Dataset Information

Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans.

ABSTRACT: Duloxetine (DLX) is a potent drug investigated for the treatment of depression and urinary incontinence. DLX is extensively metabolized in the liver by two P450 isozymes, CYP2D6 and CYP1A2. Propolis (PPL) is one of the popular functional foods known to have effects on activities of CYPs, including CYP1A2. Due to the high probability of using DLX and PPL simultaneously, the present study was designed to investigate the potent effect of PPL on pharmacokinetics (PKs) of DLX after co-administration in humans. A PK study was first conducted in 18 rats (n = 6/group), in which the plasma concentration of DLX and its major metabolite 4-hydroxy duloxetine (4-HD) with or without administration of PPL was recorded. Population PKs and potential effects of PPL were then analyzed using NONMEM software. Lastly, these results were extrapolated from rats to humans using the allometric scaling and the liver blood flow method. PPL (15,000 mg/day) exerts a statistically significant increase in DLX exposures at steady state, with a 20.2% and 24.6% increase in DLX C m a x , s s and the same 28.0% increase in DLX A U C s s when DLX (40 or 60 mg) was administered once or twice daily, respectively. In conclusion, safety issues are required to be attended to when individuals simultaneously use DLX and PPL at high doses, and the possibility of interactions between DLX and PPL might be noted.

SUBMITTER: Ngo TL

PROVIDER: S-EPMC7084906 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans.

Ngo Thi Lien TL Lee Chung-Hee CH Han Nayoung N Back Hyun-Moon HM Rhee Su-Jin SJ Noh Keumhan K Yun Hwi-Yeol HY Kang Wonku W Chae Jung-Woo JW

International journal of molecular sciences 20200309 5

Duloxetine (DLX) is a potent drug investigated for the treatment of depression and urinary incontinence. DLX is extensively metabolized in the liver by two P450 isozymes, CYP2D6 and CYP1A2. Propolis (PPL) is one of the popular functional foods known to have effects on activities of CYPs, including CYP1A2. Due to the high probability of using DLX and PPL simultaneously, the present study was designed to investigate the potent effect of PPL on pharmacokinetics (PKs) of DLX after co-administration ...[more]

PMID: 32182820

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Project description:Eliglustat is a glucosylceramide synthase inhibitor indicated as a long-term substrate reduction therapy for adults with type 1 Gaucher disease, a lysosomal rare disease. It is primarily metabolized by cytochrome P450 2D6 (CYP2D6), and variants in the gene encoding this enzyme are important determinants of eliglustat pharmacokinetics (PK) and drug-drug interactions (DDIs). The existing drug label addresses the DDIs to some extent but has omitted scenarios where both metabolizing CYPs (2D6 and 3A4) are mildly or moderately inhibited. The objectives of this study were (i) to develop and validate an eliglustat physiologically-based pharmacokinetic (PBPK) model with and without drug interactions, (ii) to simulate untested DDI scenarios, and (iii) to explore potential dosing flexibility using lower dose strength of eliglustat (commercially not available). PK data from healthy adults receiving eliglustat with or without interacting drugs were obtained from literature and used for the PBPK model development and validation. The model-predicted single-dose and steady-state maximum concentration (Cmax ) and area under the concentration-time curve (AUC) of eliglustat were within 50-150% of the observed values when eliglustat was administered alone or coadministered with ketoconazole or paroxetine. Then as model-based simulations, we illustrated eliglustat exposure as a victim of interaction when coadministered with fluvoxamine following the US Food and Drug Administration (FDA) dosing recommendations. Second, we showed that with lower eliglustat doses (21 mg, 42 mg once daily) the exposure in participants of intermediate and poor metabolizer phenotypes was within the outlined safety margin (Cmax <250 ng/mL) when eliglustat was administered with ketoconazole, where the current recommendation is a contraindication of coadministration (84 mg). The present study demonstrated that patients with CYP2D6 deficiency may benefit from lower doses of eliglustat.

Project description:BackgroundDrug-drug interactions (DDIs) are a major concern in patients' medication. It's unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples.ResultsTo address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs.ConclusionsThe results demonstrate that positive-unlabeled learning paves a new way to tackle the problem caused by the lack of experimentally verified negatives in the computational prediction of DDIs.

Dataset Information

Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans.

Publications

Application of an Inter-Species Extrapolation Method for the Prediction of Drug Interactions between Propolis and Duloxetine in Humans.

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