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EuropeanBiotechnology

Science & Industry NewsApril 2014

SPECIAL

II Big Data & IP in Life SciencesBig Data & IP in Life SciencesBig Data & IP in Life SciencesBig Data & IP in Life SciencesBig Data & IP in Life Sciences II Big Data & IP in Life Sciences

31_EBSIN4_14_Titel_Big Data_tg.indd 27 03.04.2014 14:45:38 Uhr

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32_EBSIN4_14_Dentons.indd 1 02.04.2014 16:34:17 Uhr

Euro|Biotech|NewsNº 4 | Volume 13 | 2014 33

BiG Data & iP

At BIOCOM’s recent 7th IP conference “Big Data – Big Drugs” in Berlin, Sachin Soni – the Director of Equity Research (Life Sci-ences) at Kempen & Co Merchant Bank – made it clear that integrating Big Data could have a massive impact on healthcare. He showed data suggesting that improvements in pharma ceutical R&D productivity alone could create value of US$40-70bn annual-ly. Evidence-based care could add another US$90-110bn – and that seems to be just the tip of the iceberg. Linking existing informa-tion on drug profiles with a growing body of omics data and patient records can for ex-ample identify new applications for drugs

Intro

Big Data & smart data there has been a huge rise in data collection related to medicine in the ten years since the Human Genome Project was declared finished. Big datasets that link electronic med-ical records with patient-specific diagnostic data or disease-associated genetic risk fac-tors (biomarkers) are now promising to improve the efficacy of patient pre-selection for clinical trails and therapy. But the growing flood of information in the life sciences is also presenting huge challenges when it comes to secured storage, processing, data visualisa-tion and analysis. In clinical medicine, especially oncology, “Big Data” is expected to play an increasingly important role in identifying causality of symptoms, predicting hazards of disease incidence or reoccurence, or in improving quality of care. But is the investment in projects like the “Big Data to Knowledge” (BD2K) initiative, the HAnA oncolyzer project or the free-access digital cancer treatment library “Eviti” worth the money? or is the hype surrounding Big Data masking a field with big promise, but little real current value?

that have already been approved. Integrating sequencing and outcome data can help de-termine new drug targets and compounds, according to Dr. Michele Wales from InHouse Patent Counsel LLC. That’s shown by drugs such as Benlysta, Raxibacumab, Albiglutide or Darapladib, which were derived from Hu-man Genome Sciences’ sequence databas-es. Other speakers at the conference saw further potential in areas like identifying drug responders, or finding reasons for non-compliance of therapies.

Industry is already aggressively embrac-ing new Big Data approaches, according to a survey of 70 life sciences organisations

issued in March by the IMS Institute for Healthcare Informatics. “Riding the Infor-mation Technology Wave in Life Sciences: Priorities, Pitfalls and Promise” showed that cloud-based business intelligence ap-plications and storage in non-relational parallel-processing databases, embedded analytics and systems integration has the potential to drive transformational change over the next three years – both in overall healthcare system efficiency and the ef-ficacy of treatments. The study’s authors believe that the availability and adoption of secure, healthcare-specific tools and serv-ices are key to accelerating opportunity and deriving greater value from new, expanded sources of health information to optimise patient outcomes. Cost pressure is also driving solutions that promise to improve the efficiency of pharmaceutical develop-ment. According to IMS Health, Big Phar-ma will need to reduce combined operating costs by US$36bn annually through 2017 to maintain operating margins and current levels of R&D activities.

Challenges and bottlenecks

As competition among life sciences compa-nies intensifies – and the mix of new med-icines skews toward those with relatively small target patient populations – analyt-ic systems that help bring medicines to the right patients and their physicians have be-come essential. The implementation of de-cision support algorithms can accelerate the improvement of health outcomes, while also bringing more efficiency to the entire health system. Nearly 60% of survey respondents additionally rated patient apps as extremely or very important to addressing commercial challenges, while 69% similarly rated invest-ments in physician apps.

