Evaluation of fatty tissue representative solvents in extraction of medical devices for chromatographic analysis of devices' extractables and leachables based on Abraham general solvation model

Extraction solvents used in chemical characterization (i.e., extractables and leachables testing, E&L) of fatty tissue-contacting medical devices for biocompatibility assessment per ISO 10993 have been studied by Abraham general solvation models. Chemically suitable alternative solvents to fatty tissues in solvation properties (solubility, partition, extraction, etc.) have been proposed based on Abraham's organic solvent system coefficients for water and air to condensed organic solvent phases. This evaluation is built upon the conclusion by Abraham, Acree Jr and Cometto-Muñiz that olive oil is chemically corresponding to fatty tissues. However, olive oil, if used as an extraction solvent to simulate fatty tissues, is in general not analytically expedient (realistic) per ISO 10993-18 (2020) for chromatographic analysis, and it is critical to seek alternative solvents to olive oil to perform the extraction. Although nonpolar solvents such as alkanes have been proposed and used as alternative solvents to vegetable oils, they are not equivalent to olive oil in solvation properties. Due to the practical challenge in chromatographic analysis of oil samples and the difference in migration kinetics of E&L between oil and organic solvents, the computational approach is the only realistic option to evaluate chemically alternative solvents to olive oil to simulate fatty tissue extraction. By comparing Abraham solvent system coefficients for water and air to condensed organic solvent phases distribution, a five-dimensional space distance (D) between solvents and olive oil as a reference solvent is calculated using Abraham and Martin equation to predict alternative or similar solvents to olive oil. The results of the calculation are further evaluated using E&L solubility ratio between solvents and olive oil, taking into consideration of solvent safety and physical properties. It is concluded from the study that butanone and dioxane are chemically the most suitable alternative or representative solvents to olive oil. They can be used as fatty tissue representative solvents in chemical characterization study of medical device. As Abraham solvation model is solvent system specific, not solute specific, the conclusions from this study are considered as universal.

QSPRs for Predicting Equilibrium Partitioning in Solvent–Air Systems from the Chemical Structures of Solutes and Solvents

Poly-parameter Linear Free Energy Relationships (PPLFERs) based on the Abraham solvation model are a useful tool for predicting and interpreting equilibrium partitioning of solutes in solvent systems. The focus of this work is neutral organic solutes partitioning in neutral organic liquid solvent-air systems. This is a follow-up to previous work (Brown, 2021) which developed predictive empirical correlations between solute descriptors and system parameters, allowing system parameters to be predicted from the solute descriptors of the solvent. A database of solute descriptors, and a database of system parameters supplemented by empirical predictions, form the basis for the development of new Quantitative Structure Property Relationships (QSPRs). A total of 11 QSPRs have been developed for the E, S, A, B and L solute descriptors, and the s, a, b, v, l, and c system parameters. The QSPRs were developed using a group-contribution method referred to as Iterative Fragment Selection. The method includes robust internal and external model validation and a well-defined Applicability Domain, including estimates of prediction uncertainty. System parameters can also be predicted by combining the solute descriptor QSPRs and the empirical correlations. The predictive power of PPLFERs applied using different combinations of experimental data, empirical correlations, and QSPRs are externally validated by predicting partition ratios between solvents and air. The uncertainty for predicting the log10KSA of diverse solutes in diverse solvents using only the new QSPRs and empirical correlations is estimated to be one log10 unit or less.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain while the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20,253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the three models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software packages, and source code.

Generalized molecular solvation in non-aqueous solutions by a single parameter implicit solvation scheme

In computer simulations of solvation effects on chemical reactions, continuum modeling techniques regain popularity as a way to efficiently circumvent an otherwise costly sampling of solvent degrees of freedom. As effective techniques, such implicit solvation models always depend on a number of parameters that need to be determined earlier. In the past, the focus lay mostly on an accurate parametrization of water models. Yet, non-aqueous solvents have recently attracted increasing attention, in particular, for the design of battery materials. To this end, we present a systematic parametrization protocol for the Self-Consistent Continuum Solvation (SCCS) model resulting in optimized parameters for 67 non-aqueous solvents. Our parametrization is based on a collection of ≈6000 experimentally measured partition coefficients, which we collected in the Solv@TUM database presented here. The accuracy of our optimized SCCS model is comparable to the well-known universal continuum solvation model (SMx) family of methods, while relying on only a single fit parameter and thereby largely reducing statistical noise. Furthermore, slightly modifying the non-electrostatic terms of the model, we present the SCCS-P solvation model as a more accurate alternative, in particular, for aromatic solutes. Finally, we show that SCCS parameters can, to a good degree of accuracy, also be predicted for solvents outside the database using merely the dielectric bulk permittivity of the solvent of choice.

