Partition Coefficients Of Chemicals Research Articles

Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish.

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.

Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals

Tissue-to-blood partition coefficients (Ptb) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive models. With the help of machine learning algorithms, i,e., random forest (RF), and artificial neural network (ANN), this study developed multi-task (MT) models that can simultaneously predict Ptb values for various mammalian tissues, including liver, muscle, brain, lung, and adipose. Single-task (ST) models using partial least squares regression, RF, and ANN algorithms for each endpoint were established for comparison. Overall, the performances of MT models were superior to those of ST models. The MT model using ANN algorithms showed the highest prediction accuracy with determination coefficients ranging from 0.704 to 0.886, root mean square errors between 0.223 and 0.410, and mean absolute errors ranging from 0.178 to 0.285 log units. Results showed that lipophilicity and polarizability of molecules significantly influence their partition behavior in organisms. Applicability domains (ADs) of the models were characterized by weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes. When constrained by ADs, the models displayed enhanced predictive accuracy, making them valuable tools for the risk assessment and management of chemicals.

A dynamic inventory database for assessing age-, gender-, and route-specific chronic internal exposure to chemicals in support of human exposome research

In this study, we proposed a dynamic inventory database to evaluate chronic internal exposure to chemicals at a population level, which enables users to perform modeling exercises specific to a particular chemical, route of exposure, age group, and gender. The database was built based on the steady-state solution of physiologically based kinetic (PBK) models. The biotransfer factors [BTF, the steady-state ratio between the chemical concentration in human tissues and the average daily dose (ADD) of the chemical] of 931 organic chemicals in major organs and tissues were simulated for a total of 14 population age groups for males and females. The results indicated that infants and children had the highest simulated BTFs of chemicals, and middle-aged adults had the lowest simulated BTFs. The route-specific analysis of the simulated BTFs indicated that the biotransformation half-life and octanol-water partition coefficient of chemicals had a profound impact on the BTFs. Organ- and chemical-specific results indicated that the biotransfer potential of chemicals in human bodies was primarily determined by bio-thermodynamic variables (e.g., lipid contents). In conclusion, the proposed inventory database can be conveniently used to access chronic internal exposure doses of chemicals by multiplying the route-specific ADD values for different population groups. In future studies, we recommend incorporating human biotransformation data, partition coefficients of ionizable chemicals, age-specific vulnerable indicators (e.g., the degree of maturation of immune systems), physiological variations within the same age group (e.g., intensity of daily physical activities), growth rates (i.e., the dilution effect on chemical biotransfer), and all possible target organs of carcinogenicity (e.g., bladder) into the proposed dynamic inventory database to help promote human exposome research.

Predicting low density polyethylene-water partition coefficients based on pp-LFER and QSPR models using molecular descriptors

The partition coefficients of chemicals between sorbent material and water (KPE-w) are important to determine water phase chemical concentrations using passive sampling devices. Due to the wide application of low density polyethylene (LDPE) films, the polyparameter linear free energy relationship (pp-LFER) and quantitative structure property relationship (QSPR) models were established for predicting KPE-w values. With the pp-LFER model, V (McGowan's molar volume), B (hydrogen bond acceptor capacity) and A (hydrogen bond donor capacity) were screened as significant variables. However, this model had low correlation coefficient (R2) and cross-validation coefficient (Q2). To comprehensively study the mechanisms of the partition process, a QSPR model with 4 influential descriptors (CrippenLogP (Crippen octanol-water partition coefficient), CIC0 (neighborhood symmetry of 0-order), MATS3i (Moran autocorrelation-lag3/weighted by first ionization potential) and A (hydrogen bond donor capacity)) was developed. The values of R2 and Q2 ranged from 0.771 to 0.921 and 0.739 to 0.912, respectively, indicating that both models had good predictive capacity and robustness. Mechanism interpretation indicated that the elements influencing the distribution procedure between LDPE and water were the McGowan's molar volume and hydrophobic interactions. In the present study, a superb model was established to predict the log KPE-w values for a wide range of common hydrophobic organic pollutants.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (Kpew). For chemicals with the octanol–water partition coefficient (log Kow) <8, a TLSER model with Vx (McGowan volume) and qA− (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R2) and cross-validated coefficient (Q2). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log KOW>8, a TLSER model with Vx and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water.

