Can you tell us about the research at yourcompany and what the ultimate aims are?Roche is a company that, uniquely, is not only oneof the world’s leading pharmaceutical companies,but also the world’s largest diagnostics company.Therefore, our research is geared towardsdeveloping better medicines as well as towardscoming up with novel diagnostics.As you mayimagine, this puts us in a very interesting positionwhen it comes to understanding a particularmolecular disease mechanism better, andapplying this knowledge then simultaneouslyand synergistically towards innovative tests andinnovative drugs. It puts us in a pole positionfor the era of more personalized medicines.

There is now much excitement about thepotential developments withinpharmaceutical/biotechnology research asa result of the advances in genomics andproteomics.Are you personally excited bythis? Or are you more concerned that wemay have to navigate our way through avery tough labyrinth before any benefitscan begin to be realized?It’s obvious to anyone who knows biology orbiomedical research, that we now have tools

that we could have only dreamed of10–20 years ago, and they allow us tounderstand the molecular biology much morethoroughly and at a much deeper level.At thesame time, we have come to realise that thingsare even more complicated and much moredifficult then we had originally already assumed.I’m talking about the diseases that we all worryabout – the common complex diseases. I’m nottalking about the rare genetic diseases in whichthese technologies are even more powerful,

because these diseases are, to a very largeextent, dependent on genetic factors.When itcomes to the common complex diseases, genesonly contribute some 20–50% to the cause, sothe remaining 50–80% is caused by non-geneticfactors i.e. environmental and life-styleinfluences. So we need to understand that inthese diseases, genetics will always be only partof the answer, and more often than not, otherfactors will predominate in contributing tothese illnesses. So, the excitement about ournew tools needs to be viewed in adifferentiated and rational way: while it is clearthat pretty much all future progress in this fielddepends on these tools, they will not turnmedicine or the pharmaceutical industry on its

head.They will lead, in an evolutionary fashion,to increasingly better health care.

Looking at it in this way might help tounderstand another phenomenon we arecurrently confronted with: the much maligneddraught in many of the industry’s pipelines.This, I believe, has a lot to do with a shift intarget-selection that has recently occurred.Five, 10, 15 years ago we chose a project or anew target based on a body of literature thathad already been established and was availableto us – thanks usually to many years ofacademic research that had been done up front– and was generally paid by public fundingagencies.These days, molecular genetic studiesoften give us a new target about which verylittle or nothing is known – and it is now ourburden to gather all the evidence andunderstanding that allows us to assess whether,from a physiological and pharmacologicalstandpoint, this new target is indeed viable.

This takes time and costs money and so,paradoxically, it is likely that it will take longer,on average, not shorter to make a new drug.Incidentally, this is what the much-quoted 2001Boston Consulting study showed: the expecteddiscovery and development time for a newmedicine has gone up to 15 years and the costto US$ 880 million (from 8–12 years andUS$ 500 million).Also, while the lion share ofcosts used to be in clinical development, it hasnow shifted, with two-thirds being spent inpreclinical research.There is another reasonwhy things have become more challenging:while automation and miniaturization havemade compound screening faster and cheaper,we are increasingly confronted with biologicaltargets that do not belong to any one of the‘classical’ druggable families – towards whichour currently available libraries are all distinctlybiased, because that’s what they weregenerated for, historically.Thus, these newtargets – even if they are per se tractable – maygenerate few, if any, hits in currently availablechemical libraries.

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UPDATE

INTERVIEW

Klaus Lindpaintner discusses drugdiscovery in the ‘post-genomic era’

Interview by Rachel Wood

Klaus Lindpaintner,Vice President of Research and Head, Roche Genetics, Switzerland.A native of Innsbruck,Austria, Klaus Lindpaintner graduated from the University ofInnsbruck Medical School with a degree in Medicine and from Harvard University with adegree in Public Health. He pursued postgraduate training and specialization in InternalMedicine, Cardiology, and Genetics in the United States and Germany and holds boardcertifications in these specialties. He practiced cardiology and pursued research in the area of cardiovascular disease and molecular genetics and genetic epidemiology, mostrecently as an Associate Professor of Medicine at Harvard Medical School in Boston,Massachusetts. He joined Roche Basel in 1997 as Head of Preclinical Research inCardiovascular Diseases. Since 1998, he has been VP of Research and Director of RocheGenetics, and since 2001, also Director of the Roche Center of Medical Genomics,coordinating the company’s efforts and activities in genetics, genomics, and proteomics. Hehas co-authored more than 100 original scientific papers, holds an honorary professorshipat University of London, and serves on the editorial board of several scientific journals.

