Download - Diagnostics and personalized medicine

Diagnos(cs and Personalized Medicine

Dynamics in the Biotechnology and Life Science Industry

Tuesday, February 6, 2007

Objec(ves

•  Challenge common wisdom: help you think •  Prepare you for pitches •  Provide basic background

© 2013 Winton Gibbons 2

Topics to Cover

•  Overview of diagnos(c market •  Nomenclature •  Marker mining and valida(on •  Personalized medicine •  Miscellaneous and Q & A – Recent, major acquisi(ons – Point-‐of-‐care


Overall Market Size and Structure

Source: BBC, Amersham and WG analysis

In-vitro

81%

In-vivo

19%

100%=$41.6 billion


IVD Market Size and Structure $7,997

$6,582

$6,034

$1,861

$1,827

$1,740

$1,295

$1,217

$1,228

$4,088

Diabetes

Infectious Disease

Clinical Chemistry

Hematology

Immunology

Endocrinology

Coagulation

Cancer

Cardiac

Other

Source: BBC and WG analysis


IVD Market Size and Structure


US

37%

Europe

35%

Japan

10%

ROW

18%


IVD Market Size and Structure


Lab

75%

PST

18%

Ambulatory

7%


Roche Dominates the IVDs, Especially A`er GE’s Move

Roche

21%

Abbott (pre GE)

12%

J&J

10%

Bayer (Siemens)

9%

Beckman

7%

Dade

6%

Other

35%



Other Thoughts on Industry Structure •  Top 4 Diagnos(cs players part of Larger Medical Product Firm (Roche, GE, J&J and

Siemens) –  Compe((ve Informa(on Spoey

•  Overlap with Life Science firms –  Diagnos(cs uses much of the same technology as Life Sciences, so a number of

companies straddle both (Beckman, BioRad, Cepheid, Celera and even Roche). –  However, Diagnos(cs is different due to regulatory, medical prac(ce, reimbursement,

razor / razor blade and larger, diversified players. •  In-‐vitro Diagnos(cs is a large ($34 billion), but generally grows about the same

rate as nominal GDP; however, there are a few fast-‐growing sub-‐sectors and some niche opportuni(es –  Molecular diagnos(cs (e.g., DNA) –  Blood Glucose –  Novel protein markers (e.g., BNP and others)


M.D.s Rx firms

Device firms

Dx Hospitals

Pharmacies Distribution

Stronger

Poli(cal Power for IVD Firms Typically is not Strong


Dimension RxL Max Chemistry/Immunochemistry Analyzer

GeneXpert

Triage

Diagnos(c Instruments Vary in Size and Complexity


Large System Purchases Typically Don’t Depend on Single Markers

• 5- to 6-year repurchase cycle • Labor savings (2/3 of cost)– Laboratory automation

• 12- to 24-month selling cycle – Ease of use– Easy maintenance / reliability

•Important analytes on the menu: Troponin I, HbA1c, BNP/NT-proBNP • Menu should cover 90%+ of volume high-sensitivity TSH and HCG

Source: William Blair & Company, L.L.C. analysis

Purchasing Behavior for Mainframe Immunodiagnostic Analyzers


Some Myths in Diagnos(cs •  Best test

–  Standardiza(on / installed based—VHS versus Betamax (e.g., Troponin I versus T; BNP versus NT-‐proBNP?) –  Plaoorm migra(on (NA to IA to CC) –  Trial and error (e.g., sta(ns)

•  POC –  Cost center versus total cost –  Lab Director power –  MD office

•  Work flow •  Profit (Stark II—July 26)

•  Pharmacogenomics –  Metabolizing enzymes (CYP450s)

•  Yes •  Drug-‐drug interac(ons

–  Individualized medicine •  Not always •  Except certain cancers or orphans •  Drugs to target big markets, just using new biology


Nomenclature •  Sensi(vity

–  Percent with disease who test posi(ve •  Specificity

–  Percent of without disease who test nega(ve •  Posi(ve Predic(ve Value

–  Prevalence*Sensi(vity/(Prevalence*Sensi(vity+(1-‐Prevalence)*(1-‐Specificity)) •  Nega(ve Predic(ve Value

–  (1-‐ Prevalence)*Specificity/((1-‐ Prevalence)*Specificity+Prevalence*(1-‐Sensi(vity) •  Odds Ra(o

–  Odds/Odds –  Odds=p/(1-‐p)

•  ROC Curve –  True Posi(ve Frac(on versus False Posi(ve

Disease Present Disease AbsentPositive Test A B A+BNegative Test C D C+D

A+C B+D

Sensitivity A/A+CSpecificity D/B+D


Reading List •  Believability of rela(ve risks and odds ra(os in abstracts: cross sec(onal study.

