Interview with Jonathan Chainey

What pre-competitive projects today, between biopharma groups, are driving efforts to standardize clinical trial data and facilitate open innovation?

There are a few pre-competitive projects that come to mind. These are the CDISC, Transcelerate Biopharma and PhUSE. CDISC is a global, non-profit charitable organization that develops data standards to streamline clinical research and enable connections to healthcare. It has been a key Standards Development Organization (SDO) in the Pharmaceutical Industry for over 20 years and provides an evolving set of standards to support the consistent definition, collection, tabulation, analysis, and exchange of clinical trial data, ultimately across all disease areas in which clinical research is being conducted. 

Since Q4 2016 the so called 'submission data standards models' provided by CDISC (the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM)) became mandated for regulatory submissions by the FDA in the U.S. and by the PMDA in Japan. i.e. all clinical trial data submitted by trial sponsor organizations as part of an electronic submission has to adhere to these standards.

TransCelerate BioPharma Inc. is a non-profit organization with a mission to collaborate across the biopharmaceutical research and development community to identify, prioritize, design and facilitate the implementation of solutions to drive efficient, effective and high-quality delivery of new medicines, improving the health of people around the world.

Since its launch in 2012 it prioritized clinical data standards as an area it would invest, partnering very closely with CDISC to advance the creation of CDISC SHARE - an electronic library to help CDISC manage the lifecycle of CDISC standards and deploy them to industry in machine-readable formats thereby accelerating their seamless adoption. Transcelerate Biopharma invested pharmaceutical industry financial and human resources provided by its sponsor companies, into the extension of CDISC standards across multiple disease areas. Disease areas upon which to prioritize are agreed upon by the Coalition For Accelerating Standards & Therapies (CFAST) - a collaborative effort between CDISC and the Critical Path Institute that prioritizes based on input from the FDA, NCI, and Transcelerate.

PhUSE is an independent, not-for-profit organisation run by volunteers. It is a society with a membership exceeding 8,000, across more than 30 countries worldwide and has become the industry voice to regulatory agencies and standards organisations such as the FDA, EMA & CDISC.

PhUSE runs an annual 'PhUSE - FDA Computational Science Symposium' which brings together FDA representatives and pharmaceutical industry experts tasked with driving the adoption of CDISC standards into their own organizations. The PhUSE community has become an important voice in the industry in this space, and it continues to focus on delivering tangible solutions that support CDISC adoption e.g. it developed a "reviewer's guide" which has become a key document in the exchange of clinical data from a trial sponsor to the FDA.

Are precision medicine strategies, and initiatives to support the exchange of clinical research and healthcare data, overhauling the entire biopharma business model?

In my opinion, yes. At Roche, and like most other biopharma companies, we have invested heavily in the acquisition and analysis of real world data over the past few years. Moreover and like many others, we have established strategic partnerships with companies such as Flatiron and Foundation Medicine, in order to accelerate the gaining of insights from oncology real world evidence and genomic data respectively.

More recently, and again like most other biopharma companies, we are investing heavily in order to maximize the opportunities being presented by the digitization of healthcare. This includes significant investment in putting business capabilities in place that allow us to share and integrate data from both internal and external data sources, and from various domains including early and late stage clinical trials, diagnostics, genomics, proteomics, and the real world healthcare space.

By giving research teams the ability to transform real-world data sources into evidence, how will this drive more efficiency in drug development?

There are a number of ways in which RWD is already doing this, and I believe the efficiency gains will only increase. When we accept that once a product has been on the market for 1+ years, in essence only 5% of the data available about how safe a product truly is and how well it works in a patient population was data from the clinical trials that led to its regulatory approval, we can start to understand the terrific potential in real world data for driving effective decision-making in drug development.

For example by taking the clinical outcomes from clinical trial data and real world evidence, identifying patients that responded really well to a particular treatment, and integrating these insights with the patients genomic and proteomic data, we can start to identify the genes and molecular pathways that might be underpinning this high response rate. These insights can then be fed back into the early research and development arena via so called 'reverse translation' which helps us to focus precious and limited resources, and increase the so called 'probability of technical success' of the products we invest in.

What are the obstacles in moving patient data from ‘human readable’ to ‘machine processable’? How are they being overcome? How are those efforts facilitating new data analytics tools such as artificial intelligence, to achieve precision medicine at scale?

As we continue to see in the clinical trial arena, which you would expect is far more highly controlled than the real world healthcare setting, moving from 'implicit' human readable data to 'explicit' machine readable data forces us to ensure that along with any data we wish to gain insights from, the accuracy and reliability in these insights is underpinned by having a precise and accurate definition and meaning of data, captured in its metadata.

And here in a nutshell, is the primary obstacle that we have to overcome, across all the domains of data upon which we wish to integrate and gain new insights.

This is where data governance and standardization efforts on an industrial scale are so important, including CDISC for clinical trial data and OMOP for real world data. While I'm not qualified to comment on OMOP, for CDISC the challenge is 'simply' gaining approval across its 450+ member companies for any standard it defines, while remaining 'plugged in' to scientific breakthroughs in clinical research across all disease areas, and and ensuring the standards remain relevant and up to date.

Artificial intelligence can definitely help accelerate the translation of data into insights in a number of ways. For example and in my specific area, given the variability in the format in which we receive data across the domains mentioned above, data transformation tools with a machine learning capability are already accelerating this process. That said, I tend to take the view that a machine is only as good as what you teach it, and I would certainly not underestimate the critical role that qualified staff will continue to play in this space, it's just that tools with a machine-learning capability can definitely allow them to cover more ground, and far more efficiently.

What pathways are being establishing for international harmonization on data sharing?

While there are colleagues at Roche better qualified to answer this, I can cite a couple of examples I'm aware of. For example the establishing of ClinicalStudyDataRequest.com was a step in the right direction to allow researchers to request and subsequently gain access to clinical trial data from the sponsor companies that are participating in this. 

Building trust with the public at large is so crucial, and educating the world at large on how we can maintain patient privacy, while openly sharing their data in order to advance biomedical research is going to be central to any successful strategies in this area.

Who are you hoping to meet at the Big Data in Precision Medicine Summit in the fall in Washington DC?

I'm looking forward to meeting and listening to experts working in the precision medicine arena, perhaps looking at the same challenge from many different angles and perspectives. This will help tremendously in my own personal understanding as I embark on a highly visible precision medicine initiative at Roche.

I'm particularly looking forward to meeting David Bobbitt, the new CDISC CEO in person for the first time.

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