Amorphous solid dispersions (ASDs) are crucial in enhancing the solubility and stability of water-insoluble drugs, maximizing therapeutic efficacy. This blog presents a shelf life study of ASD formulations under long-term storage conditions. The study demonstrates the power of integrating physics-based simulations with experimental data. It is one of the few publications combining theoretical work with experimental data, providing a robust approach to understanding shelf life. At amofor, we are proud to offer this cutting-edge stability prediction method to our customers.
Challenges
Amorphous Solid Dispersions (ASDs) stabilize drugs by incorporating them into a highly viscous amorphous polymer matrix. The time a drug remains stable in its amorphous state directly impacts its shelf life, which is crucial for regulatory approval.
Understanding the stability of ASDs is critical, particularly as environmental factors like temperature and humidity can accelerate drug crystallization. This complex interplay of molecular mobility and API supersaturation within the polymer matrix presents significant challenges. Addressing these requires formulators to consider key questions about crystallization risk, the feasibility of stability studies, and long-term storage. Addressing these points is essential for improving ASD performance and ensuring regulatory compliance.
Experimental Design and Model Development
To predict the onset of crystallization or shelf life of ASDs under various storage conditions, we first conducted extensive long-term experimental stability studies. We tested different API/polymer concentrations across various temperature and humidity levels and measured crystallization kinetics.
For this, we combined the following four physical model via classical nucleation theory.
- The PC-SAFT model: Provides accurate thermodynamic predictions, focusing on crystallization drivers such as temperature, supersaturation, and water uptake.
- Molecular mobility model: Assesses mobility dynamics within ASDs, considering factors like diffusion rates and glass transition.
- Glass-transition model of the ASD, also in presence of moisture or additional excipients.
- Crystal growth and nucleation model: Clarifies physical phase transition dependencies of the amorphous matrix to the API crystals.
We focused on simplifying the model equations and reducing the number of required parameters. This makes the model more applicable and easier to use in ASD development.
Using this robust modeling approach, we derived a robust framework that allows us to see how these factors influence each other at various stages to extrapolate and predict the shelf life of these ASD formulations.
Key Findings
Our comparative analysis of predicted and measured shelf life for ASDs using two different typical ASD polymers, PVPVA64 and Soluplus®, revealed two important insights:
1/ Consistent Predictive Accuracy: Both predicted and observed shelf lives are closely aligned, consistently falling within the same order of magnitude. This demonstrates a high level of agreement between our model predictions and actual experimental outcomes.
2/ Robust Model Performance: Despite variable experimental conditions (storage temperatures, relative humidities, and API concentrations), our model accurately predicted stability, demonstrating its robustness and adaptability.
Benefits For Drug Formulators
Our model speeds up and helps to design better ASD formulations. Key advantages are:
- Minimal Data Requirements: Initial predictions of ASD shelf life can be made with minimal data, including:
- One differential scanning calorimetry (DSC) measurement to determine solubility/supersaturation and glass-transition temperatures.
- One crystallization-kinetics measurement for each condition, above and below the glass transition temperature.
- Specific and Adaptable: The parameters are tailored to each API/polymer combination and manufacturing method. They also adapt to API loads and storage conditions, such as temperature and relative humidity.
- Streamlined Formulation Process:
- Quick initial assessment: This step involves quick, predictive assessments. Initial testing can be performed in a few days using a limited training dataset, such as a few solubilities in organic solvents.
- Verification stage: This stage employs more comprehensive data from failed formulations to refine and validate the model, ensuring its accuracy.
- Experimentally Validated: The in silico model has already been validated on over 150 data points for ASD shelf life measurements ranging from one to three years. This extensive, long-term storage data underlines the model’s high performance and robustness. To our knowledge, no other validated and reliable model of this scale exists in the industry that captures all the mentioned complex dependencies.
This study bridges the gap between theoretical predictions and experimental validations and sets a new standard for formulating more stable and effective pharmaceutical products. These physics-based simulations allow ASD shelf life predictions (crystallization onset) for any API load, temperature, and relative humidity. For more information, download our white paper on ASD shelf life.
More questions about the model and its applications?
Discover more in the peer-reviewed publication:
The shelf life of ASDs: 2. Predicting the shelf life at storage conditions
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