In silico pharmacology for drug discovery: applications to targets and beyond.

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediting the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Examination of purported probes of human CYP2B6.

7-Ethoxy-4-trifluoromethylcoumarin (7-EFC) was examined as a substrate for cytochrome P450 (P450) in microsomes from human livers and expressed in B-lymphoblastoid cells. The O-deethylation of 7-EFC to 7-hydroxy-4-trifluoromethylcoumarin (7-HFC) varied over a liver bank (n = 19) by a factor of 13 (40-507 pmol min-1 mg-1 protein). When compared with the ability of the bank of human liver samples to metabolize form-selective substrates of the P450, 7-HFC formation correlated strongly with the formation of the S-mephenytoin metabolite, nirvanol (r2 = 0.86, p < 0.0001). alpha-Napthoflavone (ANF), diethyldithiocarbamate (DDC) and chloramphenicol (CAP) inhibited the O-deethylation of 7-EFC by microsomes from human livers by greater than 60%. Orphenadrine (ORP), a reported specific CYP2B6 inhibitor, was a less potent inhibitor of 7-HFC formation by microsomes from human liver than DDC or ANF. Using microsomes from B-lymphoblastoid cells expressing specific P450s, CYP2B6 and CYP1A2 were found to produce substantial levels of 7-HFC whereas CYP2E1 and CYP2C19 produced detectable amounts of this metabolite. ORP inhibited expressed CYP2E1 and CYP2B6 mediated 7-HFC formation to a greater extent than the inhibition observed for CYP1A2. Methoxychlor and S-mephenytoin inhibited expressed CYP2B6 but not CYP1A2 mediated 7-EFC O-deethylation. Livers (n = 5) with high relative rates of 7-HFC formation displayed biphasic enzyme kinetics with the low K(m) site (average K(m) = 3.3 microM) demonstrating allosteric activation. Five livers with low relative rates of 7-HFC formation also exhibited biphasic kinetics but lacked evidence of an allosteric mechanism being involved in the low K(m) component (average K(m) = 2.4 microM). Furthermore, expressed CYP2B6 and CYP2E1 converted 7-EFC to 7-HFC with allosteric activation indicated, while CYP1A2 mediated metabolism of 7-EFC to 7-HFC best fit the classic Michaelis-Menten model. A commercially available antibody to rat CYP2B, suggested to be specific for CYP2B6, was found to cross react with all members to the CYP2 family examined including CYP2C19, which possessed a nearly identical electrophoretic mobility to that of CYP2B6 in the system examined. In total, the evidence presented indicates that multiple P450s are involved in the formation of 7-HFC from 7-EFC, therefore this does not appear to be a useful or a selective probe of CYP2B6 catalytic activity. Furthermore, the specificity of both antibody and chemical inhibitor (ORP) probes previously suggested to be specific for CYP2B6 is also questioned.

Three and four dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2D6 inhibitors.

Three- and four-dimensional quantitative structure activity relationship (3D/4D-QSAR) pharmacophore models of competitive inhibitors of CYP2D6 were constructed using data from our laboratory or the literature. The 3D-QSAR pharmacophore models of the common structural features of CYP2D6 inhibitors were built using the program Catalyst (Molecular Simulations, San Diego, CA, USA). These 3D-QSAR models were compared with 3D and 4D-QSAR partial least squares (PLS) models which were constructed using molecular surface-weighted holistic invariant molecular (MS-WHIM) descriptors of size and shape of inhibitors. The first Catalyst model was generated from multiple conformers of competitive inhibitors (n = 20) of CYP2D6 mediated bufurolol 1'-hydroxylation. This model demonstrated a correlation of observed and predicted Ki (apparent) values of r = 0.75. A second Catalyst model was constructed from literature derived Ki (apparent) values (n = 31) for the inhibition of CYP2D6. This model provided a correlation of observed and predicted inhibition for CYP2D6 of r = 0.91. Both Catalyst Ki pharmacophores were then validated by predicting the Ki (apparent) of a test set of known CYP2D6 inhibitors (n = 15). Ten out of 15 of these Ki (apparent) values were predicted to be within one log residual of the observed value using our CYP2D6 inhibitor model, while the literature model predicted nine out of 15 values. Similarly, 3D- and 4D-QSARs derived from PLS MS-WHIM for our dataset yielded predictable models as assessed using cross-validation. The corresponding cross-validated PLS MS-WHIM model for the literature dataset yielded a comparable 3D-QSAR and improved 4D-QSAR value. Such computational models will aid in future prediction of drug-drug interactions.

