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Redefining Disease, New Molecular
  Definitions and Personalised
            Medicine

              Dr Harsukh Parmar
          Global Discovery Medicine
   Respiratory & Inflammation Therapy Area
      harsukh.parmar@astrazeneca.com
U.S. Drug Industry R&D Expenditures and
       Drug Approvals, 1963-2000
                      60                                                                                                         27

                                                                                                R&D Expenditures




                                                                                                                                      R&D Expenditures
                                                                                                                                      (Billions of 2000$)
     NCE Approvals




                      40                                                                                                         18




                                        NCE Approvals
                      20                                                                                                         9




                          0                                                                                                      0
                      63

                               65

                                    67

                                          69

                                               71

                                                     73

                                                          75

                                                                77

                                                                     79

                                                                           81

                                                                                83

                                                                                      85

                                                                                           87

                                                                                                 89

                                                                                                      91

                                                                                                            93

                                                                                                                 95

                                                                                                                       97

                                                                                                                            99
                     19

                              19

                                   19

                                         19

                                              19

                                                    19

                                                         19

                                                               19

                                                                    19

                                                                          19

                                                                               19

                                                                                     19

                                                                                          19

                                                                                                19

                                                                                                     19

                                                                                                           19

                                                                                                                19

                                                                                                                      19

                                                                                                                           19
 R&D expenditures adjusted for inflation
Source: Tufts CSDD Approved NCE Database, PhRMA
Main Reasons for Termination of Development
      LACK OF EFFICACY & SAFETY !
         One Size Does NOT Fit ALL !
 Clinical Safety             Toxicology
     20.2%                     19.4%                    Clinical
                                                   Pharmacokinetics/
                                                     Bioavailability
                                                         3.1%
                                       Other
                                       6.2%        Preclinical efficacy
                                                          3.1%

                                                      Preclinical
                                                   Pharmacokinetcs/
                                        Various     Bioavailability
                                         10%             1.6%

                                                      Formulation
  Portfolio                                              0.8%
Considerations                                    Patent or Commercial
    21.7%          Clinical Efficacy                      Legal
                                                          0.8%
                        22.5%
                                                       Regulatory
                                                         0.8%
Current Treatment is Population Based
What is Personalised Medicine?
Personalised Medicine links the patient to a disease (segment or
part of the disease) to a drug using a diagnostic or biomarker or
clinical test that:

              • Defines the disease and/or
              • Predicts response and risk and/or
              • Determines dose
Leading to improved patient outcomes, targeted therapies and new
commercial opportunities. Personalised Medicine involves testing
patients prior to treatment to enable clinicians to prescribe:
              •   The Right Drug
              •   At the Right Dose
              •   For the Right Disease
              •   To the Right Patient
Pharmacogenomics –Redefining Disease
   Making Personalised Medicines
Patient Segmentation is Not New
•Historically we have always done this using
 Clinical, Biochemical, Histological features:

  !Inclusion/Exclusion Criteria in Clinical
   Trials
  !Regulatory Approved Data sheets often
   define the approved indications and
   subset of patients suitable for the
   approved therapy
Redefining Disease Personalised Medicine2
Pharmacogenomics
       Importance is clear and growing

• BMS - Taxol: first cancer                  NSCLC treatment with
  blockbuster, now facing generic                  TAXOL
                                               39
  competition                           40                   Taxol Response rate
• Novel taxanes have entered                                 (%)
                                                             Median survival
  market                                30                   (months)


• Beta-tubulin gene contains            20
  mutations that predict for                          10

  patterns of response and              10
                                                               0       2

  resistance                             0
• Beta-tubulin pharmacogenomic                Wild-
                                              Type
                                                             Mutated
                                                              N=16
                                              N=33
  test for differential prescription:                      Genotype
  Taxol or taxane
So What Has Changed ?
•The vast array of technology to define patient subgroups
•These range from biochemical, immunocytochemistry,
 genetics, proteomics, to new evolving technology such as
 real time chemotaxis assays
•Molecular re-classification of disease through genotype
•Better understanding & use of biomarkers for patient
 stratification
•Better understanding & use of biomarkers for patient
 segmentation & enriched clinical trials
•Greater societal expectation on efficacy and safety
•Increasing costs leading to better targeted therapies
Discovery Medicine
                        Utilize and Integrate Human
                    Pathophysiology and Disease Models




                                                                                ProteinDomain
                                                            COPD2
Target Validation


