Three core components of the EU drug safety system
“Explain and critically evaluate how the three core components of the EU drug safety system operate[…and how] a bio-pharmaceutical company use[s] the system to minimise the risks of harm from its medicines.”
NB: Please refer to assignment feedback for guidance.
Adverse drug reactions (ADRs) represent significant causes of morbidity and mortality (Pillans, 2008) accounting for the third most common cause of death in the USA (Aspinall et al., 2002) even despite known issues of under-reporting (Avong et al., 2018). In the EU, ADRs are thought to account for up to 5% of hospitalisations alone and nearly 200 thousand deaths, representing a cost of nearly £70b (European Commission, 2008).
Prior to licencing, short-term drug safety and efficacy are determined by clinical trials which are tightly controlled environments and involve a comparably very small, unrepresentative patient population (Wysowski and Swartz, 2005). To detect ADRs affecting 1:10000 individuals, a trial population would need to exceed 30,000, which is clearly not possible in the clinical setting (WHO, 2002). Drugs must therefore be monitored throughout their life cycle in order to identify these ADRs and to take the appropriate action.
Bio-pharmaceutical companies in their role a marketing authorisation holders (MAHs), national competent authorities (NCAs) and the European Medicines Agency (EMA) collaborate to monitor drug safety from the point of licensing approval (Sharrar and Dieck, 2013). The EMA established the Pharmacovigilance Risk Assessment Committee (PRAC) in order to oversee the ongoing drug safety assessments. The committee is comprised of members from each NCA in addition to experts in their field, healthcare professionals (HCPs) and individuals from patient groups (EMA, n.d. a). EudraVigilance (EV), is a database established to monitor ADRs from across the EU and represent a central store for all ADR reports generated from any stage of clinical development and real-world use (EMA, n.d. b).
“1. Individual Case Safety Reports”
Spontaneous reporting of ADRs are critical for the detection of safety signals and are recorded as Individual Case safety reports (ISCRs). In the EU, ISCRs are reported to the EV database. These reports provide all the necessary information that related to a single incidence of a specific ADR report, from an individual patient (Streefland, 2018).
Spontaneous reports are made by healthcare professionals or patients and are forwarded to MAHs for review. MAHs must validate the report, ensuring there is information on the patient, the reporter, the presumed drug, and the reported reaction (Comfort et al., 2018). The seriousness of the report and how likely it is to be related to the drug must also be determined. The reported events are then coded with the correct medical terms and technical MedDRA compliant language. This validation process often requires extensive follow-up from the MAH.
Patient reporting represents an important aspect of ADR collection that would otherwise not be accessible, patients provide first-hand accounts that may be more detailed than those made by HCPs (Rolfes et al., 2014) However, only 8.5% of UK patients are thought to be aware of the option to make direct repots (Avery et al., 2011).
Regulations do not require that a physician reviews ICSRs which can make validation challenging. Even where a HCP has reviewed the case, by virtue of a patient reporting it to them, additional work from the MAH to determine if the reported event is indeed medically sound may be required (Streefland, 2018). The validated ISCR must be reported to the NCA within 15 or days of being made aware of it, depending on its seriousness, for each of their marketed products. Almost 70% of safety signals are thought to originate from ICSRs and nearly 15 million ICSRs were reported to EV by the end of 2018 which accounts to well over 8 million individual cases (EMA, 2019a).
This process is administratively burdensome especially as there is growing patient awareness over the reporting of ADRs (Streefland, 2018). Patients are also able to make reports directly to the MHRA, through the ‘Yellow Card’ initiative (Avery et al., 2011). Furthermore, there is growing interest in using social media to provide safety signals however, these reports tend to be of low quality further complicate the challenging processing of these signals into valid ISCRs. Current EU regulations only require MAHs to report ADRs reported on their own platforms (Comfort et al., 2018) this could change in future. As the number of reports increases, the signal:noise ratio decreases, which makes the reporting system potentially less useful. Effective drug safety assessments can only be made with good quality information (Dal Pan, 2013).
ICSR Electronic Data Interchange (EDI) is an electronic information sharing system used to speed up and streamline the routine reporting process of ICSRs to EV. However, EV may automatically reject these reports if they are not formatted in the correct way, which can increase the administrative burden this system aims to reduce (EMA, 2004).