Application of in silico approaches to predicting drug--drug interactions.

In an environment driven to find the next blockbuster drug, failure years into a project should not be an option. Recent studies have shown that poor absorption, distribution, metabolism, and excretion (ADME), and the related properties of toxicity and pharmacokinetics are responsible for a large proportion of failures. One way to understand and potentially predict molecules likely to be successful in humans as drugs from an ADME point of view is to use simulations. Such simulations may include simple rule-based approaches, structure--activity relationships, three-dimensional quantitative structure--activity relationships (3D-QSAR), and pharmacophores. All of these represent useful tools in understanding metabolism by the cytochromes P450, predicting drug--drug interactions (DDIs), and other pharmacokinetic parameters. The present paper briefly reviews the application of some computational tools applied to predicting DDIs and will provide the reader with an idea of their utility.

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.

Present and future in vitro approaches for drug metabolism.

The 1980s through 1990s witnessed the widespread incorporation of in vitro absorption, distribution, metabolism, and excretion (ADME) approaches into drug development by drug companies. This has been exemplified by the integration of the basic science of cytochrome P450s (CYPs) into most drug metabolism departments so that information on the metabolic pathways of drugs and drug-drug interactions (DDIs) is no longer an academic exercise, but essential for regulatory submission. This has come about due to the application of a variety of new technologies and in vitro models. For example, subcellular fractions have been widely used in metabolism studies since the 1960s. The last two decades has seen the increased use of hepatocytes as the reproducibility of cell isolations improved. The 1990s saw the rejuvenation of liver slices (as new slicers were developed) and the utilization of cDNA expressed enzymes as these technologies matured. In addition, there has been considerable interest in extrapolating in vitro data to in vivo for parameters such as absorption, clearance and DDIs. The current philosophy of drug development is moving to a 'fail early--fail cheaply' paradigm. Therefore, in vitro ADME approaches are being applied to drug candidates earlier in development since they are essential for identifying compounds likely to present ADME challenges in the latter stages of drug development. These in vitro tools are also being used earlier in lead optimization biology, in parallel with approaches for optimizing target structure activity relationships, as well as identification of DDI and the involvement of metabolic pathways that demonstrate genetic polymorphisms. This would suggest that the line between discovery and development drug metabolism has blurred. In vitro approaches to ADME are increasingly being linked with high-throughput automation and analysis. Further, if we think of perhaps the fastest available way to screen for successful drugs with optimal ADME characteristics, then we arrive at predictive computational algorithms, which are only now being generated and validated in parallel with in vitro and in vivo methods. In addition, as we increase the number of ADME parameters determined early, the overall amount of data generated for both discovery and development will increase. This will present challenges for the efficient and fast interpretation of such data, as well as incorporation and communication to chemistry, biology, and clinical colleagues. This review will focus on and assess the nature of present in vitro metabolism approaches and indicate how they are likely to develop in the future.

Autoactivation and activation of the cytochrome P450s.