However, Big Data itself can be highly di-verse and uninformative without preproc-essing. Current limitations include selec-tion bias, sample size, missing values, ac-curacy, completeness and the nature of reporting resources. Although a few first successes are surfacing, many challeng-es remain. “Data protection is key,” says Andrew Litt from Dell. “Confidence in ge-nome research cannot undermined by in-adequate data protection.”� B

IBM’s Watson Foundation presenting new visualisation tools for Big Data at CEBIt.

© IB

M

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34 Euro|Biotech|News Nº 4 | Volume 13 | 2014

Reinhard Buettner studied medicine at the Universities of Mainz, Munich, London, and Cologne, and received his MD at the Pettenkofer Institute for Virology in Munich. After postdoctoral fellowships at the Gene Center Munich and the MD Anderson Cancer Center in Houston, he became a staff pathologist at the University of Regensburg. From 1999–2001 he was a full Professor for Pathology at the RWTH Aachen. In 2001, Buettner was appointed Professor and Chairman for Pathology at the University Hospital Bonn, and since 2011 he has been a Professor and Chairman for Pathology at the University Hospital of Cologne’s Center for Integrated Oncology.

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Integrating Big DataThe analysis and interpretation of massive amounts of biological data

poses a significant bottleneck for researchers and clinicians seeking

to understand and diagnose diseases. Prof. Dr. Reinhard Büttner ex-

plains how his laboratory addressed these challenges, and where he

sees the value of pre-tailored commercial data processing systems.

? Prof. Büttner, your lab was one of the first in Europe to implement next-generation se-quencing (NGS) into routine clinical diagnos-tics. How do you use this technology today?

! BUeTTneR:We use NGS in cancer diagnostics and initially started with lung cancer. Today, we have estab-lished additional tests for melanoma, chron-ic lymphomatic leukemia (CLL) and gastro-intestinal (GI) tumors. However, with about 3,500 cases a year, lung cancer is still the most frequently performed test in our lab followed by GI, melanoma and CLL. In total, we perform almost 5,000 NGS-based tests annually.

?

How long did it take to set it up?

! BUeTTneR:The initial set-up of the entire workflow to its routine clinical implementation took us about a year. This is because we needed some time to gain experience with the NGS technology and validate our approach. In fact, we went through a very excessive val-idation phase to ensure the accuracy of our results. But still, we’re not there where we want to be. I think of this as a process dur-ing which we’re continuously implementing new technologies and also new gene sets. It’s really about continuous development, and the current system we use is already our second version lung cancer test.

? Are you looking into the entire genome, or a particular set of genes?

! BUeTTneR:We’re working with gene panels that cover a set of around 20-40 genes, depending on the type of cancer. As of today, I don’t think that it makes sense to cover the exome or even entire genome in routine clinical diag-nostics. This is because you have only a limit-ed number of potential drugs at hand linked to biomarkers. In addition, it is still quite challenging in terms of sequencing costs and the amount of data you generate.

? How much data do you have to process for one patient?

! BUeTTneR:It’s usually one gigabyte of data per test. This is because we sequence every gene in our panel at a very high coverage, in our case 5,000-fold.

? This must be a significant challenge for your workflow?

! BUeTTneR:I think that we have adapted quite well. When we initially set-up the workflow, we developed our own proprietary data-analysis pipeline, which first helps us to

automatically filter the data to identify rel-evant mutations based upon different al-gorithms reflecting our quality standards – for instance, the quality of data or cover-age depth. The subsequent clinical interpre-tation is still manual work. This is the most time-consuming part of the entire analysis process – about a day if you deal with 3-5 mutations – but we found the automatic algorithms we tried to implement weren’t reliable enough.

? Is the entire pipeline based upon a propri-etary system?

! BUeTTneR:We ended up with a mix of analytical tools that are freely available, such as the Inte-grative Genomics Viewer and own tools developed in collaboration with our IT de-partment.

