Today, if you are diagnosed with high cholesterol, your doctor’s decision about which of seven FDA-approved statin drugs to prescribe is largely a trial-and-error proposition. Unfortunately, many patients never ask to try a second or third drug, unaware that a different medication might better match their unique body chemistry, delivering better results with fewer side effects.
This scenario is the opposite of “precision medicine.” It’s the kind of impersonal, scatter-shot medicine doctors have been forced to practice – and patients to accept – for too long.
However, that blood test you took last week could tell your doctor which of those seven drugs is best for you – if your doctor had the tools to analyze it. On a global scale, the same information that could give your doctor those insights can also be used to find new, previously unrecognized applications for existing drugs. It could guide researchers to the chemical or biological compounds – and the millions of potential combinations – that will cure a chronic disease, or even be used to craft truly personalized medicine, including custom genetic treatments to boost a patient’s unique immune system so that it can defeat that patient’s unique cancer.
“As a result of breakthroughs in data analysis, we stand on the verge of a life sciences industry powered not by trial and error, but by a virtuous cycle of experimental evidence [that] brings new insights, new applications, new refinements and new successes.”
Glen de Vries
What is this miracle tool? Data, and the power to analyze, interpret and act on what data can tell us.
At Medidata, where we help life science companies conduct digitalized clinical trials, we have amassed one of the world’s largest secure, regulatory-grade repositories of anonymized clinical data, representing more than 6 million patients and counting. For the most part, those data have been used to answer a small list of very specific questions about one specific treatment, especially: Did it work and was it safe for the 100-500 people, on average, who tried it in a clinical trial?
Attached to those data, however, are numerous deep insights about the people who participated, including information such as their specific blood chemistry and their genetic makeup. At Medidata, we have the data-analysis capabilities to learn significantly more about groups of patients with specific traits, just by pooling, anonymizing and analyzing the data in different ways using powerful AI-driven algorithms.
What if we look at not just the 100 or 500 patients who participated in an experimental trial, but the 1,000 or 10,000 in our database who have the same physiological characteristics as those who were helped by the tested treatment? And what if we also look at data from all the tests conducted on all of the people who recently had physicals? What more can we learn? How many more can we help?
6 million
The number of patient records held in Medidata’s secure, regulatory-grade repositories of anonymized clinical data, the world’s largest such stores.
This vast store of treatment-generated data, which we recently demonstrated can be managed to accurately parallel the clinical trial data we have already collected, exponentially expands what we call “the digital fabric” – the ability to “weave together” existing data in new ways to gain new insights, identify new applications for existing treatments and accelerate the discovery of new treatments.
As a result of these breakthroughs in data analysis, we stand on the verge of a life sciences industry powered not by trial and error, but by a virtuous cycle of experimental evidence that begins in the laboratory, moves into clinical trial settings and expands through analysis of the broad population. Each cycle will bring new insights, new applications, new refinements and new successes.
Together with Dassault Systèmes and the power of the 3DEXPERIENCE platform to connect the dots among practitioners, life sciences professionals and patients, we can achieve these breakthrough insights – and deliver on them – faster than ever before. The same platform used to evaluate drugs during the research cycle will also speed treatment to the marketplace and bring valuable real-world insights back to the lab for use in the next virtual cycle. And, when a doctor identifies a patient who can be helped by a customized treatment, the data required to create that customized treatment is already in the platform, speeding its formulation and delivery back to the patient.
Trial and error may not entirely disappear. But we will do significantly less of it, benefiting individual patients and society as a whole. The era of personalized, precision medicine is here, and we at Medidata and Dassault Systèmes are excited by our role in accelerating this very positive disruption.
Editor’s Note: Glen de Vries’ book, The Patient Equation: The Precision Medicine Revolution in the Age of COVID-19 and Beyond, will be published by Wiley Publishers in September 2020.
