Prediction Of Drug Metabolism Research Articles

3D Primary Hepatocyte Culture Systems for Analyses of Liver Diseases, Drug Metabolism, and Toxicity: Emerging Culture Paradigms and Applications.

Recent research has shown that the maintenance of relevant liver functions ex vivo requires models in which the cells exhibit an in vivo-like phenotype, often achieved by reconstitution of appropriate cellular interactions. Multiple different models have been presented that differ in the cells utilized, media, and culture conditions. Furthermore, several technologically different approaches have been presented including bioreactors, chips, and plate-based systems in fluidic or static media constituting of chemically diverse materials. Using such models, the ability to predict drug metabolism, drug toxicity, and liver functionality have increased tremendously as compared to conventional in vitro models in which cells are cultured as 2D monolayers. Here, the authors highlight important considerations for microphysiological systems for primary hepatocyte culture, review current culture paradigms, and discuss their opportunities for studies of drug metabolism, hepatotoxicity, liver biology, and disease.

Humanized UGT2 and CYP3A transchromosomic rats for improved prediction of human drug metabolism

Although "genomically" humanized animals are invaluable tools for generating human disease models as well as for biomedical research, their development has been mainly restricted to mice via established transgenic-based and embryonic stem cell-based technologies. Since rats are widely used for studying human disease and for drug efficacy and toxicity testing, humanized rat models would be preferred over mice for several applications. However, the development of sophisticated humanized rat models has been hampered by the difficulty of complex genetic manipulations in rats. Additionally, several genes and gene clusters, which are megabase range in size, were difficult to introduce into rats with conventional technologies. As a proof of concept, we herein report the generation of genomically humanized rats expressing key human drug-metabolizing enzymes in the absence of their orthologous rat counterparts via the combination of chromosome transfer using mouse artificial chromosome (MAC) and genome editing technologies. About 1.5 Mb and 700 kb of the entire UDP glucuronosyltransferase family 2 and cytochrome P450 family 3 subfamily A genomic regions, respectively, were successfully introduced via the MACs into rats. The transchromosomic rats were combined with rats carrying deletions of the endogenous orthologous genes, achieved by genome editing. In the "transchromosomic humanized" rat strains, the gene expression, pharmacokinetics, and metabolism observed in humans were well reproduced. Thus, the combination of chromosome transfer and genome editing technologies can be used to generate fully humanized rats for improved prediction of the pharmacokinetics and drug-drug interactions in humans, and for basic research, drug discovery, and development.

SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server.

Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing. SMARTCyp 3.0 is an updated version of our previous web server for prediction of site-of-metabolism for Cytochrome P450-mediated metabolism, now in Python 3 with increased structural coverage and new features. The SMARTCyp program is a first principle-based method using density functional theory determined activation energies for more than 250 molecules to identify the most likely site-of-metabolism. New features include a similarity measure between the query molecule and the model fragment, a new graphical interface and additional parameters expanding the structural coverage of the SMARTCyp program. The SMARTCyp server is freely available for use on the web at smartcyp.sund.ku.dk. Supplementary data are available at Bioinformatics online.

A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study.

The phenotyping approach to predict drug metabolism activity is often hampered by a lack of correlation between the probe and the drug of interest. In this article, we present a strategy to refine the phenotyping approach based on a physiologically based pharmacokinetic simulation (implemented in Simcyp Simulator version 17) using previously published models. The apparent clearance (CL/F) of erlotinib was better predicted by the sum of caffeine and i.v. midazolam CL/F (r 2 = 0.60) compared to that of either probe drug alone. The clearance of atorvastatin and repaglinide had a strong correlation (r 2 = 0.70 and 0.63, respectively) with that of pitavastatin (a SLCO1B1 probe). Use of multiple probes for drugs that are predominantly metabolized by more than one cytochrome P450 (CYP) enzyme should be considered. In a case in which hepatic uptake transporters play a significant role in the disposition of a drug, the pharmacokinetic of a transporter probe will provide better predictions of the drug clearance.

Identification and quantification of domestic feline cytochrome P450 transcriptome across multiple tissues.