–  BMJ, Jul 2006; 333: 231 -‐ 234

•  Evidence of bias and varia(on in diagnos(c accuracy studies –  Can. Med. Assoc. J., Feb 2006; 174: 469 -‐ 476.

•  Tips for learners of evidence-‐based medicine: 5. The effect of spectrum of disease on the performance of diagnos(c tests –  Can. Med. Assoc. J., Aug 2005; 173: 385 -‐ 390

•  Predic(on of cancer outcome with microarrays: a mul(ple random valida(on strategy. –  Lancet. 2005;365:488-‐92.

•  Can Genentech Double Its NHL Franchise? Focus on Fc Receptors –  William Blair & Company Research Note. December 2, 2004

•  Limita(ons of the Odds Ra(o in Gauging the Performance of a Diagnos(c, Prognos(c, or Screening Marker –  Am. J. Epidemiol., May 2004; 159: 882 -‐ 890.

•  When can a risk factor be used as a worthwhile screening test? –  BMJ, Dec 1999; 319: 1562.

•  Drug Metabolism and Variability among Pa(ents in Drug Response –  N. Engl. J. Med., May 2005; 352: 2211 -‐ 2221.

•  Codeine Intoxica(on Associated with Ultrarapid CYP2D6 Metabolism –  N. Engl. J. Med., Dec 2004; 351: 2827 -‐ 2831.

•  Developmental Pharmacology — Drug Disposi(on, Ac(on, and Therapy in Infants and Children –  N. Engl. J. Med., Sep 2003; 349: 1157 -‐ 1167.


One Week’s Worth of Gene(c Biomarker Discovery

•  “Gene(c fingerprints iden(fy brain tumors' origins” (Feb 1) •  “Mayo Clinic Research Shows 35 Percent Of 49 Young People Who Died Suddenly And Inexplicably Had

Gene(c Heart Defects” (Jan 31) •  “UCLA Researchers Discover Genes Linked To Lymphoma, Opening Way For New Targeted Drugs” (Jan 31) •  “Study finds genes that predict transplant rejec(on” (Jan 30) •  “A Form Of The Alcohol Dehydrogenase Gene May Protect Afro-‐Trinidadians From Developing

Alcoholism” (Jan 30) •  “Autoimmune Disease Breakthrough Gained By Iden(fica(on Of 30 Errant Genes” (Jan 29) •  “Gene 'could predict ADHD drug reac(on'” (Jan 29) •  “50% of Americans have gene that affects how body burns sugar” (Jan 28) •  “Scien(sts link paternal gene, au(sm” (Jan 26) •  “Gene(c Risk Factor For Parkinson's Found” (Jan 25) •  “Calculated Risk: Scien(sts Discover Gene(c Risk Factor For Smoking-‐linked Head And Neck Cancer” (Jan

25)

Source: National Office of Public Health Genomics (NOPHG)


Senator Barack Obama Introduces the Genomics and Personalized Medicine Act The Personalized Medicine Coali(on welcomes the introduc(on of S.3822, the Genomics and Personalized Medicine Act, and looks forward to working with Senator Barack Obama, the bill's author, and his colleagues in Congress, to hasten the introduc(on of personalized medicine. The legisla(on, among other things, aims to improve the coordina(on of public and private efforts to facilitate the development of safer and more effec(ve drugs, create a biobanking ini(a(ve, expand the genomics workforce, and improve the quality of clinical gene(c tes(ng.


