Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure-activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was -93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 inhibitors.

Determining buffering capacity of polydimethylsiloxane-based passive dosing for hydrophobic organic compounds in large-volume bioassays

Polydimethylsiloxane (PDMS) is the most widely used material for passive dosing. However, the ability of PDMS to maintain constant water concentrations of chemicals in large-volume bioassays was insufficiently investigated. In this study, we proposed a kinetic-based method to determine the buffering capacity of PDMS for maintaining constant water concentrations of hydrophobic organic contaminants (HOCs) in large-volume bioassays. A good correlation between log Kow and PDMS-water partitioning coefficients (log KPW) was observed for HOCs with log Kow values ranging from 3.30 to 7.42. For low-molecular-weight HOCs, volatile loss was identified as the primary cause of unstable water concentrations in passive dosing systems. Slow desorption from PDMS resulted in a reduction of water concentrations for high-molecular-weight HOCs. The volume ratio of PDMS to water (RV) was the key factor controlling buffering capacity. As such, buffering capacity was defined as the minimum RV required to maintain 90% of the initial water concentration and was determined to be 0.0076-0.032 for six representative HOCs. Finally, passive dosing with an RV of 0.014 was validated to effectively maintain water concentrations of phenanthrene in 2-L and 96-h toxicity tests with adult mosquitofish. By determining buffering capacity of PDMS, this study recommended specific RV values for cost-efficient implementation of passive dosing approaches in aquatic toxicology, particularly in large-volume bioassays.

Metrics for estimating vapour pressure deviation from ideality in binary mixtures

A novel method is introduced for estimating the degree of interactions occurring between two different compounds in a binary mixture resulting in deviations from ideality as predicted by Raoult's law. Metrics of chemical similarity between binary mixture components were used as descriptors and correlated with the Root-Mean Square Error (RMSE) associated with Raoult's law calculations of total vapour pressure prediction, including Abraham descriptors, sigma moments, and several chemical properties. The best correlation was for a quantitative structure-activity relationship (QSAR) equation using differences in Abraham parameters as descriptors (r2 = 0.7585), followed by a QSAR using differences in COSMO-RS sigma moment descriptors (r2 = 0.7461), and third by a QSAR using differences in the chemical properties of log KAW, melting point, and molecular weight as descriptors (r2 = 0.6878). Of these chemical properties, Δlog KAW had the strongest correlation with deviation from Raoult's law (RMSE) and this property alone resulted in an r2 of 0.6630. These correlations are useful for assessing the expected deviation in Raoult's law estimations of vapour pressures, a key property for estimating inhalation exposure.

Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds

Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.

Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis

Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.

Implications of polar organic chemical integrative sampler for high membrane sorption and suitability of polyethersulfone as a single-phase sampler

Polar organic chemical integrative sampler (POCIS) contains sorbent, which is typically enclosed between two polyethersulfones (PES) membranes. A significant PES uptake is reported for many contaminants, yet, aqueous concentration is mainly correlated with the sorbent uptake using first-order kinetics. Under high PES sorption, the first-order kinetics often provide erroneous sampling rate for the sorbent phase due to increased membrane resistance. This work evaluated the uptake of four high PES sorbing chemicals, i.e., three Cl- and CH₃-substituted nitrobenzenes and one chlorinated aniline using POCIS and the potential of a single-phase PES sampler using laboratory experiments. POCIS calibration results demonstrated that both sorbent and membrane had similar affinity for the target compounds. A rapid PES sorption occurred in the earlier days (<7 days) followed by a gradual increase in the PES phase concentration (equilibrium not achieved after 60 days). Especially, the membrane was the primary sink for 3,4-dichloroaniline and 3,4-dichloronitrobenzene for up to 14 and 31 days, respectively. On the other hand, the single-phase PES sampler showed similar mass uptake as POCIS and reached equilibrium within 19 days under static condition, indicating its potential suitability in the equilibrium regime. PES-water partition coefficient of the target compounds was between 1.2 and 6.5 L/g. Finally, we present a poly-parameter linear-free energy relationship (pp-LFER) using published data to predict the PES-water partition coefficients. The pp-LFER models showed moderate predictability as indicated by R²_adj values between 0.7 and 0.9 for both internal and external data set consisting of a wide range of hydrophobic and hydrophilic compounds (-0.1 ≤ logK_OW ≤ 7.4). The proposed pp-LFER model can be used to screen high PES-sorbing chemicals to increase the reliability and accuracy of aqueous concentration prediction from POCIS sampling and to select the most appropriate sampling approach for new compounds.

