Early Prediction and Impact Assessment of CYP3A4-Related Drug-Drug Interactions for Small-Molecule Anticancer Drugs Using Human-CYP3A4-Transgenic Mouse Models
Open accessAbstract
Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method. SIGNIFICANCE STATEMENT: The described method offers an alternative for the early detection and assessment of potential clinical impact of CYP3A4-related drug-drug interactions. The model was able to adequately describe the inhibition of CYP3A4 metabolism and the subsequent magnitude of change in exposure. However, it was unable to accurately predict the magnitude of change in exposure of victim drugs in combination with an inducer.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The CYP3A4 induction and inhibition assays highlight the potential of iHLOs for dynamic pharmacological testing.
“The CYP3A4 induction and inhibition assays also highlight the potential of iHLOs for dynamic pharmacological testing, a key aspect of drug safety evaluations35.”
From Temporal proteomic profiling of iPSC-derived human liver organoids reveals optimal maturation for drug metabolism and toxicology (2026) · cites this paper as [35]
The Introduction (page unavailable) links CYP3A4 drug–drug interaction testing to safety during drug development, and the Discussion (page unavailable) evaluates predictions of CYP3A4 inhibition and induction. But this paper uses transgenic mouse models; our copy does not describe iHLO assays or show that they have potential for dynamic pharmacological testing. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 17 · Cited by 2
Research works printed but not linked: 13
- [2]2. Chen, J ∙ Xu, H ∙ Pawlak, S ... The effect of rifampin on the pharmacokinetics and safety of lorlatinib: results of a phase one, open-label, crossover study in healthy participants Adv Ther. 2020; 37:745-758 Crossref Scopus (26) PubMed Google Scholar
- [3]3. Damoiseaux, D ∙ Li, W ∙ Martínez-Chávez, A ... Predictiveness of the human-CYP3A4-transgenic mouse model (Cyp3aXAV) for human drug exposure of CYP3A4-metabolized drugs Pharmaceuticals (Basel). 2022; 15:860 Crossref Scopus (4) PubMed Google Scholar
- [4]4. Hasegawa, M ∙ Kapelyukh, Y ∙ Tahara, H ... Quantitative prediction of human pregnane X receptor and cytochrome P450 3A4 mediated drug-drug interaction in a novel multiple humanized mouse line Mol Pharmacol. 2011; 80:518-528 Full Text Full Text (PDF) Scopus (77) PubMed Google Scholar
- [5]5. Hennig, S ∙ Waterhouse, TH ∙ Bell, SC ... A d-optimal designed population pharmacokinetic study of oral itraconazole in adult cystic fibrosis patients Br J Clin Pharmacol. 2007; 63:438-450 Crossref Scopus (0) PubMed Google Scholar
- [6]6. Isoherranen, N ∙ Kunze, KL ∙ Allen, KE ... Role of itraconazole metabolites in CYP3A4 inhibition Drug Metab Dispos. 2004; 32:1121-1131 Full Text Full Text (PDF) Scopus (228) PubMed Google Scholar
- [8]8. Kilford, PJ ∙ Chen, K-F ∙ Crewe, K ... Prediction of CYP-mediated DDIs involving inhibition: approaches to address the requirements for system qualification of the Simcyp Simulator CPT Pharmacometrics Syst Pharmacol. 2022; 11:822-832 Crossref Scopus (19) PubMed Google Scholar
- [9]9. Klein, K ∙ Zanger, UM Pharmacogenomics of cytochrome P450 3A4: recent progress toward the “missing heritability” problem Front Genet. 2013; 4:12 Crossref Scopus (182) PubMed Google Scholar
- [12]12. Molenaar-Kuijsten, L ∙ Van Balen, DEM ∙ Beijnen, JH ... A review of CYP3A drug-drug interaction studies: practical guidelines for patients using targeted oral anticancer drugs Front Pharmacol. 2021; 12 670862. Crossref Scopus (18) PubMed Google Scholar
- [13]13. Patel, M ∙ Chen, J ∙ McGrory, S ... The effect of itraconazole on the pharmacokinetics of lorlatinib: results of a phase I, open-label, crossover study in healthy participants Invest New Drugs. 2020; 38:131-139 Crossref Scopus (20) PubMed Google Scholar
- [14]14. Takayama, K ∙ Ito, K ∙ Matsui, A ... In vivo gene expression profile of human intestinal epithelial cells: from the viewpoint of drug metabolism and pharmacokinetics Drug Metab Dispos. 2021; 49:221-232 Full Text Full Text (PDF) Scopus (19) PubMed Google Scholar
- [15]15. Tugnait, M ∙ Gupta, N ∙ Hanley, MJ ... Effects of strong CYP2C8 or CYP3A inhibition and CYP3A induction on the pharmacokinetics of brigatinib, an oral anaplastic lymphoma kinase inhibitor, in healthy volunteers Clin Pharmacol Drug Dev. 2020; 9:214-223 Crossref Scopus (19) PubMed Google Scholar