Coupling passive sampling with in vitro bioassays and chemical analysis to understand combined effects of bioaccumulative chemicals in blood of marine turtles

Conventional target analysis of biological samples such as blood limits our ability to understand mixture effects of chemicals. This study aimed to establish a rapid passive sampling technique using the polymer polydimethylsiloxane (PDMS) for exhaustive extraction of mixtures of neutral organic chemicals accumulated in blood of green turtles, in preparation for screening in in vitro bioassays. We designed a PDMS–blood partitioning system based on the partition coefficients of chemicals between PDMS and major blood components. The sampling kinetics of hydrophobic test chemicals (polychlorinated dibenzo-p-dioxins; PCDDs) from blood into PDMS were reasonably fast reaching steady state in <96h. The geometric mean of the measured PDMS–blood partition coefficients for PCDDs, polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) was 14LbloodkgPDMS−1 and showed little variability (95% confidence interval from 8.4 to 29) across a wide range of hydrophobicity (logKow 5.7–8.3). The mass transfer of these chemicals from 5mL blood into 0.94g PDMS was 62–84%, which is similar to analytical recoveries in conventional solvent extraction methods. The validated method was applied to 15 blood samples from green turtles with known concentrations of PCDD/Fs, dioxin-like PCBs, PBDEs and organochlorine pesticides. The quantified chemicals explained most of the dioxin-like activity (69–98%), but less than 0.4% of the oxidative stress response. The results demonstrate the applicability of PDMS-based passive sampling to extract bioaccumulative chemicals from blood as well as the value of in vitro bioassays for capturing the combined effects of unknown and known chemicals.

Prediction of Phospholipid-Water Partition Coefficients of Ionic Organic Chemicals Using the Mechanistic Model COSMOmic.

The partition coefficient of chemicals from water to phospholipid membrane, K(lipw), is of central importance for various fields. For neutral organic molecules, log K(lipw) correlates with the log of bulk solvent-water partition coefficients such as the octanol-water partition coefficient. However, this is not the case for charged compounds, for which a mechanistic modeling approach is highly necessary. In this work, we extend the model COSMOmic, which adapts the COSMO-RS theory for anisotropic phases and has been shown to reliably predict K(lipw) for neutral compounds, to the use of ionic compounds. To make the COSMOmic model applicable for ionic solutes, we implemented the internal membrane dipole potential in COSMOmic. We empirically optimized the potential with experimental K(lipw) data of 161 neutral and 75 ionic compounds, yielding potential shapes that agree well with experimentally determined potentials from the literature. This model refinement has no negative effect on the prediction accuracy of neutral compounds (root-mean-square error, RMSE = 0.62 log units), while it highly improves the prediction of ions (RMSE = 0.70 log units). The refined COSMOmic is, to our knowledge, the first mechanistic model that predicts K(lipw) of both ionic and neutral species with accuracies better than 1 log unit.

Mathematical Simulation and Experimental Investigation of Propranolol Hydrochloride Loading/releasing from Ion Exchange Fibers

Ion-exchange fibers are promising drug delivery carriers,so it is important to predict the loading efficiency and loading capacity in the loading process and accurate prediction and control of the drug release profile(s) from the fibers for a given fiber-drug system under precise loading conditions. Impact of initial drug concentration, drug solution volume and fiber dose on the drug loading process and effect of loading drug concentration drug solution volume, fiber dose on the drug releasing process were assessed experimentally and theoretically. Two mathematical models based on Donnan theory and ion exchange equilibrium theory taking into account the chemical partition coefficient of D+, Na+and Cl-were developed to simulate the drug loading onto ion exchange fiber and releasing process respectively with the aim to gain mechanistic understanding on the factors affecting these two processes. Good correspondence between the experiment and theory has been established.

Modeling bioaccumulation in humans using poly-parameter linear free energy relationships (PPLFERS)

Chemical partition coefficients between environmental media and biological tissues are a key component of bioaccumulation models. The single-parameter linear free energy relationships (spLFERs) commonly used for predicting partitioning are often derived using apolar chemicals and may not accurately capture polar chemicals. In this study, a poly-parameter LFER (ppLFER) based model of organic chemical bioaccumulation in humans is presented. Chemical partitioning was described by an air–body partition coefficient that was a volume weighted average of ppLFER based partition coefficients for the major organs and tissues constituting the human body. This model was compared to a spLFER model treating the body as a mixture of lipid (≈ octanol) and water. Although model agreement was good for hydrophobic chemicals (average difference 15% for log K OW > 4 and log K OA > 8), the ppLFER model predicted ~ 90% lower body burdens for hydrophilic chemicals (log K OW < 0). This was mainly due to lower predictions of muscle and adipose tissue sorption capacity for these chemicals. A comparison of the predicted muscle and adipose tissue sorption capacities of hydrophilic chemicals with measurements indicated that the ppLFER and spLFER models' uncertainties were similar. Consequently, little benefit from the implementation of ppLFERs in this model was identified.

