Dataset Information

Solubility of Azilsartan in Methanol, Ethanol, Acetonitrile, n-Propanol, Isopropanol, Tetrahydrofuran, and Binary Solvent Mixtures between 293.15 and 333.15 K.

ABSTRACT: A precise determination method of azilsartan solubility between 293.15 and 333.15 K in several ordinary solvents and some of their aqueous mixtures was established by high-performance liquid chromatography. In all tested solvents, its solubility shows exponential growth with the increase in temperature. This trend is especially pronounced in methanol and ethanol. The order of solubility of azilsartan can be expressed as ethanol > tetrahydrofuran > ethanol/water (8/2, v/v) > methanol > methanol/water (8/2, v/v) > n-propanol > isopropanol > ethanol/Water (5/5, v/v) > acetonitrile. The solubility data of azilsartan were well correlated by the λh model. Moreover, the thermodynamic data including the dissolving enthalpy, entropy, and Gibbs free energy of azilsartan in each solvent were calculated which is crucial to its preparation technology study.

SUBMITTER: Xu H

PROVIDER: S-EPMC7097992 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Solubility of Azilsartan in Methanol, Ethanol, Acetonitrile, <i>n</i>-Propanol, Isopropanol, Tetrahydrofuran, and Binary Solvent Mixtures between 293.15 and 333.15 K.

Xu Haojie H Kang Lingling L Qin Jinmei J Lin Jiayu J Xue Min M Meng Zihui Z

ACS omega 20200313 11

A precise determination method of azilsartan solubility between 293.15 and 333.15 K in several ordinary solvents and some of their aqueous mixtures was established by high-performance liquid chromatography. In all tested solvents, its solubility shows exponential growth with the increase in temperature. This trend is especially pronounced in methanol and ethanol. The order of solubility of azilsartan can be expressed as ethanol > tetrahydrofuran > ethanol/water (8/2, v/v) > methanol > methanol/w ...[more]

PMID: 32226897

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Project description:Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available. Scientific contribution Our research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.

Dataset Information

Solubility of Azilsartan in Methanol, Ethanol, Acetonitrile, n-Propanol, Isopropanol, Tetrahydrofuran, and Binary Solvent Mixtures between 293.15 and 333.15 K.

Publications

Solubility of Azilsartan in Methanol, Ethanol, Acetonitrile, <i>n</i>-Propanol, Isopropanol, Tetrahydrofuran, and Binary Solvent Mixtures between 293.15 and 333.15 K.

Similar Datasets

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