Understanding of cytochrome P450 (CYP) isoform distribution and function in the domestic feline is limited. Only a few studies have defined individual CYP isoforms across metabolically relevant tissues, hampering the ability to predict drug metabolism and potential drug-drug interactions. Using RNA sequencing (RNA-seq), transcriptomes from the 99 Lives Cat Genome Sequencing Initiative databank combined with experimentally acquired whole transcriptome sequencing of healthy, adult male (n=2) and female (n=2) domestic felines, expression of 42 CYP isoforms were identified in 20 different tissues. Thirty-seven of these isoforms had not been previously reported in cats. Depending on the tissue, three to twenty-nine CYP isoform transcripts were expressed. The feline genome annotations did not differentiate CYP2E1 and 2E2 genes, demonstrating poor annotation for this gene using the reference genome. As the majority of the sequences are based on automated pipelines, complete cDNA sequences for translation into CYP protein sequences could not be determined. This study is the first to identify and characterize 37 additional CYP isoforms in feline tissues, increasing the number of identified CYP from the previously reported seven isoforms to 42 across 20 tissues.

Prediction of Tacrolimus Drug Dosing and Metabolism based on CYP3A5 Polymorphism in Indian Renal Transplant Recipients

Prediction of Tacrolimus Drug Dosing and Metabolism based on CYP3A5 Polymorphism in Indian Renal Transplant Recipients

Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods.

Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally; computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multiclassification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction.

Recent Advances of Computational Modeling for Predicting Drug Metabolism: A Perspective.

Absorption, Distribution, Metabolism, Excretion (ADME) properties along with drug induced adverse effects are the major reasons for the late stage failure of drug candidates as well as the cause for the expensive withdrawal of many approved drugs from the market. Considering the adverse effects of drugs, metabolism factor has great importance in medicinal chemistry and clinical pharmacology because it influences the deactivation, activation, detoxification and toxification of drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and metabolism followed by adverse effects, as they serve the integration of information on several levels to enhance the reliability of outcomes. In silico profiling of drug metabolism can help progress only those molecules along the discovery chain that is less likely to fail later in the drug discovery process. This positively impacts the very high costs of drug discovery and development. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true influence on drug discovery at different levels. If applied in a scientifically consequential way, computational tools may improve the capability to identify and evaluate potential drug molecules considering pharmacokinetic properties of drugs. Herein, current trends in computational modeling for predicting drug metabolism are reviewed highlighting new computational tools for drug metabolism prediction followed by reporting large and integrated databases of approved drugs associated with diverse metabolism issues.

NANPDB: A Resource for Natural Products from Northern African Sources.

Natural products (NPs) are often regarded as sources of drugs or drug leads or simply as a "source of inspiration" for the discovery of novel drugs. We have built the Northern African Natural Products Database (NANPDB) by collecting information on ∼4500 NPs, covering literature data for the period from 1962 to 2016. The data cover compounds isolated mainly from plants, with contributions from some endophyte, animal (e.g., coral), fungal, and bacterial sources. The compounds were identified from 617 source species, belonging to 146 families. Computed physicochemical properties, often used to predict drug metabolism and pharmacokinetics, as well as predicted toxicity information, have been included for each compound in the data set. This is the largest collection of annotated natural compounds produced by native organisms from Northern Africa. While the database includes well-known drugs and drug leads, the medical potential of a majority of the molecules is yet to be investigated. The database could be useful for drug discovery efforts, analysis of the bioactivity of selected compounds, or the discovery of synthesis routes toward secondary metabolites. The current version of NANPDB is available at http://african-compounds.org/nanpdb/ .

