Roustem Saiakhov

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.

Multiple computer-automated structure evaluation model of the plasma protein binding affinity of diverse drugs

A drug protein binding model was constructed on the basis of protein-affinity data for154 drugs. The Multiple Computer-Automated Structure Evaluation program (M-CASE) was used for the construction of the model, which separates the total data set into groups of drugs with common structural features. For each of these groups, a multiparameter Quantitative Structure–Activity Relationship (QSAR) was obtained. The most general structural fragment for all investigated drugs is a part of the phenyl ring. The lipophilicity represented by the octanol–water partition coefficient was also found to be a significant parameter for each local QSAR. The model was shown to be able to predict correctly the percentage of drug bound in plasma for ∼80% of compounds with an average error of only ∼14%.

Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts.

Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores, or biophores. They are the main building blocks/classifying units of the model, and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining an appropriate applicability domain is required as part of the OECD guidelines for the validation of quantitative structure-activity relationships (QSARs). In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts, and different alerts of a model usually represent different toxicity mechanisms and cover different structural space; the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction trade-offs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the models training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a models full range of sensitivity-specificity spectrum, and therefore, the methodology developed in this study can be of benefit in improving the predictive ability of the alert based expert systems.

MultiCASE Expert Systems and the REACH Initiative

ABSTRACT This article is a review of the MultiCASE Inc. software and expert systems and their use to assess acute toxicity, mutagenicity, carcinogenicity, and other health effects. It is demonstrated that MultiCASE expert systems satisfy the guidelines of the Organisation for Economic Cooperation and Development (OECD) principles and that the portfolio of available endpoints closely overlaps with the list of tests required by REACH.

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.

Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

PurposeOral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process.MethodsWe employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation.ResultsThe external predictivity of %F values was poor (R2 = 0.28, n = 995, MAE = 24), but was improved (R2 = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as “low”, %F ≥ 50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%.ConclusionsIn this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.

Effectiveness of CASE Ultra Expert System in Evaluating Adverse Effects of Drugs

Purpose of this pilot study is to test the QSAR expert system CASE Ultra for adverse effect prediction of drugs. 870 drugs from the SIDER adverse effect dataset were tested using CASE Ultra for carcinogenicity, genetic, liver, cardiac, renal and reproductive toxicity. 47 drugs that were withdrawn from market since the 1950s were also evaluated for potential risks using CASE Ultra and compared them with the actual reasons for which the drugs were recalled. For the whole SIDER test set (n=870), sensitivity and specificity of the carcinogenicity predictions are 66.67 % and 82.17 % respectively; for liver toxicity: 78.95 %, 78.50 %; cardiotoxicity: 69.07 %, 57.57 %; renal toxicity: 46.88 %, 67.90 %; and reproductive toxicity: 100.00 %, 61.10 %. For the SIDER test chemicals not present in the training sets of the models, sensitivity and specificity of carcinogenicity predictions are 100.00 % and 88.89 % respectively (n=404); for liver toxicity: 100.00 %, 51.33 % (n=115); cardiotoxicity: 100.00 %, 20.45 % (n=94); renal toxicity: 100.00 %, 45.54 % (n=115); and reproductive toxicity: 100.00 %, 48.57 % (n=246). CASE Ultra correctly recognized the relevant toxic effects in 43 out of the 47 withdrawn drugs. It predicted all 9 drugs that were not part of the training set of the models, as unsafe.

META V. A model of photodegradation for the prediction of photoproducts of chemicals under natural-like conditions

Our goal was to create a photodegradation model based on the META expert system [G. Klopman, M. Dimayuga, J. Talafous, J. Chem. Inf. Comput. Sci. 34 (1994a) 1320-1325]. This requires the development of a dictionary of photodegradation pathways. Equipped with such a dictionary, we found that META successfully predicts degradation pathways of organic compounds under UV light. Our model was tested on a wide range of industrial compounds for which literature data exists. The results were excellent as the hit/miss ratio was better than 92%. This work complements our previous elaboration of equivalent mammal metabolism, aerobic and anaerobic biodegradation models.

Benchmark performance of MultiCASE Inc. software in Ames mutagenicity set.

The predictive performances of MC4PC were evaluated using its learning machine functionality. Its superior characteristics are demonstrated in this following up study using the newly published Ames mutagenicity benchmark set.

COMPUTER-ASSISTED EVALUATION OF ANAEROBIC BIODEGRADATION PRODUCTS

Our objective in this study was to create a computer package which, when challenged by the structure of an organic molecule, will be able to predict the mechanism of its biodegradation in presence of anaerobic bacteria. A series of mechanistic models based on previous studies of the anaerobic biodegradation of organic molecules have been encoded into targetkransform pairs. A total of 384 rules have been coded into a dictionary and made available to our META program. Endowed with this dictionary, the META program accurately predicts the mechanism of anaerobic degradation of molecules. Examples of the results obtained with the program are presented for benzoic acid, and polychlorinated nitrobenzenes. This program complements our previous derivation of equivalent mammal metabolism and aerobic biodegradation simulators.