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

Article abstract of DOI:10.1021/acs.chemrestox.9b00006

Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using in vitro studies with human liver microsomes and modeling approaches, yet knowledge of the individual enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways. CYP2C19 and 3A4 contributed to all pathways, and so, we targeted them for steady-state analyses with recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4 similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions. Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate to quantitative kinetic constants, yet some results for CYP3A4 and CYP2C19 agreed qualitatively with preferred reaction steps and pathways. Clinical data on drug interactions support the CYP3A4 role in terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and mitigating the impact of P450 variations in toxic risks posed to patients.

Full text of DOI:10.1021/acs.chemrestox.9b00006

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Article
CYP2C19 and 3A4 dominate metabolic clearance and bioactivation
of terbinafine based on computational and experimental approaches
Mary Alexandra Davis, Dustyn A Barnette, Noah R FLynn, Anirudh S
Pidugu, S. Joshua Swamidass, Gunnar Boysen, and Grover Paul Miller
Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.9b00006 • Publication Date (Web): 29 Mar 2019
Downloaded from http://pubs.acs.org on March 31, 2019
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CYP2C19 and 3A4 dominate metabolic clearance and bioactivation of terbinafine based on
computational and experimental approaches
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Mary A. Davis1, Dustyn A. Barnette1, Noah R. Flynn2, Anirudh S. Pidugu3, S. Joshua Swamidass2,
Gunnar Boysen4, Grover P. Miller1*
1Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little
Rock, AR, 72205; 2Department of Pathology and Immunology, Washington University, St. Louis, MO,
63130; 3Department of Neuroscience and Behavioral Biology, Emory University, Atlanta, GA 30322;
4Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences,
Little Rock, AR, 72205
*To whom correspondence should be addressed: Grover P. Miller, PhD, Department of Biochemistry and
Molecular Biology, University of Arkansas for Medical Sciences, 4301 W. Markham, Slot 516, Little
Rock, AR 72205, USA; Telephone: 501.526.6486; Fax: 501.686.8169; Email: MillerGroverP@uams.edu;
ORCID 0000-0002-4743-8187
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TOC IMAGE
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ABSTRACT
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Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic
hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-
ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using
in vitro studies with human liver microsomes and modeling approaches, yet knowledge of individual
enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational
tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor
phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways.
CYP2C19 and 3A4 contributed to all pathways and so, we targeted them for steady-state analyses with
recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4
similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps
leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for
CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions.
Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine
bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate
to quantitative kinetic constants, yet results for CYP3A4, but not CYP2C19, agreed qualitatively with
preferred reaction steps and pathways. Clinical data on drug interactions support CYP3A4 role in
terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s
responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and
mitigating the impact of P450 variations in toxic risks posed to patients.
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INTRODUCTION
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Lamisil (terbinafine) is an allylamine antifungal agent used for treatment of onychomycosis 1. Patient
dosing leads to drug accumulation in adipose tissue and keratin-containing tissues such as dermis, epidermis,
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and nails in humans . Terbinafine action occurs via inhibition of squalene epoxidase to disrupt cell
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membrane function and cell wall synthesis leading to eventual death of the fungus 1,3. Terbinafine side
effects are often minor and include mild gastrointestinal, skin, and taste disturbances 4–6. Terbinafine has
been found to interact with cyclosporine, cimetidine, terfenadine, and caffeine, yet drug-drug interactions
are typically minor and often asymptomatic with the exception of exceedingly rare cases of major
interactions with perphenazine, amitriptyline, and theophylline often in already medically-compromised
patients 2,7. Given these qualities, terbinafine has proven to be much safer and more effective than other
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drugs prescribed for nail fungal infections such as itraconazole, fluconazole, and griseofulvin . In 2010,
1.5 million prescriptions for terbinafine were written in the United States indicating its wide prevalence in
use 9.
Despite favorable pharmacological traits, terbinafine use can compromise hepatobiliary function
leading to idiosyncratic liver toxicity. This side effect is often asymptomatic and reversible. However, one
in every forty-five thousand patients taking terbinafine develop severe symptomatic cholestatic injury that
requires liver transplant or causes death 10. Periodic blood tests must be administered to patients prescribed
terbinafine to ensure proper liver function and to reduce the number of severe drug reactions. Even after a
compromised patient ceases therapy, terbinafine can remain at therapeutic levels in the body for up to a
month due to bioaccumulation 11. This intervention strategy only minimizes liver damage severity 12.
Insights on the possible cause for toxicity by formation of a reactive metabolite, TBF-A, were proposed
by Iverson and Uetrecht 10,12. We identified metabolic pathways leading to and competing with TBF-A
formation through a combination of computational and experimental modeling of in vitro hepatic N-
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dealkylation of terbinafine . As a tertiary amine, three N-dealkylation pathways for terbinafine lead to
TBF-A formation (Fig. 1). Pathway 1 (red) is a direct formation of N-methyl-1-naphthyl-methylamine and
TBF-A from terbinafine. Pathway 2 (blue) involves N-demethylation of terbinafine to desmethyl-
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terbinafine and subsequent metabolism to TBF-A. Pathway 3 (green) involves metabolism of terbinafine
to 1-naphthaldehyde and N-methyl-6,6-dimethyl-2-hepten-4-yn-1-amine and consecutive metabolism to
TBF-A 13.
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In our study, we combined computational modeling and experimental in vitro studies to assess
competing N-dealkylation pathways of terbinafine leading to formation of TBF-A. First, a deep learning
model of N-dealkylation predicted a high probability for N-demethylation (Pathway 2) to yield desmethyl-
terbinafine followed by N-dealkylation to TBF-A and marginal contributions from other possible pathways.
Second, 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 (Pathway 2) and insignificance of N-denaphthylation (Pathway 3) in
terbinafine metabolism yet differed on importance of direct TBF-A formation (Pathway 1). Based on
experimental steady-state conditions, the direct pathway (Pathway 1) was the most important source of the
reactive metabolite. In the clinic, therapeutic dosing leads to accumulation of desmethyl-terbinafine due to
poor clearance so likely sources for TBF-A would draw from metabolism of both the major metabolite and
parent drug based on our modeling and experimental studies 14. Vickers et al. reported several cytochromes
P450 were responsible for those pathways, yet their roles on individual pathways remain unknown 15.
