Rhabdomyolysis risk from the use of two‐drug combination of antidyslipidemic drugs with antihypertensive and antidiabetic medications: a signal detection analysis

Gosho, Masahiko

doi:10.1111/fcp.12435

Cited by 8 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Musculoskeletal ADEs have been reported for allopurinol [35], and rhabdomyolysis is distributed as a severe ADE in the Japanese package insert [14]. However, no study has reported ADEs due to DDIs associated with the concomitant use of pitavastatin and allopurinol.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, many drugs such as solivudine, cerivastatin, mibefradil, cisapride, and terfenadine have been withdrawn from the market owing to the occurrence of ADEs due to DDIs post-marketing [6][7][8]. Therefore, it is important to detect DDIs using realworld databases such as the spontaneous reporting system (SRS) to identify ADEs in the early stage of post-marketing and to get feedback for clinical practice [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments

Negishi¹,

Oshima²,

Mutoh³

et al. 2022

Int. J. Med. Sci.

View full text Add to dashboard Cite

Adverse drug events due to drug-drug interactions can be prevented by avoiding concomitant use of causative drugs; therefore, it is important to understand drug combinations that cause drug-drug interactions. Although many attempts to identify drug-drug interactions from real-world databases such as spontaneous reporting systems have been performed, little is known about drug-drug interactions caused by three or more drugs in polypharmacy, i.e., multiple drug-drug interactions. Therefore, we attempted to detect multiple drug-drug interactions using decision tree analysis using the Japanese Adverse Drug Event Report (JADER) database, a Japanese spontaneous reporting system. First, we used decision tree analysis to detect drug combinations that increase the risk of rhabdomyolysis in cases registered in the JADER database that used six statins. Next, the risk of three or more drug combinations that significantly increased the risk of rhabdomyolysis was validated with in vivo experiments in rats. The analysis identified a multiple drug-drug interaction signal only for pitavastatin. The reporting rate of rhabdomyolysis for pitavastatin in the JADER database was 0.09, and it increased to 0.16 in combination with allopurinol. Furthermore, the rate was even higher (0.40) in combination with valsartan. Additionally, necrosis of leg muscles was observed in some rats simultaneously treated with these three drugs, and their creatine kinase and myoglobin levels were elevated. The combination of pitavastatin, allopurinol, and valsartan should be treated with caution as a multiple drug-drug interaction. Since multiple drug-drug interactions were detected with decision tree analysis and the increased risk was verified in animal experiments, decision tree analysis is considered to be an effective method for detecting multiple drug-drug interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments

Negishi¹,

Oshima²,

Mutoh³

et al. 2022

Int. J. Med. Sci.

View full text Add to dashboard Cite

show abstract

“…Gosho (2018) used the chi-square statistics model and he Ω shrinkage measure model to examine the clinical drug–drug interactions that cause hypoglycemia and rhabdomyolysis (Gosho, 2019).…”

Section: Statistical Methodologymentioning

confidence: 99%

Review of Statistical Methodologies for Detecting Drug–Drug Interactions Using Spontaneous Reporting Systems

2019

View full text Add to dashboard Cite

Concomitant use of multiple drugs for therapeutic purposes is known as “polypharmacy situations,” which has been recognized as an important social problem recently. In polypharmacy situations, each drug not only induces adverse events (AEs) but also increases the risk of AEs due to drug–drug interactions (DDIs). The proportion of AEs caused by DDIs is estimated to be around 30% of unexpected AEs. The randomized clinical trials in pre-marketing typically focus emphasis on the verification of single drug safety and efficacy rather than the surveys of DDI, and therefore, patients on multiple drugs are usually excluded. However, unlike pre-marketing randomized clinical trials, in clinical practice (= post marketing), many patients use multiple drugs. The spontaneous reporting system is one of the significant sources drug safety surveillance in post-marketing. Commonly, signals of potential drug-induced AEs detected from this source are validated in real-world settings. Recently, not only methodological studies on signal detection of “single” drug, but also on several methodological studies on signal detection of DDIs have been conducted. On the other hand, there are few articles that systematically summarize the statistical methodology for signal detection of DDIs. Therefore, this article reviews the studies on the latest statistical methodologies from classical methodologies for signal detection of DDIs using spontaneous reporting system. This article describes how to calculate for each detection method and the major findings from the published literatures about DDIs. Finally, this article presented several limitations related to the currently used methodologies for signal detection of DDIs and suggestions for further studies.

show abstract

“…Several researches have recently examined DDIs on these databases. For example, Gosho investigated the increased risk of hypoglycemia or rhabdomyolysis by concomitant use of antidiabetic, antidyslipidemic, and antihypertensive drugs using the JADER [13,14]. Antonazzo et al investigated the increased risk of myopathy by concomitant use of DPP4i and statins using the FAERS [15].…”

Section: Introductionmentioning

confidence: 99%

Increased risk of urinary tract infection and pyelonephritis under concomitant use of sodium‐dependent glucose cotransporter 2 inhibitors with antidiabetic, antidyslipidemic, and antihypertensive drugs: An observational study

Tada

Gosho

2022

Fundamemntal Clinical Pharma

Self Cite

View full text Add to dashboard Cite

Urinary tract infection (UTI) and pyelonephritis cause urosepsis, which can become life threating. Sodium‐dependent glucose cotransporter 2 (SGLT2) inhibitors, a class of oral anti‐diabetic drugs, may increase the risk of UTI and pyelonephritis, as observed from their mechanism of action. Patients with diabetes receiving SGLT2 inhibitors are often concomitantly administered other antidiabetic, antidyslipidemic, or antihypertensive drugs. To determine if drug–drug interactions (DDIs) between SGLT2 inhibitors and these medications increased the risk of UTI and pyelonephritis, we analyzed the Food and Drug Administration Adverse Event Reporting System (FAERS) database. We applied Norén's and Gosho's methods for detecting DDIs. FAERS contains records describing 14 526 398 patient characteristics, 54 290 663 drug properties, and 47 252 416 reactions/events. We found 23 drug combinations that could induce UTI and pyelonephritis, such as SGLT2 inhibitors administered with dipeptidyl peptidase‐4 inhibitors, thiazolidinediones, glinides, statins, angiotensin II receptor blockers, and calcium channel blockers. Combination therapy with the drugs detected in our analyses would show potential interactions that could result in UTI and pyelonephritis in patients with diabetes mellitus. However, owing to various limitations, these results must be confirmed by additional analyses.

show abstract

Rhabdomyolysis risk from the use of two‐drug combination of antidyslipidemic drugs with antihypertensive and antidiabetic medications: a signal detection analysis

Cited by 8 publications

References 28 publications

Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments

Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments

Review of Statistical Methodologies for Detecting Drug–Drug Interactions Using Spontaneous Reporting Systems

Increased risk of urinary tract infection and pyelonephritis under concomitant use of sodium‐dependent glucose cotransporter 2 inhibitors with antidiabetic, antidyslipidemic, and antihypertensive drugs: An observational study

Contact Info

Product

Resources

About