The Genomics and Personalized Medicine Act of 2006

•  Sponsoring Research. The bill sets aside $150 million to sponsor research on genomics. It enables a na(onal biobanking ini(a(ve and sets up a system to pool and synthesize genomic data from local sources. This act establishes an interagency task force to accelerate the transla(on of research into medical prac(ce. Finally, the legisla(on invests in the next genera(on genomics workforce by encouraging the recruitment and reten(on of genomic professionals, and promotes the integra(on of genomics across all clinical and public health disciplines.

•  Encouraging InnovaAon. The legisla(on provides a 100 percent tax credit for the development of companion diagnos(c tests that can improve the effec(veness or safety of certain drugs. In addi(on, the Na(onal Academies will conduct a study to determine what addi(onal incen(ves are needed, and how they should be structured.

•  Modernizing the FDA and CMS. The bill requires that FDA and CMS study and update regulatory processes to assure the quality of genomic tests through improved oversight and regula(on.

•  ProtecAng Consumers. The legisla(on protects consumers by reaffirming Congress commitment to stopping gene(c discrimina(on and protec(ng gene(c privacy. In addi(on, direct-‐to-‐consumer marke(ng of gene(c tests would receive greater scru(ny and regula(on.


Cytochrome p450 Enzymes •  The superfamily has undergone divergent evolu(on, and

the ancestral gene is likely 2 to 3 1/2 billion years old. •  The recent 'burst' in new P450 genes, par(cularly in the II

family during the past 800 million years, appears to be the result of 'animal-‐plant warfare'.

•  Due to the presence or absence of a par(cular P450 gene in one species but not the other, it may not be correct to extrapolate toxicity or cancer data from rodent to human.

•  Increases in the P450 gene product (enzyme induc(on) almost always reflect an elevated rate in gene transcrip(on, although there are several excep(ons.


Posted on: Monday, 22 January 2007, 21:00 CST GENETIC MEDICINE ; Some Heart PaAents Get DNA Tests to Determine Correct Drug Dose By Linda A. Johnson Personalized medicine, the tailored treatments that a few pa(ents now get based on their own DNA, is finally headed for the masses: the many heart pa(ents at risk of deadly blood clots. At least 2 million Americans with an abnormal, clot-‐triggering heart rhythm take the pill warfarin, also sold as Coumadin. Gewng too liele can lead to a stroke, and too much can cause life-‐threatening bleeding. To find the right dose for each pa(ent, doctors use trial and error -‐-‐ and the errors lead to tens of thousands of hospitaliza(ons and deaths every year. Star(ng this month, about 1,000 pa(ents who have a condi(on known as atrial fibrilla(on will take part in a project that will match their Coumadin dose to their specific gene(c needs. This gene(c fingerprin(ng should single out the many people whose bodies break down warfarin faster or slower than normal, and their doctors can immediately adjust their dosage to prevent dangerous complica8ons. "Twenty percent to 30 percent of people are either very fast or very slow" to metabolize many drugs but don't know it, said Dr. Robert Epstein, chief medical officer at prescrip(on benefit manager Medco Health Solu(ons of Franklin Lakes, N.J., which is collabora(ng in the effort with the Mayo Clinic, based in Rochester, Minn.


Effect of CYP450 Muta(ons •  Rapid metabolizers

–  Carry mul(ple copies (3-‐13) of func(onal alleles and produce excess enzyma(c ac(vity

•  Normal metabolizers –  Possess normal func(onal alleles

•  Intermediate metabolizers –  Possess one reduced ac(vity allele or

one null allele •  Poor metabolizers

–  Carry two mutant alleles which result in complete loss of enzyme ac(vity

•  2D6 gene duplica(ons –  Ethiopians 16.0% –  Saudi Arabians 10.4% –  Spaniards 10% –  Italians 8.3% –  Zimbabweans 2% –  Germans 1.8% –  Chinese 1.3%

•  2D6 Intermediate and poor metabolizers –  Caucasians 7-‐8% –  Japanese ~1% –  Chinese ~1% –  African Americans~6%

