Extension of a predictive substrate model for human cytochrome P4502D6

Pharmacogenetics of Breast Cancer Treatments: A Sub-Saharan Africa Perspective

Breast cancer is the most frequent cause of cancer death in low- and middle-income countries, in particular among sub-Saharan African women, where response to available anticancer treatment therapy is often limited by the recurrent breast tumours and metastasis, ultimately resulting in decreased overall survival rate. This can also be attributed to African genomes that contain more variation than those from other parts of the world. The purpose of this review is to summarize published evidence on pharmacogenetic and pharmacokinetic aspects related to specific available treatments and the known genetic variabilities associated with metabolism and/or transport of breast cancer drugs, and treatment outcomes when possible. The emphasis is on the African genetic variation and focuses on the genes with the highest strength of evidence, with a close look on CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5, CYP19A1, UGT1A4, UGT2B7, UGT2B15, SLC22A16, SLC38A7, FcγR, DPYD, ABCB1, and SULT1A1, which are the genes known to play major roles in the metabolism and/or elimination of the respective anti-breast cancer drugs given to the patients. The genetic variability of their metabolism could be associated with different metabolic phenotypes that may cause reduced patients’ adherence because of toxicity or sub-therapeutic doses. Finally, this knowledge enhances possible personalized treatment approaches, with the possibility of improving survival outcomes in patients with breast cancer.

Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches

Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements.

Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present.

Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches.

This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.

New insights into the structural characteristics and functional relevance of the human cytochrome P450 2D6 enzyme

To date, the crystal structures of at least 12 human CYPs (1A2, 2A6, 2A13, 2C8, 2C9, 2D6, 2E1, 2R1, 3A4, 7A1, 8A1, and 46A1) have been determined. CYP2D6 accounts for only a small percentage of all hepatic CYPs (< 2%), but it metabolizes approximately 25% of clinically used drugs with significant polymorphisms. CYP2D6 also metabolizes procarcinogens and neurotoxins, such as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, 1,2,3,4-tetrahydroquinoline, and indolealkylamines. Moreover, the enzyme utilizes hydroxytryptamines and neurosteroids as endogenous substrates. Typical CYP2D6 substrates are usually lipophilic bases with an aromatic ring and a nitrogen atom, which can be protonated at physiological pH. Substrate binding is generally followed by oxidation (5-7 A) from the proposed nitrogen-Asp301 interaction. A number of homology models have been constructed to explore the structural features of CYP2D6, while antibody studies also provide useful structural information. Site-directed mutagenesis studies have demonstrated that Glu216, Asp301, Phe120, Phe481, and Phe483 play important roles in determining the binding of ligands to CYP2D6. The structure of human CYP2D6 has been recently determined and shows the characteristic CYP fold observed for other members of the CYP superfamily. The lengths and orientations of the individual secondary structural elements in the CYP2D6 structure are similar to those seen in other human CYP2 members, such as CYP2C9 and 2C8. The 2D6 structure has a well-defined active-site cavity located above the heme group with a volume of approximately 540 A(3), which is larger than equivalent cavities in CYP2A6 (260 A(3)), 1A2 (375 A(3)), and 2E1 (190 A(3)), but smaller than those in CYP3A4 (1385 A(3)) and 2C8 (1438 A(3)). Further studies are required to delineate the molecular mechanisms involved in CYP2D6 ligand interactions and their implications for drug development and clinical practice.

Understanding CYP2D6 interactions

Owing to the polymorphic nature of CYP2D6, clinically significant issues can arise when drugs rely on that enzyme either for clearance, or metabolism to an active metabolite. Available screening methods to determine if the compound is likely to cause drug-drug interactions, or is likely to be a victim of inhibition of CYP2D6 by other compounds will be described. Computational models and examples will be given on strategies to design out the CYP2D6 liabilities for both heme-binding compounds and non-heme-binding compounds.

