Januari 13, 2017
female speaker:-- the tenth in our topic series. our speaker this morning is dr. howard mcleod, who recentlymoved to the moffitt cancer center in tampa. he's a medical director at the debartolo familypersonalized medicine institute, as well as a senior member of the division of populationsciences. dr. mcleod is a leader in the field of pharmacogenomics, a relatively new disciplinethat explores how genetic information influences our response to drugs. his research has alreadyhad several effects on fda policies; for example, he and others have shown that genetic variantsplay a role in patients' response to warfarin, a blood thinner prescribed to more than twomillion people in the united states. based on these analyses, the fda issued new dosingguidelines based on the genotyping of two
genes. as the new medical director of the personalizedmedical institute -- medicine institute, dr. mcleod will be involved with the moffitt'stotal cancer care study to create and share targeted cancer treatments that will improvepatient outcomes. during this morning's lecture, dr. mcleod will be expanding on both thesestories, as well as telling us -- as well as telling us about other developments inpharmacogenomics. please join me in welcoming in dr. mcleod to the nih campus this morning. howard mcleod:well thank you, it's a pleasure to be back and to update you on what's happening. every-- these things happen every two years in
this course and it's always surprising howmuch has happened, and how little has happened in two years, and it's true with all of thedisciplines. there's things that we're still talking about today that were -- that werediscovered decades ago, and there are things that have moved on to the point where we don'teven talk about it anymore because they've become routine. and so that's -- certainly-- certainly true in pharmacogenomics. i'm going to talk a little bit about the -- someof the emerging trends in the field, some of the ways that we've been thinking abouthow to advance the discipline, and also some of the ways we're trying to make sure thatit's not just the rich countries, or in our case, the formerly rich countries, that are-- that are benefitting from the genomic -- the
genomic advances that are happening. now ilike to start pretty much every presentation that i give not with this, although i will[unintelligible] with that, but i do -- i'm on the board of directors on a small companydown in rtp that's a pharma services company, but is unrelated to the topics that i'm speakingabout today. i like to start with this particular quote,and that is, "a surgeon who uses the wrong side of the scalpel cuts her own fingers,not the patient. if the same applied to drugs, they would have been investigated very carefullya long time ago." this quote is supposedly from 1849, it's a journal that i don't read,but of the drugs that are approved by the usfda in 2014, or since -- since the beginningof time until 2014, there are none of them
that we really know the mechanism of action.we know something, we call them something. we call them a 'cyclooxygenase 2 inhibitor'or a 'topoisomerase 1 inhibitor.' but cox-2 inhibitors have activity in cox-2 knockoutmice. there's something else going on, and so, that's true in all of the medicines outthere. we know something about them, but not a lot about them, and so there's a lot ofadvances still in terms of discovering what the genes are that regulate the effect ofthese drugs. how can we use that information to guide therapy? or, how can we use thatinformation to even just counsel the patients better in terms of what to expect in caseswhere we don't have a lot of alternatives? thankfully, in modern times for most diseases-- i know this is the nih campus, and so there
are a lot of extremely rare cases that areseen in this building. but for most diseases, certainly the common diseases, there are manyactive treatments that are available for use in modern times. and so if you take an extremeexample, for the treatment of high blood pressure, the usfda has approved more than 100 drugsor drug combinations for the treatment of high blood pressure. and so when you sit downwith a patient and try to decide which medicine should we give this particular person, howdo you choose? well you choose the one you know how to spell, is almost the way we doit -- there really isn't a lot of science that goes into it, it's more clinician familiarityand sometimes cost, sometimes other elements, and often it's trial and error. we'll trya beta blocker, we'll try an ace inhibitor,
we'll try a whatever, and see if it works,and then try it again. and so even though there are a lot of medicines, there aren'tnecessarily a lot of objective ways of choosing which medicines to use for an individual patient.and that's where some of the promise around not only pharmacogenomics, but pharmaco-proteomicsand pharmaco-whatever-omics - in terms of trying to choose people in a little bit moreuseful way. variation response is also the norm. now,the bacterial infections, there's a lot of success. bone disease, a lot of success. butfor most diseases, we get it right approximately 50 percent of the time. so whether it's mentalhealth disorders, or cancer, some other of your favorite illnesses. often the first therapywill work in around half of the people, and
then the other half need a second therapy,either sequentially or simultaneously, or a third therapy, or a fourth therapy. graduallythere might be some benefit brought to the patient, but often it will take a few triesto get there. so this variation is not only a waste of resourcesand a waste of opportunity, because for many diseases the first opportunity is the bestopportunity. but it's also -- it decreases the trust in the whole health system as thingsgo on. when -- if you can't get it right after the fourth time, can you blame someone fortrying an unproven alternative approach? because certainly the proven approach is not workingvery well for them. and so you can see the need to really get it right the first timefrom an economics standpoint, from a patient-care
standpoint, and from a health system-processstandpoint. toxicities also remain relatively unpredicted.of course you tell the patient the most common toxicity, but any of you that have watch tv,and seen a tv ad where the last seven seconds of the ad was someone talking extremely fastsaying a bunch of toxicities that could happen to you or to your loved one. and certainlytoxicities can happen in a very vague sort of way, but to an individual patient, we oftencannot predict who's going to have trouble and who's not. toxicity matters a lot whenit comes to the benefits of medicines. so i think most of you would agree that statins,the anti-cholesterol class of medications have been shown to have an amazing publichealth impact. some have argued that it has
the most important health -- public healthimpact of any medicines that have ever been developed. but that public health impact isonly true for those people that take the medicine. those people who don't take it, even whenprescribed, do not get that public health benefit. it's not just a benefit by association,it's benefit by actually taking the pills. and so what we've found, and what others havefound as well is that by the end of the first year after being prescribed statins, onlyabout one in three patients are taking the medicines as prescribed. the reason why most have stopped -- some havestopped because they don't really visualize themselves having the disease, and just can'tcare. some people stop because the drug is
just too expensive. but the majority stopbecause of the muscle pains they are getting. not rhabdomyolysis, the muscles aren't shredding,but rather just the inconvenient pain that -- that occurs in day-to-day life. and theythink, "oh, i just can be bothered. oh, i'm going to take a weekend off, because i havethe family reunion. or, i'm going to take a week off for this cruise, or, or, take alittle bit of time." and before you know it they're not taking it at all, or taking itvery little. and so, toxicities matter not only because of the acute event, but alsobecause the whole public health benefit of giving a medicine in the first place doesnot -- is not realized when toxicities are occurring. and so there's not only an individualpatient care element of this, but really a
health system and societal aspect to toxicitiesand getting it right the first time. toxicities are also something that happento the patient and not the prescriber, and certainly in the areas that i work, toxicitiescan be extreme, to the point that we don't even care about them. and of course we careabout them, but we don't really acknowledge them. so, with chemotherapy, one of the mostcommon toxicities is chemotherapy-induced diarrhea. not a topic that one would necessarilytalk about it in the morning, but it is, it's true. now if -- when i go to a study centerto look at the toxicities -- toxicities are graded from zero, meaning it didn't happenat all, to five, meaning the patient actually died from the toxicity. so when i go to thedata center i'll ask the statisticians, "just
-- just give me the grade three, grade fourtoxicities." thankfully there's not a lot of grade five. i don't even bother with thesmall stuff. well, i can tell you, if i had grade one diarrhea from chemotherapy rightnow, i'd be talking to you from somewhere out in the hallway, hopefully by audio andnot video. it's not a trivial thing to the patient, even though as an investigator, idon't even care about it. it's not a motive enough to even beg my attention. so toxicities are something that really havenot had the full service that some of the disease aspects have in terms of genomics,and in terms of other aspects of trying to figure out what's happening. we'll sequencetumors to try to figure out which drive to
give, but we won't sequence the person's germline with the purpose of choosing which drug based on toxicity. and i can tell you in oncologyand in most areas therapeutic selection is a tie-breaker exercise. you have two equaltherapies, and you're trying to just break the tie. it's not awesome therapy versus suckytherapy, and you have to have a really good reason not to give awesome. it's two equals,often not so awesome, and you're trying to decide, "well which of these do i give thepatient?" and it's just -- a feather will shift the scale. it's not something that needs,necessarily, amazing data. and toxicity is usually that feather that will cause a shiftto one therapy versus another for many disease areas.