Donnan Equilibrium of Ionic Drugs in pH-Dependent Fixed Charge Membranes: Theoretical Modeling

We have studied theoretically the partition equilibrium of a cationic drug between an electrolyte solution and a membrane with pH-dependent fixed charges using an extended Donnan formalism. The aqueous solution within the fixed charge membrane is assumed to be in equilibrium with an external aqueous solution containing six ionic species: the cationic drug (DH +), the salt cations (Na + and Ca 2+), the salt anion (Cl −), and the hydrogen and hydroxide ions. In addition to these mobile species, the membrane solution may also contain four fixed species attached to the membrane chains: strongly acid sulfonic groups (SO 3 −), weakly acid carboxylic groups in dissociated (COO −) and neutral (COOH) forms, and positively charged groups (COO···Ca +) resulting from Ca 2+ binding to dissociated weakly acid groups. The ionization state of the weak electrolyte groups attached to the membrane chains is analyzed as a function of the local pH, salt concentration, and drug concentration in the membrane solution, and particular attention is paid to the effects of the Ca 2+ binding to the negatively charged membrane fixed groups. The lipophilicity of the drug is simulated by the chemical partition coefficient between the membrane and external solutions giving the tendency of the drug to enter the membrane solution due to hydrophobic interactions. Comparison of the theoretical results with available experimental data allows us to explain qualitatively the effects that the pH, salt concentration, drug concentration, membrane fixed charge concentration, and Ca 2+ binding exert on the ionic drug equilibrium. The role of the interfacial (Donnan) electric potential difference between the membrane and the external solutions on this ionic drug equilibrium is emphasized throughout the paper.

Sensitivity analysis for validating expert opinion as to ideal data set criteria for transport modeling

Environmental fate modeling results are often used in risk assessment without adequately considering uncertainty in exposure predictions. Sensitivity analysis is fundamental to model validation and error prediction since sensitive model input parameters account for the largest variance in model prediction. Once identified, sensitive model input parameters can be used to propagate parametric uncertainty in numerical predictions. Output sensitivity to variation in input code sequences was investigated for the pesticide root zone model (PRZM 3) using Plackett-Burman analysis for six runoff and leaching data sets. The analysis utilized an incomplete block factorial design with even parameter weighting and uniform proportional input perturbation. Timing and duration of key period rainfall were assumed a priori to be dominant sensitive inputs. Thus, meteorological data were fixed, allowing identification of additional input components contributing to model sensitivity. Results validated expert modeler assumptions concerning parameters most critical for model validation. For leaching data sets, the application rate, soil bulk density (an indicator of available water-holding capacity), chemical partition coefficient, and pesticide degradation rates were commonly the most sensitive inputs. For runoff data sets, the in-crop runoff curve number was the most significant input governing pesticide loss in runoff and erosion flux. The chemical partition coefficient, soil and foliar decay rates, and soil bulk density were also common sensitive components for runoff predictions. These commonly observed sensitive components for runoff and leaching prediction need to be carefully considered in the design and conduct of relevant field studies, modeling assessment of such studies, and future improvements in algorithms for environmental transport modeling.

A Model-based Dependence of the Human Tissue/Blood Partition Coefficients of Chemicals on Lipophilicity and Tissue Composition

Partitioning of up to thirty-six nonionizable chemicals between seven tissues (fat, liver, brain, kidney, muscle, lung, heart) and blood in humans was modeled using membrane accumulation, protein binding, and distribution in the aqueous phases as relevant processes. The extent of membrane accumulation and protein binding of individual compounds was described as the Collander-type function of their lipophilicity expressed by the 1-octanol/water partition coefficients. The resulting model-based expression described satisfactorily the tissue/blood partition coefficients as a nonlinear function of lipophilicity and tissue composition (a standard content of lipids, proteins, and water). The calibrated model is suitable for prediction of the tissue/blood partition coefficients for non-amphiphilic nonionizable chemicals with the 1-octanol/water partition coefficients varying between 0.01 and 100,000, i.e. practically in the whole range for which equilibrium tissue/blood distribution can be used in physiologically based pharmacokinetic models.