                                       COPD0


                                                    COPD1
                          Clinical Data
                      NS




Platforms




                                                                                                Cytoband
                               HS


                                                                                  Deliverables




                                                                    NA
•Genetics
•Genomics




                                                                         GO
•Proteomics          15        19     18            9       16      2         •Validated targets
•Metabonomics                                                                 •Pathophysiological
•Lipidomics                                                                    understanding
•Glycomics                                                                    •Biological Mechanism
•Imaging                                                                      •Disease stratification
                                                                                           Annots
•Epidemiology                                                                 •Biomarkers
•Physiology                                                                   •Patient segmentation
      20/04/2005
                    Bioinformatics and Informatics
                                               15
Redefining Disease Personalised Medicine2
Benefit-Risk of Biomarkers in R & D
Benefits                                        Risks

1.   For NMEs with a novel mechanism of         1.   Biomarkers that are nonspecific and
     action, biomarkers are key to                   do not correlate with clinical outcome
     understanding PoM and establishing              may lead to incorrect conclusions.
     PoP/PoC.                                   2.   Biomarkers associated with only a
2.   Biomarkers should help contain the
                                                     portion of the clinical outcome, may
     cost of drug development by allowing
                                                     not identify all of the relevant effects of
     early termination or rapid progression
     to Launch.                                      the therapy, including adverse effects.
3.   Biomarkers may help pre-select             3.   Biomarker analysis can be expensive
     patient populations that are most likely        and time-consuming.
     to benefit.                                4.   Biomarker-based decisions could
4.   Biomarkers that predict the course of           become biased unless a priori criteria
     disease may serve as a useful tool for          are set up for decision-making in
     clinicians, health care systems.                addition to biomarker data.
5.   Diagnostic kits could be developed         5.   Patient pre-selection using biomarkers
     where appropriate patient
                                                     may reduce the potential market size.
     segmentation may reduce the size of
     trials required
Redefining Disease Personalised Medicine2
Biomarkers & Clinical Outcomes
                   •In a 15,000 patient study,
                   independent drug safety
                   committee recommended
                   stopping further development
                   since mortality was about 60%
                   (82 versus 51) higher in
                   Torcetrapib group.

                   •Biomarkers did not predict.

                   •However human genetics
                   (CTEP) in Japanese study did
                    potentially predict poor
                   outcome because of ineffective
                   “HDL” produced by such
                   inhibition

                   •Increase in BP may be another
                   factor for increased mortality
Disease reclassification at the
      molecular level
Molecular classification of Acute Leukaemia
 Golub TR et al. Science 1999; 286: 531

                              !Genes distinguishing ALL
                              from AML The 50 genes that
                              correlate most highly between
                              ALL and AML are shown.

                              !The top panel shows genes
                              that are highly expressed in
                              ALL, whereas the bottom panel
                              shows genes more highly
                              expressed in AML.

                              !While as a group, these genes
                              are correlated with pathologic
                              class, no single gene is
                              uniformly expressed across the
                              class, illustrating the value of
                              whole-genome expression
                              analysis in class prediction
Acute Myeloid Leukaemia
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Similarly with the EGFR Antibody, Erbitux,
Approved as Personalised Medicine, Based on EGFR Expression
Rheumatoid Arthritis
GENE EXPRESSION ANALYSIS USING GENELOGIC DATA
GenelogicTM Expression Data

!Pathways that are significant to the pathophysiology of
Rheumatoid Arthritis and Anti-TNF treatments have been

highlighted in the table.


!Knowledge of immune response genes can potentially be
useful for identification of surrogate markers of clinical endpoint

or disease/treatment/response markers according to the project

needs.
Overview of Analysis
• Gene expression data from three types of sample
  populations analyzed:

   ! WBC samples from Normal individuals
   ! WBC samples from Rheumatoid Arthritis patients.
   ! WBC samples from RA patients, 6 weeks after
     Remicade Infusion.