OBJECTIVE: In order to reliably predict in vivo pharmacokinetic parameters from in vitro data, we must thoroughly understand the systems we currently use to determine enzyme kinetic parameters. There have been a number of reports of atypical Michaelis-Menten kinetics for cytochrome- (CYP) P4503A mediated metabolism in vitro but little discussion of its clinical relevance. In this manuscript, we examined the scope of CYP autoactivation and confirmed that CYP1A2 demonstrates atypical Michaelis-Menten kinetics in vitro. MATERIALS: Human liver microsomes, baculovirus-expressed CYP1A2, CYP1A2 in the RECO format, and E. coli expressed CYP1A2 were utilized. METHODS: Enzyme kinetics were performed using the various human CYP1A2 sources and ethoxyresorufin O-deethylation as a prototypical biotransformation. The data were fit to various models of enzyme kinetics. In some cases the data best fit the Hill equation, which was used to empirically model allosteric-type autoactivation kinetics. RESULTS: RECO CYP1A2 and E. coli expressed CYPIA2 both demonstrated autoactivation kinetics for ethoxyresorufin O-deethylation. When the data were fit to the Hill equation, n (the slope factor) was found to be 1.4 and 1.8 for RECO and E. coli expressed CYP1A2, respectively. Human liver microsomal and insect expressed sources of CYP1A2 illustrated classical Michaelis-Menten kinetics for the O-deethylation of ethoxyresorufin. CONCLUSION: Data generated in the current study and previous work suggest many CYPs, not only CYP3A, appear to behave as allosteric enzymes. We would argue that this is not necessarily a classical allosteric mechanism because n is frequently a non-integer. This autoactivation appears to be a function of several factors including substrate physicochemical characteristics, specific interactions of the substrates (activators) with the enzyme active site, and presence of other enzyme modulators. These factors interact to increase the catalytic activity of CYP and thus the complexity of predicting enzyme kinetic parameters or drug interactions.

Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays.

INTRODUCTION: The increasing abuse of amphetamine-like compounds presents a challenge for clinicians and clinical laboratories. Although these compounds may be identified by mass spectrometry-based assays, most clinical laboratories use amphetamine immunoassays that have unknown cross-reactivity with novel amphetamine-like drugs. To date, there has been a little systematic study of amphetamine immunoassay cross-reactivity with structurally diverse amphetamine-like drugs or of computational tools to predict cross-reactivity. METHODS: Cross-reactivities of 42 amphetamines and amphetamine-like drugs with three amphetamines screening immunoassays (AxSYM(®) Amphetamine/Methamphetamine II, CEDIA(®) amphetamine/Ecstasy, and EMIT(®) II Plus Amphetamines) were determined. Two- and three-dimensional molecular similarity and modeling approaches were evaluated for the ability to predict cross-reactivity using receiver-operator characteristic curve analysis. RESULTS: Overall, 34%-46% of the drugs tested positive on the immunoassay screens using a concentration of 20,000 ng/mL. The three immunoassays showed differential detection of the various classes of amphetamine-like drugs. Only the CEDIA assay detected piperazines well, while only the EMIT assay cross-reacted with the 2C class. All three immunoassays detected 4-substituted amphetamines. For the AxSYM and EMIT assays, two-dimensional molecular similarity methods that combined similarity to amphetamine/methamphetamine and 3,4-methylenedioxymethampetamine most accurately predicted cross-reactivity. For the CEDIA assay, three-dimensional pharmacophore methods performed best in predicting cross-reactivity. Using the best performing models, cross-reactivities of an additional 261 amphetamine-like compounds were predicted. CONCLUSIONS: Existing amphetamines immunoassays unevenly detect amphetamine-like drugs, particularly in the 2C, piperazine, and ß-keto classes. Computational similarity methods perform well in predicting cross-reactivity and can help prioritize testing of additional compounds in the future.

Future directions for drug transporter modelling.

Since the late 1980s computational methods such as quantitative structure-activity relationship (QSAR) and pharmacophore approaches have become more widely applied to assess interactions between drug-like molecules and transporters, starting with P-glycoprotein (P-gp). Identifying molecules that interact with P-gp and other transporters is important for drug discovery, but it is normally ascertained using laborious in vitro and in vivo studies. Computational QSAR and pharmacophore models can be used to screen commercial databases of molecules rapidly and suggest those likely to bind as substrates or inhibitors for transporters. These predictions can then be readily verified in vitro, thus representing a more efficient route to screening. Recently, the application of this approach has seen the identification of new substrates and inhibitors for several transporters. The successful application of computational models and pharmacophore models in particular to predict transporter binding accurately represents a way to anticipate drug-drug interactions of novel molecules from molecular structure. These models may also see incorporation in future pharmacokinetic-pharmacodynamic models to improve predictions of in vivo drug effects in patients. The implications of early assessment of transporter activity, current advances in QSAR, and other computational methods for future development of these and systems-based approaches will be discussed.

Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms.

The authors have previously applied two integrated platforms, MetaCore and MetaDrug, for the assembly and analysis of human biological networks as a useful method for the integration and functional interpretation of high-throughput experimental data. The present study demonstrates in detail the specific algorithms that are used in both software platforms. Using a standard set of genes as input, namely CYP3A4 (an enzyme), PXR (a nuclear hormone receptor), MDR1 (a transporter) and hERG (an ion channel) related to the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) of xenobiotics, we have now generated networks with each algorithm. The relative advantages and disadvantages of these algorithms are explained using these examples as well as appropriate instances of utility to illustrate further the particular circumstances for their use. In addition, the benefits of the different network algorithms are identified when compared with algorithms available in other products, where this information is available.

Short-term maintenance of phase I and II metabolism in precision-cut liver slices in dynamic organ culture.

Testosterone and 7-ethoxycoumarin were used as substrates to quantify the maintenance of phase I and II enzymes in precision-cut rat liver slices in dynamic organ culture. Testosterone hydroxylations, 7-ethoxycoumarin O-deethylation, 7-hydroxycoumarin sulfate, and 7-hydroxycoumarin glucuronide formation were all maintained at initial levels in the slice incubation media after incubation for up to 4 hr. The activities of various cytochrome P450 isozymes, measured using the stereospecific and regiospecific hydroxylation of testosterone and the maintenance of phase I and II metabolism using 7-ethoxycoumarin, are therefore suggested to be stable over short-term incubations in a physiological buffer. The testosterone hydroxylation assay is also suggested as a versatile slice metabolic viability marker for various P450 activities over longer periods.

In silico approaches to predicting drug metabolism, toxicology and beyond.

The discovery and optimization of new drug candidates is becoming increasingly reliant upon the combination of experimental and computational approaches related to drug metabolism, toxicology and general biopharmaceutical properties. With the considerable output of high-throughput assays for cytochrome-P450-mediated drug-drug interactions, metabolic stability and assays for toxicology, we have orders of magnitude more data that will facilitate model building. A recursive partitioning model for human liver microsomal metabolic stability based on over 800 structurally diverse molecules was used to predict molecules with known log in vitro clearance data (Spearman's rho -0.64, P <0.0001). In addition, with solely published data, a quantitative structure-activity relationship for 66 inhibitors of the potassium channel human ether-a-gogo (hERG) that has been implicated in the failure of a number of recent drugs has been generated. This model has been validated with further published data for 25 molecules (Spearman's rho 0.83, P <0.0001). If continued value is to be realized from these types of computational models, there needs to be some applied research on their validation and optimization with new data. Some relatively simple approaches may have value when it comes to combining data from multiple models in order to improve and focus drug discovery on the molecules most likely to succeed.

Three-dimensional quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance.

Future alternatives to the presently accepted in vitro paradigm of prediction of intrinsic clearance, which could be used earlier in the drug discovery process, would potentially accelerate efforts to identify better drug candidates with more favorable metabolic profiles and less likelihood of failure with regard to human pharmacokinetic attributes. In this study we describe two computational methods for modeling human microsomal and hepatocyte intrinsic clearance data derived from our laboratory and the literature, which utilize pharmacophore features or descriptors derived from molecular structure. Human microsomal intrinsic clearance data generated for 26 known therapeutic drugs were used to build computational models using commercially available software (Catalyst and Cerius(2)), after first converting the data to hepatocyte intrinsic clearance. The best Catalyst pharmacophore model gave an r of 0.77 for the observed versus predicted clearance. This pharmacophore was described by one hydrogen bond acceptor, two hydrophobic features, and one ring aromatic feature essential to discriminate between high and low intrinsic clearance. The Cerius(2) quantitative structure activity relationship (QSAR) model gave an r(2) = 0.68 for the observed versus predicted clearance and a cross-validated r(2) (q(2)) of 0.42. Similarly, literature data for human hepatocyte intrinsic clearance for 18 therapeutic drugs were also used to generate two separate models using the same computational approaches. The best Catalyst pharmacophore model gave an improved r of 0.87 and was described by two hydrogen bond acceptors, one hydrophobe, and 1 positive ionizable feature. The Cerius(2) QSAR gave an r(2) of 0.88 and a q(2) of 0.79. Each of these models was then used as a test set for prediction of the intrinsic clearance data in the other data set, with variable successes. These present models represent a preliminary application of QSAR software to modeling and prediction of human in vitro intrinsic clearance.