- [16]16. Wilkins, JJ ∙ Savic, RM ∙ Karlsson, MO ... Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption Antimicrob Agents Chemother. 2008; 52:2138-2148 Crossref Scopus (129) PubMed Google Scholar
- [18]18. Yamashita, F ∙ Sasa, Y ∙ Yoshida, S ... Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data PLoS One. 2013; 8 e70330. Crossref Scopus (79) Google Scholar Figures (16) Article metrics Related Articles Advertisement Fig. 1 Figure fx1 Figure fx2 Figure fx3 Figure fx4 Figure fx5 Figure fx6 Figure fx7 Figure fx8 Figure fx9 Figure fx10 Figure fx11 Figure fx12 Figure fx13 Figure fx14 Fig. 2 Hide CaptionDownloadSee figure in Article Toggle Thumbstrip Fig. 1 Fig. 1 Figure Figure fx1 Figure Figure fx2 Figure Figure fx3 Figure Figure fx4 Figure Figure fx5 Figure Figure fx6 Figure Figure fx7 Figure Figure fx8 Figure Figure fx9 Figure Figure fx10 Figure Figure fx11 Figure Figure fx12 Figure Figure fx13 Figure Figure fx14 Fig. 2 Fig. 2 Download .PPT Home Access for Developing Countries Articles & Issues Articles in Press Current Issue List of Issues For Authors Submit a Manuscript Guide for Authors Open Access Permissions Researcher Academy Journal Info About the Journal Abstracting/Indexing Activate Online Access Contact Information Editorial Board Info for Advertisers Reprints New Content Alerts Subscribe Society website The content on this site is intended for healthcare professionals. All content on this site: Copyright © 2026 Elsevier Inc., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply. Privacy Policy Terms and Conditions Accessibility Help & Contact Responsible AI RELX Thanks for sharing! AddToAny More… We use cookies that are necessary to make our site work. We may also use additional cookies to analyze, improve, and personalize our content and your digital experience. You can manage your cookie preferences using theCookie settings link. For more information, see ourCookie Policy Company Logo Opt-Out Request Honored Cookie Preference Center We use cookies which are necessary to make our site work. We may also use additional cookies to analyse, improve and personalise our content and your digital experience. For more information, see our Cookie Policy and the list of Google Ad-Tech Vendors. You may choose not to allow some types of cookies. However, blocking some types may impact your experience of our site and the services we are able to offer. See the different category headings below to find out more or change your settings. You may also be able to exercise your privacy choices Cookie Settings | Your Privacy Choices as described in our Privacy Policy Manage Consent Preferences Strictly Necessary Cookies Always active These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work. Functional Cookies These cookies enable the website to provide enhanced functionality and personalisation. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. Performance Cookies These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. Targeting Cookies These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. If you do not allow these cookies, you will experience less targeted advertising. Cookie List Consent Leg.Interest label label label Powered by Onetrust
- Authors
- Damoiseaux, D., Beijnen, J. H., Huitema, A. D. R., & Dorlo, T. P. C.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Damoiseaux, David
- Venue
- Drug Metabolism and Disposition
- Publisher
- Elsevier BV
- Published
- 2024-11
- Volume
- 52
- Issue
- 11
- Pages
- 1217-1223
- ISSN
- 0090-9556 (ISSN-L)
- OpenAlex
- W4403096605
- Cited by
- 2
- References
- 17
- Citations by year
- 2026: 2
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Metabolism and Disposition (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- drug-drug interactions, CYP3A4, small molecule anticancer agents, brigatinib, lorlatinib, itraconazole, rifampicin, CYP3A4 inhibition, CYP3A4 induction, drug exposure prediction, model-based prediction
- MeSH terms
- Animals, Antineoplastic Agents, Drug Interactions, Humans, Mice, Transgenic, Rifampin, Itraconazole, Mice, Cytochrome P-450 CYP3A, Cytochrome P-450 CYP3A Inhibitors, Cytochrome P-450 CYP3A Inducers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