Developing a QSPR Model of Organic Carbon Normalized Sorption Coefficients of Perfluorinated and Polyfluoroalkyl Substances

Perfluorinated and polyfluoroalkyl substances (PFASs) are known for their long-distance migration, bioaccumulation, and toxicity. The transport of PFASs in the environment has been a source of increasing concerned. The organic carbon normalized sorption coefficient (Koc) is an important parameter from which to understand the distribution behavior of organic matter between solid and liquid phases. Currently, the theoretical prediction research on log Koc of PFASs is extremely limited. The existing models have limitations such as restricted application fields and unsatisfactory prediction results for some substances. In this study, a quantitative structure–property relationship (QSPR) model was established to predict the log Koc of PFASs, and the potential mechanism affecting the distribution of PFASs between two phases from the perspective of molecular structure was analyzed. The developed model had sufficient goodness of fit and robustness, satisfying the model application requirements. The molecular weight (MW) related to the hydrophobicity of the compound; lowest unoccupied molecular orbital energy (ELUMO) and maximum average local ionization energy on the molecular surface (ALIEmax), both related to electrostatic properties; and the dipole moment (μ), related to the polarity of the compound; are the key structural variables that affect the distribution behavior of PFASs. This study carried out a standardized modeling process, and the model dataset covered a comprehensive variety of PFASs. The model can be used to predict the log Koc of conventional and emerging PFASs effectively, filling the data gap of the log Koc of uncommon PFASs. The explanation of the mechanism of the model has proven to be of great value for understanding the distribution behavior and migration trends of PFASs between sediment/soil and water, and for estimating the potential environmental risks generated by PFASs.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R² are 0.940 and 0.976) than the published NN-LFER model (R² are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log C_e, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Simulating drug concentrations in PDMS microfluidic organ chips

Microfluidic organ-on-a-chip (Organ Chip) cell culture devices are often fabricated using polydimethylsiloxane (PDMS) because it is biocompatible, transparent, elastomeric, and oxygen permeable; however, hydrophobic small molecules can absorb to PDMS, which makes it challenging to predict drug responses. Here, we describe a combined simulation and experimental approach to predict the spatial and temporal concentration profile of a drug under continuous dosing in a PDMS Organ Chip containing two parallel channels separated by a porous membrane that is lined with cultured cells, without prior knowledge of its log P value. First, a three-dimensional finite element model of drug loss into the chip was developed that incorporates absorption, adsorption, convection, and diffusion, which simulates changes in drug levels over time and space as a function of potential PDMS diffusion coefficients and log P values. By then experimentally measuring the diffusivity of the compound in PDMS and determining its partition coefficient through mass spectrometric analysis of the drug concentration in the channel outflow, it is possible to estimate the effective log P range of the compound. The diffusion and partition coefficients were experimentally derived for the antimalarial drug and potential SARS-CoV-2 therapeutic, amodiaquine, and incorporated into the model to quantitatively estimate the drug-specific concentration profile over time measured in human lung airway chips lined with bronchial epithelium interfaced with pulmonary microvascular endothelium. The same strategy can be applied to any device geometry, surface treatment, or in vitro microfluidic model to simulate the spatial and temporal gradient of a drug in 3D without prior knowledge of the partition coefficient or the rate of diffusion in PDMS. Thus, this approach may expand the use of PDMS Organ Chip devices for various forms of drug testing.

Coupling a Feedforward Network (FN) Model to Real Adsorbed Solution Theory (RAST) to Improve Prediction of Bisolute Adsorption on Resins

When predicting bisolute adsorption, the adsorbed solution theory (AST) and real adsorbed solution theory (RAST) either frequently show high prediction deviations or require bisolute adsorption data. Emerging feedforward network (FN) models can provide high prediction accuracy but lack broad applicability. To avoid those limitations, adsorption experiments were performed for a total of 12 single solutes and 55 bisolutes onto two widely used resins (MN200 and XAD-4). Different FN-based models were then built and compared with AST and RAST, based on which a new modeling strategy coupling FN to RAST and requiring only single-solute data was proposed. The root-mean-square error (RMSE) of predictions by the FN-RAST is 0.082 log units for 50 bisolute adsorption on MN200, much lower than that by AST (0.164) and slightly higher than that by RAST (0.069) or the best FN model (0.068). The FN-RAST model further provided satisfactory predictions for 5 bisolute adsorption on XAD-4 (RMSE = 0.10), which is comparable to that by RAST (0.10) and much lower than those by AST (0.26) and FN model (0.38). Therefore, the FN-RAST enjoys both satisfactory prediction accuracy and some broad applicability. The values of Abraham descriptors E and S were also founded to help assess/compare the nonideal behavior in different bisolute mixtures.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Predicting plant cuticle-water partition coefficients (K_cw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental K_cw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict K_cw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logK_cw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R²_adj,tra) of 0.93, bootstrapping coefficient (Q²_BOOT) of 0.92, external validation coefficient (Q²_ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13-31%), double bond (9-17%) or nitrogen-containing heterocycles (9-17%), π/n-electron pairs interactions exhibit obvious positive contributions to logK_cw. In conclusion, the proposed pp-LFER model is beneficial for predicting logK_cw of potential organic pollutants directly from their molecular structures.

Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning

Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent-adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23-0.31 versus 0.23-0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R²) of 0.949 and cross-validation coefficient (Q_Loo²) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.