Characterization of transgenic mouse strains using six human hepatic cytochrome P450 probe substrates.

1. Transgenic mice were evaluated with six human cytochrome P450 (CYP) selective probe substrates, as little is known about their metabolism in the mouse. Mouse strains characterized include C57BL/SJL, FVB/N, mdr 1a/1b (-/-), ob/ob and ACCA. 2. Human CYP probe substrates used for characterization of mouse CYP activities included bufuralol, testosterone, dextromethorphan, phenacetin, diclofenac and S-mephenytoin. Activities were compared with those obtained in human liver microsomes and in human recombinant enzyme preparations. All transgenic mouse strains showed similar apparent K(m) with bufuralol, testosterone and dextromethorphan which compared favourably with those observed in human liver microsomes. 3. K(m) for phenacetin O-deethylase and S-mephenytoin 4'-hydroxylation were more variable across strains and in some cases demonstrated biphasic kinetics. Phenacetin O-deethylase activity was low in all mouse strains except FVB/N and mdr 1a/1b (-/-). Diclofenac 4-hydroxylation did not occur to any significant extent in the five strains of mouse evaluated here. 4. The findings suggest the validity of using five of the probes for transgenic mouse hepatic CYP characterization and gross comparison with data generated with human CYP.

Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites.

Structure activity relationships (SAR), three-dimensional structure activity relationships (3D-QSAR), and pharmacophores represent useful tools in understanding cytochrome P450 (CYP) active sites in the absence of crystal structures for these human enzymes. These approaches have developed over the last 30 years such that they are now being applied in numerous industrial and academic laboratories solely for this purpose. Such computational approaches have helped in understanding substrate and inhibitor binding to the major human CYPs 1A2, 2B6, 2C9, 2D6, 3A4 as well as other CYPs and additionally complement homology models for these enzymes. Similarly, these approaches may assist in our understanding of CYP induction. This review describes in detail the development of pharmacophores and 3D-QSAR techniques, which are now being more widely used for modeling CYPs; the review will also describe how such approaches are likely to further impact our active site knowledge of these omnipresent and important enzymes.

Three-dimensional-quantitative structure activity relationship analysis of cytochrome P-450 3A4 substrates.

To gain a better understanding of the active site of cytochrome P-450 (CYP) 3A4, a three-dimensional-quantitative structure activity relationship model was constructed using the structures and K(m (apparent)) values of 38 substrates of human liver microsomal CYP3A4. This pharmacophore was built using the program Catalyst and consisted of four features: two hydrogen bond acceptors, one hydrogen bond donor, and one hydrophobic region. The pharmacophore demonstrated a fit value (r) of observed and expected K(m(apparent)) value of 0.67. The validity of the CYP3A4 substrate model was tested by twice permuting (randomizing) the activity values and substrate structures. The results of this validation procedure indicated that the original model was a significant representation of the features required of CYP3A4 substrates. The second validation method used the Catalyst model to predict the K(m(apparent)) values of a test set of structurally diverse substrates for CYP3A4 not included in the 38 molecules used to build the model. Two fitting algorithms included in this software were examined: fast fit and best fit. The fast fitting method resulted in predictions for all 12 substrates that were within 1 log unit for the residual [i.e., the difference between predicted and observed K(m(apparent))]. In contrast, the best fit algorithm poorly predicted the K(m (apparent)) values (i.e., residual >1 log unit) of 4 of 12 substrates. These poor fits with the best fit function suggest that the fast fit method within Catalyst is more representative of the observed K(m(apparent)) values for CYP3A4 substrates and enables good in silico prediction of this activity. A Catalyst common features pharmacophore was also constructed from three molecules known to activate their own metabolism included in the 38 molecules of the initial CYP3A4 model. This demonstrated that activators of CYP3A4 possess multiple hydrophobic regions that might correspond with a region in the active site away from the metabolic site.

