Gut Microbiome Integration in Drug Discovery and Development of Small Molecules.

Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.

Peripheral tryptophan, serotonin, kynurenine, and their metabolites in major depression: A case-control study.

AIM: Tryptophan is the sole precursor of both peripherally and centrally produced serotonin and kynurenine. In depressed patients, tryptophan, serotonin, kynurenine, and their metabolite levels remain unclear. Therefore, peripheral tryptophan and metabolites of serotonin and kynurenine were investigated extensively in 173 patients suffering from a current major depressive episode (MDE) and compared to 214 healthy controls (HC). METHODS: Fasting plasma levels of 11 peripheral metabolites were quantified: tryptophan, serotonin pathway (serotonin, its precursor 5-hydroxytryptophan and its metabolite 5-hydroxyindoleacetic acid), and kynurenine pathway (kynurenine and six of its metabolites: anthranilic acid, kynurenic acid, nicotinamide, picolinic acid, xanthurenic acid, and 3-hydroxyanthranilic acid). RESULTS: Sixty (34.7%) patients were antidepressant-drug free. Tryptophan levels did not differ between MDE patients and HC. Serotonin and its precursor (5-hydroxytryptophan) levels were lower in MDE patients than in HC, whereas, its metabolite (5-hydroxyindoleacetic acid) levels were within the standard range. Kynurenine and four of its metabolites (kynurenic acid, nicotinamide, picolinic acid, and xanthurenic acid) were lower in MDE patients. CONCLUSION: Whilst the results of this study demonstrate an association between the metabolites studied and depression, conclusions about causality cannot be made. This study uses the largest ever sample of MDE patients, with an extensive assessment of peripheral tryptophan metabolism in plasma. These findings provide new insights into the peripheral signature of MDE. The reasons for these changes should be further investigated. These results might suggest new antidepressant therapeutic strategies.

Direct and quantitative evaluation of the major human CYP contribution (fmCYP) to drug clearance using the in vitro Silensomes™ model.

1. We have applied the concept of using MBIs to produce CYP-Silensomes to quantify the contribution of the major CYPs to drug metabolism (fmCYP). 2. The target CYPs were extensively and selectivity inhibited by the selected MBIs, while non-target CYPs were inhibited by less than 20% of the homologous control activities. Only CYP2D6-Silensomes exhibited a CYP2B6 inhibition that could be easily and efficiently encountered by subtracting the fmCYP2B6 measured using CYP2B6-Silensomes to adjust the fmCYP2D6. 3. To validate the use of a panel of 6 CYP-Silensomes, we showed that the fmCYP values of mono- and multi-CYP metabolised drugs were well predicted, with 70% within ± 15% accuracy. Moreover, the correlation with observed fmCYP values was higher than that for rhCYPs, which were run in parallel using the same drugs (<45% within ±15% accuracy). Moreover, the choice of the RAF substrate in rhCYP predictions was shown to affect the accuracy of the fmCYP measurement. 4. These results support the use of CYP1A2-, CYP2B6-, CYP2C8-, CYP2C9-, CYP2D6 and CYP3A4-Silensomes to accurately predict fmCYP values during the in vitro enzyme phenotyping assays in early, as well as in development, phases of drug development.

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.

Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.

Direct and quantitative evaluation of the human CYP3A4 contribution (fm) to drug clearance using the in vitro SILENSOMES model.

1. Among the different in vitro studies recommended by the regulatory agencies, no gold-standard model can easily and directly measure the quantitative CYP450 contributions to drug biotransformation. In this article, we propose an original strategy, called SilensomesTM, to produce human liver microsomes silenced for one specific CYP450, thanks to specific mechanism-based inhibitors (MBI). 2. Using azamulin as a specific CYP3A4 MBI, we demonstrated the proof of concept that CYP3A4 can be totally, specifically (even against 3A5) and permanently (at least for six years) inhibited by our process. Thus, comparing clearance in control and CYP3A4-SilensomesTM, CYP3A4 contributions were determined for 11 CYP3A4 substrates which correlated with known in vivo contributions and revealed accuracy with less than 10% error. In comparison, contributions determined using recombinant human CYP450 (rhCYP450s) were less accurate (more than 10% error for 30% of the tested CYP3A4 substrates). 3. This easy and ready-to-use in vitro method combines the advantages of existing models (specificity of rhCYP450s and representativeness of HLM) without their drawbacks. The same strategy could be used to silence other major CYP450s one-by-one to provide a complete direct CYP450 quantitative phenotyping kit.

