High Performance Computing

(1)

Workshop Report

High Performance Computing

in

Health Research

Held on

1-2 October 2014

(2)

2

LEGAL NOTICE

By the Commission of the European Union, Communications Networks, Content & Technology Directorate-General.

Neither the European Commission nor any person acting on its behalf is responsible for the use which might be made of the information contained in the present publication.

The European Commission is not responsible for the external web sites referred to in the present publication. The views expressed in this publication are those of the authors and do not necessarily reflect the official European Commission view on the subject.

(3)

3

Report on Workshop on High Performance Computing in Health Research

(held on 1-2 October 2014 in Brussels)

Executive Summary

A workshop on High Performance Computing (HPC) in Health Research was held in Brussels on 1st to 2nd October 2014. The workshop was well attended with over 90 registered participants from the health research and High Performance Computing (HPC) communities as well as other stakeholders. 1.1 Objectives

The objectives of the workshop on HPC in Health Research were to: explore the key challenges facing health research, the role of HPC in this area and potential future actions and to explore research and innovation opportunities offered by Horizon 2020 e-Infrastructure call for Centres of Excellence in HPC for health research.

The achievement of these objectives was facilitated through the delivery of key-note speeches, presentations and through panel debates used to gather evidence from the key stakeholders in health research and HPC who attended the workshop.

1.2 Key Findings

The discussion during the work shop addressed several topics and broadly covered the current and potential applications of HPC in health care research. The key findings were:

• There are considerable opportunities for the integration of more HPC applications in health research with a high potential supporting clinical research, drug development and clinical applications (e.g. for personalised medicine1 approaches and in the context in research on rare diseases).

• The use of HPC in health research is currently fragmented and lacks an overall strategy to support further developments.

• Strengthened management, storage, mining, data integration, data quality assurance and analysis of large volumes of distributed heterogeneous “Big Data” have a high potential to achieve improvements in health research. However, these processes have to be adapted and appropriately implemented to support health research, taking into account data storage. e.g. the use of clouds to store data.

• Health research could substantially benefit from working together as a fully representative community, integrating both health research and methodical expertise in HPC in Centres of Excellence (CoE) focused on health research with high clinical impact.

• There is a need for discussion and agreement upon future regulatory requirements involving all relevant stakeholders, scientists, clinicians, HPC experts, patients, citizens and regulators. The protection of private data is a major challenge that has to be addressed, to ensure regulatory compliance while allowing the application of HPC to support progress in health research.

• To exploit the full potential there is a need to establish a ‘common strategy for health research supported by ICT’ and a roadmap for the use of HPC in health research (see chapter 4.0 Conclusions, Recommendations and Next Steps).

• Software is crucial: there is a need to improve availability of software adapted to health research needs, suitable to run HPC applications on multiple processor systems and clusters. Currently, programmes like PRACE (The Partnership for Advanced Computing in Europe) are set up to support only limited areas in health research. Therefore the services offered by

1_{“Personalized medicine does not literally mean the creation of drugs or medical devices that are unique to a patient but} rather the ability to classify individuals into sub-populations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventative and therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not.”

(5)

5

PRACE should be adapted to support more aspects of health research and / or alternative HPC systems should be considered.

• Multidisciplinary teams need to be formed with experts with all the skills necessary to develop and support health care research using HPC. These multidisciplinary teams should include relevant basic scientists, experts in clinical and health research, HPC hardware and software experts, data and networking experts and civil society organisations. The above findings need to be addressed by the health research, HPC, data and networking communities as well as patient organisations, so that a coherent approach is taken to health research in Europe with the greatest possible acceptance of this new technology.

2.0 Report on the Workshop 2.1 Introduction

A workshop “High Performance Computing (HPC) in Health Research” was held by the European Commission on 1-2 October 2014 in Brussels. A wide range of participants attended the workshop from the health research and healthcare sectors as well as representatives from the High Performance Computing and bioinformatics sectors, research funders, commercial companies and patient organisations.

The main aim of the workshop, facilitated through the delivery of key note speeches, presentations and through panel debates, was to explore the potential role of HPC in health research and future opportunities such as the Horizon 2020 e-Infrastructure call for Centres of Excellence in HPC for health research.

There were three different panel debates addressing: 1) Current needs for HPC in health research;

2) Can existing infrastructures answer identified HPC needs for health research? 3) Future activities and implementation.

The workshop agenda, copies of the presentation slides, brief Curriculum Vitae of the presenters and panel members as well as a list of participants can be found on the web site for the workshop at

http://ec.europa.eu/digital-agenda/en/news/workshop-high-performance-computing-hpc-health-research

The presentation slides of each of the speakers and panel members can be found by clicking on the person’s name in the agenda on the web site above.