now, the other element that none of us wantto talk about is the cost of healthcare, the cost of medications. and truly, it's somethingthat as academics, we want to focus on shrinking tumors, and avoiding stevens-johnson syndrome,or some severe toxicity. we don't really think about the cost element. but yet, it's profoundfor the patient and often causes them to make decisions that we just cannot understand.why would you not want to get this therapy? well the fact is, even the well-insured havesignificant out-of-pocket expenses. you know, many of the therapies for cancer, the newbiologics, the new kinase inhibitors, will be somewhere around $10,000 to $20,000 permonth, with a 10 percent with -- a well-insured person will have a 10 percent copay -- andit is capped at some point. but most people
don't have a -- an extra $1,000 or $2,000sitting around their -- that they wondered what to do with. and often people will befaced with the decision, "do i mortgage my house to make sure i can pay for my care?" so the economics have to be part of the decisionas we go forward. we can't be analyzing just, you know, what's the pet scan look like forthe tumor, but how do we put this all together, and we'll come back to that point towardsthe end, because we don't -- i'm not intending that someone who's an amazing genomic scientist,or an amazing clinician, or an amazing biochemist, should suddenly become a health economicsperson, but rather interacting with those folks to ask smart questions with sometimeswith financial endpoints, is an element that
we need to be focusing on a little more oftenthan we are now. now, this slide is really hard to see fromhere, so you probably can't see it at all from -- from there. it's from a science translationalmedicine article that geoff ginsburg, jeanette mccarthy, and myself, put out late last year.and it really -- the reason for showing it is not to go over it in detail but ratherto depict there's a lot of different areas that have to be taken into account as we tryto optimize therapy based on genomics. there's new diagnostics that are coming through, andso lung cancer is not lung cancer anymore, it's one of many of different elements, sub-typesof cancer. the early diagnosis aspect is not only happening in terms of childhood maladies,like you heard about last week, but it also
is happening in terms of predicting whichdiseases one might have. or early prediction that one might have a recurrence, or not,of their disease, or subsequent resistance. is there a sub-clone, a resistant sub-clonethat may be only a half a percent of the total population currently that could emerge andbe taking over, over time? those types of things are really coming forward. so, it'sno -- it's no longer one snip or one gene type equals therapy decision, but rather aconstellation of information that's helping inform how a patient is managed, not justat this visit, but longitudinally over the course of their care? and so we have to bethinking about a lot of different aspects, and we'll hit on some of those over the nextfew minutes.
now in terms of pharmacogenomics, there isa lot of different activity happening under that name, and really the interaction betweendrugs and the genome does offer a lot of different opportunities. so there's still a lot of discoveryto be -- to be made. with all the variants that have been discovered, you would thinkthere wouldn't be too many left, and yes there are rare variants, but there's also variantsthat are unique to populations that have not been very well studied. so, for example, insome of the work that moffitt's doing, [unintelligible] cancer's doing and in puerto rico. i've foundthat the lung cancers, there's some variants in lung cancer that affect therapeutic decisionsthat occur in around 10 percent of the us based population both the white and africanamerican population, but it occurs in around
30 percent of the puerto rican population.and so puerto ricans that are not a population that had been well-studied in the previouscancer studies, now we're finding interesting facts -- that's just a small little anecdote,but there are many throughout the literature, and throughout the current scientific exploration,where populations do have some unique features, where discovery is still relevant at the sequencelevel. even for common things, that are common for that population, not necessarily commonfor all people. there still a lot of difference in phenotypes.so whether this is the incidence of drowsiness after a certain medication, or whether theincidence of blood level, or whatever it might be. still a lot of explanation going on, interms of genetic exploration. there is still
of course those rare individuals -- we havepeople who we give one dose of oxaliplatin and their nerves just are broken up basedon -- just on a single exposure to these drugs. there are other people who get a single dose,or short course of carbamazepine and get stevens-johnson syndrome. these very extreme events are stilla very rich source of information and have been the subject of not only a lot of high-profilepublications, but a lot of data that has resulted in changes in fda package inserts, and changesin the routine care in many different countries. clinical trial inclusion/exclusion is nowa very much full of genetic information. it used to be there was a traditional phase one,phase two, phase three type of drug development, and we still pretend that that's the case,but the reality is that often companies are
trying to stack the odds in their favor earlyon, not from a marketing standpoint, but from a drug development economic standpoint. soif you know that the extensive metabolizers have a better theoretical chance of outcome,or practical chance of outcome, compared to the poor metabolizers, you might do a studyjust in this group, compare the results to a competitor medicine, and see whether thisselect group clears the bar in terms of having superior of outcomes. if it does, then youcan go to a traditional phase one - phase two type of model, maybe using all patients,not even selecting. but you've done that initial experiment, and people who are enriched forsuccess, and if it doesn't work there, you kill that drug right now, where you've onlywasted a million dollars, or a couple million
dollars, and not a hundred million dollars.and so we see that a lot now in early drug development for all different classes goinginto a genetically defined population as a -- almost a phase zero, or phase 0.5, typeof study, to find whether there is some proof of principle, proof of concept, and then expandout into the formats that are required for fda approval. and then we'll hit on some ofthe practice elements there, but a lot of different aspects happening there for thistopic. the other thing is that there's more thanone genome that's relevant to the patient. i'm showing you here a picture that depictsalterations in a tumor, and alterations to normal tissue, both of relevance to a cancerpatient, but this could easily be an hiv patient,
or a hepatitis b patient, or the virus genomeis as or more important to the treatment than the what's going on in the patient's normalgenome. as soon as a hepatitis story has taught us a lot, that it's both a viral genome, andthe patient's germ line genome that has a -- it has clinical relevance in terms of someof the therapies that we are using. and so we cannot be thinking that this simple religious-basedapproach that has been taken in the past, where either you're a believer in the somaticgenome, or a believer in the germ line genome, and you will defend your genome to the hilt.but rather we need to be thinking about, how do we improve care for this patient who happensto have both within them -- both need to be accounted for? and so this approach wherewe are taking into account both genomes simultaneously,
it's really been a much more rewarding strategy,risk/benefit, not just benefit or risk in isolation. and so that's an important elementas we go forward. now the way pharmico-genetics, or pharmacogenomicsis being applied is really rather simple at this point in routine practice. it's oftenused retrospectively clinically to explain an untoward event. so someone received [unintelligible]or capecitabine, a [unintelligible] drug, had a very severe reaction, and you want toknow, was it dihydropyrimidine dehydrogenase deficiency that caused them to get that extremeevent? because if it was, you now know to either not use that drug, or use extremelysmall doses. whereas if it wasn't that event, you now need to use a different strategy interms of managing the patient. and so that's
a common example. there are also areas where there's low utilitythat end up being requirements for insurance coverage. so if you have a colon cancer thathas a mutation in the kras gene, you will not benefit from some of these expensive antibodytherapies, and therefore, without evidence of genotype, insurance companies will notpay for the medicine. and so as you can imagine, as an economic requirement, that test is donevery faithfully, because there's not just clinical but economic reasons to make surethat happens. you have evidence for dose selections, in terms of whether someone needs a normalstandard dose, or a higher dose, therapy selection in terms of whether one gets a -- the mostcommonly used medicine in the case of clopidogrel
for stint placement in the heart, clopidogrelor plavix is a commonly used medicine for there, but in patients who have mutationsin c19, they can't activate it as faithfully. often people will use a different medicinethat's more expensive, but is -- that doesn't -- that bypasses this particular activationstep. and in a preemptive example, so this particulargenotype is used for the hiv drug abacavir, but there are other hla markers for severehypersensitivity reactions. some of them occur more commonly in east asian populations, andyou'll see in countries like taiwan, thailand, and china, these tests are routine in termsof patients receiving carbamazepine, some of the hiv drugs, allopurinol. they're paidfor by the government, they're routine test,
because it's such a high frequency of event,whereas in the united states, it's not used as frequently, used more commonly on the westcoast than the east coast, but these tests have not become as popular in terms of clinicalmanagement. now in terms of pharaco-genomics in 2014,there are quite a few examples where application is happening. it's not something that mayhappen someday, although it will increase in time just like every other -- as knowledge-- as knowledge goes forward. a number of these examples are tumor aberrations. someof them quite old. there are tumor examples, quite old -- where a tumor abnormality andcopy number and sequence, and rearrangement will lead to selection of a medicine, or ade-selection of a medicine in these cases.
