Software‐aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery

Cece-Esencan, Esra Nurten; Fontaine, Fabien; Plasencia, Guillem; Teppner, Marieke; Brink, Andreas; Pähler, Axel; Zamora, Ismael

doi:10.1002/rcm.7429

Cited by 10 publications

(5 citation statements)

References 33 publications

(80 reference statements)

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“…Monitoring metabolic stability by a kinetic approach allows not only the half-life calculation for the substrate but also reduction in false positives in the characterization of metabolites. Samples for each time point were analyzed by LC–MS/MS, and raw data were analyzed using Mass-MetaSite software , in the WebMetabase platform. − Figure C shows that the PBS/ACN-DMSO protocol significantly increases the stability of 1 ( dBet1 ) during analysis. Therefore, the final method for the metabolic stability assay used in this study included the PBS/ACN-DMSO protocol (see the Methods section for the whole procedure), to reduce the risk of further degradation of the substrate in the autosampler during analysis.…”

Section: Resultsmentioning

confidence: 99%

“…To a solution of 45 (0.100 g, 0.248 mmol) in ACN (3.0 mL), K 2 CO 3 (0.086 g, 0.620 mmol), KI (0.021 g, 0.124 mmol), and propargyl bromide solution (80 wt.% in toluene) (0.031 g, 0.258 mmol) were added, and the mixture was refluxed for 4 h. Then, the solvent was evaporated to dryness and the crude residue was diluted with water, yielding a solid, which was collected by filtration, tritured by DEE, and filtered off to afford the title compound as a yellow-orange solid (0.069 g, 69% yield). 1 4-(4-Fluoro-3-(4-(hex-5-ynoyl)piperazine-1-carbonyl)benzyl)phthalazin-1(2H)-one (68). Under a nitrogen atmosphere, a solution of 45 (0.150 g, 0.372 mmol), 5-hexynoic acid (0.042 g, 0.372 mmol), HBTU (0.175 g, 0.465 mmol), and Et 3 N (0.1 mL, 0.744 mmol) in dry DMF (2.0 mL) was stirred at room temperature for 2 h. Then, the reaction mixture was poured in ice-water and extracted with EA (×3).…”

Section: -((3-chlorophenylmentioning

confidence: 99%

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Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

et al. 2020

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Hetero-bifunctional PROteolysis TArgeting Chimeras (PROTACs) represent a new emerging class of small molecules designed to induce polyubiquitylation and proteasomal-dependent degradation of a target protein. Despite the increasing number of publications about the synthesis, biological evaluation, and mechanism of action of PROTACs, the characterization of the pharmacokinetic properties of this class of compounds is still minimal. Here, we report a study on the metabolism of a series of 40 PROTACs in cryopreserved human hepatocytes at multiple time points. Our results indicated that the metabolism of PROTACs could not be predicted from that of their constituent ligands. Their linkers’ chemical nature and length resulted in playing a major role in the PROTACs’ liability. A subset of compounds was also tested for metabolism by human cytochrome P450 3A4 (CYP3A4) and human aldehyde oxidase (hAOX) for more in-depth data interpretation, and both enzymes resulted in active PROTAC metabolism.

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Section: Resultsmentioning

confidence: 99%

Section: -((3-chlorophenylmentioning

confidence: 99%

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

et al. 2020

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“…Given data sets that span the metabolic capabilities of E. coli , the proposed machine-learning inspired parameterization strategy demonstrated that it is indeed possible to train a single model to predict the genetic and environmentally perturbed phenotypes with fidelity. In the same spirit, the same multi-data set parameterization concepts can be leveraged for applications of kinetic models in personalized healthcare36, biomarker identification37, drug discovery38 and modelling of microbial communities39.…”

Section: Discussionmentioning

confidence: 99%

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

2016

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Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).

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“…RapidFire™ MS is fully automated and completes integrated sample purification and MS analysis with reduced sample cycle times [2,19]. In reaction-phenotyping experiments, high-resolution mass spectrometry is able to follow the disappearance of the parent compound and concurrently identify and monitor the formation of metabolites in each enzymatic system being evaluated for the NCE [20].…”

Section: In Vitro Assaysmentioning

confidence: 99%

Application of in vitro CYP and Transporter Assays to Predict Clinical Drug–Drug Interactions

Volpe

Balimane

2018

Bioanalysis

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Drug transporters and drug-metabolizing enzymes participate in the processes of absorption, disposition, metabolism and elimination, which influence the pharmacokinetics and pharmacodynamics of a new chemical entity (NCE). Therefore, understanding an NCE's potential as a substrate, inhibitor or inducer of a drug-metabolizing enzyme or transporter has become an essential element of risk assessment during drug discovery, development and regulatory review [1][2][3][4][5][6][7]. Drug metabolismDrugs are metabolized by Phase I and/or II enzymes in several organs, especially the liver, intestine and kidneys. The aim of drug metabolism is to increase the polarity and solubility of the molecule to enable its elimination. Phase I metabolic reactions introduce reactive and polar groups into the substrate NCE. Cytochrome P450 (CYP) enzymes integrate an oxygen atom into nonactivated hydrocarbons, resulting in either the introduction of hydroxyl groups or the N-, O-and S-dealkylation of substrates. Other Phase I enzymes include the flavin monooxygenases, monoamine oxidases and xanthine oxidase/aldehyde oxidase. Outcomes of Phase I metabolism include inactivation of the drug by production of pharmacologically inactive metabolites or generation of active metabolite(s). Phase II metabolic reactions include glucuronidation, sulfonation, methylation, acetylation and glutathione conjugation. Phase II enzymes include uridine-5 -diphospho-glucuronosyltransferases, sulfotransferases, glutathione S-transferases and N-acetyltransferases. Metabolites formed during Phase II reactions are not likely to be pharmacologically active. Drug transportUptake and efflux transporters determine plasma and tissue concentrations of both endogenous and exogenous substances. The transporters are found in the small intestines, liver and kidneys, which are critical for drug absorption and elimination. They are also located in vital blood-tissue barriers such as the blood-brain and blood-placenta barriers. Solute carrier transporters facilitate the uptake of compounds into cells through facilitated diffusion or via cotransport with intracellular and/or extracellular ions. ATP-binding cassette transporters efflux compounds out of cells against a concentration gradient driven by the hydrolysis of ATP as an energy source. Drug interactionsDrug-drug interactions occur when one drug ('perpetrator') interacts with another drug ('victim') by altering its metabolism and/or transport following coadministration. Drug-drug interactions can have serious clinical effects by decreasing the therapeutic efficacy or increasing the toxicity of a victim drug. Polypharmacy, the simultaneous use of multiple drugs in a patient for one or more conditions, increases the risk for drug interactions.Evaluation

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Software‐aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery

Abstract: This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

Cited by 10 publications

References 33 publications

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Application of in vitro CYP and Transporter Assays to Predict Clinical Drug–Drug Interactions

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