Download LA MEDICINA PERSONALIZZATA APPLICATA AL DIABETE MELLITO

 L’INNOVAZIONE TECNOLOGICA E TERAPEUTICA NELLA GESTIONE DEL DIABETE MELLITO E DELLE SUE COMPLICANZE CRONICHE. LA MEDICINA PERSONALIZZATA APPLICATA AL DIABETE MELLITO • Do# . Francesco Chiaramonte • Roma 27/9/14 πρόσωπον la medicina personalizzata Cells and DNA • Human body contains ~100 trillion cells • Each cell contains 23 pairs of chromosomes (= genome) • Chromosomes contain DNA • DNA is made of 4 nucleoTde bases (Adenine, Guanine, Cytosine & Thymine) = AGCT sequence Ed Radclyffe, CSIRO • Every cell (except a few) in an individual contains the same exact genome u Completamento Genoma Umano
Ø Sequenza lineare di DNA sui Cromosomi. L’intero genoma contiene da
Ø Enorme sforzo tecnologico pubblico e privato
Ø Molto ‘rumore’ non un vantaggio immediato
u Il sequenziamento del Genoma Umano promette una grande mole
di informazioni biologiche
Ø comprensione del profilo genetico dell’ individuo “Medicina
personalizzata”
Ø Individuazione precoce dei fattori di rischio
u Molte patologie hanno una componente genetica
Ø Cancro (30%), malattie cardiache (30%), Diabete ,
disordini neurodegenerativi, malattie mentali
Ø L’identificazione dei geni coinvolti potrebbe suggerire nuove
terapie (anche a livello genetico) o essere d’aiuto per la
diagnosi precoce
il sequenziamento del genoma Ome, Omics • Genomics: all the genes • pharmacogenomics : choice of personalized medicine • nutrigenomics : choice of best diet • toxicogenomics : predicTon of toxicity • Epigenomics: all epigeneTc changes in genome • Transcriptomics: all the mRNAs → microarrays • Proteomics : all the proteins • Interactomics : all interacTons between all proteins • Metabolomics (or metabonomics) : all metabolites • … Human genome variaTons – Single nucleoTde polymorphisms (SNPs) • ~ 97 % of the genome between any two individuals in idenTcal • ~ 1% of the differences are single nucleoTde variaTons (SNPs) • ~2% Other changes – copy number variaTons, deleTons • Between 11-­‐12 million SNPs have been idenTfied Examples of individual DNA tests • Carrier tests
• Parent and New born screen
• Disease Risk tests
• Drug Response tests
GeneTc loci associated with T2DM R.J. Smith J Clin Endocrinol Metab, April 2010 Major Genomewide Association (GWA) Studies of Type 2 Diabetes
McCarthy MI. N Engl J Med 2010;363:2339-2350
Pathways to Type 2 Diabetes Implicated by Identified Common Variant Associations
McCarthy MI. N Engl J Med 2010;363:2339-2350
A causal model of T2D pathogenesis.
Das S K Diabetes 2014;63:2901-2903
Copyright © 2014 American Diabetes Association, Inc.