there are also examples that are associatedwith toxicity that will require altered dosing. examples associated with hyper-sensitivityreactions, examples in terms of drug therapy selection. many different types of examplesthat are used. now what's shown on this side are the examplesthat have made it into the dosing and administration section of the fda prescribing recommendations,so the package insert, as they're more commonly called. now the reason that's important isthat there are 140 different drugs -- or more than 140 now -- that have genetic informationsomewhere in the fda insert. but this list here, they're in the dosing and administrationsection, which is the section that is supposed to be read by prescribers, it's the sectionthat is read by the iphone apps that you use
to prescribe. it is the section that's readby the insurance companies, and unfortunately it's the section read by the litigators. andso we see a lot of litigation emerging where someone did not do a genotype, something badhappened, and then they can use that to beat up some poor sap who was doing the best theycould. in some cases the event happened before the fda even acted, but yet that doesn't stopthe litigation attorneys in terms of trying to take a genomic-driven approach for that.but there are examples now, and the list increases as the data allows. now i'm going to hit on a couple of differentpoints over the next little period of time. one is around discovery, because there stillis a lot of discovery to be done. if you look
at the fda approved drugs, i'm sorry, if youlook at the top 200 prescribed drugs, there's only about a fifth of them that have had seriousgenomic analysis of any type, at least in the public -- published literature. now, manyof those are old drugs, and so there is no sugar daddy to pay for that study, the nihhas not been a big funder of pharmaco-genomic studies. there -- it's been more in industryor in foundation. so many of the old drugs are kind of old and boring and have not necessarilyreceived that type of evaluation. and so there's a lot to be done still. and even some of theexamples where work has been done, there's still elements to be -- to be defined. now i'd like to show this slide as a way ofreminding ourselves that we know something,
but that we don't know enough. this is ananti-cancer drug. it goes into cells, and it's pumped out through an active transport,it's inactivated through b450s in the liver, it's activated in the plasma -- to this metabolite,which is pumped out, which is inactivated, it hits the cellular target, cell death occurs.i mean, look how smart we are. i mean we're geniuses. and if i had a better graphic artist,i'd be even smarter. except here's the real pathway. especially in the area of pharmacodynamics,it's like, i don't know if it's yogi berra or donald rumsfeld, but, "we know what weknow, but we don't know what we don't know." i mean we have a situation where someone haslooked at these particular genes and has seen some sort of effect, in cells, in mice, inman, somewhere. but it's not as if someone
has asked the question, "which genes are mostimportant, and are regulating?" let the biology tell us where to go. and so it's often thesituation where i've got an assay for sif 385 [spelled phonetically] running in my lab,so i don't care what the question is, the answer is sif 385. and we see a lot of thateven in modern science, where people, i've invested in this assay, so i'm going to runthis sucker. and we need to be stepping back from that, because often it's leading us downblind alleys. so a lot of discovery is still going on inmouse, in man, in family studies, and all sorts of approaches, to try to -- to try tohelp, but really not a lot has been done. we're very early in this field. the term pharmacogeneticswas coined over 50 years ago, but the science
in terms of really trying to aggressivelydefine which genes are important is rather new. as of yesterday, there were 2228 genomeassociation studies in nhgri gwas catalog. seventy-three of those studies had a drug-relatedphenotype of some sort. so less than, only 3 percent or so. very few of them had a largesample size. a minority of them found no significant hits at all. there's just around half havea replication cohort of some sort, and that's an improvement, because a few years ago wheni looked it was a lot fewer that had replication cohorts. but even though we had this messand this, we've hardly even started to try, there have been 11 of these studies that havecontributed to package insert changes at the fda.
so there are some bits of gold to be pulledout of the mine, but it takes effort to get that out and really we're just starting totry in terms of finding, what are the genes that are important. in some cases there willbe no genes that are important, either because the effect of any one gene is so small, orbecause its post-genetic affects that are critical. or there will be some cases wherethey get all the way to the point where we're driving patient care based on some of thesechanges. but we have to do those studies so there's a lot of work still to be done. we've also stepped back in some areas andtried to do things in a little bit different way, so if you can imagine where have thebig successes happen in terms of gene-finding,
mice have been a huge success for diseasein general. family studies have also been a huge success even in the next gen-sequencingerror family studies have been very valuable source of finding real genes that hold upas being clinically important. and so that's great except family studies are tough to dowith certain therapeutic classes of medicines like anti-cancer drugs in general are veryhard to give to normal volunteers. the risks are just too great. you've bringing volunteersinto your lab or your clinic to do a study. "sorry about grandma" is not something youwant to be saying to your volunteers because you gave them neutropenic fever and died of[unintelligible]. it's not like there's any inconvenient rash. it's a little more thansomething like that will often happen in these
cases. and so one of the things that we and othershave done is tried to step back and say, "well, what else can we do?" and one of this is usingan immortalized b cells ebb transformed b cells from large families. now some of thesefamilies are these sef [spelled phonetically] families shown there for you french-speakers.the benefit of those is first of all, they're multi-generational and have large numbersof children, but also much of the human genome project has used these cells and thereforethe genomics are in place, and so you just have to do the phenotyping and you get thegenomics for free. and now there are resources through the nih and otherwise where largenumbers of unrelated individuals with genomic
data available and intact can be brought intothis same scenario. so one of the things that you can do, i'mshowing you a 96 boil plate because it's prettier. we mainly do 384 or 1536 well plates, butone can do two different drugs on a plate in quadruplicate with increasing drug concentrationseveral different types of controls on there, and in case of toxicity do a 72-hour assaywith an oxidated stress dye as the phenotype. and you can see that some cell lines can addvery rapid killing and these are three separate experiments not three separate plates, butthree separate days or three separate occasions with the air bar shown there. it's prettytight replication, admittedly using robots and bar coding, and things that allow youto get better data like that.