Source: Roche © 2013 Winton Gibbons 21

CYP450 Substrates (drugs) 1A2 2B6 2C8 2C19 2C9amitriptyline bupropion paclitaxel Proton Pump Inhibitors: NSAIDs:caffeine cyclophosphamide torsemide lansoprazole diclofenacclomipramine efavirenz amodiaquine omeprazole ibuprofenclozapine ifosfamide cerivastatin pantoprazole lornoxicamcyclobenzaprine methadone repaglinide rabeprazole meloxicamestradiol E-3810 S-naproxen=>Norfluvoxamine piroxicamhaloperidol Anti-epileptics: diazepam=>Nor suprofen imipramine N-DeMe phenytoin(O)mexilletine S-mephenytoin Oral Hypoglycemic Agents:naproxen phenobarbitone tolbutamideolanzapine glipizide ondansetron amitriptylinephenacetin=> carisoprodol Angiotensin II Blockers:acetaminophen=>NAPQI citalopram losartanpropranolol clomipramine irbesartanriluzole cyclophosphamideropivacaine hexobarbital Sulfonylureas:tacrine imipramine N-DeME glyburide/theophylline indomethacin glibenclamidetizanidine R-mephobarbital glipizideverapamil moclobemide glimepiride(R)warfarin nelfinavir tolbutamidezileuton nilutamidezolmitriptan primidone amitriptyline

progesterone celecoxibproguanil fluoxetinepropranolol fluvastatin glyburideteniposide nateglinideR-warfarin=>8-OH phenytoin=>4-OH

rosiglitazonetamoxifentorsemideS-warfarin

Source: CYTOCHROME P450 DRUG-‐INTERACTION TABLE-‐-‐Last Updated: Tue Oct 17 2006-‐-‐Indiana University Department of Medicine, Division of Clinical Pharmacology


CYP450 Substrates (drugs)-‐-‐con(nued 2E1

Beta Blockers: alprenolol Anesthetics: Macrolide antibiotics: Steroid 6beta-OH:carvedilol amphetamine enflurane clarithromycin estradiolS-metoprolol aripiprazole halothane erythromycin (not 3A5) hydrocortisonepropafenone atomoxetine isoflurane NOT azithromycin progesteronetimolol bufuralol methoxyflurane telithromycin testosterone

chlorpheniramine sevofluraneAntidepressants: chlorpromazine Anti-arrhythmics: alfentanylamitriptyline codeine (=>O-desMe) acetaminophen quinidine=>3-OH (not 3A5) aprepitantclomipramine debrisoquine =>NAPQI aripiprazoledesipramine dexfenfluramine aniline Benzodiazepines: buspironeimipramine dextromethorphan benzene alprazolam cafergotparoxetine duloxetine chlorzoxazone diazepam=>3OH caffeine=>TMU

encainide ethanol midazolam cilostazolAntipsychotics: flecainide N,N-dimethyl formamide triazolam cocainehaloperidol fluoxetine theophylline codeine- N-demethylationperphenazine fluvoxamine =>8-OH Immune Modulators: dapsonerisperidone=>9OH lidocaine cyclosporine dexamethasonethioridazine metoclopramide tacrolimus (FK506) dextromethorphanzuclopenthixol methoxyamphetamine docetaxel

mexilletine HIV Antivirals: domperidoneminaprine indinavir eplerenonenebivolol nelfinavir fentanylnortriptyline ritonavir finasterideondansetron saquinavir gleevecoxycodone haloperidolperhexiline Prokinetic: irinotecanphenacetin cisapride LAAMphenformin lidocainepromethazine Antihistamines: methadonepropranolol astemizole nateglinidesparteine chlorpheniramine odanestrontamoxifen terfenidine pimozidetramadol propranololvenlafaxine Calcium Channel Blockers: quetiapine

amlodipine quininediltiazem risperidonefelodipine NOT rosuvastatinlercanidipine salmeterolnifedipine sildenafilnisoldipine sirolimusnitrendipine tamoxifenverapamil taxol

terfenadineHMG CoA Reductase Inhibitors: trazodoneatorvastatin vincristinecerivastatin zaleplonlovastatin ziprasidoneNOT pravastatin zolpidemsimvastatin