• Set of 25 genes were identified as a marker set for
  patient stratification in future novel NME target
  discovery and development.
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Speed and Simplicity                 Verigene Mobile
                                     !The next generation
Since it is based on direct
genomic detection and not target     Verigene Mobile will transfer
amplification, ClearRead makes       the power and accuracy of the
molecular testing faster and         Verigene AutoLab to an
simpler. Current methods require     affordable, hand-held device.
highly specialized scientists and
lab technicians for processing and   !Its portability will make it
interpretation, while ClearRead      ubiquitous at point-of-care
assays are easy to perform and       settings such as doctor's
produce definitive results.
                                     offices, hospital bedsides and
                                     even in patients' homes.
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Drugs with Personalised Medicine Properties/Potential
   •Antibiotics are Personalised Medicines
   •Herceptin in Oncology
   •Protease Inhibitors in HIV
   •Protease Inhibitors in HCV
   •Diabetic Treatment & Monitoring
   •Neuroamidase Inhibitors in Influenza e.g. Tamiflu, Relenza
   •Rituximab, Anti-CD20 in NHL, RA etc
   •Xolair, Anti-IgE in asthma
   •Anti-TNF’s & Anti-IL1 in RA
   •Campostar in Oncology
   •Xeloda, Gemcitabine, Velcade in Oncology
   •Taxol & Taxanes in Oncology
   •UDF in Oncology
   •EGFR Antibodies & TK inhibitors e.g. Tarceva, Iressa, Erbitux
   •Potentially VEGF Antibodies (Avastin) and TK inhibitors
   •Various Monoclonal Antibody Targets
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2
Redefining Disease Personalised Medicine2

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Redefining Disease Personalised Medicine2

