Verification of a cocktail approach for quantitative drug–drug interaction assessment: a comparative analysis between the results of a single drug and a cocktail drug

Miura, Mikihiko; Uchida, Shinya; Tanaka, Shimako; Kamiya, Chiaki; Katayama, Naoki; Hakamata, Akio; Odagiri, Keiichi; Inui, Naoki; Kawakami, Junichi; Watanabe, Hiroshi; Namiki, Noriyuki

doi:10.1080/00498254.2020.1867330

Cited by 4 publications

(6 citation statements)

References 44 publications

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“…All the individual tested cocktails showed that there were no mutual metabolic interactions among the included drugs at the respective doses, except that whereas caffeine, losartan, omeprazole, and quinine had no significant change when administered together compared with administered alone, an inhibition of debrisoquine metabolism was observed when concurrent administration of the five drugs in the Karolinska cocktail 55 . The Inje cocktail was further validated by the typical CYP inducer rifampicin or inhibitors cimetidine and fluvoxamine, and the degrees of interaction are consistent with the single agent studies 64 …”

Section: Introductionsupporting

confidence: 65%

“…55 The Inje cocktail was further validated by the typical CYP inducer rifampicin or inhibitors cimetidine and fluvoxamine, and the degrees of interaction are consistent with the single agent studies. 64 Clinically validated cocktails involving transporter mediated DDIs Three clinically validated cocktails involving transporters were shown in Table S1, including the publication time for the final version of the cocktail, drugs in the cocktail and their doses/routes, and the applications of the cocktail with no change or minimal changes.…”

Section: Notementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of the drug–drug interaction potential for therapeutic proteins with pro‐inflammatory activities

Henrich

Wang

2023

Clinical Translational Sci

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It is well‐recognized that therapeutic proteins (TPs) with pro‐inflammatory activities elevate the pro‐inflammatory cytokines and result in cytokine‐drug interactions. In the current review, several pro‐inflammatory cytokines, including IL‐2, IL‐6, IFN‐γ, and TNF‐α, as well as an anti‐inflammatory cytokine IL‐10, were summarized for their respective effect on major cytochrome P450 enzymes and efflux transporter PgP. Pro‐inflammatory cytokines are generally associated with suppression of CYP enzymes across assay systems but have varied effect on Pgp expression levels and activities depending on the individual cytokines and assay systems, whereas IL‐10 had no significant impact on CYP enzymes and P‐gp. A cocktail drug‐drug interaction (DDI) study design could be an ideal approach for simultaneously assess the impact of TPs with pro‐inflammatory activities on multiple CYP enzymes. Clinical DDI studies using the cocktail approach have been conducted for several TPs with pro‐inflammatory activities and for those TPs with pro‐inflammatory activities which had no clinical DDI study conducted, languages for potential DDI risk due to cytokine‐drug interaction were included in the label. Up to date drug cocktails, including clinically validated and unvalidated for DDI assessment, were summarized in this review. Most clinically validated cocktails focused either on CYP enzymes or transporters. Additional effort was needed to validate a cocktail to include both the major CYP enzymes and key transporters. In silico methods for assessment of the DDI for TPs with pro‐inflammatory activities were also discussed.

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Section: Introductionsupporting

confidence: 65%

Section: Notementioning

confidence: 99%

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Assessment of the drug–drug interaction potential for therapeutic proteins with pro‐inflammatory activities

Henrich

Wang

2023

Clinical Translational Sci

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“…With potential drug targets, drugs may be repurposed for more relevant disease. Interactions between drug molecules may either reduce or enhance drug efficacy, which may help design novel schemes for drug combination in disease treatment, such as drug cocktail 92 . Additionally, the clinical use of potential drug combinations or drug repurposing requires high confidence in the molecular interactions for patients' safety, so the accuracy of current algorithms can be improved, since it may not have good performance when testing on different dataset.…”

Section: Learning-based Methods For Clustering and Classificationmentioning

confidence: 99%

Systems biology in disease modeling and control, analysis and perspectives

Yue¹,

Dutta²

2021

Preprint

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Omics and drug molecules become increasingly influential in identifying disease mechanisms and drug response. Because diseases and drug responses are co-expressed and regulated in the relevant omics interactions, the traditional way that grabbing molecular data from single isolated layers cannot always obtain valuable inference. Also, adverse effects exist in drugs that impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is then applied to predict potential molecular interaction elements by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines’ therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among omics and drug molecules for modeling disease mechanism and drug response. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are discussed for its clinic uses.

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“…Thus, it is critical to select enzyme substrates and rationally optimize incubation system conditions [34]. In this study, a cocktail method based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to detect the in vitro effect of LKMS on CYP450 enzymes and the mechanism of drug interaction [35,36] to provide data support for the clinical application of LKMS and the avoidance of adverse interactions between other drugs.…”

Section: Introductionmentioning

confidence: 99%

Inhibitory Mechanisms of Lekethromycin in Dog Liver Cytochrome P450 Enzymes Based on UPLC-MS/MS Cocktail Method

Sun,

Cao,

Qiu

et al. 2023

Molecules

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Lekethromycin (LKMS) is a synthetic macrolide compound derivative intended for use as a veterinary medicine. Since there have been no in vitro studies evaluating its potential for drug–drug interactions related to cytochrome P450 (CYP450) enzymes, the effect of the inhibitory mechanisms of LKMS on CYP450 enzymes is still unclear. Thus, this study aimed to evaluate the inhibitory effects of LKMS on dog CYP450 enzymes. A cocktail approach using ultra-performance liquid chromatography–tandem mass spectrometry was conducted to investigate the inhibitory effect of LKMS on canine CYP450 enzymes. Typical probe substrates of phenacetin, coumarin, bupropion, tolbutamide, dextromethorphan, chlorzoxazone, and testosterone were used for CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2D6, CYP2E1, and CYP3A4, respectively. This study showed that LKMS might not be a time-dependent inhibitor. LKMS inhibited CYP2A6, CYP2B6, and CYP2D6 via mixed inhibition. LKMS exhibited mixed-type inhibition against the activity of CYP2A6 with an inhibition constant (Ki) value of 135.6 μΜ. LKMS inhibited CYP2B6 in a mixed way, with Ki values of 59.44 μM. A phenotyping study based on an inhibition assay indicated that CYP2D6 contributes to the biotransformation of LKMS. A mixed inhibition of CYP2D6 with Ki values of 64.87 μM was also observed. Given that this study was performed in vitro, further in vivo studies should be conducted to identify the interaction between LKMS and canine CYP450 enzymes to provide data support for the clinical application of LKMS and the avoidance of adverse interactions between other drugs.

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Verification of a cocktail approach for quantitative drug–drug interaction assessment: a comparative analysis between the results of a single drug and a cocktail drug

Cited by 4 publications

References 44 publications

Assessment of the drug–drug interaction potential for therapeutic proteins with pro‐inflammatory activities

Assessment of the drug–drug interaction potential for therapeutic proteins with pro‐inflammatory activities

Systems biology in disease modeling and control, analysis and perspectives

Inhibitory Mechanisms of Lekethromycin in Dog Liver Cytochrome P450 Enzymes Based on UPLC-MS/MS Cocktail Method

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