‘we now have tools that we couldhave only dreamed of 10–20 yearsago, and they allow us to understandthe molecular biology... at a muchdeeper level.’

‘the much maligned draught inmany of the industry’s pipelines...has a lot to do with a shift in target-selection that has recently occurred.’

‘the expected discovery anddevelopment time for a new medicinehas gone up to 15 years and the costto US$ 880 million (from 8–12 yearsand US$ 500 million)... while the lion share of costs used to be inclinical development, it has nowshifted, with two-thirds being spentin preclinical research.’

The general population is hoping thatadvances by scientists in the post-genomicera will pave the way for the rapiddiscovery of new, highly effectivetherapeutics for this new century. Howrealistic is this expectation and what canwe do as scientists to make the reality ofthe situation more understandable to thelayperson?I think we are doing all of this because webelieve this is the way forward.There istremendous value in using these approaches understand biology at a much morefundamental level than we could before, but Ithink we should be very, very careful andcautious in making grandiose predictions forsome of the reasons I just mentioned.This isnot going to be a revolution; this is going to bean evolutionary process.We have added littlebits and pieces to the toolkit we use to doscience all along, ever since Paracelsus andLeonardo.And of course, we keep getting moresophisticated. I always cautiously look at this ina way that yes, this is a new arrow in ourquiver, it is perhaps even a particularly sharparrow, but it’s still just one of many, and it iscertainly not a panacea. I think we really needto be careful because there has always been atendency to be overly enthusiastic about howthis is going to turn things around.

In 5–10 years time, we will have some newmedicines that will be better than thoseavailable today, otherwise we are obviouslybarking up the wrong tree. But let me be clear:these will be better medicines, but they willnot be perfect medicines.Another thing is that as we raise these very high, exaltedexpectations, we create this aura of what isoften called genetic exceptionalism. If we takethe undifferentiated position that genetics is so powerful that it is going to make us theMaster of all things right away, then of coursethis power could also be abused – and thiscreates concerns and fear.This is what we arealready experiencing in the broader publictoday – there is a great deal of concern and agreat deal of worry about where this is allgoing to lead… How is this all going to changeour lives?

It must be understood that, in the contextof common complex diseases, genetics is justone among many sources of information thatwill allow us to diagnose disease somewhatmore accurately and, hopefully, to create better

therapeutics. Genetics will not provide us withblack-and-white prognostication, it will notprovide us with absolutely effective medicine.If these limitations are understood, then thereis also no reason to see these data as sotremendously sensitive and potentiallydangerous and ominous. Unfortunatley, by andlarge, anytime the word ‘genetics’ is used, thegeneral public thinks about Mendelian theoriesbecause that’s what they associate withgenetics: a straight line correlation betweencarrying a particular mutation and developingthe disease.This is certainly not the case for97% of the health problems that we, as asociety, are confronted with.What we areactually looking at is the probability of whethera person is more or less likely to develop adisease, or to respond positively – ornegatively – to the medicine. … However,notwithstanding these facts, it is obviouslytoday a very sensitive issue in the eyes of thepublic, and we must take this into account andact accordingly…

I think we need to be very careful to use adifferentiated view that clearly sets apart thecommon illnesses that all of us suffer from, andthe very rare classic genetic diseases.Theserare classic genetic diseases are incrediblyimportant for those affected by them – theydominate their lives – and they have been veryimportant for science because they provide uswith the proof-of-principle for how all thisworks; however, when it comes to commoncomplex disease then the importance and thepower of genetics drops off sharply…

This problem comes, I think, from manydifferent directions. One direction is certainlythe geneticists themselves. Over the past100 years, geneticists have cultivated aterminology that is ever-so forbidding andever-so obscure and difficult to understand.This terminology excludes the general publicfrom what it all means, when actually theprinciples are not all that complicated and arerelatively straight forward.This has resulted inwhat appears to be a closed society and thishas led to the perception of danger. Geneticistshave also been talking up the power ofgenetics.And since power – more often thannot – is abused and used against individuals’interests, clearly, people are afraid.

