# Comparative Assessment of Statistical and Thermodynamic Prediction Methods for Solvate Formation: A Case Study with Curcumin and Its Derivatives

## TL;DR

This paper compares methods to predict which solvents form solvates with curcumin derivatives, finding that combining COSMO-RS with hydrogen bond propensity gives the best results.

## Contribution

The study introduces a novel combination of COSMO-RS and hydrogen bond propensity for improved prediction of solvate formation.

## Key findings

- Hydrogen bond propensity (HBP) was the best individual predictor for solvate formation.
- Combining COSMO-RS with HBP improved predictive accuracy over standalone methods.
- New solvated and hydrated forms of curcumin derivatives were identified through crystallization screens.

## Abstract

This
study compares statistical and thermodynamic methodologies
for predicting solvate formation using curcumin (CUR) and its derivatives
demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) as models.
We evaluated the performance of Statistical Frequency of Interaction
for Multicomponent Prediction (SFIMP) and Conductor-like Screening
Model for Realistic Solvents (COSMO-RS) methods to identify solvents
likely to form solvates. A comprehensive crystallization screen yielded
several new solvated and hydrated forms. Our results show that hydrogen
bond propensity (HBP) performed best among individual predictors,
while COSMO-RS combined with HBP yielded superior predictive accuracy
overall. These insights aid rational design and screening of multicomponent
solid forms in pharmaceutical development.