Synthesis,in vitro antimicrobial assessment,and computational investigation of pharmacokinetic and bioactivity properties of novel trifluoromethylated compounds using in silico ADME and toxicity prediction tools

1,3-Dipolar cycloaddition reactions of a sugar-based trifluoromethylated nitrone-to-maleimide and allyl bromide afforded a series of cycloadducts in good yield. The same nitrone reacts with propargyl acetate lead, after rearrangement of 4-isoxazoline, to aziridine with good yield. The obtained compounds were evaluated for their in vitro antimicrobial potency. Additionally, we are interested in predicting their physicochemical parameters such as lipophilicity and bioactivity score as well as their pharmacokinetic properties such as absorption, distribution, metabolism, and excretion (ADME) such as plasma protein binding (PPB) penetration of the blood–brain barrier (BBB), human intestinal absorption (HIA), cellular permeability (PCaco-2), cell permeability of Madin–Darby canine kidney (PMDCK), P-glycoprotein (P-gp) efflux, CYP inducers, substrates and inhibitors’ skin and permeability (PS), and their toxicological behavior [mutagenicity, carcinogenicity, acute, environmental, cardiotoxicity (hERG inhibition)] using in silico computational methods. Also, we aimed to validate QSAR models for the elucidation of their antitarget using 32 sets of end-points (IC50, Ki and Kact). The obtained result provides good information about the pharmacotherapy potential and toxicity of the examined molecules with good compliance between in vitro antimicrobial and the predicted properties. Findings indicated and encouraged the use of these compounds and their derivatives for further in vivo evaluations in the design and the elucidation of the intrinsic mechanisms as well as the efficacy of the selected powerful drug.     

Applications of high throughput microsomal stability assay in drug discovery

High throughput in vitro microsomal stability assays are widely used in drug discovery as an indicator for in vivo stability, which affects pharmacokinetics. This is based on in-depth research involving a limited number of model drug-like compounds that are cleared predominantly by cytochrome P450 metabolism. However, drug discovery compounds are often not drug-like, are assessed with high throughput assays, and have many potential uncharacterized in vivo clearance mechanisms. Therefore, it is important to determine the correlation between high throughput in vitro microsomal stability data and abbreviated discovery in vivo pharmacokinetics study data for a set of drug discovery compounds in order to have evidence for how the in vitro assay can be reliably applied by discovery teams for making critical decisions. In this study the relationship between in vitro single time point high throughput microsomal stability and in vivo clearance from abbreviated drug discovery pharmacokinetics studies was examined using 306 real world drug discovery compounds. The results showed that in vitro Phase I microsomal stability t(1/2) is significantly correlated to in vivo clearance with a p-value<0.001. For compounds with low in vitro rat microsomal stability (t(1/2)<15 min), 87% showed high clearance in vivo (CL>25 mL/min/kg). This demonstrates that high throughput microsomal stability data are very effective in identifying compounds with significant clearance liabilities in vivo. For compounds with high in vitro rat microsomal stability (t(1/2)>15 min), no significant differentiation was observed between high and low clearance compounds. This is likely owing to other clearance pathways, in addition to cytochrome P450 metabolism that enhances in vivo clearance. This finding supports the strategy used by medicinal chemists and drug discovery teams of applying the in vitro data to triage compounds for in vivo PK and efficacy studies and guide structural modification to improve metabolic stability. When in vitro and in vivo data are both available for a compound, potential in vivo clearance pathways can be diagnosed to guide further discovery studies.     

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.     

A high throughput metabolic stability screening workflow with automated assessment of data quality in pharmaceutical industry

One of the most commonly performed in vitro ADME assays during the lead generation and lead optimization stage of drug discovery is metabolic stability evaluation. Metabolic stability is typically assessed in liver microsomes, which contain Phase I metabolizing enzymes, mainly cytochrome P450 enzymes (CYPs). The amount of parent drug metabolized by these CYPs is determined by LC/MS/MS. The metabolic stability data are typically used to rank order compounds for in vivo evaluation. We describe a streamlined and intelligent workflow for the metabolic stability assay that permits high throughput analyses to be carried out while maintaining the standard of high quality. This is accomplished in the following ways: a novel post-incubation pooling strategy based on c Log D_3.0 values, coupled with ultra-performance liquid chromatography/tandem mass spectrometry (UPLC/MS/MS), enables sample analysis times to be reduced significantly while ensuring adequate chromatographic separation of compounds within a group, so as to reduce the likelihood of compound interference. Assay quality and fast turnaround of data reports is ensured by performing automated real-time intelligent re-analysis of discrete samples for compounds that do not pass user-definable criteria during the pooling analysis. Intelligent, user-independent data acquisition and data evaluation are accomplished via a custom visual basic program that ties together every step in the workflow, including cassette compound selection, compound incubation, compound optimization, sample analysis and re-analysis (when appropriate), data processing, data quality evaluation, and database upload. The workflow greatly reduces labor and improves data turnaround time while maintaining high data quality.     