34-35_EBN_Advert-Qiagen_tg.indd 34 04.04.2014 12:55:26 Uhr


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? Was it a major cost factor for your lab, or is the investment in bioinformatics rather negligible?

! Buettner:It is a significant cost factor. For instance, we had to exchange all the switches and the connections to the existing IT system be-cause the regular connection was too slow to transfer the amount of data we’re work-ing with. So we had to invest in hardware. But you also have to invest in people who generate your algorithms to make sense out of the data. However, I believe these are the typical starting costs associated with get-ting into NGS.

? Do you think this approach would also be the way to go for other labs?

! Buettner:We have a very large discovery unit around lung cancer, so it made sense for us to invest in bioinformatics infrastructure and per-sonnel. However, there are also vendors out there like QIAGEN that work on pre-tailored commercial bioinformatics solutions, and if you’re a pure diagnostic lab that wants to do NGS-based tests, I think it’s probably sound-er to buy such a solution. In fact, I believe this is ideal for larger diagnostic labs that are focused on routine diagnostics and simply want to have a streamlined analysis pipeline with state-of-the-art technology.

? But some argue that relying too much on integrated systems might harm profession-al standards …

! Buettner:There is little difference to other areas of modern medicine. Just think of an inten-sive care unit – you have no chance to con-trol the technical credibility of all the para-meters provided by various instruments. We are dependent on technology. This does not mean that you shouldn’t perform regular controls of your workflow. But the idea that someone can visually control the plausibil-ity of gigabytes of data is simply not prac-ticable; this would lead to many more er-

rors. So I rather see this as a purely psycho-logical factor.

? So where do you see the role of the pathol-ogist in this setting?

! Buettner:Applied to cancer, I believe that you need a solution that delivers to the pathologist comprehensive and credible information about all validated mutations and genom-ic alternations in the sample. But then, the pathologist needs to integrate this informa-tion into a comprehensive diagnosis – a con-clusive report – which connects the genomic information with data from microscopy and immunochemistry as well as other clinical information about the patient and the par-ticular disease. This is something that you shouldn’t do automatically, in my view.

? Speaking of integrating information, how do you handle the exchange with other in-stitutions or external databases?

! Buettner:Other institutions aren’t directly connected to our genome data. The external institutions we work with – around 100 hospitals and pa-thologists in Germany – receive a standard-ized report containing a table that summariz-es which genes have been sequenced and at which coverage, what is the allele frequency and the potential functionality of the muta-tion, and what would be the potential clinical interpretation in terms of treatment or inclu-sion in a study.

? How do you ensure that your findings and recommendations are up-to-date?

! Buettner:This is a very critical point. When it comes to the clinical interpretation of mutations, we refer to guidelines that make state-ments about which genes should be tested for in what tumor entities. However, guide-lines don’t include lists of drivable muta-tions, since this knowledge is continuous-ly expanding at a very rapid pace. This can be quite challenging if you find novel or rare

mutations. You might have analyzed the tu-mor according to guidelines, but still don’t know what to tell the patient. So I think that over time, we need a database that connects even novel and rare mutations to clinical out-comes. This brings us to clinical studies. To-day, we have a good overview of all studies performed here at the University of Cologne, and we know precisely the inclusion criteria and can direct patients to the studies if nec-essary. However, it is much more challenging when you start looking for external studies, especially when new compounds are in an early stage of development. This can be ex-tremely time -consuming. So I think that in both cases there is room for improvement.

? Which other challenges do you see in the application of personalized approaches like these?

