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Redefining Disease Personalised Medicine2
 

Redefining Disease Personalised Medicine2

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    Redefining Disease Personalised Medicine2 Redefining Disease Personalised Medicine2 Presentation Transcript

    • 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
    • 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
    • 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
    • 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
    • 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.
    • 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.
    • 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