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Gilles Klopman

Publications and source records attributed to Gilles Klopman.

11 recordsLinked to original sources
PubMed2006

A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals.

This report describes the construction, optimization and validation of a battery of quantitative structure-activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC software to predict seven general reprotox classes: male and female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. The reprotox QSARs incorporate a weight of evidence paradigm using rats, mice, and rabbit reprotox study data and are designed to identify trans-species reprotoxicants. The majority of the reprotox QSARs exhibit good predictive performance properties: high specificity (>80%), low false positives (<20%), significant receiver operating characteristic (ROC) values (>2.00), and high coverage (>80%) in 10% leave-many-out validation experiments. The QSARs are based on 627-2023 chemicals and exhibited a wide applicability domain for FDA regulated organic chemicals for which they were designed. Experiments were also performed using the MC4PC multiple module prediction technology, and ROC statistics, and adjustments to the ratio of active to inactive (A/I ratio) chemicals in training data sets were made to optimize the predictive performance of QSAR models. Results revealed that an A/I ratio of approximately 40% was optimal for MC4PC. We discuss specific recommendations for the application of the reprotox QSAR battery.

Abnormalities, Drug-Induced↗
PubMed2005

Recent methodologies for the estimation of n-octanol/water partition coefficients and their use in the prediction of membrane transport properties of drugs.

The lipophilicity of drug molecules (represented as the logarithm of the n-octanol/water partition coefficient) often strongly correlates with their pharmacological and toxic activities. It is therefore, not surprising that there is considerable interest in developing mathematical models capable to accurately predict their value for new drug candidates. In this review, current major approaches for estimating partition coefficients are described and some of their advantages and disadvantages are discussed. Recent uses of these partition coefficient algorithms in the development of membrane transport models are also discussed.

Algorithms↗
PubMed2003

Screening of high production volume chemicals for estrogen receptor binding activity (II) by the MultiCASE expert system.

A structurally and functionally diverse and cross-validated quantitative structure-activity knowledge database generated by the MultiCASE expert system was used to screen 2526 high production volume chemicals (HPVCs) for their estrogen receptor binding activity. 73 HPVCs were found to contain structural features or biophores that have been documented as having the ability to bind to the estrogen receptor. Potential chemicals were ranked according to their quantitatively predicted ER binding potential and the details of the biophores found in them are discussed.

Animals↗
PubMed2003

Structure-activity relationship study of a diverse set of estrogen receptor ligands (I) using MultiCASE expert system.

The MultiCASE expert system was used to construct a quantitative structure-activity relationship model to screen chemicals with estrogen receptor (ER) binding potential. Structures and ER binding data of 313 chemicals were used as inputs to train the expert system. The training data set covers inactive, weak as well as very powerful ER binders and represents a variety of chemical compounds. Substructural features associated with ER binding activity (biophores) and features that prevent receptor binding (biophobes) were identified. Although a single phenolic hydroxyl group was found to be the most important biophore responsible for the estrogenic activity of most of the chemicals, MultiCASE also identified other biophores and structural features that modulate the activity of the chemicals. Furthermore, the findings supported our previous hypothesis that a 6 A distant descriptor may describe a ligand-binding site on an ER. Quantitative structure-activity relationship models for the chemicals associated with each biophore were constructed as part of the expert system and can be used to predict the activity of new chemicals. The model was cross validated via 10 x 10%-off tests, giving an average concordance of 84.04%.

Animals↗
PubMed2003

Multiple computer-automated structure evaluation study of aquatic toxicity. III. Vibrio fischeri.

An acute toxicity model was constructed on the basis of 901 chemicals tested for toxicity against the luminescent bacteria Vibrio fischeri (formerly Photobacterium phosphoreum, the Microtox test). The model was created using the Multiple Computer-Automated Structure Evaluation (M-CASE) program. The model can correctly predict acute toxicity for 92% of the compounds with an error averaging 0.55 log units per median effect concentration (EC50). The main toxicophores, corresponding to polar and nonpolar narcosis, and other types of reactive chemicals were identified.

Animal Testing Alternatives↗
PubMed2002

An MCASE approach to the search of a cure for Parkinson's Disease.

BACKGROUND: Parkinson's disease is caused by a dopamine deficiency state in the fore brain area. Dopamine receptor agonists, MAO-B inhibitors, and N-Methyl-D-Aspartate (NMDA) receptor antagonists are known to have antiparkinson effect. Levodopa, a dopamine structural analog, is the best currently available medication for the treatment of Parkinsons disease. Unfortunately, it also induces side effects upon long administration time. Thus, multidrug therapy is often used, in which various adjuvants alleviate side effects of levodopa and enhance its antiparkinsonian action. RESULTS: Computer models have been created for three known antiparkinson mechanisms using the MCASE methodology. New drugs for Parkinsons disease can be designed on the basis of these models. We also speculate that the presence of biophores belonging to different groups can be beneficial and designed some potential drugs along this line. The proposed compounds bear pharmacophores of MAO-B inhibitors, dopamine agonists and NMDA antagonists, which could synergistically enhance their antiparkinson effect. CONCLUSIONS: The methodology could readily be expanded to other endpoints where drugs with multiple activity mechanisms would be desirable.

Antiparkinson Agents↗
PubMed2002

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.

Adsorption↗
PubMedDate not supplied

ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases.

We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenicity of a diverse set of chemicals using MCASE male/female rats and mice carcinogenicity modules.

Algorithms↗
PubMedDate not supplied

A structural analogue approach to the prediction of the octanol-water partition coefficient.

A new strategy for the calculation of n-octanol/water partition coefficients is presented. Log P calculations of unknown chemicals are based on their closest structural analogues from a database of molecules with known experimental log P values. The contribution of the differing molecular parts is then estimated from a compilation of fragment contributions. Such a strategy is found to be superior to conventional group contribution methods and promises an overall enhancement of the prediction's accuracy.

Journal Article↗
PubMedDate not supplied

MCASE study of the multidrug resistance reversal activity of propafenone analogs.

A database containing 130 propafenone type chemicals which have been tested for their multidrug resistance (MDR) reversal activity was compiled. Using the Multiple Computer-Automated Structure Evaluation (MCASE) program to analyze this database, an underlying relationship between MDR reversal activity and octanol/water partition coefficient was found. An MDR reversal model was created based on this database by the baseline activity identification algorithm (BAIA) of the MCASE program. The main phamacophores relevant to MDR reversal activity were identified.

Algorithms↗