{"id":36083,"date":"2018-11-13T19:41:50","date_gmt":"2018-11-14T00:41:50","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/roche-machine-learning-brings-a-big-pharma-business-model-under-siege\/"},"modified":"2018-11-13T19:41:50","modified_gmt":"2018-11-14T00:41:50","slug":"roche-machine-learning-brings-a-big-pharma-business-model-under-siege","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/roche-machine-learning-brings-a-big-pharma-business-model-under-siege\/","title":{"rendered":"Roche: Machine Learning Brings a Big Pharma Business Model Under Siege"},"content":{"rendered":"<p><span style=\"font-weight: 400\">The traditional business model in pharma hinges on blockbuster drug development, large-scale clinical trials and a robust market access and marketing engine. But the drug-hunting approach of old\u2014with a cycle time of a decade and a price tag of $2.7 billion and growing\u2014is unsustainable for Roche and its fellow pharmaceutical giants. [1] For Roche in particular, a biologics \u201cpatent cliff\u201d looms over its portfolio of cancer drugs, which generated $21 billion in sales last year but will face competition from cheaper biosimilar copies by the end of this year. [2]<\/span><\/p>\n<p><span style=\"font-weight: 400\">Roche\u2019s traditional competitive advantages across each stage of the chain\u20141) Discovery &amp; Development, 2) Clinical Testing, and 3) Go-to-Market \u2014 are being threatened by upstarts across technology and health care armed with new sources of data and machine learning techniques.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Machine Learning in Drug Discovery: Advances in technology and the introduction of new types of gene and cell therapies have given rise to more and better-funded early-stage biotechs. These biotechs are taking advantage of i) declining cost of genomics, ii) huge volumes of new patient data, and iii) applications of machine learning techniques to computational biology. Machine learning startups such as BenevolentAI claim to be upending and outpace traditional R&amp;D models in pharma with target validation success rates that are 4 times as high. [3]<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Digital Biomarkers in Clinical Testing: As the number of high-potential drug targets grows, the bottleneck for pharmaceutical companies will increasingly become clinical trials. Today, trials are expensive affairs with big logistical challenges (30% of Phase 3 trials are terminated due to enrollment difficulties). [4] Yet companies like Roche will face competition from technology giants that are better positioned to collect digital biomarkers from large samples through virtual, \u201csiteless\u201d trials\u00a0(e.g., via Apple Watch or Alphabet\u2019s Project Baseline) and apply machine learning techniques to improve clinical trial design and efficacy.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Go-to-Market and Revenue Models: With physicians becoming more engaged in designing custom therapeutic interventions that move beyond pharmacology to include behavioral and digital approaches, the results from the clinical trial won\u2019t have the last word\u2014pharmaceutical companies will be challenged on outcomes and prices by providers, insurance companies, and governments. Providers and payers will look to apply machine learning techniques to a wealth of patient data when determining whether and how to prescribe and pay for drugs. <\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">In the last 18 months, Roche has responded to these challenges to its business model through i) a strategy of partnerships with drug discovery companies, ii) acquisitions of genomic profiling companies that will help it personalize cancer treatments, and iii) a focus on clinical decision support tools that combine in vitro diagnostics with other patient data to give Roche leverage with providers. These tactics\u2014all fundamentally bets on the value of machine learning techniques\u2014are designed to address the emerging threats to Roche\u2019s business model. In turn: <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Drug Discovery: To strengthen its target pipeline and better position itself against new drug discovery startups that use machine learning, Roche entered a strategic partnership with GNS Healthcare, a company that uses ML techniques to help identify new drug candidates. [5]<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Acceleration of Clinical Trials: In July 2018, Roche acquired Foundation Medicine, a genomic testing and profiling company that will help Roche a) better identify which patients are suited for clinical trials for its drug candidates, and b) expand testing to more potential candidates for treatment through the development of liquid biopsies. [6] This comes on top of its February acquisition of Flatiron Health, an oncology data startup that could give Roche sufficient real-world evidence to have to forgo control groups entirely\u2014a development that would help Roche defend itself against technology companies seeking to introduce \u201cvirtual\u201d clinical trials. [7]<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Go-to-Market and Revenue Models: With payers applying increasing pricing pressure and providers recommending more personalized