Covering Disruptive Technology Powering Business in The Digital Age

Home > Archives > Blog > Right Patient, Right Drug, Right Time: How Can Big Data Cure the Incurable?
image
Right Patient, Right Drug, Right Time: How Can Big Data Cure the Incurable?
image
February 12, 2016 Blog big data

This article was originally published by www.mddionline.com and can be viewed in full here

This past fall, at the esteemed Milken Institute’s London Summit, I had the privilege of joining several colleagues on stage for a spirited discussion about the power of big data to transform healthcare. It was a wide-ranging conversation about the enormous potential for data to rewrite the narrative in biomedical research—from revolutionizing how we develop and administer drugs to recasting the roles of healthcare provider and patient.

Like all compelling conversations, it inspired additional dialogue in the subsequent days—with one recurring realization: The future of big data is already visible in several corners of the healthcare field.

Each time we pick a program on Netflix or scroll through our Facebook feeds, we witness high-powered data processing at work. But no field stands to offer more positive impact to society through this technological sea change than healthcare, where big data promises to detect diseases earlier, develop appropriate medicines faster, and identify personalized options more effectively.

To be sure, we’ve only scratched the surface of this reality. But in research labs and healthcare facilities around the world, data-driven efforts that would marvel many of Silicon Valley’s best minds are already well underway.

Revolutionizing R&D to Customize Treatment

In research and development, big data is playing a critical role in unlocking key insights that are revolutionizing drug discovery. By linking real-world clinical, genomic, and lifestyle data, researchers are identifying new disease pathways and improving the drug discovery process.  For example, through a partnership with genetic testing startup 23andme, Pfizer researchers have been able to study the genetic profiles of patients with lupus to determine which ones are most likely to experience severe flares, as well as those most likely to respond to our treatment. Armed with that information, we are now able to explore customized treatment options that vary with genetic, dietary, and lifestyle factors.

Pfizer’s Precision Medicine Analytics Ecosystem, which is designed to connect the dots between several large, disparate datasets, also enables us to create targeted drugs based on what we know about patients. In lung cancer, for example, medical research revealed that about 5% of patients weren’t engaged in high-risk lifestyles, like heavy smoking, coal mining, or other activities that abused their lungs. Their common thread was a mutation in their ALK gene.  Supported by predictive analytics, Pfizer succeeded in developing a drug approved in 2011 specifically for lung cancer patients with the ALK gene mutation.

 Improving Clinical Trials with Sensor Data and Patient Pinpointing

Another area ripe for big data-fueled innovation is clinical trials. Pfizer has worked to integrate sensor data into our clinical trials to ensure medication compliance, predict events, evaluate new clinical endpoints, and demonstrate how treatment impacts a patient’s overall quality of life. We have completed at least 10 studies with wearable devices during the past five years, and, going forward, many of our studies across a range of indications will include companion technology, such as wearables, to generate consistent streams of data from participating patients.

Big data can also help determine which patients may benefit most from a drug and therefore should be included in clinical trials.  And it enables us to target very specific patient profiles. For example, subsequent to such an analysis, we could elect to focus a study only on female breast cancer patients over age 50, of Asian ancestry, who have tested positive for the BRCA1 gene mutation, and have failed first-round chemotherapy, and develop a more meaningful treatment for this specific population. Without big data, that kind of granular pinpointing just wouldn’t be possible.

 Building Coalitions to Overcome Challenges

Still, despite the incredible progress, we, as a sector, have much to overcome. To start, healthcare organizations must improve mechanisms for protecting and securing patient data. Deidentifying and anonymizing patient data is one necessary step. And, of course, more must be done to prevent data breaches in the healthcare field (2015 was dubbed the “year of the healthcare hack,” unfortunately for good reason).  Regulators should do more to facilitate the use of these data by public or private organizations (provided the anonymity and security of data are well guarded). This will enhance our ability to produce medical solutions and better serve public health interest.

Additionally, although FDA and other regulatory bodies around the world are moving swiftly to accommodate big data, private and public sector organizations must work closer together as we transition to a more data-driven paradigm. Increasing access to clinical trial data and results holds a great deal of promise, but simply opening the floodgates is not the answer. Regulators will also need to address issues with pharmacovigilance reporting that will emerge, as a result of mining and correlating massive databases.

We must work as a healthcare community to define and build robust and sustainable ways to share data and ensure interoperability. To that end, we are working with a group of multinational government, patient, and industry stakeholders to identify practical strategies for advancing clinical trial transparency in fair and respectful ways.  Together with academic institutions, nonprofits, healthcare companies, and others in the global research community, we’re establishing data-sharing models that will ensure meaningful and scalable progress.

Now, more than ever before, we have the power to significantly improve the odds that we’ll die from old age—and in good health. We just need to bring the data-driven future we’re building to the mainstream.

(0)(0)