! Buettner:We are generating tremendous numbers of new biomarkers for all kinds of different cancers. These biomarkers literally flood clinical practice, but I’m still observing some skepticism towards targeted treatment ap-proaches – in Germany and elsewhere. We need a broader understanding that we’re truly entering into a completely new age of medicine – an age of rational oncology that examines the molecular alterations driving a tumor and matches it with val-id therapeutic concepts. However, cases in which healthcare professionals ask for a full mutation scan only to decide to stick with standard chemotherapy are still common-place. This is the wrong path, and one which puts our entire healthcare system at risk. We need to invest in modern diagnostic tech-nologies to determine which patients can benefit from particular treatments and act accordingly. If you try to save money in diag-nostics, you will lose this money through im-precise therapies. There is still a lot of work that needs to be done here. ii

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34-35_EBN_Advert-Qiagen_tg.indd 35 04.04.2014 12:55:36 Uhr

Nº 4 | Volume 13 | 2014

Big Data & iP

In the race to discover and develop companion diagnostics successfully, many pharmaceutical companies are partnering with biotech companies that are expert in the appropriate diagnostic or technology. Working with a diagnostic partner to develop a companion diagnostic, which ultimately also requires obtaining market authorisation, complicates the already complex drug development and approval process. An intellectual property (IP) strategy is part of this complex process, and plays a key role in the development and commercialisation of companion diagnostics.

IP strategies for companion diagnostics include a deep understanding of type of discovery, business objectives, patent eligibility and infringement considerations. While it is beyond the scope of this article to examine all IP considerations for all major jurisdictions, it is important to briefly discuss how patent law is evolving in the US and Europe. In this first installment of a series of articles, we look at types of discovery and US patent eligibility issues.

type of discovery

The strength of patent protection is most often correlated with the type of discovery. Patent claims directed at compounds are the strongest type of protection. Such

Companion DiagnostiCs

IP considerations associated with CDx

Ramin Ronny Amirsehhi, Amirsehhi Intellectual Property Law; Martinsried

many pharmaceutical companies are considering a biomarker strategy for their drugs as advances in diagnostic technologies, the growth of biosimilars, a rising number of expiring patents, and concerns about profit margins drive them to ad-just their approach.

protection could be used to exclude others from using the compound or novel biomarker in any type of diagnostic for any type of drug.

In most cases, biomarkers are known proteins. A discovery involves their correlation with a particular disease and drug that is under study. This type of discovery may result in methodoftreatment claims, which can be very valuable – especially if such method claims read on a drug and/or diagnostic label.

Methodoftreatment claims need to be drafted with both the drug label and diagnostic label in mind. The labels can be in general terms or include a specific assay. A claim directed at a specific diagnostic assay or technology would include limitations associated with how the presence of the biomarker is determined. Such claims can be vital in the long run, as they may extend the exclusivity of the drug against generics and biosimilars, since to meet FDA requirements, any generic or biosimilar equivalent would also be required to include reference to the diagnostic assay.

The discovery and patenting of new platform technologies can also be valuable to a diagnostic company. It is important to consider in advance how a potential competitor may attempt to avoid patent infringement while at the same time demonstrating the equivalence of its device to a legal

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36-37_EBSIN_4_14_Special_Amirsahhi_tg.indd 36 02.04.2014 16:40:15 Uhr

Nº 4 | Volume 13 | 2014

Big Data & iP

ly marketed device by submitting a 510(k) premarket submission to the FDA. There-fore, it is important to consider other fea-sible methods or techniques for measur-ing the bio marker and include these in the specification and/or claims.

Discoveries that do not impact labels are less valuable, and are usually easi-er to get around. These include, for ex-ample, the discovery of specific assay reagents.

Patent eligibility

By now, most people in the life science community have become aware of the US Supreme Court’s decision in Mayo Collaborative Services vs. Prometheus Laboratories, Inc. 132 S.Ct. 1289, (2012 –“Mayo” decision), as well as in Associ-ation for Molecular Pathology et al. vs. Myriad Genetics, et al., 133 S.Ct. 2107, (2013 – “Myriad” decision). It is impor-tant to highlight the difference between these two rulings.