In this study, we assessed the metabolic kinetics for the most dominant P450 isozymes in the metabolic
clearance and bioactivation of terbinafine. Initially, we carried out inhibitor phenotyping of terbinafine
metabolism by pooled human liver microsomes to reveal the possible relative significance of nine P450
isozymes generating metabolites from all three N-dealkylation pathways. Those efforts revealed CYP2C19
and 3A4 were critical determinants in terbinafine metabolism, and thus we measured their steady-state
kinetic mechanisms and constants for N-dealkylation pathways using recombinant P450 Supersomes. The
interpretation of those results required further study to account for deviations from the expected 1:1 ratio
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of co-metabolites N-methyl-1-naphthyl-methylamine and TBF-A and to explain unexpectedly efficient
formation of naphthaldehyde by the recombinant enzymes relative to human liver microsomes. Given this
complexity, computational models may provide a more rapid approach to assessing the relative importance
of P450 isozymes in terbinafine metabolism. As a complement to our experimental studies, we investigated
the ability of our individual P450 isozyme models to predict the probability of competing pathways
involved in metabolic clearance and bioactivation of terbinafine 16. We then compared the findings from
both approaches and discussed their meaning in the context of terbinafine metabolism and bioactivation.
EXPERIMENTAL PROCEDURES
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Materials
All chemical solvents were purchased from Thermo Fisher Scientific (Waltham, MA). Substrate
terbinafine hydrochloride and metabolites N-desmethyl-terbinafine hydrochloride, N-methyl-1-naphthyl
methylamine hydrochloride, 1-naphthyl methylamine, naphthoic acid, and 1-napthaldehyde were purchased
from Millipore-Sigma (Burlington, MA), while N-desmethyl-terbinafine hydrochloride and E-6,6-
dimethylhept-2-ene-4-ynal (TBF-A) were obtained from Toronto Research Chemicals (Toronto, ON,
Canada). Labeling agents dansyl chloride and dansyl hydrazine and internal standards fluoxetine
hydrochloride and dimethyl benzaldehyde were purchased from Millipore-Sigma. Isozyme inhibitors
alpha-naphthoflavone, (+)-N-3-benzylnirvanol, ketoconazole, 4-methylpyrazole hydrochloride,
montelukast sodium, quinidine, sulfaphenazole, ticlopidine hydrochloride, and tranylcypromine sulfate
were also purchased from Millipore-Sigma. Lastly, recombinant Supersomes CYP2C19 and 3A4 were
purchased from Corning (Woburn, MA).
P450 isozyme inhibitor phenotyping assays
We carried out inhibitor phenotyping experiments to qualitatively identify probable P450 isozymes
catalyzing terbinafine metabolic pathways present in our original pooled human liver microsomes studies
13. Reactions contained 0.1 mg/mL protein (HLM150), 200 μM terbinafine or desmethyl-terbinafine,
specific P450 inhibitors, and 2.5% (final) methanol co-solvent due to solubility limits of the substrate in 50
mM potassium phosphate buffer pH 7.4. Choice and concentration of inhibitors were based on existing
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literature demonstrating selective bias, if not exclusivity, in inhibiting specific P450 isozymes. For each
experiment, we included the following as final concentrations: 16 μM α-naphthoflavone (ANF) for
CYP1A2 17, 2 μM tranylcypromine (TCP) for CYP2A6, 3 μM ticlopidine (TIC) for CYP2B6 18,19, 16 μM
montelukast (MTK) for CYP2C8 20, 10 μM sulfaphenazole (SPA) for CYP2C9, 16 μM (+)-N-3-
benzylnirvanol (BZV) for CYP2C19, 2 μM quinidine (QND) for CYP2D6, 30 μM 4-methylpyrazole (4MP)
for CYP2E1, and 1 μM ketoconazole (KCZ) for CYP3A4 18,19,21. Each inhibitor solution was prepared in
potassium phosphate buffer pH 7.4 with 1% methanol (final) as a co-solvent, so that the contribution of
methanol from the inhibitor solution was insignificant (<0.1%).
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After 15 min pre-incubation with shaking at 37°C, reactions were initiated by addition of an NADPH
regenerating system (2 μU/μL 1-glucose-6 phosphate dehydrogenase, 10 mM glucose 6-phosphate, 2 mM
MgCl2, 500 μM NADP+) and shaken at 37°C. Reactions lacking the NADPH regenerating system served
as negative controls for metabolite background signals. After a 20 min, reactions were quenched with an
eight-fold volume of an ice-cold acetonitrile containing 200 µM fluoxetine and 0.5 µM dimethyl
benzaldehyde (internal amine and aldehyde standards) and incubated on ice for 5 min to promote protein
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and phosphate buffer precipitation . Quenched reactions were centrifuged for 15 min at 4°C and 2500
RPM (2800G) using a Beckman GPR centrifuge. The supernatant was transferred to a microplate and
labeled with dansyl hydrazine or dansyl chloride to improve metabolite stability and detectability as
described previously by our group 13. Once complete, labeling reactions were evaporated to dryness and
resuspended in mobile phase for UHPLC-MS analysis.
Each set of reactions was conducted in triplicate and repeated two to four times. After correcting for
the background, the resultant values were normalized to reaction rates observed in absence of inhibitors to
yield a percent inhibition value. Statistically different normalized rates due to the presence of P450-specific
inhibitors were determined by the Mann-Whitney test (p-value = 0.05) using GraphPad Prism 7.0 from
GraphPad Software, Inc. (San Diego, CA).
Steady-state terbinafine kinetics by recombinant CYP2C19 and 3A4
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Based on inhibitor phenotyping results, CYP2C19 and 3A4 were likely important contributors to
generation of reactive metabolites and overall clearance in all three N-dealkylation pathways and thus, we
determined the kinetic mechanisms and constants for recombinant forms of the isozymes. The experimental
design for the recombinant enzyme reactions were similar to those used for HLM studies. Reactions
initially contained 100 nM P450 Supersomes, terbinafine ranging from 3.125 to 500 µM, and 2.5%
methanol (final) co-solvent due to solubility limits of terbinafine in 50 mM potassium phosphate buffer pH
7.4. After 15 min pre-incubation with shaking, reactions were initiated upon addition of an NADPH
regenerating system and shaken. After 30 min, reactions were quenched, processed, and analyzed by
UHPLC-MS as previously described. Reactions lacking the NADPH regenerating system were prepared
and used as negative controls. All steady-state reactions were performed in triplicate and each set was
replicated two to four times. After correcting for the background, initial rates were calculated and plotted
against substrate concentration. The data in resultant kinetic profile was then fit to the Michaelis-Menten
and Hill equations and the most statistically probable mechanism determined by the extra sum-of-squares
F test to yield the best-fit kinetic constants using GraphPad Prism 7.0 from GraphPad Software, Inc (San
Diego, CA). Similar steady-state studies were carried out for secondary metabolites N-methyl-1-naphthyl
methylamine and desmethyl-terbinafine to aid in the interpretation of results for terbinafine metabolism by
CYP2C19 and 3A4.