“2. Signal Detection and Escalation”
Signal detection is a central pillar of drug safety assessment (Egberts, 2007). A signal is any potential, link between a medicine and an adverse event. Within the EU, this focuses on adverse outcomes and involves a process to determine whether, data from EV or scientific literature presents any previously unknown risks or suggests a change to the risk profile of a drug (EMA, 2017a). Safety monitoring is an ongoing process MAHs, NCA and the EMA review signals to ensure that any changes to the known safety profile of a drug are detected and validated quickly so that the required mitigation and minimisation activities can be passed to HCPs and patients in an expeditious manner, in order to safeguard those at risk. Spontaneous ADRs make up over half of all signals entered into EV. Depending on the seriousness of the reaction, multiple ADR reports may be required to generate a signal, especially if the reported information is of poor quality or is incomplete (Stahl et al., 2003). ISCRs entered on EV are a significant source of signals and provide real-world information (RWI) on drug use and any emerging potential safety concerns. Good Pharmacovigilance Practices and StrengtheningCollaboration for Operating Pharmacovigilance in Europe (SCOPE) (Potts et al., 2019) outline best practice for signal detection and escalation. MAHs must report validated signals to the EMA and NCAs in all markets their products are available (EMA, 2017a).
An example of good signal detection and escalation is illustrated by canagliflozin. Data from two clinical trials showed an increased risk of adverse events leading to toe amputation. From the analysis, PRAC recommended Direct Healthcare Professional Communication to highlight this emerging signal. At the same time, the drug class was put under a review which led to alterations of the medicine labels to highlight this issue to patients (EMA, 2017b). Modifications of drug product information have taken place in over half of all PRAC assessed signals (EMA, 2019b).
Once detected, signals should be followed up with detailed investigations including pharmacoepidemiologic studies (Egberts, 2007) and regulatory action (Campbell, Gossell-Williams and Lee, 2015), issued by PRAC. This may include changes to product information, Summary of product Characteristics (SmPCs) or Patient Information Leaflets (PILs). PRAC may also suggest: Direct healthcare professional communication; placing adverts; publications; alterations of the drug monitoring strategy or an update to the Risk Management Plan, which may include the initiation of a Post-authorisation safety studies (PASS) (Santoro et al., 2017).
ADR under‐reporting represents one of the biggest shortfalls in this process. It is thought that as few as 6% ADRs are actually reported (Avong et al., 2018). This lack of conversion of event to reports means that the detection of safety signals is limited which ultimately, impacts on the success of the entire drug safety system and its aim to maintain safety and protect patients.
Electronic health records (EHRs) may represent source of information on ADRs and lead to earlier signal detection however, these can be hard to identify as reported ADRs may be due to confounding factors or underlying health status of the patients. If EHRs are going to be used as a safety signal source, the data must be appropriately mined to account for these discrepancies (Haerian et al., 2012). The 2008 EU-ADR Project aims to produce an integrated system for the early detection of drug safety signals and has shown to be able to detect ADRs that both frequent and often unreported (Coloma et al., 2013). This shows the clear necessity to continue to develop methods of signal detection and how the use of technology can improve and further reintegrated to safeguard patient health.
“3. Post-authorisation safety studies (PASS)”
ADRs are most commonly identified during clinical trials and inform the and the benefit:risk ratio. However, rarer events may not be detected until the drug has already been approved for use under the benefit:risk ratio (Cohet et al., 2017). Post-authorisation safety studies (PASS) are performed on licenced drugs. A regulator may require the MAH to conduct a PASS as a condition of approval if there are risk concerns, or sufficient uncertainties surrounding the safety profile of the drug (EC, 2006), especially if authorisation is granted under exceptional circumstances. PASS reports must be submitted to PRAC or the NCA withing 1 year of the study close.
PASS can also be voluntarily carried out by MAHs, or as part of the pre-defined risk management plan (RMP) (EMA, 2017c). PASS protocols are reviewed and results of this process are presented to the MAH. GVP Module VIII (Rev 2) also outlines where PASS are not required but, are recommended to be carried out. The MAH is required to present study results to EU‐PAS, which is a digital repository for all PASS in the EU, maintained by European Network of Pharmacovigilance and Pharmacoepidemiology (ENCePP) and the EMA (Engel et al., 2016).
PASS allow MAHs to monitor how drugs are used in real-world contexts. This information can also be used by MAHs to support other activities, such has, reimbursement assessments, although this is discouraged as it may interfere with the primary outcomes of determining safety. If Research Ethics Committees (RECs) do not feel the proposed protocol will meet this aim, it may reject the proposal (Engel et al., 2016).
Once the PASS is approved, the MAH may apply for an ENCePP certification mark (Blake et al., 2012) to show that the proposed study complies with their regulatory requirements and best practice however, compliance with ENCePP guidance is not an absolute requirement (ENCePP, 2010) and many studies can have restrictive study population criteria and lack comparator arms can be difficult to assemble (Cohet et al., 2017), which impact on the ability of PASS to identify safety signals.