Evaluation of log KOW and tissue lipid content as predictors of chemical partitioning to fish tissues

AbstractIn vitro equilibrium chemical partition coefficients were determined for six chemicals in selected tissues from four species of fish. Log‐transformed values were then regressed in stepwise fashion against chemical log octanol/water partition coefficient (Kow) and the log of tissue lipid content to derive a series of linear one‐ and two‐variable models. Equations derived for fat indicate that n‐octanol is a good surrogate for nonpolar lipid in the range of chemical log Kow tested (1.46 &lt; log Kow &lt; 4.04). These equations also support the conclusion that previously developed Kow‐bioconcentration factor relationships are largely a reflection of chemical accumulation in fat. Fitted slope and intercept terms for “lean” tissues differed from those expected from chemical partitioning to lipid only and were instead consistent with the suggestion that partitioning to nonlipid‐nonwater cellular constituents contributes substantially to chemical accumulation. A general equation is presented for prediction of tissue/water and blood/water partitioning from chemical log Kow and tissue (or blood) lipid content. It is suggested, however, that tissue‐ and blood‐ specific equations be used to estimate the tissue/blood partitioning relationships needed for kinetic modeling efforts.

A Quantitative Structure-toxicokinetic Relationship Model for Highly Metabolised Chemicals

The aim of the present study was to develop a quantitative structure-toxicokinetic relationship (QSTkR) model for highly metabolised chemicals (HMCs). The proposed QSTkR model is essentially a physiologically based toxicokinetic (PBTK) model, in which the blood:air and tissue:blood partition coefficients (PCs) are predicted from the molecular structure of chemicals, and the liver blood flow rate (Q1) is used to describe hepatic clearance. Molecular structure-based prediction of the blood:air and tissue:blood PCs was performed from the n-octanol:water and water:air PCs of chemicals obtained with the conventional fragment constant methods. The validity of incorporating Q1 instead of metabolic rate constants, as the hepatic clearance factor, in PBTK models for HMCs (extraction ratio &gt; 0.7) was verified by comparing the simulations of venous blood concentration (Cv) profiles obtained with both the QSTkR and PBTK model approaches for 1,1-dichloroethylehe, trichloroethylene and furan in the rat. Following the validation of this alternative approach for describing hepatic clearance of HMCs, a QSTkR model for dichloromethane was constructed. This model used molecular structure information as the sole input, and provided simulations of Cv for human exposure to low concentrations of dichloromethane. The QSTkR model simulations were similar to those obtained with the previously validated, conventional human PBTK model with experimentally determined PCs and metabolic rate constants (Vmax, Km and Kf) for dichloromethane. The present methodology is the first validated example of a mechanistically based prediction of the inhalation toxicokinetics of HMCs made solely from information on molecular structure.

Partitioning of nonionic organic compounds between multicomponent organic phase and water

The partitioning of nonionic organic compounds between multicomponent organic phase and water was investigated. It was found that the partition coefficients of chemicals between multi‐component organic phase and water are approximately the linear combination of those between single‐component organic phase and water based on the volume fraction of each component. The contribution of each component to the extracting capacity of multicomponent organic phase for chemicals is dependent on the polarity of chemicals. The increasing volume fraction of polar solvents in organic phase will greatly enhance the partitioning of polar chemicals into organic phase. In contrast, the partitioning of nonpolar chemicals is less sensitive to the change of composition of organic phase.

Parameters affecting the desorption of hydrophobic organic chemicals from suspended sediments

AbstractRecent work has demonstrated that both the adsorption and desorption of hydrophobic organic chemicals to sediments and soils can be quite slow and that chemical equilibrium may not be a good approximation in many real situations. By means of long‒term batch experiments, this has been recently and clearly shown for the adsorption of hexachlorobenzene and three polychlorinated biphenyls to suspended sediments. In the present study, long‒term desorption experiments are performed with three hydrophobic organic chemicals (hexachlorobenzene and two polychlorinated biphenyls) and suspended sediments. Desorption times are on the order of a month to several years. It is shown that the desorption rates are dependent on the particle/floc size and density distributions, the type of water, the amount of organic carbon in the sediments, the time of adsorption before desorption, and the chemical partition coefficient. It is also shown that a simple chemical diffusion model with an effective diffusion coefficient dependent on the porosity of the particle/floc, the organic content of the sediments, and the chemical partition coefficient, along with proper consideration of particle/floc size and density distributions is sufficient to explain the experimental results.

