Co-Former Screening Method for Multicomponent Crystals Based on Partial Least Squares Regression: A Case Study of Ciprofloxacin

Abstract

Multicomponent crystal is an effective way to improve the physicochemical properties of drugs. However, screening for co-formers that form multicomponent crystals with active pharmaceutical ingredients is still a challenge. In this study, a co-former prediction method based on partial least squares (PLS) regression was developed. First, 101 positive and negative samples reported were traced from the literature as a training set, based on which seven prediction models were built. Ciprofloxacin (CIP) was selected as the model drug, and the prediction models developed were adopted to predict co-formers which might form multicomponent crystals with CIP. Seven CIP multicomponent crystal systems were successfully prepared experimentally, six of which were hit by the two-parameter prediction model with the Hansen solubility parameter and the conductor-like screening model for real solvents, which achieved an overall accuracy and positive accuracy of 85 and 75%, respectively, higher than those of other models. All seven multicomponent crystal systems showed an increase in process solubility in pH 6.8 phosphate buffer solution (1.06–3.92-fold) and exhibited two different types of dissolution behaviors, wherein three samples exhibited a supersaturated peak in the dissolution process, while the other four did not. This difference was mainly due to the different rates of CIP trihydrate precipitation. The hygroscopic stability of all multicomponent crystals at room temperature and 95% relative humidity was substantially improved compared to raw CIP. This study provides a simple and effective strategy for the predictive screening of multicomponent crystal systems.