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Pharmacovigilance the OTHER discovery process in Pharma Daniel Mayer, Product Marketing Manager Olivier Feller, Life Sciences Consultant 7th Text Mining.

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Presentation on theme: "Pharmacovigilance the OTHER discovery process in Pharma Daniel Mayer, Product Marketing Manager Olivier Feller, Life Sciences Consultant 7th Text Mining."— Presentation transcript:

1 Pharmacovigilance the OTHER discovery process in Pharma Daniel Mayer, Product Marketing Manager Olivier Feller, Life Sciences Consultant 7th Text Mining Symposium Oct 5-6th 2009

2 Copyright © 2009 TEMIS - All Rights Reserved - Slide 2 Agenda A process view of safety in pharma Why is pharmacovigilance necessary ? Clinical Trials have key benefits but also some limits But how important and urgent is this issue ? Where does pharmacovigilance fit in the process ? A new definition of Pharmacovigilance What role does Text Mining play in Pharmacovigilance ? How is Luxid® used for this purpose ?

3 Copyright © 2009 TEMIS - All Rights Reserved - Slide 3 The Safety Process In Pharmaceuticals Prevent harm to patients Prescribe the right dosage of the right drug to the right patient Rigorously screen Clinical Trial Reports Optimize Drug Labelling for available drugs Healthcare Providers Regulatory Authorities Pharma companies Perform Clinical Trials Remove low b/h ratio drugs from the market as soon as possible

4 Copyright © 2009 TEMIS - All Rights Reserved - Slide 4 Agenda A process view of safety in pharma Why is pharmacovigilance necessary ? Benefits and Limits of Clinical Trials Importance and Urgency Where does it fit in the process ? A new definition of Pharmacovigilance What role does Text Mining play in Pharmacovigilance ? How is Luxid® used for this purpose ?

5 Copyright © 2009 TEMIS - All Rights Reserved - Slide 5 Benefits and Limitations of Clinical Trials Clinical Trials proove with some certainty that the product is effective and identify the common serious adverse events but have limitations

6 Copyright © 2009 TEMIS - All Rights Reserved - Slide 6 Benefits and Limitations of Clinical Trials Clinical Trials proove with some certainty that the product is effective and identify the common serious adverse events but have limitations Relatively Homogeneous populations Relatively healthy patients with only one disease Patients with complicated medical conditions often excluded Not sufficiently ethnically diverse Specific groups such as pregnant women, children, and elderly people are mostly excluded

7 Copyright © 2009 TEMIS - All Rights Reserved - Slide 7 Benefits and Limitations of Clinical Trials Clinical Trials proove with some certainty that the product is effective and identify the common serious adverse events but have limitations Relatively Homogeneous populations Small sample size rarely more than 3000 patients reduces the chance of finding rare adverse effects

8 Copyright © 2009 TEMIS - All Rights Reserved - Slide 8 Benefits and Limitations of Clinical Trials Clinical Trials proove with some certainty that the product is effective and identify the common serious adverse events but have limitations Relatively Homogeneous populations Small sample size Limited duration long term consequences such as cancer cannot be discovered

9 Copyright © 2009 TEMIS - All Rights Reserved - Slide 9 Benefits and Limitations of Clinical Trials Clinical Trials proove with some certainty that the product is effective and identify the common serious adverse events but have limitations Relatively Homogeneous populations Small sample size Limited duration Difficulty to predict the real world Patients receiving certain concurrent drugs are often excluded Drug interactions can therefore almost never be predicted from clinical trials, even though they may be substantial Food-drug interactions are also not covered

10 Copyright © 2009 TEMIS - All Rights Reserved - Slide 10 Benefits and Limitations of Clinical Trials Clinical Trials proove with some certainty that the product is effective and identify the common serious adverse events but have limitations Relatively Homogeneous populations Small sample size Limited duration Difficulty to predict the real world … but how urgent and important is it to address this issue ?