Genetic polymorphism in cytochrome P450 2D6 (CYP2D6): Population distribution of CYP2D6 activity

Cytochrome P-450 2D6 (CYP2D6) is involved in the metabolism of many therapeutic drugs even though the enzyme represents a small proportion of the total CYP content of human liver. In vivo phenotyping with probe drug substrates such as debrisoquine and dextromethorphan showed a clear separation between poor metabolizers (PM) and extensive metabolizers (EM). This polymorphism may affect susceptibility to environmental disease, as suggested by molecular epidemiologic studies that found an association between CYP2D6 metabolizer phenotype and cancer risk; however, this association is not consistent. There are only a few examples of CYP2D6 involvement in toxicant mechanism of action, but this has not been extensively studied. Gene probe studies documented a number of genetic polymorphisms that underlie CYP2D6 metabolizer phenotypes. The EM group carries the wild-type (*1) or active (*2) variant alleles, while the PM group carries the *3, *4, *5, or *6 alleles, all of which code for a protein that has lower or null CYP2D6 activity. The current analysis characterizes (a) influence of genotype on phenotype based upon in vivo metabolism studies of probe drugs and (b) frequency of the major genotypes in different population groups is also characterized. These data were then incorporated into Monte Carlo modeling to simulate population distributions of CYP2D6 activity. This analysis reproduced the bimodal distributions commonly seen in phenotyping studies of Caucasians and found extensive population variability in enzyme activity, as indicated by the 9- to 56-fold difference between the PM modal median and the total population median CYP2D6 activity. This substantial degree of interindividual variability in CYP function indicates that assessments involving CYP2D6 substrates need to consider the full distribution of enzyme activity in refining estimates of internal dose in health assessments of xenobiotics.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

Metabolic aromatization of N-alkyl-1,2,3,4-tetrahydroquinoline substructures to quinolinium by human liver microsomes and horseradish peroxidase

Metabolic aromatization of xenobiotics is an unusual reaction with some documented examples. For instance, the oxidation of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine to the neurotoxic pyridinium ion metabolite 1-methyl-4-phenylpyridinium by monoamine oxidase (MAO) B in the brain has been of interest to a number of investigators. It has also been reported that although the aromatization of N-methyl-tetrahydroisoquinoline occurs with MAO B, the metabolism does not proceed for its isomer, N-methyl-tetrahydroquinoline, by the same enzyme. The aromatization of an N-alkyl-tetrahydroquinoline substructure was identified during in vitro metabolite profiling of compound A, which was designed as a potent renin inhibitor for the treatment of hypertension. The N-alkylquinolinium metabolite of compound A was identified by liquid chromatography-tandem mass spectrometry of human liver microsomal incubates and proton NMR of the isolated metabolite. Further in vitro metabolism studies with a commercially available chemical (compound B), containing the same substructure, also generated an N-alkylquinolinium metabolite. In vitro cytochrome P450 (P450) reaction phenotyping of compound A revealed that the metabolism was catalyzed exclusively by CYP3A4. Although compound B was a substrate for several P450 isoforms, its quinolinium metabolite was also generated predominantly by CYP3A4. Neither compound A nor compound B was a substrate of MAOs. The quinolinium metabolites were readily produced by horseradish peroxidase, suggesting that aromatization of the N-alkyltetrahydroquinoline could occur via a mechanism involving single electron transfer from nitrogen. Although dihydro intermediates from the tetrahydroquinoline substrates were not observed in the formation of quinolinium metabolites, cyanide trapping results indicated the occurrence of iminium intermediates.

Functional interaction of nitrogenous organic bases with cytochrome P450: A critical assessment and update of substrate features and predicted key active-site elements steering the access, binding, and orientation of amines

The widespread use of nitrogenous organic bases as environmental chemicals, food additives, and clinically important drugs necessitates precise knowledge about the molecular principles governing biotransformation of this category of substrates. In this regard, analysis of the topological background of complex formation between amines and P450s, acting as major catalysts in C- and N-oxidative attack, is of paramount importance. Thus, progress in collaborative investigations, combining physico-chemical techniques with chemical-modification as well as genetic engineering experiments, enables substantiation of hypothetical work resulting from the design of pharmacophores or homology modelling of P450s. Based on a general, CYP2D6-related construct, the majority of prospective amine-docking residues was found to cluster near the distal heme face in the six known SRSs, made up by the highly variant helices B', F and G as well as the N-terminal portion of helix C and certain beta-structures. Most of the contact sites examined show a frequency of conservation < 20%, hinting at the requirement of some degree of conformational versatility, while a limited number of amino acids exhibiting a higher level of conservation reside close to the heme core. Some key determinants may have a dual role in amine binding and/or maintenance of protein integrity. Importantly, a series of non-SRS elements are likely to be operative via long-range effects. While hydrophobic mechanisms appear to dominate orientation of the nitrogenous compounds toward the iron-oxene species, polar residues seem to foster binding events through H-bonding or salt-bridge formation. Careful uncovering of structure-function relationships in amine-enzyme association together with recently developed unsupervised machine learning approaches will be helpful in both tailoring of novel amine-type drugs and early elimination of potentially toxic or mutagenic candidates. Also, chimeragenesis might serve in the construction of more efficient P450s for activation of amine drugs and/or bioremediation.

