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Cited by 20Piperaceae Chemical and Biological StudiesPharmacogenetics and Drug MetabolismDrug Transport and Resistance Mechanisms

The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index; none of the 32 sources its claims cite is checked for support yet.

Predicting Food–Drug Interactions between Piperine and CYP3A4 Substrate Drugs Using PBPK Modeling

Open access
Lin, Feifei, Hu, Yingchun, Zhang, Yifan, Zhao, Lijuan, Zhong, Dafang, Liu, Jia·2024·International Journal of Molecular Sciences
DOI 10.3390/ijms252010955PMC PMC11506926
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Abstract

Piperine has been reported to inhibit the enzyme activity of cytochrome P450 (CYP) 3A4. The aim of this study was to develop and validate a physiologically based pharmacokinetic (PBPK) model for piperine and to predict potential food–drug interactions (FDIs) between piperine and CYP3A4 substrate drugs using these models. The PBPK model for piperine was successfully developed and validated. Using this model, FDIs with ten CYP3A4 substrate drugs were simulated. The predicted area under the curve (AUC) ratios (with and without piperine, following a 7-day intake of 20 mg/day) for six drugs were found to exceed 1.25, with significant increases in AUC observed for ritonavir (31%), nifedipine (34%), cyclosporine (35%), triazolam (36%), alfentanil (39%), and simvastatin (59%) in humans. These findings suggest that caution should be exercised when consuming amounts of black pepper equivalent to a daily intake of 20 mg piperine during treatment with CYP3A4 substrate drugs, as it may significantly alter their pharmacokinetics.

Overview

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TL;DR

A PBPK model for piperine was developed and validated in Simcyp and used to simulate food–drug interactions with 10 CYP3A4 substrate drugs. After simulated 7-day intake of 20 mg/day piperine, six drugs had predicted AUC ratios ≥1.25, with the largest AUC increase for simvastatin (59%).

Key findings

  • The piperine PBPK model was validated against clinical data: predicted-to-observed fold-error was 1.3 for Cmax and 1.4 for AUClast after a single 20 mg dose, and 1.0 and 1.1 after 20 mg/day for 7 days, all within the two-fold acceptance range.
  • Simulated piperine co-administration increased AUC for carbamazepine (11%), clarithromycin (17%), midazolam (20%), itraconazole (24%), ritonavir (31%), nifedipine (34%), cyclosporine (35%), triazolam (36%), alfentanil (39%), and simvastatin (59%).
  • Predicted AUC ratios with versus without piperine were ≥1.25 for ritonavir (1.31), nifedipine (1.34), cyclosporine (1.35), triazolam (1.36), alfentanil (1.39), and simvastatin (1.59).
  • The paper concludes caution is warranted for consuming black pepper equivalent to 20 mg/day piperine during treatment with CYP3A4 substrate drugs, especially narrow therapeutic index drugs such as alfentanil and cyclosporine.

Methods

  • In vitro intrinsic clearance of piperine was measured in human liver microsomes using LC-MS/MS; in-house CLint values ranged from 10.6 ± 0.9 to 34.0 ± 1.3 μL/min/mg protein across tested HLM and piperine concentrations.
  • PBPK modeling used Simcyp Version 18 with the ADAM absorption model, Rodgers and Rowland Method 2 for Vss, and whole-organ metabolic clearance with an average CLint of 24.2 μL/min/mg protein; interaction parameters included Kapp 6.74 μM, kinact 0.558 1/h, and fumic 0.863.
  • The model was validated against published clinical piperine data after single-dose 20 mg and multiple-dose 20 mg/day for 7 days in Chinese healthy volunteers; simulations used a virtual population of 100 subjects (10 trials × 10 subjects), and interaction simulations used Simcyp built-in models for 10 CYP3A4 substrate drugs with or without 20 mg/day piperine for 7 days.

Limitations

  • Stated: metabolic clearance was calculated using the whole-organ metabolic clearance approach due to the absence of clinical data, and the authors note PBPK models should be updated with new mechanistic insights such as enzyme or transporter inhibition or induction.
  • Stated/evident: the food–drug interaction findings are simulation-based; for drugs with predicted AUC ratio ≥1.25, the paper states that a clinical interaction study should be conducted, so clinical FDI confirmation was not performed in this study.
  • Evident: the paper attributes high SEMs in the interaction results to inter-individual variability and CYP3A4 polymorphism in the virtual population (CV 41% for enzyme abundance and 68% for turnover rate), and the simulations used a limited virtual population of 100 subjects and only 10 CYP3A4 substrate drugs.