Amyotrophic lateral sclerosis and denervation alter sphingolipids and up-regulate glucosylceramide synthase.

Amyotrophic lateral sclerosis (ALS) is a fatal adult-onset disease characterized by upper and lower motor neuron degeneration, muscle wasting and paralysis. Growing evidence suggests a link between changes in lipid metabolism and ALS. Here, we used UPLC/TOF-MS to survey the lipidome in SOD1(G86R) mice, a model of ALS. Significant changes in lipid expression were evident in spinal cord and skeletal muscle before overt neuropathology. In silico analysis also revealed appreciable changes in sphingolipids including ceramides and glucosylceramides (GlcCer). HPLC analysis showed increased amounts of GlcCer and downstream glycosphingolipids (GSLs) in SOD1(G86R) muscle compared with wild-type littermates. Glucosylceramide synthase (GCS), the enzyme responsible for GlcCer biosynthesis, was up-regulated in muscle of SOD1(G86R) mice and ALS patients, and in muscle of wild-type mice after surgically induced denervation. Conversely, inhibition of GCS in wild-type mice, following transient peripheral nerve injury, reversed the overexpression of genes in muscle involved in oxidative metabolism and delayed motor recovery. GCS inhibition in SOD1(G86R) mice also affected the expression of metabolic genes and induced a loss of muscle strength and morphological deterioration of the motor endplates. These findings suggest that GSLs may play a critical role in ALS muscle pathology and could lead to the identification of new therapeutic targets.

Ultra performance liquid chromatography - mass spectrometry studies of formalin-induced alterations of human brain lipidome.

The development of 'omics' sciences offers new opportunities for the study of neurodegenerative diseases but increases at the same time the sample demand on brain banks that collect and store valuable human post-mortem tissue. Our study aims to evaluate in lipidomics the potential of formalin-fixed tissue compared with the cryopreservation method, considered as the gold standard for biochemical research. Two complementary liquid chromatography/mass spectrometry analytical platforms were used on the basis of hybrid quadrupole time-of-flight and triple quadrupole mass spectrometers. Untargeted fingerprinting, semitargeted profiling of specific lipid classes and targeted monitoring of lipid species were performed in formalin-fixed and cryopreserved samples to provide detailed information at the molecular level on the formalin-induced alterations of the brain tissue. In vitro incubations of lipid standards were also performed to further describe the degradation processes induced by formaldehyde. Phospholipid compounds were found to be extensively hydrolysed, whilst the sphingolipid ones were preserved. N-methylation and N-formylation of amine-containing phospholipids have also been evidenced. These findings show that the potential detrimental effect of formalin on the analytes of interest must be taken into account when analysing formalin-fixed samples.

Comparing translational population-PBPK modelling of brain microdialysis with bottom-up prediction of brain-to-plasma distribution in rat and human.

The prediction of brain extracellular fluid (ECF) concentrations in human is a potentially valuable asset during drug development as it can provide the pharmacokinetic input for pharmacokinetic-pharmacodynamic models. This study aimed to compare two translational modelling approaches that can be applied at the preclinical stage of development in order to simulate human brain ECF concentrations. A population-PBPK model of the central nervous system was developed based on brain microdialysis data, and the model parameters were translated to their corresponding human values to simulate ECF and brain tissue concentration profiles. In parallel, the PBPK modelling software Simcyp was used to simulate human brain tissue concentrations, via the bottom-up prediction of brain tissue distribution using two different sets of mechanistic tissue composition-based equations. The population-PBPK and bottom-up approaches gave similar predictions of total brain concentrations in both rat and human, while only the population-PBPK model was capable of accurately simulating the rat ECF concentrations. The choice of PBPK model must therefore depend on the purpose of the modelling exercise, the in vitro and in vivo data available and knowledge of the mechanisms governing the membrane permeability and distribution of the drug.

