Drug interactions between tacrolimus and clotrimazole troche: a data mining approach followed by a pharmacokinetic study

Uno, Takaya; Wada, Keita; Hosomi, Kouichi; Matsuda, Sachi; Ikura, Megumi; Takenaka, Hiromi; Terakawa, Nobue; Oita, Akira; Yokoyama, S.; Kawase, Atsushi; Takada, Mitsutaka

doi:10.1007/s00228-019-02770-6

Cited by 5 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although several methods for detecting signals of drug interaction have been reported, it is difficult to say that these methods are used because many of them are complicated in calculation. Therefore, there were several cases [ 11 , 12 , 13 ] where subset analysis using the algorithm for detecting signals of single drugs (e.g., ROR [ 6 ]) was used for signal detection of drug–drug interactions before its validity was verified.…”

Section: Discussionmentioning

confidence: 99%

“…In previous studies [ 11 , 12 , 13 ], if the signal for drug D 2 was detected in the subset of a patient group using drug D 1 or if the signal for drug D 1 was detected in the subset of a patient group using drug D 2 , this signal was considered the drug–drug interaction signal. The criterion that a signal only needs to be detected from a subset of either patient group is ambiguous, highlighting the two shortcomings mentioned earlier.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, in order to detect the drug–drug interaction signals between drug D 1 and drug D 2 , the subset analysis that detects the signal of drug D 1 using the ROR, which is easy to calculate in a subset of patient groups, is often reported [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Of previous studies, several [ 11 , 12 ] have used animal experiments and/or pharmacological data to ensure signal reliability, but the signals obtained with this analysis model are not strictly drug–drug interaction signals; they only showed the effect of drug combinations for the following two reasons [ 14 ]:…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

2020

View full text Add to dashboard Cite

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

2020

View full text Add to dashboard Cite

show abstract

“…Clotrimazole inhibits CYP3A function [28]. To date, oral clotrimazole lozenges have been used for prevention of opportunistic infections at NCVC, but we have experienced a need for dose adjustment of TAC by hospitalization when this drug is discontinued 6 months after HTx [29][30][31]. Herein, we have switched to oral amphotericin B for treatment, and since then, it has succeeded in maintaining stable pharmacokinetics of TAC [32].…”

Section: Calcineurin Inhibitors (Cnis)mentioning

confidence: 99%

Role of the Transplant Pharmacist

Ikura¹,

Nakagita²,

Uno³

et al. 2022

Heart Transplantation - New Insights in Therapeutic Strategies

Self Cite

View full text Add to dashboard Cite

At the National Cerebral and Cardiovascular Center, Japan, pharmacists have been involved in drug treatment management and patient care as members of multidisciplinary heart transplant teams that include surgeons, physicians, recipient transplant coordinators, and nurses during the waiting period for heart transplantation (HTx), HTx surgery, and post-HTx. During the waiting period, pharmacists play an important role in adjusting the use of antibiotics, anticoagulants, and antiarrhythmics by patients receiving a ventricular assist device (VAD). During HTx surgery and post-HTx, pharmacists advise physicians regarding the individualized medication protocol for immunosuppression and infection prevention to be used for each patient based on the patient’s pre-HTx characteristics as well as gene polymorphisms. They thus contribute to reducing the burden on the physician through the sharing of tasks. Throughout all three phases of HTx, pharmacists repeatedly provide medication and adherence education to the patients and caregivers. It is hoped that an academic society-led training protocol as well as transplant pharmacists will be established in Japan and other developed countries, and that these specialized transplant pharmacists would then provide individualized pharmacotherapy for the use of various antibiotics, anticoagulants, and immunosuppressive agents that have a narrow range of treatment in VAD and HTx patients.

show abstract

Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

2020

View full text Add to dashboard Cite

Drug interactions between tacrolimus and clotrimazole troche: a data mining approach followed by a pharmacokinetic study

Cited by 5 publications

References 46 publications

Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

Role of the Transplant Pharmacist

Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

Contact Info

Product

Resources

About