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Metabolites
Special Issues
The Role of Metabolites in Translational and Clinical Pharmacology
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The Role of Metabolites in Translational and Clinical Pharmacology

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Pharmacology and Drug Metabolism".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 4259

Special Issue Editors

Dr. Mayur K. Ladumor

E-Mail Website
Guest Editor
Transporters & In Vitro Technologies, ADMET & BA, Merck & Co. Inc., West Point, PA 19486, USA
Interests: drug metabolism & transport; pharmacokinetics; drug-drug in-teractions; special populations, PBPK and PK/PD modeling & simulation
Dr. Xinning Yang

E-Mail Website
Guest Editor
Office of Clinical Pharmacology, Center of Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA
Interests: drug-drug interactions; drug-food interactions; metabolism; transport; PBPK modeling; clinical pharmacology; regulatory science
Dr. Eva Berglund

E-Mail Website
Guest Editor
Certara Strategic Consulting, Integrated Drug Development, Certara, Princeton, NJ 08540, USA
Interests: drug-drug interactions; pediatrics; metabolism; transport; PBPK modeling; clinical pharmacology; special populations, phar-macogenetics; regulatory science; MIDD
Prof. Dr. Rheem A. Totah

E-Mail Website
Guest Editor
Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA
Interests: drug metabolism; DDI; reactive metabolites; protein-drug ad-ducts; time dependent inhibition; CYP2J2; S-Methyl transferases in health and disease

Special Issue Information

Dear Colleagues,

Drug metabolism can lead to the formation of pharmacologically active, inactive, or reactive metabolites. Factors, including intrinsic (e.g., disease, age and genetics) or extrinsic (e.g., co-medication and food), can affect the pharmacokinetics (PK) of active drug metabolites and consequently their pharmacodynamics (PD). In addition, drug metabolites can impact the activity of drug-metabolizing enzyme- or transporter (DMET) proteins, leading to changes in the exposure of co-administered drugs and raising concerns about their safety and efficacy, especially for narrow-therapeutic index drugs. Therefore, regulatory agencies emphasize studying the PK, PK/PD, and drug–drug interaction (DDI) potential of major drug metabolites.

This Special Issue aims to provide a comprehensive overview of the importance of drug metabolites in drug discovery and development within translational and clinical pharmacology domains. We invite original research articles, reviews, and short communications related to the field of metabolites. Interested authors are encouraged to send pre-submission inquiries and discuss potential fee waivers (depending on the merit).

Topics of interest include, but are not limited to, the following areas:

  • Absorption, distribution, metabolism, and excretion (ADME) or PK studies of drug metabolites.
  • In vitro or in vivo evaluation of drug metabolites as substrates or perpetrators (inhibitors or inducers) of DMET proteins.
  • Prediction of PK of drug metabolites in healthy or special populations using physiologically based pharmacokinetic (PBPK) models.
  • Prediction of the DDI potential of drug metabolites using basic, static mechanistic, or dynamic PBPK models.
  • Endogenous biomarkers for assessing the DMET-mediated DDI potential.
  • Use of metabolite monitoring in DDI studies to improve the mechanistic interpretation of results.
  • Mechanistic PK and PK/PD modeling of drug metabolites or parent metabolites.

Dr. Mayur K. Ladumor
Dr. Xinning Yang
Dr. Eva Gil Berglund
Prof. Dr. Rheem A. Totah
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolism
  • transport
  • ADME
  • PK
  • DDI
  • special populations
  • endogenous metabolite biomarker
  • PBPK and PK/PD modeling