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UHPLC-MS analysis of metabolic reactions
For reaction analyses, we compared and relied on co-elution with standards, fragmentation patterns,
and mass spectral response for LC-MS analysis to identify, resolve and quantitate analytes based on mass
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to charge ratio (m/z) and co-elution with authentic standards as described previously by our group
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Reaction metabolites were separated using a Cortecs C-18 2.7 µm column (4.6 x 50 mm) on a Waters
Acquity Arc UHPLC system connected to a Waters Acquity QDa single quadrupole MS system with a 20V
cone voltage setting to detect masses from 150 to 650 in positive ion mode (Milford, MA). Total flow rate
was 0.5 mL/min and run time was 15 min. The mobile phase comprised of two solvents: ultrapure water
with 0.01% formic acid (solvent A) and acetonitrile with 0.01% formic acid (solvent B). The gradient
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method began at 65% solvent A for 1 min, decreased to 20% over 5 min, and then maintained at 20% for 2
min. Solvent A was increased back to 65% over 1 min and was maintained for remainder of run. Peak
areas of analytes were normalized to internal standards and when available, quantitated relative to authentic
metabolite standards after correction with negative control reactions lacking NADPH. Serial dilution
standard curves were used for quantitation of analytes.
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A second set of 500 µM terbinafine reactions by CYP2C19 and CYP3A4 were conducted to confirm
the identity of TBF-A by LC-MS/MS. Samples were prepared as previously described and injected onto
an Agilent Technologies 1290 Infinity UHPLC. Analytes were then resolved with an InfinityLab Poroshell
120 EC-C18 column (2.1 x 150 mm, 2.7 Micron). The mobile phase (solvents A and B) was the same from
the previously described UHPLC-MS method, but at a flow of 0.35 mL/min and using different gradient.
The gradient began at 40% solvent B for 1 min, increased to 100% solvent B over 9 min, and was then held
at 100% B for 1 min before returning to 40% solvent B over a 1-min period. Analytes were scanned with
an Agilent Technologic 6490 Triple Quad MS. The ESI source was in positive ion mode and ion spectra
were acquired in full ion mode monitoring an m/z range of 50-1000 Da. Product ion spectra were produced
by collision-induced dissociation at 20 eV from the theoretical precursor m/z 384 Da.
Assessment of competing N-dealkylation reactions during terbinafine metabolism
We previously developed studies and further elaborate here a novel strategy to determine qualitatively
preferences for specific metabolic pathways relative to one another based on either experimental kinetic
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studies or modeling outputs for CYP2C19 and 3A4 . Like metabolic flux studies 23–25, we calculated a
metabolic split ratio using reaction rates at each nodal point of a metabolic pathway to describe the
partitioning of metabolites. For experimental data, the Michaelis-Menten equation describes the observed
turnover rate for the reaction. In our case, we limited conditions to a low substrate concentration, so that
the observed rate simplifies to Vmax[S]/Km, wherein S denotes substrate. This condition is reasonable for
terbinafine given its reported low plasma levels (1 mg/L) and high (>99%) protein binding 26,27. We then
calculated metabolic split ratios by dividing the rate of the reaction by the sum of all rates at that nodal
point in the pathway. Substrate concentration is the same for the reactions and thus factors out of the
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equation making it independent of concentration. The metabolic split ratio is then a ratio of the catalytic
efficiency of each reaction over the sum of catalytic efficiencies for all reactions. This calculation holds
true as long as substrate concentration is low enough to make observed rates linearly dependent on
substrate concentration. By contrast, model predictions are not rates, and there is no evidence that they
correlate with reaction rates. The model outputs reflect the likelihood for a reaction to occur and, so we
used the values to calculate analogous model split ratios for competing reactions. In this case, the ratio for
a reaction is the corresponding prediction divided by the sum of all reaction predictions for the substrate of
interest. We then multiplied the metabolic split ratios from experimental data or model split ratios from
modeling data for each pathway to yield the fraction of the parent drug leading to formation of a specific
metabolite like TBF-A. This fractional yield for a common metabolite through different pathways will not
sum to 1.0 due to pathway branches and will only reflect pathways of interest, such as N-dealkylations.
Nevertheless, through this approach we were able to directly compare the relative significance of single
and multi-step pathways toward formation of a common metabolite as well as the performance of the model
predictions against experimental studies.
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RESULTS
CYP2C19 and 3A4 catalyzed all N-dealkylation pathways based on microsomal inhibitor phenotyping
Inhibitor phenotyping experiments suggested that multiple P450 isozymes catalyzed N-dealkylation in
all three pathways, though metabolism was seemingly less specific in Pathway 3, as evidenced by lower
degrees of inhibition following introduction of single P450-specific inhibitors (Fig. 1). TBF-A and N-
methyl-1-naphthyl methylamine are co-metabolites in Pathway 1 and their formation generally yielded
similar inhibition patterns via possible major catalysts CYP1A2, 2C9, 2C19, 2D6 and 3A4 (Fig. 2, Panels
A and B, respectively). N-Demethylation of terbinafine in Pathway 2 was likely catalyzed by CYP1A2,
2C8, 2C9, 2C19, 2D6 and 3A4 with minor contributions from 2B6 (Fig. 2, Panel C). Pathway 3 was the
least affected by the inhibitors with only CYP2A6, 2C19 and 3A4 implicated in formation of 1-
naphthaldehyde during terbinafine metabolism (Fig. 2, Panel D). Given the weak inhibition, we combined
inhibitors for CYP2C19 and 3A4 to demonstrate additivity and thus confirm their collective contribution
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to this reaction step. Formation of terbinafine dihydrodiols is a multi-step process that relied on
contributions from several enzymes including CYP1A2, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, and 3A4
although their roles in specific metabolic steps remain unclear (Fig. S1, Panel A). Comparatively fewer
enzymes, namely CYP2A6, 2B6, 2C19, and 2E1, generated hydroxyterbinafine (Fig. S2, Panel B). As a
major in vivo metabolite, we extended phenotyping studies to desmethyl-terbinafine metabolism and
revealed that loss of the methyl group decreased the number of P450 isozymes involved in metabolism.
CYP2A6, 2B6, 2C8, 2C19 and 3A4 catalyzed TBF-A formation from desmethyl-terbinafine in Step 2.2a,
while CYP1A2, 2C9, 2D6, and 2E1 did not (Fig S2, Panel A). N-denaphthylation (Pathway 3) was again
carried out only by CYP2C19 and 3A4 (Fig. S2, Panel B).
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Though not the primary focus of this work, formation of desmethyl-terbinafine dihydrodiols and
hydroxydesmethyl-terbinafine was also measured (Fig. S2). Although effects were limited to <25%
inhibition, studies demonstrated CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4 generated desmethyl-
terbinafine dihydrodiols. Production of hydroxydesmethyl-terbinafine was catalyzed by CYP1A2, 2A6,
2B6, 2C8, 2C19, and 3A4. Compared to terbinafine phenotyping results, loss of the N-methyl group
decreased enzymatic specificity in formation of both hydroxy- and dihydrodiol oxidative products (Fig. S2,
Panels C and D). Based on inhibitor phenotyping, CYP2C19 and 3A4 contributed to all three N-
dealkylation pathways and thus likely play important roles in formation of TBF-A as well as overall
clearance of terbinafine.
