{"id":30999,"date":"2018-11-13T00:57:17","date_gmt":"2018-11-13T05:57:17","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/flatirons-machine-learning-solutions-benefit-cancer-patients-and-big-pharma\/"},"modified":"2018-11-13T00:58:04","modified_gmt":"2018-11-13T05:58:04","slug":"flatirons-machine-learning-solution-benefits-cancer-patients-and-big-pharma","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/flatirons-machine-learning-solution-benefits-cancer-patients-and-big-pharma\/","title":{"rendered":"Flatiron\u2019s Machine Learning Solution Benefits Cancer Patients and Big Pharma"},"content":{"rendered":"<p>Clinical trials can be a bottleneck in the drug development process, particularly for anti-cancer therapies. Not only are clinical trials extremely expensive, but enrollment for cancer patients can be slow and difficult. The average cost of drug development was estimated to be more than $2.5 billion in 2015, with 60% of the costs comprised of clinical trials.<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a> Flatiron Health vows to speed up the drug development by reducing the amount of extensive clinical trials currently required by the FDA (Figure 1).<\/p>\n<p>By standardizing electronic health records (EHR) and using proprietary machine learning technology, Flatiron is helping to pave the way for data-driven precision medicine. The value of Flatiron\u2019s EHR platform and its machine learning technology was attractive enough to be acquired by Roche Group in February 2018 for $1.9 billion.<a href=\"#_ftn2\">[2]<\/a><\/p>\n<p><figure id=\"attachment_30932\" aria-describedby=\"caption-attachment-30932\" style=\"width: 942px\" class=\"wp-caption aligncenter\"><a href=\"#_ftn2\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-30932\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-1.png\" alt=\"\" width=\"942\" height=\"625\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-1-300x199.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-1-768x510.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-1-600x398.png 600w\" sizes=\"auto, (max-width: 942px) 100vw, 942px\" \/><\/a><figcaption id=\"caption-attachment-30932\" class=\"wp-caption-text\"><span style=\"font-size: 16px\">Figure 1: The drug development process hinges a several phases of clinical trials that are both costly and difficult to run.<\/span><a style=\"font-size: 16px\" href=\"#_ftn3\" name=\"_ftnref3\"><strong>[3]<\/strong><\/a><\/figcaption><\/figure>The short-term outlook from Flatiron focuses on EHRs and their ability to continue to collect both structured and unstructured data and make it useful. Their software platform has been in use by some of the biggest players in the pharmaceutical industry. During this time Flatiron\u2019s database and algorithms can expand, with some saying that \u201cFlatiron now has data from 20% of active cancer patients in the U.S., and it\u2019s extremely well structured\u201d.<a href=\"#_ftn4\" name=\"_ftnref4\">[4]<\/a> Flatiron also has plans to continue to partner with additional pharmaceutical companies outside of Roche. Flatiron looks to gain more real-world evidence by increasing both its structured health record data and the unstructured data that has been contextualized using Flatiron software. This would give Flatiron the benefit of a larger real-world sample set and more opportunities to test their real-world clinical endpoints. Previous collaborations included work on real-world endpoints with the pharma giant, Pfizer, (Figure 2). In May 2018 Flatiron announced a three-year collaboration with another large pharmaceutical company, Bristol-Myers Squibb.<a href=\"#_ftn5\" name=\"_ftnref5\">[5]<\/a><\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-2-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-30933\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-2-1.png\" alt=\"\" width=\"433\" height=\"503\" \/><\/a><\/p>\n<p style=\"text-align: center\">Figure 2: Flatiron uses real world endpoints to successfully predict results in Pfizer\u2019s PALOMA-2 clinical trial for metastatic breast cancer.<a href=\"#_ftn6\" name=\"_ftnref6\"><strong>[6]<\/strong><\/a><\/p>\n<p>Flatiron\u2019s future over the next few years looks bright. Flatiron\u2019s patent pending abstraction process uses machine learning to improve and develop their data model.<a href=\"#_ftn7\" name=\"_ftnref7\">[7]<\/a> This feedback loop allows their model to learn from new data coming in from their database and continue to learn, improve, and optimize (Figure 3). With the additional influx of capital from the $1.9 billion Roche deal, I would expect to see Flatiron make significant investments its scalability.<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-3-2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-30943\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-3-2.png\" alt=\"\" width=\"670\" height=\"449\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-3-2.png 670w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-3-2-300x201.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-3-2-600x402.png 600w\" sizes=\"auto, (max-width: 670px) 100vw, 670px\" \/><\/a><\/p>\n<p style=\"text-align: center\">Figure 3: Data models are updated and learn from new data being extracted into the database. [7]<\/p>\n<p><strong>What can Flatiron Do to Maintain its Momentum?<\/strong><\/p>\n<p>Flatiron needs to continue to innovate and scale its reach over the next few years. To utilize machine learning accurately, the quality of the patient data set will be paramount. Expanding their data globally to increase variety and remove biases from the population sample set could make their data more reliable and widely applicable.