Measurement and correlation of solubility of lifitegrast in four mixtures of (diethylene glycol monoethyl ether, glycerol, PEG 400, and propylene glycol + water) from 288.15 K to 308.15 K

Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation

In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the effective delivery and therapeutic performance of rivaroxaban, as its low aqueous solubility limits oral bioavailability and necessitates innovative approaches for drug formulation. The study explored the role of primary alcohols combined with dichloromethane in improving solubility, emphasizing their industrial relevance in crystallization, purification, and drug manufacturing processes. To complement experimental insights, machine learning models were employed to predict rivaroxaban solubility based on temperature, solvent type, and mass fraction of dichloromethane. Three models-AdaBoost Gaussian process regression (ADAGPR), AdaBoost multilayer perceptron (ADAMLP), and AdaBoost LASSO regression (ADALASSO)-were evaluated using [Formula: see text], RMSE, and MAPE metrics. Among these, ADAGPR demonstrated superior performance with an R² score of [Formula: see text], outperforming ADAMLP [Formula: see text] and [Formula: see text]. It also achieved the lowest total RMSE [Formula: see text] and MAPE [Formula: see text], confirming its predictive precision and reliability. Optimal solubility conditions were identified at [Formula: see text] with a mass fraction of 0.8190 in a dichloromethane-methanol mixture, yielding a predicted solubility of [Formula: see text]. These findings highlight the potential of combining chemical engineering principles with advanced predictive modeling to optimize solubility in complex solvent systems, offering significant value to pharmaceutical development and process optimization.

Effect of Citric Acid and Tromethamine on the Stability of Eyedrops Containing Lifitegrast

BACKGROUND/OBJECTIVES

Lifitegrast is an effective treatment for dry eye disease, reducing inflammation and improving the ocular surface condition. Owing to its high sensitivity to oxidation and hydrolysis, formulation studies are required to maintain the physicochemical stability of lifitegrast. This study aimed to overcome the instability of lifitegrast by developing a more stable eyedrop formulation by using citric acid and tromethamine to prevent the degradation of lifitegrast.

METHODS

Based on the Design of Experiment (DoE) approach, formulations were prepared at various concentrations of two stabilizers, citric acid and tromethamine. The stabilizers were carefully controlled to reduce the generation of degradation products. The eyedrops were stored under accelerated test conditions, and parameters such as appearance, pH, drug content, and impurities were evaluated.

RESULTS

The results showed that all critical quality attributes (CQAs) including appearance, pH, drug content, and impurities were maintained at stable levels under accelerated conditions, meeting established criteria. In addition, it was suggested that citric acid provided protection against oxidative stress, while tromethamine prevented hydrolysis caused by pH fluctuations.

CONCLUSIONS

Consequently, it was concluded that the developed lifitegrast-containing eyedrop formulation exhibited improved physicochemical stability, validated through statistical analyses. These findings contribute to the development of stable eyedrops and provide a foundation for commercial production and clinical applications.