so this particular cell life, very rapid killingwith increasing drug concentration for this sort of toxic drug. and in this cell life,same drug, same concentrations, very little killing to the point where it never even reached50 percent killing rate for this drug. and so you see this type of variation and thenone can ask some really fundamental questions. one of the questions is, is the trait heritable?now it seems pretty stupid that we didn't do this before. it's embarrassing to thinkhow many millions of dollars i personally spent on genomic analysis without asking thequestion, is the trait actually inherited? now heritability is a phenotype that can beinfluenced by a number of things. it's not just a useful predictor of whether there'sa gene involved. for example, i can't really
see because of the lighting, but i imaginethat the majority of you have two arms. and there were genes that were involved in that,even though the heritability would be quite low. so you can see that heritability is amajor of variability, as well as genomic influence. so one can ask that question the familiesof cell lines and this is data from 14 different families about 150 different participantslooking at the 29 most commonly prescribed anti-cancer drugs and i wish it was 30, becausei hate that it is 29. but there was one of the drugs where we had solubility problemswhere we couldn't trust the data, so i hate that it's not 30. but what one can see withouteven being able to read down here, is that there is some drugs with very high heritabilityup in the 60 percent range, some drugs very
low inheritability, similar to the controls,the vehicle controls. and so this gradient is present, and one can now say well not necessarilythat these would not have any genes involved, but certainly you can prioritizing up on thisend of the scenario. now at the top of the list here is a drugcalled temozolomide which is used for brain tumor treatment. it's an alkylator agent andso we then used a collection of 563 of unrelated individuals, took their cell lines, lookedat temozolomide in that environment, did a genome-wide association study using in vitrodata, and what we found here is a hit here on chromosome 10, and even without the greenlines you could probably see this, something that just came out a couple of months agowith chet brown [spelled phonetically] as
the first author. and you can see here, thishit. now the good news is that this hit was methylguanine-methyltransferase,a gene which repairs dna adducts, perfect. the bad news is that biochemist had alreadyshown this gene to be involved 10 years ago or more using traditional biochemical analysis.so, the positive spin of course is that we validate our approach by finding truth, butthe reality is in this case we found something somebody had already found before. but wenow can take this and look at large numbers of other drugs where we have hits that havenot been associated previously with these drugs as the start of a series of biochemicalanalyses using shra, et cetera, to try to credential which of these genes heretoforehave not included on our list of important
genes really have some impact in terms ofthe effect of these drugs. so this sort of discovery approach is justone of many, but the idea that we still need to do discovery is so important because oftenwe think, oh well, the [unintelligible] is we are well into the second decade all thatdiscovery has been done, we just now to apply it. and that is certainly not true, we doneed to apply it, but there is a lot of discovery still to be made. as a matter of fact therefew people trying to do the discovery that it's no surprise that we're still slow interms of advancing the science. a second aspect is validation, and we reallyneed robust data sets and what we've found is that there are very few high quality biobanksout there. there are biobanks in terms of
flesh that is stuffed in a freezer, but interms of high quality annotated data there's really very little. and it's shocking howlittle is happening within the nah clinical trials portfolio. there are some areas likecancer where now the nci is funding either blood or where possible tumor accrual, butmany of the other institutes have not mandated a collection of blood otherwise in minh hasdone a good job, but there's still a lot of work to be done, and so there's a lot of missedopportunities. one of the things that we did a few yearsago in our cancer area is started integrated blood sampling when possible tumor sampling.and you have scenarios where instead of 46 breast cancers from tampa, florida, you'llhave 4,600 samples from centers all across
the united states and canada where you havecaptured the variability of multi-center treatment but in the context of a prospective clinicaltrial with audited data for both toxicity efficacy censor review of the imaging, allof those kinds of control measures one needs to trust your phenotype in terms of analysis.in terms of some of the drugs there you have some very nice grading with well-defined criteriathat have been put forward by the nci or by the other institutes, so you can really haveuniform measurement of toxicity in there, so here's a study. the clinical trial waspublished two years ago, dan hertz has a paper on neuropathy that is coming out, there'smore data to be published soon. but this was a study in prostate cancer where docetaxelthat chemotherapy drug, and placebo was compared
with docetaxel and bevacizumab, which wasan anti-vascular agent. and the bottom line clinically was that thesetwo arms were not different in terms of survival. there was a difference in terms of diseaseprogression, but not in terms of survival. and so one can go in and do genetic analysis.here is the most common toxicity neutropenia, you got neuropathy, hypertension, thrombosis,hemorrhage, et cetera, and you could use those phenotypes to try to analyze things further.and i'm skipping some of these in the interest of time. and so one can see a study design where onecould now do validation or even discovery or look at patients who were treated on thetrial who experience neuropathy or who did
not experience neuropathy in terms of thatparticular phenotype, except it is not as simple as that, there are many other thingsthat need to be taken into account, competing events as they are called. and so it couldbe that the patients disease progressed prior to having the chance of them getting neuropathy,or that they died, or they had some other toxicity or withdrew from the study. thereare -- so the statistical modeling needs to catch up with the clinical reality. we havedecent models for a traditional type of strategy, but the competing risk analysis models thatare out there are okay, but not nearly what we need in terms of trying to definitivelyanswer these questions. i'm going to skip over some of this in theinterest of time, but the bottom line is that
on this trial patients got neuropathy shownin the red line and this bottom line for those of you that are colorblind. but you had othertoxicities like death and progression or other adverse events that occurred much more commonlyi mean you have to take into account these competing measures. you can't just rely ona yes/no type of phenotype in terms of that. we also often need to take into account, dose.because many of the toxicities are dose related in addition to just the presence of the drug,and so the level of sophistication that needs to be put in. it's the same as disease genomics.you know diabetes, no diabetes. it's really not very useful. diabetes, early onset, ortiming to onset, or diabetes well controlled, but still having kidney damage. there areother phenotypes that need to be brought into
play to really define the patients. and thesame is true with drug effects. we need to be looking much more sophisticatedly in termsof how we're defining the drug effects. not just saying a yes/no and then wondering whyyou don't find anything. now in terms of this particular analysis,most of you are familiar with genome-wide association analysis, it's been talked aboutover the last few weeks of this course, but this is going on as chromosome 1, chromosome2, chromosome 3, et cetera. and on this axis is the negative log p value, so the higherthe value, the more significant each one of these dots is a region of the genome wherethere was a single nucleotide polymorphism that gave some level of data, and these areso-called manhattan plots, because when you
have a positive finding, you have some sortof gleaming spires like you'd see in manhattan, new york. unfortunately, often it looks morelike manhattan, kansas. and in this particular case, i had to put red circles around thedots or you wouldn't even see them. and yes, they were above a certain threshold statistically,but often with these phenotypes there are complex traits, they are not a simple mendelian-styletrait, and so one has a number of genes contributing a little in terms of its prediction. now when you look at the list of genes thatare there you see that some of them are when adjusted are genome-wide significant, othersare almost, but every one of the genes has that perfect story for why it should be included.and so, here's stabilizes something in charcot-marie-tooth,
