A pharmacist-driven Food and Drug Administration incident surveillance and response program for compounded drugs

Abstract Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose To provide an overview of compounding under sections 503A and 503B of the Federal Food, Drug, and Cosmetic Act, and to describe the pharmacist’s role within the US Food and Drug Administration’s (FDA’s) Compounding Incidents Program, whose efforts are aimed at protecting the public against poor-quality compounded drugs through surveillance, review and response to adverse events and complaints. Summary Compounded drugs may serve an important medical need for patients who cannot be treated with medications approved by FDA; however, compounded drugs are not approved by FDA and are not subject to premarket review for safety, efficacy, or manufacturing quality; thus, they may pose safety risks to patients. Prompt reporting of adverse events or complaints related to compounding is important in identifying these risks and implementing safeguards to protect the public. FDA’s Compounding Incidents Program consists of a team of pharmacists dedicated to the surveillance and review of adverse events and complaints and follow-up actions related to safety risks associated with compounded drugs. Pharmacists are a vital component of FDA’s Compounding Incidents Program, utilizing their clinical skill set and regulatory knowledge to review and act on safety issues that affect public health. Conclusion As FDA continues to expand the Compounding Incidents Program and its efforts to protect the public against poor-quality compounded drugs, we encourage the continued submission of adverse event reports by healthcare professionals and consumers to FDA’s MedWatch reporting system in addition to adverse event reporting compliance by outsourcing facilities.

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Statin-Associated Muscular and Renal Adverse Events: Data Mining of the Public Version of the FDA Adverse Event Reporting System

PLoS ONE ◽

10.1371/journal.pone.0028124 ◽

2011 ◽

Vol 6 (12) ◽

pp. e28124 ◽

Cited By ~ 38

Author(s):

Toshiyuki Sakaeda ◽

Kaori Kadoyama ◽

Yasushi Okuno

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Event Reporting ◽

The Public ◽

Events Data

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Adverse Events Associated With Colchicine Drug Interactions: Analysis Of The Public Version Of The Fda Adverse Event Reporting System

Value in Health ◽

10.1016/j.jval.2013.03.1105 ◽

2013 ◽

Vol 16 (3) ◽

pp. A218 ◽

Cited By ~ 1

Author(s):

Z. Almalki ◽

J.J. Guo ◽

C.M. Kelton ◽

P.R. Wigle

Keyword(s):

Adverse Event ◽

Adverse Events ◽

Drug Interactions ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Event Reporting ◽

The Public

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Basics and Essentials of Medical Devices Safety Surveillance

10.5772/intechopen.97248 ◽

2021 ◽

Author(s):

Vivekanandan Kalaiselven ◽

Shatrunajay Shukla ◽

Nikita Mishra ◽

Pawan Kumar

Keyword(s):

Adverse Event ◽

Adverse Events ◽

Medical Device ◽

Medical Devices ◽

Adverse Event Reporting ◽

Scientific Data ◽

Event Reporting ◽

Healthcare Facilities ◽

The Public ◽

Regulatory Policies

Medical devices are being used in healthcare facilities for diagnosis, monitoring, prevention and treatment of an array of diseases. To ensure user/patient safety associated with the medical devices being used in healthcare industry, it is of utmost importance to closely monitor the adverse events associated with the medical devices through a robust, sustainable and scaled surveillance. Materiovigilance Programme of India (MvPI) provides a reliable system to report adverse events associated with medical devices. Under MvPI, various modalities to report adverse events associated with medical devices have been developed. These modalities include an editable medical device adverse event reporting form, a toll-free helpline number and a field safety corrective action form (FSCA). FSCA form is used to notify the regulatory authority and healthcare professionals on corrective actions or recall by the manufacturer. Due to the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, one-page editable form has been developed to boost the adverse event reporting of Personal Protective Equipments (PPEs). MvPI also coordinates with healthcare facilities and medical device industries across the country for reporting the medical device-related adverse events. The collected scientific data is utilized to develop regulatory policies and enhance measures to ensure the quality of medical devices. All the healthcare workers are, therefore, encouraged to report adverse events to MvPI. This chapter aims to describe the systems, procedures and modalities available for the reporting of Medical Device Adverse Events (MDAEs) in India, in order to intensify the nature of reporting and creating an environment that encourages the public to perform MDAE reporting.

