Special Issue "Drug Safety and Pharmacovigilance"

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A special issue of Pharmaceutics (ISSN 1999-4923).

Deadline for manuscript submissions: closed (15 September 2012)

Special Issue Editor

Guest Editor
Dr. Tomomi Kimura

Head of Epidemiology, Janssen Pharmaceutical K.K., 5-2, Nishi-kanda 3-chome, Chiyoda-ku, Tokyo 101-0065, Japan
E-Mail
Interests: clinical epidemiology; database research; ethnic sensitivities

Keywords

  • pharmacovigilance
  • drug safety
  • active surveillance
  • signal detection
  • pharmacoepidemiology
  • database
  • algorithm
  • validation
  • outcomes

Published Papers (5 papers)

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Research

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Open AccessArticle Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic
Pharmaceutics 2013, 5(1), 179-200; doi:10.3390/pharmaceutics5010179
Received: 6 November 2012 / Revised: 1 March 2013 / Accepted: 4 March 2013 / Published: 14 March 2013
Cited by 1 | PDF Full-text (262 KB) | HTML Full-text | XML Full-text
Abstract
Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a
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Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds. Full article
(This article belongs to the Special Issue Drug Safety and Pharmacovigilance)
Open AccessArticle Safety Monitoring in Clinical Trials
Pharmaceutics 2013, 5(1), 94-106; doi:10.3390/pharmaceutics5010094
Received: 6 November 2012 / Revised: 9 January 2013 / Accepted: 10 January 2013 / Published: 17 January 2013
Cited by 4 | PDF Full-text (442 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring patient safety during clinical trials is a critical component throughout the drug development life-cycle. Pharmaceutical sponsors must work proactively and collaboratively with all stakeholders to ensure a systematic approach to safety monitoring. The regulatory landscape has evolved with increased requirements for risk
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Monitoring patient safety during clinical trials is a critical component throughout the drug development life-cycle. Pharmaceutical sponsors must work proactively and collaboratively with all stakeholders to ensure a systematic approach to safety monitoring. The regulatory landscape has evolved with increased requirements for risk management plans, risk evaluation and minimization strategies. As the industry transitions from passive to active safety surveillance activities, there will be greater demand for more comprehensive and innovative approaches that apply quantitative methods to accumulating data from all sources, ranging from the discovery and preclinical through clinical and post-approval stages. Statistical methods, especially those based on the Bayesian framework, are important tools to help provide objectivity and rigor to the safety monitoring process. Full article
(This article belongs to the Special Issue Drug Safety and Pharmacovigilance)
Open AccessArticle Quantitative Evaluation of Compliance with Recommendation for Sulfonylurea Dose Co-Administered with DPP-4 Inhibitors in Japan
Pharmaceutics 2012, 4(3), 479-493; doi:10.3390/pharmaceutics4030479
Received: 11 June 2012 / Revised: 8 August 2012 / Accepted: 30 August 2012 / Published: 19 September 2012
Cited by 4 | PDF Full-text (240 KB) | HTML Full-text | XML Full-text
Abstract
After the launch of dipeptidyl peptidase-4 (DPP-4), a new oral hypoglycemic drug (OHD), in December 2009, severe hypoglycemia cases were reported in Japan. Although the definite cause was unknown, co-administration with sulfonylureas (SU) was suspected as one of the potential risk factors. The
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After the launch of dipeptidyl peptidase-4 (DPP-4), a new oral hypoglycemic drug (OHD), in December 2009, severe hypoglycemia cases were reported in Japan. Although the definite cause was unknown, co-administration with sulfonylureas (SU) was suspected as one of the potential risk factors. The Japan Association for Diabetes Education and Care (JADEC) released a recommendation in April 2010 to lower the dose of three major SUs (glimepiride, glibenclamide, and gliclazide) when adding a DPP-4 inhibitor. To evaluate the effectiveness of this risk minimization action along with labeling changes, dispensing records for 114,263 patients prescribed OHDs between December 2008 and December 2010 were identified in the Nihon-Chouzai pharmacy claims database. The adherence to the recommended dosing of SU co-prescribed with DPP-4 inhibitors increased from 46.3% before to 63.8% after the JADEC recommendation (p < 0.01 by time-series analysis), while no change was found in those for SU monotherapy and SU with other OHD co-prescriptions. The adherence was significantly worse for those receiving a glibenclamide prescription. The JADEC recommendation, along with labeling changes, appeared to have a favorable effect on the risk minimization action in Japan. In these instances, a pharmacy claims database can be a useful tool to evaluate risk minimization actions. Full article
(This article belongs to the Special Issue Drug Safety and Pharmacovigilance)

Review

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Open AccessReview Signal Detection and Monitoring Based on Longitudinal Healthcare Data
Pharmaceutics 2012, 4(4), 607-640; doi:10.3390/pharmaceutics4040607
Received: 4 September 2012 / Revised: 15 November 2012 / Accepted: 26 November 2012 / Published: 13 December 2012
Cited by 14 | PDF Full-text (456 KB) | HTML Full-text | XML Full-text
Abstract
Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudinal data. This paper gives a broad overview of
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Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudinal data. This paper gives a broad overview of the signal detection process and introduces some types of data sources typically used. The most commonly applied signal detection algorithms are presented, covering simple frequentistic methods like the proportional reporting rate or the reporting odds ratio, more advanced Bayesian techniques for spontaneous and longitudinal data, e.g., the Bayesian Confidence Propagation Neural Network or the Multi-item Gamma-Poisson Shrinker and methods developed for longitudinal data only, like the IC temporal pattern detection. Additionally, the problem of adjustment for underlying confounding is discussed and the most common strategies to automatically identify false-positive signals are addressed. A drug monitoring technique based on Wald’s sequential probability ratio test is presented. For each method, a real-life application is given, and a wide set of literature for further reading is referenced. Full article
(This article belongs to the Special Issue Drug Safety and Pharmacovigilance)

Other

Jump to: Research, Review

Open AccessCommentary Vaccine Safety Surveillance Systems: Critical Elements and Lessons Learned in the Development of the US Vaccine Safety Datalink’s Rapid Cycle Analysis Capabilities
Pharmaceutics 2013, 5(1), 168-178; doi:10.3390/pharmaceutics5010168
Received: 31 December 2012 / Revised: 16 February 2013 / Accepted: 18 February 2013 / Published: 12 March 2013
Cited by 2 | PDF Full-text (165 KB) | HTML Full-text | XML Full-text
Abstract
Since the late 1990s, there have been tremendous strides made in improving the capacity for carrying out routine active surveillance of new vaccines in the United States. These strides have led to new surveillance systems that are now in place. Some of the
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Since the late 1990s, there have been tremendous strides made in improving the capacity for carrying out routine active surveillance of new vaccines in the United States. These strides have led to new surveillance systems that are now in place. Some of the critical elements that are part of successful vaccine or drug safety surveillance systems include their use of (i) longitudinal data from a discrete enumerated population base, (ii) frequent, routine transfers of small amounts of data that are easy to collect and collate, (iii) avoidance of mission creep, (iv) statistical capabilities, (v) creation of an “industrialized process” approach and (vi) political safe harbor. Full article
(This article belongs to the Special Issue Drug Safety and Pharmacovigilance)

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