Detecting Safety issues: will new scientific developments strengthen - - PowerPoint PPT Presentation

Detecting Safety issues: will new scientific developments strengthen public health protection? Developments in the last 10 years for dr. Thomas Lönngren

• Prof. dr. Miriam CJM Sturkenboom

MCJM Sturkenboom

Methods/resources for evaluation of drug safety

1950s 1970s 1990s 2010

Case series Spontaneous reports

Field studies on drug use. Safety, registries

Insurance claims DBs and electronic medical records Generation of signals

Disproportionality analyses

Drug use safety signal testing

Databases with ISCR: Vigibase, AERS, VAERS, Eudravigilance

RMP/ Drug safety monitoring

MCJM Sturkenboom

Safety signal detection

Traditionally, regulatory agencies relied on health care professionals to send reports of suspected adverse events Initially global introspection rule-based methods (qualitative) and simply reporting ratio’s Last ten years: quantitative datamining methods on WHO, AERS, EUDRAVIGILANCE data (disproportionality

analysis, proportional reporting ratios, Bayesian confidence propagation neural network, reporting odds ratio, knowledge discovery in databases, information content, probability filtering algorithm (PROFILE), R test, Sets test, the cuscore test, and the chi square test)

But,

Greener, M. EMBO Rep. 2008 March; 9(3): 221–224

MCJM Sturkenboom

Drug withdrawals in last 10 years created discussion

1 2 3 4 5 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

MI/stroke/C V /cardiac depression/suicide

• verdose

hepatotoxicity rahbdomyolysis P ML

• ther rare

cerivastatin rapacuronium trovafloxacin co-proxamol rofecoxib mixedamphetamines hydromorphone thioridazine pemoline ximelagatra tegaserod aprotinin lumiracoxib rimonabant efalizumab sibutramine gemtuzumab Rosiglitazone

MCJM Sturkenboom

Examples and criticisms

2004: Vioxx withdrawn because of increased risk of MI and stroke: after 5 yrs of marketing and more than 80 million persons exposed globally 2007: Avandia debated: risk of MI, European Medicines Agency did not withdraw but changed label, finally withdrawn in 2010 Lumiracoxib withdrawn after 8 million exposed persons (detected with ICSR)

As David Graham: “If there were an average of 150 to 200 people

• n an aircraft, this range of 88,000 to 138,000 (excess MI/SUD)

would be the rough equivalent of 500 to 900 aircraft dropping from the sky” (testimony www.senate.gov)

MCJM Sturkenboom

Difficulties in detection of signals

Timing Acute Delayed Frequency Frequent (>1/100) trials ? Moderate ? ? Rare (< 1/10,000) ICSR ICSR?

“Vioxx is often quoted as an example of the failure of regulators to detect an adverse reaction once a medicine is marketed—but trying to differentiate between the effects of a medicine and the ‘normal' events that occur in everyday life is not always straightforward,” the EMEA commented by e-mail. Many middle-aged people suffer heart attacks and the same age group typically took Vioxx; therefore, ascribing causality is difficult (Greener, 2008). E.g. Hepatoxicity, rhabdomyolisis, PML E.g. Myocardial infarction, stroke, arrhythmia

MCJM Sturkenboom

What changed after 2004 (Vioxx):

Loke: “Regulators and companies wait to see what reports drop into their letter-box,”. “It is time that the regulators start adopting new, more robust methodologies. Many techniques other than spontaneous reports are required to build a complete picture of a drug's safety.” (Greener 2008) New developments 1) EU-RMP 2) More use of existing datasources and upscaling EC: providing funding FDA-AA : > 100,000 million subjects to be monitored ENCePP: database resources 3) Development of new methods for signal detection on longitudinal health records

MCJM Sturkenboom

WHAT CHANGED?

