Gilles Klopman

Estimation of the aqueous solubility of organic molecules by the group contribution approach.

Several group contribution methods to estimate the aqueous solubility of organic molecules are proposed and evaluated for their ability to predict the water solubility of new molecules. The learning set consisted of 1168 organic compounds with experimental data taken from the literature after critical evaluation. The best method, based on a new fragment atom scheme, leads to a squared correlation coefficient of 0.95 and an average absolute calculation error of 0.50 log unit, which is superior to other group contribution methods currently available. One of the advantages of this model is that it has upper and lower limits so that the predicted solubilities cannot be unrealistily high or low.

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.

META. 1. A Program for the Evaluation of Metabolic Transformation of Chemicals

A new metabolism program, META, is introduced. In this paper, the basic principles on which the program operates are described. META is an expert system, capable of predicting the sites of potential enzymatic attack and the nature of the chemicals formed by such metabolic transformations. It operates from dictionaries of transformation operators, created by experts to represent known metabolic paths.

The structural basis of the mutagenicity of chemicals in Salmonella typhimurium: The Gene-Tox Data Base

The CASE structure-activity methodology has been applied to a Gene-Tox derived Salmonella mutagenicity data base consisting of 808 chemicals. Based upon qualitative structural features, CASE identified 29 activating and 3 inactivating structural determinants which correctly predicted the probability of carcinogenicity of 93.7% of the known mutagens and non-mutagens in the data base (sensitivity = 0.998, and specificity = 0.704). Additionally, based upon a qualitative structure-activity analysis, CASEs performance was even better, leading to a sensitivity of 0.981 and a specificity of 1.000. Using the structural determinants identified in this data base, CASE gave excellent predictions of the mutagenicity of chemicals not included in the data base. The identified biophores and biophobes can also be used to investigate the structural basis of the mutagenicity of various chemical classes.

META. 2. A dictionary model of mammalian xenobiotic metabolism

META is a new knowledge-based expert system that provides computer simulation of the biotransformation of chemicals. The program is based on the recognition of key functional groups within the complete chemical structure and therefore can predict the metabolites of new xenobiotics. Here, we describe a comprehensive knowledge base built for the purposes of modeling mammalian metabolism with META methodology.

ADME evaluation: 2. A computer model for the prediction of intestinal absorption in humans

PURPOSE To develop a computational method to rapidly evaluate human intestinal absorption, one of the drug properties included in the term ADME (Absorption, Distribution, Metabolism, Excretion). Poor ADME properties are the most important reason for drug failure in clinical development. METHODS The model developed is based on a modified contribution group method in which the basic parameters are structural descriptors identified by the CASE program, together with the number of hydrogen bond donors. RESULTS The human intestinal absorption model is a quantitative structure-activity relationship (QSAR) that includes 37 structural descriptors derived from the chemical structures of a data set containing 417 drugs. The model was able to predict the percentage of drug absorbed from the gastrointestinal tract with an r2 of 0.79 and a standard deviation of 12.32% of the compounds from the training set. The standard deviation for an external test set (50 drugs) was 12.34%. CONCLUSIONS The availability of reliable and fast models like the one we propose here to predict ADME/Tox properties could help speed up the process of finding compounds with improved properties, ultimately making the entire drug discovery process shorter and more cost efficient.

Relationship between polarographic reduction potential and mutagenicity of nitroarenes

A series of nitropyrenes and other nitroarenes were reduced electrochemically with a dropping mercury electrode. The half-wave potentials (E1/2) corresponding to the reduction of the various nitro functions mutagenicities exhibited by these chemicals in Salmonella typhimurium strains TA98 and TA1538 was demonstrated. A linear relationship between E1/2 and the calculated energies of the Lowest Unoccupied Molecular Orbital (LUMO) was also established. This indicates that the mutagenicities of nitroarenes can be predicted from calculated LUMO energies.

Structure-genotoxic activity relationships of pesticides: comparison of the results from several short-term assays

The Computer-Automated Structure Evaluation (CASE) program has been applied to the analysis of the genotoxic activity of 54 pesticides (31 insecticides, 15 herbicides and 8 fungicides) in 5 different short-term test systems measuring gene mutation and DNA damage. The database contains compounds presenting diverse structures including carbamates, thiocarbamates, organophosphates, halo-aromatics and other functionalities. Some significant relationships between common structural features and the genotoxic activity displayed by these chemicals have been found. Among the most relevant fragments, automatically selected by the program, a methoxyphosphinyl and a chlorovinyl group appear as the common structural subunits responsible for the activities detected in the battery composed of the Salmonella typhimurium histidine reversion assay, the mouse lymphoma gene mutation assay and recombination in the yeast Saccharomyces cerevisiae.

Development, Characterization and Application of Predictive-toxicology Models

The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover, the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.

META. 3. A GENETIC ALGORITHM FOR METABOLIC TRANSFORM PRIORITIES OPTIMIZATION

META is a knowledge-based expert system that simulates the biotransformation of xenobiotics. It operates with the help of a dictionary (knowledge base) to seek target fragments in a compound and transform them to products. Here, a genetic algorithm is introduced to help build the knowledge base and optimize the performance of the methodology.