A direct link to the CVs of the presenters and panel members is here: Short CVs and Pictures

A direct link to the list of the workshop participants is here: List of participants

A direct link to final synthesis slides is here: Final synthesis slides

Significant progress has been made in Europe over the years on the development of prevention, diagnosis and treatment of disease, leading to increased life expectancy of the population. However, due to societal developments like an increasingly ageing population, there is a demand for innovative treatments to address unmet medical needs. Furthermore the demand for better quality healthcare is increasing, whilst there are limited resources to support health care systems.

Some examples of the medical conditions that lead to very high treatment costs are:

• Cardiovascular disease causes 2 million deaths annually in the E.U. and costs 192 billion Euros;

(6)

6

• Cancer treatment costs 129 billion Euros annually in the E.U.;

• Diabetes has over 27 million sufferers in the E.U. and 120 million suffer from rheumatic and musculoskeletal conditions, with high treatment costs;

• In England the cost of treating Alzheimer’s disease costs over £17 billion annually;

• Treatments for rare diseases are usually treatments at the edge of technological development and are amongst the most expensive treatments per patient.

The escalating costs are not sustainable. Thus, to address this challenge there is a need to strengthen health research to improved diagnosis, prevention and treatment of medical conditions.

One area with great potential is the better and more efficient analysis of health research data from a huge number of different sources. The increasing availability of data from patients (e.g. genomics, images and health records) as well as data from clinical studies from academic research or pharmaceutical industry are a chance. The innovative analysis of huge highly integrated data sets can support health research by identifying trends, responses of different sub-groups of patients (patient stratification) to specific treatments and help develop better diagnostic tools and effective treatments. These results can contribute to the development of personalised medicine approaches, i.e. approaches to medicine targeting stratified groups of population, providing tailored preventive and therapeutic options that best answer patients' needs, also taking into account healthcare professionals and carers for the benefit of the society as a whole (see section 3.4.1.).

Another area with a very high potential is modelling and simulation approaches which are already used for innovative drug design early in the development or even in late stages e.g. PK/PD models in the clinical development phase.

One way to improve the understanding of complex biological systems is to develop models such as 'modelling the brain' in the EC funded Human Brain Project (HBP2). The Virtual Physiological Human (VPH3) project aims to provide digital representations of the entire human body. Thus it has the potential to enable academic, clinical and industrial researchers to improve their understanding of human physiology and pathology, to derive predictive hypotheses and simulations supporting the development of new therapies. These challenging projects heavily rely on High Performance Computing.

The overall drivers for the use of High Performance Computing in health research are therefore to:

• Better facilitate the research outcomes of health research, so that there is a positive impact on improving medical patient diagnosis, disease prevention and treatment, thereby improving the health of the population;

• Reduce the time and cost of drug discovery and design which at the present time is becoming uneconomical in some cases;

• Compare very large data sets including images to carry out patient stratification, to find trends so as to develop personalised medicine;

• Improve the ability to model complex biological systems leading to the better understanding of physiology, etc.;

• Reduce the overall costs of the provision of healthcare.

The availability of sufficient computing resources are a prerequisite, especially when analysis of large volumes is required to apply modelling and simulation approaches or analysis of huge highly integrated data sets.

The presentations and panel discussions held at the workshop explored the issues of better facilitating health research using High Performance Computing and other supporting infrastructures.

2

https://www.humanbrainproject.eu/ 3_{http://www.vph-institute.org/}

(7)

7 2.2 HPC Strategy in Europe

At present there is a well defined strategy for HPC in Europe. The strategy is composed of three pillars: (a) developing the next generation of HPC technologies, applications and systems towards Exascale; (b) providing access to the best supercomputing facilities and services for both industry including SMEs and academia; and (c) achieving excellence in HPC application delivery and use. This mainly involves the development of the European Technology Platform for HPC (ETP4HPC)4 and also the strategy developed by the Partnership for Advanced Computing in Europe (PRACE)5 along with other HPC initiatives, mainly exploring the development of Exascale HPC systems. The European Technology Platform for HPC is an industry led forum for HPC stakeholders, whose mission is to define research priorities and action plans on a number of technological areas aiming to improve EU growth, competitiveness and sustainability requires major research and technological advances in the medium to long term. The ETP4HPC developed so far the following main recommendations:

• To launch a research program with the aim to develop European technology in all segments of the HPC solutions value chain;

• To focus this program on specific technical domains and on relevant operational priorities.

The ETP4HPC develops policies and sets up activities based on the input from its membership. At the present time there is no direct health research organisation or association represented in its membership, so the ETP4HPC is therefore not directly engaged with the health research agenda. Widening the scope and including representatives from health research and civil society organisations, notably patient groups, would be an opportunity for the ETP4HPC to strengthen its contribution to the future developments of HPC in health research.