N-Dealkylation in Pathway 1 was similarly catalyzed by CYP2C19 and 3A4 Supersomal systems
To further assess responsible P450s in terbinafine metabolism to TBF-A, we utilized recombinant P450
Supersomes to conduct steady-state kinetic studies and elucidate individual P450 catalytic efficiencies. For
Pathway 1, TBF-A and N-methyl-1-napthyl methylamine are formed at a 1:1 ratio, yet the decay of TBF-
A during the course of the reaction makes the results for N-methyl-1-napthyl methylamine a more reliable
reflection of the kinetics for this pathway 13. CYP2C19 and CYP3A4 shared similar Vmax and Km values
for N-methyl-1-napthyl methylamine; however, slight differences led to CYP3A4 catalyzing the reaction
with an almost two-fold higher efficiency (Vmax/Km) (Fig. 3, Panel A; Tables 1 and 2). Secondarily,
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reactions were conducted similarly for N-methyl-1-naphthyl methylamine metabolism to 1-naphthaldehyde
as a measure of Pathway 1.2. CYP3A4 was found to be four-fold more catalytically efficient than CYP2C19
(Fig. 3, Panel B).
9
Unlike N-methyl-1-napthyl methylamine, the TBF-A co-metabolite kinetics yielded similar Km but not
Vmax values among data sets for each P450 isozyme, which implied further unreliability of direct TBF-A
quantification as evidenced previously 13. This problem was only observed for Supersomes and not human
liver microsomes, and so we investigated the impact of enzyme source on observed TBF-A levels. We
conducted time-course decay experiments for 50 µM TBF-A in HLM, control Supersomes, and isozyme
Supersomes. When compared to controls, TBF-A decay was 13% and 55% faster in HLM when normalized
per mg/protein and compared to CYP2C19 and CYP3A4 Supersomes, respectively. The end-point plateau
of CYP2C19 was roughly two-fold higher than end-point plateaus for both HLM and control Supersomes
(Fig. S3, Panels A and B). The end-point plateau for CYP3A4 was nearly four-fold higher than that of
HLM but was not statistically different to control Supersomes. These differences were statistically analyzed
and confirmed as significant via comparison of plateau and half-life 95% confidence intervals for each
enzymatic environment. The findings suggest higher TBF-A apparent rates of formation during the reaction
would be more likely observed with Supersomes versus microsomes.
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Alternatively, parent m/z may not directly correlate with actual co-metabolite levels. As a test, we
carried out an activity assay for each isozyme using 500 μM terbinafine and assessed observed initial rates
based on either parent m/z or characteristic mass transition for loss of the dansyl group (236 Da) by UHPLC-
MS/MS. The observed rates for both isozyme reactions showed significant variability in responses for
TBF-A from different LC-MS instruments and choice of spectral responses (fragment m/z versus the parent
m/z) (Fig. S4, Panels A and B). The UHPLC-MS instrument yielded four-fold higher responses when
compared to the UHPLC-MS/MS. In the latter case, the parent to fragment response was 14-fold higher
for the CYP2C19 reaction while only 11-fold higher for the CYP3A4 reaction. By contrast, instrument
choice or spectral response did not impact observed rates for N-methyl-1-naphthyl methylamine. Moreover,
there was little to no background signal for N-methyl-1-naphthyl methylamine in all reactions based on
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similarity in signal responses (Fig. 4S, Panels C and D). Though decay of TBF-A and instrument choice
seemed to play a role in detection of the metabolite, we were unable to resolve why its quantitation was
highly variable compared to its co-metabolite. Taken together, kinetic data based on N-methyl-1-naphthyl
methylamine would be a more accurate indicator of kinetics for Pathway 1.
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CYP2C19 was more efficient than CYP3A4 at all reaction steps in Pathway 2
TBF-A formation through Pathway 2 involves a two-step process whereby CYP2C19 predominated
over CYP3A4. CYP2C19 was eight-fold more efficient than CYP3A4 at N-demethylation of terbinafine
due to a combination of a higher Vmax and lower Km. (Fig. 4, Panels A; Tables 1 and 2). Loss of the parent
drug N-methyl group significantly impacted the relative kinetics of the reactions for each P450 isozyme
and between them (Fig. 4, Panels B and C; Tables 1 and 2). N-Denaphthylation of desmethyl-terbinafine
by CYP2C19 demonstrated a nearly three-fold higher rate of turnover (Vmax) than for the parent drug. For
CYP3A4, the desmethyl-terbinafine Vmax was about two-fold less than the reaction for terbinafine. The
CYP2C19 Km for desmethyl-terbinafine was relatively low but still about five-fold higher than the value
observed for terbinafine N-denaphthylation (Pathway 3). The loss of the N-methyl group led to a lower
affinity for desmethyl-terbinafine based on a three-fold higher Km than that for terbinafine. Collectively,
desmethyl-terbinafine kinetics yielding 1-napthaldehyde showed that CYP2C19 and CYP3A4 were
similarly efficient as determined by catalytic efficiency ratios (Vmax/Km). As previously shown in
terbinafine reactions, TBF-A kinetic profiles were not reliable when compared to formation of its co-
metabolite, i.e. 1-naphthyl methylamine. The efficiency of 1-naphthyl methylamine formation in Pathway
2.2a by CYP2C19 was about three-fold higher than that for 3A4, while CYP3A4 and CYP2C19 efficiencies
for 1-naphthaldehyde generation via Pathway 2.2b were comparable. Taken together, CYP2C19 more
efficiently catalyzes the first step in the pathway while CYP2C19 and 3A4 compete almost equally for
generation of TBF-A as measured by 1-naphthyl methylamine kinetics or 1-naphthaldehyde by CYP3A4.
N-Denaphthylation for Pathway 3 was surprisingly efficient for CYP2C19 and 3A4
Unlike microsomal metabolism of terbinafine 13, 1-naphthaldehyde was a major metabolite generated
by both P450 isozymes in Pathway 3 (Fig. 5). CYP2C19 demonstrated a moderate Vmax for this N-
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dealkylation (Pathway 1) step, but the lowest Km in this study resulting in the most efficient reaction. By
comparison, CYP3A4 catalyzed N-denaphthylation (Pathway 3) with a greater than ten-fold rate of turnover.
Nevertheless, when combined with a higher Km value, the efficiency (Vmax/Km) was three-fold less than that
observed for CYP2C19. The kinetics indicate Pathway 3 as one of the most efficient pathways catalyzed
by these P450 isozymes. The resulting metabolite N-methyl-6,6-dimethylhept-2-ene-4-ynal is not
commercially available and thus, we were not able to determine kinetics for the secondary reaction steps in
Pathway 3 yielding TBF-A. In fact, CYP2C19 and 3A4 efficiencies for N-denaphthylation were much
higher than that observed for human liver microsomes in which Pathway 3 was the least efficient among
the three 13.
