Computational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5)

Energy Decomposition Analysis of the Nature of Coordination Bonding at the Heme Iron Center in Cytochrome P450 Inhibition

Drug compounds or their metabolic intermediates (MIs) sometimes inhibit the function of cytochrome P450 enzymes (P450s) by forming a coordination bond with the Fe(III) heme or Fe(II) heme of P450s. Such inhibition is one of the major causes of drug-drug interactions (DDIs), a subject of longstanding academic and practical interest. However, such coordination bonding is not fully understood at the quantum mechanical level, thus hampering rational improvement of the accuracy of DDI-related predictions. In this work, we employed density functional theory (DFT) and the generalized Kohn-Sham energy decomposition analysis (GKS-EDA) scheme to investigate the nature of the coordination bonding formed in the reversible and quasi-irreversible inhibition of P450s. The GKS-EDA results highlighted a previously unrecognized role of the electron correlation effect in P450 inhibition. The correlation effect tends to be larger in Fe(II) complexes of MI-type inhibitors and is particularly prominent for the nitrosoalkane ligand. An additional natural bond orbital (NBO) analysis provided insight into the relative significance of the σ donation and π backdonation effects in various heme-inhibitor complexes.

High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus

One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with antibiotic activity only in the presence of copper. Recently, our group demonstrated that previously unknown staphylococcal CDIs were frequently present in a small pilot screen. Here, we report the outcome of a larger industrial anti-staphylococcal screen consisting of 40 771 compounds assayed in parallel, both in standard and in copper-supplemented media. Ultimately, 483 had confirmed copper-dependent IC50 values under 50 μM. Sphere-exclusion clustering revealed that these hits were largely dominated by sulfur-containing motifs, including benzimidazole-2-thiones, thiadiazines, thiazoline formamides, triazino-benzimidazoles, and pyridinyl thieno-pyrimidines. Structure-activity relationship analysis of the pyridinyl thieno-pyrimidines generated multiple improved CDIs, with activity likely dependent on ligand/ion coordination. Molecular fingerprint-based Bayesian classification models were built using Discovery Studio and Assay Central, a new platform for sharing and distributing cheminformatic models in a portable format, based on open-source tools. Finally, we used the latter model to evaluate a library of FDA-approved drugs for copper-dependent activity in silico. Two anti-helminths, albendazole and thiabendazole, scored highly and are known to coordinate copper ions, further validating the model's applicability.

Computational prediction of cytochrome P450 inhibition and induction

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Variation in the Response of Clozapine Biotransformation Pathways in Human Hepatic Microsomes to CYP1A2- and CYP3A4-selective Inhibitors

The atypical antipsychotic agent clozapine (CLZ) is effective in many patients who are resistant to conventional antipsychotic drugs. Cytochromes P450 (CYPs) 1A2 and 3A4 oxidize CLZ to norCLZ and CLZ N-oxide in human liver. Concurrent treatment with inducers and inhibitors of CYP1A2 modulates CLZ elimination that disrupts therapy. Drug-drug interactions involving CYP3A4 are also significant but less predictable. To further characterize the factors underlying these interactions, we used samples from a cohort of human livers to assess variation in CLZ oxidation pathways in relation to intrinsic CYP3A4 and CYP1A2 activities and the effects of the corresponding selective inhibitors ketoconazole (0.2 and 2 μM) and fluvoxamine (1 and 10 μM). The CYP3A4-selective inhibitor ketoconazole (2 μM) impaired CLZ N-oxide formation in all 14 of the livers used in inhibition studies (≥50% inhibition) while the CYP1A2-selective inhibitor fluvoxamine (10 μM) decreased norCLZ formation in nine. Ketoconazole effectively inhibited CLZ metabolism in five of seven livers that catalysed CYP3A4-dependent testosterone 6β-hydroxylation at or above the median rate and in four other livers with lower intrinsic CYP3A4 activity. Similarly, fluvoxamine (10 μM) readily inhibited CLZ oxidation in seven livers with high CYP1A2-mediated 7-ethoxyresorufin O-deethylation activity (at or above the median) and three livers with lower intrinsic CYP1A2 activity. In three livers, CLZ biotransformation was impaired by both ketoconazole and fluvoxamine, consistent with a major role for both CYPs. These findings suggest that the intrinsic activities of CYPs 1A2 and 3A4 are unrelated to the response to CYP-selective inhibitors and that assessment of the activities in vivo may not assist the prediction of drug-drug interactions.

