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International Clinical Trials

New Saviour

Simon Beaulah, Paul Denny-Gouldson and Robin Munro of IDBS discuss the opportunities for successful collaboration between industry and academia, and the platforms that can be used to further cement this important relationship

There has always been collaboration between academia and industry groups to support basic research, screening, preclinical studies and, in particular, clinical trials. The relationship is beneficial to both groups: bringing essential subjects for clinical trials to pharma and biotech companies; and attracting industry-funded partnerships for academic medical centres to perform groundbreaking translational research, leading to improved outcomes in their organisations. The well-documented pressures of pricing policies, regulations, failure rates and safety on the drug industry have resulted in many mergers and acquisitions, as well as new restructuring and investments in biologics-based treatments. However, for industry and academia the most radical move is towards a more personalised approach to drug development and disease treatment across the health science ecosystem.

The Center for Technology Innovation at Brookings has stated that “The capture and storage of genomic information will redefine health informatics data flows. Our (US) healthcare system requires a seamless and rapid flow of digital information, including genomic, clinical outcome, and claims data, in order to become more efficient, effective and truly personalised (1).”

Academic medical centres are also under pressure from falling grant levels for basic research, as well as the loss of publicly funded software packages such as caBIGTM. To counterbalance these pressures there is a growing opportunity for academia and industry to work more closely together, driven by the growing availability of well populated, high quality patient and specimen sets that can be the basis for clinical trials, biomarker studies, comparative assessment studies and outcomes analysis. This article will look at the growing availability of such data and the informatics challenges that need to be overcome in delivering a secure collaborative research platform (CRP) for translational science.

The growth in the use of electronic medical records (EMR), especially in the US where the stimulus package has driven adoption, brings with it the opportunity for the secondary use of such data for clinical research (2). Detailed patient and family histories, treatments, diagnosis and outcomes are vital information for accurate cohort selection in translational medicine research. These better populated patient data sets are complemented by well-established biobanking procedures, especially in cancer. Many healthcare and academic groups are now seeing that the combination of large scale biobanks with a well-characterised, high quality set of patient data is a major attraction for industry partners. In the US, the Moffitt Cancer Center is building a wide network of cancer patients, while in New York State the Healthcare Association (HANYS) is working on bringing together large scale patient sets across the state as part of their Partnership to Advance Clinical Electronic Research (PACER) project (3,4).

In Europe, Cancer Research UK is sponsoring the collection and genetic analysis of cancer samples from six major cancer centres in the UK to build a resource to support stratified medicines research (5). This project is running in parallel with multiple projects that are sponsored by the UK’s Technology Strategy Board to develop a collaborative IT framework to support translational medicine and low-cost wet lab tests that will identify cancer relevant genetic mutations (6). In addition, the Integrated Medicines Initiative is sponsoring various biomarker-orientated large scale studies across Europe.

The opportunities for collaboration around such data sets are extensive, and provide many secondary uses for high quality patient specimens and associated data sets (see Figure 1). For many CROs, pharma and biotech companies, the opportunity of being able to query large sets of well-populated, consented data sets for prospective clinical trials design is a significant attraction. Clinical trials can experience recruitment issues once they are underway because they are unable to accurately predict the number of patients available for recruitment from different sites. With high quality patient data prepared for secondary use, medical research groups can easily see how many possible subjects meet certain criteria, specified by an institutional review board (IRB) or other body.


A CRP provides the essential informatics infrastructure to support secure stratified/translational medicine research and collaboration, including data handling, storage, retrieval and analysis services that will acquire, pseudonymise and integrate data from a wide variety of diverse sources. To be effective, data from a range of different genome-wide measurements and experimental modalities must be integrated with historical and current clinical records in an environment that secures patient confidentiality while enabling access by research scientists. Such systems must be delivered across multiple treatment and research centres in order to enable meta-analysis of larger cohorts, facilitating the evaluation of possible subjects. The system must facilitate cohort selection, biomarker identification and patient tissue profiling, and support clinical risk assessment.