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We then carried out further studies to validate that the high efficiencies for 1-naphthaldehyde formation
were not a reflection of secondary metabolic processes despite the use of steady-state conditions. Human
liver microsomes may selectively metabolize 1-naphthaldehyde to decrease its apparent kinetics so that
they are underestimated relative to those for Supersomes. The incubation of 50 µM 1-naphthaldehyde under
reaction conditions lead to a steady rate decay or disappearance, but it was not significantly affected by
active or heat-inactivated human liver microsomes or the presence or absence of NADPH, suggesting
possible limited evaporative loss for microsomal reactions (Fig. S5). We then compared depletion of 10
and 50 µM 1-naphthaldehyde in the presence of NADPH with either human liver microsomes or
Supersomes for CYP2C19 and 3A4 and found that Supersome reactions led to a more significant decrease
in 1-naphthaldehyde after 30 min (Fig. S6). Supersome-mediated decay of 1-naphthaldehyde could be
inhibited by terbinafine (Fig. S7) and required NADPH as well as active (not heat-treated) enzyme
preparations (Fig. S8). Despite evidence for 1-naphthaldehyde metabolism, analysis of all reactions by
HPLC-MS in positive and negative ion mode did not yield detectable levels of 1-naphthol, 1-naphthoic
acid, or the dihydrodiol of 1-naphthaldehyde as possible reaction metabolites. Rather than human liver
microsomes, Supersomes were more prone to inducing 1-naphthaldehyde decay, which likely involved an
NADPH-dependent enzyme. This observation is apparently due to preparatory differences between the
two-enzymatic systems, but could not be resolved in this study. For steady-state studies, 1-naphthaldehyde
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decay would have minimal effect due to reaction inefficiency and ability of terbinafine to outcompete the
aldehyde during metabolism. Moreover, results gained from microsomal studies are more relevant in
apparent formation rates of 1-naphthaldehyde, given that microsomal systems are more reflective of liver
metabolism.
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In addition, we explored whether alternate secondary metabolic pathways yielding 1-naphthaldehyde
may contribute to the apparent kinetics of Supersomes but not microsomes, leading to overestimation of
Pathway 3 kinetics. We determined the N-denaphthylation steady-state kinetics for N-methyl-1-naphthyl
methylamine in Pathway 1 and desmethyl-terbinafine in Pathway 2 for CYP2C19 and 3A4 (Fig. 3, Panel
B and Fig. 4, Panel C; Tables 1 and 2), yet the reactions were too inefficient to contribute to the apparent
kinetics for 1-naphthaldehyde from terbinafine metabolism. Taken together, the more efficient 1-
naphthaldehyde kinetics for CYP2C19 and 3A4 versus human liver microsomes are not due to loss of the
metabolite in the microsomal reactions or formation from alternate pathways in Supersome reactions.
Deep learning models predicted N-dealkylations of terbinafine by CYP2C19 and 3A4
As an alternate, more rapid approach to gaining insights on metabolism, we explored the potential for
our CYP2C19 and 3A4 models to predict N-dealkylation pathways (Fig. 6). These models are freely
available for use at http://swami.wustl.edu/xenosite/. For each P450 isozyme, the model predicts molecular
sites-of-metabolism established with a test set of 680 known P450 substrates across nine isozymes and
assigns each atom a score from 0 to 1.0. Based upon a cut-off score of 0.5, the model has an average correct
compound bioactivation output of 87% across the incorporated P450 isozymes and human liver microsomes.
This prediction is not a probability but an unscaled likelihood for reaction occurrence 16. Generally, model
bioactivation likelihoods were higher at less sterically hindered atoms and at atoms further from electron-
withdrawing groups, which agrees with established trends of chemical reactivity (Fig. 6). A statistical
analysis of significant differences among values was then not possible; rather, variations in predictions
provide a qualitative analysis of patterns favoring reaction steps relative to one another for the two P450
isozymes (Fig. 7). For CYP2C19, the likelihood for direct TBF-A formation in Pathway 1 was very low
compared to the first step in the other two pathways subsequently leading to the reactive metabolite. Despite
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relative ease in N-demethylation, there was no improvement in the possibility of TBF-A formation for
Pathway 2. By contrast, the loss of 1-naphthaldehyde in Pathway 3 did increase the likelihood for
generation of TBF-A during metabolism. This pattern for the metabolic pathway predictions was
essentially identical for CYP3A4. Taken together, models for CYP2C19 and 3A4 predicted similar
metabolic patterns for each reaction by the isozymes in contrast to the experimental data.
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Catalytic capacities of CYP2C19 and 3A4 favored different pathways to TBF-A
We calculated metabolic split ratios from experimental data and leveraged them to determine the
fraction of the parent drug leading to formation of TBF-A at low terbinafine levels (Fig. 7). For the
experimental studies, we were limited to determining the fractional yields of TBF-A through Pathways 1
and 2 due to the inability to measure kinetics for the second step in Pathway 3. For CYP2C19, direct
formation of TBF-A in Pathway 1 was more than two-fold more productive than Pathway 2 despite a highly
efficient first step in that pathway. By contrast, CYP3A4 was three-fold more effective at generating TBF-
A from Pathway 2 than Pathway 1. Moreover, CYP3A4 was less efficient at pathways competing with
formation of TBF-A such that the isozyme yielded higher fractional conversions of terbinafine to TBF-A
than CYP2C19 for Pathways 1 and 2. A potential caveat for this outcome is the impact of reaction
conditions on CYP2C19 activity; the relatively high level of methanol necessary for solubilizing terbinafine
(2.5% final) may have suppressed CYP2C19 activity relative to that for CYP3A4 28.
Although not rates, model predictions reflected the relative likelihood for the reaction to occur and thus,
may be amenable to a similar analysis using model split ratios to assess the relative preference for
competing pathways. Unlike experimental studies, modeling studies were able to predict fractional TBF-
A yields from terbinafine for all three N-dealkylation pathways. For CYP2C19, there was no difference in
the TBF-A yield between Pathways 1 and 2, while Pathway 3 was two-fold more favorable for TBF-A
formation than the other pathways. The CYP3A4 model predicted similar amounts of TBF-A generated
through Pathways 1 and 2 but a six-fold higher preference for TBF-A from Pathway 3. Taken together, the
models did not predict the isozyme-specific differences in TBF-A formation for Pathways 1 and 2 based
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on experimental data, yet they provided an intriguing, but experimentally untestable prediction that Pathway
3 is the most productive route to formation of the reactive metabolite.
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Recombinant CYP2C19 and 3A4 catalyze oxidative non-dealkylation reactions
9
Although not the focus on this study, we were able to gain information on the role of CYP2C19 and
3A4 in other oxidation pathways, namely, formation of hydroxyterbinafine and a pair of dihydrodiols (Fig.