The Mayo decision is entirely independ-ent of the “law of nature“; it is focused on process claims. The Myriad decision did not review process claims, and focused on the genes. There are numerous publica-tions analysing the two decisions. This ar-ticle will therefore propose strategies for obtaining claims that relate to diagnostic methods or biomarkers. It is important to remember that the following approach-

es are completely applicable only in the US. For other jurisdictions – such as Eu-rope – different scope of claims may be obtained. When drafting applications, ap-plicants should therefore consider these differences and include them in the spec-ification and claims.

Proposed solutions

According to the Mayo decision, correla-tions between a biomarker and efficacy are a natural law, and are therefore not patentable. The possible approaches in light of this can be claims 1) directed at a method of diagnosing a disease by de-tecting a novel biomarker, 2) use of a spe-cific reagent (as mentioned above, these types of claims can be easy to circumvent for competitors and should be considered carefully), 3) adding a treatment step (the treatment step can be a particular drug or therapy), or 4) detecting a combination of biomarkers.

Genomic DNA is considered a product of nature, and based on the Myriad deci-sion is therefore not patentable. Possible strategies for bypassing this roadblock can be claims directed at cDNA, non-naturally occurring DNA (such as mu-tagenized or chemically modified DNA), synthetic RNA or synthetic proteins (it is important to avoid using the term “iso-lated”), as well as their respective probes and primers. �

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38 Euro|Biotech|News Nº 4 | Volume 13 | 2014

BIG DATA & IP

Cancer is the second most common cause of death in the developed world. In fact, one in three people will be diagnosed with car-cinoma at some stage in their life. To tackle this global problem, pharma fi rms are in-vesting billions of dollars every year in the development of cancer treatments. Even though R&D costs for drug development are exploding, there are currently about 1,900 cancer medicines in the pipeline.

At the same time, the volume, velocity, variety and veracity of data is rising expo-nentially in medicine. We know today that cancer is often related to genetic factors. To sequence insightful DNA and achieve better treatment options, billions of sam-ples need to be compared. That is gener-ating huge sets of data, so that drug de-velopment for oncology has increasingly become a data-driven science bringing to-gether physicians, pharmacologists, mo-lecular biologists, computer scientists and mathematicians to solve complex prob-lems that none of these disciplines could solve individually.

The Big Data challenge

This complexity is also refl ected in the re-lated data itself, which are often referred to as the characteristics of “Big Data”:

ONCOLOGY

Leveraging Big Data for new medicines

Manuela Müller-Gerndt, Healthcare & Pharma/Life Sciences, IBM Germany; Dr. Wolfgang Hildesheim, Watson Group, IBM Europe; Fatema Maher, IBM Germany

Almost US$200bn are spent annually on R&D in science-driven sectors such as health-care, life sciences, consumer products or chemicals. An estimated 60% of pharmaceu-tical R&D investments, however, are spent on products that will never reach the mar-ket. This article looks at how best practices for R&D workspaces could be applied to en-sure successful drug development. It all starts with a step-by-step approach towards Big Data, for example based on IBM’s Watson Foundations.

– Volume of data: Research data is spread across huge databases in a number of different areas, including patents, com-pounds, journals, biomarkers, structure activity relationships, medical records and genomics. For example, around one million human genomes were sequenced in 2013, and we expect around fi ve mil-lion human genomes to be sequenced by the end of 2014 worldwide with around 15 million new cancer patients each year worldwide. Over 26 million unique mole-cules are available for new drug develop-ment from 400+ different sources in the ChemSpider chemical database.

– Velocity of data: Real-time analysis of de-vice data, images and alerts will change the role of monitoring devices in health-care outcomes and patient well-being. Bedside monitoring devices today capture more than 1,000 vital signs per second.

– Variety of data: Around 80% of all data to-day is unstructured, and this percentage will increase dramatically over time. As health and personalised medicine make advances in the population, even more data inputs can be expected from medi-cal records, notes and dictation, public health reports, scientifi c papers, social media and the Internet.

– Veracity: How trustworthy is the data?

