Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition

Identification of CYP3A4 inhibitors as potential anti-cancer agents using pharmacoinformatics approach

Biguanide derivatives exhibit a wide variety of therapeutic applications, including anti-cancer effects. Metformin is an effective anti-cancer agent against breast cancer, lung cancer, and prostate cancer. In the crystal structure (PDB ID: 5G5J), it was found that metformin is found in the active site of CYP3A4, and the associated anti-cancer effect was explored. Taking clues from this work, pharmacoinformatics research has been carried out on a series of known and virtual biguanide, guanylthiourea (GTU), and nitreone derivatives. This exercise led to the identification of more than 100 species that exhibit greater binding affinity toward CYP3A4 in comparison to that of metformin. Selected six molecules were subjected to molecular dynamics simulations, and the results are presented in this work.

Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape

Scientists' ability to detect drug-related metabolites at trace concentrations has improved over recent decades. High-resolution instruments enable collection of large amounts of raw experimental data. In fact, the quantity of data produced has become a challenge due to effort required to convert raw data into useful insights. Various cheminformatics tools have been developed to address these metabolite identification challenges. This article describes the current state of these tools. They can be split into two categories: Pre-experimental metabolite generation and post-experimental data analysis. The former can be subdivided into rule-based, machine learning-based, and docking-based approaches. Post-experimental tools help scientists automatically perform chromatographic deconvolution of LC/MS data and identify metabolites. They can use pre-experimental predictions to improve metabolite identification, but they are not limited to these predictions: unexpected metabolites can also be discovered through fractional mass filtering. In addition to a review of available software tools, we present a description of pre-experimental and post-experimental metabolite structure generation using MetaSense. These software tools improve upon manual techniques, increasing scientist productivity and enabling efficient handling of large datasets. However, the trend of increasingly large datasets and highly data-driven workflows requires a more sophisticated informatics transition in metabolite identification labs. Experimental work has traditionally been separated from the information technology tools that handle our data. We argue that these IT tools can help scientists draw connections via data visualizations and preserve and share results via searchable centralized databases. In addition, data marshalling and homogenization techniques enable future data mining and machine learning.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

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.

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

In silico and in vitro inhibition of cytochrome P450 3A by synthetic stilbenoids

Inhibition of cytochrome P450 3A4 (CYP3A4), the major drug metabolizing enzyme, by dietary compounds has recently attracted increased attention. Evaluating the potency of the many known inhibitory compounds is a tedious and time consuming task, yet it can be achieved using computing tools. Here, CDOCKER and Glide served to design model inhibitors in order to characterize molecular features of an inhibitor. Assessing nitro-stilbenoids, both approaches suggested nitrostilbene to be a weaker inhibitor of CYP3A4 than resveratrol, and stronger than dimethoxy-nitrostilbene. Nitrostilbene and resveratrol, but not dimethoxy-nitrostilbene, engage electrostatic interactions in the enzyme cavity, and with the haem. In vitro assessment of the inhibitory capacity supported the in silico predictions, suggesting that evaluating the electrostatic interactions of a compound with the prosthetic group allows the prediction of inhibitory potency. Since both programs yielded related results, it is suggested that for CYP3A4, computing tools may allow rapid identification of potent dietary inhibitors.

New amino acid-Schiff base derived from s-allyl cysteine and methionine alleviates carbon tetrachloride-induced liver dysfunction

