Literature DB >> 30076845

Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches.

Dustyn A Barnette1, Mary A Davis1, Na L Dang2, Anirudh S Pidugu3, Tyler Hughes2, S Joshua Swamidass2, Gunnar Boysen4, Grover P Miller5.   

Abstract

Lamisil (terbinafine) may cause idiosyncratic liver toxicity through a proposed toxicological mechanism involving the reactive metabolite 6,6-dimethyl-2-hepten-4-ynal (TBF-A). TBF-A toxicological relevance remains unclear due to a lack of identification of pathways leading to and competing with TBF-A formation. We resolved this knowledge gap by combining computational modeling and experimental kinetics of in vitro hepatic N-dealkylation of terbinafine. A deep learning model of N-dealkylation predicted a high probability for N-demethylation to yield desmethyl-terbinafine followed by N-dealkylation to TBF-A and marginal contributions from other possible pathways. We carried out steady-state kinetic experiments with pooled human liver microsomes that relied on development of labeling methods to expand metabolite characterization. Those efforts revealed high levels of TBF-A formation and first order decay during metabolic reactions; actual TBF-A levels would then reflect the balance between those processes as well as reflect the impact of stabilizing adduction with glutathione and other biological molecules. Modeling predictions and experimental studies agreed on the significance of N-demethylation and insignificance of N-denaphthylation in terbinafine metabolism, yet differed on importance of direct TBF-A formation. Under steady-state conditions, the direct pathway was the most important source of the reactive metabolite with a Vmax/Km of 4.0 pmol/min/mg protein/µM in contrast to model predictions. Nevertheless, previous studies show that therapeutic dosing leads to accumulation of desmethyl-terbinafine in plasma, which means that likely sources for TBF-A would draw from metabolism of both the major metabolite and parent drug based on our modeling and experimental studies. Through this combination of novel modeling and experimental approaches, we are the first to identify pathways leading to generation of TBF-A for assessing its role in idiosyncratic adverse drug interactions.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bioactivation; Liver toxicity; Reactivity; TBF-A; Terbinafine

Mesh:

Substances:

Year:  2018        PMID: 30076845      PMCID: PMC6188815          DOI: 10.1016/j.bcp.2018.07.043

Source DB:  PubMed          Journal:  Biochem Pharmacol        ISSN: 0006-2952            Impact factor:   5.858


  19 in total

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Authors:  Na Le Dang; Tyler B Hughes; Grover P Miller; S Joshua Swamidass
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Journal:  Am J Med Sci       Date:  2003-05       Impact factor: 2.378

8.  Pharmacokinetics of terbinafine and of its five main metabolites in plasma and urine, following a single oral dose in healthy subjects.

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Review 10.  Deleterious effects of reactive metabolites.

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  8 in total

1.  CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches.

Authors:  Mary A Davis; Dustyn A Barnette; Noah R Flynn; Anirudh S Pidugu; S Joshua Swamidass; Gunnar Boysen; Grover P Miller
Journal:  Chem Res Toxicol       Date:  2019-04-10       Impact factor: 3.739

Review 2.  The application of artificial neural networks in metabolomics: a historical perspective.

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3.  Comprehensive kinetic and modeling analyses revealed CYP2C9 and 3A4 determine terbinafine metabolic clearance and bioactivation.

Authors:  Dustyn A Barnette; Mary A Davis; Noah Flynn; Anirudh S Pidugu; S Joshua Swamidass; Grover P Miller
Journal:  Biochem Pharmacol       Date:  2019-10-09       Impact factor: 5.858

Review 4.  The potential applications of artificial intelligence in drug discovery and development.

Authors:  H Farghali; N Kutinová Canová; M Arora
Journal:  Physiol Res       Date:  2021-12-30       Impact factor: 2.139

5.  Impacts of diphenylamine NSAID halogenation on bioactivation risks.

Authors:  Mary Alexandra Schleiff; Sasin Payakachat; Benjamin Mark Schleiff; S Joshua Swamidass; Gunnar Boysen; Grover Paul Miller
Journal:  Toxicology       Date:  2021-06-06       Impact factor: 4.571

Review 6.  Examining the Potential of Developing and Implementing Use of Adiponectin-Targeted Therapeutics for Metabolic and Cardiovascular Diseases.

Authors:  Ying Liu; Vivian Vu; Gary Sweeney
Journal:  Front Endocrinol (Lausanne)       Date:  2019-12-11       Impact factor: 5.555

7.  Significance of Multiple Bioactivation Pathways for Meclofenamate as Revealed through Modeling and Reaction Kinetics.

Authors:  Mary Alexandra Schleiff; Noah R Flynn; Sasin Payakachat; Benjamin Mark Schleiff; Anna O Pinson; Dennis W Province; S Joshua Swamidass; Gunnar Boysen; Grover P Miller
Journal:  Drug Metab Dispos       Date:  2020-11-25       Impact factor: 3.922

8.  XenoNet: Inference and Likelihood of Intermediate Metabolite Formation.

Authors:  Noah R Flynn; Na Le Dang; Michael D Ward; S Joshua Swamidass
Journal:  J Chem Inf Model       Date:  2020-06-29       Impact factor: 4.956

  8 in total

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