The principal technical challenges of these CRPs relate to inherent data complexity, reflecting the multiple sources of data, including sequencing, genotyping, expression arrays, protein-based diagnostics, imaging, prescription history, clinical, pathology, chemistry and so on. This is compounded by the need to deploy the platform across multiple institutions, who may all use different formats, standards, capture procedures and controlled vocabularies, and who will need to maintain data privacy. This requires a comprehensive set of capabilities, as detailed in the architectural overview in Figure 2 and in the following sections:

Types of Collaboration
Partnerships across industry, healthcare systems and academia vary significantly (see Figure 3). A CRP requires that the full nature of collaborations be definable and supportable through the provision of highly flexible and controllable data, sharing and collaboration models, across different types of research.

Types of Research
The platform needs to support the broad spectrum of research operations from basic research, requiring high levels of flexibility, to industry-sponsored clinical trials, which requires compliance of the platform with good laboratory and clinical practice standards.

Data Sharing
The CRP must support a variable model whereby data sharing occurs across a range of models, from open access to highly controlled. The consortium model must be designed to meet the full spectrum of data sharing models in line with the different types of collaboration.

Security and Access Control
The platform must be secured at a data level to ensure that access is only granted to data that is agreed to as part of any specific collaboration, as well as IRB protocol. The security model must facilitate access on a highly granular – often subproject – level to ensure compliance with the different types of collaboration.

Scalable Data Model
The data model will need to scale from single organisational to cross-organisational requirements, meeting the needs of capturing both repeatable (ongoing longitudinal views) and ad hoc data requirements (clinical trials efficacy data).

Intellectual Property
Organisational requirements of ensuring IP is managed and controlled can be met through the formalisation of how collaboration is designed through stringent management of data and security.


Pseudonymisation of patient data is key to maintaining patient confidentiality and thereby enabling appropriate secondary use of data for research purposes. Pseudonymisation is the process by which patient identifiers are de-identified, or more commonly anonymised, and replaced with research identifiers before clinical data can be used for research purposes. The de-identification process removes all fields that can directly identify a patient, either by itself or in combination with other attributes available in the same data set (for example, names and date of birth). Mappings, or the master patient index, are maintained by a trusted third party (often referred to as the ‘honest broker’) and are only accessible by clinicians with appropriate permissions and with a public key infrastructure (PKI) certificate and private key. With a pseudonymised data set, human subjects are only identifiable by a small subset of clinicians who can then contact individuals of interest – something that is not possible with a fully anonymised data set. A similar process can also be applied to specimen and sample identifiers.

As well as patient data, such collaboration systems need to support data management and analysis of molecular data. The cost and timeframes for performing large scale molecular analysis have dropped significantly since the completion of the Human Genome Project, from the globally-driven $3 billion public effort to around $10,000, and are now available to users of next generation sequencing instruments from the likes of SOLiD, Illumina and Roche 454 (7,8). Many labs are also using gene expression, Genome Wide Association Studies (GWAS) and proteomics techniques, meaning there is a vast quantity of molecular data to be analysed and associated with patient information. The rapid fall in the cost, and the increase in data being generated, are causing serious issues that require a more structured approach to metadata management and the sharing of genetic and other molecular variation.

To support long-term outcome studies, precision cohort selection and comparative effectiveness longitudinal data management is required. This ensures that all significant clinical events are captured on a timeline that shows when a test was taken as well as the result. Due to the complexity of most hospital IT systems, covering electronic medical records (EMR) and non-EMR, a CRP needs to support established data interchange standards such as HL7, CDISC and DICOM. CRPs need powerful data mapping technology to represent the source information in a consistent fashion. The semantically normalised data needs to be harmonised in a single repository with data logically organised and secured, while allowing their dynamic integration regardless of their type or source. Source system data both within and between institutions tends to be strongly purposed and semantically disparate, which makes it hard to compare, much less correlate.

