نبذة مختصرة : Given the common challenge of low solubility of many drugs in water, it is vital to implement novel methods to improve it. One method that is widely used across various applications is the use of co-solvents. In particular, Deep Eutectic Solvents have been recognized for their potential in the pharmaceutical industries. This potential is due to their environmental compatibility, cost-effectiveness, and desirable properties. Due to the large number and variety of deep eutectic solvents, it is not possible to perform experimental studies to determine the effect of Deep Eutectic Solvents on the solubility of drugs in water. Thus, it is vital to have thermodynamic models, to help researchers to estimate how the co-solvents enhance the solubility of drugs. This research investigates the performance of five relevant thermodynamic models. The investigated models are all empirical and require regression on the experimental data of each system to be used, which makes them non-predictive. Therefore, to overcome this issue, for the first time, the Khayam-Rajabi-Haghbakhsh model (KRH) has been developed as the first comprehensive and accurate predictive model for estimating the solubility of various drugs in water considering Deep Eutectic Solvents as co-solvents. For the development of this model, a comprehensive data bank including 1489 experimental data points for 13 different drugs and 17 Deep Eutectic Solvents has been used. The AARD% of this model has been calculated to be 13.00, indicating a high level of accuracy. Statistical analysis demonstrates acceptable and unbiased performance across all Deep Eutectic Solvents and drugs investigated. This model is widely utilized for various drug systems, water, and Deep Eutectic Solvents as co-solvents due to its comprehensiveness, accuracy, and capability to estimate drug solubility without needing experimental data.
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