Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF?) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r ²?=?0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(log?BSF? ^Obs???log?BCF? ^Calc)?≤?0.75)) for 59 chemicals included within the model applicability domain was 80%.     

Response-surface analyses for toxicity to Tetrahymena pyriformis: reactive carbonyl-containing aliphatic chemicals

A response-plane has been developed with Tetrahymena pyriformis population growth impairment toxicity data [log 1/50% growth inhibitory concentration (IGC50)], the 1-octanol/water partition coefficient (log Kow), and the energy of the lowest unoccupied molecular orbital (Elumo). A statistically robust plane [log 1/IGC50 = 0.530 (log Kow) -0.890 (Elumo) -0.271, n = 50, s = 0.295, r2 = 0.855, F = 145] was found for reactive carbonyl-containing aliphatic chemicals. These compounds had a variety of electrophilic mechanisms of action and included aldehydes acting as Schiff-base formers, alpha,beta-unsaturated aldehydes and alpha,beta-unsaturated ketones acting as Michael-type acceptors, and selected alpha-diones acting as selective binders to arganine residues; gamma-diones acting as selective binders to tubulin; and beta-diones with unknown mechanisms of action. Outliers to this model broadly fell into two groups: small reactive molecules (e.g., acrolein) that were more toxic than predicted and molecules in which the reactive center was sterically hindered by an alkyl group (e.g., 2,4-dimethyl-2,6-heptadienal) that were less toxic than predicted.     

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.     

Structural Properties Governing Retention Mechanisms on Immobilized Artificial Membrane (IAM) HPLC Columns

The aims of this study were to investigate whether three commercially available immobilized artificial membrane (IAM) HPLC columns yield collinear data for neutral compounds, and whether IAM scales are distinct from the log P_oct (partition coefficient in the octanol/H₂O system) scale. With these objectives, the retention mechanisms on the IAM HPLC columns were analysed by linear solvation free‐energy relationships (LSERs). A set of 68 neutral model compounds with known solvatochromic parameters and log P_oct values was investigated, allowing a regular and broad exploration of property space. The resulting solvatochromic equations clearly indicate that the three IAM stationary phases retain small neutral solutes by a balance of intermolecular forces closely resembling those underlying partitioning in octanol/H₂O and retention on a reversed‐phase LC‐ABZ HPLC column. For all systems, the solute's size and hydrogen‐bond‐acceptor basicity are the two predominant factors, whereas dipolarity/polarisability and hydrogen‐bond‐donor acidity play only minor roles.     

Determination of octanol-water partition coefficient for terpenoids using reversed-phase high-performance liquid chromatography

Octanol-water partition coefficients (Kow) for 57 terpenoids were measured using a RP-HPLC method. Sample detection was achieved with standard UV and refractive index detectors and required no special column treatment. Measured log Kow values for the terpenoids ranged from 1.81 to 4.48 with a standard error of between 0.03 and 0.08 over the entire range. Partition coefficients determined by the RP-HPLC method were compared against shake flask, atom/fragment contribution, fragment and atomistic methods. The HPLC values were found to give the best correlation with shake flask results. Log Kow values calculated by the atom/fragment contribution method gave the best correlation with the HPLC values when compared to fragment and atomistic methods.     

An in silico expert system for the identification of eye irritants

This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient chemicals for eye irritancy potential, which is based upon exclusion rules of five physicochemical properties – molecular weight (MW), hydrophobicity (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA) and polarizability (Pol). These rules were developed using the ADMET Predictor software and a dataset of 917 eye irritant chemicals. The dataset was divided into 826 (90%) chemicals used for training set and 91 (10%) chemicals used for external validation set (every 10th chemical sorted by molecular weight). The sensitivity of these rules for the training and validation sets was 72.3% and 71.4%, respectively. These rules were also validated for their specificity using an external validation set of 2011 non-irritant chemicals to the eye. The specificity for this validation set was revealed as 77.3%. This method facilitates rapid screening and prioritization of data poor chemicals that are unlikely to be tested for eye irritancy in the Draize test.     

Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation.

A reliable and generally applicable aqueous solubility estimation method for organic compounds based on a group contribution approach has been developed. Two models have been established based on two different sets of parameters. One has a higher accuracy, while the other has a more general applicability. The prediction potentials of these two models have been evaluated through cross-validation experiments. For model I, the mean cross-validated r2 and SD for 10 such cross-validation experiments were 0.946 and 0.503 log units, respectively. While for model II, they were 0.953 and 0.546 log units, respectively. Applying our models to estimate the water solubility values for the compounds in an independent test set, we found that model I can be applied to 13 out of 21 compounds with a SD equal to 0.58 log unit and model II can be applied to all the 21 compounds with a SD equal to 1.25 log units. Our models compare favorably to all the current available water estimation methods. A program based on this approach has been written in FORTRAN77 and is currently running on a VAX/VMS system. The program can be applied to estimate the water solubility of the water solubility of any organic chemical with a good or fairly good accuracy except for except for electrolytes. Applying our aqueous solubility estimation models to biodegradation studies, we found that although the water solubility was not the sole factor controlling the rate of biodegradation, ring compounds with greater solubilities were more likely to biodegrade at a faster rate. The significance of the relationship between water solubility and biodegradation activity has been illustrated by predicting the biodegradation activity of 27 new chemicals based solely on their estimated solubility values.