Comparative molecular field analysis and QSAR on substrates binding to cytochrome P450 2D6

In this study, we utilized comparative molecular field analysis (CoMFA) to gain a better understanding of the steric and electrostatic features of the cytochrome p450 2D6 (CYP2D6) active site. The training set consists of 24 substrates with reported K(M) values from liver microsomal CYP2D6 spanning an activity range of almost three log units. The low energy conformers were fit by root mean square (RMS) to minaprine at the site of metabolism and to the protonated nitrogen. In this manner, we constructed two CoMFA models, one model with a distance constraint and another without. The model with the distance parameter (non-cross-validated R(2)=0.99) was approximately equal to the CoMFA without a distance parameter (non-cross-validated R(2)=0.98). Validation of our CoMFA was accomplished by predicting the K(M) values of 15 diverse CYP2D6 substrates not in the original training set resulting in a predictive R(2)=0.62. Finally, we also pursued correlations of pK(a) and log P with CYP2D6 substrate K(M) in an effort to investigate other physicochemical properties.

Pharmacophore modeling of cytochromes P450

Understanding the binding of ligands in the active site of a membrane-bound protein is difficult in the absence of a crystal structure. When these proteins are the enzymes involved in drug metabolism, it leaves little option but to use site-directed mutagenesis and in vitro studies to provide critical information relating to determinants of binding affinity. Pharmacophore models and three-dimensional quantitative structure-activity relationships have been used either alone or in combination with protein homology models to provide this information for cytochrome P450s. At present, their application has been directed to the major enzymes but this may escalate in future as more in vitro data are generated for other P450s. The following review outlines the methodologies and models as well as future prospects for applying these technologies to P450s in the hope that future drugs will be selected with increased metabolic stability and fewer incidences of undesirable drug-drug interactions.

Progress in predicting human ADME parameters in silico

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

A novel approach to predicting P450 mediated drug metabolism. CYP2D6 catalyzed N-dealkylation reactions and qualitative metabolite predictions using a combined protein and pharmacophore model for CYP2D6

A combined protein and pharmacophore model for cytochrome P450 2D6 (CYP2D6) has been extended with a second pharmacophore in order to explain CYP2D6 catalyzed N-dealkylation reactions. A group of 14 experimentally verified N-dealkylation reactions form the basis of this second pharmacophore. The combined model can now accommodate both the usual hydroxylation and O-demethylation reactions catalyzed by CYP2D6, as well as the less common N-dealkylation reactions. The combined model now contains 72 metabolic pathways catalyzed by CYP2D6 in 51 substrates. The model was then used to predict the involvement of CYP2D6 in the metabolism of a "test set" of seven compounds. Molecular orbital calculations were used to suggest energetically favorable sites of metabolism, which were then examined using modeling techniques. The combined model correctly predicted 6 of the 8 observed metabolites. For the well-established CYP2D6 metabolic routes, the predictive value of the current combined protein and pharmacophore model is good. Except for the highly unusual metabolism of procainamide and ritonavir, the known metabolites not included in the development of the model were all predicted by the current model. Two possible metabolites have been predicted by the current model, which have not been detected experimentally. In these cases, the model may be able to guide experiments. P450 models, like the one presented here, have wide applications in the drug design process which will contribute to the prediction and elimination of polymorphic metabolism and drug-drug interactions.

Novel approach to predicting P450-mediated drug metabolism: development of a combined protein and pharmacophore model for CYP2D6

A combined protein and pharmacophore model for cytochrome P450 2D6 (CYP2D6) has been derived using various computational chemistry techniques. A combination of pharmacophore modeling (using 40 substrates), protein modeling, and molecular orbital calculations was necessary to derive a model which incorporated steric, electronic, and chemical stability properties. The initial pharmacophore and protein models used to construct the combined model were derived independently and showed a high level of complementarity. The combined model is in agreement with experimental results concerning the substrates used to derive the model, with site-directed mutagenesis data available for the CYP2D6 protein, and takes into account the site-directed mutagenesis results for a variety of other 2-family P450s.