A physiologically based modeling strategy during preclinical CNS drug development.

Physiologically based pharmacokinetic (PBPK) modeling of the central nervous system (CNS) provides the opportunity to predict the relevant drug concentrations at the therapeutic target site during preclinical and clinical development. In order to successfully interpret model results, and to provide confidence in the subsequent human predictions, it is essential that an appropriate model structure is chosen at the preclinical stage which takes into account both physiological and drug-specific knowledge. However, the models published to date in the literature show significant variation in the approaches applied by different authors, which can lead to difficulties in the interpretation of model parameter estimates. We aimed to develop a coherent PBPK modeling approach in the rat, which would also be adaptable depending on the quantity and quality of in vivo data obtained during drug development. Based on a sensitivity analysis of the model parameters, and using three CNS drugs as case studies (atomoxetine, acetaminophen, and S 18986), we proposed a decision tree to aid in the appropriate parametrization and structure of the model according to the data available. We compared our parameter estimates to those originally published, and considered the impact of the respective approaches on the mechanistic interpretation of the parameter values. Since the measurement of brain extracellular fluid (ECF) concentrations using microdialysis is not routinely performed in the industrial environment, we also evaluated the bottom-up scaling of in vitro permeability data from the Caco-2 cell line to predict BBB passive permeability in the absence of measured ECF concentrations. Our strategy demonstrates the value of PBPK as a prediction tool throughout the development process of CNS-targeting drugs.

Physiologically based pharmacokinetic modelling of drug penetration across the blood-brain barrier--towards a mechanistic IVIVE-based approach.

Predicting the penetration of drugs across the human blood-brain barrier (BBB) is a significant challenge during their development. A variety of in vitro systems representing the BBB have been described, but the optimal use of these data in terms of extrapolation to human unbound brain concentration profiles remains to be fully exploited. Physiologically based pharmacokinetic (PBPK) modelling of drug disposition in the central nervous system (CNS) currently consists of fitting preclinical in vivo data to compartmental models in order to estimate the permeability and efflux of drugs across the BBB. The increasingly popular approach of using in vitro-in vivo extrapolation (IVIVE) to generate PBPK model input parameters could provide a more mechanistic basis for the interspecies translation of preclinical models of the CNS. However, a major hurdle exists in verifying these predictions with observed data, since human brain concentrations can't be directly measured. Therefore a combination of IVIVE-based and empirical modelling approaches based on preclinical data are currently required. In this review, we summarise the existing PBPK models of the CNS in the literature, and we evaluate the current opportunities and limitations of potential IVIVE strategies for PBPK modelling of BBB penetration.

Development of a physiologically based pharmacokinetic model for the rat central nervous system and determination of an in vitro-in vivo scaling methodology for the blood-brain barrier permeability of two transporter substrates, morphine and oxycodone.

A whole-body physiologically based pharmacokinetic (PBPK) model was developed for the prediction of unbound drug concentration-time profiles in the rat brain, in which drug transfer across the blood-brain barrier (BBB) was treated mechanistically by separating the parameters governing the rate (permeability) of BBB transfer from brain binding. An in vitro-in vivo scaling strategy based on Caco-2 cell permeability was proposed to extrapolate the active transporter-driven component of this permeability, in which a relative activity factor, RAF, was estimated by fitting the model to rat in vivo profiles. This scaling factor could be interpreted as the ratio of transporter activity between the in vitro system and the in vivo BBB, for a given drug in a given in vitro system. Morphine and oxycodone were selected to evaluate this strategy, as substrates of BBB-located efflux and influx transporters, respectively. After estimation of their respective RAFs using the rat model, the PBPK model was used to simulate human brain concentration profiles assuming the same RAF, and the implications of this were discussed.

