In silico solid state perturbation for solubility improvement.

Area of Science:

• Pharmaceutical Science
• Computational Chemistry
• Drug Discovery

Background:

• Solubility is a critical challenge in pharmaceutical development, impacting drug efficacy and formulation.
• Existing methods for solubility enhancement often require extensive experimental screening.
• Proactive strategies for improving intrinsic solubility are essential for efficient drug development.

Purpose of the Study:

• To develop and validate a novel in silico methodology for predicting and improving intrinsic drug solubility.
• To assess the applicability of the method for identifying key molecular modifications in crystalline solids.
• To evaluate the approach using a series of benzodiazepine compounds.

Main Methods:

• Utilized in silico prediction of crystal structures to identify and perturb key interactions.
• Employed a two-dimensional molecular structure as the sole input for the computational model.
• Applied the methodology to a set of benzodiazepine molecules to predict intrinsic solubility trends.

Main Results:

• The methodology successfully predicted the overall trend in intrinsic solubility for the tested benzodiazepine set.
• The approach demonstrated suitability for identifying strategic molecular substitutions to enhance solubility, particularly for high melting point compounds ('brick dust' compounds).
• Accurate prediction of solubility trends was achieved using only 2D molecular structures.

Conclusions:

• The developed in silico method offers a valuable tool for rational compound design in early-stage drug development.
• This predictive approach can guide medicinal chemists in optimizing drug solubility by modifying crystalline solid-state interactions.
• The methodology provides a proactive strategy to address solubility challenges, reducing experimental burden.