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Integrity signals

Registry

Crossref · 10.3390/ijms252010955

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Chemical structure of piperine.
Figure 1 Chemical structure of piperine.CC BY · PubMed Central
Figure 2 Workflow for development, optimization, and evaluation of PBPK models.
Figure 2 Workflow for development, optimization, and evaluation of PBPK models.CC BY · PubMed Central
Figure 3 The linear regression of natural logarithmic remaining percentage of piperine against incubation time. Each value is the mean of three measurements.
Figure 3 The linear regression of natural logarithmic remaining percentage of piperine against incubation time. Each value is the mean of three measurements.CC BY · PubMed Central
Figure 4 Simulated and observed piperine plasma C-T profile in Chinese healthy volunteers: (A) single dose (20 mg) and (B) multiple dose (consecutive 7 days of piperine at 20 mg/day). The 10 trial profiles are displayed as a grey line, and the mean value of simulations are presented as a black line, 5% and 95% percentile are shown as a dashed line. Open circles indicate observed values reported by Wang et al. [35].
Figure 4 Simulated and observed piperine plasma C-T profile in Chinese healthy volunteers: (A) single dose (20 mg) and (B) multiple dose (consecutive 7 days of piperine at 20 mg/day). The 10 trial profiles are displayed as a grey line, and the mean value of simulations are presented as a black line, 5% and 95% percentile are shown as a dashed line. Open circles indicate observed values reported by Wang et al. [35].CC BY · PubMed Central
Figure 5 FDI prediction of plasma C-T profiles of oral administration of CYP3A4 victim drugs: (A) carbamazepine, 400 mg, (B) clarithromycin, 250 mg, (C) midazolam, 5 mg, (D) itraconazole_fed capsule, 200 mg, (E) ritonavir, 100 mg, (F) nifedipine, 20 mg, (G) cyclosporine, 200 mg, (H) triazolam, 0.25 mg, (I) alfentanil, 0.043 mg, (J) simvastatin, 40 mg, with or without consecutive 7 days of piperine at 20 mg/day in healthy volunteers. The mean value of 10 trial simulations are displayed, with the single dose of victim drugs as a blue line and victim drugs with a consecutive 7 days of piperine at 20 mg/day as a red line; 5% and 95% percentile are shown as a dashed line.
Figure 5 FDI prediction of plasma C-T profiles of oral administration of CYP3A4 victim drugs: (A) carbamazepine, 400 mg, (B) clarithromycin, 250 mg, (C) midazolam, 5 mg, (D) itraconazole_fed capsule, 200 mg, (E) ritonavir, 100 mg, (F) nifedipine, 20 mg, (G) cyclosporine, 200 mg, (H) triazolam, 0.25 mg, (I) alfentanil, 0.043 mg, (J) simvastatin, 40 mg, with or without consecutive 7 days of piperine at 20 mg/day in healthy volunteers. The mean value of 10 trial simulations are displayed, with the single dose of victim drugs as a blue line and victim drugs with a consecutive 7 days of piperine at 20 mg/day as a red line; 5% and 95% percentile are shown as a dashed line.CC BY · PubMed Central

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Citations

Cites

Show all 51 references

Research works printed but not linked: 4

  1. Linke, 2017, Physiologically based pharmacokinetic (PBPK) modeling to predict human internal exposure to industrial chemicals—an evaluation and case study using the simulation software GastroPlus™, Naunyn-Schmiedebergs Arch. Pharmacol., 390, S82
  2. (2024, March 02). FDA Public Workshop ‘Development of Best Practices in Physiologically Based Pharmacokinetic Modeling to Support Clinical Pharmacology Regulatory DecisionMaking’, Available online: https://www.fda.gov/drugs/news-events-human-drugs/development-best-practices-physiologically-based-pharmacokinetic-modeling-support-clinical.
  3. Fu, J.W. (2013). Preparation, Characterization and Evaluation of Compound Curcumin Encapculating in Cubic Liquid Crystalline Nanoparticles. [Ph.D. Thesis, Guangzhou University of Chinese Medicine].
  4. Wang, 2010, Pharmacokinetics of piperine capsules in healthy volunters, Cent. South Pharm., 8, 513

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Metadata

Authors
Lin, F., Hu, Y., Zhang, Y., Zhao, L., Zhong, D., & Liu, J.
Affiliations
Chinese Academy of Sciences (CN)
Corresponding author
Liu, Jia
ORCID
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2024-10-11
Volume
25
Issue
20
Pages
10955
ISSN
1422-0067 (ISSN-L)
DOI
10.3390/ijms252010955
PMC
PMC11506926
OpenAlex
W4403318846
Cited by
20
References
51
Citations by year
2026: 13 · 2025: 6 · 2024: 1
FWCI
4.63
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Piperaceae Chemical and Biological Studies
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
  • Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
Keywords
piperine, food-drug interactions, CYP3A4 inhibition, cytochrome P450, black pepper, physiologically based pharmacokinetic modeling, ritonavir, nifedipine, cyclosporine, triazolam, alfentanil, simvastatin, drug-drug interactions, pharmacokinetic simulation
MeSH terms
Alkaloids, Humans, Models, Biological, Nifedipine, Piperidines, Triazolam, Alfentanil, Food-Drug Interactions, Area Under Curve, Simvastatin, Cytochrome P-450 CYP3A, Benzodioxoles, Polyunsaturated Alkamides, Cytochrome P-450 CYP3A Inhibitors

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