Through PRACE the academic sector is pooling its leadership-class computing systems as a single infrastructure and makes them available to all researchers in the EU, based on scientific excellence. In this context, for access to use of PRACE services the submission of a project proposal application is required. Hosted by PRACE the use of various systems is available including Tier-0 and Tier-1 systems. It is a pre-requisite that the applicants have experience of using HPC. There is a peer reviewed preparatory access route for timeslots of 2 to 6 months in duration. The preparatory access calls give researchers the possibility to apply for access to perform code scalability tests and code optimizations and support for code development and optimisation, possibly with the help of software experts amongst the PRACE members. However, this access mode does not allow major runs of computer models, simulations or other codes.

PRACE can be accessed by industrial users but they may only use the facilities and services provided by the infrastructure for basic research and development purposes. The condition associated with the free access is for the industrial users to publish all the results obtained at the end of the grant period. PRACE runs an SME HPC Adoption Programme in Europe (SHAPE). The programme allows SMEs to gain access to PRACE facilities, including expertise to develop and port application codes. This could be a route for SMEs carrying out health research to gain access to HPC services.

4

http://www.etp4hpc.eu/ 5_{http://www.prace-ri.eu/}

(8)

8

Important projects such as the Human Brain Project (HBP) have effectively engaged with PRACE as well as some researchers in biological sciences.

HPC is a transformation tool for health research and in particular, compared to standard computing platforms, HPC can cut down computing time by at least an order of magnitude. E.g. a commercial drug matching application used at Janssen labs has had its run time cut from 41 hours to 2 hours using HPC, thereby allowing faster drug development.

The trend for computing power to move to becoming available on the desktop as developments proceed mean that in a few years some desktop systems may become available which have the capabilities of current smaller scale HPC systems. These may satisfy some uses in assisting clinical diagnosis in a hospital environment. However, many areas such as drug discovery/development will still need large scale HPC systems and as more sophisticated techniques are developed for diagnosis, more computing power will be required.

2.3 Challenges and needs of health research for High Performance Computing

There are several different areas of health research, some of which have comparable requirements and others which have distinct specific requirements for efficient implementation of HPC. The following sections describe the needs and requirements based on the presentations and panel discussions that took place during the workshop.

2.3.1 HPC in drug development for small populations

Drug discovery and development is a key area in health research. The development of new drugs for effective clinical treatments is exceedingly expensive and time consuming. The cost of developing a new drug from the initial idea phase to finally reaching the market varies from hundreds of million until billions of Euros. Time scales for the development of an innovative therapeutic drug can need up to 15 years. Drug development is a business with a high risk of failure, even in late stages of clinical development.

There is clearly a need for improvement of the whole process of drug development, from pre-clinical research to marketing authorisation and even later during the life cycle management of medicinal products.

The Director of Science Policy of the European Federation of Pharmaceutical Industries and Associations (EFPIA) stated that there was considerable scope for speeding-up of drug research by improving co-operation, e.g. by harnessing all sources of data (clinical trials, real world, EHR, registries) and applying HPC approaches. The Innovative Medicines Initiative (IMI) aims to improve health by speeding up drug development strategies and regulatory procedures and offers a “safe harbour” to test new collaborations and approaches. IMI is the world's biggest public-private partnership (PPP) in life sciences.

Pre-commercial research on pharmaceutical research is frequently carried out by academia in collaboration with industry. The researchers should have access to publically funded HPC facilities at a national level (Tier 1 and 2 HPC facilities) or through PRACE calls for Tier-0 or Tier-1 HPC resources and through the SHAPE industrial collaboration initiative. However, commercial companies who carry out drug research and development, need to either have their own HPC facilities or purchase HPC services from commercial providers or from a publically funded HPC centres which fully recover costs (to avoid the use public funding to distort the market6).

6

Public funding cannot be used to fund any activity which may distort a competitive market. Within the context of free competition in the internal market, and specifically the opening up of public services to competition, Member States sometimes intervene through the use of public resources to promote certain economic activities or to protect national

(9)

9

Drugs which target the general population may not be suitable to effectively treat all members of the population, may be ineffective for some patients or may have a higher risk for side effects in specific patients. There is therefore a need to move towards “Personalized Medicine” and the development of drugs for use in personalized medicine.

HPC can support the development of personalized medicine concepts by providing access to and analysis of electronic medical records, personalized genomic data available for clinical use and physicians access to electronic decision support tools.

HPC support for drug research and development and in particular for personalized medicine can be categorized into two main areas:

1. Storage, integration and analysis of quality data sets from Big Data sources for:

• Supporting integrated research to identify biomarkers and therapeutic targets for personalized medicine;

• Improving patient prognostic classification;

• Enhancing diagnostics and population health risk assessment and management;

• Improving post-market drug surveillance;

• Enhance and verify data quality and

2. Modelling and Simulations for e.g.: HPC Virtual Screening; Chemical Informatics;

Membrane Permeation; Searching for Unexpected Targets; Support for Adverse Drug Reaction Prediction; Mapping Free Energy; Metabolic stability; Population pharmacokinetics and PK/PD Modelling, pattern recognition in health care records, identification of common mechanism across diseases, effect of combination of therapy, understanding natural history of diseases, etc...