Influence of Amlodipine Enantiomers on Human Microsomal Cytochromes P450: Stereoselective Time-Dependent Inhibition of CYP3A Enzyme Activity

Amlodipine (AML) is available as a racemate, i.e., a mixture of R- and S-enantiomers. Its inhibitory potency towards nine cytochromes P450 (CYP) was studied to evaluate the drug–drug interactions between the enantiomers. Enzyme inhibition was evaluated using specific CYP substrates in human liver microsomes. With CYP3A, both enantiomers exhibited reversible and time-dependent inhibition. S-AML was a stronger reversible inhibitor of midazolam hydroxylation: the K_i values of S- and R-AML were 8.95 µM, 14.85 µM, respectively. Computational docking confirmed that the enantiomers interact differently with CYP3A: the binding free energy of S-AML in the active site was greater than that for R-AML (−7.6 vs. −6.7 kcal/mol). Conversely, R-AML exhibited more potent time-dependent inhibition of CYP3A activity (K_I 8.22 µM, K_inact 0.065 min⁻¹) than S-AML (K_I 14.06 µM, K_inact 0.041 min⁻¹). R-AML was also a significantly more potent inhibitor of CYP2C9 (K_i 12.11 µM/S-AML 21.45 µM) and CYP2C19 (K_i 5.97 µM/S-AML 7.22 μM. In conclusion, results indicate that clinical use of S-AML has an advantage not only because of greater pharmacological effect, but also because of fewer side effects and drug–drug interactions with cytochrome P450 substrates due to absence of R-AML.

Potential Role of Patients' CYP3A-Status in Clozapine Pharmacokinetics

BACKGROUND

The atypical antipsychotic clozapine is effective in treatment-resistant schizophrenia; however, the success or failure of clozapine therapy is substantially affected by the variables that impact the clozapine blood concentration. Thus, elucidating the inter-individual differences in clozapine pharmacokinetics can facilitate the personalized therapy.

METHODS

Since a potential role in clozapine metabolism is assigned to CYP1A2, CYP2C19, CYP2D6 and CYP3A enzymes, the association between the patients' CYP status (CYP genotypes, CYP expression) and clozapine clearance was evaluated in 92 psychiatric patients.

RESULTS

The patients' CYP2C19 or CYP2D6 genotypes and CYP1A2 expression seemed to have no effect on clozapine serum concentration, whereas CYP3A4 expression significantly influenced the normalized clozapine concentration (185.53±56.53 in low expressers vs 78.05±29.57 or 66.52±0.25 (ng/mL)/(mg/kg) in normal or high expressers, P<.0001), in particular that the patients expressed CYP1A2 at a relatively low level. The functional CYP3A5*1 allele seemed to influence clozapine concentrations in those patients who expressed CYP3A4 at low levels. The dose requirement for the therapeutic concentration of clozapine was substantially lower in low CYP3A4 expresser patients than in normal/high expressers (2.18±0.64 vs 4.98±1.40 mg/kg, P<.0001). Furthermore, significantly higher plasma concentration ratios of norclozapine/clozapine and clozapine N-oxide/clozapine were observed in the patients displaying normal/high CYP3A4 expression than in the low expressers.

CONCLUSION

Prospective assaying of CYP3A-status (CYP3A4 expression, CYP3A5 genotype) may better identify the patients with higher risk of inefficiency or adverse reactions and may facilitate the improvement of personalized clozapine therapy; however, further clinical studies are required to prove the benefit of CYP3A testing for patients under clozapine therapy.

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015)

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t_1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t_1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitroMtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivoMtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t_1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data

PURPOSE

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability.

METHODS

Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).

RESULTS

"Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.