  • 1. Redefining Disease, New Molecular Definitions and Personalised Medicine Dr Harsukh Parmar Global Discovery Medicine Respiratory & Inflammation Therapy Area harsukh.parmar@astrazeneca.com
  • 2. U.S. Drug Industry R&D Expenditures and Drug Approvals, 1963-2000 60 27 R&D Expenditures R&D Expenditures (Billions of 2000$) NCE Approvals 40 18 NCE Approvals 20 9 0 0 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 R&D expenditures adjusted for inflation Source: Tufts CSDD Approved NCE Database, PhRMA
  • 3. Main Reasons for Termination of Development LACK OF EFFICACY & SAFETY ! One Size Does NOT Fit ALL ! Clinical Safety Toxicology 20.2% 19.4% Clinical Pharmacokinetics/ Bioavailability 3.1% Other 6.2% Preclinical efficacy 3.1% Preclinical Pharmacokinetcs/ Various Bioavailability 10% 1.6% Formulation Portfolio 0.8% Considerations Patent or Commercial 21.7% Clinical Efficacy Legal 0.8% 22.5% Regulatory 0.8%
  • 4. Current Treatment is Population Based
  • 5. What is Personalised Medicine? Personalised Medicine links the patient to a disease (segment or part of the disease) to a drug using a diagnostic or biomarker or clinical test that: • Defines the disease and/or • Predicts response and risk and/or • Determines dose Leading to improved patient outcomes, targeted therapies and new commercial opportunities. Personalised Medicine involves testing patients prior to treatment to enable clinicians to prescribe: • The Right Drug • At the Right Dose • For the Right Disease • To the Right Patient
  • 6. Pharmacogenomics –Redefining Disease Making Personalised Medicines
  • 7. Patient Segmentation is Not New •Historically we have always done this using Clinical, Biochemical, Histological features: !Inclusion/Exclusion Criteria in Clinical Trials !Regulatory Approved Data sheets often define the approved indications and subset of patients suitable for the approved therapy
  • 9. Pharmacogenomics Importance is clear and growing • BMS - Taxol: first cancer NSCLC treatment with blockbuster, now facing generic TAXOL 39 competition 40 Taxol Response rate • Novel taxanes have entered (%) Median survival market 30 (months) • Beta-tubulin gene contains 20 mutations that predict for 10 patterns of response and 10 0 2 resistance 0 • Beta-tubulin pharmacogenomic Wild- Type Mutated N=16 N=33 test for differential prescription: Genotype Taxol or taxane
  • 10. So What Has Changed ? •The vast array of technology to define patient subgroups •These range from biochemical, immunocytochemistry, genetics, proteomics, to new evolving technology such as real time chemotaxis assays •Molecular re-classification of disease through genotype •Better understanding & use of biomarkers for patient stratification •Better understanding & use of biomarkers for patient segmentation & enriched clinical trials •Greater societal expectation on efficacy and safety •Increasing costs leading to better targeted therapies
  • 11. Discovery Medicine Utilize and Integrate Human Pathophysiology and Disease Models ProteinDomain COPD2 Target Validation COPD0 COPD1 Clinical Data NS Platforms Cytoband HS Deliverables NA •Genetics •Genomics GO •Proteomics 15 19 18 9 16 2 •Validated targets •Metabonomics •Pathophysiological •Lipidomics understanding •Glycomics •Biological Mechanism •Imaging •Disease stratification Annots •Epidemiology •Biomarkers •Physiology •Patient segmentation 20/04/2005 Bioinformatics and Informatics 15
  • 13. Benefit-Risk of Biomarkers in R & D Benefits Risks 1. For NMEs with a novel mechanism of 1. Biomarkers that are nonspecific and action, biomarkers are key to do not correlate with clinical outcome understanding PoM and establishing may lead to incorrect conclusions. PoP/PoC. 2. Biomarkers associated with only a 2. Biomarkers should help contain the portion of the clinical outcome, may cost of drug development by allowing not identify all of the relevant effects of early termination or rapid progression to Launch. the therapy, including adverse effects. 3. Biomarkers may help pre-select 3. Biomarker analysis can be expensive patient populations that are most likely and time-consuming. to benefit. 4. Biomarker-based decisions could 4. Biomarkers that predict the course of become biased unless a priori criteria disease may serve as a useful tool for are set up for decision-making in clinicians, health care systems. addition to biomarker data. 5. Diagnostic kits could be developed 5. Patient pre-selection using biomarkers where appropriate patient may reduce the potential market size. segmentation may reduce the size of trials required
  • 15. Biomarkers & Clinical Outcomes •In a 15,000 patient study, independent drug safety committee recommended stopping further development since mortality was about 60% (82 versus 51) higher in Torcetrapib group. •Biomarkers did not predict. •However human genetics (CTEP) in Japanese study did potentially predict poor outcome because of ineffective “HDL” produced by such inhibition •Increase in BP may be another factor for increased mortality
  • 16. Disease reclassification at the molecular level
  • 17. Molecular classification of Acute Leukaemia Golub TR et al. Science 1999; 286: 531 !Genes distinguishing ALL from AML The 50 genes that correlate most highly between ALL and AML are shown. !The top panel shows genes that are highly expressed in ALL, whereas the bottom panel shows genes more highly expressed in AML. !While as a group, these genes are correlated with pathologic class, no single gene is uniformly expressed across the class, illustrating the value of whole-genome expression analysis in class prediction
  • 24. Similarly with the EGFR Antibody, Erbitux, Approved as Personalised Medicine, Based on EGFR Expression
  • 26. GENE EXPRESSION ANALYSIS USING GENELOGIC DATA
  • 27. GenelogicTM Expression Data !Pathways that are significant to the pathophysiology of Rheumatoid Arthritis and Anti-TNF treatments have been highlighted in the table. !Knowledge of immune response genes can potentially be useful for identification of surrogate markers of clinical endpoint or disease/treatment/response markers according to the project needs.
  • 28. Overview of Analysis • Gene expression data from three types of sample populations analyzed: ! WBC samples from Normal individuals ! WBC samples from Rheumatoid Arthritis patients. ! WBC samples from RA patients, 6 weeks after Remicade Infusion. • Set of 25 genes were identified as a marker set for patient stratification in future novel NME target discovery and development.
  • 31. Speed and Simplicity Verigene Mobile !The next generation Since it is based on direct genomic detection and not target Verigene Mobile will transfer amplification, ClearRead makes the power and accuracy of the molecular testing faster and Verigene AutoLab to an simpler. Current methods require affordable, hand-held device. highly specialized scientists and lab technicians for processing and !Its portability will make it interpretation, while ClearRead ubiquitous at point-of-care assays are easy to perform and settings such as doctor's produce definitive results. offices, hospital bedsides and even in patients' homes.
  • 41. Drugs with Personalised Medicine Properties/Potential •Antibiotics are Personalised Medicines •Herceptin in Oncology •Protease Inhibitors in HIV •Protease Inhibitors in HCV •Diabetic Treatment & Monitoring •Neuroamidase Inhibitors in Influenza e.g. Tamiflu, Relenza •Rituximab, Anti-CD20 in NHL, RA etc •Xolair, Anti-IgE in asthma •Anti-TNF’s & Anti-IL1 in RA •Campostar in Oncology •Xeloda, Gemcitabine, Velcade in Oncology •Taxol & Taxanes in Oncology •UDF in Oncology •EGFR Antibodies & TK inhibitors e.g. Tarceva, Iressa, Erbitux •Potentially VEGF Antibodies (Avastin) and TK inhibitors •Various Monoclonal Antibody Targets