<\/p>\n<p>Flatiron will also need to reach out to regulatory agencies, such as the FDA, to engage regulators on how drug development and clinical trials may change with the introduction of machine learning over the next 5-10 years.<\/p>\n<p>Flatiron will also need to keep an eye on their competition. The market for AI in health care is booming (Figure 4). To stay competitive, Flatiron should make significant investments in its technology and software platform. They should aim to increase the number of active cancer patients that are in this database over the next few years. Network effects will benefit the technology that Flatiron has to offer. As more patients, doctors, and pharmaceutical companies input data into the system, there will be increased benefits for all stakeholders. In this market, a significant increase in competition, especially in electronic health records, may hinder advancements due to a lack of standards for inputting medical data.<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-30935\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4-1024x768.png\" alt=\"\" width=\"640\" height=\"480\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4-1024x768.png 1024w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4-300x225.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4-768x576.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4-600x450.png 600w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Figure-4.png 1142w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><\/p>\n<p style=\"text-align: center\">Figure 4: Healthcare funding for AI technologies has been increasing rapidly since 2013.<a href=\"#_ftn8\" name=\"_ftnref8\"><strong>[8]<\/strong><\/a><\/p>\n<p><strong>Looking Forward<\/strong><\/p>\n<p>As the health care community eagerly waits for machine learning to be further integrated into general practice, there is, at this point, no approved AI-developed drugs.<a href=\"#_ftn9\" name=\"_ftnref9\">[9]<\/a> Whether or not, the implications of machine learning on drug development is all hype or will have a real impact on the future of pharma is still to be decided. There is no question that big pharmaceutical companies have found these technologies attractive, but will these investments pay off?<\/p>\n<p>(Word Count: 730)<\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> John Lechleiter, \u201cPharmaceutical Companies Sponsor Clinical Trials, But Don\u2019t Directly Conduct Them,\u201d <em>Forbes<\/em>, July 15, 2015, https:\/\/tinyurl.com\/y7zqlj6q , accessed November 2018.<\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a> Reenita Das, \u201cThe Flatiron Health Acquisition is a Shot in the Arm for Roche\u2019s Oncology Real World Evidence Needs, \u201d <em>Forbes<\/em>, February 26, 2018, <a href=\"https:\/\/tinyurl.com\/ya45eamq\">https:\/\/tinyurl.com\/ya45eamq<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> \u201cThe Future of Clinical Trials: How AI &amp; Big Tech Could Make Drug Development Cheaper, Faster, &amp; More Effective,\u201d CB Insights, August 7, 2018, <a href=\"https:\/\/tinyurl.com\/y94arzh6\">https:\/\/tinyurl.com\/y94arzh6<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref4\" name=\"_ftn4\">[4]<\/a> Erika Fry, \u201cTech\u2019s Next Big Wave: Big Data Meets Biology,\u201d Fortune, March 19, 2018, <a href=\"https:\/\/tinyurl.com\/yauzffzh\">https:\/\/tinyurl.com\/yauzffzh<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref5\" name=\"_ftn5\">[5]<\/a> Matthew Herper, \u201cFlatiron Health, Purchased by Roche, Signs Three-Year Deal with Bristol Myers\u201d, <em>Forbes<\/em>, May 2, 2018, <a href=\"https:\/\/tinyurl.com\/yc7ya36k\">https:\/\/tinyurl.com\/yc7ya36k<\/a> accessed November 2018.<\/p>\n<p><a href=\"#_ftnref6\" name=\"_ftn6\">[6]<\/a> Flatiron Health, \u201cLife Sciences: Accelerate research with the most advanced real-world evidence platform in oncology,\u201d <a href=\"https:\/\/flatiron.com\/real-world-evidence\/\">https:\/\/flatiron.com\/real-world-evidence\/<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref7\" name=\"_ftn7\">[7]<\/a> Gil Shklarski, \u201cExtracting Facts from Unstructured Data,\u201d US Patent Application: US 20170039341 A1. <a href=\"https:\/\/tinyurl.com\/y7ye9mas\">https:\/\/tinyurl.com\/y7ye9mas<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref8\" name=\"_ftn8\">[8]<\/a> \u201cThe AI Industry Series: Top Healthcare AI Trends to Watch\u201d CB Insights, <a href=\"https:\/\/tinyurl.com\/y82nxafy\">https:\/\/tinyurl.com\/y82nxafy<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref9\" name=\"_ftn9\">[9]<\/a> Nic Fleming, \u201cHow artificial intelligence is changing drug discovery,\u201d <em>Nature<\/em>, May 30, 2018, <a href=\"https:\/\/www.nature.com\/articles\/d41586-018-05267-x\">https:\/\/www.nature.com\/articles\/d41586-018-05267-x<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Big Pharma Digs Deeps into their Wallets to Gain Access to Flatiron Health\u2019s Machine Learning Technology, but will it be worth the price tag?<\/p>\n","protected":false},"author":11342,"featured_media":31015,"comment_status":"open","ping_status":"closed","template":"","categories":[4055,346],"class_list":["post-30999","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-digital-healthcare","category-machine-learning","hck-taxonomy-organization-flatiron-health","hck-taxonomy-industry-health","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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