Evidence map›Paper›PMID 27695293›Full record

ArticleDrug design, development and therapy2016

Application of physiologically based pharmacokinetic modeling in predicting drug-drug interactions for sarpogrelate hydrochloride in humans.

Jee Sun Min, Doyun Kim, Jung Bae Park, Hyunjin Heo, Soo Hyeon Bae, Jae Hong Seo, Euichaul Oh, Soo Kyung Bae

Open access · goldAbstract read
In one paragraph

Article in Drug design, development and therapy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.3field-weighted citation impact, top 17% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 1 institution in 1 country.

Jee Sun MinIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Doyun KimIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Jung Bae ParkIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Hyunjin HeoIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Soo Hyeon BaeDepartment of Pharmacology, College of Medicine, The Catholic University of Korea, Seocho-gu, Seoul, South Korea.
Jae Hong SeoIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Euichaul OhIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Soo Kyung BaeIntegrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Catholic University of Korea · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvaluating the potential risk of metabolic drug-drug interactions (DDIs) is clinically important.

objectiveTo develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (

methodsThe PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol.

resultsThe developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition.

conclusionThis study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor.

Indexed as

Computer SimulationCytochrome P-450 CYP2D6DimethylaminesDrug InteractionsHumansModels, BiologicalPropanolsSuccinatesCytochrome P-450 CYP2D6DimethylaminesPropanols(R,S)-1-(2-(2-(3-methoxyphenyl)ethyl)-phenoxy)-3-(dimethylamino)-2-propanolsarpogrelateSuccinatesCYP2D6 inhibitionM-1PBPK modelingsarpogrelate hydrochloride

Identifiers

PMID27695293
PMCPMC5028085
OpenAlexW2519792490

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.