The above require different kinds of HPC facility.

For big data analysis HPC will still be required to carry out rapid comparisons and analysis of data, but high speed access to well organized distributed data sets via a high bandwidth network are also requirements. The prerequisites for achieving this are given in section 2.3.2.

For molecular simulations large-scale compute resources (CPU/GPU) are required with a platform independent modular software architecture, but with software ported and optimized for specific hardware. Software license constraints can be a barrier to the use of HPC so ideally software licenses should either be free or available at low cost. During the discussions there was a concern that drug discovery was being limited to narrow areas of chemical space (and thereby limiting the possibility of new drug discovery) mainly because there were not enough resources available to conduct wider searches using HPC. In order to examine more chemical space to widen the search for drugs, very high power HPC systems are required.

The success of drug development requires collaboration between experts from several different fields including pre-clinical research, clinicians, regulatory experts, HPC software experts, HPC hardware experts, data experts and networking experts. Local, multi-disciplinary teams need to be formed and encouraged to work together effectively.

industries. By favouring certain firms over their competitors, this State aid is liable to distort competition. State aid is prohibited under the Treaty on the Functioning of the European Union. Nevertheless, some exceptions authorise aid justified by common interest objectives, i.e. for services of general economic interest, as long as they do not distort competition in such a way as to be against the public interest. The monitoring of State aid carried out by the European Commission therefore consists of striking a balance between the positive and negative effects of aid.

(10)

10

Current clinical trials are conducted in stages with large patient trials at the final stage. The process is very costly and time consuming. The conduct of pivotal clinical trials will remain essential for drug development. But modelling and simulation approaches have a high potential to provide valuable contributions to the early and late stages in drug development, e.g. population pharmacokinetic modelling in paediatrics.

It was reported that real HPC molecular simulations are not sufficiently integrated in the standard workflow used in drug discovery because the HPC infrastructure available as standard to industry is limited and there is a lack of validated HPC software accessible for industrial applications. It is clear that improvements could be made in these areas.

2.3.2 Current Needs in Health Research

There are many generic and specific needs to support effective health research. These can be categorised into several areas:

• Availability of data for health research;

• Data infrastructure and strategies for data integration and analysis;

• HPC infrastructure.

Availability of data for health research

In the health care profession there is much emphasis put on patient confidentiality and there are ethical issues about the revealing of data about individuals under clinical care to others unless they are directly involved with the clinical process.

Under EU law7, personal data can only be gathered legally under strict conditions, for a legitimate purpose. Furthermore, persons or organisations which collect and manage personal information must protect these data from misuse and must respect the rights of the data owners which are guaranteed by EU and national law. It is not allowed to process personal data for any purpose other than confirmed by given informed consent e.g. of a volunteer in a clinical study. Furthermore personal data cannot be transferred to third parties without permission being given by the individual.

Data protection legislation frames the use of personal data in health research. Clinical data sets frequently used in health research are often personal data. Clinical data sets are not only crucial for the analysis of health data (cohorts, data basis) they are also frequently the basis for modelling and simulation approaches. Clinicians attending the workshop expressed the view that in their practical experience patients under treatment have also an interest to make their data available for clinical research because of a potentially direct benefit. At the same time, specific safeguards have to be considered to prevent unnecessary and unauthorised processing of health data in compliance with ethical and legal requirements, notably those related to data protection and protection of patients’ rights on their health data.

Experts stressed that procedures ensuring data protection are crucial and have to take in account specific characteristics of HPC approaches e.g. data processing in the cloud.

A further aspect of data availability for health research can be limited access due to intellectual property concerns: experts mentioned during the workshop that academic research groups and infrastructures, but also e.g. pharmaceutical companies, sometimes consider their data as important intellectual property, not to be made available to competitors. This represents a barrier to making data, such as clinical trial data, freely available to health researchers. The 'European Medicines Agency

7

Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data

(11)

11

policy on publication of clinical data for medicinal products for human use' (policy 70)8 is aimed at facilitating access to clinical data generated for the purpose of registration of medicinal products. In the United Kingdom the NHS (National Health Service) intends making freely available health care data for research. These are unique opportunities for research groups and infrastructures active in the field of big data analysis applying HPC applications.

Experts stated that further development and open data initiatives are however needed e.g. also for the non-clinical data sets in health research. Open standards and data sharing policies have the potential to strengthen data-driven health research. Experts expressed their opinion that research would benefit when more data sets from publicly funded health research but also the results and data sets from global data analysis using HPC would be made publicly available. Experts also suggested that options for a more ‘Open Consent’ should be explored to improve efficient use of personal data in health research. Discussions could be facilitated through platforms like the Research Data Alliance.