Systems must allow researchers to interact with the data using terms and classifications (ontologies) with which they feel comfortable. For example, the same data might be viewed using an ICD-10 view by one researcher and an internally configured ontology view by another. This pragmatic research focus helps to mitigate the tremendous challenge of generalised semantic normalisation. These data are presented in a single physical repository that allows their dynamic integration regardless of their type or source. This power and flexibility overcomes key issues including data protection, interoperability and re-use and IP management that have troubled other systems.

Interest in hosted and cloud-based systems to support life science research is growing due to the reduced internal costs and flexibility of storage and analytical power available. This is especially true for projects involving next generation sequencing and large image data sets, which can fill many terabytes of storage space. The attraction of the cloud is considerable where close collaboration and data sharing between parties is required because each group can access and analyse data from wherever they are located. Security is the first consideration in such systems, with the industry standard being a two-factor sign in process with hardware authenticators. Also, being able to support 21 CFR Part 11, HIPAA compliance and operating in a validated research environment is essential in working with and attracting industry partners, something that can be challenging when using open source software.


The opportunities for successful collaborations between academia and industry partners can be greatly enhanced with the growing use of CRPs. When the technical capabilities are put in place they will truly empower translational medicine in the context of both patient outcomes in the clinic and cohort selection, as well as biomarker validation in the context of pharma and biotech clinical trials.

  1. Center for Technology Innovation at Brookings, Enabling Personalised Medicine through Health IT, 2011
  2. Goedert J, Where Does the $2 Billion Go?, Health Data Management, February 2009, stimulus-27749-1.html
  3. Moffit Cancer Center,
  4. Partnership to Advance Clinical Electronic Research (PACER),
  5. AstraZeneca and Pfizer Join Cancer Research UK’s stratified medicine programme, Drug Discovery Today, February 2011, 15555/astrazeneca-and-pfizer-join-cancer-research-uksstratified- medicine-programme
  6. Technology Strategy Board press release, Research and development into tumour profiling will lead to improved cancer care, press_release_stratmed_tpdc_9jun11.pdf
  7. National Human Genome Research Institute, National Institute of Health,
  8. Illumina Halves Sequence Costs, Launches iPad App, Bio-IT World, 10 June 2011, www.bioitworld. com/2011/06/10/illumina-halves-sequencingcosts- launches-iPad-app.html

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As Marketing Director of Translational Medicine, Simon Beaulah is responsible for the promotion of IDBS’ market leading capabilities in personalised medicine across the pharmaceutical, diagnostic and academic medical centre space. Simon has been working in life science and healthcare informatics for more than 20 years, initially in research and over the past 12 years for informatics vendors including LION bioscience, BioWisdom and InforSense (now part of IDBS). Simon has degrees from Aston University and Cranfield Institute of Technology. Email:

As VP of Translational Medicine, Paul Denny-Gouldson leads the IDBS Healthcare Group’s strategic planning and execution on the advancement of personalised medicines in the healthcare and pharmaceutical industry. He joined IDBS in 2005 as part of the acquisition of his ELN company and has spearheaded the drive to make E-WorkBook the acknowledged technology and market leader in this space. Prior to this, he was Senior Scientist at Sanofi-Synthelabo (now Sanofi) for just under five years, where he managed a multidisciplinary biology department. Paul obtained his PhD in Computational Biology from Essex University in 1996, and has authored over 25 scientific papers and book chapters. Email:

As Director of Translational Medicine Solutions, Robin Munro is responsible for the direction of the IDBS Translational Medicine Solution and heads the global team of Solution Analysts and Scientific Presales. He has over 15 years of experience in bioinformatics and extensive knowledge of the pharmaceutical and healthcare industries with in-depth knowledge of translational medicine, biomarker discovery and molecular analytics as well as the drug discovery process, clinical and healthcare information management systems. Before he joined IDBS he led teams to develop bioinformatics solutions for expression analysis, target tracking, protein and pathway interactions at companies such as LION bioscience and InforSense (now part of IDBS). Robin holds a Computational Biochemistry PhD from University College London and an MSc in Biological Computation from York University. Email:

Simon Beaulah
Paul Denny-Gouldson
Robin Munro
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