How to Increase Drug Safety. Evolutions in Pharmacovigilance and Risk Management.

Transcription

1 How to Increase Drug Safety. Evolutions in Pharmacovigilance and Risk Management. Erik Kerkhofs - Patrick De Locht - Franky De Cooman 1

2 Introduction Importance of pharmacovigilance Compliant platform Flexibility of the tools: A practical example Domain expertise 2

3 How to Increase Drug Safety. Evolutions in Pharmacovigilance and Risk Management. Business & Decision Pharmacovigilance context Data flow, case reporting, aggregate reporting, medical queries & authorities. IT Requirements Specifics: MedDRA versioning, SMQs, within patient queries. Tools screen shots. Custom code Conclusion 3

4 Business & Decision group European consultancy group/software Integrator, headquarters in Paris Founded in consultants Core Business Management Consulting Business Intelligence CRM E-business Present in Europe Benelux, France, Spain, Switzerland and UK Listed on Euronext Paris Euronext New market in 02/2001 Growth above IT-market standards Customers 1000 projects realised for 900 European customers 21,2 M ,7 M 48,5 M 54,8 M + 17 % Consolidated revenue in Euros 64,3 M a Growth above market standards Growth in personnel

5 About the Benelux Offices in Brussels, Amsterdam & Luxembourg Solutions: CRM, Financial, Risk, Pharma-BI Cross-Industry: Distribution, Finance, Manufacturing, Media, Pharmaceutical, Public, Telco + 20 years experience in consulting services 95 people Turn-over > 9,5 Million Euro Consolidated revenue in Euros Benelux 5,400,000 6,000,000 5,200,000 5,025,

6 Pharmacovigilance Department within pharmaceutical companies where human drug safety, e.g. adverse events, on medicinal products is managed. Investing in high quality means keeping a drug safe (medication leaflet) and on the market. Decisions have major impact on sales figures and safe drug usage. Big portion of cases received originate from post-marketing data. Stringent reporting timelines of individual case safety reports (ICSRs). Covers: capturing (data entry), verifying (quality), reporting (authorities) & analysis. 6

7 Pre-marketing Post-marketing Clinical Data Phase I Phase II Phase III Phase IV Case Receipt Data Entry Quality Control Manual Input Distributing Storage systems Analysis Process Aggregate reporting Epidemiology/ risk management Local Authorities EMEA/ FDA Internal processing/ doc management 7

8 Analytical/Visualisation Tools To manage huge volumes and complex medical information several SAS tools are utilized. Transactional data need to be reproducible and consistently queried. Different versions of for example the MedDRA codelist and complex queries need to be managed. Datamining and text-mining provide important information for company physicians. Predictive statistics have to be tuned and adapted to product portfolio to show only relevant signals. These custom program source code can be incorporated and managed with the SAS platform. 8

9 IT Requirements Web based Validated systems, 21CFR part 11 compliant (audit trails, role based security ). Performant global systems +/- 24h/24h availability Connectivity to other data sources. Several output formats (E2B) Workflow/ document management Resolve complex medical queries 9

10 MedDRA Codelist Adverse events are coded using MedDRA (Medical Dictionary for Regulatory Activities) MedDRA is an international medical terminology designed to support the classification, retrieval, presentation, and communication of medical information throughout the medical product regulatory cycle. MedDRA was developed under the auspices of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). SOC: Nervous system disorders HLGT: Headaches HLT: Headache NEC PT: Headache LLT: Headache SOC: Vascular disorders HLGT: Cerebrovascular and spinal vascular disorders NEC HLT: Cerebrovascular and spinal vascular disorders NEC PT: Migraine without aura LLT: Headache vascular 10

11 Complex queries Retrieval of Standardised Medical Queries (SMQs) e.g.: several groups of Primary Terms (PTs) are defined and patients with at least x out of y events are retrieved.(see next slide) Example: Find all patients with at least two events from an event cluster like for example: anxiety, dizziness, headache. 11