Data infrastructure

The availability of large volumes of data for analysis in health research requires a number of technical and methodological improvements. The main challenges are:

• The management and storage of large volumes of data: “Big Data” needs to be better developed and implemented;

• Long term data curation and the secure preservation of data needs to be addressed and standardised;

• The processing, integration and analysis of heterogeneous data sets require innovative methodologies to ensure the interoperability of data across multiple platforms. This will require the development of standard ontology and metadata; there is a need for common algorithms in HPC data analysis;

• The increasing use of mobile health applications for smart phones or mobile patient monitor systems will generate data of high interest for health research; the required technical resources and methods to handle research in this field have to be developed;

• Tools are required for the processing of data including security, big data management, movement and data mining, quality assurance, etc.;

• Using data clouds is very beneficial as a mechanism for storing heterogeneous data sets. There are several issues that need to be addressed especially data protection and cloud security, cross-border movement of data within clouds, data flow across networks between clouds and interfaces needed between different types of clouds;

• However, in a number of cases the data free movement of data will not be possible due to data protection requirements. Recourses and technical solutions have to be implemented to facilitate HPC data analysis within individual institutions (e.g. hospitals) so processing could be brought to the data and not vice versa.

HPC infrastructure

The HPC infrastructure required to support health care research is not uniform across all types of applications. Even more important than the available HPC capacity is an appropriate configured HPC infrastructure to suit health research needs and in particular for supporting clinical practice. The discussions and presentations at the workshop revealed a broad variety of requirements for HPC in health research depending on the application. The main needs discussed and the resulting requirements for health research are given below:

• The services offered by PRACE are not well aligned with the requirements of potential applications in health research. PRACE is batch driven, whereas a number of potential applications in health research require timely processing. The mode of operation of PRACE

(12)

12

has to be adapted to these requirements or, alternatively, new specific HPC facilities should be implemented to support these types of applications.

• HPC provision needs to have the appropriate capability (to execute health codes) as well as the capacity focussed for health research.

• There is a need for data intensive HPC with high data I/O rates. This is not always available on HPC systems which are often designed for maximum compute power rather than maximum I/O rates. (However, it should be noted that PRACE has several initiatives researching into to improving I/O rates for data).

• Appropriate infrastructure has to be established for storage, data integration and analysis of medical records, personalised genomic data and further important data sets from clinical and public health research.

• High data storage capacity and fast links to clouds for long term storage are required. In some areas of modelling & simulation much more compute power is required and therefore Exascale computing will be needed.

• Generally, appropriate resources and compute power has to be available; lack of processing capacities should not be a bottle-neck for health research.

• The algorithms for health research applications need to be improved and code libraries of validated codes need to be made available.

• Technical support for developing and maintaining Health Research codes is required by using multidisciplinary teams.

• End users need to be involved in the development of appropriate infrastructures, taking advice from infrastructure experts. To support involvement of scientists from health researchers in both development and use of fitting HPC infrastructures and software training and collaboration with HPC software experts is required. Multidisciplinary teams to support HPC use for health research applications should be established on local level to meet these requirements.

• New forms of visualisation application are required so that clinical staff can understand data.

• HPC applications need to be adapted/simplified for use in clinical practice.

• Training and skill development for clinicians in the use of HPC clinical applications is required so they can use them a.o. for diagnosing

• Training and skill development for patient representatives is necessary to support the adherence and acceptance to HPC-based clinical applications and clinical decisions downstream.

The varied nature of health research will require specific HPC hardware and software resources, appropriate data access facilities, high-performance networking and the formation of multidisciplinary teams with all the necessary skills to support the development, reporting and scaling of HPC health research applications.

Imaging Data for Health

The use of High resolution medical imaging data has, in recent years, greatly advanced medical practice. Processing highly complex imaging data is probably a most advanced field for HPC applications.

High-resolution medical imaging produces large data files which for single patient are not generally a problem to deal with. However, this can pose a challenge to the current (local) infrastructures.

For example neuroimaging will have significant impact on clinical care and has a huge potential to contribute to:

• Deepen the understanding of the aetiopathogenesis of mental illnesses and other diseases.

• Objective diagnosis and prognosis of mental disorders compared to psychiatric conditions;

• Detection of severe psychiatric disorders at early or prodromal stages;

(13)

13

• Refinement of psychiatric nosology based on neurobiological signatures;

• Identification of novel targets for drug development;

There are requirements for:

• Better visualization tools;

• Combining advanced statistical learning tools and established descriptors of psychosis risk to extract multivariate clinical, neurocognitive and MRI-based prediction models of psychosis;

• Larger sample sizes to validate models and to develop robust scanner calibration methods;

• Significantly larger, multi-centre sample sizes covering primary, secondary and tertiary healthcare are needed in order to uncover the true clinical utility of biomarkers in psychiatry. There is a huge potential for developing a better understanding in many indications using techniques to compare and analyse multiple large databases of images to identify common factors and develop new diagnosis and treatment options.

These techniques need the development of large distributed image database access, sophisticated statistical learning tools and computer modelling and will require very high bandwidth networks to transport the data. The validation of the models to be used in clinical practice will also be required and for clinical diagnosis the results need to be available within 2 hours. This will require specially optimised HPC facilities at a local level or alternatively access to HPC facilities in real time over the network.