Pharmacometabonomic investigation of dynamic metabolic phenotypes associated with variability in response to galactosamine hepatotoxicity.

Galactosamine (galN) is widely used as an in vivo model of acute liver injury. We have applied an integrative approach, combining histopathology, clinical chemistry, cytokine analysis, and nuclear magnetic resonance (NMR) spectroscopic metabolic profiling of biofluids and tissues, to study variability in response to galactosamine following successive dosing. On re-challenge with galN, primary non-responders displayed galN-induced hepatotoxicity (induced response), whereas primary responders exhibited a less marked response (adaptive response). A systems-level metabonomic approach enabled simultaneous characterization of the xenobiotic and endogenous metabolic perturbations associated with the different response phenotypes. Elevated serum cytokines were identified and correlated with hepatic metabolic profiles to further investigate the inflammatory response to galN. The presence of urinary N-acetylglucosamine (glcNAc) correlated with toxicological outcome and reflected the dynamic shift from a resistant to a sensitive phenotype (induced response). In addition, the urinary level of glcNAc and hepatic level of UDP-N-acetylhexosamines reflected an adaptive response to galN. The unique observation of galN-pyrazines and altered gut microbial metabolites in fecal profiles of non-responders suggested that gut microfloral metabolism was associated with toxic outcome. Pharmacometabonomic modeling of predose urinary and fecal NMR spectroscopic profiles revealed a diverse panel of metabolites that classified the dynamic shift between a resistant and sensitive phenotype. This integrative pharmacometabonomic approach has been demonstrated for a model toxin; however, it is equally applicable to xenobiotic interventions that are associated with wide variation in efficacy or toxicity and, in particular, for prediction of susceptibility to toxicity.

Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?

In 2005, a survey compared a number of commercial PBPK software available at the time, mainly focusing on 'ready to use' modelling tools. Since then, these tools and software have been further developed and improved to allow modellers to perform WB-PBPK modelling including ADME processes at a high level of sophistication. This review presents a comparison of the features, values and limitations of both the 'ready to use' software and of the traditional user customizable software that are frequently used for the building and use of PBPK models, as well as the challenges associated with the various modelling approaches regarding their current and future use. PBPK models continue to be used more and more frequently during the drug development process since they represent a quantitative, physiologically realistic platform with which to simulate and predict the impact of various potential scenarios on the pharmacokinetics and pharmacodynamics of drugs. The 'ready to use' PBPK software has been a major factor in the increasing use of PBPK modelling in the pharmaceutical industry, opening up the PBPK approach to a broader range of users. The challenge is now to educate and to train scientists and modellers to ensure their appropriate understanding of the assumptions and the limitations linked both to the physiological framework of the 'virtual body' and to the scaling methodology from in vitro to in vivo (IVIVE).

Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery.

Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Metabonomic studies on human hepatocyte in primary culture.

Mechanisms involved in induction processes have been investigated using fresh human hepatocytes in culture as a cellular model and using mass spectrometry-based metabonomics as a global investigation tool. Sample preparation to data analysis have been detailed in an approach enabling to separate drug-induced (endogenous metabolites) and drug-related (drug metabolites) biomarkers for reference inducers. Rifampicin, a nuclear pregnane X receptor (PXR) ligand; CITCO, a nuclear constitutive androstane receptor (CAR) ligand; and phenobarbital, which activates both CAR and PXR, have been used. Specific intra-cellular metabolites have been isolated for rifampicin and CITCO as potential endogenous biomarkers of their respective induction mechanism. A mixture of these two types of biomarkers modified in the same way after treatment with either rifampicin or CITCO on the one hand and with phenobarbital on the other hand has been found.