(R)-, (S)-, and racemic fluoxetine N-demethylation by human cytochrome P450 enzymes.

Fluoxetine is one of the most widely prescribed selective serotonin reuptake inhibitors (SSRIs) that is marketed worldwide. However, details of its human hepatic metabolism have been speculative and incomplete, possibly due to the sensitivity of analytical techniques and selectivity of specific in vitro probes and reagents used. Studies with (R)-, (S)-, and racemic fluoxetine were undertaken to determine the stereospecific nature of its metabolism and estimate intrinsic clearance contributions of each CYP for fluoxetine N-demethylation. Measurable fluoxetine N-demethylase activity was catalyzed by CYP1A2, -2B6, -2C9, -2C19, -2D6, -3A4, and -3A5. All enzymes catalyzed this reaction for both enantiomers and the racemate, and intrinsic clearance values were similar for the enantiomers for all CYP enzymes except CYP2C9, which demonstrated stereoselectivity for R- over the S-enantiomer. Scaling the intrinsic clearance values for the individual CYP enzymes to estimate contributions of each in human liver microsomes suggested that CYP2D6, CYP2C9, and CYP3A4 contribute the greatest amount of fluoxetine N-demethylation in human liver microsomes. These data were corroborated with the examination of the effects of CYP-specific inhibitors quinidine (CYP2D6), sulfaphenazole (CYP2C9), and ketoconazole (CYP3A4) on fluoxetine N-demethylation in pooled human liver microsomes. Together, these findings suggest a significant role for the polymorphically expressed CYP2D6 in fluoxetine clearance and are consistent with reports on the clinical pharmacokinetics of fluoxetine.

Three- and four-dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors.

The interaction of competitive type inhibitors with the active site of cytochrome P450 (CYP) 2C9 has been predicted using three- and four-dimensional quantitative structure activity relationship (3D-/4D-QSAR) models constructed using previously unreported and literature-derived data. 3D-QSAR pharmacophore models of the common structural features of CYP2C9 inhibitors were built using the program Catalyst and compared with 3D- and 4D-QSAR partial least-squares models, which use molecular surface-weighted holistic invariant molecular descriptors of the size and shape of inhibitors. The Catalyst models generated from multiple conformers of competitive inhibitors of CYP2C9 activities contained at least one hydrophobic and two hydrogen bond acceptor/donor regions. Catalyst model 1 was constructed with Ki(apparent) values for inhibitors of tolbutamide and diclofenac 4'-hydroxylation (n = 9). Catalyst model 2 was generated from literature Ki(apparent) values for (S)-warfarin 7-hydroxylation (n = 29), and Catalyst model 3 from literature IC50 values for tolbutamide 4-hydroxylation (n = 13). These three models illustrated correlation values of observed and predicted inhibition for CYP2C9 of r = 0.91, 0.89, and 0.71, respectively. Catalyst pharmacophores generated with Ki(apparent) values were validated by predicting the Ki(apparent) value of a test set of CYP2C9 inhibitors also derived from the literature (n = 14). Twelve of fourteen of these Ki(apparent) values were predicted to be within 1 log residual of the observed value using Catalyst model 1, whereas Catalyst model 2 predicted 10 of 14 Ki(apparent) values. The corresponding partial least-squares molecular surface-weighted holistic invariant molecular 3D- and 4D-QSAR models for all CYP2C9 data sets yielded predictable models as assessed using cross-validation. These 3D- and 4D-QSAR models of CYP inhibition will aid in future prediction of drug-drug interactions.