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Utilizing Molecular Simulations to Examine Nanosuspension Stability
Andrew P Latham1,2, Elizabeth S Levy1, Benjamin D Sellers2
1Small Molecule Pharmaceutical Sciences, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
Pharmaceutics
|January 23, 2024
Summary
Computational tools predict drug nanosuspension stability. Hydrophobic interactions and polar surface area are key factors, guiding excipient selection for improved drug bioavailability and rational formulation design.
Area of Science:
- Pharmaceutical Sciences
- Computational Chemistry
- Materials Science
Background:
- Drug nanosuspensions enhance bioavailability for poorly soluble compounds.
- Excipients are crucial for nanosuspension stability, but selection lacks clear rationale.
- Molecular-level understanding of drug-excipient interactions is needed for rational formulation design.
Purpose of the Study:
- To computationally investigate drug-excipient interactions using molecular dynamics simulations.
- To identify key molecular properties predicting nanosuspension stability.
- To develop a predictive model for rational nanosuspension formulation design.
Main Methods:
- Molecular dynamics simulations to study drug-excipient interactions.
- Analysis of hydrophobic interactions and polar surface area for stability prediction.
- Prospective application of the model to a novel drug compound (GDC-0810).
Main Results:
- Hydrophobic interactions were identified as the primary driver of excipient adsorption to drug nanoparticles.
- The fraction of polar surface area accurately predicted experimental nanosuspension stability.
- Simulations correctly predicted that a salt form of GDC-0810 would yield more stable nanosuspensions.
Conclusions:
- Computational tools offer molecular insights into drug-excipient interactions for rational formulation design.
- Predictive models based on molecular properties can guide excipient selection and improve drug bioavailability.
- Simplified, 2D molecular properties can rationalize nanosuspension design, reducing the need for computationally intensive simulations.
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