1) through experimental and computational studies. Nevertheless, the lack of standards for quantitation
permitted only the identification of the metabolic mechanism, insights on binding interactions, and relative
Vmax values and efficiencies for the reactions. CYP2C19 and 3A4 oxidation of the naphthyl group or alkyl
terminus for terbinafine led to cooperative mechanisms (Fig. S9, Panels A through D, Fig. S10; Tables S2
and S3). CYP2C19 formation of the dihydrodiols involved negative cooperativity while that of
hydroxyterbinafine involved positive cooperativity. By contrast CYP3A4 demonstrated positive
cooperativity for both terbinafine reactions. For both isozymes, loss of the methyl group on the substrate
favored the Michaelis-Menten kinetic mechanism for all oxidative non-dealkylations of desmethyl-
terbinafine. The CYP2C19 and 3A4 affinities for the parent drug and major metabolite based on Km were
relatively moderate (< ~100 μM). Formation of the dihydrodiols involves oxidation at two sites on the
naphthyl moiety and based on the CYP2C19 and 3A4 models, the likelihood for those reactions were very
low (mostly < 0.1 with some <0.29). By contrast, the CYP2C19 model predicted a high likelihood for
oxidation of one of the methyl groups (> 0.72) while the prediction of CYP3A4 was very low (< 0.08).
Collectively, these data provided an opportunity to assess structure-function relationships for the two
isozymes from experimental data and model performance for those findings.
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Both isozymes oxidized the parent drug and major metabolite at the alkyl terminus and the naphthyl
moiety, suggesting terbinafine and desmethyl-terbinafine bind in two opposing orientations relative to the
active heme. The CYP2C19 Vmax was slightly lower in formation of the terbinafine dihydrodiols and S50
was greater than five-fold higher when compared to that for CYP3A4. Loss of the methyl group led to an
approximately ten-fold higher Vmax and slightly lower Km for CYP2C19 formation of the desmethyl-
terbinafine dihydrodiols. The same trends for the relative kinetic constants between the isozymes were also
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observed for oxidation of the alkyl termini for the substrates. These findings indicate that (1) CYP2C19
was less efficient at oxidation of the naphthyl moiety than 3A4 and (2) CYP3A4 substrate binding
significantly was lost upon removal of the N-methyl group, which was not the case for CYP2C19. By
contrast, the models did not predict either of these differences in metabolism between the isozymes.
DISCUSSION
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Inference of TBF-A kinetics was made through its amine co-metabolites in reactions
Our dansyl hydrazine and dansyl chloride labeling methods for aldehyde and amine metabolites
provided a powerful approach to identify and track formation of the broadest array of terbinafine
13
metabolites to date . TBF-A apparent rates were measurable but not a reliable indicator of its actual
formation during metabolism of terbinafine or desmethyl-terbinafine as reported previously 13. The reactive
metabolite rapidly decays over time; however, we observed differences in the rate and end-point of decay
among human liver microsomes and the Supersomes. The type of enzyme preparation then impacted decay
suggesting proteins present stabilize TBF-A, yielding a measurable, but relatively minor effect in these
studies. By contrast, we showed that the co-metabolites of TBF-A reactions, i.e. N-methyl-1-naphthyl-
methylamine and 1-naphthyl-methylamine in Steps 1.1 and 2.2a, were stable, reproducibly measurable
metabolites for those reactions. In fact, we are the first to report the kinetics for 1-naphthyl-methylamine,
13
because low yields and high variability precluded its measurement in human liver microsomes
.
Consequently, we were able to infer TBF-A kinetics through the use of its amine co-metabolites formed in
the same reaction and could make direct comparisons of alternate reaction steps leading to formation of the
reactive metabolite.
P450 isozymes were promiscuous in reactions toward terbinafine
The potential importance of P450 isozymes in terbinafine metabolism was revealed through inhibitor
phenotyping experiments with pooled human liver microsomes. Utilization of inhibitor phenotyping is
limited to qualitative assessment and cannot accurately represent the importance of individual P450s in
metabolism. These limitations are due in part to the capability of individual P450 isozymes to compensate
for the inactivation of other isozymes. Additionally, chemical inhibitors of individual isozymes were
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selected due to their relative specificity to a singular enzyme, but off-target effects can influence the activity
of other isozymes in human liver microsomes. As indicated by others 15, nearly all P450 isozymes
metabolize terbinafine; however, we report a breakdown of the specificity of each isozyme for individual
pathways and their potential level of contribution to them. Six of the nine tested P450 isozymes could N-
dealkylate terbinafine into TBF-A and N-methyl-1-naphthyl-methylamine in Pathway 1 and most of those
catalyzed the N-demethylation of terbinafine in Pathway 2. These isozymes were similarly responsible for
downstream generation of TBF-A from desmethyl-terbinafine in Pathway 2 indicating that loss of the
methyl group does not impact specificity for the reaction among the isozymes. There was higher selectivity
for oxidative removal of the sterically larger N-naphthyl group, as evidenced by CYP2A6, 2C19 and 3A4
involvement in the reaction. For the oxidative non-dealkylations, eight P450 isozymes likely participated
in formation of dihydrodiol metabolites, while hydroxylation of the heptanyl chain terminus involved only
1A2 and CYP2 family members. The metabolic promiscuity of P450 isozymes for these reactions is
reasonable given the diversity of structural elements comprising terbinafine. The drug contains small and
large alkyl groups, a large planar biphenyl moiety and a positive charge – all of which provide effective
elements for the isozymes to bind and target for metabolism. Nevertheless, the capacity to drive these
reactions ultimately determines their relevance in generation of the reactive metabolite TBF-A and
clearance of the parent in general.
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CYP2C19 was most efficient, but CYP3A4 more effective at pathways leading to TBF-A
Steady-state studies for CYP2C19 and 3A4 validated the qualitative inhibitor phenotyping results to
quantitatively reveal their importance in all three N-dealkylation pathways. Between the two P450
isozymes, CYP2C19 was the most efficient at all N-dealkylation reactions for terbinafine including direct
formation of TBF-A in Pathway 1. The efficiencies of N-dealkylations in Pathways 2 and 3 were similar
for CYP2C19 indicating a more accommodating active site to form catalytically competent substrate-bound
complexes than CYP3A4, which preferentially catalyzed only N-denaphthylation in Pathway 3. The most
significant difference in their catalytic capacities was CYP2C19 N-demethylation of terbinafine in Pathway
2 to yield desmethyl-terbinafine. The ten-fold higher efficiency in the reaction suggests that CYP2C19
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may be important in formation of this major plasma metabolite following multi-dosing for patients 14. Even
for desmethyl-terbinafine, CYP2C19 remained more efficient at TBF-A formation in Pathway 2 based on
1-naphthyl-methylamine kinetics. For Pathway 3, both P450 isozymes efficiently catalyzed N-
denaphthylation of terbinafine. The resulting N-methyl-6,6-dimethylhept-2-ene-4-ynal metabolite is not
commercially available which rendered us unable to determine the kinetics for its further metabolism to
generate TBF-A. Interestingly, both P450 isozymes exhibited a significant (~15-fold) decrease in N-
denaphthylation catalytic efficiencies from terbinafine to desmethyl-terbinafine. This effect derived mainly
from an increase in Km suggesting the methyl group is important in substrate binding for metabolism.
