COSMOplex: self-consistent simulation of self-organizing inhomogeneous systems based on COSMO-RS

Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles.

Local Structure of Nonuniform Fluid Mixtures Containing Associating and Chainlike Molecules from a Multilayer Quasichemical Model

In this work, we develop a theory for predicting details of the local structure in nonuniform multicomponent fluids that may contain chainlike and associating components. This theory is an extension─to the fluid interfaces and mesoscopic structures of different geometry─of the multilayer quasichemical model originally proposed by Smirnova to describe liquid solution in the vicinity of a planar solid wall. The basis of the theory is the "cut-and-bond" approach, much in spirit of SAFT, where an infinite attraction between the separated monomeric units of a chainlike molecule mimics the chemical bonds of the chain. We describe the equilibrium structure of the mixture, including the spatial distribution of the monomeric units and the local orientation of the chemical bonds in chainlike molecules, and discuss the contribution of chemical bonds to the local chemical potential in a nonuniform fluid. To test the new theory, we apply it to mixtures containing combinations of model components: a strongly associating solvent, an inert substance of varying chain length, and a chainlike amphiphile. To compare predictions from the multilayer model with the results of continuous description of nonuniform fluids, we also address the square-gradient theory and derive an analytical expression for the influence parameter that takes into account pair correlations in the quasichemical approximation. The multilayer quasichemical model developed in this work predicts formation of aggregates in liquid solution and describes the local structure of the interfaces between the coexisting liquid phases in the mixture. Our theoretical predictions agree on a qualitative level with the accumulated knowledge about the structure of different types of systems studied in this work.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

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.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

First-principles prediction of critical micellar concentrations for ionic and nonionic surfactants

The concentration of surfactant in solution for which micelles start to form, also known as critical micelle concentration is a key property in formulation design. The critical micelle concentration can be determined experimentally with a tensiometer by measuring the surface tension of a concentration series. In analogy with experiments, in-silico predictions can be achieved through interfacial tension calculations. We present a newly developed method, which employs first principles-based interfacial tension calculations rooted in COSMO-RS theory, for the prediction of the critical micelle concentration of a set of nonionic, cationic, anionic, and zwitterionic surfactants in water. Our approach consists of a combination of two prediction strategies for modelling two different phenomena involving the removal of the surfactant hydrophobic tail from contact with water. The two strategies are based on regular micelle formation and thermodynamic phase separation of the surfactant from water and both are required to take into account a wide range of polarity in the hydrophilic headgroup. Our method yields accurate predictions for the critical micellar concentration, within one log unit from experiments, for a wide range of surfactant types and introduces possibilities for first-principles based prediction of formulation properties for more complex compositions.

Concentration of Antioxidant Compounds from Calendula officinalis through Sustainable Supercritical Technologies, and Computational Study of Their Permeability in Skin for Cosmetic Use

The growing interest in the cosmetic industry in using compounds of natural and sustainable origin that are safe for humans is encouraging the development of processes that can satisfy these needs. Chlorogenic acid (CHA), caffeic acid (CAF) and ferulic acid (FA) are three compounds widely used within the cosmetic industry due to their functionalities as antioxidants, collagen modifiers or even as radiation protectors. In this work, two advanced separation techniques with supercritical CO2 are used to obtain these three compounds from Calendula officinalis, and these are then evaluated using a computational skin permeability model. This model is encompassed by the COSMO-RS model, the calculations of which make it possible to study the behaviour of the compounds in the epidermis. The results show that both CAF and FA are retained in the stratum corneum, while CHA manages to penetrate to the stratum spinosum. These compounds were concentrated by antisolvent fractionation with super-critical CO2 using a Response Surface Methodology to study the effect of pressure and CO2 flow rate. CHA, CAF and FA were completely retained in the precipitation vessel, with concentrations between 40% and 70% greater than in the original extract. The conditions predicted that the optimal overall yield and enrichment achieved would be 153 bar and 42 g/min.

Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery

The surfactant flooding becomes an attractive method among several Enhanced Oil Recovery (EOR) processes to improve the recovery of residual oil left behind in the reservoir after secondary oil recovery process. The designing of a new effective surfactant is a comparatively complex and often time consuming process as well as cost-effective due to its dependency on the crude oil and reservoir properties. An alternative chemical computational approach is focused in this article to optimize the performance of effective surfactant system for EOR. The molecular dynamics (MD), dissipative particle dynamics (DPD) and density functional theory (DFT) simulations are mostly used chemical computational approaches to study the behaviour in multiple phase systems like surfactant/oil/brine. This article highlighted a review on the impact of surfactant head group structure on oil/water interfacial property like interfacial tensions, interface formation energy, interfacial thickness by MD simulation. The effect of entropy in micelle formation has also discussed through MD simulation. The polarity, dipole moment, charge distribution and molecular structure optimization have been illustrated by DFT. A relatively new coarse-grained method, DPD is also emphasized the phase behaviour of surfactant/oil/brine as well as polymer-surfactant complex system.

Use of the normalized hydrophilic-lipophilic-deviation (HLDN) equation for determining the equivalent alkane carbon number (EACN) of oils and the preferred alkane carbon number (PACN) of nonionic surfactants by the fish-tail method (FTM)

The standard HLD (Hydrophilic-Lipophilic-Deviation) equation expressing quantitatively the deviation from the "optimum formulation" of Surfactant/Oil/Water systems is normalized and simplified into a relation including only the three more meaningful formulation variables, namely (i) the "Preferred Alkane Carbon Number" PACN which expresses the amphiphilicity of the surfactant, (ii) the "Equivalent Alkane Carbon Number" EACN which accurately reflects the hydrophobicity of the oil and (iii) the temperature which has a strong influence on ethoxylated surfactants and is thus selected as an effective, continuous and reversible scanning variable. The PACN and EACN values, as well as the "temperature-sensitivity-coefficient"τ of surfactants are determined by reviewing available data in the literature for 17 nonionic n-alkyl polyglycol ether (C_iE_j) surfactants and 125 well-defined oils. The key information used is the so-called "fish-tail-temperature" T* which is a unique data point in true ternary C_iE_j/Oil/Water fish diagrams. The PACNs of C_iE_j surfactants are compared with other descriptors of their amphiphilicity, namely, the cloud point, the HLB number and the PIT-slope value. The EACNs of oils are rationalized by the Effective-Packing-Parameter concept and modelled thanks to the COSMO-RS theory.