• 1. EU-RISK MANAGEMENT

PLAN

MCJM Sturkenboom

EU-RMP: Centrally authorised products (substances) with and without additional Risk minimization activities

3 17 14 21 17 25 30 27 32 17 28 33 27 29 1 1 2 1 1 1 1 2 5 15 6 21 14

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Without additional RMAs With additional RMAs

Courtesy: Zomerdijk I, Erasmus MC

MCJM Sturkenboom

EU-RMP: Centrally authorized products

18 11 5 5 4 2 2 1 1 2 5 2 1 1 3 1 2 1 5 11 3 3 2 2 1 1 1 6 31 24 7 5 10 15 20 25 30 35 40 45 50 (J) Antiinfectives for systemic use (A) Alimentary tract and metabolism (L) Antineoplastic and immunomodulating (N) Nervous system (C) Cardiovascular system (G) Genito urinary system and sex hormones (B) Blood and blood forming organs (V) Various (S) Sensory organs (M) Musculo-skeletal system (R) Respiratory system (H) Systemic hormonal preparations, (D) Dermatologicals (P) Antiparasitic products, insecticides and repellents Without additional RMAs With additional RMAs (only educational material) With additional RMAs (educational material and other additional RMAs)

Courtesy: Zomerdijk I, Erasmus MC

Most frequent measures

• 1. Educational materials
• 2. Patient monitoring
• 3. Control of prescription
• 4. Pregnancy prevention

programmes

• 5. registries

What is the effectiveness of RMA? No systematic assessment

Effectiveness of RMA

Crijns HJ, Straus SM, Gispen-de Wied CH, de Jong-van den Berg LT. Compliance with Pregnancy Prevention Programs

• f isotretinoin in Europe: a systematic review. Br J Dermatol. 2010 Aug 12 .

Isotretinoin PPP In 6-26% isotretinoin was prescribed in full accordance with the PPP. Pregnancy incidence was seen in 0.2-1.0 per 1000 women of childbearing age using isotretinoin. Between 65-87% of these pregnancies were terminated.

MCJM Sturkenboom

Example: Rx for cough and cold medications in children < 2 years

Sen et al. Br J Clin Pharmacol 2010

Sen et al. Br J Clinical Pharmacol 2011 (in press)

WHAT CHANGED? CONDUCT OF MULTI- DATABASE STUDIES

• 2. FUNDING AND COLLABORATIONS

MCJM Sturkenboom

Collaborations in the area of pharmacoepidemiology Based on abstracts to ICPE till 2009

Courtesy of Schuemie, M

With all collaborative projects we hope to create more interconnections in EU

MCJM Sturkenboom

EC Funding of drug safety projects: boosted the field

Safety topics from Pharmacovigilance Working Party ICT in Health and patient safety PPPS to boost EU pharmaceutical research EU-Vaccine safety network Capacity building

TRANSFORM

MCJM Sturkenboom

How do we collaborate? combining data

Meta-analysis of individual studies Common protocol studies and sharing

• f coefficients

Pooling of aggregated data (not individual level) Pooling of elaborated data (individual level) Combining of raw data in central datawarehouse

Example: Person time in source population for background rate project VAESCO (H1N1 monitoring) distributed model

Total more than 260 million PY 50 million subjects

Sex and age specific incidence rate of Guillain Barré for observed / expected analyses

IR per 100,000 PY

MCJM Sturkenboom

WHAT CHANGED?

• 3. METHODS FOR SIGNAL DETECTION IN

HEALTHCARE DATABASES

Mining of electronic records and biom edical know ledge for drug safety m onitoring

References: Coloma P et al. PDS 2010 Trifiro’ G et al. PDS 2009 Avillach P, JAMIA 2009

4 medical record DBs 4 record linkage: total 30 million persons www.euadr- project.org

Drug Safety Signal Generation

Signal generation in distributed data m odel

DB1 Extracted information Local Aggregated data DB2 Extracted information Local Aggregated data Text- mining Signal generation Shared Signals Signal substantiation

• Generation of signals using

combined aggregated data Specific events extracted: UGIB, MI, Rhabdo, Anaphylactic shock, acute renal insufficiency (being increased to 15)

Methods flow for signal detection

Basic Methods for disproportionality assessment

• GPS/ BPCNN/Fisher exact (traditional

case based)

• Incidence rate based (Exact test)
• Correlation (cumulative exposure)

Chance? Confounding? Bias? Bonferroni FDR Bayesian Adjustment Design (CC, SCCS/CCO)

Across databases?