12 Complex Queries Example: Find all patients with at least two events from a respiratory adverse event set. Adverse Event Set/ Results of Interest Anxiety Dizziness Headache 12

13 SQL alternative SELECT a.code as Code, a.name as Name, count(b.ncode)from patient a, nmmaster b WHERE a.code = b.code and a.status = 1 and b.status = 1 and b.ncode <> 'a10' and trunc(last_updated_date) <= trunc(sysdate-13) group by a.code, a.name UNION SELECT a.code as Code, a.name as Name, count(b.ncode)from patient a, nmmaster b WHERE a.code = b.code and a.status = 1 and b.status = 1 and b.ncode <> 'a10' and trunc(last_updated_date) > trunc(sysdate-13) group by a.code, a.name; 13

14 Business Case (with Multiple Records per Patient) For a Total of 3 Cases Oral Sub lingual Syrup Tablet Tablet Event Counts AE: Anxiety 2.. Dizziness. 2 1 Headache 1.. Hypoglycaemia. 1. Palpitation 1.. Vomiting. 1. Sum of above N Patients per route/ form. Sum

15 Analytical/Visualisation Tools Frequently, queries & query strategies are maintained by business users for correct medical interpretation and timely response to authorities. There is a need for visualization/data warehousing (additional sales data) and statistics (early warning, trends, signals ) Only build on a solid foundation: SAS Drug Development, the compliant platform for pharmacovigilance. SAS Insight, SAS Enterprise Miner, SAS Enterprise Guide : analysis tools for maximal flexibility. 15

16 SAS/Insight Point Point and and click, click, dynamic dynamic interface interface One, One, two two or or dimension dimension analysis analysis Quickly Quickly find find best best model model GLM GLM Because Because it it is is integrated integrated with with the the SAS SAS System, System, SAS/INSIGHT SAS/INSIGHT software software can can read read output output from from any any SAS SAS procedure. procedure. 16

17 SAS Enterprise Guide Point Point and and click click interface interface Analytics Analytics and and report report tasks tasks Workflow Workflow Various Various output output formats formats 17

18 SAS Enterprise Miner Advanced Advanced analytical analytical workbench workbench From From signal signal detection detection to to signal signal prediction prediction Meet Meet new new requirements requirements of of proactive proactive pharmacoviligance pharmacoviligance 18

19 SAS Drug Development (SDD) Pharmacovigilance platform Web Web enabled enabled Centralized Centralized object object repository, repository, versioning versioning and and electronic electronic signature signature Compliant Compliant environment environment (audit (audit trail, trail, versioning, versioning, security ) security ) CFR21 CFR21 part part Data Data transformation transformation and and integration integration Data Data exploration exploration Statistical Statistical analysis analysis and and reporting reporting Study Study reports reports and and submissions submissions 19

20 Custom Code Integration Pharmacovigilance covers drug safety and all companies have specific statistical and reporting needs. Standardize on PV platform (SDD), allow for maximal flexibility from a usage/tool point-of-view Existing statistical procedures need to be finetuned/optimised and integrated with existing software. Such an optimised statistical method for risk management will be explained by Franky De Cooman. 20

21 Some Adverse Events are more frequent than others Which AE should we be aware of for each suspect drug? 21

22 Signal Detection Data come from the FDA website: And represent the following data: The files listed on this page contain raw data extracted from the AERS database for the indicated time ranges and are not cumulative. The files include: demographic and administrative information and the initial report image ID number (if available); drug information from the case reports; reaction information from the reports; patient outcome information from the reports; information on the source of the reports; 22

23 Data Mining approach Split database in two distinct parts Model on each part 23

24 Preferred term Frequency Percent Row Pct drg1 drg2 drg3 drg4 Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ reac ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ reac ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ reac ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ reac ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total

25 Assessment Construct Confidence interval around estimates Look if the confidence interval the from the test dataset overlaps with the CI from the training dataset 25

