Gerd Binnig, PhD

Pharmaceutical companies and clinical oncology teams are collaborating to harness large sets of patient data, paving the way for more informed and individualized decision-making about treatments.

Large or complex sets of information, known as big data, are being leveraged in nearly every industry worldwide, enabling companies and organizations to gain deeper insights and make better decisions than ever before. Medicine has unfortunately been slower to effectively incorporate big data than most other industries, due to various issues in standardization, digitization, and technology adoption. The field of oncology, in particular, has struggled in this regard; as of yet, there are not enough patient data available in digital form to make an impact on the spectrum of medicine from drug discovery and development to the clinical routine.

This is all about to change, however. New technologies are emerging that can collect, correlate, and structure data in a meaningful way. In the near future we will see a noticeable increase in the use of big data in oncology. This data revolution is set to have a significant effect on the role of specialty oncology pharmacists (SOPs), both in the pharmacy setting and in their work in clinical trials and on clinical teams.

Taking a big data approach in drug research—specifically, by combining and correlating quantified tissue image data with genomic information and clinical outcomes&mdash;will redefine the drug development process. This approach will ultimately support personalized medicine, and it has several implications for the roles and responsibilities of SOPs across the spectrum.

It has been forecast that there will be an increased demand for oncology pharmacists to serve as principal investigators or co-investigators in clinical trials, particularly for trials involving patients whose site of care is at the home or the oncology office. For SOPs acting in this capacity, a big data approach is critical and will eventually become part of the standard process.

Clinical trials will soon be conducted very differently than they were in the past. The industry is realizing the need for close, interdisciplinary collaboration, which creates a culture where seemingly unrelated experts talk to one another and share information they have gathered. For example, a statistics professional has a very different mindset and role than an oncologist or pharmacist, but these experts will come together and work as a team in order to obtain all the information needed to come to the right conclusions about drug development and patient treatments.

With an interdisciplinary approach, much more data can be gathered and analyzed. The clinical trial also becomes an opportunity to learn something new and to connect those findings with all of the currently existing knowledge possessed by researchers, pharmacists, and clinicians. Pharmaceutical manufacturers will rely on all the experts in the chain of oncology research and patient treatment to support drug trials in an effort to obtain stronger results and operate a more effective drug development process.

SOPs are increasingly expected to collect, analyze, and share various kinds of patient data with audiences that include payers, clinicians, and pharmaceutical manufacturers. True personalized medicine needs to be holistic and include data from every stage, which means that SOPs must be able to provide structured feedback on the outcome of drugs, drug combinations, and specialized treatment plans in the patients they are serving. This is especially important with the use of combination therapies, which are poised to play a significant role in cancer treatment, since the complexities of dosing, adverse effects (AEs), and outcomes are even greater when combining multiple drugs. With this kind of outcomes data from SOPs, researchers and specialty pharmaceutical manufacturers can take a more informed approach to the development of diagnostics and targeted drugs, and clinicians can improve treatment plans for individual patients.

This feedback loop is already beginning to take shape, but it is not yet standardized. As big data technologies become available and are adopted, feedback loops between SOPs and the rest of the oncology spectrum will become mainstream.

Another emerging role for the SOP is in the field of pharmacogenomics, where pharmacists are intimately involved in studies of how variations in the human genome affect the response to oncology medications, and how laboratory tests can be used to determine which patients will have the best chance of benefiting and which patients will not realize any clinical improvement from a certain drug.

A big data approach can help SOPs working in pharmacogenomics make better sense of their data, compare and correlate the data with other data banks, and ultimately support better decision making. This is already happening: huge data banks have been created to collect genetic information and correlate it with clinical outcomes of patients. Through this process, an immense body of information is gained about how individual patients will respond to various treatments. With this knowledge, clinicians and clinical oncology teams can test patients and know ahead of time whether they will respond to a certain treatment. Pharmaceutical companies are using this data to create better diagnostics and therapies. In the future, however, genetic data will increasingly become connected to other crucial types of data, such as those derived from medical images.

Applying a big data approach enables the industry to gain knowledge otherwise unavailable and apply it in the clinical routine so patients get the right treatment. This is the revolution taking place right now. For this to be effective, there needs to be a greater volume of data available, meaning additional cancer centers and universities need to join the movement, more big data technologies must be adopted, and more SOPs need to be involved in pharmacogenomics. This will likely happen over time. There has been an enormous increase in the number of experts working in this field in the past several years due to excitement over what big data and pharmacogenomics can do for the future of cancer care, and interest will only continue to grow.

Beyond research, a big data approach on the clinical side can close the loop on drug development and patient treatment, facilitate a more holistic system, and support more personalized therapeutic regimens.

For SOPs working on clinical oncology teams to support the development of specialized treatment plans, having access to more targeted therapies is extremely important. There are many more treatment options available now than ever before, and with so many to choose from, it can be difficult to determine what to prescribe. Clinical oncology teams need more information in order to make more informed decisions, and big data is necessary to accomplish this. The ability to correlate a patient’s response data with that of other similar patients across the country or globe can support clinical oncology teams developing more personalized and innovative treatment plans.

Big data in principal is a new technology for science and for clinical trials. By collecting and comparing data on drug response, toxicity, and AEs, a body of knowledge is created through which more patient-specific and innovative care plans can ultimately be developed. SOPs on clinical oncology teams have the ability to contribute this key drug information, but doing so requires the use of big data technologies and a consistent and standardized approach industrywide.

For SOPs in the pharmacy setting, having more information about the way drugs interact, their AEs, and how certain patients respond can enable them to be more effective pharmacists and help them provide a better treatment experience, even if they are not on a clinical oncology team. With structured patient response data, SOPs can better advise, monitor, and support patients; communicate with clinicians; and, when appropriate, recommend alternative drug options for patients to ask their oncologists about. A big data approach will enable them to collect, store, and use this kind of information in their daily practice.

Steps to Achieving a True Big Data Approach 

Currently, big data are applied in a siloed way. For example, in genomics, much of the data are segmented, meaning genomic data are mainly compared with other genomic data versus other kinds of important, related data. Moving forward, the industry needs to take a more holistic approach and incorporate big data every step of the way. Rather than looking at genomics in isolation, for example, all kinds of data—including radiology data, images from tissue samples, blood values, findings in clinical journals, early stage research, patient results, clinician findings, SOP feedback, and more&mdash;need to be catalogued in 1 system where clinicians and SOPs can review it in order to support patients more effectively.

In order to accomplish this, experts at every stage will need a standardized way to collect data and enter it into data mining tools where it can be connected and correlated. This holistic, big data approach has a much higher value than data that is concentrated in just 1 area. This is the industry’s mission.

The big data approach is truly a revolution: it is coming, and it will change the industry for the better. Similar to the invention of the telephone, the printing press, the television, and the Internet, big data will transform our capabilities to communicate and exchange knowledge. Beyond that, it will allow knowledge to be created automatically. As more medical universities, cancer centers, and pharmaceutical manufacturers begin to develop data banks and apply a big data approach, and as we have access to better tools that enable structured data collection, mining, and delivery, this approach will filter down naturally to the SOP and become ingrained in practice. 

 is founder, chief technology officer, and member of the executive board of Definiens. Dr. Binnig was awarded the Nobel Prize in physics for his work in scanning tunneling microscopy. He has more than 30 years of experience in the physics community and developed Definiens’ patented Cognition Network Technology.

Gerd Binnig, PhD

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