The ambition to deal with these four ’V’s’ can be understood as the defi nition of the Big Data trend, which we see in oncology, but also in healthcare and nearly all oth-er industries.

A business need in pharma R&D

Oncology R&D researchers are challenged by huge amounts of data from different sources in heterogeneous formats that they have to digest and turn into new products more quickly. Given the sensitivity of health records and medical information, as well as the need to protect intellectual property, one of the most compelling issues is security.

More and more R&D employees, admin-istrators, managers and senior executives are asking for role-specific workplaces with instant access and proactive delivery of changes to many kinds of information from many internal systems – both within their own organisation and external databases/online libraries. They want access to pa-tient information, physician opinions, clini-cal data, medical research studies, product and market information, and regulatory & compliance standards.

Getting up to speed

To stay at the forefront, R&D departments in oncology need to tackle the Big Data chal-lenge and prepare to take advantage of the upcoming era of Cognitive Computing. To get up to speed, typically, they go through three phases: – The fi rst phase starts with Information Ex-

ploration & Discovery to enable complete visibility into new and historical internal data sets, as well as external research re-sults and publications.

– The second phase concentrates on Con-tent Analytics by applying text mining, pat-tern recognition and predictive analytics (e.g. for target discovery and validation) in order to fi nd correlations between genes and diseases.

– Phase three introduces Cognitive Com-puting, adding natural interaction with on-cologists and systems that learn through interactions, deliver evidence-based medical responses and drive better out-comes in the near future.

38-39_EBSIN_4_14_Special_IBM_tg.indd 38 02.04.2014 16:42:00 Uhr


BIG DATA & IP

Since most oncology R&D departments are currently in the fi rst phase, or even a pre-stage of it, this article introduces how organisations can get started with information exploration. Usually the fi rst step is selecting a defi ned pilot, work-ing with an innovative group and tying to-gether a few internal and external infor-mation sources.

State-of-the-art data exploration

Modern R&D workplaces ideally includeintelligent information exploration that virtually integrates multiple sources into one single access point, provides person-alised content, uses best-of-breed search and unique, automatic clustering and cat-egorisation capabilities, and allows us-ers to browse all kinds of historical re-search information within the organisa-tion and beyond.

Dynamic clustering helps users to gain new insights that have not been discov-ered before. Having the ability to tag, bookmark and comment on documents helps foster collaboration and improve treatment outcomes. Proactive alerts and “push intelligence” let people know when new or updated information is available. The rapid application of building capabili-ties helps to quickly present customised, role-specifi c views to the end user.

This kind of information exploration is available today, allowing organisations

to maximise the value of their intellectu-al property, reduce their product devel-opment cycle and signifi cantly decrease R&D costs.

One real-life example

A leading pharmaceutical company that has been developing innovative medicines for decades needed to focus on competi-tiveness. That involved transforming in-formation and data sharing capabilities to support faster time to market and new product introductions. R&D needed in-stant access to patient and research in-formation across the company’s intranet, to many different applications and fi le sys-tems, as well as information from several external toxicity databases and subscrip-tion sources. The company’s executives created initiatives to increase data trans-parency and productivity throughout the enterprise, worldwide.

Data Exploration Initiative

The Watson Data Explorer software was de-ployed within 10 weeks and gave 30,000 em-ployees worldwide an authorised access to intranet portal-like applications. These por-tals were built rapidly to cover real-time internal and external information, includ-ing networked fi le systems, the enterprise content management system, fi les in Mi-crosoft SharePoint, an employee directory

and external internet pages and subscrip-tion sources. The solution preserved exist-ing security parameters, so that employees could only access content they were author-ised to view. Security is supported at group, user, document and the even more granu-lar fi eld levels.