In spite of the tremendous stride in modern medicine, conventional drugs used in the hepatotoxic management are mostly inadequate. The present study aims in the synthesis of novel Schiff base compound derived using s-allyl cystiene and methionine. The newly synthesized compound, 2-((2-((2-(allylthio)-1-carboxyethyl)imino)ethylidene)amino)-4-(methylthio)butanoic acid (ACEMB) was characterized using UV-visible spectrophotometer, FTIR, ¹HNMR, and GC-MS. ACEMB showed potent in vitro antioxidant property. Chronic administration of ACEMB prior to CCl₄ intoxication: i) attenuated the leakage of liver injury markers, such as, enzymes (AST, ALT, GGT, ALP and LDH) and biomolecules (bilirubin) into the blood circulation; ii) normalized the concentration of total proteins, albumin and globulin to control level; and iii) protected the liver against dyslipidemia. These effects of ACEMB show the preservation of endoplasmic reticulum function against CCl₄ toxicity in the liver. The protective effect of ACEMB was due to its antioxidant property, which was revealed by reduced oxidative stress (TBARS and HP) and enhanced functions of the endogenous antioxidative system (SOD, catalase, GPx, GST, GSH, vitamin E and C) against CCl₄ intoxication. Also, ACEMB protected the functional activities of the various mitochondrial tricarboxylic acid cycle and oxidative phosphorylation enzymes. The biochemical alterations are in concurrence with the histological observations, wherein ACEMB pretreatment prevented the vacuolation, degeneration of nuclei and necrosis of hepatocytes. In addition, in silico analysis reveals the interaction of ACEMB in the active site of cytochrome P450. ACEMB mediates hepatoprotective effect by substituting itself as an antioxidant and decreasing oxidative stress, thereby diminishing the intracellular organelle dysfunction against CCl₄ toxicity in the liver.

In silico prediction of chronic toxicity with chemical category approaches

Chemical chronic toxicity, referring to the toxic effect of a chemical following long-term or repeated sub lethal exposures, is an important toxicological end point in drug design and environmental risk assessment.

Getting the most out of PubChem for virtual screening

INTRODUCTION

With the emergence of the 'big data' era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.

AREAS COVERED

This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.

EXPERT OPINION

PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

Inhibition of cytochrome P450 3A by acetoxylated analogues of resveratrol in in vitro and in silico models

Many dietary compounds, including resveratrol, are potent inhibitors of CYP3A4. Here we examined the potential to predict inhibition capacity of dietary polyphenolics using an in silico and in vitro approaches and synthetic model compounds. Mono, di, and tri-acetoxy resveratrol were synthesized, a cell line of human intestine origin and microsomes from rat liver served to determine their in vitro inhibition of CYP3A4, and compared to that of resveratrol. Docking simulation served to predict the affinity of the synthetic model compounds to the enzyme. Modelling of the enzyme’s binding site revealed three types of interaction: hydrophobic, electrostatic and H-bonding. The simulation revealed that each of the examined acetylations of resveratrol led to the loss of important interactions of all types. Tri-acetoxy resveratrol was the weakest inhibitor in vitro despite being the more lipophilic and having the highest affinity for the binding site. The simulation demonstrated exclusion of all interactions between tri-acetoxy resveratrol and the heme due to distal binding, highlighting the complexity of the CYP3A4 binding site, which may allow simultaneous accommodation of two molecules. Finally, the use of computational modelling may serve as a quick predictive tool to identify potential harmful interactions between dietary compounds and prescribed drugs.

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

Use of In Vitro and Predictive In Silico Models to Study the Inhibition of Cytochrome P4503A by Stilbenes

CYP3A4 is recognized as the main enzyme involved in the metabolism of drugs and xenobiotics in the human body and its inhibition may lead to undesirable consequences. Stilbenes, including resveratrol, belong to a group of dietary health-promoting compounds that also act as inhibitors of CYP3A4. The aim of this study was to examine the use of computer modeling of enzyme-ligand interactions to analyze and predict the inhibition of structurally related compounds. To this end, an aldehyde group was attached to resveratrol and the interactions of CYP3A4 with resveratrol, its aldehyde analogue (RA) and a known synthetic inhibitor were studied and compared in two biological models. Specifically, the metabolism of testosterone was examined in a human intestine cell line (Caco-2/TC7) and in rat liver microsomes (RLM). The results demonstrated a weak inhibitory effect of RA on CYP3A4, as compared to resveratrol itself, in both biological models. Human CYP3A4 was more susceptible to inhibition than the commonly used model isozyme from rat. Modeling of the binding site of CYP3A4 revealed a combination of three types of interactions: hydrophobic interactions, electrostatic interactions and hydrogen bonds. A docking simulation revealed that the RA lacked an important binding feature, as compared to resveratrol, and that that difference may be responsible for its lower level of affinity for CYP3A4. Software analysis of binding affinity may serve as a predictive tool for designing new therapeutic compounds in terms of inhibition of CYP3A4 and help to reveal the biochemical nature of the interactions of dietary compounds, herbal compounds and drugs whose metabolism is mediated by this enzyme.