Actual Value of Subgroup Analyses to Support Regulatory Applications

In the era of personalized medicine, genetic or molecular profiling serves the purpose, to identify a more stratified and homogenous subgroup of patients for benefit risk evaluation of a given therapeutical agent. However, subgroup analyses always bear the risk of a loss of power in the original outcome analyses. By illustrative examples, the participants will be sensitised in which regulatory situations, subgroup analyses may serve for evidence gain, and in which applications, subgrouping may rather not lead to valid conclusions. The benefit and the risk of using genetic profiling and subgroup analyses in regulatory applications will be illustrated We analysed the biomarker drug pairs of drugs that were approved during the six past years by the European Medicine Agency, with regard to their role in defining subgroups used to derive regulatory decisions related to drug approval. We classified biomarkers due to their role in prediction of drug efficacy, side effects, or pharmacokinetics and drug metabolism. By this systematic review we found that the functional evidence of biomarkers that are used to derive regulatory decisions related to drug approval is heterogeneous. A specific biomarker classification system may be helpful to specify the role of biomarkers in the context of companion diagnostics and drug approval. This classification should consider the type of the biomarker and the kind of phenotype the biomarker measures in the drug’s action, i.e. the biological plausibility. The second aspect concerns the precision or the specificity with which the biomarker can predict the clinical outcome in drug efficacy or safety. Evaluation of a given biomarker in these two dimensional aspects may help to estimate the usefulness of biomarker based subgroup analyses in regulatory decisions. Thus, for example it may help to decide at which point biomarker negative patients may be excluded or rather analyzed as subgroup in drug development. Stratified medicine offers a promising vision on the development of new drugs in areas with a high medical need. Assuming that some drugs may act differently in different patients, precision medicine is searching for a relevant interaction between patient and treatment resulting in an improved efficacy or tolerability in a given subgroup of patients resulting in a certain degree of treatment personalization.

Recent advances in the prediction of non‐CYP450‐mediated drug metabolism

Computational models of drug metabolism prediction have focused mainly on cytochrome P450 enzymes, because drug–drug interactions, reactive metabolite formation, hepatotoxicity, idiosyncratic adverse drug interactions, and/or loss of efficacy of many drugs were the results of interactions with CYP450s. Metabolic regioselectivity and isoform specificity prediction models for CYP450‐catalyzed reactions have reached approximately 95% accuracy. Thus, a new drug candidate is less likely to show unexpected metabolic profile due to metabolism via CYP450 pathways. For such candidates, secondary metabolic Phase I and II enzymes are likely to play an expected (or unexpected) role in drug metabolism. The importance of flavin monooxygenases (FMOs), aldehyde and alcohol dehydrogenase, monoamine oxidase from the Phase I and UDP‐glucuronosyltransferase (UGT), sulfotransferase, glutathione S‐transferase, and methyltransferase from Phase II has increased and United States Food and Drug Administration guidelines on NDA have specific recommendations for in vitro and in vivo testing against these enzymes. Thus, there is an urgent requirement of reliable predictive models for drug metabolism catalyzed by these enzymes. In this review, we have classified drug metabolism prediction models (site of metabolism, isoform specificity, and kinetic parameter) for these enzymes into Phase I and II. When such models are unavailable, we discuss the Quantitative Structure Activity Relationship (QSAR), pharmacophore, docking, dynamics, and reactivity studies performed for the prediction of substrates and inhibitors. Recently published models for FMO and UGT are discussed. The need for comprehensive, widely applicable, sequential primary and secondary metabolite prediction is highlighted. Potential difficulties and future prospectives in the development of such models are discussed. WIREs Comput Mol Sci 2017, 7:e1323. doi: 10.1002/wcms.1323This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Computer and Information Science > Chemoinformatics Software > Molecular Modeling

Construction and verification of CYP3A5 gene polymorphisms using a Saccharomycescerevisiae expression system to predict drug metabolism.

The present study evaluated the ability of a Saccharomyces cerevisiae expression system to predict the pharmacokinetic (PK) activity of a calcium channel blocker in patients with distinct cytochrome P450 3A5 (CYP3A5) polymorphisms. The blood pressure lowering activity of amlodipine in 57 hypertensive patients with CYP3A5*1/*1, CYP3A5*1/*3, CYP3A5*4 and CYP3A5*6 polymorphisms was evaluated by the current study. Subsequently, a Saccharomyces cerevisiae expression system for CYP3A5 gene polymorphisms was constructed to examine the PK activity of CYP3A5*1/*1, CYP3A5*4 and CYP3A5*6 polymorphisms. This system was used to predict the PK of amlodipine and was compared with the in vivo data from different gene polymorphism groups. The blood pressure lowering activity of amlodipine in hypertensive patients varied among CYP3A5 polymorphisms. The in vivo results demonstrated that CYP3A5*6 exhibited the highest metabolic rate, followed by CYP3A5*1/*1, CYP3A5*4 and CYP3A5*1/*3. The difference between CYP3A5*6 and CYP3A5*1/*1 was not statistically significant (P=0.5). In accordance with in vivo data, CYP3A5*1/*1 exhibited the highest in vitro metabolic rate, followed by CYP3A5*6 and CYP3A5*4. With the exception of the comparison between CYP3A5*6 and CYP3A5*1/*1, polymorphisms exhibited statistically significant differences compared with CYP3A5*1/*1 (P<0.05). The Saccharomyces cerevisiae expression system may be a cost effective and potentially useful tool for assessing the PK activity of drugs that are metabolized by CYP3A5.