In silico prediction of cytochrome P450-mediated drug metabolism

The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.     

Recent advances in high throughput screening for ADME properties

With the increase in the numbers of molecules synthesized in a typical drug discovery program, as well as the large amount of information utilized in the selection of a drug candidate, there is a need for a plethora of drug metabolism and pharmacokinetic (DMPK) information to be regularly generated in discovery. Over the past decade, many in vitro, and even in vivo, DMPK screens have been developed and routinely deployed to generate this information in support of drug discovery efforts. In the past few years, newer methods, or adaptations to methods, have been published, and this review attempts to summarize these advances. In particular, advances have been reported for experimental approaches to metabolic clearance, CYP inhibition, in vivo exposure, and distribution, as well as in silico determinations of absorption, distribution, metabolism, and excretion (ADME) properties. Bioanalytical approaches aimed at optimizing analyte method development, sample preparation, and analyte detection, have also been reported. Future advances will further improve the ability to make decisions on molecules earlier in drug discovery.     

In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery

Drug metabolism can have profound effects on the pharmacological and toxicological profile of therapeutic agents. In the pharmaceutical industry, many in vitro techniques are in place or under development to screen and optimize compounds for favorable metabolic properties in the drug discovery phase. These in vitro technologies are meant to address important issues such as: (1) is the compound a potent inhibitor of drug metabolising enzymes (DMEs)? (2) does the compound induce the expression of DMEs? (3) how labile is the compound to metabolic degradation? (4) which specific enzyme(s) is responsible for the compound's biotransformation? and (5) to which metabolites is the compound metabolized? Answers to these questions provide a basis for judging whether a compound is likely to have acceptable pharmacokinetic properties in vivo. To address these issues on the increasing number of compounds inundating the drug discovery programs, high throughput assays are essential. A combination of biochemical advances in the understanding of the function and regulation of DMEs (in particular, cytochromes P450, CYPs) and automated analytical technologies are revolutionizing drug metabolism research. Automated LC-MS based metabolic stability, fluorescence, radiometric and LC-MS based CYP inhibition assays are now in routine use. Automatible models for studying CYP induction based on enzyme activity, quantitative RT-PCR and reporter gene systems are being developed. We will review the utility and limitations of these HTS approaches and highlight on-going developments and emerging technologies to answer metabolism questions at the different stages of the drug discovery process.     

Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data

The human cytochrome P450 (CYP450) isozymes are the most important enzymes in the body to metabolize many endogenous and exogenous substances including environmental toxins and therapeutic drugs. Any unnecessary interactions between a small molecule and CYP450 isozymes may raise a potential to disarm the integrity of the protection. Accurately predicting the potential interactions between a small molecule and CYP450 isozymes is highly desirable for assessing the metabolic stability and toxicity of the molecule. The National Institutes of Health Chemical Genomics Center (NCGC) has screened a collection of over 17,000 compounds against the five major isozymes of CYP450 (1A2, 2C9, 2C19, 2D6, and 3A4) in a quantitative high throughput screening (qHTS) format. In this study, we developed support vector classification (SVC) models for these five isozymes using a set of customized generic atom types. The CYP450 data sets were randomly split into equal-sized training and test sets. The optimized SVC models exhibited high predictive power against the test sets for all five CYP450 isozymes with accuracies of 0.93, 0.89, 0.89, 0.85, and 0.87 for 1A2, 2C9, 2C19, 2D6, and 3A4, respectively, as measured by the area under the receiver operating characteristic (ROC) curves. The important atom types and features extracted from the five models are consistent with the structural preferences for different CYP450 substrates reported in the literature. We also identified novel features with significant discerning power to separate CYP450 actives from inactives. These models can be useful in prioritizing compounds in a drug discovery pipeline or recognizing the toxic potential of environmental chemicals.     