But having said all this, I actually do notagree with the innuendo behind the commonlyasked question: what will all this do for us?Namely, that so far this technology has not yet

delivered… It’s true to some extent that manypharmaceutical company pipelines may looksomewhat drier than they did 10–15 years ago.But this is essentially a function of some ofthem perhaps focusing too much on too manynovel targets, which just takes a lot morework. On the other hand, the impact thatmolecular biology, molecular genetics,biotechnology etc have had on our currentmodern pharmacopoeia is already impressive:almost all drugs that have been introduced inthe last 5–10 years have had biotechnology as asignificant contributor – be it to design ascreen with a recombinant molecule, or toproduce the drug using biotechnology.

Is the biotech sector now realizing thatgene and protein structures themselves donot reveal much about their potentialrelevance to disease, making theidentification of novel drug targets difficult?I think if we are talking about a known proteinfamily that has druggability, structuralinformation is very important.When it comesto novel targets, I think that even if we have a3D structure of that novel target it’s still a verylarge and bold jump to then go to the drawingboard and design a molecule that will fit thetarget. Often these molecules are very dynamicand pliable when they react with otherchemical entities, in ways that we still are verypoor in predicting.What is often more helpfulis, first, to screen a chemical library and, second(after one finds a molecule that binds to thetarget) co-crystallize it with the target. Basedon that data, structural modeling may wellprovide a very powerful tool in assistingmedicinal chemistry. But structural informationis not a standalone tool.

If a VC asks the CSO how they intend to overcome this problem, is there any real answer?Overcoming the problem of predictingchemical interaction and structure? No, notthat I know of. I think every day we moveforward – as our databases grow we’ll learnmore and we are going to get so much betterat this. But proteins, even when you crystallizethem and know the structure, are incrediblyflexible and malleable (fluid almost). So, by thetime you apply the ligand and it binds and you

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UPDATE

INTERVIEW

‘...these will be better medicines, butthey will not be perfect medicines.’

‘What we are actually looking at isthe probability of whether a personis more or less likely to develop adisease, or to respond positively – or negatively – to the medicine.’

‘This is not going to be a revolution;this is going to be an evolutionaryprocess.’

co-crystalize that, suddenly that tertiarystructure may look very different. Using thatthen may allows you to, as I said before, refinethe design of a potential drug in silico.

Some say that functional genomic andproteomic strategies for treating humandisease now appear to be stifled by theneed for more genetic research.Willemploying solely genomics and proteomics,or integrating the two new fields withgenetics, successfully deliver the nextgeneration of viable drugs and proteintargets, in your opinion?I think it is, in many ways, not very productiveto make a categorical differentiation betweengenetics genomics, proteomics and the rest ofcell biology. It’s all part of one continuum – weare evolving from one set of tools to anotherand then yet another.They all have a place andthey all do certain jobs well and other jobspoorly.We cannot do meaningful biology anymore without genetics/genomics and to someextent proteomics, and we still have a lot tolearn and develop in terms of thesetechnologies, especially in proteomics.Theseare, however, quintessential tools – we cannothope to make any progress without these tools – but at the same time we must be very,very careful not to expect that they are goingto turn everything around.

The chemical genomic approach issometimes labeled as ‘a small chemicalpartner for every gene product’. Is such anapproach truly feasible given that the fullcomplement of proteins made by our genescan number more than half a million? Do we need a more well-designedapproach to identify which gene productswill serve as the most effective drug targets?The short answer is that we will need all thehelp we can get! And that’s what I was gettingat – the genetics/genomics will help us to findkey targets, but we still have to find the smallmolecule that modulates that target into theappropriate direction (up or down regulation)and genetics/genomics is not going to help usthere in a really fundamental new way.

Chemical genomics or chemical genetics is anill-defined term – as many of the terms used inthis field are.The way I see it, it means that ifyou understand the evolutionary relationshipbetween the novel target and a target for whichwe have already found a small molecule ligand,then we will be guided into a certain area ofchemistry that will be more successful.There isa little evidence that this may work, and to someextent it makes sense that it will work.Theproblem is going to be that some of the new

targets we are going to tackle will besufficiently far away from the targets we havesuccessfully tackled in the past that chemicalgenetics/genomics will likely NOT help in amajor way.And, as you know, we are nowfinding 7-transmembrane receptors that wethink are perhaps GPCR-like, but usingGPCR-derived knowledge we can’t actually findthem in the genome.That means that they aresufficiently different that chemical genomicsmay not have an overwhelmingly high chance ofbeing successful. If, and when, chemicalgenetics/genomics shows success for an orphantarget, this will of course be very helpful, butagain, just like everything else, it isn’t going to bea panacea. I think that so far the success we havehad with this approach, even though it soundsvery appealing theoretically, is rather limited.