11 Copyright © 2009 TEMIS - All Rights Reserved - Slide 11 Quantifying The Costs Of Adverse Drug Reactions Approximately 5% of all hospital admissions are caused by ADRs 2% of these admitted patients die 4% of hospital capacity 72% avoidable

12 Copyright © 2009 TEMIS - All Rights Reserved - Slide 12 Quantifying The Costs Of Adverse Drug Reactions Approximately 5% of all hospital admissions are caused by ADRs 2% of these admitted patients die 4% of hospital capacity 72% avoidable Direct costs in US estimated at US$ 130 Billion annually

13 Copyright © 2009 TEMIS - All Rights Reserved - Slide 13 Quantifying The Costs Of Adverse Drug Reactions Approximately 5% of all hospital admissions are caused by ADRs 2% of these admitted patients die 4% of hospital capacity 72% avoidable Direct costs in US estimated at US$ 130 Billion annually Drug-related litigation costs Pharmas Billions

14 Copyright © 2009 TEMIS - All Rights Reserved - Slide 14 Quantifying The Costs Of Adverse Drug Reactions Approximately 5% of all hospital admissions are caused by ADRs 2% of these admitted patients die 4% of hospital capacity 72% avoidable Direct costs in US estimated at US$ 130 Billion annually Drug-related litigation costs Pharmas Billions The faster you address a problem the better

15 Copyright © 2009 TEMIS - All Rights Reserved - Slide 15 Alternative definition of Pharmacovigilance It is important and urgent to put in place a process that completes Clinical Trials

16 Copyright © 2009 TEMIS - All Rights Reserved - Slide 16 Alternative definition of Pharmacovigilance It is important and urgent to put in place a process that completes Clinical Trials Post-marketing monitoring for safety [FDA] Detection, assessment, understanding and prevention of Adverse Drug Reactions (ADRs), particularly long-term and short-term side effects of medicines [WHO]

17 Copyright © 2009 TEMIS - All Rights Reserved - Slide 17 Alternative definition of Pharmacovigilance It is important and urgent to put in place a process that completes Clinical Trials Post-marketing monitoring for safety [FDA] Detection, assessment, understanding and prevention of Adverse Drug Reactions (ADRs), particularly long-term and short-term side effects of medicines [WHO] A discovery process focused on contra-indications linked to Specific therapies Specific populations Specific interactions

18 Copyright © 2009 TEMIS - All Rights Reserved - Slide 18 Where does Pharmacovigilance fit ? Prevent harm to patients Prescribe the right dosage of the right drug to the right patient Rigorously screen Clinical Trial Reports Optimize Drug Labelling for available drugs Healthcare Providers Regulatory Authorities Pharma companies Perform Clinical Trials Remove low b/h ratio drugs from the market as soon as possible Understand and qualify unexpected ADRs as fast as possible Report unexpected ADRs Minimize Social cost Minimize liabilities

19 Copyright © 2009 TEMIS - All Rights Reserved - Slide 19 Agenda A process view of safety in pharma Why is pharmacovigilance necessary ? Clinical Trials have key benefits but also some limits But how important and urgent is this issue ? Where does pharmacovigilance fit in the process ? A new definition of Pharmacovigilance Why Text Mining in Pharmacovigilance ? How is Luxid® used for this purpose ?

20 Copyright © 2009 TEMIS - All Rights Reserved - Slide 20 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Doctor/Pharmacist reports Medwatch / AFSSAPS / NIMH Consumer Forums Call center transcripts Summary of Product Characteristics Scientific literature (Pubmed) Some structured databases Regulatory authorities reports and Approval packages Internal documents

21 Copyright © 2009 TEMIS - All Rights Reserved - Slide 21 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Enables information discovery even though a large part of the content is unstructured Including relations between disorder, treatment, population, ADR

22 Copyright © 2009 TEMIS - All Rights Reserved - Slide 22

23 Copyright © 2009 TEMIS - All Rights Reserved - Slide 23 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Enables information discovery even though a large part of the content is unstructured Helps to contextualize new information Link new information to other sources Gain insight into the bigger picture

24 Copyright © 2009 TEMIS - All Rights Reserved - Slide 24 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Enables information discovery even though a large part of the content is unstructured Helps to contextualize new information Accelerates the contraindication discovery process