Watson Explorer has enabled touch points throughout the drug discovery process, from initial research to clinical trials. R&D staff obtained an easy navigation tool and a way to fi lter data quickly. Scientists can now re-trieve an overview, drill down into specifi c topics and discover content that might re-main uncovered. They can also scan multiple synonyms for medical terms and phrases, while critical R&D employees receive alerts of changes in support-of-compliance ef-forts. Collaboration capabilities allow users to identify relevant content to colleagues by commenting and tagging results. By globally leveraging past research around the histo-ry of compounds for formulation and noted effects, R&D was able to drastically reduce duplicate efforts. In addition, sales repre-sentatives became more productive by gain-ing access to the newest policies, external news, marketing collateral and a doctor’s latest purchases.

Finally, the company knowledge base supported hiring and retraining transition-ing employees. A knowledge-sharing and collaboration culture was created, as well as subject-matter experts, who maximise one of the company’s most valuable assets – their data.

Watson Data Explorer has already de-livered measurable business value to many pharma companies. In the exam-ple described above, the effi ciency of R&D was improved by 90%. Cutting search by 50% saved the company millions of dol-lars in the fi rst year alone. In addition, sales rose by 4.1%, training costs were reduced by 10% and new staffing re-quirements decreased by 1.2%, saving US$13.4m per year.

References

[1] Jaruzelski, B., Loehr, J., Holman, R.: THE GLO-BAL INNOVATION 1000: Making Ideas Work. B. Company,Editor. 2012.

[2] IBM Institute for Business Value analysis.

[3] Cancer.org, DKFZ 2013 Watson Explorer – a fi rst step towards intelligent 360º view workplaces including structured and unstructured data.

Create unified view of ALL information for real-time

monitoring

Identify areas of information risk & ensure data compliance

Analyze customer data to unlock true customer value

Increase productivity & leverage past work

increasing speed to market

Improve customer service & reduce call times

Watson Explorer

Data access & integration • Index structured & unstructured data—in place

• Support existing security • Federate to external sources

• Leverage MDM, governance, and taxonomies

Discovery & navigation • Clustering & categorization • Contextual intelligence

• Easy-to-deploy applications • All at the scale required for today’s big data

challenges

Providing unified, real-time access and fusion of big data unlocks

greater insight and ROI

Data access & integration Discovery & navigation Clustering & categorization

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monitoring






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challenges





monitoring






Watson Explorer






challenges




38-39_EBSIN_4_14_Special_IBM_tg.indd 39 02.04.2014 16:42:09 Uhr

40 Euro|Biotech|News N º– 4 | Volume 13 | 2014

BIG DATA & IP

IntervIew

Big Data can’t make gold from bad data Life sciences companies are growing increasingly interested in Big Data as they seek to combine information about disease pathways with patient data and critical inclusion cri-teria for clinical trials or electronic medical records. EuroBiotechNews spoke with SAP’s Peter Langkafel, the software giant’s General Manager Public Sector/Healthcare MEE, about the impact of Big Data and its current limitations in life sciences applications.

tion of drug responders in areas like on-cology, the identification of new uses for already approved drugs, establishing elec-tronic medical records, etc.?

LAnGKAFeL: !There are many existing cases that can il-lustrate this process. We’ve tried to under-stand what has happened in the past with traditional reporting. Currently we are on our way to understanding the situation in real-time. And Big Data technology will help us to predict and look into the fu-ture…so it’s not just about what has hap-pened, but what is happening and what will happen.

Euro|BioTech|News ?What tools are needed and used to man-age inherently imprecise data types like that involving stratification biomarkers?

LAnGKAFeL: !The golden rule of any business intelli-gence project is: garbage in — garbage out. If you have bad data, you can’t turn it into gold dust through some miraculous proc-ess. We do provide tools for data cleansing

Dr. med. Peter Langkafel is SAP AG’s General Manager Public Sector/Healthcare MEE (Middle and Eastern Europe) and the President of the Berlin/Brandenburg German Association of Medical Informatics (BVMI e.V.) He has over 20 years of experience in healthcare and information technology. With a PhD in medicine, a degree in medical informatics, and an executive MBA, Langkafel was a clinician and researcher himself for many years. He has written a long list of publications, and has appeared as a speaker at over 100 healthcare conferences.