a heredity peripheral neuropathy syndrome.a perfect neuropathy, neuropathy syndrome, beautiful. this gene here is involved in thedorsal root ganglion and maintaining neurons. perfect. neuro-outgrowth, it even says itin the name, great. so what we find is that our statisticians will label these gene 1,gene 2, and gene 3 for us, and by doing that we have a much more objective discussion aboutwhich is important and which is not. remember pat brown talking about the earlydays of expression arrays, where they did a gene expression for breast tumor and normalbreast ducts and they got the list of genes and they spent the afternoon going througheach gene talking about why this one made sense and that one made sense. and then thenext morning the statisticians came in and
said "sorry, there was a coding error here'sthe list of genes." every gene had made perfect sense before but it just was not true. andunfortunately the way we have named genes in the genome, anything that has cell deathin the name or something like that makes a perfect story for any phenotype you care about.and so, yes we find genes, yes they seem to have some biological plausibility, but there'sstill a lot of work to be done in terms of replication and validation, and then implementationin terms of how we use it in practice. skip over that in the interest of time. now,the other thing that one can do is not only take advantage of the clinical trials, buttake advantage of the health systems. so this is an example of something that started atmoffitt by bill dalton back in 2006. i had
nothing to do with it at that time, so getno credit for it, but what bill did is he developed something called total cancer carewhich really most of it is total cancer collection. there is a care aspect to it. but what's happenedis that you can go in and from day one patients are enrolled in terms of clinical follow up,clinical data warehouse retrieval, but also in terms of biobanking. and so, for example of a couple of weeks ago,there were 105,000 tumors that had been banked at moffitt, all with extensive clinical informationavailable longitudinally, consented from day one to allow a whole genome analysis if youwant to do that in terms of way forward. and there have been, for example, 16,000 of thesehave had gene expression analysis done, 4,000
have had targeted exome. there's been a smallnumber that have had whole genome, et cetera. so if one builds a biobank longitudinallyand lets it grow with purposeful investment, one can start reaping rewards. now, most biobanks were designed for deposit and not withdrawal. if you put your money into a bank that wasfor deposit and not withdraw, you'd be pissed. you know, you want an atm on every corner;you don't just want to be able to go in and get your money out, you want to have it beconvenient. as a matter of fact you want an app, where you can just do the transfer onthe app. you don't want an app you have to go to the bank ever again. biobanks traditionally have not been designedwith people in mind. they've been designed
with sticking pieces of flesh in a freezer.and we need to be rethinking how we're doing biobanking. because biobanks are great ifyou want to know tumor versus normal or one tissue versus another tissue, but if you wantto discover what's associated with some sort of outcome toxicity, efficacy, whatever youneed to have high quality phenotype. phenotype rules the day. you can sequence whatever youwant, but if you have crappy phenotype you'll learn nothing. and so, we really need a lotmore attention placed on that in order to improve the infrastructure that we have inthis country for doing this sort of work. skip that in the interest of time. now thelast part of what i want to spend time on is application. and some of this will be spenton traditional application, what are we doing
for the patient that is in clinic today? andthen some of it will be application in terms of public health use of pharmacogenomic datawhich is a little bit different than maybe what you were expecting, but also an opportunityin terms of trying to help developing countries in particular make decisions about which medicinesare available in their countries. now i showed this list before lots of different types ofdrugs that are available and what's interesting is that many of these drugs have very littleapplication data that's out there. there are very few implementation science studies thathave been done with any of these examples. there has been some quality of data that hasgot into a prestigious journal that has led the fda to make the change, but traditionallynot a lot of, how do we actually implement
it in routine practice? now one example that's a bit controversialand i'll tell you about that, but has really taught us a lot is one that i'm going to showyou here. we've published some on this and then there's some of this data that is notyet published, but the concepts were important enough that i thought i would take that risk.now tamoxifen is a drug used for breast cancer, it's an old drug. the drug itself is not sopotent in terms of an anti-estrogen, as a matter of fact it's not very potent at all,but it needs to be activated to metabolites which are potent anti-estrogens. and wheni trained 4-hydroxy tamoxifen was the main drug that acted as a metabolite and therewere a bunch of different enzymes involved
in activating it, and so there was not reallya great variability and therefore that was the end of the story. but a few years ago, almost a decade ago now,vered stearns and david flockhart, when they were at georgetown, saw a woman who had breastcancer, was receiving tamoxifen, was getting the hot flashes that one gets with the peri-menopausalsyndrome as you block estrogen, you get this type of syndrome, many of the patients haveit. she also had clinical depression was seen by a, i'm not sure if it was a psychiatristor a family medicine person, but anyway was given an anti-depressant and her hot flasheswent away. and they went away very quickly. so if it was me i'd be quite excited, hotflashes went away somewhere around the next
four or six weeks, the depression will probablybenefit. anybody with scottish blood loves two for the price of one. so fantastic, right?well vered's an [unintelligible] israeli, she didn't think that was right that somethingthat took four to six weeks to work for depression would right away affect hot flashes, somethingwas going on and through a number of years of very difficult analysis, they identifiedthat there was a metabolite that they called endosulfan. it was a known metabolite butnot a prioritized metabolite was a very potent anti-estrogen. it was also something thatwas formed mainly through this two-step process down here. and the reason it was relevantis that the anti-depressant that was given blocked cyp2d6. so the reason the hot flasheswent away is the active drug was not being
formed anymore. now, the good news is that you block formationof active metabolites you don't have hot flashes. the bad news is you have no anti-cancer protection.and so what we saw dramatically as this was presented in june of a few years back at americansociety of clinical oncology meeting. almost immediately looking back at prescribing data,almost immediately people stopped using those types of anti-depressants in patients receivingtamoxifen. it was just a wholesale stopped doing it. patients that were already on themwere switched to other anti-depressants. they still used anti-depressants to try to fighthot flashes, but not the ones that block this step of activation. and so it was very widelyaccepted to this day still is that you do
not want to mess with cyp2d6 in terms of tamoxifentherapy. now the funny thing is kind of a genetic exceptionaltype of story, so with a drug interaction, but it was blindly accepted that you don'twant to mess here and therefore will not use those drugs, but yet ten percent of you inthe room and ten percent of you watching are missing this gene, either missing both copiesof the gene from deletion or having a very low or no function based on point mutation.and so those folks you think would be at or more at risk of a bad affect than the peoplewith drug reactions is drug interactions might occur and is very much blood level dependent,et cetera, whereas genetics, it does occur there's not a lot of variation there. andso there have been a number of studies that
telling a story similar to this one wherethe people with two copies, the extensive metabolizers have the better outcome comparedto people with one working copy or no working copies of this particular genes. so the poormetabolizers as they're called have a worse outcome. now, while this is an independent effect comparedto other factors you'll see that even the good risk scoop. every time there's a littleblimp here someone's breast cancer has recurred and so it still does happen, it's not likethis is the gene that cures breast cancer, but it does seem to have some affect, andthere have been a number of studies showing that this type of scenario. there have alsobeen some studies that have shown that this
does not occur. now in some of the cases,a very high dose of tamoxifen was given or a lot of extra chemotherapy was given. insome cases the tissue of -- in one case it was a prevention study that's a differentscenario in terms of incidence. in the last two cases, tumor dna was used and so there'ssome confusion there about the relevance because about 30 percent of breast cancers have adeletion in the region of this gene, and therefore when you genotype with breast cancer tissue,what are you really genotyping? there's still a lot of controversy about whether these studiestold us that this gene is irrelevant or rather that this -- the studies showed us that weneed to use the right tissue so it's still to be defined with that.