25 Copyright © 2009 TEMIS - All Rights Reserved - Slide 25 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Enables information discovery even though a large part of the content is unstructured Helps to contextualize new information Accelerates the contraindication discovery process Enables faster response time to unexpected ADRs Investigation / Qualification of ADR cases Relabelling Recall

26 Copyright © 2009 TEMIS - All Rights Reserved - Slide 26 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Enables information discovery even though a large part of the content is unstructured Helps to contextualize new information Accelerates the contraindication discovery process Enables faster response time to unexpected ADRs Reduces public exposure to potentially serious ADRs

27 Copyright © 2009 TEMIS - All Rights Reserved - Slide 27 Why Text Mining For Pharmacovigilance ? Enables unified access to many heterogeneous sources Enables information discovery even though a large part of the content is unstructured Helps to contextualize new information Accelerates the contraindication discovery process Enables faster response time to unexpected ADRs Reduces public exposure to potentially serious ADRs Minimizes social and corporate risk

28 Copyright © 2009 TEMIS - All Rights Reserved - Slide 28 What typical Pharmacovigilance questions does Text Mining need to address ? Which unexpected ADRs have been reported for this drug ? Which populations have been exposed to unexpected ADRs ? Age Ethnic characteristics Which treatment caused ADRs ? Dosages, Duration, Frequency, Administration Route For which Indication What was tested during the Clinical Trials ?

29 Copyright © 2009 TEMIS - All Rights Reserved - Slide 29 Agenda A process view of safety in pharma Why is pharmacovigilance necessary ? Clinical Trials have key benefits but also some limits But how important and urgent is this issue ? Where does pharmacovigilance fit in the process ? A new definition of Pharmacovigilance Why Text Mining in Pharmacovigilance ? How is Luxid® used for this purpose ?

30 Copyright © 2009 TEMIS - All Rights Reserved - Slide 30 Front-end Luxid® Annotation Factory Luxid® platform overview Luxid® Productivity Tools Knowledge Mgr Skill Cartridge Mgr Luxid® Content Pipeline Back-end webservice Luxid® Toolbar Luxid® SkillCartridge TM Library Luxid® Information Analytics

31 Copyright © 2009 TEMIS - All Rights Reserved - Slide 31 Copyright © 2009 TEMIS - All Rights ReservedSlide 31 Overview - The "Vigitermes" Project A research project from the National Research Agency The purpose is to build a global platform dedicated to pharmacovigilance A dedicated Skill Cartridge has been developed based on the "Medical Entity Relationship" Skill Cartridge

32 Copyright © 2009 TEMIS - All Rights Reserved - Slide 32 Pharmacovigilance sentence samples - Entities The authors report 2 cases of hepatocellular tumour in children treated with anabolic androgens for aplastic anemia. A 10 year old girl with HCV infection was treated with Ribavirin for 12 months at 1,2 mg/day and developed anemia. We report a 41-year-old female, treated with etanercept for a rheumatoid arthritis, who developed a cutaneous lupus. A 32-year-old woman was treated for severe aplastic anemia with norethandrolone over a period of 4 years, with a cumulative dose of 25 g. Legend: PatientSymptomDrugDisorderDrug Usage

33 Copyright © 2009 TEMIS - All Rights Reserved - Slide 33 Pharmacovigilance sentence samples - Relations The authors report 2 cases of hepatocellular tumour in children treated with anabolic androgens for aplastic anemia. A 10 year old girl with HCV infection was treated with Ribavirin for 12 months at 1,2 mg/day and developed anemia. We report a 41-year-old female, treated with etanercept for a rheumatoid arthritis, who developed a cutaneous lupus. A 32-year-old woman was treated for severe aplastic anemia with norethandrolone over a period of 4 years, with a cumulative dose of 25 g. "Therapy" relationships "Patient with Disease" relationships