Main Pathophysiological Defects in T2DM pancreaTc insulin secreTon increTn effect -
gut carbohydrate delivery & absorpTon pancreaTc glucagon secreTon ? HYPERGLYCEMIA
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hepaTc glucose producTon peripheral glucose uptake Adapted from: Inzucchi SE, Sherwin RS in: Cecil Medicine 2011 inefficacia dei farmaci Danger of drugs: • 6.7% of paTents in hospitals experience serious drug reacTons Polygenic Determinants of Drug Response Differences in drug sensiTvity and renal clearance Nine possible combinaTons of drug-­‐metabolism and drug-­‐
receptor genotypes and the corresponding drug-­‐response phenotypes. Each yields a different therapeuTc index (efficacy:toxicity raTos) ranging from 13 (65 percent:5 percent) to 0.125 (10 percent:
80 percent). h#p://content.nejm.org/cgi/content/full/348/6/538 Old Paradigm: New Paradigm: Future Paradigm: Initial Treatments for Various Diabetes Subtypes
McCarthy MI. N Engl J Med 2010;363:2339-2350
Switching from Insulin to Oral Sulfonylureas in PaUents with Diabetes Due to Kir6.2 MutaUons Pearson ER.: N.Engl.J Med 2006 VariaUon in TCF7L2 Influences TherapeuUc Response to Sulfonylureas :A GoDARTs Study Pearson ER Diabetes 2007 Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Me_ormin and Lifestyle IntervenUon in the D P P Kathleen A. Jablonski Diabetes 2010 Kathleen A. Jablonski Diabetes 2010 Ø geni coinvolU nel metabolismo dei farmaci e dei trasportatori Ø geni codificanU bersagli farmacologi e recebori Ø geni coinvolU nel percorso del DM2 che hanno la capacità di modificare l’effebo dei farmaci Ø altri nuovi geni (fabore crescita insulinosimile ,fabori di trascrizione atassia e telengectasia mutaU) Genotype Score in AddiUon to Common Risk Factors for PredicUon of Type 2 Diabetes Genotype Score in AddiUon to Common Risk Factors for PredicUon of Type 2 Diabetes Conclusions A genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly beber predicUon of risk than knowledge of common risk factors alone Meigs Jemes B New Engl J Med 2008 Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes Valeriya Lyssenko Nengl J med 2008 Effect of geneUc tesUng for risk of typ DM2 on health behaviors and outcomes: study raUonale, development and design Cho et al. BMC Health Services Research 2012 Effect of geneUc tesUng for risk of typ DM2 on health behaviors and outcomes: study raUonale, development and design Cho et al. BMC Health Services Research 2012, Clinical UTlity of GeneTc Risk TesTng in Primary CareThe Example of Type 2 Diabetes However, the early studies looking at the actual clinical uTlity and clinical outcomes of providing geneTc risk tesTng in clinical care have not revealed addiTve or differenTal effects on health behaviors or diabetes-­‐related clinical measures as compared with tradiTonal risk factors and prevenTve informaTon. T Cho A Personalized Medicine 2013 The clinical applicaUon of geneUc tesUng in type 2 diabetes:a paUent and physician survey Grant R.W. Diabetologia 2009 The clinical applicaUon of geneUc tesUng in type 2 diabetes:a paUent and physician survey • Conclusions/interpretaTon Despite the paucity of current • data, physicians and paTents reported high expectaTons that • geneTc tesTng would improve paTent moTvaTon to adopt • key behaviours for the prevenTon or control of type 2 • diabetes. This suggests the testable hypothesis that ‘geneUc’ • risk informaTon might have greater value to moTvate • behaviour change compared with standard risk informaTon. Grant R.W. Diabetologia 2009 Cost of genome sequencing • • • • • • Genome of C Venter : 100x106 $ Genome of J Watson : < 1.5x106 $ X Prize FoundaTon challenge : 10,000 $ Race for $ 1,000 genome : 1,000 $ Complete Genomics in 2009 : 4,400 $ Cost of the detecTon of a single mutaTon by Belgian geneTcs laboratories : 300 ∈ EBM: what is and it isn’t “ the conscien*ous,explicit and judicious use of current best evidence in making decisions about the care of individual pa*ents “ Sake# Dl Cln.Orthop.Relat.Res 1996 Challenge of evidence in Individualized medicine Krat K Personalized medicine 2012 il presente • ancora costosa • tecnologie da migliorare • mancano le infrastrubure • implementazione culturale • sicuramente è il futuro P.M. la medicina traslazionale • ricerca molecolare • genomica • proteomica • bioinformaTca • farmacogeneTca Ø predizione personale del rischio Ø progressione malaua e complicanze Ø terapia individualizzata Ø medicina personalizzata gli esempi AMD & SID DIABETES, VOL. 62, OCTOBER 2013 The Future of Genomics in Medicine ©2007 by American College of Chest Physicians Tebbub S J et al. Chest 2007;131:1216-­‐1223 “E 'molto più importante conoscere quale persona ha una mala?a piu@osto che quale mala?a ha quella la persona” Ippocrate