CONCLUSIONS

Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Proadifen sensitizes resistant ovarian adenocarcinoma cells to cisplatin

Proadifen (SKF-525A) is a P450 monooxygenase inhibitor with potential anti-proliferative activity and the ability to potentiate the toxicity of hypericin-mediated photodynamic therapy and mitoxantrone via alteration of ABC transport proteins. Elevated expression of some ABC transporters may also determine the efficacy of cisplatin-based chemotherapy. Thus, the purpose of this study was to investigate the ability of proadifen to sensitize A2780 and A2780cis ovarian cancer cells to cisplatin (CDDP). Herein, we show for the first time that proadifen sensitized resistant ovarian cancer cells to CDDP-induced cell death. The chemosensitizing effect of proadifen on CDDP action was also confirmed by MTT assays in multicellular spheroids. The possible mechanisms responsible for the enhanced cytotoxicity of proadifen/CDDP combined treatment may be attributed to a decrease of reduced relative glutathione levels, downregulation of multidrug resistance-associated proteins 1 and 2 (MRP1, MRP2) and attenuation of survivin expression. Taken together, our results indicate that proadifen is a promising compound for further in vivo experiments related to overcoming multidrug resistance and sensitization of resistant ovarian carcinoma to CDDP.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Background

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.

Methodology/Principal Findings

In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC₅₀ < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi.

Conclusions/ Significance

We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The disease is endemic to Latin America but is increasingly found in North America and Europe, primarily through immigration, and the spread of this disease is bringing new attention to the need for novel, safe, and effective therapeutics to treat T. cruzi infection. We have used data from a phenotypic screen to build Bayesian models to predict anti-parasitic activity against T. cruzi in vitro. These models were used to score various small libraries of molecules. We selected less than 100 compounds for testing and found in vitro actives, some of which were tested in an in vivo efficacy model. We identified the antimalarial pyronaridine as having in vivo efficacy and provides us with a new starting point for further investigation and optimization.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Computational prediction and validation of an expert's evaluation of chemical probes

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis

Tuberculosis is a major, neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event data sets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose-response, single-point and dual-event dose-response as well as combined data sets for three distinct data sets from the same laboratory. We used a fourth data set of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose-response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual-event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25 000-350 000 molecules. Our results have implications for justifying further high-throughput screening versus focused testing based on model predictions.

Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis

Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an animal model of the disease is a challenge that takes considerable time and money. As an example, recent years have seen whole-cell phenotypic screens of millions of compounds yielding over 1500 inhibitors of Mycobacterium tuberculosis (Mtb). These must be prioritized for testing in the mouse in vivo assay for Mtb infection, a validated model utilized to select compounds for further testing. We demonstrate learning from in vivo active and inactive compounds using machine learning classification models (Bayesian, support vector machines, and recursive partitioning) consisting of 773 compounds. The Bayesian model predicted 8 out of 11 additional in vivo actives not included in the model as an external test set. Curation of 70 years of Mtb data can therefore provide statistically robust computational models to focus resources on in vivo active small molecule antituberculars. This highlights a cost-effective predictor for in vivo testing elsewhere in other diseases.

Modelling cytochromes P450 binding modes to predict P450 inhibition, metabolic stability and isoform selectivity

The cytochromes P450 (P450) superfamily is a diverse group of enzymes involved in the metabolism of xenobiotics, whose orientations within the catalytic site can lead to different binding modes, namely productive, nonproductive, and inhibitory. This article collects the most recent approaches that individually study P450- ligand interactions, including a novel in silico technology, developed in the framework of the Human Cytochrome P450 Consortium initiative, that provides reliable in silico predictions of P450 inhibition, metabolic stability and isoform selectivity.

Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis

The search for compounds active against Mycobacterium tuberculosis is reliant upon high-throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 μM to 10.2 μM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to the same series including five quinolones (including ciprofloxacin), three molecules with long aliphatic linkers and three singletons. This approach represents a rapid method to prioritize compounds for testing that can be used alongside medicinal chemistry insight and other filters to identify active molecules. Such models can significantly increase the hit rate of HTS, above the usual 1% or lower rates seen. In addition, the potential targets for the 11 molecules were predicted using TB Mobile and clustering alongside a set of over 740 molecules with known M. tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation

The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multidrug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external data set. A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine, or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual data sets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three data sets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest 5-fold cross-validation ROC scores can outperform other models in a test set dependent manner. We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Data set fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date.

Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery

PURPOSE

Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested.

METHODS

A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate.

RESULTS

In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models.

CONCLUSION

Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.

Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models

High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15–28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.

