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A disproportionality analysis of CDK4/6 inhibitors in the FDA Adverse Event Reporting System (FAERS)

EXPERT OPINION ON DRUG SAFETY 2025年第1期24卷 25-33页

作者： Peng, Yuan Zhou, Yuying Zhou, Xuanyi Jia, Xu Zhong, Yan North Sichuan Med Coll Dept Pharm Affiliated Hosp Nanchong Sichuan Peoples R China North Sichuan Med Coll Sch Pharm Nanchong Sichuan Peoples R China Xuzhou Med Univ Clin Med Coll 1 Xuzhou Jiangsu Peoples R China Hosp Chengdu Off Peoples Govt Tibetan Autonomous R Hosp C T Dept Pharm 20 Xi Mian Qiao Heng St Chengdu Sichuan Peoples R China

ObjectiveThe FDA Adverse Event Reporting System (FAERS) was used to mine and evaluate adverse events (AEs) associated with cyclin-dependent kinase (CDK) 4/6 inhibitors, thereby providing a reference for clinical rational drug *** data related to CDK4/6 inhibitors from the first quarter of 2015 to the first quarter of 2023 were acquired from FAERS, while the signal mining was processed using the reporting odds ratio (ROR) method and bayesian confidence propagation neural network (BCPNN) *** number of AE reports for CDK4/6 inhibitors was, respectively, 132,494 for palbociclib, 56,151 for ribociclib, and 7,014 for abemaciclib. The corresponding numbers of AE signals were 319, 517, and 59, with the number of involved System Organ Class (SOC) being 23, 23, and 15, mainly involving blood and lymphatic system disorders, respiratory, thoracic and mediastinal disorders, hepatobiliary disorders, skin and subcutaneous tissue disorders, ***4/6 inhibitors could lead to pulmonary toxicity, myelosuppression, skin reactions, etc. Special attention should be paid to abemaciclib for interstitial lung disease (ILD), erythema multiforme, and thrombosis risk;ribociclib for cardiac toxicity, hepatotoxicity, and musculoskeletal toxicity;palbociclib for neurocognitive impairment and osteonecrosis of the jaw.

关键词： Cyclin-dependent kinase 4/6 inhibitors adverse events reporting odds ratio method bayesian confidence propagation neural network method FDA Adverse Event Reporting System

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

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FUNDAMENTAL & CLINICAL PHARMACOLOGY 2019年第3期33卷 339-346页

作者： Gosho, Masahiko Univ Tsukuba Dept Biostat Fac Med 1-1-1 Tennodai Tsukuba Ibaraki 3058575 Japan

Rhabdomyolysis is one of the most concerning complications of antidyslipidemic drugs. Most patients with dyslipidemia take multiple medications. Our objective was to explore which two-drug combinations lead to a higher risk of rhabdomyolysis. We analyzed data from the Japanese Adverse Drug Event Report (JADER) database between April 2004 and September 2017. The primary outcome was the report of rhabdomyolysis. We assessed the risk of rhabdomyolysis for the two-drug concomitant use of antidyslipidemic drugs (statin, fibrate, and ezetimibe) with antihypertensive and antidiabetic medications. The Noren and Gosho methods were used for detecting two-drug interactions. The JADER contained 468 292 records for patient characteristics, 2 973 172 drug information records, and 741 016 adverse reactions records. Rhabdomyolysis was reported in 5 017 patients. Concomitant use of pravastatin/fenofibrate, simvastatin/mefruside, fluvastatin/temocapril, bezafibrate/temocapril, bezafibrate/spironolactone, bezafibrate/metoprolol, bezafibrate/losartan, fluvastatin/mitiglinide, or fenofibrate/glibenclamide was detected as the signal of rhabdomyolysis in this analysis. Combination therapy with the drugs listed above has the potential of drug-drug interactions that could result in rhabdomyolysis in patients with dyslipidemia.

关键词： bayesian confidence propagation neural network method drug interaction Japanese Adverse Drug Event Report reporting odds ratio rhabdomyolysis signal detection

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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

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FUNDAMENTAL & CLINICAL PHARMACOLOGY 2022年第6期36卷 1106-1114页

作者： Tada, Keisuke Gosho, Masahiko Sanofi KK Biostat & Programming Tokyo Opera City Tower Tokyo Japan Univ Tsukuba Grad Sch Comprehens Human Sci Ibaraki Japan Univ Tsukuba Fac Med Dept Biostat Ibaraki Japan

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 Noren'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.

关键词： bayesian confidence propagation neural network method drug-drug interaction reporting odds ratio signal detection spontaneous reporting system

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Risk of Hypoglycemia After Concomitant Use of Antidiabetic, Antihypertensive, and Antihyperlipidemic Medications: A Database Study

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JOURNAL OF CLINICAL PHARMACOLOGY 2018年第10期58卷 1324-1331页

作者： Gosho, Masahiko Univ Tsukuba Dept Biostat Fac Med Tsukuba Ibaraki 3058575 Japan

Hypoglycemia is the most important complication of antidiabetic medications. Most patients with diabetes mellitus take multiple medications. In this study, we explored clinical drug-drug interactions that result in hypoglycemia by analyzing the Japanese Adverse Drug Event Report (JADER) database. The primary outcome was the report of hypoglycemia. The Noren and Gosho methods, which quantitatively measure the discrepancy between the observed and expected number of adverse events under the combination of 2 drugs, were used as the criteria for detecting drug-drug interactions. The JADER database contained patient characteristics with 468292 records, drug information with 2973172 records, and adverse reactions with 741016 records. We noted that hypoglycemia was reported in 6208 patients. Concomitant use of linagliptin/glibenclamide, sitagliptin/glibenclamide, sitagliptin/buformin, exenatide/voglibose, mitiglinide/perindopril, repaglinide/barnidipine, alogliptin/cilnidipine, teneligliptin/barnidipine, teneligliptin/urapidil, exenatide/candesartan, voglibose/barnidipine, voglibose/guanabenz, or exenatide/tocopherol was noted using the 2 criteria for drug-drug interactions. Concomitant use of teneligliptin and barnidipine produced the highest values for the 2 criteria. The second-and third-highest values were observed for teneligliptin/urapidil and exenatide/candesartan combinations, respectively. The same analysis was performed using the US Food and Drug Administration Adverse Event Reporting System database to assess the validity of the result from the JADER database. The concomitant use of sitagliptin/buformin, exenatide/voglibose, repaglinide/barnidipine, teneligliptin/barnidipine, teneligliptin/urapidil, exenatide/candesartan, voglibose/barnidipine, voglibose/guanabenz, or exenatide/tocopherol was detected in both the JADER and Food and Drug Administration Adverse Event Reporting System databases. Combination therapy with the drugs listed above would show potential in

关键词： bayesian confidence propagation neural network method drug-drug interactions JADER reporting odds ratio signal detection

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