Evidence map›Paper›PMID 31044521›Full record

ArticleCPT: pharmacometrics & systems pharmacology2019

Evaluation of the Drug-Drug Interaction Potential of Acalabrutinib and Its Active Metabolite, ACP-5862, Using a Physiologically-Based Pharmacokinetic Modeling Approach.

Diansong Zhou, Terry Podoll, Yan Xu, Ganesh Moorthy, Karthick Vishwanathan, Joseph Ware, J Greg Slatter, Nidal Al-Huniti

Open access · goldAbstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 46 citations in OpenAlex.

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  10. Recommendations on the use of azole antifungals in hematology-oncology patients.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Diansong ZhouQuantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA.
Terry PodollDMPK/Clinical Pharmacology, Acerta Pharma, South San Francisco, California, USA.
Yan XuDMPK/Clinical Pharmacology, Acerta Pharma, South San Francisco, California, USA.
Ganesh MoorthyQuantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA.
Karthick VishwanathanQuantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA.
Joseph WareDMPK/Clinical Pharmacology, Acerta Pharma, South San Francisco, California, USA.
J Greg SlatterDMPK/Clinical Pharmacology, Acerta Pharma, South San Francisco, California, USA.
Nidal Al-HunitiQuantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA.
Pharmacology Research Institute · USAstraZeneca (United States) · USAstraZeneca (Japan) · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acalabrutinib, a selective, covalent Bruton tyrosine kinase inhibitor, is a CYP3A substrate and weak CYP3A/CYP2C8 inhibitor. A physiologically-based pharmacokinetic (PBPK) model was developed for acalabrutinib and its active metabolite ACP-5862 to predict potential drug-drug interactions (DDIs). The model indicated acalabrutinib would not perpetrate a CYP2C8 or CYP3A DDI with the sensitive CYP substrates rosiglitazone or midazolam, respectively. The model reasonably predicted clinically observed acalabrutinib DDI with the CYP3A perpetrators itraconazole (4.80-fold vs. 5.21-fold observed) and rifampicin (0.21-fold vs. 0.23-fold observed). An increase of two to threefold acalabrutinib area under the curve was predicted for coadministration with moderate CYP3A inhibitors. When both the parent drug and active metabolite (total active components) were considered, the magnitude of the CYP3A DDI was much less significant. PBPK dosing recommendations for DDIs should consider the magnitude of the parent drug excursion, relative to safe parent drug exposures, along with the excursion of total active components to best enable safe and adequate pharmacodynamic coverage.

Indexed as

BenzamidesClinical Trials, Phase I as TopicComputer SimulationCytochrome P-450 CYP3A InhibitorsDrug InteractionsHumansModels, BiologicalPyrazinesacalabrutinibBenzamidesCytochrome P-450 CYP3A InhibitorsPyrazines

Identifiers

PMID31044521
PMCPMC6656940
OpenAlexW2943666771

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.