Overall, CYP2C19 and 3A4 demonstrated similar patterns in efficiencies yet CYP2C19 was consistently
more efficient at reaction steps leading to TBF-A formation. Consequently, lower efficiencies for reactions
competing with TBF-A formation led to a higher flux of terbinafine to the reactive metabolite by CYP3A4
when compared to CYP2C19.
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CYP3A4 model performance was better than that for CYP2C19
Unlike experimental approaches, metabolism models for CYP2C19 and 3A4 provided a rapid
assessment of all possible reaction steps for N-dealkylation pathways. Their application yielded outputs
scaled from 0 to 1.0 resembling a probability with three important caveats. First, the absence of a way to
statistically analyze the significance of differences in model outputs limited the findings to a qualitative
assessment of reaction possibilities. Second, model predictions are not scaled to kinetic constants (Vmax
,
Km, or Vmax/Km), so that a direct comparison of model performance relative to experimental data is not
possible. As a solution, we calculated model split ratios for estimating the preference for pathways leading
to TBF-A. Third, predictions from one model are not scaled to other models such that it is impossible to
directly compare the likelihood for a reaction to be carried out by one isozyme relative to another. Rather,
the magnitude of model predictions scales to the training set for reactions catalyzed and not catalyzed by
that specific P450 isozyme. In other words, higher predictions for CYP3A4 do not necessarily mean the
reaction is more likely to occur relative to CYP2C19 and vice versa. Nevertheless, the model outputs alone
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and in combination provided insights on the preference for reaction steps and subsequently pathways to
formation of TBF-A.
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Although limited to reactions in this study, use of the CYP2C19 and 3A4 models provided an
opportunity to assess their performance relative to experimental findings. Overall, the CYP3A4 model
yielded higher likelihoods for individual reactions than those predicted by the CYP2C19 model. This
outcome may reflect a sufficiently representative training set for CYP3A4 reactions and/or the known
higher substrate promiscuity reported for the isozyme 29. The general patterns for CYP2C19 and 3A4 model
outputs were similar, wherein N-demethylations (Pathway 2) and denaphthylations (Pathway 3) were much
more likely than N-dealkylations (Pathway 1) to yield TBF-A. This trend held true for the catalytic
efficiencies for the isozymes; however, the experimental data showed that CYP2C19 was much more
efficient at N-demethylation (Pathway 2) than CYP3A4 and comparable in N-denaphthylation. The models
further predicted the loss of the bulky naphthyl group increased the likelihood for generation of TBF-A in
Pathway 3. Unfortunately, we were not able to experimentally test the model prediction due to the lack of
commercially available N-methyl-6,6-dimethylhept-2-ene-4-ynal substrate. Although not rates, model
predictions yielded the likelihood for all reactions to occur for the three N-dealkylation pathways leading
to formation of TBF-A and hence, we leveraged the predictions to calculate model split ratios. Analysis of
the modeling results mirrored those for individual predictions in that there was no overall difference in
preference between Pathways 1 and 2. This observation was true for CYP3A4 based on fractional
contributions of the pathways using experimental data; however, that was not the case for CYP2C19 in
which Pathway 1 was more important than Pathway 2. Interestingly, the models predicted an intriguing,
but experimentally untestable prediction that Pathway 3 is the most productive route to formation of the
reactive metabolite. Taken together, model predictions seemed more in line with experimental findings for
CYP3A4 than that for 2C19. These differences may be due to (1) low model accuracy, (2) lack of model
scaling to kinetic constants, and/or (3) scaling differences between the P450 isozyme models. Further
application of the model to experimental data would likely lend insights into the weight of those possibilities
in model predictions and guide future iterations for their improvement.
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Highly efficient N-denaphthylation by CYP2C19 and 3A4 was not observed with microsomes
The CYP2C19 and 3A4 kinetics for N-dealkylation pathways shared many similarities to those for
human liver microsomes with a notable difference; the N-denaphthylation (Pathway 3) of terbinafine was
a minor pathway for the microsomal reaction 13, but a major one for both P450 isozymes. This surprising
finding led to a series of control studies to validate this observation. The high 1-naphthaldehyde formation
rates for CYP2C19 and 3A4 Supersomes did not reflect contributions from secondary metabolism based
on steady-state conditions for experiments and relatively inefficient alternate reactions, so that the observed
kinetics were due to the first step in Pathway 3. Moreover, the low 1-naphthaldehyde rates for human liver
microsomes were not due to subsequent metabolism of the metabolite as levels were stable during the
duration of the assay. Rather, 1-naphthaldehyde was only metabolized by enzymatic activity dependent on
NADPH in Supersomes, although the rates were not significant enough to alter the observed formation rates
during terbinafine metabolism. Taken together, these findings support an efficient generation to 1-
naphthaldehyde from Pathway 1.2 that occurred with CYP2C19 and 3A4 Supersomes but not in human
liver microsomes. Interestingly, the model for metabolism by human liver microsomes predicted a low
likelihood for N-denaphthylation (Pathway 3), while CYP2C19 and 3A4 models predicted high likelihoods
for the reaction (Fig. 7). Replication of this general trend for the experimental data suggests that training
data for the models may contain information on the impact of enzyme source on the likelihood for the
reaction to occur. An explanation for higher N-denaphthylation efficiency for recombinant P450 isozymes
relative to human liver microsomes containing them remains elusive and important to the differences in
metabolite stability seen in this study.
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Clinical data support CYP3A4 role in terbinafine metabolism
Results from in vitro kinetic studies with CYP2C19 and 3A4 reflected patterns observed for terbinafine
metabolism by human liver microsomes and in a broader, more relevant context, explain clinical
observations. For N-dealkylations, the Km values for reaction steps for CYP2C19 and 3A4 were within
two-fold of those reported in human liver microsomal studies, indicating comparable substrate specificity
and affinity. Moreover, the patterns of metabolic efficiencies for the reactions in Pathways 1 and 2 were
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similar among the enzyme preparations (Fig. 7). Trends for the kinetic mechanisms and constants as well
as the inhibitor phenotyping results support important roles for CYP2C19 and 3A4 in hepatic terbinafine
metabolism including formation of TBF-A. Nevertheless, hepatic CYP2C19 and 3A4 protein levels
determine Vmax and thus will modulate their relative significance in metabolism. In the general population,
there is wide variation in P450 isozyme expression due to factors such as age, sex, physiological status,
genetic polymorphisms, and chemical inducers like drugs and pollutants 29–32. A recent meta-analysis of
P450 isozyme expression data found hepatic CYP2C19 levels at 11 pmol/mg protein (89% CV, range 0 –
67) and those for CYP3A4 at 93 pmol/mg (91% CV, range 0 – 601) 33. On average, the higher CYP3A4
levels would make it more likely the dominant enzyme for Pathways 1 and the first step of Pathway 3
despite their comparable metabolic efficiencies with CYP2C19. N-demethylation (Pathway 2) of
terbinafine is far more efficiently carried out by CYP2C19, so that it would likely remain the most dominant
isozyme responsible for generation of the major metabolite in plasma 14. Reported drug-drug interactions
provide clinical evidence in support of CYP3A4 in the overall clearance of terbinafine. Co-administered
drugs increase terbinafine metabolism through CYP3A4 induction or decrease its metabolism due to
CYP3A4 inhibition 27,34. There is little evidence for clinical relevance for CYP2C19; however, studies have
primarily focused on overall terbinafine clearance and not individual pathways such as the potential
importance of CYP2C19 in N-demethylation of the drug. Notably, no clinical studies have investigated the
role of CYP2C19 and 3A4 (or other isozymes) in TBF-A formation and subsequent potential idiosyncratic
adverse drug events. Our findings provide novel insights on those possibilities.
