature, enthalpy of fusion, and lipophilicity.
- A simpler model using molecular weight and enthalpy of fusion also provided good predictions.
Conclusions:
- Statistical models incorporating readily accessible parameters can effectively predict long-term amorphous drug stability.
- These models can accelerate the decision-making process in drug formulation development.
- Understanding key factors influencing amorphous stability is vital for developing stable and effective drug products.
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