Characterisation of the water–isopropyl myristate system

Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water

A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal adsorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Q_m) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Q_m (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Q_m and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K- nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R² of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption.

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.

Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars

The increasing accumulation of pharmaceuticals in aquatic ecosystems could impair freshwater quality and threaten human health. Despite the adsorption of pharmaceuticals on biochars is one of the most cost-effective and eco-friendly removal methods, the wide variation of experimental designs and research aims among previous studies pose significant challenge in selecting biochar for optimal removal. In this work, literature data of 1033 sets with 21 variables collected from 267 papers over ten years (2010-2020) covering 19 pharmaceuticals onto 88 biochars were assessed by different machine learning (ML) algorithms i.e., Linear regression model (LM), Feed-forward neural networks (NNET), Deep neutral networks (DNN), Cubist, K-nearest neighbor (KNN), and Random forest (RF), to predict equilibrium adsorption capacity (Q_e) and explore adsorption mechanisms. LM showed the best performance on ranking importance of input variables. Except for initial concentration of pharmaceuticals, Q_e was strongly governed by biochars' properties including specific surface area (BET), pore volume (PV), and pore structure (PS) rather than pharmaceuticals' properties and experimental conditions. The most accurate model for estimating Q_e was achieved by Cubist, followed by KNN, RF, KNN, NNET and LM. The generalization ability was observed by the tuned Cubist with 26 rules for the prediction of the unseen data. This study not only provides an insightful evidence for data-based adsorption mechanisms of pharmaceuticals on biochars, but also offers a potential method to accurately predict the biochar adsorption performance without conducting any experiments, which will be of high interests in practice in terms of water/wastewater treatment using biochars.

Experimental determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for pesticides and other contaminants: new measurements and recommendations

Well-calibrated polyparameter linear free energy relationships (pp-LFERs) are an accurate way to predict partition coefficients (K) for neutral organic chemicals. In this work, pp-LFER substance descriptors of 111 environmentally relevant substances, mainly pesticides, were determined experimentally using gas chromatographic (GC) retention times and liquid/liquid partition coefficients. The complete set of descriptors for 50 compounds are being reported here for the first time. Validation of the measured substance descriptors was done by comparing predicted and experimental log K for the systems octanol/water (Kow), water/air (Kwa), and organic carbon/water (Koc), all of which indicated a high reliability of pp-LFER predictions based on the determined descriptors (e.g., a root mean squared error of 0.39 for log Kow). The descriptors presented in this work in combination with existing pp-LFER system equations substantially extend (and in some cases correct) our knowledge on partition properties of these 111 chemicals. In addition, the results of this work provide insight on some general guidelines with respect to the method combination best suited for deriving descriptors for environmentally relevant compounds.

Biomimetic membranes with aqueous nano channels but without proteins: impedance of impregnated cellulose ester filters

Earlier we have shown that many important properties of ionic aqueous channels in biological membranes can be imitated using simple biomimetic membranes. These membranes are composed of mixed cellulose ester-based filters, impregnated with isopropyl myristate or other esters of fatty acids, and can be used for high-throughput drug screening. If the membrane separates two aqueous solutions, combination of relatively hydrophilic polymer support with immobilized carboxylic groups results in the formation of thin aqueous layers covering inner surface of the pores, while the pore volume is filled by lipid-like substances. Because of these aqueous layers biomimetic membranes even without proteins have a cation/anion ion selectivity and specific (per unit of thickness) electrical properties, which are similar to typical properties of biological membranes. Here we describe frequency-dependent impedance of the isopropyl myristate-impregnated biomimetic membranes in the 4-electrode arrangement and present the results as Bode and Nyquist diagrams. When the membranes are placed in deionized water, it is possible to observe three different dispersion processes in the frequency range 0.1 Hz to 30 kHz. Only one dispersion is observed in 5 mM KH(2)PO(4) solution. It is suggested that these three dispersion features are determined by (a) conductivity in aqueous structures/channels, formed near the internal walls of the filter pores at high frequencies, (b) dielectric properties of the whole membrane at medium frequencies, determined by polymer support, aqueous layers and impregnating oil, and, finally, (c) by the processes in hydrated liquid crystal structures formed in pores by impregnating oil in contact with water at low frequencies.

Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

Predicting tissue and environmental distribution of chemicals is of major importance for environmental and life sciences. Most of the molecular descriptors used in computational prediction of chemicals partition behavior consider molecular structure but ignore the nature of the partition system. Consequently, computational models derived up-to-date are restricted to the specific system under study. Here, a free energy-based descriptor (DeltaG(k)) is introduced, which circumvent this problem. Based on DeltaG(k), we developed for the first time a single linear classification model to predict the partition behavior of a broad number of structurally diverse drugs and other chemicals (1300) for 38 different partition systems of biological and environmental significance. The model presented training/predicting set accuracies of 91.79/88.92%. Parametrical assumptions were checked. Desirability analysis was used to explore the levels of the predictors that produce the most desirable partition properties. Finally, inversion of the partition direction for each one of the 38 partition systems evidences that our models correctly classified 89.08% of compounds with an uncertainty of only +/-0.17% independently of the direction of the partition process used to seek the model. Other 10 different classification models (linear, neural networks, and genetic algorithms) were also tested for the same purposes. None of these computational models favorably compare with respect to the linear model indicating that our approach capture the main aspects that govern chemicals partition in different systems.

A data base for partition of volatile organic compounds and drugs from blood/plasma/serum to brain, and an LFER analysis of the data

Literature values of the in vivo distribution (BB) of drugs from blood, plasma, or serum to rat brain have been assembled for 207 compounds (233 data points). We find that data on in vivo distribution from blood, plasma, and serum to rat brain can all be combined. Application of our general linear free energy relationship (LFER) to the 207 compounds yields an equation in log BB, with R2=0.75 and a standard deviation, SD, of 0.33 log units. An equation for a training set predicts the test set of data with a standard deviation of 0.31 log units. We further find that the in vivo data cannot simply be combined with in vitro data on volatile organic and inorganic compounds, because there is a systematic difference between the two sets of data. Use of an indicator variable allows the two sets to be combined, leading to a LFER equation for 302 compounds (328 data points) with R2=0.75 and SD=0.30 log units. A training equation was then used to predict a test set with SD=0.25 log units.

Chemical Toxicity Correlations for Several Fish Species Based on the Abraham Solvation Parameter Model

The Abraham solvation parameter model is used to construct mathematical correlations for describing the nonspecific aquatic toxicity of organic compounds to the fathead minnow, guppy, bluegill, goldfish, golden orfe, and high-eyes medaka. The derived mathematical correlations describe the observed published toxicity data to within an overall average standard deviation of approximately 0.28 log units. In the case of ester solutes, the descriptions were improved by introducing an indicator variable into the basic model. Derived correlations can be used to estimate aquatic toxicities of organic chemicals to the six fish species studied and to help in identifying compounds whose toxic mode of action might involve chemical specific reactivity, rather than nonpolar or polar narcosis. A principal component analysis of the correlation equations shows that the water-octanol system is a poor model for nonspecific aquatic toxicity but that the water-isobutanol and water-pentanol systems are much better models.