Mechanism-based inhibition profiles of erythromycin and clarithromycin with cytochrome P450 3A4 genetic variants

Inhibition of cytochrome P450 (CYP) 3A4 is the major cause of drug-drug interactions (DDI). We have previously reported that the genetic variation of CYP3A4 significantly affected the inhibitory profiles of typical competitive inhibitors. In addition to competitive inhibition, some clinically significant DDI are attributable to mechanism-based inhibition (MBI). However, the differences in the MBI kinetics among CYP3A4 genetic variants remain to be characterized. In this study, we quantitatively investigated the inhibition kinetics of MBI inhibitors, erythromycin and clarithromycin, on the CYP3A4 variants CYP3A4.1, 4.2, 4.7, 4.16, and 4.18. The activity of CYP3A4 was assessed using testosterone 6β-hydroxylation with recombinant CYP3A4. Both erythromycin and clarithromycin decreased the activity of CYP3A4 in a time-dependent manner. The maximum inactivation rate constants, k(inact,max), of erythromycin for CYP3A4.2 and CYP3A4.7 were 0.5-fold that for CYP3A4.1, while that for CYP3A4.16 and CYP3A4.18 were similar to that for CYP3A4.1. The K(I) values of erythromycin for CYP3A4.2, 4.7, 4.16, and 4.18 were 1.2-, 0.4-, 2.2- and 0.72-fold those of CYP3A4.1, respectively. Similar results were obtained for clarithromycin. In conclusion, the inhibitory profiles of MBI inhibitors, as well as competitive inhibitors, may possibly differ among CYP3A4 variants. This difference may contribute to interindividual differences in the extent of DDI based on MBI.

Computational models for tuberculosis drug discovery

The search for small molecules with activity against Mycobacterium tuberculosis increasingly uses -high-throughput screening and computational methods. Previously, we have analyzed recent studies in which computational tools were used for cheminformatics. We have now updated this analysis to illustrate how they may assist in finding desirable leads for tuberculosis drug discovery. We provide our thoughts on strategies for drug discovery efforts for neglected diseases.

In silico repositioning of approved drugs for rare and neglected diseases

One approach to speed up drug discovery is to examine new uses for existing approved drugs, so-called 'drug repositioning' or 'drug repurposing', which has become increasingly popular in recent years. Analysis of the literature reveals many examples of US Food and Drug Administration-approved drugs that are active against multiple targets (also termed promiscuity) that can also be used to therapeutic advantage for repositioning for other neglected and rare diseases. Using proof-of-principle examples, we suggest here that with current in silico technologies and databases of the structures and biological activities of chemical compounds (drugs) and related data, as well as close integration with in vitro screening data, improved opportunities for drug repurposing will emerge for neglected or rare/orphan diseases.

Discovery of novel SERCA inhibitors by virtual screening of a large compound library

Two screening protocols based on recursive partitioning and computational ligand docking methodologies, respectively, were employed for virtual screens of a compound library with 345,000 entries for novel inhibitors of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA), a potential target for cancer chemotherapy. A total of 72 compounds that were predicted to be potential inhibitors of SERCA were tested in bioassays and 17 displayed inhibitory potencies at concentrations below 100 μM. The majority of these inhibitors were composed of two phenyl rings tethered to each other by a short link of one to three atoms. Putative interactions between SERCA and the inhibitors were identified by inspection of docking-predicted poses and some of the structural features required for effective SERCA inhibition were determined by analysis of the classification pattern employed by the recursive partitioning models.

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Metabolism of ramelteon in human liver microsomes and correlation with the effect of fluvoxamine on ramelteon pharmacokinetics

Ramelteon is a melatonin receptor agonist used as a treatment for insomnia. It is subject to a remarkably large drug-drug interaction (DDI) caused by fluvoxamine coadministration, resulting in a more than 100-fold increase in exposure. The objective of this study was to determine whether the DDI could be estimated using in vitro metabolism data. Ramelteon was shown to undergo hydroxylation in human liver microsomes to eight metabolites via six pathways. The main routes of metabolism included hydroxylation on the ethyl side chain and the benzylic position of the cyclopentyl ring, as assessed through enzyme kinetic measurements. Hydroxylation at the other benzylic position was observed in human intestinal microsomes. Ramelteon metabolism was catalyzed by CYP1A2, CYP2C19, and CYP3A4 as shown through the use of recombinant human cytochrome P450 enzymes and specific inhibitors. In liver, CYP1A2, CYP2C19, and CYP3A4 were estimated to contribute 49, 42, and 8.6%, respectively, whereas in intestine only CYP3A4 contributes. The in vitro data were used to estimate the magnitudes of DDI caused by ketoconazole, fluconazole, and fluvoxamine. The DDIs caused by the former were reliably estimated (1.82-fold estimated versus 1.82-fold actual for ketoconazole; 2.99-fold estimated versus 2.36-fold actual for fluconazole), whereas for fluvoxamine it was underestimated (11.4-fold estimated versus 128-fold actual). This suggests that there may be a limit on the magnitude of DDI that can be estimated from in vitro data. Nevertheless, the example of the fluvoxamine-ramelteon DDI offers a unique example wherein one drug can simultaneously inhibit multiple enzymatic pathways of a second drug.

