this are built.
• Signal detection methods- age as a confounder or effect modifier
Age appears to be an effect modifier rather than a confounder. Age adjustment was systematically demonstrated to decrease signal detection performance and should be avoided. Age stratification can increase sensitivity (especially in paediatrics) and lead to discovery of new signals therefore can be used complementary to standard methods.
• Predictors for new safety issues
Newly approved drugs should be monitored with greater caution since the knowledge of their benefit-risk profile is still less mature. Post-approval exposure seems to be a determinant of safety issues, at least in the initial period on the market. Special attention during signal detection should be given to drugs with potential for high and rapid market uptake, at least until they achieve a certain exposure threshold. The exact threshold, estimated in our study at approximately 10,000 patient-years should be investigated in further research. Since the studies investigating the relation between drug exposure and frequency of safety issues have different results, more research in this area is recommended.
Multi-national reporting and report quality should be considered when prioritizing signals. In contrast, reporter qualification should not be considered as a prioritization criteria since it was not proven to be associated with true signals.
• More testing of currently available prioritization criteria and frameworks should be done, as this would support creation of a robust evidence-based prioritization process.
• Drug exposure data
Drug utilisation data have an increasingly important role in the review of benefit-risk of medicinal products post-marketing. Signal detection is no exception. To ensure optimal signal management, efforts should be made to improve collection and accessibility of drug exposure information, since exposure is needed to estimate the public health impact.
CONCLUSION
In conclusion, the dynamic nature of the drug safety field, both in the scientific and in the regulatory aspects, drives the continuous update of existing methods and exploration of other sources for investigating drug safety. There is a need to create big networks of EHR, to support signal detection and evaluation processes, to increase access to drug utilisation data and to invest in prioritisation systems.
519883-L-sub01-bw-Pacurariu 519883-L-sub01-bw-Pacurariu 519883-L-sub01-bw-Pacurariu 519883-L-sub01-bw-Pacurariu Processed on: 5-6-2018 Processed on: 5-6-2018 Processed on: 5-6-2018
Processed on: 5-6-2018 PDF page: 138PDF page: 138PDF page: 138PDF page: 138
Chapter 7
138
REFERENCES
1 Ishiguro C, Hall M, Neyarapally GA, Dal Pan G. Post-market drug safety evidence sources: an analysis of FDA drug safety communications. Pharmacoepidemiol Drug Saf. 2012 Oct 1;21(10):1134–6.
2 Institute of Medicine. The Future of Drug Safety: Promoting and Protecting the Health of the Public [Internet]. 2006 [cited 2018 Jan 12]. Available from: https://www.nap.edu/catalog/11750/the-future-of-drug-safety- promoting-and-protecting-the-health
3 Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS, Kauffman RE. Developmental pharmacology - Drug disposition, action, and therapy in infants and children. N Engl J Med. 2003;349(12):1157–67. 4 Gispen-de Wied CC, Leufkens HGM. From molecule to market access: drug regulatory science as an
upcoming discipline. Eur J Pharmacol. 2013 Nov 5;719(1–3):9–15.
5 Advancing regulatory science [Internet]. Available from: https://www.fda.gov/ScienceResearch/ SpecialTopics/RegulatoryScience/default.htm
6 Meagher E. Efficient drug approval and monitoring must rely on sound regulatory science : Nature Medicine : Nature Research [Internet]. [cited 2017 Feb 12]. Available from: http://www.nature.com/nm/journal/v17/ n12/abs/nm1211-1535.html
7 Stricker BHC, Psaty BM. Detection, verification, and quantification of adverse drug reactions. BMJ. 2004 Jul 3;329(7456):44–7.
8 Krumholz HM, Ross JS, Presler AH, Egilman DS. What have we learnt from Vioxx? BMJ. 2007 Jan 20;334(7585):120–3.
9 Eichler H-G, Oye K, Baird LG, Abadie E, Brown J, Drum CL, et al. Adaptive licensing: taking the next step in the evolution of drug approval. Clin Pharmacol Ther. 2012 Mar;91(3):426–37.
10 Eichler H-G, Baird LG, Barker R, Bloechl-Daum B, Børlum-Kristensen F, Brown J, et al. From adaptive licensing to adaptive pathways: delivering a flexible life-span approach to bring new drugs to patients. Clin Pharmacol Ther. 2015 Mar;97(3):234–46.
11 Vandenbroucke JP. In defense of case reports and case series. Ann Intern Med. 2001 Feb 20;134(4):330–4. 12 Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of pharmacovigilance
signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther. 2013 Jun;93(6):539–46.
13 Coloma PM, Schuemie MJ, Trifirò G, Gini R, Herings R, Hippisley-Cox J, et al. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf. 2011 Jan;20(1):1–11.
14 Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012 Dec 30;31(30):4401–15.
15 Wisniewski AFZ, Bate A, Bousquet C, Brueckner A, Candore G, Juhlin K, et al. Good Signal Detection Practices: Evidence from IMI PROTECT. Drug Saf. 2016;39:469–90.
16 Schuemie MJ, Coloma PM, Straatman H, Herings RMC, Trifirò G, Matthews JN, et al. Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Med Care. 2012 Oct;50(10):890–7.
17 Trifirò G, Patadia V, Schuemie MJ, Coloma PM, Gini R, Herings R, et al. EU-ADR healthcare database network vs. spontaneous reporting system database: preliminary comparison of signal detection. Stud Health Technol Inform. 2011;166:25–30.
18 Harpaz R, Vilar S, Dumouchel W, Salmasian H, Haerian K, Shah NH, et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J Am Med Inform Assoc JAMIA. 2013 May 1;20(3):413–9.
19 PROTECT Home [Internet]. [cited 2018 Jan 12]. Available from: http://www.imi-protect.eu/symposium.shtml 20 Edwards I, Karimi G, Bergvall T, Caster O, Asiimwe A, Star K, Bhayat F, Soriano-Gabarro M, Thakrar B, Thompson M, Bate A, Norén G. Risk identification in healthcare records: comparison to epidemiological studies. Pharmacoepidemiol Drug Saf. 2013;22(S1):311–312.
21 Dhanwal DK, Dennison EM, Harvey NC, Cooper C. Epidemiology of hip fracture: Worldwide geographic variation. Indian J Orthop. 2011;45(1):15–22.
519883-L-sub01-bw-Pacurariu 519883-L-sub01-bw-Pacurariu 519883-L-sub01-bw-Pacurariu 519883-L-sub01-bw-Pacurariu Processed on: 5-6-2018 Processed on: 5-6-2018 Processed on: 5-6-2018
Processed on: 5-6-2018 PDF page: 139PDF page: 139PDF page: 139PDF page: 139