Estimating system parameters for solvent-water and plant cuticle-water using quantum chemically estimated Abraham solute parameters

Polyparameter Linear Free Energy Relationships (pp-LFERs) using Abraham system parameters have many useful applications. However, developing the Abraham system parameters depends on the availability and quality of the Abraham solute parameters. Using Quantum Chemically estimated Abraham solute Parameters (QCAP) is shown to produce pp-LFERs that have lower root mean square errors (RMSEs) of predictions for solvent-water partition coefficients than parameters that are estimated using other presently available methods. pp-LFERs system parameters are estimated for solvent-water, plant cuticle-water systems, and for novel compounds using QCAP solute parameters and experimental partition coefficients. Refitting the system parameter improves the calculation accuracy and eliminates the bias. Refitted models for solvent-water partition coefficients using QCAP solute parameters give better results (RMSE = 0.278 to 0.506 log units for 24 systems) than those based on ABSOLV (0.326 to 0.618) and QSPR (0.294 to 0.700) solute parameters. For munition constituents and munition-like compounds not included in the calibration of the refitted model, QCAP solute parameters produce pp-LFER models with much lower RMSEs for solvent-water partition coefficients (RMSE = 0.734 and 0.664 for original and refitted model, respectively) than ABSOLV (4.46 and 5.98) and QSPR (2.838 and 2.723). Refitting plant cuticle-water pp-LFER including munition constituents using QCAP solute parameters also results in lower RMSE (RMSE = 0.386) than that using ABSOLV (0.778) and QSPR (0.512) solute parameters. Therefore, for fitting a model in situations for which experimental data exist and system parameters can be re-estimated, or for which system parameters do not exist and need to be developed, QCAP is the quantum chemical method of choice.

Quantum Chemically Estimated Abraham Solute Parameters Using Multiple Solvent-Water Partition Coefficients and Molecular Polarizability

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.

Predicting Abraham model solvent coefficients

Background

The Abraham general solvation model can be used in a broad set of scenarios involving partitioning and solubility, yet is limited to a set of solvents with measured Abraham coefficients. Here we extend the range of applicability of Abraham’s model by creating open models that can be used to predict the solvent coefficients for all organic solvents.

Results

We created open random forest models for the solvent coefficients e, s, a, b, and v that had out-of-bag R² values of 0.31, 0.77, 0.92, 0.47, and 0.63 respectively. The models were used to suggest sustainable solvent replacements for commonly used solvents. For example, our models predict that propylene glycol may be used as a general sustainable solvent replacement for methanol.

Conclusion

The solvent coefficient models extend the range of applicability of the Abraham general solvation equations to all organic solvents. The models were developed under Open Notebook Science conditions which makes them open, reproducible, and as useful as possible.

Graphical Abstract

Estimation of Abraham solvation equation coefficients for hydrogen bond formation from Abraham solvation parameters for solute acidity and basicity

Abraham solvation equations find widespread use in environmental chemistry and pharmaco-chemistry. The coefficients in these equations, which are solvent (system) descriptors, are usually determined by fitting experimental data. To simplify the determination of these coefficients in Abraham solvation equations, this study derives equations, based on Abraham solvation parameters for hydrogen acidity and basicity of the solvents involved, to estimate the value of the coefficients for hydrogen bond formation. These equations were applied to calculate Abraham solvation parameters for hydrogen acidity and basicity for polyoxymethylene, polyacrylate, sodium dodecylsulfate, some ionic liquids, alkanoyl phosphatidyl cholines, and lipids for which fitted values for Abraham coefficients for hydrogen bond formation were available.

Solvation thermodynamics and the physical-chemical meaning of the constant in Abraham solvation equations

Abraham solvation equations find widespread use in environmental chemistry. Until now, the intercept in these equations was determined by fitting experimental data. To simplify the determination of the coefficients in Abraham solvation equations, this study derives theoretical expressions for the value of the intercept for various partition processes. To that end, a modification of the description of the Ben-Naim standard state into the van der Waals volume is proposed. Differences between predicted and fitted values of the Abraham solvation equation intercept for the enthalpy of solvation, the entropy of solvation, solvent-water partitioning, air-solvent partitioning, partitioning into micelles, partitioning into lipid membranes and lipids, and chromatographic retention indices are comparable to experimental uncertainties in these values.

An algorithm for 353 odor detection thresholds in humans

One hundred and ninety three odor detection thresholds, ODTs, obtained by Nagata using the Japanese triangular bag method can be correlated as log (1/ODT) by a linear equation with R(2) = 0.748 and a standard deviation, SD, of 0.830 log units; the latter may be compared with our estimate of 0.66 log units for the self-consistency of Nagata's data. Aldehydes, acids, unsaturated esters, and mercaptans were included in the equation through indicator variables that took into account the higher potency of these compounds. The ODTs obtained by Cometto-Muñiz and Cain, by Cometto-Muñiz and Abraham, and by Hellman and Small could be put on the same scale as those of Nagata to yield a linear equation for 353 ODTs with R(2) = 0.759 and SD = 0.819 log units. The compound descriptors are available for several thousand compounds, and can be calculated from structure, so that further ODT values on the Nagata scale can be predicted for a host of volatile or semivolatile compounds.

The biological and toxicological activity of gases and vapors

A large amount of data on the biological and toxicological activity of gases and vapors has been collected from the literature. Processes include sensory irritation thresholds, the Alarie mouse test, inhalation anesthesia, etc. It is shown that a single equation using only five descriptors (properties of the gases and vapors) plus a set of indicator variables for the given processes can correlate 643 biological and non-lethal toxicological activities of 'non-reactive' compounds with a standard deviation of 0.36 log unit. The equation is scaled to sensory irritation thresholds obtained by the procedure of Cometto-Muñiz, and Cain provides a general equation for the prediction of sensory irritation thresholds in man. It is suggested that differences in biological/toxicological activity arise primarily from transport from the gas phase to a receptor phase or area, except for odor detection thresholds where interaction with a receptor(s) is important.