34 Copyright © 2009 TEMIS - All Rights Reserved - Slide 34 Enrich existing concepts (drug, adverse effect) - Lexicons Theriaque A database of all drugs available in France containing official regulatory information and validated bibliographic information Thesorimed Another database of all drugs available in France MedDRA (Medical Dictionary for Regulatory Activities) A clinically validated international medical terminology used by regulatory authorities and the regulated biopharmaceutical industry RX Norm Normalized names for clinical drugs with links to many commonly used drug vocabularies (First Databank, Micromedex, MediSpan, Gold Standard Alchemy and Multum) Drug Bank Unique bioinformatics and cheminformatics resource that combines detailed drug (chemical, pharmacological and pharmaceutical data) with comprehensive drug target (sequence, structure and pathway information)

35 Copyright © 2009 TEMIS - All Rights Reserved - Slide 35 Data sources involved Scientific Literature (PubMed) Pharmacovigilance Reports Summary of Product Characteristics (SPC) Meyler's Side Effects of Drugs (The International Encyclopedia of Adverse Drug Reactions and Interactions) Doctor/Pharmacist reports MedWatch / AFSSAPS reports Regulatory authorities reports and EMEA/FDA approval packages …

36 Copyright © 2009 TEMIS - All Rights Reserved - Slide 36 Cells and Tissues Clinical Trials Diagnostic Methods Diseases Enzymes Patients Age Ethnic Origin Treatments Drug Dose Drug Duration Drug Frequency Administration Route Symptoms Extracted Entities

37 Copyright © 2009 TEMIS - All Rights Reserved - Slide 37 Cell Disease Relationships between Cells and Tissues, Symptoms and Disorders Cell Diagnostic Methods Relationships between Cells and Tissues and Diagnostic Methods Cell Treatment Relationships between Cells and Tissues and Treatments Clinical Research Relationships between Clinical Trials, Disorders, Patients Diagnosis Relationships between Disorders, Diagnostic Methods, Cells and Tissues and patients Patients with Disease Relationships between Patients and Disease Therapy Relationships between Disorders, Treatments, Symptoms and Patients Treatment Effects A negative effect of a treatment in relation with Symptoms, Disorders and patients A treatment without effect or with a neutral one in relation with Symptoms, Disorders and patients A positive effect of a treatment in relation with Symptoms, Disorders and patients Extracted Relationships

38 Copyright © 2009 TEMIS - All Rights Reserved - Slide 38 Extraction overview in IDE Demo Client Therapy (relationship) Dosage - Frequency - Administration - Dose - Duration Treatment - External (from lexicon) Living Being - Age - Ethnic Origin - Gender Disease - MeSH disease

39 Copyright © 2009 TEMIS - All Rights Reserved - Slide 39 Therapy relationship example Dosage: Frequency, Administration Therapy (Relationship) Dosage: Dose, Duration Treatment: External (from lexicon) Disease: MeSH disease Living Being - Age - Ethnic Origin - Gender Daily oral Capsaicin at 3 mg/kg for 1 year administered to 60 year old Chinese men to regulate hay fever.

40 Copyright © 2009 TEMIS - All Rights Reserved - Slide 40 Entities hierarchy Dosage - Administration - Dose - Duration - Frequency Treatment - External (from lexicon) Disease: MeSH disease Living Being - Ethnic Origin - Gender Living Being: Age Treatment - External (from lexicon) Daily oral Capsaicin at 3 mg/kg for 1 year administered to 60 year old Chinese men to regulate hay fever.

41 Copyright © 2009 TEMIS - All Rights Reserved - Slide 41 Relationship hierarchy (Therapy example) Therapy (Relationship) Treatment Living Being A Therapy Relationship is based on a Treatment and a Patient

42 Copyright © 2009 TEMIS - All Rights Reserved - Slide 42 Overview of potential relationships

43 Copyright © 2009 TEMIS - All Rights Reserved - Slide 43 Links between an Symptom, a Patient and a Treatment Dosage Patient Symptom

44 Copyright © 2009 TEMIS - All Rights Reserved - Slide 44 Links between entities and attributes (1/2) Dosage Prevalence Patient Inheritance EntitiesAttributes

45 Copyright © 2009 TEMIS - All Rights Reserved - Slide 45 Links between entities and attributes (2/2) Dosage Patient Prevalence Inheritance