Further issues arise in the conduct of PASS such as the availability of data sources and difficulty in managing bias (GPP, 2015). Rare events are by definition, uncommon and their incidence means data is not always available. Database PASS can be used to identify signals but they must be independently validated by blinded professionals in order to prevent bias and misclassification (Ehrenstein et al., 2016).
Disincentives for incomplete studies, such as fines are only levied at the time of the final study report (EMA, 2017c) which may go some way to explain why, despite an increase in PASS registrations, finalised studies are not as common as anticipated (Engel et al., 2016).
Aspinall, M., Whittle, J., Aspinall, S., Maher, R. and Good, C. (2002). Improving adverse-drug-reaction reporting in ambulatory care clinics at a Veterans Affairs hospital. American Journal of Health-System Pharmacy, 59(9), pp.841-845.
Avery, A., Anderson, C., Bond, C., Fortnum, H., Gifford, A., Hannaford, P., Hazell, L., Krska, J., Lee, A., McLernon, D., Murphy, E., Shakir, S. and Watson, M. (2011). Evaluation of patient reporting of adverse drug reactions to the UK ‘Yellow Card Scheme’: literature review, descriptive and qualitative analyses, and questionnaire surveys. Health Technology Assessment, 15(20).
Avong, Y., Jatau, B., Gurumnaan, R., Danat, N., Okuma, J., Usman, I., Mordi, D., Ukpabi, B., Kayode, G., Dutt, S., El-Tayeb, O., Afolabi, B., Ambrose, I., Agbaji, O., Osakwe, A., Ibrahim, A., Ogar, C., Nosiri, H., Avong, E., Adekanmbi, V., Uthman, O., Abimiku, A., Oni, Y., Mensah, C., Dakum, P., Mberu, K. and Ogundahunsi, O. (2018). Addressing the under-reporting of adverse drug reactions in public health programs controlling HIV/AIDS, Tuberculosis and Malaria: A prospective cohort study. PLOS ONE, 13(8), p.e0200810.
Blake, K., deVries, C., Arlett, P., Kurz, X. and Fitt, H. (2012). Increasing scientific standards, independence and transparency in post-authorisation studies: the role of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance. Pharmacoepidemiology and Drug Safety, 21(7), pp.690-696.
Campbell, J., Gossell-Williams, M. and Lee, M. (2015). A Review of Pharmacovigilance. West Indian Medical Journal.
Cohet, C., Rosillon, D., Willame, C., Haguinet, F., Marenne, M., Fontaine, S., Buyse, H., Bauchau, V. and Baril, L. (2017). Challenges in conducting post-authorisation safety studies (PASS): A vaccine manufacturer's view. Vaccine, 35(23), pp.3041-3049.
Coloma, P., Trifirò, G., Patadia, V. and Sturkenboom, M. (2013). Postmarketing Safety Surveillance. Drug Safety, 36(3), pp.183-197.
Comfort, S., Perera, S., Hudson, Z., Dorrell, D., Meireis, S., Nagarajan, M., Ramakrishnan, C. and Fine, J. (2018). Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media. Drug Safety, 41(6), pp.579-590.
Dal Pan, G. (2013). Ongoing Challenges in Pharmacovigilance. Drug Safety, 37(1), pp.1-8.
EC (2006). Commission Regulation (EC) No 507/2006. [online] Ec.europa.eu. Available at: https://ec.europa.eu/health//sites/health/files/files/eudralex/vol-1/reg_2006_507/reg_2006_507_en.pdf [Accessed 14 Feb. 2020].
Egberts, T. (2007). Signal Detection. Drug Safety, 30(7), pp.607-609.
Ehrenstein, V., Petersen, I., Smeeth, L., Jick, S., Benchimol, E., Ludvigsson, J. and Sørensen, H. (2016). Helping everyone do better: a call for validation studies of routinely recorded health data. Clinical Epidemiology, p.49.
EMA (2004). Note For Guidance Onthe Electronic Data Interchange (Edi) Of Individual Case Safety Reports (ICSRS)1 And Medicinal Product Reports (Mprs)In Pharmacovigilance During The Pre-And Post-Authorisation Phase In The European Economic Area (EEA). [online] EMA. Available at: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/note-guidance-electronic-data-interchange-edi-individual-case-safety-reports-icsrs1-medicinal_en.pdf [Accessed 14 Feb. 2020].
EMA (2017a). Guideline on good pharmacovigilance practices (GVP)Module IX – Signal management (Rev 1). [online] Ema.europa.eu. Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacovigilance-practices-gvp-module-ix-signal-management-rev-1_en.pdf [Accessed 14 Feb. 2020].
EMA (2017b). SGLT2 inhibitors: information on potential risk of toe amputation to be included in prescribing information. [online] Ema.europa.eu. Available at: https://www.ema.europa.eu/en/documents/referral/sglt2-inhibitors-previously-canagliflozin-article-20-procedure-sglt2-inhibitors-information_en.pdf [Accessed 22 Feb. 2020].