[Chemoinformatics and virtual screening of molecules for therapeutic use]. / Apports de la chémo-informatique dans la recherche et l'optimisation des molécules d'intérêt thérapeutique.

Successful identification of new chemical entities with drug-like properties in pharmaceutical and academic research groups involves an early screen and the use of a large number of public and proprietary chemical libraries. Before applying high-throughput experimental screening approaches, virtual screening strategies have been put in place in order to sort and filter this massive amount of compounds and data available at these very early stages. Chemoinformatic tools have a crucial role in this selection process and enable therapeutic chemists to focus very early on promising candidates. Virtual screening has conventionally been based either on models of the target or the ligand (molecule), but today these models include biopharmaceutical filters addressing right from the start of the project the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the molecules. Above all, chemoinformatic tools help chemists understand better the chemical diversity they can work with, especially when comparing chemical libraries. This paper will focus on exemples of the day-to-day use of chemoinformatics in screening programs. A large part will be dedicated to new tools (chemographic and pharmacographic approaches) being developed for the representation and analysis of chemical diversity, but also for combining chemical and biological information to expedite research programs.

Integrated comparison of drug-related and drug-induced ultra performance liquid chromatography/mass spectrometry metabonomic profiles using human hepatocyte cultures.

The biochemical variations induced in human primary hepatocyte cultures by reference activators of xenoreceptor CAR (NR1I3) and PXR (NR1I2), i.e., rifampicin, phenobarbital, and 6-(4-chlorophenyl)imidazo[2,1-b] [1,3]thiazole-5-carbaldehyde O-3,4-dichlorobenzyl) oxime (CITCO), were investigated using a global metabonomics approach. Cultured human hepatocytes were treated with the three drugs before analysis of intracellular and extracellular media by ultra performance liquid chromatography/time-of-flight-mass spectrometry (UPLC/TOF-MS) technique, in order to list endogenous compounds potentially related to a PXR or CAR induction mechanism and to identify drug metabolites related to each treatment. The emphasis was put on the quality of the analytical data (dilution/filtration strategy before data processing) and on the appropriate pattern recognition techniques. In cellular media, the most significant variations seen in the data are not related to the treatments but to the source of hepatocytes, illustrating the importance of the genetic and/or environmental background in human liver experiments. However when applying classical multivariate statistical approaches (principal component analysis (PCA) and orthogonal partial least squares (O-PLS)), the statistical weight due to drug metabolites, present only in the treated groups, hinders the interpretation because of their predominance compared to most of the changes seen in endogenous metabolites. A new statistical approach, called shared and unique structure (SUS) plot, enabling the comparison of different treatments having the same control has been applied, allowing separation of clearly exogenous variables (drug metabolites) from endogenous biomarkers. Endogenous variables (either up- or down-regulated) have been attributed specifically to the impact of rifampicin (PXR ligand), CITCO (CAR ligand), and phenobarbital (CAR and PXR activator) on the biological regulation pathways of the hepatocytes. This global approach coupled to a statistical pretreatment of the data, enabling the separate capture of both drug related and drug induced biomarkers, represents a powerful technique for future mechanistic studies using cellular tools.

[Biomarkers: "Found in translation"]. / Les biomarqueurs : "Found in translation".