but this idea that we have a group that does-- well, a group that does poorly, they can stay on the one pill-a-day, 20 milligramsof tamoxifen they need to have something else, easy. except 40 percent of women are in thisgroup here this middle line. and so as oncologist started to do testing many of us started toget phone calls saying, "hey, what do i do with these folks?" and the reality is we didn'tknow, we didn't know what to do. and so, some studies that were done at one of the paperscame out a couple years ago, there's another paper that is under review right now on this.the first 119 patients have been published -- as i showed you down here, an additional500 patients which is now being reviewed. what we did in this study was really simple.we took patients that has been on tamoxifen
for at least four months, so based on thepharmacokinetics they reached study state. we then majored active metabolite level shownon this axis. we also did clea-level genotyping, clinical grade genotyping for cyp2d6. andwhat we found is what others have found. there was a statistical difference between the intermediatemetabolizers and the extensive metabolizers. people that have two normal copies or onlyone working copy of the gene have statistically different active metabolite levels. no surprise,but it was nice still to see that. what we did is these folks here they stayedon one pill a day and four months later there was a slight decrease not statistically different,probably due to adherence in terms of taking the meds, but not a lot of change that occurredthere. these folks here with the low levels
we did something really simple we had themtake two pills instead of one, nothing earth shattering, nothing crazy, just that. andthe fda approved dosings between 10 and 40 milligrams. so they went from 20 milligramsa day to 40 milligrams a day. we didn't even have to file and ind because it was withinthe approved dosing. and what we found is that there was no long a physical differencebetween blood levels at that point. we had normalized blood level. now, two additional studies, one from tokyo,and one from new york city have now replicated this finding. so it's something that definitelydoes occur. there's a significant difference here and we can normalize it based on genotypederived dosing, one pill versus two, really
simple stuff. what's funny about it is thatthis sort of data is exactly what's behind most of the fda dosing recommendations. soif you dose based on kidney function it's a pharmacokinetic study that derived thatdosing. pharmacokinetics was 50 percent different therefore you give a 50 percent differentdose. drug interactions are almost always pharmacokinetic based reactions and the goalis to normalize by taking into account drug interactions, organ dysfunction, and age whateverthe factor might be. and so from that standpoint we've now defined and others have replicateda way of normalizing blood levels. what's controversial is whether this sortof normalization will impact survival at all. and so we went and enrolled a total of 500women from across north carolina. i was up
at the university of north carolina, chapelhill at that time. we enrolled about 500 women. the outcome now data is now maturing. we needfive year survival data, so it's going to take a few years to do that. but we have beenable to show that in the 500 patients, we indeed can replicate this finding, where thereis significant difference at the start that one can normalize based on this interaction.the other thing is that we were able to do this study in 64 of the 100 north carolinacounties. we took this study out of the academic center, out into the community, and were ableto enroll a genotype-guided therapy study out there just fine. matter of fact, we thoughtwe would enroll 100 patients in five years. we enrolled 119 in four months, expanded itto 500, and enrolled 500 in 14 months.
the reason why is that it was a very simplestudy. it was a simple concept that any oncologist could understand and any patient can understand.and we had the weird scenario where we didn't have to advertise; the patients did it. thevery first patient that came in, i met with her along with billy urban [spelled phonetically]and some of the others that were involved, told her about the study and she's like "yeah,i want to be involved with that, sure." fine. she left the room, we then called in the nextpatient and we were in quiet phase, just making sure we had not only the approval, but makingsure the forms were correct, whatever. the patient came in and said, "i want to be onthe dna study." like, how did you find out about it? "oh, a woman came out and went aroundand told everybody in the waiting room." patients
think we already do personalized medicine.they think we already do this stuff. they want to have their care to be shaped aftertheir body, the way that they -- their liver acts, or whatever it might be. and so, patientsare on board with this sort of stuff, but we need to do the trials to show that it reallyis the right thing to do, and what we'll find out in terms of that. another area that is important is with opportunisticinfections. so, whether it's hiv or cancer, or whatever it might be, often it's not thedisease that kills the patient, it's the opportunistic infection. and certainly in the case of myeloidmalignancies, acute myeloid leukemia in particular, you have a very high risk of fungal infectionand many patients will die of fungal infection.
and so there have been studies that have shownthat -- sorry for that complex drawing here -- that voriconazole, an antifungal drug,needs to be -- it works by itself, but it can be inactivated by a number of genes, includingcyp2c19 that we show you here. and we already know that people who have two bad copies ofthe gene have very low blood levels, and then there are some people who have extra copiesof the gene that have very high levels. and so those people that have high levels, andoften get hallucination and other effect, that there's others that can chew the drugup really fast and get -- they do not reach therapeutic range in terms of their bloodlevels. indeed, this is an ugly slide that i needto -- and apologies to the authors -- but
this is an ugly slide. what we found is thatnormally the rate of ultra-rapid metabolizers is somewhere around 20 percent or so. andyet, 80 percent of the patients with -- that did not achieve therapeutic blood levels hadthis particular genotype. so, there's an enrichment. the people who chew up the drug too fast can'tget therapeutic levels and are the ones that have fungal infection. now the reason forbelaboring this story is -- we wanted to go and start implementing this in our cancerpatients, leukemia patients in particular. but we felt, you know, we need to do an economicanalysis to see whether this is really relevant. what we hadn't realized is using our own institutionaldata, a person who gets a fungal infection costs an extra $30,000. well, $29,500 to -- andcompared to someone who does not get a fungal
infection. so we did a number of different economic analyses,and you know, mason [spelled phonetically] from our group did this. we could not finda scenario where it wasn't cost-effective to genotype everybody because if you preventone case, you've paid for everyone in spades. and so, often we have to include these economicanalyses in addition to the science to make sure that we can make the case for that. youknow, if i could keep a few leukemic patients from getting a fungal infection, but bankruptmy entire institution, i'm not doing anyone any favors. and so we have to bring in notonly the science of implementation and the science of discovery and validation, but alsoget the economists and others involved in
terms of the application. now, i also want to mention a little bit aboutthe complexities that are becoming normal in terms of cancer care. you know, it wasn'tthat long ago that we would say, "oh, there is a tumor in the bowel, therefore this isbowel cancer." or maybe we'd get fancy and call it colorectal cancer. that was great.well, then you can look at it under a microscope and stain it and say, "oh, it's an adenocarcinoma."there's some ducts there, so there's a glands, there's a -- so there's an adenocarcinomaof the colon, very fancy. very fancy. oh, well now we can genotype it, in this casefor kras gene, and say, "oh, it's a kras mutant. it's a codon 12 kras mutant adenocarcinomaof the colon." wow, we're starting to get
pretty fancy here, that's right. well, now we start getting into some of theserealties, and this is a -- i've used my iphone to take a picture of this report, of sequencingof one of our patient's tumors, and maybe you can see up here that there is an abnormalityin p53, in ep300, and ddx3x gene is lost. well that's great, and now we have to go andfigure out, well where are those genes, and should it matter? and the report from thecompany says that this really has no relevance in terms of fda-approved therapy, and thereare no clinical trials involved, so are we really any smarter with -- and, there's alist of a bunch of other genes that are abnormal that no one has a clue what to do with becausethese variations are just -- and the handwriting
is on here because we get together and tryto figure out what in the heck do we do with this stuff? so, it's no longer the case where it's a simplelittle colon cancer, or a simple little leukemia. we're getting a high layer of complexity suchthat the informaticists and the biochemists are heavily involved in trying to help usunderstand what to do with the clinical data. folks that were used to being in another buildingand we never met except in the cafeteria are now an integral part in terms of how we manageour patients because we need their expertise in terms of trying to interpret. it's really-- we're at the point -- you know, the early stage of consensus opinion, not at the stagewhere we have definitive data saying yes,
if you have a hist1h1e mutation at codon 47,we knew exactly what to do. we don't have a clue what to do, and we need to dig intoit and find out are there trials, is it likely to be important, what do we actually do withall this stuff? another -- switching gears from the tumorside, another part is what do we do with the rest of the world? we -- it's great that wecan do genotype-guided whatever for cancer or for hiv or for whatever your favorite diseaseis, but what do we do in most of the world? most people don't have access to the genome.most people don't have access to any of this stuff we have been talking about in this wholeseries. so, what does it mean? and modern therapy has been a key component of improvinghealth, and really is a sizeable part of most
