The effects of agomelatine and imipramine on liver cytochrome P450 during chronic mild stress (CMS) in the rat

Haduch, Anna; Bromek, Ewa; Rysz, Marta; Pukło, Renata; Papp, Mariusz; Gruca, Piotr; Łasoń, Magdalena; Niemczyk, Monika

doi:10.1007/s43440-020-00151-w

Cited by 6 publications

(7 citation statements)

References 66 publications

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“…Imipramine is metabolized by CYP2D in rat liver microsomes to form 2-hydroxy imipramine, which undergoes an arene-oxide intermediate formation, and covalently binds to the CYP2D protein as a reactive metabolite (Haduch et al, 2020; Masubuchi et al, 1996). The MPNN model classifies imipramine as positive, with a putative substructure of 5-methyl-10,11-dihydro-5H-dibenzo[b,f]azepine.…”

Section: Resultsmentioning

confidence: 99%

Development of a novelin silicoclassification model to assess reactive metabolite formation in the cysteine trapping assay and investigation of important substructures

Umemori,

Handa,

Yoshimura

et al. 2024

Preprint

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Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of its mechanism. The production of reactive metabolites is one of the major causes of DILI, particularly idiosyncratic DILI. The cysteine trapping assay is one of the methods to detect reactive metabolites which bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed anin silicoclassification model for predicting a positive/negative outcome in the cysteine trapping assay to accelerate the drug discovery process. In this study, we collected 475 compounds (436 in-house compounds and 39 publicly available drugs). Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. The 5-fold cross-validation (CV) and hold-out test were evaluated in random- and time-split trials. Additionally, we investigated the substructures that contributed to positive results in the cysteine trapping assay through the framework of the MPNN model. In the random-split dataset, the AUC-ROC of MPNN and RF were 0.698 and 0.811 in the 5-fold CV, and 0.742 and 0.819 in the hold-out test, respectively. In the time-split dataset, AUC-ROC of MPNN and RF were 0.729 and 0.617 in the 5-fold CV, and 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that thein silicoMPNN classification model for the cysteine trapping assay have better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous reports such as propranolol, verapamil, and imipramine. This is a new machine learning model that can determine the outcome of the cysteine trapping assay, namely accurately predicting the covalent binding risk as the one of factors of idiosyncratic DILI. We believe that this can contribute to mitigating DILI risk for reactive metabolites at the early stages of drug discovery.

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Section: Resultsmentioning

confidence: 99%

Development of a novelin silicoclassification model to assess reactive metabolite formation in the cysteine trapping assay and investigation of important substructures

Umemori,

Handa,

Yoshimura

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It is believed that in certain cases, cellular function is compromised and organ toxicity can occur. In a study on human liver microsomes, 5 reduced glutathione-trapped adducts and 2 semicarbazide-trapped aldehydes were uncovered [12] . These reactive metabolites have a certain incubation period for the consumption of reducing substances in vivo, which is also related to the susceptibility of patients.…”

Section: Discussionmentioning

confidence: 99%

“…It has been observed that the bioactivation of AGM leads to the formation of epoxides. These reactive metabolites may be closely related to AGM toxicity [12] . Cytochrome P450 family 1 subfamily A member 2 (CYP1A2) activity has been directly correlated with AGM metabolism [13] .…”

Section: Introductionmentioning

confidence: 99%

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CYP1A2 polymorphism may contribute to agomelatine-induced acute liver injury

Wang

et al. 2021

Medicine

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Rationale:Liver function monitoring is recommended when agomelatine is prescribed, although liver enzymes are not considered predictive biomarkers. Most patients present with acute liver injury, with only a few presenting with levels of liver enzymes that are over 30 times the upper limit of normal. The patient-specific risk factors that are associated with liver injury remain unclear. Thus, this report provides new insights into the mechanism of agomelatine-induced acute hepatocellular injury based on cytochrome P450 family 1 subfamily A member 2 (CYP1A2) polymorphism.Patient concerns:We present a case of acute hepatocellular injury in a 75-year-old man who was taking agomelatine at a dose of 50 mg/qn. All hepatitis virus test results were negative. No history of liver disease was observed. As CYP1A2 is the main metabolic enzyme of agomelatine, CYP1A2 AA (rs762551) genetic polymorphism was analyzed.Diagnosis:The patient's transaminases level exceeded the critical value on day 72 after starting oral agomelatine.Interventions:The patient received intravenous magnesium isoglycyrrhizinate, a liver cell-protecting agent, followed by the withdrawal of agomelatine.Outcomes:There was an improvement in the levels of the liver enzymes and no subsequent organ dysfunction was observed.Lessons:Here, we report a case of acute hepatocellular injury characterized by a very high aspartate aminotransferase level. Periodic liver function testing throughout the treatment period can help in the rapid and appropriate diagnosis of acute liver injury, particularly in the absence of typical clinical manifestations. Agomelatine hepatic toxicity might be related to an idiosyncratic metabolic reaction that depends on individual patient differences. As it is the main metabolic enzyme of agomelatine, CYP1A2 genetic polymorphism may contribute to liver injury by affecting its metabolites.

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“…Clozapine significantly enhanced CYP2D activity in the truncus cerebri ( Haduch et al, 2011 ). Additionally, mirtazapine and imipramine affected expression and activity of CYP2D in liver ( Daniel et al, 2002 ; Haduch et al, 2020a ), but not in brain ( Haduch et al, 2011 ). The effects of psychotropic drugs on brain CYP2D expression and activity are complicated, but all these results indicate brain CYP2D is susceptible.…”

Section: Alterations In Expressions and Functions Of Cytochrome P450s And Udp-glucuronosyltransferases Under Diseases And Their Impacts Omentioning

confidence: 99%

Alterations of Cytochrome P450s and UDP-Glucuronosyltransferases in Brain Under Diseases and Their Clinical Significances

Yun

Yang

et al. 2021

Front. Pharmacol.

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Cytochrome P450s (CYPs) and UDP-glucuronosyltransferases (UGTs) are both greatly important metabolic enzymes in various tissues, including brain. Although expressions of brain CYPs and UGTs and their contributions to drug disposition are much less than liver, both CYPs and UGTs also mediate metabolism of endogenous substances including dopamine and serotonin as well as some drugs such as morphine in brain, demonstrating their important roles in maintenance of brain homeostasis or pharmacological activity of drugs. Some diseases such as epilepsy, Parkinson’s disease and Alzheimer’s disease are often associated with the alterations of CYPs and UGTs in brain, which may be involved in processes of these diseases via disturbing metabolism of endogenous substances or resisting drugs. This article reviewed the alterations of CYPs and UGTs in brain, the effects on endogenous substances and drugs and their clinical significances. Understanding the roles of CYPs and UGTs in brain provides some new strategies for the treatment of central nervous system diseases.

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The effects of agomelatine and imipramine on liver cytochrome P450 during chronic mild stress (CMS) in the rat

Cited by 6 publications

References 66 publications

Development of a novelin silicoclassification model to assess reactive metabolite formation in the cysteine trapping assay and investigation of important substructures

Development of a novelin silicoclassification model to assess reactive metabolite formation in the cysteine trapping assay and investigation of important substructures

CYP1A2 polymorphism may contribute to agomelatine-induced acute liver injury

Alterations of Cytochrome P450s and UDP-Glucuronosyltransferases in Brain Under Diseases and Their Clinical Significances

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