Zahra Anita Trippe1,2, Bruno Brendani3, Christoph Meier4, David Lewis3,5. 1. Patient Safety, Novartis Pharma AG, Basel, Switzerland. zahra_anita.trippe@novartis.com. 2. Division of Clinical Pharmacy and Epidemiology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland. zahra_anita.trippe@novartis.com. 3. Patient Safety, Novartis Pharma AG, Basel, Switzerland. 4. Division of Clinical Pharmacy and Epidemiology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland. 5. School of Life and Medical Sciences, University of Hertfordshire, Hatfield, England, UK.
Abstract
INTRODUCTION: The distribution and use of substandard medicines (SSMs) is a public health concern worldwide. The detection of SSMs is currently limited to expensive large-scale assay techniques such as high-performance liquid chromatography (HPLC). Since 2013, the Pharmacovigilance Department at Novartis Pharma AG has been analyzing drug-associated adverse events related to 'product quality issues' with the aim of detecting defective medicines using spontaneous reporting. The method of identifying SSMs with spontaneous reporting was pioneered by the Monitoring Medicines project in 2011. METHODS: This retrospective review was based on data from the World Health Organization (WHO) Global individual case safety report (ICSR) database VigiBase® collected from January 2001 to December 2014. We conducted three different stratification analyses using the Multi-item Gamma Poisson Shrinker (MGPS) algorithm through the Oracle Empirica data-mining software. In total, 24 preferred terms (PTs) from the Medical Dictionary for Regulatory Activities (MedDRA®) were used to identify poor-quality medicines. To identify potential SSMs for further evaluation, a cutoff of 2.0 for EB05, the lower 95% interval of the empirical Bayes geometric mean (EBGM) was applied. We carried out a literature search for advisory letters related to defective medicinal products to validate our findings. Furthermore, we aimed to assess whether we could confirm two SSMs first identified by the Uppsala Monitoring Centre (UMC) with our stratification method. RESULTS: The analysis of ICSRs based on the specified selection criteria and threshold yielded 2506 hits including medicinal products with an excess of reports of product quality defects relative to other medicines in the database. Further investigations and a pilot study in five authorized medicinal products (proprietary and generic) licensed by a single marketing authorization holder, containing valsartan, methylphenidate, rivastigmine, clozapine, or carbamazepine, were performed. This resulted in an output of 23 potential SSMs. The literature search identified two communications issued to health professionals concerning a substandard rivastigmine patch, which validated our initial findings. Furthermore, we identified excess reporting of product quality issues with an ethinyl estradiol/norgestrel combination and with salbutamol. These were categorized as confirmed clusters of substandard/spurious/falsely labelled/falsified/counterfeit (SSFFC) medical products by the UMC in 2014. CONCLUSION: This study illustrates the value of data mining of spontaneous adverse event reports and the applicability of disproportionality analysis to identify potential SSMs.
INTRODUCTION: The distribution and use of substandard medicines (SSMs) is a public health concern worldwide. The detection of SSMs is currently limited to expensive large-scale assay techniques such as high-performance liquid chromatography (HPLC). Since 2013, the Pharmacovigilance Department at Novartis Pharma AG has been analyzing drug-associated adverse events related to 'product quality issues' with the aim of detecting defective medicines using spontaneous reporting. The method of identifying SSMs with spontaneous reporting was pioneered by the Monitoring Medicines project in 2011. METHODS: This retrospective review was based on data from the World Health Organization (WHO) Global individual case safety report (ICSR) database VigiBase® collected from January 2001 to December 2014. We conducted three different stratification analyses using the Multi-item Gamma Poisson Shrinker (MGPS) algorithm through the Oracle Empirica data-mining software. In total, 24 preferred terms (PTs) from the Medical Dictionary for Regulatory Activities (MedDRA®) were used to identify poor-quality medicines. To identify potential SSMs for further evaluation, a cutoff of 2.0 for EB05, the lower 95% interval of the empirical Bayes geometric mean (EBGM) was applied. We carried out a literature search for advisory letters related to defective medicinal products to validate our findings. Furthermore, we aimed to assess whether we could confirm two SSMs first identified by the Uppsala Monitoring Centre (UMC) with our stratification method. RESULTS: The analysis of ICSRs based on the specified selection criteria and threshold yielded 2506 hits including medicinal products with an excess of reports of product quality defects relative to other medicines in the database. Further investigations and a pilot study in five authorized medicinal products (proprietary and generic) licensed by a single marketing authorization holder, containing valsartan, methylphenidate, rivastigmine, clozapine, or carbamazepine, were performed. This resulted in an output of 23 potential SSMs. The literature search identified two communications issued to health professionals concerning a substandard rivastigmine patch, which validated our initial findings. Furthermore, we identified excess reporting of product quality issues with an ethinyl estradiol/norgestrel combination and with salbutamol. These were categorized as confirmed clusters of substandard/spurious/falsely labelled/falsified/counterfeit (SSFFC) medical products by the UMC in 2014. CONCLUSION: This study illustrates the value of data mining of spontaneous adverse event reports and the applicability of disproportionality analysis to identify potential SSMs.
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