Prediction of cytochrome P450 mediated metabolism

Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected from the predictions, and how they could potentially be improved.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools

In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.

Interactions between CYP3A4 and Dietary Polyphenols

The human cytochrome P450 enzymes (P450s) catalyze oxidative reactions of a broad spectrum of substrates and play a critical role in the metabolism of xenobiotics, such as drugs and dietary compounds. CYP3A4 is known to be the main enzyme involved in the metabolism of drugs and most other xenobiotics. Dietary compounds, of which polyphenolics are the most studied, have been shown to interact with CYP3A4 and alter its expression and activity. Traditionally, the liver was considered the prime site of CYP3A-mediated first-pass metabolic extraction, but in vitro and in vivo studies now suggest that the small intestine can be of equal or even greater importance for the metabolism of polyphenolics and drugs. Recent studies have pointed to the role of gut microbiota in the metabolic fate of polyphenolics in human, suggesting their involvement in the complex interactions between dietary polyphenols and CYP3A4. Last but not least, all the above suggests that coadministration of drugs and foods that are rich in polyphenols is expected to stimulate undesirable clinical consequences. This review focuses on interactions between dietary polyphenols and CYP3A4 as they relate to structural considerations, food-drug interactions, and potential negative consequences of interactions between CYP3A4 and polyphenols.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Insights on cytochrome p450 enzymes and inhibitors obtained through QSAR studies

The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹⁵N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutant—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹⁵N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹⁵N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold² descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Structural insights into the inhibition of cytosolic 5'-nucleotidase II (cN-II) by ribonucleoside 5'-monophosphate analogues

Cytosolic 5′-nucleotidase II (cN-II) regulates the intracellular nucleotide pools within the cell by catalyzing the dephosphorylation of 6-hydroxypurine nucleoside 5′-monophosphates. Beside this physiological function, high level of cN-II expression is correlated with abnormal patient outcome when treated with cytotoxic nucleoside analogues. To identify its specific role in the resistance phenomenon observed during cancer therapy, we screened a particular class of chemical compounds, namely ribonucleoside phosphonates to predict them as potential cN-II inhibitors. These compounds incorporate a chemically and enzymatically stable phosphorus-carbon linkage instead of a regular phosphoester bond. Amongst them, six compounds were predicted as better ligands than the natural substrate of cN-II, inosine 5′-monophosphate (IMP). The study of purine and pyrimidine containing analogues and the introduction of chemical modifications within the phosphonate chain has allowed us to define general rules governing the theoretical affinity of such ligands. The binding strength of these compounds was scrutinized in silico and explained by an impressive number of van der Waals contacts, highlighting the decisive role of three cN-II residues that are Phe 157, His 209 and Tyr 210. Docking predictions were confirmed by experimental measurements of the nucleotidase activity in the presence of the three best available phosphonate analogues. These compounds were shown to induce a total inhibition of the cN-II activity at 2 mM. Altogether, this study emphasizes the importance of the non-hydrolysable phosphonate bond in the design of new competitive cN-II inhibitors and the crucial hydrophobic stacking promoted by three protein residues.

Nucleotidase activity is part of a biological process that allows the cell to regulate the intracellular pools of nucleotides involved in many signaling pathways. During cancer therapy with cytotoxic nucleoside analogues, the role of cN-II is unclear. Therefore, the development of specific inhibitors against this enzyme is of great interest for understanding its implication in cancer biology and drug resistance. Ribonucleoside phosphonates are of major importance because they behave as bioisosteric analogues of the natural cN-II substrates and contain a chemically and enzymatically stable phosphorus-carbon linkage. Taking the advantages of docking methods, we predicted the inhibitory potential of these compounds. Their binding strength was explained by an impressive interaction network involving mainly three residues of the enzyme (acting as hydrophobic tweezers). These new characterized inhibitors will constitute a valuable tool for elucidating the role of cN-II in cancer cells and may be used in combination with cytotoxic nucleosidic drugs in order to increase their antitumor activity. Furthermore, the strategy taking into account the hydrophobic clamp for designing new inhibitors may be applied to other nucleotidases of the HAD family as two of the three identified residues are present in the substrate binding site of cytosolic 5′-nucleotidase III and 5′-deoxynucleotidase-I.