2D6 3A4,5,7



CYP450 Inhibitors


1A2 2C19 2C9 2D6 3A4,5,7amiodarone chloramphenicol amiodarone amiodarone HIV Antivirals:cimetidine cimetidine fenofibrate bupropion delaviridineciprofloxacin felbamate fluconazole celecoxib indinavirfluoroquinolones fluoxetine fluvastatin chlorpheniramine nelfinavirfluvoxamine fluvoxamine fluvoxamine chlorpheniramine ritonavirfurafylline indomethacin isoniazid chlorpromazineinterferon ketoconazole lovastatin cimetidine amiodaronemethoxsalen lansoprazole phenylbutazone citalopram aprepitantmibefradil modafinil omeprazole probenicid clemastine NOT azithromycin

oxcarbazepine sertraline clomipramine chloramphenicol2B6 probenicid sulfamethoxazole cocaine cimetidinethiotepa ticlopidine sulfaphenazole diphenhydramine clarithromycinticlopidine topiramate teniposide doxepin diethyl- dithiocarbamate

voriconazole doxorubicin diltiazem2C8 2E1 zafirlukast duloxetine erythromycintrimethoprim diethyl- dithiocarbamate escitalopram fluoxetine fluconazolequercetin disulfiram halofantrine fluvoxamineglitazones histamine H1 receptor antagonists gestodenegemfibrozil hydroxyzine grapefruit juicemontelukast levomepromazine imatinibtrimethoprim methadone itraconazole

metoclopramide ketoconazolemibefradil mifepristonemidodrine nefazodonemoclobemide norfloxacinparoxetine norfluoxetineperphenazine mibefradilquinidine star fruitranitidine verapamilred-haloperidol voriconazoleritonavirsertralineterbinafineticlopidinetripelennamine


CYP450 Inducers


1A2 2C19 3A4,5,7broccoli carbamazepine HIV Antivirals:brussel sprouts norethindrone efavirenzchar-grilled meat NOT pentobarbital nevirapineinsulin prednisonemethyl cholanthrene rifampin barbituratesmodafinil carbamazepinenafcillin 2C9 efavirenzbeta- naphthoflavone rifampin glucocorticoidsomeprazole secobarbital modafiniltobacco nevirapine

2D6 phenobarbital2B6 dexamethasone phenytoinphenobarbital rifampin rifampinrifampin St. John's wort

2E1 troglitazone2C8 ethanol oxcarbazepinerifampin isoniazid pioglitazone

rifabutin


Personalized Medicine-‐Pros and Cons

•  Desire for efficacy •  Desire for safety •  Because we can…

•  Clinical study size and cost –  Efficacy –  Safety

•  COGS •  Marke(ng expense •  Design around

–  Dosing (case studies) –  Ligand binding (case study) –  Alterna(ve target or MOA


Future Rx Targets Not Likely; Orphan Drugs

Existing Rx Today Practical with Proper Data

Only Dosing Modifications for

Metabolism

One-Size Rationalize PersonalizeOne Drug A Few Drugs Many Drugs

Economic Pressure

Scientific Pressure

Outlook for Personalized Medicine


Cases

•  Rituxan and NHL •  MRI for stroke (Lancet 2007) •  Gene signature for breast cancer (NEJM 2007)


Thoughts on the Cases •  Read the literature and do your homework •  Design around personaliza(on

–  Genitope and Favrille s(ll have a chance •  Understand the clinical environment •  Odd Ra(os •  Comparison against normals versus mimics or common differen(al (all comers)

•  Over-‐fiwng –  Degrees of freedom –  Algorithms –  Sta(s(cal tests

•  Valida(on, valida(on, valida(on


Performance of Rituxan Varies by Fc Polymorphism


An(-‐idiotype Vaccines S(ll Appear to Perform Best


High Heaven

Med

Orphan Drugs

Low Hell

Low Med High

(Number and Effect)

Power of

Target

Degree of Polymorphism

Efficacy

Higher Throughput Screening Needed

Target Selec(on Strategy


Almost 20% of ERs May Not Have Any Access to MRI

Source: Biosite Investor R&D Day 2006


Available MRIs Take Long and Are Not Available All Shi`s

Source: Biosite Investor R&D Day 2006


Odds Ra(os Must Be Quite High to Be Useful

Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker. Am. J. Epidemiol., May 2004; 159: 882 - 890.


Sources of Bias in DiagnosAcs


Evidence of bias and variation in diagnostic accuracy studies. Can. Med. Assoc. J., Feb 2006; 174: 469 - 476.

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