46 Copyright © 2009 TEMIS - All Rights Reserved - Slide 46 Clinical Research - Entities involved Clinical trial Patient Disorder

47 Copyright © 2009 TEMIS - All Rights Reserved - Slide 47 Agenda A process view of safety in pharma Why is pharmacovigilance necessary ? Clinical Trials have key benefits but also some limits But how important and urgent is this issue ? Where does pharmacovigilance fit in the process ? A new definition of Pharmacovigilance Why Text Mining in Pharmacovigilance ? How is Luxid® used for this purpose ? Conclusion

48 Copyright © 2009 TEMIS - All Rights Reserved - Slide 48 Value Proposition in Pharmacovigilance Boost the productivity of Contraindication Discovery Scientists process a higher number of more relevant sources in less time and develop faster and deeper insight into the specific context. Discover and Investigate unexpected ADRs faster Discard ADRs that are expected Develop Contraindication insights faster

49 Copyright © 2009 TEMIS - All Rights Reserved - Slide 49 Value Proposition in Pharmacovigilance Boost the productivity of Contraindication Discovery Minimize social costs associated with adverse events Avoid un-necessary exposure of sensitive populations to the drug Modify Drug Labelling faster Remove toxic products from the market earlier

50 Copyright © 2009 TEMIS - All Rights Reserved - Slide 50 Value Proposition in Pharmacovigilance Boost the productivity of Contraindication Discovery Minimize social costs associated with adverse events Minimize corporate risk associated with ADRs Reduced public exposure means reduced number of legal proceedings

51 Copyright © 2009 TEMIS - All Rights Reserved - Slide 51 Value Proposition in Pharmacovigilance Boost the productivity of Contraindication Discovery Minimize social costs associated with adverse events Minimize corporate risk associated with ADRs Open new areas for growth Reuse ADR knowledge in future Research Some unexpected side effects can be considered therapeutic effects elsewhere Discover alternative uses for a given drug

52 Copyright © 2009 TEMIS - All Rights Reserved - Slide 52 Unique benefits of TEMIS offering in Life Sciences Long experience with the Pharma industry and specialized in-house expertise

53 Copyright © 2009 TEMIS - All Rights Reserved - Slide 53 Unique benefits of TEMIS offering in Life Sciences Long experience with the Pharma industry and specialized in-house expertise Specialized components for Scientific Discovery and unique technical differentiator : Relationships detection The powerful semantic models embedded in our off-the shelf components (Biological, Chemical and Medical SkillCartridges ) detect entities such as disorders, targets, leads, and side-effects, as expressed in scientific litterature, AS WELL AS the relationships that bind them. You dont need to invest any time or effort in developping semantic expertise to benefit from advanced extraction capabilities. We do that for you.

54 Copyright © 2009 TEMIS - All Rights Reserved - Slide 54 Unique benefits of TEMIS offering in Life Sciences Long experience with the Pharma industry and specialized in-house expertise Specialized components for Scientific Discovery and unique technical differentiator : Relationships detection Fully customizable Platform Standard SkillCartridges can be further customized and bespoke SkillCartridges can be developped to adjust specifically to your therapeutic areas of focus or R&D strategies. We provide the tools, training and services required to expand the out-of- the-box capabilities of the platform and customize them to your own way of working.

55 Copyright © 2009 TEMIS - All Rights Reserved - Slide 55 Unique benefits of TEMIS offering in Life Sciences Long experience with the Pharma industry and specialized in-house expertise Specialized components for Scientific Discovery and unique technical differentiator : Relationships detection Fully customizable Platform Low TCO thanks to Enterprise Platform approach Luxid provides Text Mining benefits across the entire organization, not only to departments focused on Scientific Discovery. Our capabilities include Competitive Intelligence, Sentiment Analysis and Pharmacovigilance. This creates opportunities for economies of scale in several areas, including administration and training. We can even reduce administration costs further by hosting the platform for you.

56 Thank you – Q& A Daniel Mayer, Product Marketing Manager Olivier Feller, Life Sciences Consultant


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