An in vitro gas equilibration method for determination of chemical partition coefficients in fish

1. 1. A gas equilibration method for the determination of in vitro chemical partition coefficients in mammals was adapted for use with fish. 2. 2. In vitro blood: water and tissue: blood partition coefficients were determined for three chlorinated ethanes in rainbow trout ( Oncorhynchus mykiss). 3. 3. In vitro partition coefficients accurately predicted chemical concentrations in tissues of exposed trout.

Evaluation of the procedures for the measurement of water solubility and n-octanol/water partition coefficient of chemicals results of a ring test in Japan

A ring test by 19 labolatories in Japan was carried out in 1984 for the evaluation of the experimental manuals for the measurements of water solubility and partition coefficient (n-octanol/water). The experimental data obtained by the procedures in the manuals were collected by Environmental Agency, Japan. In the measurement of water solubility, this manual applies a modified batch method (the glass beads technique), whereas the OECD Test Guidelines suggests the use of the column elution method for chemicals having water solubility lower than 10 mg/L. Anthracene and flouranthene were used as test chemicals for the measurement of water solubility, and γ -hexachlorocyclohexane (γ -HCH) and 1,2,3-trichlorobenzene were used for partition coefficient. Good agreements were observed at a practical basis between the OECD methods and our methods for the results of mesurements of water solubility and partition coefficient. We concluded that the present ring test showed an advantage of the use of the glass beads method which requires only simple apparatus in water solubility measurement, and the HPLC method is very useful to measure partition coefficient because of the simplicity of procedure and the wide range of measurement, especially at higher values of partition coefficient.

Effects of biosolubility on pulmonary uptake and disposition of gases and vapors of lipophilic chemicals.

Since statistical analysis proved the intercorrelation of tissue-gas partition coefficients of chemicals with similar chemical structures, bioavailability is controlled by one parameter dependent on the physicochemical properties of the chemicals and two constants distinguishing the tissues. Oil-gas partition coefficients are suggested to describe the biosolubility of volatile halogenated aliphatic chemicals. Tissue-gas partition coefficients derived from oil-gas partition coefficients were substituted in a pharmacokinetic model in order to study the effect of biosolubility on uptake, distribution, and elimination of inhaled chemicals. The simulation was focused on occupational exposures (8 h/day, 5 days/wk). Desaturation curves for all tissues show three exponential decays. The analysis of the simulation data indicates three patterns in behavior of inhaled vapors and gases in the body. Tissue uptake of poorly soluble chemicals (oil-gas partition coefficient less than 10) is flow limited at the beginning of exposure, but the partial pressures of such chemicals in the body equilibrate very rapidly with ambient air. Increased pulmonary uptake compensates for metabolic clearance. The rapid response of tissue concentrations to changes in exposure concentrations indicates that the toxic effect can easily be induced by short-term increase of exposure concentration, and that emergence from the reversible effect is rapid when exposure ceases. Tissue uptake of chemicals with oil-gas partition coefficients between 10 and 10(4) is flow limited during the entire 8-h exposure. Tissue concentrations increase slowly. Pulmonary uptake, being restricted by alveolar ventilation, compensates at steady state only for the amount of chemical removed by metabolic clearance. Therefore, tissue concentrations at steady state are lower than biosolubility. Accumulation during occupational exposure is obvious. Dumping of inhaled chemicals in adipose tissue protects the target organ from the occasional short-term increases in the exposure concentration. Tissue uptake of highly soluble chemicals (oil-gas partition coefficients greater than 10(4)) is limited by alveolar ventilation and exposure concentration. The rising and declining of tissue concentrations is very slow, half-times being in the magnitude of months and years. Metabolism reduces the half-time significantly. A lagging acute toxic effect can develop as the chemical accumulates in the body; the effect is most likely to persist long after the termination of the exposure.(ABSTRACT TRUNCATED AT 400 WORDS)