Aqueous and cosolvent solubility data for drug-like organic compounds

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Accurate prediction of drug solubility is crucial for pharmaceutical development.
• Existing models may lack the scope to cover diverse chemical spaces.
• In silico methods offer efficient alternatives for solubility assessment.

Purpose of the Study:

• To develop and validate quantitative structure-property relationship (QSPR) models for predicting drug solubility.
• To establish models for both intrinsic aqueous solubility and solubility in polyethylene glycol 400 (PEG 400) cosolvent systems.
• To provide reliable datasets for future solubility modeling efforts.

Main Methods:

• Development of two QSPR-based in silico models.
• Collection of a literature dataset of 321 structurally diverse drugs for aqueous solubility.
• Generation of experimental solubility data for 122 drugs in PEG 400/water mixtures at varying compositions.

Main Results:

• Successfully developed two predictive models for drug solubility.
• The models cover a wide range of physicochemical properties (log P: -5 to 7.5, MW: 100 to >600 g/mol).
• Standardized experimental procedures ensured high-quality data for the cosolvent model.

Conclusions:

• The developed in silico models offer robust tools for predicting drug solubility.
• The high-quality experimental datasets are valuable resources for the scientific community.
• These models and data can aid in optimizing drug formulation and delivery.