Prediction of Drug Loading in the Gelatin Matrix Using Computational Methods

Area of Science:

• Drug delivery systems
• Computational chemistry
• Nanotechnology

Background:

• Drug delivery research is critical for economic, health, and societal advancements.
• Selecting optimal drug formulations (carrier and drug) is a significant challenge, involving substantial annual investment.
• Experimental determination of drug loading capacity is time-consuming and costly.

Purpose of the Study:

• To investigate the use of computational tools to predict drug loading into drug delivery systems.
• To demonstrate a methodology that can accelerate the drug formulation process.
• To highlight the potential economic, environmental, and health benefits of computational approaches in drug delivery.

Main Methods:

• Data mining of existing literature.
• Principal component analysis (PCA) and hierarchical clustering analysis (HCA).
• Partial least squares regression (PLSR), autocovariance calculations, molecular dynamics (MD) simulations, and molecular docking.

Main Results:

• Successfully predicted drug loading into model drug delivery systems (gelatin nanospheres) using computational tools.
• Demonstrated the efficacy of a combined computational approach for formulation studies.
• Validated the potential of theoretical chemistry to expedite the identification of suitable drug carriers.

Conclusions:

• Computational chemistry offers a powerful and efficient alternative to traditional experimental methods in drug formulation.
• The developed methodology has the potential to significantly impact and transform drug formulation studies globally.
• This approach can lead to substantial savings in costs, time, and chemical waste, benefiting the economy, environment, and public health.