Atom/fragment contribution method for estimating octanol-water partition coefficients.

Atom/fragment contribution values, used to estimate the log octanol-water partition coefficient (log P) of organic compounds, have been determined for 130 simple chemical substructures by a multiple linear regression of 1120 compounds with measured log P values. An additional 1231 compounds were used to determine 235 "correction factors" for various substructure orientations. The log P of a compound is estimated by simply summing all atom/fragment contribution values and correction factors occurring in a chemical structure. For the 2351 compound training set, the correlation coefficient (r2) for the estimated vs measured log P values is 0.98 with a standard deviation (SD) of 0.22 and an absolute mean error (ME) of 0.16 log units. This atom/fragment contribution (AFC) method was then tested on a separate validation set of 6055 measured log P values that were not used to derive the methodology and yielded an r2 of 0.943, an SD of 0.408, and an ME of 0.31. The method is able to predict log P within +/- 0.8 log units for over 96% of the experimental dataset of 8406 compounds. Because of the simple atom/fragment methodology, "missing fragments" (a problem encountered in other methods) do not occur in the AFC method. Statistically, it is superior to other comprehensive estimation methods.

Methods of assessing dermal absorption with emphasis on uptake from contaminated vegetation.

Dermal absorption may be an important route of exposure in several exposure scenarios for workers and the general public. Because criteria (e.g. RfDs or MRLs) for chemical exposures are usually expressed in units of mg/kg/day, risk assessments often attempt to convert dermal exposure data to units of mg/kg/day absorbed dose. For some types of dermal exposure involving direct and continuous contact with liquids, Fick's first law may be used. In other cases, such as those involving spills onto the skin surface or dermal exposure to contaminated vegetation, the applicability of Fick's first law is limited. This analysis focuses on a method for estimating absorbed dose from dermal contact with contaminated vegetation or other surfaces. The method involves two steps: estimating the transfer rate from contaminated vegetation to the skin surface, and estimating the extent of absorption from the skin surface into the body. A generic equation can be derived for estimating the transfer rate (TR) from dislodgeable foliar residues (DFR): logTR = 1.09 logDFR + 0.05. Given the surface area of the exposed skin and the duration of contact, this equation can be used to estimate the amount of chemical deposited on the surface of the skin. This equation is based on data from eight different studies using 16 different pesticides. Excluding one outlier (Vinclozolin), the squared correlation coefficient for this equation is 0.78, and the model is significant at p < 0.00001. Data from a series of studies by Feldmann and Maibach (1969, 1970, 1974) are used to estimate dermal absorption. These studies were selected as the most relevant for risk assessment because most of the experimental subjects are human and because of the nature of dermal exposures was closely related to many exposure scenarios used in risk assessments. For all 47 compounds included in this series of studies, there were no significant correlations between commonly available physical or chemical properties and dermal absorption. For those compounds with a K0/w > 1.85, however, the average daily absorption rate (AR) over a five-day postexposure period can be estimated from the molecular weight: logAR = -0.04MW + 1.5. The squared correlation coefficient for this equation is 0.68, and the model is significant at p < 0.00001. The usefulness of this approach is evaluated using a study by Harris and Solomon (1992) in which the absorption of 2,4-D from contaminated turf was measured in a group of volunteers. The estimated absorbed dose using the equations above is very close to the measured values.

Development of a predictive model for biodegradability based on BIODEG, the evaluated biodegradation data base.

A file of evaluated biodegradation data was used to develop a model for predicting aerobic biodegradability from chemical structure alone. Chemicals were initially divided into three groups: (i) chemicals that degrade rapidly under most environmental conditions without requiring acclimation; (ii) chemicals that degrade slowly or not at all; and (iii) chemicals that are biodegradable, but only after an acclimation period. Chemicals in the first two groups were then used to develop a model for classifying chemicals as rapidly or not rapidly biodegradable. The model is based on linear regression against 34 preselected substructures, and correctly classifies 92% (211 or 229) of the chemicals in the final training set.