As biotech enters the post-genomic era,new companies are hoping to convertgenomics and proteomics findings intonovel drugs and targets for disordersranging from cancer to CNS disorders.In your opinion, what is the outlook forthese therapeutic areas, in light of the new developments?It isn’t so much a matter of biotech andgenomics or proteomics: what matters is amore fundamental and differentiatedunderstanding of disease causation that willprovide new opportunites – here and there,but also not always, and not by default – ofcoming up with better medicines. Let’s notforget that today’s pharmacopea is, by a verywide margin, strictly based on theunderstanding of physiology, not pathology,and the drugs are symptomatic and palliative,but not causative agents by and large.

You mention CNS and cancer – and I agree,these may be two areas that might particularlybenefit from insights into the fundamentalmolecular pathology – to a large extentbecause our current pharmacopea in theseareas is still very marginal. Cancer, in particular,of course also lends itself to very powerfulexperimental approaches, and so thecombination of improved understanding ofcontributing factors and the ease of testingthus derived new therapeutic approaches holdsspecial promise. In CNS disease, on the otherhand, yes, we know very little about themolecular pathology, and that’s where the

promise of new approaches lies; but let’s notbe naïve: it is so much more difficult to do theresearch first – it’s easy to get a piece oftumor, but near impossible to get a brainbiopsy! The testing of any newly developedtherapeutic is still extremely difficult andsort-of on the opposite end of thestraight-forward paradigms that can be appliedto tumor growth and regression…

To propel developments, researchers willneed to access, analyze, and mine vastvolumes and multiple types of data, whilstusing information from many sources,including microarray expression analysis andSNP analysis. Is this one of the biggestchallenges in the post genomic era? Arethere any strategies, in your personalopinion that may simplify this? Will companyconfidentiality inhibit advancements?It’s a matter of data management and dataanalysis, basically, and it is of course true thatwe are increasingly confronted with very largesets of data.We will need to find some sensibleways to analyse this data, because if we can’tanalyse it there’s no point in us generating thedata. Management is one thing that is alreadypretty difficult. For example, our microarrayresults database is already larger than ourentire corporate financial database, so… it is afairly monumental task. I think we are in direneed of lots and lots of smart people to helpput all this together.We are creating, to someextent, more data than we can feasiblyaccommodate or analyse.We analyse the datawith the tools that we have today which maynot be the most powerful ones.Also, I am justtalking about individual experiments now.

There is also this notion that we will throwall of this data together into some huge,boundless database and then mine thisdatabase and come up with all the answers.However, unless you know very precisely thatthe experimental conditions of one experimentperfectly match those of a differentexperiment, it will be very difficult to derivesome cumulative or joint comparative valuefrom databases that are being combined fromqualitatively vastly different sources. Obviously,to some extent, that is a tempting thing to dobut I think we have to be rational about it and

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UPDATE

INTERVIEW

‘...chemical genomics may not have an overwhelmingly high chance of being successful... for anorphan target...’

‘Management is one thing that isalready pretty difficult. For example,our microarray results database isalready larger than our entirecorporate financial database...’

go back to the bare bones of good biologicalexperiments, where you should run yourseparate, new set of controls with each newiteration of the experiment. Never assume thatthe controls are static and don’t change.Thebiological systems that we use are socomplicated that I think it is an illusion that wewill ever have enough data to do the properadjustments to make all these data setscomparable. I mean, we use one methodologyor supplier today, but maybe (as things evolve)we will shift to using something else, and sohow can you really compare experiments anddata? I think it will be important to somehowdistill, from the morass of data, things that areactually biologically true, as it were: pathwaysand protein–protein interactions, for example.I don’t think this will be achieved by throwingall the data together – it will come fromanalysing experiment A from one lab and thenexperiment B and C from two other, differentlabs and, if they all come up with the sameanswer, then I believe we are getting there.Thisinformation – rather than all the raw data –should go into some sort of general databaseand – my hunch is – that a lot of this well-annotated data will be in the public domain.Some of it will be proprietary, but the majoritywill still be public – simply because there areso many more people out there in academiathan there are in industry working on this.I think it is somewhat unlikely that there willbe huge corporate databases containing uniqueinformation that is not available fromelsewhere as well.This field is so large andresearch has to go in so many differentdirections that industry won’t have the time orthe money to be all-encompassingly broad..