Despite continued increase in global Pharma R & D expenditure, the number of innovative drugs obtaining market approval has declined since 1994. The pharmaceutical industry is now entering a crucial juncture where increasing rates of attrition in clinical drug development as well as increasing development timelines are impacted by external factors such as intense regulatory pricing and safety pressures, increasing sales erosion due to generics, as well as exponential increases in the costs of bringing a drug to market. Despite these difficulties, numerous opportunities exist such as multiple unmet medical needs, the increasing incidence of certain diseases such as Alzheimer's disease, cancer, diabetes and obesity due to demographic changes, as well as the emergence of evolving markets such as China, India, and Eastern Europe. Consequently, Pharma is now responding to this challenge by improving both the productivity and the innovation in its drug discovery and development pipelines. In this regard, the advent of new technologies and expertise such as genomics, proteomics, structural biology, and molecular informatics in an integrated systems biology approach also provides a powerful opportunity for Pharma to address some of these difficulties. The key features behind this new strategy imply a discovery process based on an improved understanding of the molecular mechanism of diseases and drugs, translational research that places the patient at the center of the research process, and the application of biomarkers throughout the discovery and development phases. Moreover, new paradigms are required to improve target validation and develop more predictive cellular and animal models of human pathologies, a greater capacity in informatics-based analysis, and, consequently, a greater access to the vast sources of accumulating biological data and its integrated analysis. In the present review, we will address some of these issues and in particular emphasize how the application of biomarkers could potentially lead to improved productivity, quality, and innovation in drug discovery and ultimately better and safer medicines with improved therapeutic efficacy in specific pathologies for targeted patients.

Development of an in vitro rat intestine segmental perfusion model to investigate permeability and predict oral fraction absorbed.

PURPOSE: The aims of the study are to develop and evaluate an in vitro rat intestine segmental perfusion model for the prediction of the oral fraction absorbed of compounds and to assess the ability of the model to study intestinal metabolism. METHODS: The system consisted of a perfusion cell with a rat intestinal segment and three perfusion circulations (donor, receiver, and rinsing circulation). Lucifer yellow (LY) was applied as internal standard together with test compounds in the donor circulation. To validate the model, the permeability of eight noncongeneric passively absorbed drugs was determined. Intestinal N-demethylation of verapamil into norverapamil was followed in the donor and receiver circulations by high-performance liquid chromatography analysis. RESULTS: The in vitro model allowed ranking of the tested compounds according to their in vivo absorption potential. The Spearman's correlation coefficient between the oral fraction absorbed in humans and the ratio of permeation coefficient of test compound to the permeation coefficient of LY within the same experiment was 0.98 (P < 0.01). Moreover, intestinal N-demethylation of verapamil, its permeation, and the permeation of its metabolite norverapamil could be assessed in parallel. CONCLUSIONS: Up to six permeation kinetics can be obtained per rat, and the method has shown to be a valuable tool to estimate human oral absorption.

Characterization of microsomal cytochrome P450-dependent monooxygenases in the rat olfactory mucosa.

Nasal administration of a drug ensures therapeutic action by rapid systemic absorption and/or the entry of some molecules into the brain through different routes. Many recent studies have pointed out the presence of xenobiotic-metabolizing enzymes in rat olfactory mucosa (OM). Nevertheless, very little is known about the precise identity of isoforms of cytochrome P450 (P450)-dependent monooxygenases (P450) and their metabolic function in this tissue. Therefore, we evaluated mRNA expression of 19 P450 isoforms by semiquantitative reverse transcriptase-polymerase chain reaction and measured their microsomal activity toward six model substrates. For purposes of comparison, studies were conducted on OM and the liver. Specific activities toward phenacetin, chlorzoxazone, and dextromethorphan are higher in OM than in the liver; those toward lauric acid and testosterone are similar in both tissues, and that toward tolbutamide is much lower in OM. There are considerable differences between the two tissues with regard to mRNA expression of P450 isoforms. Some isoforms are expressed in OM but not in the liver (CYP1A1, 2G1, 2B21, and 4B1), whereas mRNA of others (CYP2C6, 2C11, 2D2, 3A1, 3A2, and 4A1) is present only in hepatic tissue. Although expression of CYP1A2, 2A1, 2A3, 2B2, 2D1, 2D4, 2E1, 2J4, and 3A9 is noticed in both tissues, there are a number of quantitative differences. On the whole, our results strongly suggest that CYP1A1, 1A2, 2A3, 2E1, 2G1, and 3A9 are among the main functional isoforms present in OM, at least regarding activities toward the six tested substrates. The implication of olfactory P450-dependent monooxygenases in toxicology, pharmacology, and physiology should be further investigated.