Blood glucose related adverse drug reaction of antitumor monoclonal antibodies: a retrospective analysis using Vigibase

Yang, Jincheng; Zhao, Bin; Zhou, Haiyan; Jia, Bei; Chen, Lianzhen

doi:10.1590/s2175-97902020000118893

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Half of the studies (12 out of 24) used spontaneous reports from the FDA adverse events reporting system (FAERS) database [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], six from Vigibase [ 26 , 27 , 28 , 29 , 30 , 31 ], three from the French pharmacovigilance database [ 32 , 33 , 34 ], two from the Japanese adverse drug report (JADERS) database [ 35 , 36 ] and one from the Italian spontaneous reporting system [ 37 ]. Bevacizumab was the most studied drug (13 studies) followed by sunitinib (11 studies), sorafenib (10 studies) and axitinib (nine studies).…”

Section: Resultsmentioning

confidence: 99%

“…Comparing the records of metabolism and nutrition disorders with total retrieved records, sorafenib showed the highest ratio (around 10%) [ 30 ]. In a second study, the same authors focused on AEs associated with monoclonal antibody drugs, and bevacizumab was reported to have higher reporting for hyperglycemic records [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…In total, 24 articles were included in this scoping review (six were excluded because they did not use SRSs and one because it did not report information on AADs) (Figure 1). Half of the studies (12 out of 24) used spontaneous reports from the FDA adverse events reporting system (FAERS) database [14][15][16][17][18][19][20][21][22][23][24][25], six from Vigibase [26][27][28][29][30][31], three from the French pharmacovigilance database [32][33][34], two from the Japanese adverse drug report (JADERS) database [35,36] and one from the Italian spontaneous reporting system [37]. Bevacizumab was the most studied drug (13 studies) followed by sunitinib (11 studies), sorafenib (10 studies) and axitinib (nine studies).…”

Section: Study Characteristicsmentioning

confidence: 99%

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Uncovering Knowledge Gaps in the Safety Profile of Antiangiogenic Drugs in Cancer Patients: Insights from Spontaneous Reporting Systems Studies

et al. 2023

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Global repositories of postmarketing safety reports improve understanding of real-life drug toxicities, often not observed in clinical trials. The aim of this scoping review was to map the evidence from spontaneous reporting systems studies (SRSs) of antiangiogenic drugs (AADs) in cancer patients and highlight if the found disproportionality signals of adverse events (AEs) were validated and thus mentioned in the respective Summary of product Characteristics (SmPC). This scoping review was conducted according to PRISMA guidelines for scoping reviews. A knowledge gap on the safety of AADs was found: firstly, several cardiovascular AEs were not mentioned in the SmPCs and no pharmacovigilance studies were conducted despite the well-known safety concerns about these drugs on the cardiovascular system. Second, a disproportionality signal (not validated through causality assessment) of pericardial disease was found in the literature for axitinib with no mention in SmPC of the drug. Despite the exclusion of pharmacoepidemiological studies, we believe that this scoping review, which focuses on an entire class of drugs, could be considered as a novel approach to highlight possible safety concerns of drugs and as a guide for the conduction of a target postmarketing surveillance on AADs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

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Uncovering Knowledge Gaps in the Safety Profile of Antiangiogenic Drugs in Cancer Patients: Insights from Spontaneous Reporting Systems Studies

et al. 2023

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“…Among the endocrine dysfunctions, hyperglycemia was the third (after hypothyroidism and hyperthyroidism) at all-grade immune-related adverse events (irAEs) and the first at grade III or higher irAEs [5,6]. Our previous research indicated that Nivolumab and Pembrolizumab were positive hyperglycemia-causing drugs [7,8]. In 2021, JAMA Oncology reported that risks of chronic irAEs should be integrated into treatment decision making [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

Yang

Lin

et al. 2022

Journal of Healthcare Engineering

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Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions ( n u = 0.5649 × e − case / 6984 , gamma = 9.005 × 10 − 4 × case − 4.877 × 10 − 8 × case 2 ). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.

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Long-Acting Strategies for Antibody Drugs: Structural Modification, Controlling Release, and Changing the Administration Route

Wang,

Song,

et al. 2024

Eur J Drug Metab Pharmacokinet

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Blood glucose related adverse drug reaction of antitumor monoclonal antibodies: a retrospective analysis using Vigibase

Cited by 4 publications

References 18 publications

Uncovering Knowledge Gaps in the Safety Profile of Antiangiogenic Drugs in Cancer Patients: Insights from Spontaneous Reporting Systems Studies

Uncovering Knowledge Gaps in the Safety Profile of Antiangiogenic Drugs in Cancer Patients: Insights from Spontaneous Reporting Systems Studies

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

Long-Acting Strategies for Antibody Drugs: Structural Modification, Controlling Release, and Changing the Administration Route

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