PRIMARY SCREENING METHODS

Refinement Consistency

Leopard (new)

LEOPARD

Longitudinal Evaluaton Of Profiles of Adverse Reactions to Drugs

Detection of protopathic bias

Stomach pain Proton pump inhibitor (PPI) Stomach bleeding Example: Did the PPI cause the stomach bleeding?

Schuemie M. LEOPARD. Pharmacoepidemiolgy & Drug safety 2010

Leopard: Two drugs and upper GI bleeding

P < 0.001 P = 1.000

Schuemie M. LEOPARD. PDS 2010

Once a signal is generated, w e need to find out w hether there is a possible biological explanation for the signal: signal substantiation

EU-ADR: SI GNAL SUBSTANTI ATI ON

Ranked signal list

know n signals taken out

Knowledge Knowledge Sources: Sources: litherature litherature

“New ” list Drug-target Drug-target Tar Target et-e

• event

vent Pathways Pathways Other Other Evidence

Evidence combination Evidence combination

Re- ranked signal list

Validation:

• Retrospective
• Prospective

W eb Services W eb Services

m etabolites Biological pathw ays drug event Gene/ protein Gene/ protein Signal substantiation

Courtesy of Bauer A, Furlong, L, Sanz F, Mestres J et al.

event Gene/ protein Gene/ protein I s the event know n to be associated to a gene/ protein?

• Mining of a com prehensive database on gene-disease

associations

• Data extracted from expert curated gene-disease

associations ( OMI M, Pharm GKB, CTD, UniProt)

• sem antic gene-disease associations autom atically

extracted from biom edical literature

Gene/ protein-event m apping

Bauer A, Furlong, L, Sanz F, Mestres J et al.

Netw ork of genes around EU-ADR events

Courtesy of Laura Furlong

m etabolites drug event Gene/ protein Gene/ protein Drug-target in silico profiling Drug-target in silico profiling

Bauer A, Furlong, L, Sanz F, Mestres J et al.

drug Gene/ protein Gene/ protein W hich are the targets of drug?

• Drug-target profiles by in silico m ethods, w hich

capitalize on prior know ledge for m any targets of therapeutic relevance.

• A com pound w ill be active against a target if it is

sim ilar to a certain degree to a set of know n ligands

• f this target

Drug-target in silico profiling

Bauer A, Furlong, L, Sanz F, Mestres J et al.

drug Gene/ protein Gene/ protein W hich are the targets of drug?

• Targets of the drugs are obtained by querying annotated

chem ical libraries ( ACL)

• I n addition, the in silico profiling m ethods can predict

novel targets for a given drug Drug-target in silico profiling

Bauer A, Furlong, L, Sanz F, Mestres J et al.

Exp+ Com p predicted target profiling of UGI B drugs

GPCRs Cyt Targets

Bauer A, Furlong, L, Sanz F, Mestres J et al.

ketoprofen Upper GI bleeding Drug-target in silico profiling hROAT1 COX-1

I nterleukin-8

NOS2 COX-2 COX-1 Gene/ protein- event m apping Signal substantiation: intersection of a protein

ketoprofen Upper GI bleeding Drug-target in silico profiling hROAT1 COX-1

I nterleukin-8

NOS2 COX-2 COX-1 Gene/ protein- event m apping ketoprofen Upper GI bleeding COX-1

binds to is associated to

Signal substantiation Signal substantiation: intersection of a protein

Detecting Safety issues: will new scientific developments strengthen public health protection?

MCJM Sturkenboom

Will public health improve?

EU-RMP: effectiveness needs to be measured, the RMP itself is not sufficient More funding -> more collaborations Better methods development Better use of resources and development of tools Accessibility of healthcare data improved Transparency improved (mapping/benchmarking) More data on background rates, actual use etc. Signal detection in health care databases Both OMOP, WHO-UMC and EU-ADR do methods development Methods require comparison against standard information and validation Future will tell whether these methods are an addition

You certainly left a revolutionized field of pharmacovigilance and pharmacoepidemiology behind Thanks on behalf of all scientists!!

Nature Biotechnology 2 2 , 1341 (2004) doi: 10.1038/ nbt1104-1341 Profile: Thomas Lönngren Sabine Louët1 Dublin Abstract I n his attem pts to stream line the European Medicines Agency, Thom as Lönngren's style is one of evolution rather than revolution.