Euro|BioTech|News ?Dr Langkafel, is the idea of Big Data be-ing over hyped?

LAnGKAFeL: !“Big Data” in healthcare is an El Dorado for some people, but it is still a very diffuse term. There is a huge potential in some ar-eas – such as better integrating research data and clinical day-to-day data. But Big Data can also mean “going beyond bor-ders”, which means the integration of am-bulatory and in-house care and the anal-ysis of what is happening there. This is at times a complex topic due to organisation-al and legal issues. But there’s no doubt there is huge potential.

That said, there is hype as well. For some people, Big Data is pretty much only as-sociated with personalised medicine – with genomic analysis, which “promis-es” cures for all kinds of diseases in the

future. For me, the so-called “potential” here is hype.

Euro|BioTech|News ?In which areas does SAP see the greatest market potential for Big Data solutions within the healthcare industry?

LAnGKAFeL: !We’re involved in projects with providers like the Charité (Europe s biggest univer-sity clinic), healthcare providers like the AOK (Germany s biggest health insurer), research organisations like the cancer re-search centres in Heidelberg and Stan-ford, and Health Maintenance Organisa-tions like Kaiser Permanente in the US. I would describe three phases for Big Data against this backdrop. First, it is about un-derstanding and maybe better visualising data. Then you have to align and match data sources – which has not been possi-ble to this extent before. The third phase is to really create new data or add some data outside of the traditional ecosystem. And I’d like to point out that just a few years ago we still had technical difficulties, but to-day technology hurdles like memory rep-resent unleashed potential. Technology is no longer necessarily the obstacle.

Euro|BioTech|News ?What is Big Data about, and how can data integration help improve patient recruit-ment for clinical trials? Or the identifica-

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or master data management, but these are mainly technical tools that can’t “understand” the content of imprecise data. There is a lot of effort being put into trying to get unstructured data (like a doctor’s discharge letter) into the system. Most of these projects promise a lot, but only reach accuracy levels of 60% to 80%. Some-thing is missing, but it’s not clear what. So we can’t use this ap-proach. My recommendation is always to try to feed structured data into the system!

Euro|BioTech|News ?What are current drivers, and where do you see limitations?

LANGKAFEL: !Technology is certainly a driver, as well as the growing volume of digital data in healthcare. We’ve started to view “data” as some-thing very valuable – but we are in an early phase of really un-derstanding its power. Data security is for me not a limitation, but an absolute prerequisite. We should be asking our data se-curity officers as well about who will protect our data from NOT being used. Our main limitations are the silos in the organisa-tion and a lack of statistical understanding. Or let me put it this way: Big Data gives a lot of answers, but we have to ask the right questions first.

Euro|BioTech|News ?What has been already achieved by SAP, and what are your plans for the future?

LANGKAFEL: !SAP is the frontrunner in memory technology: With SAP HANA, we have an extremely powerful tool in our portfolio, which is now even available in a cloud offering. The SAP Healthcare plat-form brings together new ways of using and integrating data. We are delivering solutions in that area, and we are co-innovat-ing with customers – like those I mentioned before – around the world.

Euro|BioTech|News ?Data from academic institutions are often not compatible with the requirements of the biopharmaceutical industry when it comes to things like assay design, reproducibility or biomarker cut-off values. What kind of standardisation do you think is needed to support efficient translation into applications?

LANGKAFEL: !Any discussion about standardisation should ask the same open question: Why is there no standard? Who has interest in NOT hav-ing a standard? Maybe for economic reasons, to protect an area (or a market) or to protect fraud and abuse, or to protect AGAINST fraud and abuse? I am personally a big fan of the the British Med-ical Journal (BMI) Open Data Initiative. Everybody who has noth-ing to hide should allow others to have a look in – under a certain operation mode, of course (data security, IP, legal). Here we need new ways for academia and industry to collaborate. F

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