Advances in Computational Prediction of Regioselective and Isoform-Specific Drug Metabolism Catalyzed by CYP450s.

Prediction of drug metabolism is an important step in the early lead optimization phase and preclinical studies. Its main purpose is to avoid late stage (Phase I–III) or post-marketing withdrawals which usually results in a significant financial loss to a pharmaceutical company. Computational methods for identification of soft-spots in the molecules (site of metabolism; SOM) and CYP450 isoforms responsible for drug metabolism have over the past ten years proved an indispensable tool towards achieving this goal. These methods can be broadly classified into i) ligand based, ii) structure based and iii) hybrid methods. In this review we discuss some of the most successful methods of SOM and isoform specificity prediction, their application domain, advantages, and limitations along with recent developments in the last 5 years in this area. Recent applications of molecular dynamics (MD), quantum mechanics (QM) and quantum mechanics/molecular mechanics (QM/MM) methodology for regioselective and isoform specific drug metabolism predictions are also discussed. Finally, a summary of these methods along with expected developments, future challenges and opportunities are discussed.

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data.

The programme for the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25-29 October 2015) included a sunrise session presenting an overview of the state-of-the-art tools for in vitro-in vivo extrapolation (IVIVE) and mechanistic prediction of renal drug disposition. These concepts are based on approaches developed for prediction of hepatic clearance, with consideration of scaling factors physiologically relevant to kidney and the unique and complex structural organisation of this organ. Physiologically relevant kidney models require a number of parameters for mechanistic description of processes, supported by quantitative information on renal physiology (system parameters) and in vitro/in silico drug-related data. This review expands upon the themes raised during the session and highlights the importance of high quality in vitro drug data generated in appropriate experimental setup and robust system-related information for successful IVIVE of renal drug disposition. The different in vitro systems available for studying renal drug metabolism and transport are summarised and recent developments involving state-of-the-art technologies highlighted. Current gaps and uncertainties associated with system parameters related to human kidney for the development of physiologically based pharmacokinetic (PBPK) model and quantitative prediction of renal drug disposition, excretion, and/or metabolism are identified.

Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

Murine Cyp3a knockout chimeric mice with humanized liver: prediction of the metabolic profile of nefazodone in humans.

Chimeric mice with humanized livers (PXB mice) are used to investigate the metabolism and pharmacokinetics of drugs in humans. However, residual murine enzymatic activities derived from the liver and the presence of mouse small intestinal metabolism can hamper the prediction of human drug metabolism. Recently murine Cytochrome P450 3a gene knockout chimeric mice with humanized livers (Cyp3a KO CM) were developed. To evaluate the prediction of drug metabolism, nefazodone (NEF) was administered orally at 10 mg/kg to the following mouse strains: Cyp3a KO CM, murine Cyp3a gene knockout (Cyp3a KO), PXB and severe combined immunodeficiency (SCID) mice. Liquid chromatography-mass spectrometry was used for metabolic profiling of plasma, urine and bile. The prediction of human metabolite levels such as hydroxy nefazodone (OH-NEF), triazoledione form (TD), m-chlorophenylpiperazine and dealkyl metabolites in Cyp3a KO CM was superior to that in Cyp3a KO, PXB or SCID mice. Further, clinical exposure levels of NEF, OH-NEF and TD were reproduced in Cyp3a KO CM. In contrast, NEF was rapidly metabolized to TD in both PXB and SCID mice but not in Cyp3a KO mice, suggesting that murine CYP3A is involved in the elimination of NEF in these mice. These findings demonstrate that the metabolic profile of NEF in Cyp3a KO CM differs qualitatively and quantitatively from that in PXB mice due to the higher metabolic rate of NEF and its metabolites via murine CYP3A. Therefore Cyp3a KO CM might be useful in predicting the metabolic profiles of drug candidates in humans.