Has the Human Genome Project alteredthe view and practice of biology and led to paradigm changes in systems biology,and predictive and preventive medicine?Which technologies are notably drivingthese changes?I don’t like the words paradigm shift, revolutionand all that, but I would say it has been anevolving story. By having a lot more sequenceout there, things have of course changed thescale of efforts that are being undertaken.We simply were not in the position to doexpression profiling on 30,000 genes a fewyears ago because we didn’t have theinformation. So, as far as technologicalapproaches go, yes it has changed to someextent the way research is done. Researchused to be done focused on a particularquestion and people went after one hypothesis,and they basically bet their money on thathypothesis, let’s say on a particular gene.

Researchers would come up with a hypothesisand do A, B, C and D etc methodologies, all in asemi-efficient way in one lab (the smaller thelab, the less efficient the work, although thelarger labs could organise themselves better to get data out because they had a division of labour).

I think what we are seeing now, particularlyin the industrial arena as well as in the largerscale academic arena (for example, the genomesequencing centres), is that we are no longerdoing research driven solely by intellectual‘rigor’, so to speak, but to a large extent alsoby brute force – we no longer really bet onhypotheses.We’re just basically pounding awayat the entirety of biological informationcontent, looking at everything under the sun,not taking any chances that we are betting onthe wrong one. Now our problem beginswhere the analytical issues come in. If you lookat 30,000 genes at the same time yourbiostatistical approaches have a problembecause by definition you will have a lot offalse-positive results.You then have to weedout these false positives and, once again, youare arriving at some sort of hypothesis-drivenapproach, which then also brings back theinfluence of human thinking, and bias. In a way,it has changed the way we do biology, but itcertainly has not got to the point where smartthinking is no longer necessary.Automated‘database-mining’ won’t do the job.We don’thave the analytical prowess: our systems aren’tsmarter than some physician or researcherwho has spent the better part of their lifeinvolved in a particular area and carries a hugebody of highly integrated knowledge, who canmake associations that no computer ever – atleast at this point yet – can make. So theanswer is sort of yes and no.

Regarding the technologies and what, if any,are driving these changes – well, I think at thispoint it’s more the wet lab capability that isdriving it, with the analytical aspects lagging.There are interesting developments out there – there are some ‘intelligent’ types ofsoftware or programmes out there that canhelp us along, but at the end of the day eventhose will rely on human intervention as theycan’t come up with hypotheses.Validation ofthese hypotheses will require experiments – all computers can really do is come up with

better, more sensitive, smarter, safer ways ofdoing what we already do to draw associationsor correlations.Whether or not thosecorrelations are causal or simply associative –this is where the smartly designed experimentis required to answer that. It’s a cyclical thing –computers can help us ask, to some extent, theright questions, but then we still have to do thebiological experiments to get the answers.

How will diagnostics and therapeutics workmore closely together now that Roche hasstreamlined its portfolio?If we create new therapeutics based on a morefundamental understanding of disease, what weare generally talking about is a furtherbranching of the tree of differential diagnosis,and to determine the branch points, you’llneed advanced molecular diagnostics. Case inpoint: Herceptin, a drug we have that treats aparticular subgroup of breast cancer whosevery aggressive growth is influenced by theover-abundance of a growth factor. So usingthis drug on everyone with breast cancerwould not make sense, and before youprescribe it you need to do the properdiagnostic test to know whether it’s indeedindicated. More and more, as we branch outinto the more differentiated, molecularunderstanding of disease mechanisms, thisparadigm will apply. Making the properdiagnosis will require a molecular test.This isthe synergy we see, and Roche of course hasgreat strengths in providing both parts of thisnew equation, the drug as well as the test.What we do not foresee is necessarily a ‘kit’approach – the test should of course beapplicable to any and all drugs for theparticular condition, be they Roche drugs orsomeone else’s. So, while within our companyof course we’ll try to capitalize on synergiesand trade knowledge back and forth betweenPharmaceuticals and Diagnostics, in the marketplace we see the two largely as independententities and endeavours.

What would you like to have accomplishedby the end of your career?I think it’s being able to make an – if ever-sosmall – difference in peoples lives by advancingsome aspect of health care – it’s aboutimproving the human condition.

F. Hoffman-La-RocheBDLG 93/534

CH-4070 BaselSwitzerland

tel: +41 61 688 0254fax: +41 61 688 1929

e-mail: KLAUS.LINDPAINTNER@Roche.COM

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UPDATE

INTERVIEW

‘...we are no longer doing researchdriven solely by intellectual ‘rigor’,so to speak, but to a large extentalso by brute force...’