A collaborative database and computational models for tuberculosis drug discovery

The search for molecules with activity against Mycobacterium tuberculosis (Mtb) is employing many approaches in parallel including high throughput screening and computational methods. We have developed a database (CDD TB) to capture public and private Mtb data while enabling data mining and collaborations with other researchers. We have used the public data along with several cheminformatics approaches to produce models that describe active and inactive compounds. We have compared these datasets to those for known FDA approved drugs and between Mtb active and inactive compounds. The distribution of polar surface area and pK(a) of active compounds was found to be a statistically significant determinant of activity against Mtb. Hydrophobicity was not always statistically significant. Bayesian classification models for 220, 463 molecules were generated and tested with external molecules, and enabled the discrimination of active or inactive substructures from other datasets in the CDD TB. Computational pharmacophores based on known Mtb drugs were able to map to and retrieve a small subset of some of the Mtb datasets, including a high percentage of Mtb actives. The combination of the database, dataset analysis, Bayesian and pharmacophore models provides new insights into molecular properties and features that are determinants of activity in whole cells. This study provides novel insights into the key 1D molecular descriptors, 2D chemical substructures and 3D pharmacophores which can be used to mine the chemistry space, prioritizing those molecules with a higher probability of activity against Mtb.

Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin

The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. K(I) and k(inact) values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 microM for K(i) and 0.4 and 4 h(-1) for k(inact) in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in K(I), k(inact), and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.

Application of mechanism-based CYP inhibition for predicting drug-drug interactions

BACKGROUND

A mechanism-based inhibition of CYPs is characterized by NADPH-, time- and concentration-dependent enzyme inactivation and substrate protection. A significant inactivation of CYPs and particularly the main human hepatic and intestinal CYPs could result in clinical drug-drug interactions (DDIs) and adverse drug reactions.

OBJECTIVE

To address whether DDIs owing to mechanism-based CYP inhibition is predictable based on in vitro inhibitory data.

METHOD

Medline (by means of PubMed up to 26 March 2009) has been searched using proper relevant terms.

RESULT/CONCLUSION

It is possible to predict DDIs caused by mechanism-based CYP inhibition, although the in vitro data do not necessarily translate directly into relative extents of inhibition in vivo because in vivo clinical consequences depend on additional factors that are not easily accounted for in vitro and for reversible inhibition. Incorporation of other important parameters such as CYP degradation rate (k(deg)), relative contribution of the CYP inactivated to the victim drug elimination (f(m(CYP))) and inhibition of intestinal CYP-mediated first-pass metabolism of the object drug (F'(gut)/F(gut) ratio) into the prediction models significantly improves the prediction. Uncertainty of the prediction is mainly from the variability in the estimates of these critical parameters.

An in vitro evaluation of the victim and perpetrator potential of the anticancer agent laromustine (VNP40101M), based on reaction phenotyping and inhibition and induction of cytochrome P450 enzymes

