Personalised Medicine In R & D


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Personalised Medicine In R & D

  1. 1. Application of Pharmacogenomics To Personalised Medicine and R & D Dr Harsukh Parmar Global Discovery Medicine Respiratory & Inflammation Therapy Area
  2. 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. 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. 4. The co-existence of genetic polymorphisms in drug metabolizing enzymes, targets, receptors, and transporters, in the context of drug and non-drug influences, may result in high frequencies of unusual drug reaction phenotypes.
  5. 5. Current Treatment is Population Based
  6. 6. 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
  7. 7. Pharmacogenomics –Making Personalised Medicines
  8. 8. Patient Segmentation is Not New •Historically we have always done this using Clinical and Biochemical 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. 9. 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
  10. 10. Pharmacogenomics Promise Individualized Medicine •New diagnostic procedures (pharmacogenomic tests) •Better matches between patient, disease, therapy and outcome •Impact on R&D as well as Sales and Marketing
  11. 11. Importance is clear and growing • BMS - Taxol: first cancer NSCLC treatment with blockbuster, now facing generic TAXOL 39 competition 40 Taxol Response rate • Novel taxane about to enter (%) 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
  12. 12. 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. 13. Rheumatoid Arthritis
  15. 15. 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.
  16. 16. 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.
  17. 17. Micro-array Analysis in RA-Treated with Steroids • Analysis of covariance. The distribution of p-values allowed identification of genes with altered gene expression on steroids. CD68 Immunohistochemistry pre post prednisolone placebo CD68 100x Dr H Parmar Experimental Medicine
  18. 18. Biomarkers of disease progression COPD
  19. 19. Analysis of Epithelial Gene Expression in COPD Smokers with/without Brushings (bronchial epithelial cells) Primary cell-based model COPD Non-smokers Define the biochemical Microarrays Microarrays pathways initiated by COPD related stresses Bioinformatics Clinical data •Smoke (CSE) & Statistical analysis G a n a ig en n n ca e ota (G FEV1/FVC ratio Bronchial biopsies on ti O m p to on AC Identify differentially Identify differentially lo gy expressed genes expressed genes ) Confirm expression in in Confirm expression in in disease tissue disease tissue Generate hypotheses, Generate hypotheses, identify targetable identify targetable molecules in pathways molecules in pathways IHC/in situ Functional assays Cytokine production Differentiation, Proliferation Secretion, Motility Candidate Targets
  20. 20. Disease progression cluster (Gene Expresion) (GOLD 0,1 &2; decreasing FEV1) NAME nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/ tubulin, gamma 1 carboxylesterase 1 (monocyte/macrophage serine esterase 1) ProteinDomain carboxyl ester lipase (bile salt-stimulated lipase) COPD2 COPD0 COPD1 cholesterol 25-hydroxylase SPARC-like 1 (mast9, hevin) low density lipoprotein receptor (familial hypercholesterolemia) Cytoband NS HS prostate stem cell antigen NA carboxypeptidase E gastrin-releasing peptide fer-1-like 3, myoferlin (C. elegans) killer cell lectin-like receptor subfamily C, member 3 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide, Y chromosome GO ribosomal protein S4, Y-linked 15 19 18 9 16 2 killer cell lectin-like receptor subfamily C, member 3 small inducible cytokine A5 (RANTES) small inducible cytokine A5 (RANTES) secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymp secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymp Cluster Incl. AF070536:Homo sapiens clone 24566 mRNA sequence /cd S100 calcium binding protein A10 (annexin II ligand, calpactin I, light poly mucin 1, transmembrane aldehyde dehydrogenase 1 family, member A3 cytochrome P450, subfamily I (aromatic compound-inducible), polypeptid Annots cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma cytochrome P450, subfamily I (dioxin-inducible), polypeptide 1 (glaucoma annexin A3 transmembrane 4 superfamily member 1 transcobalamin I (vitamin B12 binding protein, R binder family) cystatin A (stefin A) uroplakin 1B S100 calcium binding protein P claudin 10 carcinoembryonic antigen-related cell adhesion molecule 5 carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific • Hierarchical clustering of genes carbonyl reductase 1 UDP glycosyltransferase 2 family, polypeptide B ubiquitin carboxyl-terminal esterase L1 (ubiquitin thiolesterase) hypothetical protein MGC13523 • Subjects ordered in disease progression Pirin aldo-keto reductase family 1, member B10 (aldose reductase) malic enzyme 1, NADP(+)-dependent, cytosolic • N=79, Expression data from U133A&B glutathione peroxidase 2 (gastrointestinal) phosphogluconate dehydrogenase thioredoxin aldo-keto reductase family 1, member C1 (dihydrodiol dehydrogenase 1; alcohol dehydrogenase 7 (class IV), mu or sigma polypeptide transaldolase 1 aldo-keto reductase family 1, member C3 (3-alpha hydroxysteroid dehyd NAD(P)H dehydrogenase, quinone 1
  21. 21. Human Knock-Out Initiated Projects, Entered Phase III
  22. 22. Disease reclassification at the molecular level
  23. 23. 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. 24. Nanosphere, Inc - Novel technology detects human DNA mutations
  25. 25. Speed and Simplicity Verigene Mobile Since it is based on direct genomic detection and not target !The next generation Verigene Mobile will transfer the power and accuracy amplification, ClearRead makes of the Verigene AutoLab to an molecular testing faster and affordable, hand-held device. simpler. Current methods require highly specialized scientists and !Its portability will make it ubiquitous lab technicians for processing and at point-of-care settings such as interpretation, while ClearRead doctor's offices, hospital bedsides and assays are easy to perform and even in patients' homes. produce definitive results.
  26. 26. Herceptin
  27. 27. Drugs with Personalised Medicine Properties/Potential •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 •Potentially VEGF Antibodies (Avastin) and TK inhibitors •Various Monoclonal Antibody Targets