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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15 pages, 1801 KiB  
Article
Is N1-Methylnicotinamide a Good Organic Cation Transporter 2 (OCT2) Biomarker?
by Anoud Sameer Ailabouni, Gautam Vijaywargi, Sandhya Subash, Dilip Kumar Singh, Zsuzsanna Gaborik and Bhagwat Prasad
Metabolites 2025, 15(2), 80; https://doi.org/10.3390/metabo15020080 - 29 Jan 2025
Viewed by 437
Abstract
Background/Objectives: The impact of potential precipitant drugs on plasma or urinary exposure of endogenous biomarkers is emerging as an alternative approach to evaluating drug–drug interaction (DDI) liability. N1-Methylnicotinamide (NMN) has been proposed as a potential biomarker for renal organic cation transporter 2 [...] Read more.
Background/Objectives: The impact of potential precipitant drugs on plasma or urinary exposure of endogenous biomarkers is emerging as an alternative approach to evaluating drug–drug interaction (DDI) liability. N1-Methylnicotinamide (NMN) has been proposed as a potential biomarker for renal organic cation transporter 2 (OCT2). NMN is synthesized in the liver from nicotinamide by nicotinamide N-methyltransferase (NNMT) and is subsequently metabolized by aldehyde oxidase (AO). Multiple clinical studies have shown a reduction in NMN plasma concentration following the administration of OCT inhibitors such as cimetidine, trimethoprim, and pyrimethamine, which contrasts with their inhibition of NMN renal clearance by OCT2. We hypothesized that OCT1-mediated NMN release from hepatocytes is inhibited by the administration of OCT inhibitors. Methods: Re-analysis of the reported NMN pharmacokinetics with and without OCT inhibitor exposure was performed. We assessed the effect of cimetidine on NMN uptake in OCT1-HEK293 cells and evaluated the potential confounding effects of cimetidine on enzymes involved in NMN formation and metabolism. Results: A re-analysis of previous NMN pharmacokinetic DDI data suggests that NMN plasma systemic exposure decreased by 17–41% during the first 4 h following different OCT inhibitor administration except dolutegravir. Our findings indicate that NMN uptake was significantly higher (by 2.5-fold) in OCT1-HEK293 cells compared to mock cells, suggesting that NMN is a substrate of OCT1. Additionally, our results revealed that cimetidine does not inhibit NNMT and AO activity. Conclusions: Our findings emphasize the limitations of using NMN as an OCT2 biomarker and reveal potential mechanisms behind the reduction in NMN plasma levels associated with OCT inhibitors. Instead, our data suggest that NMN could be tested further as a potential biomarker for OCT1 activity. Full article
(This article belongs to the Special Issue The Role of Metabolites in Translational and Clinical Pharmacology)
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17 pages, 1998 KiB  
Article
Disposition of Oral Nalbuphine and Its Metabolites in Healthy Subjects and Subjects with Hepatic Impairment: Preliminary Modeling Results Using a Continuous Intestinal Absorption Model with Enterohepatic Recirculation
by Swati Nagar, Amale Hawi, Thomas Sciascia and Ken Korzekwa
Metabolites 2024, 14(9), 471; https://doi.org/10.3390/metabo14090471 - 27 Aug 2024
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Abstract
Nalbuphine (NAL) is a mixed κ-agonist/μ-antagonist opioid with extensive first-pass metabolism. A phase 1 open-label study was conducted to characterize the pharmacokinetics (PKs) of NAL and select metabolites following single oral doses of NAL extended-release tablets in subjects with mild, moderate, and severe [...] Read more.
Nalbuphine (NAL) is a mixed κ-agonist/μ-antagonist opioid with extensive first-pass metabolism. A phase 1 open-label study was conducted to characterize the pharmacokinetics (PKs) of NAL and select metabolites following single oral doses of NAL extended-release tablets in subjects with mild, moderate, and severe hepatic impairment (Child–Pugh A, B, and C, respectively) compared to healthy matched subjects. NAL exposures were similar for subjects with mild hepatic impairment as compared to healthy subjects and nearly three-fold and eight-fold higher in subjects with moderate and severe hepatic impairment, respectively. Datasets obtained for healthy, moderate, and severe hepatic impaired groups were modeled with a mechanistic model that incorporated NAL hepatic metabolism and enterohepatic recycling of NAL and its glucuronidated metabolites. The mechanistic model includes a continuous intestinal absorption model linked to semi-physiological liver–gallbladder–compartmental PK models based on partial differential equations (termed the PDE-EHR model). In vitro studies indicated that cytochromes P450 CYP2C9 and CYP2C19 are the major CYPs involved in NAL oxidation, with glucuronidation mainly catalyzed by UGT1A8 and UGT2B7 isozymes. Complex formation and elimination kinetics of NAL and four main metabolites was well predicted by PDE-EHR. The model is expected to improve predictions of drug interactions and complex drug disposition. Full article
(This article belongs to the Special Issue The Role of Metabolites in Translational and Clinical Pharmacology)
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Review

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22 pages, 3364 KiB  
Review
Metabolite Measurement in Index Substrate Drug Interaction Studies: A Review of the Literature and Recent New Drug Application Reviews
by Jingjing Yu, Nathalie Rioux, Iain Gardner, Katie Owens and Isabelle Ragueneau-Majlessi
Metabolites 2024, 14(10), 522; https://doi.org/10.3390/metabo14100522 - 26 Sep 2024
Viewed by 1615
Abstract
Background/Objectives: Index substrates are used to understand the processes involved in pharmacokinetic (PK) drug–drug interactions (DDIs). The aim of this analysis is to review metabolite measurement in clinical DDI studies, focusing on index substrates for cytochrome P450 (CYP) enzymes, including CYP1A2 (caffeine), CYP2B6 [...] Read more.
Background/Objectives: Index substrates are used to understand the processes involved in pharmacokinetic (PK) drug–drug interactions (DDIs). The aim of this analysis is to review metabolite measurement in clinical DDI studies, focusing on index substrates for cytochrome P450 (CYP) enzymes, including CYP1A2 (caffeine), CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 ((S)-warfarin, flurbiprofen), CYP2C19 (omeprazole), CYP2D6 (desipramine, dextromethorphan, nebivolol), and CYP3A (midazolam, triazolam). Methods: All data used in this evaluation were obtained from the Certara Drug Interaction Database. Clinical index substrate DDI studies with PK data for at least one metabolite, available from literature and recent new drug application reviews, were reviewed. Further, for positive DDI studies, a correlation analysis was performed between changes in plasma exposure of index substrates and their marker metabolites. Results: A total of 3261 individual index DDI studies were available, with 45% measuring at least one metabolite. The occurrence of metabolite measurement in clinical DDI studies varied widely between index substrates and enzymes. Discussion and Conclusions: For substrates such as caffeine, bupropion, omeprazole, and dextromethorphan, the use of the metabolite/parent area under the curve ratio can provide greater sensitivity to DDI or reduce intrasubject variability. In some cases (e.g., omeprazole, repaglinide), the inclusion of metabolite measurement can provide mechanistic insights to understand complex interactions. Full article
(This article belongs to the Special Issue The Role of Metabolites in Translational and Clinical Pharmacology)
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