Current Topics in Medicinal Chemistry - Volume 14, Issue 11, 2014

Species difference in drug metabolism and drug toxicity is a well-established phenomenon. As a result, the classical paradigm of preclinical testing of drug candidates in animals may not be appropriate. One preclinical approach to evaluate human drug properties, especially ADMET (absorption, disposition, metabolism, elimination, and toxicity) properties, is to apply in vitro experimental systems with relevant human properties. The latest advances include the use of human hepatocytes to evaluate hepatic uptake, metabolism, efflux and toxicity. Successful cryopreservation of human hepatocytes to retain high viability, metabolic capacity, as well as the ability to be cultured allow routine application of this relevant experimental system. This review summarizes the latest findings on human hepatocytes isolation, cryopreservation, culturing, as well as application in the evaluation of metabolic stability, metabolite profiling, hepatic uptake and efflux, metabolic drug-drug interactions, and drug toxicity. The use of hepatocyte to evaluate the role of metabolism in drug toxicity represents a major advance in drug toxicity evaluation. The use of the novel integrated discrete multiple organ coculture (IdMOC) system allows the evaluation of the role of hepatic metabolism on nonhepatic toxicity.

Making reliable predictions of drug metabolites requires detailed knowledge of the chemical reactivity of drug metabolizing enzymes. Cytochrome P450 enzymes (P450s) play an important role in drug metabolism. Numerous adverse drug reactions have been identified that occur as a result of interactions with P450s. These enzymes display complex reactivity and the active oxidizing species is highly reactive and difficult to isolate, making P450s ideal candidates for computational study. Hybrid quantum mechanics/molecular mechanics calculations (QM/MM) have provided valuable insight into the reactivity of P450s, and will assist in the development of simpler predictive models. QM/MM methods have been used to model the metabolism of several drug molecules in human P450s, and have successfully rationalized experimentally observed selectivity. QM/MM calculations have been used to investigate the reactivity of other drug metabolizing enzymes, such as soluble epoxide hydrolase and glutathione transferases. Here, we review the application of QM/MM methods to modelling reactions catalyzed by drug metabolizing enzymes.

Inactivation of human drug-metabolizing cytochrome P450 3A4 (CYP3A4) could lead to serious adverse events such as drug-drug interactions and toxicity. However, when properly controlled, CYP3A4 inhibition may be beneficial as it can improve clinical efficacy of co-administered therapeutics that otherwise are quickly metabolized by CYP3A4. Currently, the CYP3A4 inhibitor ritonavir and its derivative cobicistat are prescribed to HIV patients as pharmacoenhancers. Both drugs were designed based on the chemical structure/activity relationships rather than the CYP3A4 crystal structure. To unravel the structural basis of CYP3A4 inhibition, we compared the binding modes of ritonavir and ten analogues using biochemical, mutagenesis and x-ray crystallography techniques. This review summarizes our findings on the relative contribution of the heme-ligating moiety, side chains and the terminal group of ritonavir-like molecules to the ligand binding process, and highlights strategies for a structure-guided design of CYP3A4 inactivators.

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.

The analysis of designer drugs in human plasma is highly complex, as most of these drugs are metabolized quickly, and often into multiple products. For novel designer drugs, it is common that reference compounds for these metabolites are unavailable at the time of analysis. Hence, the usage of in silico procedures to accurately predict the chemical structures of these metabolites would be very useful. In this study, the differences between several methods for prediction of site of metabolism for cytochrome P450 mediated drug metabolism are described, and their prediction accuracies are analyzed on a set of designer drugs. It is found that ligand-based methods, which are simpler and faster, are better than or at least as good as much more complex structure-based methods.

In the development of new drugs, it is very important to know the effects these may bring to those who consume them. Drugs which act upon certain diseases must not cause toxic side effects on healthy organs. These toxic side effects can be quite varied, i.e. mutagenicity, clastogenicity, teratogenicity, etc., but undoubtedly the mutagenicity officiate in the selection process, during preclinical testing, to advance in clinical trials. Mutagenic compounds are removed and cannot continue its development. There are preclinical studies of mutagenicity and genotoxicity, ranging from in vitro to in vivo studies. Particularly, Ames test is recommended by ICH as the first input in these studies. Herein, we investigated the mutagenicity of an in-house chemical library of eighty five N-oxide containing heterocycles using Ames test in Salmonella thyphimurium TA 98 with and without S9 activation and the use of neural networks in order to predict this nondesired activity. N-oxide containing heterocycles are especially relevant regarding its pharmacological activities as antitrypanosoma, anti-leishmania, anti-tuberculosis, anti-cancer, chemopreventive, anti-inflammatory, anti-atherogenic, and analgesic agents. In some cases, a relationship was found between the presence of N-oxide and mutagenicity. Specifically, benzofuroxan system seems to be responsible for the mutagenicity of certain agents against Chagas disease and certain anti-inflammatory agents. However other N-oxides, such as furoxans with anti-inflammatory and anti-atherosclerosis activities, seem to lack mutagenicity. In other cases, such as quinoxaline dioxides with anti-parasitic activity, mutagenicity shows to be substituent dependent. Applying CODES neural network two models were defined, one without metabolism and other with metabolism. These models predict the mutagenicity with and without metabolism in an excellent manner.

In this paper, the in vitro microsomal hepatic metabolism of the antiasthmatic prototype LASSBio-448 and the structural identification of its major phase I metabolites were described. Incubation with pooled rat liver microsomes converted LASSBio-448 to the following major metabolites: O-demethyl-LASSBio-448 (M1) and 3,4-dihydroxyphenyl- LASSBio-448 (M2). These metabolites were formed by the dealkylation step of 3,4-dimethoxyphenyl and 1,3- benzodioxole subunits, respectively, in agreement with the in silico prediction using MetaSite Program. The development of a reproducible analytical methodology for the major metabolites by using HPLC–MS showed that both reactions require NADPH generating system and appeared to be catalyzed by cytochrome P450 (CYP). The identification of which isoenzyme was involved in the oxidative metabolism of LASSBio-448 was carried out by pre-incubations with the selective inhibitors sulfaphenazole (CYP2C9), quinidine (CYP2D6), furafylline (CYP1A2), p-nitrophenol (CYP2E1), ticlopidine (CYP2C19) and ketoconazole (CYP3A4). CYP1A2, CYP2C19 and CYP3A4 were demonstrated to be involved in the oxidative biotransformation of LASSBio-448.

Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and wellcharacterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).