EMA (2017c). Guideline on good pharmacovigilance practices (GVP) Module VIII – Post-authorisation safety studies (Rev 3). [online] Ema.europa.eu. Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacovigilance-practices-gvp-module-viii-post-authorisation-safety-studies-rev-3_en.pdf [Accessed 22 Feb. 2020].
EMA (2019a). 2018 Annual Report on EudraVigilance for the European Parliament, the Council and the Commission. [online] Ema.europa.eu. Available at: https://www.ema.europa.eu/en/documents/report/2018-annual-report-eudravigilance-european-parliament-council-commission-reporting-period-1-january_en.pdf [Accessed 14 Feb. 2020].
EMA (2019b). Report on pharmacovigilance tasks from EU Member States and the European Medicines Agency (EMA), 2015-2018. [online] Ema.europa.eu. Available at: https://www.ema.europa.eu/en/documents/report/report-pharmacovigilance-tasks-eu-member-states-european-medicines-agency-ema-2015-2018_en.pdf [Accessed 23 Feb. 2020].
EMA (n.d. a). Pharmacovigilance Risk Assessment Committee (PRAC) - European Medicines Agency. [online] European Medicines Agency. Available at: https://www.ema.europa.eu/en/committees/pharmacovigilance-risk-assessment-committee-prac [Accessed 14 Feb. 2020].
EMA (n.d. b). EudraVigilance - European Medicines Agency. [online] European Medicines Agency. Available at: https://www.ema.europa.eu/en/human-regulatory/research-development/pharmacovigilance/eudravigilance [Accessed 14 Feb. 2020].
ENCePP (2010). Guide on Methodological Standards in Pharmacoepidemiology (Revision 4). [online] Encepp.eu. Available at: http://www.encepp.eu/standards_and_guidances/documents/ENCePPGuideofMethStandardsinPE_Rev4.pdf [Accessed 22 Feb. 2020].
Engel, P., Almas, M., De Bruin, M., Starzyk, K., Blackburn, S. and Dreyer, N. (2016). Lessons learned on the design and the conduct of Post‐Authorization Safety Studies: review of 3 years of PRAC oversight. British Journal of Clinical Pharmacology, 83(4), pp.884-893.
European Commission (2008). Strengthening pharmacovigilance to reduce adverse effects of medicines. [online] European Commission. Available at: https://ec.europa.eu/commission/presscorner/detail/en/MEMO_08_782 [Accessed 14 Feb. 2020].
GPP (2015). Guidelines for good pharmacoepidemiology practice (GPP). Pharmacoepidemiology and Drug Safety, 25(1), pp.2-10.
Haerian, K., Varn, D., Vaidya, S., Ena, L., Chase, H. and Friedman, C. (2012). Detection of Pharmacovigilance-Related Adverse Events Using Electronic Health Records and Automated Methods. Clinical Pharmacology & Therapeutics, 92(2), pp.228-234.
Pillans, P. (2008). Clinical perspectives in drug safety and adverse drug reactions. Expert Review of Clinical Pharmacology, 1(5), pp.695-705.
Rolfes, L., van Hunsel, F., Wilkes, S., van Grootheest, K. and van Puijenbroek, E. (2014). Adverse drug reaction reports of patients and healthcare professionals-differences in reported information. Pharmacoepidemiology and Drug Safety, 24(2), pp.152-158.
Santoro, A., Genov, G., Spooner, A., Raine, J. and Arlett, P. (2017). Promoting and Protecting Public Health: How the European Union Pharmacovigilance System Works. Drug Safety, 40(10), pp.855-869.
Sharrar, R. and Dieck, G. (2013). Monitoring product safety in the postmarketing environment. Therapeutic Advances in Drug Safety, 4(5), pp.211-219.
Stahl, M., Edwards, I., Bowring, G., Kiuru, A. and Lindquist, M. (2003). Assessing the Impact of Drug Safety Signals from the WHO Database Presented in ‘SIGNAL’. Drug Safety, 26(10), pp.721-727.
Streefland, M. (2018). Why Are We Still Creating Individual Case Safety Reports?. Clinical Therapeutics, 40(12), pp.1973-1980.
WHO (2002). Safety of Medicines - A guide to detecting and reporting adverse drug reactions. [online] who.int. Available at: https://apps.who.int/iris/bitstream/handle/10665/67378/WHO_EDM_QSM_2002.2.pdf [Accessed 14 Feb. 2020].
Wysowski, D. and Swartz, L. (2005). Adverse Drug Event Surveillance and Drug Withdrawals in the United States, 1969-2002. Archives of Internal Medicine, 165(12), p.1363.