health budgets. in the developing world, mostof the time the buildings are not super expensive, the people are not super expensive, the costof equipment is minimal, don't have a lot of the super expensive equipment, but themedicines, even the cheap generic medicines from india or other parts are still expensivein terms of the proportion of the healthcare budget. and when you look at selecting medicines,often it's a combination of clinical consensus, access to and cost, and familiarity. so you have these sorts of scenarios -- andmedicine prioritization is really a high stakes undertaking in most of the developing world.when you look at the who essential medicines list, which is the national formulary for-- that most countries use outside of the
richer countries, what you see on there isthat there might be five medicines for your favorite disease, and if you can only affordone or two, how do you pick? the data up until even 2014 is heavily skewed towards westerneurope, united states, and australia. we have data that's coming from certain populationsof the world, but they don't necessarily benefit or give direct data for the rest of the world.and so, instead of having data for the individual patient, we have data from certain parts ofthe world that are inferred across the rest of the world. so, if you're in this case, nicaragua or inother parts, and you're trying to make a decision about what is your -- what medicines do youpay for, you can't pay for them all, you can't
afford that. so, how do you select? and nicaraguais not a participant in the phase 3 clinical trial networks that are currently out there.there have been no trials, no patients enrolled from nicaragua in any of the fda-approvedtrials over the last 15 years. you have nothing to go on in terms of trying to make your decisionat the ministry of health level. and so, what we've been doing is -- others have been doingis trying to look at, can we use pharmacogenetic data to augment a decision that a country'sministry of health or health authority might be making to provide some local context forthat? the way we've been doing that -- and we'veworked in 104 countries so far -- is to identify the most common groups within the country.there might be ethnic, there might be racial,
there might be religious, whatever the countrydecides are those groups. enroll volunteers from these different groups, and look at genevariants that have been shown to influence toxicity or efficacy for medicines that areon the who essential medicines list. not alterations in pharmacokinetics, but alterations in actualdosing, toxicity, or efficacy. and those alterations have had to be found in at least two separatepopulations in order to be included on the list. this is something from the pharmacogeneticsfor every nation initiative, or pgenie [spelled phonetically] as it's called. think p. diddy,except pgenie. and so, one can go in -- if you take an examplewith mercaptopurine, which is used for arthritis, for inflammatory bowel disease. it's alsoused for childhood leukemia, but that's the
smallest therapeutic category, happens tobe the only fda-approved category, but it's the smallest therapeutic category in termsof its use. there are three different genetic variants in the thiopurine methyltransferasegene that inactivate -- that influence the inactivation. and so if you have one of thesedifferent alleles, you can't break the drug down very well. you get extreme toxicity andneed to be hospitalized or at the least come off the medicine. and it's been well shownby multiple groups now that there's very different dosing depending on whether you have two normalcopies of the gene or two abnormal copies of the gene. it's almost one-tenth of thenormal dose that one would take in that scenario. so, that's great.
so, this is some of the initial data froma while back now. and if you see green that means the data is similar to what's seen inthe u.s. white population. and the reason why the u.s. whites were the comparison isnot because i'm from the u.s. and white, but rather because the dosing and safety datawas almost exclusively done in the initial stages in u.s. populations. phase two andphase three then go out to other parts of the world. and so you can see there's manycountries that have green. some countries have light blue. that means that the geneticrisk is one-half or less of that seen in the u.s. white population. and then there aresome countries, bulgaria, ghana, and peru in this particular slide, that have more thandouble the genetic risk for this incidence.
and so, the connection between those threecountries is a bit perilous, but you know, there's cocoa in two of the three, and whoknows what else, but those are three countries that really stood out. now -- and here is a continuous variable.if one looks within ghana in west africa, looking at some of the more common sub-populationswithin the country, what you find is that all of them have about a 10 percent incidenceof this severe risk, very high incidence. and often what people say is, "well, geographically,why don't you just look somewhere in africa and then you'll get african data, and somewherein south america you'll get south american data." well, here's the data from one of thenigerian populations. the frequency of genetic
risk for this particular example in this populationis half of that seen in their neighbors or two small countries in between them in ghana.matter of fact, the nigerians in this case, the risk was more similar to the uk caucasiansand the u.s. caucasians than it was their neighbors in west africa. and so, one can'tjust take a blind look, just get the region and try to get it right. there's another example. this is cyp2c19,it inactivates voriconazole, an antifungal drug, as i showed you a couple slides ago.it's also involved in the activation of plavix, or clopidogrel, for cardiac disease. and whatyou can kind of see up here, this is cyp2c19*2 variants. here's one ghanaian population witha very high frequency of this variation of
this mutation. and here's another ghanaianpopulation, the [unintelligible], very different frequencies even within the same country.and so -- and then you can see some of the kenyan populations and nigerian populationsnext to them. so, the point being that by looking withinan individual country, working with the ministry of health, trying to define what is the levelof risk within the populations that they define, one can start getting some data -- it's notso much a clear decision based on this, but it's tie-breaker type data, where you seeit almost like a currency converter. you know, how many pesos equals a dollar? how -- youknow, which drugs are more likely to be safe in ghana versus another, and using this morebroadly. and one can use the data in terms
of prioritization of -- or surveillance. so,in the case of a high incidence of liver toxicity from isoniazid, one is still going to giveisoniazid. there's not a good alternative yet, but you'll monitor more carefully. andthat is the state that we've -- some of the data for liver toxicity risk from isoniazidwith countries like china, i'm using this data to help define the way they monitor thesepatients. in other cases we could have clinical algorithmsfor the available drugs, in this case for rheumatoid arthritis, where based on the geneticdata within the country, one can start breaking this down into a clear decision. this is fora small eastern-asian country called china, where one can take the drugs that are availableand help prioritize them in terms of level
of risk. so, for example methotrexate, theyhave a very high incidence of a resistance gene with [unintelligible] and you know, itwas already known that methotrex didn't work as well, now they figured out why, and soone can use this in terms of coming up with priorities for their medicines. we can also look more broadly. this is datafrom alphametrix [unintelligible] chip. looking at 7,000 individuals across 40 different countries,and you can see that here's the average prediction: warfarin dose on this axis, and you can seethat in some cases the average dose is extremely small, other cases it's very high, based onthe different geographic continental separations. g.i. risk from amodiaquine, a malaria drug.again, high risk, low risk, a lot of variation
within a continent. risk of simvastatin muscletoxicity, the same type of thing. one can start putting together these sorts of mapsand reports for the ministry of health to now not just take the who essential medicinelist and say, "oh, we got to pick one of these." but use this data to try to say, "you knowwhat? we can now prioritize based on this, and we can only afford the following three,so we'll use it in that sort of manner." in closing here, the -- i think the key thingis that we've become decent at discovery and validation. we still have a lot of work todo in terms of health economics, integration of health systems. you can tell this slideis old because there is a blackberry on there, and for you young people, blackberry's usedto be like an iphone. you probably haven't
heard of them, but they're -- and assay development,a lot of work still to be done. and i'm going to finish up with this particularstory from a friend of mine in north carolina. at the time he was 44 years old, he's thechief scientific officer of a biotech company in north carolina. he was born with what hecalls a "frog heart." he has an a.v. block due to this congenital heart defect. neededto have a pacemaker placed, but because of his anatomy had to have his chest crackedto place it. and so, he told the cardiologist, the c.t. surgeon, the anesthesiologist, andthe admitting team, these folks here, that he'd had an executive physical, that he'dhad pharmacogenetic analysis, and that he couldn't activate oxycodone very well, orcodeine, and also had to have some different
dosing of warfarin. "that's fine," they notedthat and went on with their way. successful surgery, successful placement ratherof the pacemaker. in the recovery room morphine was giving him some decent pain control, fourout of 10 in the scale. he had his chest cracked, so it's, you know, sort of a painful thing.he moved to the coronary care unit, he was switched to oral medicines, to oxycodone,and had very severe pain, was basically ignored, he was a wimp, you know, he was in pain, hejust needs to buck up. he called me from the ccu, said you know, "you have to come andrescue me." i called one of the cardiologists at that university who had trained with mewhen i was at wash. u. and said, "hey, you got to go save this guy."
