Prediction of Solubility of Drugs by Conductor-Like Screening Model for Real Solvents

COSMO Models for the Pharmaceutical Development of Parenteral Drug Formulations

The aqueous solubility of active pharmaceutical ingredients is one of the most important features to be considered during the development of parenteral formulations in the pharmaceutical industry. Computational modelling has become in the last years an integral part of pharmaceutical development. In this context, ab initio computational models, such as COnductor-like Screening MOdel (COSMO), have been proposed as promising tools for the prediction of results without the effective use of resources. Nevertheless, despite the clear evaluation of computational resources, some authors had not achieved satisfying results and new calculations and algorithms have been proposed over the years to improve the outcomes. In the development and production of aqueous parenteral formulations, the solubility of Active Pharmaceutical Ingredients (APIs) in an aqueous and biocompatible vehicle is a decisive step. This work aims to study the hypothesis that COSMO models could be useful in the development of new parenteral formulations, mainly aqueous ones.

Rapid route design of AZD7594

Multidisciplinary collaboration enables the rapid and efficient design and selection of an improved manufacturing route to a new potential medicine for the treatment of asthma.

Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS

Performance of COSMO-RS method as a tool for partition and distribution modeling in 20 solvent pairs-composed of neutral or acidic aqueous solution and organic solvents of different polarity, ranging from alcohols to toluene and hexane-was evaluated. Experimental partition/distribution data of lignin-related and drug-like compounds (neutral, acidic, moderately basic) were used as reference. Several aspects of partition modeling were addressed: accounting for mutual saturation of aqueous and organic phases, variability of systematic prediction errors across solvent pairs, taking solute ionization into account. COSMO-RS was found to predict extraction outcome for both ligneous and drug-like compounds in various solvent pairs fairly well without any additional empirical input. The solvent-specific systematic errors were found to be moderate, despite being statistically significant, and related to the solvent hydrophobicity. Accounting for mutual solubilities of the two liquids was proven crucial in cases where water was considerably soluble in the organic solvent. The root mean square error of a priori logP prediction varied, depending mainly on the solvent pair, from 0.2 to 0.7, overall value being 0.6 log units. The accuracy was higher in case of hydrophilic than hydrophobic solvents. The logD predictions were less accurate, due to pK_a prediction being an additional source of error, and also because of the complexity of modeling the behaviour of ionic species in the two-phase system. A simple correction for partitioning of free ions was found to notably improve logD prediction accuracy in case of the most hydrophilic organic phase (butanol/water).

A perspective on synthetic and solid-form enablement of inhalation candidates

The administration of compounds by a dry-powder inhaler presents significant challenges to the development and discovery chemist, owing to the stringent requirements placed upon the physical characteristics of the active pharmaceutical ingredient and the high complexity of the molecules concerned. The current state of synthetic chemistry technology is such that commercial syntheses of these compounds are demanding but achievable. While synthetic chemistry will remain a major component of the development of inhaled therapies, the main challenge facing practitioners in this area is the early identification of a suitable solid form. Further advances in the prediction of solid-form properties would significantly enable this field and may allow triage of molecules to be carried out at the design stage of projects.

Recent progress in the computational prediction of aqueous solubility and absorption

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classification system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We find that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefly discuss aspects of emerging scientific understanding that may lead to breakthroughs in the computational modeling of these properties.

Predicting Solubility of Anthracene in Non-aqueous Solvent Mixtures Using a Combination of Jouyban-Acree and Abraham Models

Quantitative structure property relationships were proposed to calculate the binary interaction terms of the Jouyban-Acree model using coefficients of Abraham solvational models. The applicability of the proposed methods for reproducing solubility data of anthracene in binary solvents has been evaluated using 56 solubility data sets collected from the literature. The mean percentage deviation (MPD) of experimental and calculated solubilities, using predicted mole fraction solubility of anthracene in solvents 1 and 2, has been computed as a measure of accuracy and the MPD of the proposed methods were 5.5 and 4.2%. The accuracy of the method was compared with that of a previously reported method where the MPD was 14.4% and the mean differences between proposed and previous methods was statistically significant. To provide a predictive model, solubility of anthracene was computed using Abraham solvational models and employed to predict the solubility in binary solvents using derived model constants of Jouyban-Acree model and the obtained MPDs were 37.9 and 22.2%, respectively.