26 Confidence intervals based on the distribution of Poisson If x i is the frequency of a combination AE-drug and we have X i ~ Po(µ i ) where µ i is a rate of occurrence for a given period. E(X i ) = µ i Var(X i ) = µ i Confidence limits: exp ln( xi ) ± 1,96 1 x i 26

27 CI based on the gamma distribution (baysian method) In the Poisson process, if X ~ Poi(λt), a priori distribution for λ is λ ~ Γ(t0, s0) and 0 ( λ ) = E var a posteriori : λ s,t ~ Γ(t*, s*) where t* = t 0 + t s* = s 0 + s E ( s, t) s t s t 0 s t 0 ( λ ) = λ = var ( st, ) s Confidence limits : ± 1, 96 t s t 2 0 λ = s ( t ) 2 27

28 DRUG PT CI 04Q1 CI 04Q2 C I04Q3 CI 04Q4 A CEREBROVASCULAR ACCIDENT CI 04Q4 baysian B BREAST CANCER [ 0, 8] [ 0, 8] [ 189, 248] [ 99, 120] A CHEST PAIN [ 2, 14] [ 0, 8] [ 5, 19] [ 153, 207] [ 83, 103] BREAST CANCER FEMALE A CARDIAC DISORDER [ 0, 8] [ 0, 10] [ 121, 169] [ 64, 82] A DYSPNOEA [ 1, 11] [ 1, 11] [ 1, 11] [ 84, 124] [ 46, 62] D DRUG INEFFECTIVE [ 33, 61] [ 43, 73] [ 26, 52] [ 68, 106] [ 68, 86] APPLICATION SITE REACTION [ 6, 22] [ 2, 14] [ 4, 16] [ 479, 569] [ 251, 284] C [ 1, 11] [ 50, 82] [ 380, 462] [ 150, 203] [ 195, 224] E [ 154, 208] [ 147, 199] [ 124, 172] [ 63, 99] [ 150, 177] A F A A G E C ADVERSE EVENT DRUG INEFFECTIVE DIZZINESS DEATH DYSPNOEA ABDOMINAL PAIN BREAST CANCER [ 0, 8] [ 26, 52] [ 0, 10] [ 1, 11] [ 20, 43] [ 32, 60] [ 0, 8] [ 0, 8] [ 2, 14] [ 0, 10] [ 10, 28] [ 26, 52] [ 43, 73] [ 13, 33] [ 38, 68] [ 0, 8] [ 4, 18] [ 11, 29] [ 25, 50] [ 20, 42] [ 58, 93] [ 50, 82] [ 46, 78] [ 37, 66] [ 35, 63] [ 30, 56] [ 30, 56] [ 36, 50] [ 46, 62] [ 27, 39] [ 23, 35] [ 33, 46] [ 42, 57] [ 35, 49] 28

29 Clustering Transposition of the data into one file by drug with the form: Patient ae1 ae2 ae3 ae4... aen Patient Patient Patient Patient R Clustering of the adverse events with PROC VARCLUS. Selection of the clusters where the correlation between AE s is 1. (propor = 1) Results: Drug Cluster AE Drug Cluster AE Drg1 Clus1 ae1 Drg1 Clus3 ae6 Drg1 Clus1 ae2 Drg2 Clus1 ae7 Drg1 Clus1 ae3 Drg2 Clus1 ae8 Drg1 Clus2 ae4 Drg2 Clus2 ae9 Drg1 Clus2 ae5 Drg2 Clus2 ae10 29

30 Example 30

31 Example (cont.) 31

32 Example (cont.) 32

33 Conclusion This complex and highly regulated environment needs support of the best, customizable platform: SAS SDD. Currently, more pro-active analysis are performed and external data incorporated to provide in-depth medical knowledge. Implementation of explorative and statistical tools, on top of the compliant platform results in visual alert signals and easy interpretation hereof. 33