2.4 HPC Infrastructure Potential and Opportunities 2.4.1 Potential

There is considerable potential for the improvement of health research by strengthening HPC infrastructure. The results can contribute to better prevention strategies, faster and more effective clinical diagnosis and better treatments, based on innovative personalised medicine approaches. The application of HPC in large scale data mining and analysis can contribute to more cost effective drug discovery approaches and a faster development of medicinal products. More details are given in section Error! Reference source not found..

The cost of conducting clinical trials especially in the final stages of drug development is high. The use of HPC offers the potential for carrying modelling and simulation approaches that can contribute to shorten the time to market and reducing costs for developing new drugs.

Modelling and simulation approaches can also be used to predict drug toxicity and thus can save time in the pre-clinical development phase. Initiatives like the virtual physical rat project furthermore aim to reduce animal experimentation carried out in health research.

The development of new treatments can be assisted by computer-based physiological models of human organs or -as vision for the future- could even be integrated in a virtual physiological human model. The simulation of human organs combined with imaging is already introduced in clinical praxis. Examples are in coronary Fractional Flow Reserve, airway disease prediction outcomes, through patient specific computational modelling and in model based measuring of bone mass and bone mineral density in osteoporosis diagnostics.

2.4.2 Opportunities

There are several opportunities to improve health research using HPC which were discussed during the workshop.

(14)

14

There is an opportunity to focus on the development and use of ICT as a tool to achieve clinical impact. Lack of collaboration between health researchers, clinicians, HPC hardware and software experts, data experts and networking experts has been identified as a bottleneck for the broader use of ICT and HPC in health research.

There was evidence presented during the workshop indicating that algorithms and software codes used in health research do not always serve the needs in an optimal way. New techniques including algorithms and software codes that match the problem need to be developed in order to reduce run times. Just the addition of more computer power is, in many cases, not the solution.

Industry representatives indicated that HPC facilities were not generally accessible for private companies, or too costly. Therefore there is a need to explore solutions to improve the availability of HPC services for reasonable costs.

There is considerable opportunity for cost savings that can be realised in specific areas of clinical diagnosis using computer assistance. The computer assistance required is not always HPC but could employ artificial intelligence based systems.

Some simulations applications, such as the virtual physiological human require significant resources. Even if a single simulation run only takes a few hours, often several thousand runs are required to simulate a representative sample. However, the results of diagnostic applications in a clinical situation have to be available within one or two hours. Running some of these diagnosis applications still need weeks which limit their clinical value. An example cited was that running a full inhalation model would involve using greater than 200,000 core hours. There is a need to increase available HPC capacities to improve this situation. In particular multi-scale modelling requires Exascale HPC solutions and the availability of PRACE resources (Tier-0 & 1) as well as national HPC resources for large health research simulations and modelling.

The use of data from diverse and distributed sources is a major challenge but also an opportunity for health research. At the moment, data is fragmented and in different formats and poses a problem when integration and analysis is to be carried out. There is a need to:

• Integrate data across multiple data sets by developing better interfaces to address technical but also scientific bottle necks;

• Develop advanced and specific software applications for data analysis addressing health research requirements to allow harmonisation and integration of data elements;

• Develop better software for image analysis and comparison.

Data quality is a major issue. The following can be carried out to improve the situation, making data more reliable and increase the value of analysis results:

• Train basic scientists & clinicians to generate standardized high quality data;

• Develop data standards (ELIXIR and others) that clinicians can understand.

The movement of large volumes of data across data networks can be impractical if the bandwidth is not available. All researchers in the EU can use their country’s National Research and Education Network (NREN) and GÉANT, the pan-European network backbone, for the purposes of research. However, at present very few NRENs directly allow their networks to be used for clinical purposes and neither does GÉANT. There is therefore an opportunity to leverage the existing network infrastructures available for research and education in the EU to support health research. This could lead to considerable cost savings when transporting large volumes is required.

(15)

15 2.5 Future Challenges

The discussions at the workshop mainly focused on the current requirements for HPC and the necessary developments required in the (near) future. The main future challenges are:

• Ensure appropriate funding for HPC services tailored to the needs of health research, against a background of cost cutting;

• Strengthen existing good cooperation in European collaborative projects in health research;

• Strengthen experience of co-operation and sharing of information e.g. via ELIXIR, EUDAT;

• Simplifying access to HPC resources via PRACE Tier-0 and Tier-1 calls (but this may be not always appropriate for Health Research and needs to be improved);

• Supporting infrastructures and on-going development of infrastructures and

e-infrastructure strategy implementation in Europe - including members states; for example by strengthening initiatives like GÉANT and the NRENs (supporting high bandwidth networking) or the development of a European Cloud Strategy through DG CONNECT initiatives.

• Develop a coherent strategy for health research that takes into account required HPC for hardware and software development.