Precision-cut liver slices from rats of different ages: basal cytochrome P450-dependent monooxygenase activities and inducibility

The biotransformation capacity – of the cytochrome P450 (CYP) system for example – is lower but inducibility is more pronounced in neonates than in adults. On the other hand, both enzyme activities and inducibility decline with senescence. Precision-cut rat liver slices are widely used as an in vitro tool for the examination of drug toxicity, xenobiotic metabolism or enzyme induction. The aim of the present study was to assess whether age-related changes in CYP activities and induction observed in vivo are also mirrored in vitro in liver slices. For this purpose, different CYP model reactions were measured in precision-cut liver slices from one-day-old, 40-day-old and one-year-old rats after in vitro exposure to various inducers. Similar to the in vivo situation, basal CYP activities were distinctly lower and inducibility was much more pronounced in liver slices from neonatal than in those from adult animals. Also, enzyme activities were mostly somewhat lower in liver slices from aged rats compared to those from 40-day-old rats. However, CYP inducibility was less pronounced than with younger animals too. Thus, precision-cut rat liver slices are a suitable in vitro tool for investigating age-related changes in CYP activities and induction as well as developmental differences in drug metabolism and toxicity.     

Quantitative analysis of highly homologous proteins: the challenge of assaying the “CYP-ome” by mass spectrometry

Cytochrome P450 enzymes comprise families of highly homologous proteins. These proteins play a pivotal role in oxidative drug metabolism and are important targets in drug discovery research. Proteomics today is a valuable tool for the analysis of proteins. In the past, qualitative analysis of the proteome was the main focus of research, but in the last few years interest in the mathematical modelling of protein networks has been growing and so has the demand on quantitative proteome analysis. As a thorough understanding of cytochrome P450 dependent metabolism is crucial for drug discovery, it is thus not astounding that cytochrome P450 enzymes are a target for quantitative proteomics research. In this article, we review the techniques available for quantitative proteome analysis and to what extent these techniques have been used for the quantification of cytochrome P450 enzymes and give a brief outlook of the techniques that have promising potential for the analysis of these proteins in the future.     

New combined model for the prediction of regioselectivity in cytochrome P450/3A4 mediated metabolism

Cytochrome P450 3A4 metabolizes nearly 50% of the drugs currently in clinical use with a broad range of substrate specificity. Early prediction of metabolites of xenobiotic compounds is crucial for cost efficient drug discovery and development. We developed a new combined model, MLite, for the prediction of regioselectivity in the cytochrome P450 3A4 mediated metabolism. In the model, the ensemble catalyticphore- based docking method was implemented for the accessibility prediction, and the activation energy estimation method of Korzekwa et al. was used for the reactivity prediction. Four major metabolic reactions, aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation reaction, were included and the reaction data, metabolite information, were collected for 72 well-known substrates. The 47 drug molecules were used as the training set, and the 25 well-known substrates were used as the test set for the ensemble catalyticphore-based docking method. MLite predicted correctly about 76% of the first two sites in the ranking list of the test set. This predictability is comparable with that of another combined model, MetaSite, and the recently published QSAR model proposed by Sheridan et al. MLite also offers information about binding configurations of the substrate-enzyme complex. This may be useful in drug modification by the structure-based drug design.     

Predicting Drug Interaction of Clopidogrel on Microbial Metabolism of Diclofenac

Seven fungal cultures were studied for the metabolism of diclofenac in order to elucidate the nature of enzymes involved in biotransformation, as diclofenac is a specific substrate to cytochrome P450 (CYP) 2C9 isozyme in mammals. The metabolites were identified by high-performance liquid chromatography–diode array detection and liquid chromatography–tandem mass spectroscopy analysis. The study included clopidogrel, a selective inhibitor of CYP2C9 isozyme, to inhibit the metabolism of diclofenac. Two-stage fermentation protocol was used to study the diclofenac metabolism and its inhibition by clopidogrel. Among the cultures studied, four have shown positive indication for drug interaction, since clopidogrel inhibited the metabolism of diclofenac in a dose-dependent manner. The results indicate that microbial cultures possess enzyme systems similar to mammals and they can be used to predict drug interactions in mammalian systems.     

Biomimetic modeling of oxidative drug metabolism

The prediction of drug metabolism is an important task in drug development. Besides well-established in vitro and in vivo methods using biological matrices, several biomimetic models have been developed. This review summarizes three different nonenzymatic strategies, including metalloporphyrins as surrogates of the active centre of cytochrome P450, Fenton’s reagent, and the electrochemical oxidation of drug compounds. Although none of the systems can simulate the whole range of cytochrome P450-catalyzed reactions adequately, the biomimetic models show some advantages over standard in vitro methods. For example, metalloporpyhrin catalysts allow the synthesis of certain metabolites in sufficient amounts and with sufficient purities to permit characterization and further pharmacological and toxicological tests. The electrochemical generation of metabolites coupled on-line to liquid chromatography/mass spectrometry is a promising tool for studying reactive metabolites and can be applied in automated high-throughput screening approaches. In this paper, detailed comparisons with cytochrome P450 catalysis are drawn, advantages and disadvantages of the respective methods are revealed, and possible applications are discussed.