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Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Current Drug Safety ◽

10.2174/1574886313666181026100000 ◽

2019 ◽

Vol 14 (1) ◽

pp. 21-26 ◽

Cited By ~ 2

Author(s):

Viswam Subeesh ◽

Eswaran Maheswari ◽

Hemendra Singh ◽

Thomas Elsa Beulah ◽

Ann Mary Swaroop

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Disproportionality Analysis ◽

Positive Signal ◽

Data Mining Algorithms ◽

Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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Adverse events following adenovirus type 4 and type 7 vaccine, live, oral in the Vaccine Adverse Event Reporting System (VAERS), United States, October 2011–July 2018

Vaccine ◽

10.1016/j.vaccine.2019.08.087 ◽

2019 ◽

Vol 37 (44) ◽

pp. 6760-6767 ◽

Cited By ~ 2

Author(s):

Michael M. McNeil ◽

Iwona Paradowska-Stankiewicz ◽

Elaine R. Miller ◽

Paige L. Marquez ◽

Srihari Seshadri ◽

...

Keyword(s):

United States ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adenovirus Type ◽

Adverse Event Reporting ◽

Event Reporting

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Descriptive epidemiology of adverse events after immunization: Reports to the Vaccine Adverse Event Reporting System (VAERS), 1991-1994

The Journal of Pediatrics ◽

10.1016/s0022-3476(97)70056-8 ◽

1997 ◽

Vol 131 (4) ◽

pp. 529-535 ◽

Cited By ~ 32

Author(s):

M.Miles Braun ◽

Susan S. Ellenberg

Keyword(s):

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Descriptive Epidemiology ◽

Event Reporting

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Safety of SGLT2 Inhibitors: A Pharmacovigilance Study From 2015 to 2020 Based on FDA Adverse Event Report System Database

10.22541/au.162109370.02304837/v1 ◽

2021 ◽

Author(s):

Xiang Zhou ◽

Xiaofei Ye ◽

Yinghong Zhai ◽

Fangyuan Hu ◽

Yongqing Gao ◽

...

Keyword(s):

Adverse Event ◽

Adverse Events ◽

Diabetic Ketoacidosis ◽

Adverse Event Reporting System ◽

Kidney Injury ◽

Adverse Event Reporting ◽

Adverse Event Report ◽

System Database ◽

Report System ◽

Euglycemic Diabetic Ketoacidosis

Aim: With the widespread use of SGLT2i, various adverse events (AEs) have been reported. This study aimed to describe the distribution of SGLT2i-related AEs in different systems, quantify the association of important medical events (IMEs) and SGLT2i regimens, and build a signal profile of SGLT2i- induced IMEs. Methods: Data from 2015 Q1 to 2020 Q4 in the FDA Adverse Event Reporting System database (FAERS) were selected to conduct disproportionality analysis. Two signal indicators, the reported odds ratio (ROR) and information component (IC), were used to evaluate the correlation between SGLT2i and IMEs. The lower end of the 95% confidence interval of IC (IC025) exceeding zero was deemed a signal. For ROR, it was defined a signal if ROR025 over one, with at least 3 cases. Results: A total of 45,771,436 records were involved, including 111,564 records related to SGLT2i, with 38,366 records of SGLT2i-induced IMEs. Overall, SGLT2i was significantly associated with IMEs (IC=0.36, 95% CI: 0.35-0.38; ROR=1.44, 95% CI: 1.42-1.46). Most SGLT2i-related adverse events occurred in monotherapy (92.93%). Diabetic ketoacidosis was the most IMEs. Specifically, acute osteomyelitis has the strongest signal of all SGLT2i (IC025=7.83), and it was unique to canagliflozin. Diabetic ketoacidosis, acute kidney injury, ketoacidosis, Fournier’s gangrene, and euglycemic diabetic ketoacidosis were common to the four FDA-approved SGLT2i. Conclusion: Our study demonstrated that different SGLT2i regimens lead to different important adverse events, but there are overlapping events. Early identification and management of SGLT2i-associated IMEs are essential for clinical practice.

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