Laromustine (VNP40101M, also known as Cloretazine) is a novel sulfonylhydrazine alkylating (anticancer) agent. Laromustine generates two types of reactive intermediates: 90CE and methylisocyanate. When incubated with rat, dog, monkey, and human liver microsomes, [(14)C]laromustine was converted to 90CE (C-8) and seven other radioactive components (C-1-C-7). There was little difference in the metabolite profile among the species examined, in part because the formation of most components (C-1-C-6 and 90CE) did not require NADPH but involved decomposition and/or hydrolysis. The exception was C-7, a hydroxylated metabolite, largely formed by CYP2B6 and CYP3A4/5. Laromustine caused direct inhibition of CYP2B6 and CYP3A4/5 (the two enzymes involved in C-7 formation) as well as of CYP2C19. K(i) values were 125 microM for CYP2B6, 297 muM for CYP3A4/5, and 349 microM for CYP2C19 and were greater than the average clinical plasma C(max) of laromustine (25 microM). There was evidence of time-dependent inhibition of CYP1A2, CYP2B6, and CYP3A4/5. Treatment of primary cultures of human hepatocytes with up to 100 microM laromustine did not induce CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, or CYP3A4/5, but the highest concentration of laromustine decreased the activity and levels of immunoreactive CYP3A4. The results of this study suggest the laromustine has 1) negligible victim potential with respect to metabolism by cytochrome P450 enzymes, 2) negligible enzyme-inducing potential, and 3) the potential in some cases to cause inhibition of CYP2B6, CYP3A4, and possibly CYP2C19 during and shortly after the duration of intravenous administration of this anticancer drug, but the clinical effects of such interactions are likely to be insignificant.

Machine learning methods and docking for predicting human pregnane X receptor activation

The pregnane X receptor (PXR) regulates the expression of genes involved in xenobiotic metabolism and transport. In vitro methods to screen for PXR agonists are used widely. In the current study, computational models for human PXR activators and PXR nonactivators were developed using recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms with VolSurf descriptors. Following 10-fold randomization, the models correctly predicted 82.6-98.9% of activators and 62.0-88.6% of nonactivators. The models were validated using separate test sets. The overall ( n = 15) test set prediction accuracy for PXR activators with RP, RF, and SVM PXR models is 80-93.3%, representing an improvement over models previously reported. All models were tested with a second test set ( n = 145), and the prediction accuracy ranged from 63 to 67% overall. These test set molecules were found to cover the same area in a principal component analysis plot as the training set, suggesting that the predictions were within the applicability domain. The FlexX docking method combined with logistic regression performed poorly in classifying this PXR test set as compared with RP, RF, and SVM but may be useful for qualitative interpretion of interactions within the LBD. From this analysis, VolSurf descriptors and machine learning methods had good classification accuracy and made reliable predictions within the model applicability domain. These methods could be used for high throughput virtual screening to assess for PXR activation, prior to in vitro testing to predict potential drug-drug interactions.

Clinically important drug interactions potentially involving mechanism-based inhibition of cytochrome P450 3A4 and the role of therapeutic drug monitoring

Cytochrome P450 (CYP) 3A4 is the most abundant enzyme of CYPs in the liver and gut that metabolizes approximately 50% currently available drugs. A number of important drugs have been identified as substrates, inducers, and/or inhibitors of CYP3A4. The substrates of CYP3A4 considerably overlap with those of P-glycoprotein. Both CYP3A4 and P-glycoprotein are subject to inhibition and induction by a number of factors. Mechanism-based inhibition of CYP3A4 is characterized by NADPH-, time-, and concentration-dependent enzyme inactivation occurring when some xenobiotics or drugs are converted by CYPs to reactive metabolites. Such an inhibition of CYP3A4 is caused by chemical modification of the heme, the protein, or both as a result of covalent binding of modified heme to the protein. To date, the identified clinically important mechanism-based CYP3A4 inhibitors mainly include macrolide antibiotics (eg, clarithromycin and erythromycin), anti-HIV agents (eg, ritonavir and delavirdine), antidepressants (eg, fluoxetine and fluvoxamine), calcium channel blockers (eg, verapamil and diltiazem), steroids and their modulators (eg, gestodene and mifepristone), and several herbal and dietary components. The inactivation of CYP3A4 by drugs often causes unfavorable and long-lasting drug-drug interactions and probably fatal toxicity, depending on many factors associated with the enzyme, drugs, and the patients. Clinicians are encouraged to have a sound knowledge of drug-induced, mechanism-based CYP3A4 inhibition; take proper cautions, and perform close monitoring for possible drug interactions when using drugs that are mechanism-based CYP3A4 inhibitors. To minimize drug-drug interactions involving mechanism-based CYP3A4 inhibition, it is necessary to choose safe drug combination regimens, adjust drug dosages appropriately, and conduct therapeutic drug monitoring for drugs with narrow therapeutic indices.