• Effective collaboration with industry and all relevant stakeholders has to be established.

• Data quality is a paramount. Systems have to be developed to improve quality of source data in data bases on a local level and strategies and networks for the validation of data sets have to be established to ensure data quality and thus quality of analysis results.

• Establish effective research institutes for health research and excellent European HPC centres

• Establish and maintain long-term multidisciplinary local teams to build up improved expertise and capacity in health research HPC applications.

2.6 Future Activities and Implementation

Infrastructure, projects and initiatives applying HPC in health research are currently fragmented. To achieve more implementation of HPC applications an ecosystem integrating and support HPC in health research is needed. A key future activity should be to establish a permanent forum for progressing adaptation of HPC strategies to health research requirements. The remit of this forum needs to be defined by consultation with basic scientists, health researchers, clinicians and all relevant stakeholders.

The forum should develop a roadmap for the future implementation of an efficient and suitable HPC ecosystem required to support health research. Initiatives should be established to support and sustain the implementation of this roadmap.

3.0 Conclusions, Recommendations and Next Steps

The workshop on High Performance Computing in Health Research successfully identified requirements and scoping problems required for HPC infrastructures in health research. ICT solutions adapted to the requirements of healthcare requirements have to be developed and implemented.

The experts' recommendations and suggested next steps based on the needs and requirements identified at the workshop are:

Technological / methodical aspects

• To exploit the full potential for HPC in Health Research there is a need to establish a forum, to develop a ‘common strategy for health research supported by ICT’ and a roadmap for the

(16)

16

use of HPC in health research. The strategy should include topics such as compute, data storage, technical requirements to ensure data protection, ensuring data quality by monitoring and validation, networking requirements, special data mining techniques, data integration methodologies, common data elements, artificial intelligence based systems for pattern recognition, strategies, methodology and common algorithms for data analysis, staff training, etc. The full remit of the forum should be defined by all the stakeholders in health research.

• The follow on from the development of a strategy and roadmap is that initiatives should be established to support the implementation of the strategy in-line with the agreed roadmap.

• Develop a standardised platform for EC projects implementing strategies and methodology agreed in the ‘common strategy for health research supported by ICT’.

• Explore the possibilities for specific access to PRACE for particular health research projects – not competing in a peer review system with all other applications from many disciplines. This is required because PRACE services are over subscribed.

• Explore the possibilities of developing a general HPC health research and healthcare platform so that HPC facilities are more accessible than the access currently available through PRACE.

• Explore joint actions at European level under H2020, e.g. aggregate European excellence from all the areas (pharmaceutical industry, academia, PRACE, hardware manufacturers, software, data and networking experts, clinicians, etc.) to implement, validate, deploy, host, maintain and support an integrated HPC computational health care research platform.

• Articulate the requirements for access to GÉANT and the NRENs for healthcare as well as health research and for accessing HPC facilities and clouds which contain health data. This is needed for high volume data transfers from clouds to HPC resources for health research and healthcare. (This is in-line with widening the role of GÉANT to become a true European Communications Commons.)

Networking and training

• Organise a network of EU health researchers who use HPC and engage with the HPC

community to exchange ideas and develop services together.

• Bid for HPC Centres of Excellence (CoE) in health research and healthcare.

• The health research community needs to engage with ETH4HPC and others in the healthcare and HPC/Data/Networking ecosystems.

• Stakeholders should reflect how to make more data publicly available from publicly supported health research projects, while securing the interests of patients.

• Support training of health researchers and clinicians both at EU and national level, as well as of patient representatives to secure awareness, understanding and better acceptance of HCP-based applications, hence of clinical decisions downstream.

• Devise a plan for local support to access national and European ICT facilities for use in health research with the aim to establish local, multi-disciplinary teams to ensure effective use of HPC in health research.

Regulatory requirements

• Analyse existing and potential regulatory requirements to be taken in account for the application of HPC in health research.

• Analyse potential options for a more ‘Open Consent’ to allow efficient use of health research data applying HPC strategies, in compliance with data protection provisions and the necessary safeguards of patient and patients’ health data.

• Establishment of framework for reuse of healthcare data with data harmonisation where possible, addressing Open Consent, Open Common Standards and Open Data sharing.

(17)

17

The suggested immediate next step would be to bring together a representative working group/forum of stakeholders from relevant disciplines in health research, HPC, data (and possibly networking) communities and all relevant stakeholders, including clinicians and patient organisations, to take forward the recommendations and start to address the requirement to develop a ‘common strategy for health research supported by ICT’ and a roadmap for implementation.