before he got in there, one of the medicalstudents and one of the pharmacists were pre-rounding. saw this man in severe pain, talked to him,found out he was a poor metabolizer for cyp2d6, so his ability to activate oxycodone and otherpain meds is not as good as most people. switched him to a different med, in this case hydromorphone,which gave him a much better degree of pain control. still five out of 10, but he hadhis chest cracked, you know? now, this happened at one of the top fivecardiac centers in the united states. a very high-profile institution that happens to beeight miles away from university of north carolina. rhymes with duke. a phenomenal place.i literally have gone there for cardiac evaluation because it's such a great place. the anesthesiologistwho was involved literally wrote the book
on post-anesthesia pain. and yet, world famouscardiologists, cardiothoracic surgeons, anesthesiologists, incredibly smart fellows, residents, and interns,didn't recognize the data when it was in front of them. now, if this had happened at unc,it would have been just as bad or worse, and we did a lot of cross-training based on thiscase where geoff ginsburg came over and trained us at unc and i went over and helped trainthem at duke. but the bottom line was even with the data in our face, we don't alwaysrecognize when it's ready for use. so we can't have a scenario where we justpublish smart papers; we have to be thinking about implementation. and the best scenariois one where no one has to know about it because it's baked into electronic health record.and this sort of data is a hard stop to switch
over to a different medicine and no one hasto remember anything because the computers do that. and that's something that is nowin place at many centers, is now at place at duke, and is something that is applied. so, i'll finish with this slide. you know,back at the last olympics, the one in london, usain bolt did not win the 4 by 100 relay.jamaica won the 4 by 100 relay, men's relay. usain bolt ran a phenomenal leg, just a terrificleg. but so did three other guys. and if they hadn't ran terrific legs, jamaica would nothave won. it's the same type of thing -- you know, when you go past a track where theyare practicing, they spent hours on the hand off. you know, how do you receive -- you know,someone's coming, you start running, you receive
the baton, or you hand the baton off a certainway, you pick up -- because if you drop the baton, you're out of the race, your teamsout of the race, your schools out of the race, your countries out of the race, it's a bigdeal. we have the same scenario in biomedical sciences. we need to go from discovery tovalidation, integration to practice, integration to policy. we can't -- one person can't doall this. it needs to be people who are really good at this, and really good at this, reallygood at that. but our hand off is lousy. often my dissemination strategy is osmosis.you know, i publish something and hope that someone accidentally reads it. as opposedto saying, "all right, who is going to use
this data, how do i interact with them, soi make sure that the data we are putting out can be most useful to them as they run forward?"you know, walk through any foyer or any atrium in any research -- biomedical research institutionin the united states, and there are batons all over the floor. you're going to trip ona baton because we've dropped so many of them over time. but we have to get better at that,and i think that's the big challenge. now, if we want to make progress, yeah, weneed to be smart. yeah, we need to have the latest technology. but we need to be thinkingabout how do we do this relay race? and how do we do it better? and that means we haveto talk to people we may not even like, and make sure that they're on board and that they'reready to receive what we're doing, because
otherwise we might get promoted, we mightget a free trip to bethesda, but are we really going to help a single person? and so, that'sthe challenge, not just for pharmacogenomics, but especially in this area, you know, howdo we do it better? and i'll stop at that point. thank you very much. [applause] any general questions, and i'll -- and thenanybody who wants can join the conversation with the -- male speaker:hi. may i ask a question? howard mcleod:hi, how are you?
male speaker:excellent talk and fascinating subject. i had a question about the complexity of thechallenge of pharmacogenomics. i had a -- the pleasure of spending a little time with thepgrn about a year and a half ago, this nih-supported pharmacogenomics research network, and theissues it seemed to confront it now, particularly in the area of cancer therapeutics, have goneup almost exponentially in complexity with the recognition that cancer tumors, particularlythe solid tumors, are not homogeneous genetically. and so, i wanted to know what your thoughtsare about how this is going to impact pharmacogenomics? howard mcleod:very much so. in terms of the cancer side, i mean now what we're doing -- so before withnext-gen sequencing of tumors, clinically,
you thought, oh, maybe we need to do 30x coverage,30 times coverage for that. what we're now doing routinely about 1,000x coverage. andthe reason why is that we know there are sub-populations that are there, and you can't see them ifyou just do 100x or -- you need to do very deep re-sequencing clinically, in a clea environment,in order to find those and act on them accordingly. we're also using serum more often now, orplasma to look at our mutation clones, mutants that are rising over time, kind of in a minimal-residualdisease type model that you'd use in leukemia, because we can -- with the technologies outthere, one can now find things way before there's an imaging change, or there's a symptomaticchange and decide whether to act on it. and so, it has changed our complexity quite dramatically,but there are some institutions like our own
that have really embraced this and are tryingto use the heterogeneity that is definitely present as an advantage in terms of choosingtherapy. it is -- there is no doubt that life -- so life never was simple. it's just thatwe like to think it was. and in some ways, we're just kind of hitting reality a littlebit harder than we want. but you are exactly right that whether it's an autoimmune diseaseor a complex cancer, life is not as simple as it used to be. male speaker:another quick question -- howard mcleod:sure. male speaker:-- if i may. do you see the benefits now of
stem cell technology in terms of setting uphigh throughput screenings for both cellular and neuropathic toxicities as a pre-eventbefore going to say, humanized animal models howard mcleod:yes, so, i'm still a little bit skeptical about that approach for the treatment of cancer.in cases where there is so much heterogeneity, because you'd have to do so many things -- evenif you introduce so -- male speaker:no, i was referring to marker and -- howard mcleod:oh, marker. oh yeah, okay. because we are looking at stem cell therapies for toxicity.you know, if you can introduce stem cell therapies to someone who has a genetic predisposition-- i skipped some of those slides -- for neuropathy,
one can try to bypass that event, and that'sstill very much a research tool, but one that is evolving. in terms of markers of stem-ness,or whatever you want to call it, at the moment they are definitely measurable, or at leastthere are measurements that are called stem-ness, but we haven't figured out -- at least, wehaven't figured out yet how to apply them in a regular basis clinically. so, they'revery much a research tool. there are people now that are, you know, flow-sorting out atmany institutions, including our own. you know, flow-orting out these different subtypesof cells, reintroducing them into mice and other systems, trying to understand does one-- is that really a sub-population that needs differential treatment? you know, how doesone treat it, it could be that, you know,
once we're smarter, we will treat that smallsub-population and ignore the rest. but at least that's the way -- i'm not sure i answeredthe question the way you meant it, but you can kind of see the direction things havebeen heading. male speaker:thank you very much. howard mcleod:great. [end of transcript]
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