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Concluding Remarks
Our combination of experimental and computational approaches yielded novel insights on the
importance of CYP2C19 and 3A4 on the metabolic clearance and activation of terbinafine as a foundation
for understanding the impact of those processes on idiosyncratic adverse drug events. Based on kinetics,
CYP2C19 is a dominating enzyme, but higher CYP3A4 protein levels compensate for lower efficiency and
render it more important in an average adult’s overall metabolic capacity. Their respective roles would
ultimately depend on clinical factors impacting their protein expression levels. Moreover, other P450
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isozymes were more selective in catalyzing specific N-dealkylation and deserve further study, given they
may also be important in generation of reactive TBF-A during terbinafine treatment. For the modeling
studies, predictions provided insights on the likelihood for reactions to occur yet did not extrapolate to
quantitative measures of metabolism like kinetics. As a bridge between modeling and experimental studies,
we expanded on our novel approach 13 to use split ratios for competing reactions and in doing so, generate
a readout on the preference for single and multi-step pathways relative to one another. This strategy is
useful for extending the potential value of modeling predictions as well as a tractable way to assess
modeling and experimental results. In this study, the CYP3A4 model performance was more accurate than
that for CYP2C19 for this small data set. The identification of model shortcomings provided insights on
which aspects of the model require adjustment for improving prediction accuracy. Moreover, the models
yielded hypotheses, such as the potential impact of loss of the naphthyl group on increasing metabolism,
even though it was not possible to test it in this case. Consequently, utilization of both approaches can lead
to a more complete picture of compound metabolism. Taken together, knowledge of CYP2C19 and 3A4
roles in terbinafine metabolic kinetics provides important clues on their potential activities in generation of
reactive TBF-A. These activities may correlate with idiosyncratic adverse drug events during oral
terbinafine treatment and will serve as a foundation for further study on their relevance in the clinic.
ACKNOWLEDGEMENTS
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Research reported in this publication was supported by the National Library of Medicine of the National
Institutes of Health under grant numbers R01LM012222 and R01LM012482. Davis is partially supported
by the National Institute of General Medical Sciences under grant number T32 GM106999. Computations
were performed using the facilities of the Washington University Center for High Performance
Computing that was partially funded by National Institutes of Health (NIH) under grant numbers
1S10RR022984-01A1 and 1S10OD018091-01. The content is solely the responsibility of the authors and
does not necessarily represent the official views of the National Institutes of Health.
ABBREVIATIONS
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HLM, human liver microsomes; TBF, terbinafine; TBF-A, 6,6-dimethyl-2-hepten-4-ynal; MNMA, N-
methyl-1-naphthyl methylamine; NMA, 1-naphthyl methylamine; dTBF, desmethyl-terbinafine; NAL, 1-
naphthaldehyde; DHD, terbinafine dihydrodiol; OL, hydroxyterbinafine.
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SUPPORTING INFORMATION
Table S1. Impact of reaction conditions on TBF-A decay in the absence of NADPH
Table S2. Oxidative non-dealkylation steady-state kinetics and modeling predictions for CYP2C19
Table S3. Oxidative non-dealkylation steady-state kinetics and modeling predictions for CYP3A4
Fig. S1. Cytochrome P450 isozymes responsible for terbinafine oxidative non-dealkylations
identified by inhibitor phenotyping
Fig. S2. Cytochrome P450 isozymes identified by inhibitor phenotyping of desmethyl-terbinafine
metabolism
Fig. S3. TBF-A decayed under reaction conditions
Fig. S4. MS response for dansylated TBF-A but not N-methyl-1-naphthyl methylamine varied
among instruments
Fig. S5. 1-Naphthylaldehyde depletion was not impacted by human liver microsomal reaction
conditions over time
Fig. S6. In the presence of NADPH, Supersomes increased 1-napthaldehyde decay relative to
microsomes
Fig. S7. Terbinafine at high levels blocked 1-napthaldehyde decay
Fig. S8. Decay of 1-napthaldehyde in the presence of Supersomes was minimal in absence of
NADPH
Fig. S9. CYP2C19 and 3A4 catalyzed oxidative non-dealkylations of terbinafine and desmethyl-
terbinafine under steady-state conditions
Fig. S10. Models predicted sites and likelihood for metabolism leading to dihydrodiols and
terminally hydroxylated metabolites
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TABLES
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Table 1. Michaelis-Menten Kinetic Constants for Terbinafine N-Dealkylation Pathways by
CYP2C19.a
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Steady-state kinetic constants for individual metabolites
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54
55
56
57
58
59
60
b
Pathway
Step
Substrate
Vmax
Km (µM)
V/K
Pathway 1
1.1
terbinafine
N-methyl-1-naphthyl-methylamine
1300 ± 83
250 ± 17
120 ± 22
1-napthaldehyde
300 ± 41
11
0.83
120
3.7
N-methyl-1-naphthyl
methylamine
1.2
2.1
Pathway 2
terbinafine
desmethyl-terbinafine
4300 ± 190
37 ± 6.7
2.2a
2.2b
3.1
desmethyl-terbinafine
desmethyl-terbinafine
terbinafine
1-naphthyl-methylamine
810 ± 92
220 ± 55
1-naphthaldehyde
150 ± 58
1300 ± 59
410 ± 14
8.7
Pathway 3e
1-naphthaldehyde
3.0 ± 0.83
140
aData fit best to the Michaelis-Menten equation over the Hill equation (P < 0.05) shown in Figs. 3, 4, and
5. Values shown with standard deviations; bUnits are pmol/min/nmol protein; cQuantitation of TBF-A
catalytic efficiency was not possible due to variable Vmax values, while Km values were consistent. The Km
values were then averaged and included in the table; dPathway Step 3.2 not studied due to absence of
authentic standards and low efficiency of previous step in HLM enzymatic system obviates the
significance of this pathway for TBF-A; eLimit of quantitation calculated as standard deviation of
response divided by slope of standard curve.
29
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