The normal metabolism of drugs can generate metabolites that have intrinsic chemical reactivity towards cellular molecules, and therefore have the potential to alter biological function and initiate serious adverse drug reactions. Here, we present an assessment of the current approaches used for the evaluation of chemically reactive metabolites. We also describe how these approaches are being used within the pharmaceutical industry to assess and minimize the potential of drug candidates to cause toxicity. At early stages of drug discovery, iteration between medicinal chemistry and drug metabolism can eliminate perceived reactive metabolite-mediated chemical liabilities without compromising pharmacological activity or the need for extensive safety evaluation beyond standard practices. In the future, reactive metabolite evaluation may also be useful during clinical development for improving clinical risk assessment and risk management. Currently, there remains a huge gap in our understanding of the basic mechanisms that underlie chemical stress-mediated adverse reactions in humans. This review summarizes our views on this complex topic, and includes insights into practices considered by the pharmaceutical industry.

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The possibilities and limitations of using mercapturic acids and protein and DNA adducts for the assessment of internal and effective doses of electrophilic chemicals are reviewed. Electrophilic chemicals may be considered as potential mutagens and/or carcinogens. Mercapturic acids and protein and DNA adducts are considered as selective biomarkers because they reflect the chemical structure of the parent compounds or the reactive electrophilic metabolites formed during biotransformation. In general, mercapturic acids are used for the assessment of recent exposure, whereas protein and DNA adducts are used for the assessment of semichronic or chronic exposure. 2-Hydroxyethyl mercapturic acid has been shown to be the urinary excretion product of five different reactive electrophilic intermediates. Classification of these electrophiles according to their acid-base properties might provide a tool to predict their preference to conjugate with either glutathione and proteins or with DNA. Constant relationships appear to exist in the cases of 1,2-dibromoethane and ethylene oxide between urinary mercapturic acid excretion and DNA and protein adduct concentrations. This suggests that mercapturic acids in some cases may also play a role as a biomarker of effective dose. It is concluded that simultaneous determination of mercapturic acids, protein and DNA adducts, and other metabolites can greatly increase our knowledge of the specific roles these biomarkers play in internal and effective dose assessment. If the relationship between exposure and effect is known, similar to protein and DNA adducts, mercapturic acids might also be helpful in (individual) health risk assessment.

ABSTRACT:The cytochrome P450 (P450) superfamily plays an important role in the metabolism of drug compounds, and it is therefore highly desirable to have models that can predict whether a compound interacts with a specific isoform of the P450s. In this work, we provide in silico models for classification of CYP1A2 inhibitors and noninhibitors. Training and test sets consisted of approximately 400 and 7000 compounds, respectively. Various machine learning techniques, such as binary quantitative structure activity relationship, support vector machine (SVM), random forest, kappa nearest neighbor (kNN), and decision tree methods were used to develop in silico models, based on Volsurf and Molecular Operating Environment descriptors. The best models were obtained using the SVM, random forest, and kNN methods in combination with the BestFirst variable selection method, resulting in models with 73 to 76% of accuracy on the test set prediction (Matthews correlation coefficients of 0.51 and 0.52). Finally, a decision tree model based on Lipinski's Rule-of-Five descriptors was also developed. This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of classification. All of the models developed in this work are fast and precise enough to be applicable for virtual screening of CYP1A2 inhibitors or noninhibitors or can be used as simple filters in the drug discovery process.Cytochromes P450 (P450s) are heme-containing enzymes found in both prokaryotes and eukaryotes, and they are involved in a wide range of cellular biotransformation functions. From a pharmaceutical perspective, the most important function is the degradation of drugs (Nebert and Russell, 2002). In general, hydrophobic compounds are converted into more hydrophilic species to facilitate excretion.The most important P450 isoforms involved in metabolism of drugs in humans are CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. CYP1A2 constitutes 12% of the total P450 content in the liver and plays an important role in the metabolic clearance of ϳ5% of currently marketed drugs. The substrates for the CYP1A subfamily are generally characterized as neutral, flat, aromatic, and lipophilic (two to four aromatic rings) with at least one putative hydrogen bond donor (Smith et al., 1997), in agreement with the observed contacts in the recent crystal structure of CYP1A2 (Sansen et al., 2007). Examples of drugs that are CYP1A2 substrates are acetaminophen, caffeine, clozapine, haloperidol, olanzapine, propranolol, tacrine, theophylline, and zolmitriptan (drug interactions: cytochrome P450 drug interaction table, Indiana University School of Medicine, http://medicine.iupui.edu/flockhart/table.htm).In silico approaches are attractive because they can be used in an early stage of the drug discovery process and thereby reduce the number of experimental studies and improve the success rates. For this purpose, various traditional in silico modeling methods and more recently developed nonlinear machine learning methods...

With the availability of an increasing number of high resolution 3D structures of human cytochrome P450 enzymes, structure-based modeling tools are more readily used. In this study we explore the possibilities of using docking and scoring experiments on cytochrome P450 1A2. Three different questions have been addressed: 1. Binding orientations and conformations were successfully predicted for various substrates. 2. A virtual screen was performed with satisfying enrichment rates. 3. A classification of individual compounds into active and inactive was performed. It was found that while docking can be used successfully to address the first two questions, it seems to be more difficult to perform the classification. Different scoring functions were included, and the well-characterized water molecule in the active site was included in various ways. Results are compared to experimental data and earlier classification data using machine learning methods. The possibilities and limitations of using structure-based drug design tools for cytochrome P450 1A2 come to light and are discussed.