This report is prepared by the rapporteur, Dr. Robin G. Arak 17 December 2014

(18)

18 Annex: Final Programme/Agenda

Final Programme

High Performance Computing in Health Research Brussels, 1 - 2 October 2014

Venue: CDMA, rue du Champ de Mars 21, 1050 Brussels

High Performance Computing (HPC) is a crucial technology for addressing complex issues in health research. This workshop aims to identify current and future health research requirements for HPC and will explore mechanisms of addressing them through co-operation of relevant stakeholders. The workshop will bring together high level experts from health-related industries, health researchers, and representatives of HPC industries and supercomputing centres. Presentations and panel debates will explore key challenges facing health research, the role of HPC and future actions. Research and innovation opportunities like Centre of Excellence in HPC for health research will also be explored.

Day 1

13.30-14.00 Registration

14:00 – 14:30 Welcome address: HPC Strategy and Health Research

[Thierry van Der Pyl, Director "Excellence in Science" DG CONNECT, European Commission]

[Ruxandra Draghia-Akli, Director "Health", DG Research & Innovation, European Commission]

[Gisele Roesems-Kerremans, Deputy Head of Unit "ICT for Health and Wellbeing" DG CONNECT, European Commission]

14:30 – 14:45 HPC Centre of Excellence: An opportunity

[Aniyan Varghese, DG Connect, European Commission]

14:45 – 15:05 Can HPC help answering medically relevant questions?

[Jesper Tegner, Karolinska University Hospital]

(19)

19

[Andrea R. Beccari, Drug Discovery Platform Manager, Dompé]

15:25 – 15:45 HPC and in silico clinical trials: AVICENNA roadmap http://www.avicenna-isct.org

[Marco Viceconti- Avicenna ]

15:45 – 16:00 The role of PRACE for Health Research http://www.prace-ri.eu

[Sanzio Bassini- PRACE Chair]

16:00 – 16:15 Coffee break

16:15 – 16:30 The needs for HPC in life sciences and health research

[Steven Newhouse, European Bioinformatics Institute,Member of BioMedBridges]

16:30 – 17:30 Panel: Current needs for HPC in health research

Panel moderator: Alf Game (Chairman of the Board of ELIXIR)

Participants:

− Charles Auffray, (Director of the European Institute for Systems Biology & Medicine, Member of the Coordinating Action Systems Medicine CASyM) https://www.casym.eu)

− Bart Vannieuwenhuyse (Janssen Pharmaceutica NV,Innovative Medicines Initiative (IMI) project EMIF http://www.emif.eu)

− Felix Schurmann (Human Brain Project (HBP) https://www.humanbrainproject.eu/)

− Martin Hofmann-Apitius (Head of the Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing)

− Hans Hofstraat (Vice-President & Member of the Healthcare Program Board at Philips Research, the Netherlands)

− Lucia Monaco (Chief Scientific Officer, Fondazione Telethon, Italy)

(20)

20

Day 2

09:00 – 09:15 Making the most of imaging data: the example of brain imaging

[Nikolaos Koutsouleris, Ludwig-Maximilians-Universität Munich,

Coordinator of PRONIA]

09:15 – 10:15 Panel: Can existing infrastructures answer identified HPC needs for health research? Panel Moderator: Wouter Spek (EuroBioForum)

Participants:

− David Manset (CEO of GNUBLA; neuGRID eInfrastructure www.neugrid.eu)

− Adriano Henney (Director, Virtual Liver Network (VLN) http://www.virtual-liver.de)

− Erwin Laure (Director of the Centre of HPC, KTH, Stockholm)

− Modesto Orozco (Group Leader, Molecular Modelling and BioInformatics)

− Peter Coveney (EUDAT (www.eudat.eu))

− Wiro Niessen (Euro-BioImaging (http://www.eurobioimaging.eu))

10:15 – 10:30 Keynote speech on future challenges

[Magda Chlebus (Director of Science Policy, European Federation of Pharmaceutical Industries and Associations)]

10:30 – 10:45 Coffee Break

10:45 – 11:45 Panel: Future activities and implementation

Panel co-moderators: Gitte Moos Knudsen (Chairman of the Neurobiology Research Unit, Director of the Centre for Integrated Molecular Brain Imaging)

Ian Cree (Yvonne Carter Professor of Pathology, Warwick Medical School)

Participants:

− Bernadette Andrietti (European Technology Platform for High

Performance Computing (ETP4HPC) www.etp4hpc.eu)

− Peter Longreen (Elixir: European Bioinformatics infrastructure http://www.elixir-europe.org)

(21)

21

− Yann le Cam (Chief Executive Officer of the European Organisation for Rare Diseases, Eurordis)

− Jan Bogaerts (Vice Director of the European Organisation for Research and Treatment of Cancer)

− Gunaretnam Rajagopal, (VP, Global Head of Computational Sciences, Discovery Sciences at Janssen)

− Yi-Ke Guo (Professor of Computing Science, Imperial College London; Innovative Medicines Initiative project eTRIKS)

11:45 – 12:30 Synthesis and next steps

[EC representative and Rapporteur]

Presentations and list of participants are available at: http://ec.europa.eu/digital-agenda/en/news/workshop-high-performance-computing-hpc-health-research

High Performance Computing

Workshop Report