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Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism

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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion

Daniel M Walden¹, Yogesh Bundey¹, Aditya Jagarapu¹

¹VeriSIM Life Inc., 1 Sansome St, Suite 3500, San Francisco, CA 94104, USA.

Molecules (Basel, Switzerland)

|January 6, 2021

Summary

This summary is machine-generated.

Molecular modeling accelerates drug development by predicting amorphous solid dispersion (ASD) properties and API-carrier interactions. This computational approach enhances formulation design for poorly soluble drugs, reducing experimental costs and time.

Keywords:

amorphous solid dispersions bioavailability drug development machine learning molecular dynamics molecular modeling solubility

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Area of Science:

Pharmaceutical Sciences
Computational Chemistry
Materials Science

Background:

Amorphous solid dispersions (ASDs) are crucial for delivering poorly soluble active pharmaceutical ingredients (APIs).
Predicting API solubility and API-carrier interactions is vital for effective drug formulation.
Experimental methods for determining these properties are often time-consuming and expensive.

Purpose of the Study:

To review the application of in silico methods for rational formulation design of low-solubility drugs.
To discuss the role of molecular modeling in predicting ASD properties and API-carrier interactions.
To highlight the potential clinical benefits of accelerated ASD formulation through computational approaches.

Main Methods:

Utilizing quantum mechanical methods to determine API-carrier non-bonding interactions.
Employing molecular dynamics simulations to predict ASD physical stability, solubility, and dissolution.
Leveraging statistical learning models for predicting drug formulation properties.

Main Results:

Molecular modeling accurately elucidates API-carrier non-bonding interactions.
Simulations predict ASD physical stability, solubility, and dissolution mechanisms.
Statistical learning models show promise for predicting ASD solubility.

Conclusions:

In silico research, including molecular modeling and statistical learning, is essential for rational ASD formulation.
Computational methods accelerate the understanding and prediction of ASD properties, reducing experimental trial and error.
Advancements in theoretical and computational approaches will expedite lead compound development for clinical applications.