Drug Metabolism Prediction. Edited by Johannes Kirchmair

<b>Drug Metabolism Prediction</b>. Edited by Johannes Kirchmair

Hepatocyte spheroid culture on fibrous scaffolds with grafted functional ligands as an in vitro model for predicting drug metabolism and hepatotoxicity

The identification of a biologic substrate for maintaining hepatocyte functions is essential to provide reliable and predictable models for in vitro drug screening. In the current study, a three-dimensional culture of hepatocytes was established on highly porous fibrous scaffolds with grafted galactose and RGD to afford extensive cell–cell and cell–scaffold interactions spatially. The pore size and ligand densities indicated significant effects on the formation of hepatocyte spheroids in balancing the cell retention, adhesion, and migration on fibrous scaffolds. Fibrous scaffolds with an average pore size of 60μm and surface grafting densities of galactose at 5.9nmol/cm2 and RGD at 6.9pmol/cm2 provided optimal microenvironments for hepatocyte infiltration and multicellular spheroid formation. Significant promotions were also demonstrated in the syntheses of albumin and urea and the activities of phase I (CYP 3A11 and CYP 2C9) and phase II enzymes. The in vitro metabolism tests on testosterone and acetaminophen by hepatocytes on the optimal scaffolds indicated the predicated clearance rates of 50.7 and 22.6ml/min/kg, respectively, which were comparable to the in vivo values of rats. The in vitro hepatotoxicity tests on amiodarone hydrochloride and acetaminophen predicted the half maximal effective concentrations (EC50) to reflect the in vivo toxic plasma concentrations in human. In addition, the enzyme activities, predicted clearance rates and hepatotoxicity values of hepatocytes on the optimal scaffolds experienced sensitive responsiveness to specific inducers or inhibitors of CYP 3A11 and phase II enzymes, exhibiting in vivo–in vitro correlations to a certain extent. These results demonstrate the feasibility of hepatocyte spheroid culture on fibrous scaffolds as an potential in vitro testing model to predict the in vivo drug metabolism, hepatotoxicity, and drug–drug interactions.

Development of Murine Cyp3a Knockout Chimeric Mice with Humanized Liver.

We developed murine CYP3A knockout ko chimeric mice with humanized liver expressing human P450S similar to those in humans and whose livers and small intestines do not express murine CYP3A this: approach may overcome effects of residual mouse metabolic enzymes like Cyp3a in conventional chimeric mice with humanized liver, such as PXB-mice [urokinase plasminogen activator/severe combined immunodeficiency (uPA/SCID) mice repopulated with over 70% human hepatocytes] to improve the prediction of drug metabolism and pharmacokinetics in humans. After human hepatocytes were transplanted into Cyp3a KO/uPA/SCID host mice, human albumin levels logarithmically increased until approximately 60 days after transplantation, findings similar to those in PXB-mice. Quantitative real-time-polymerase chain reaction analyses showed that hepatic human P450s, UGTs, SULTs, and transporters mRNA expression levels in Cyp3a KO chimeric mice were also similar to those in PXB-mice and confirmed the absence of Cyp3a11 mRNA expression in mouse liver and intestine. Findings for midazolam and triazolam metabolic activities in liver microsomes were comparable between Cyp3a KO chimeric mice and PXB-mice. In contrast, these activities in the intestine of Cyp3a KO chimeric mice were attenuated compared with PXB-mice. Owing to the knockout of murine Cyp3a, hepatic Cyp2b10 and 2c55 mRNA levels in Cyp3a KO/uPA/SCID mice (without hepatocyte transplants) were 8.4- and 61-fold upregulated compared with PXB-mice, respectively. However, human hepatocyte transplantation successfully restored Cyp2b10 level nearly fully and Cyp2c55 level partly (still 13-fold upregulated) compared with those in PXB-mice. Intestinal Cyp2b10 and 2c55 were also repressed by human hepatocyte transplantation in Cyp3a KO chimeric mice.

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.