treatments, Roche needs to protect is value proposition. To do so, Roche entered a long-term partnership with GE Healthcare to build clinical decision support tools for physicians that use machine learning and *combine* genomics, biomarker, and monitoring data\u2014getting Roche directly in front of the physician when it comes time for them to recommend treatments for patients. [8]<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Yet partnerships are not a panacea. To succeed, Roche needs to rethink the role of a pharma giant in the digital age. When ML startups can develop assets faster, tech giants can run bigger clinical trials more precisely, and providers and patients can customize holistic treatments, Roche\u2019s capabilities all fall under competitive threat. Instead of betting that it will allocate capital across drug discovery, testing, and distribution more efficiently than its competitors, Roche will need to reposition itself and focus on the segment of the value chain that it can best defend. <\/span><\/p>\n<p><span style=\"font-weight: 400\">What *is* the best, most defensible territory for old guard pharma in an age of AI drug discovery startups, patients empowered with digital health solutions, and a global health care system straining to pay for expensive, breakthrough drugs?<\/span><\/p>\n<p>(799 Words)<\/p>\n<p>[1]\u00a0Joseph A. DiMasi, Henry G. Grabowski, and Ronald W.Hansen,\u00a0<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167629616000291\">Innovation in the pharmaceutical industry: New estimates of R&amp;D costs<\/a>.\u00a0<em>Journal of Health Economics<\/em>, May 2016.<br \/>\n<span style=\"font-weight: 400\">[2]\u00a0<\/span>John Miller and Ben Hirschler,\u00a0<a href=\"https:\/\/www.reuters.com\/article\/us-roche-ceo-focus\/roche-steps-up-efficiency-drive-to-take-sting-out-of-biosimilars-idUSKCN1LT2QF\">Roche steps up efficiency drive to take sting out of biosimilars<\/a>.\u00a0<em>Reuters<\/em>, September 13, 2018.\u00a0<span style=\"font-weight: 400\"><br \/>\n[3] Biotechnology Report:\u00a0<em><a href=\"https:\/\/www.ey.com\/Publication\/vwLUAssets\/ey-beyond-borders-biotech-report-2017\/$FILE\/ey-beyond-borders-biotech-report-2017.pdf\">Beyond Borders<\/a>. Ernst &amp; Young,\u00a0<\/em>2017.<br \/>\n[4] <a href=\"https:\/\/www.cognizant.com\/whitepapers\/patients-recruitment-forecast-in-clinical-trials-codex1382.pdf\">Patients Recruitment Forecast in Clinical Trials<\/a>. <em>Cognizant<\/em>, August 2015.<br \/>\n[5]\u00a0<a href=\"https:\/\/www.gnshealthcare.com\/gns-healthcare-announces-collaboration-to-power-cancer-drug-development\/\">GNS Healthcare Announces Collaboration to Power Cancer Drug Development with REFS&#x2122; Causal Machine Learning and Simulation AI Platform<\/a>. <em>GNS Healthcare Press Release<\/em>, June 2017.<br \/>\n[6]\u00a0<a href=\"http:\/\/investors.foundationmedicine.com\/news-releases\/news-release-details\/roche-and-foundation-medicine-reach-definitive-merger-agreement\">Roche and Foundation Medicine reach definitive merger agreement to accelerate broad availability of comprehensive genomic profiling in oncology<\/a>. <em>Foundation Medicine Press Release<\/em>, June 2018.<br \/>\n[7] Lydia Ramsey, <a href=\"https:\/\/www.businessinsider.com\/why-roche-acquired-foundation-medicine-and-flatiron-health-2018-7\">Why Roche Acquired Foundation Medicine and Flatiron Health<\/a>. <em>Business Insider,<\/em>\u00a0August 2018.<br \/>\n[8]\u00a0<a href=\"https:\/\/www.roche.com\/media\/releases\/med-cor-2018-01-08.htm\">Roche and GE enter partnership to develop integrated digital diagnostics platform to improve oncology and critical care treatment.<\/a>\u00a0<em>Roche Press Release<\/em>, January 2018.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Roche, the Swiss pharmaceuticals giant, is caught in a race against time. After making a big bet on breakthrough drugs and personalized medicine, the company has spent much of the last 18 months thinking ahead to the challenges that machine learning and artificial intelligence pose to a traditional, integrated pharmaceutical business model.<\/p>\n","protected":false},"author":11929,"featured_media":36084,"comment_status":"open","ping_status":"closed","template":"","categories":[4527,1067,4319,132,2062,4396,5087,5089,5088,887,346,1686,5090,55],"class_list":["post-36083","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-alphabet","category-apple","category-big-pharma","category-business-model","category-digital-health","category-flatiron-health","category-foundation-medicine","category-ge-healthcare","category-gns-healthcare","category-health-care","category-machine-learning","category-pharmaceutical","category-roche","category-technology","hck-taxonomy-organization-roche","hck-taxonomy-industry-pharmaceutical","hck-taxonomy-country-switzerland"],"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 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Roche: Machine Learning Brings a Big Pharma Business Model Under Siege - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/roche-machine-learning-brings-a-big-pharma-business-model-under-siege\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Roche: Machine Learning Brings a Big Pharma Business Model Under Siege - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Roche, the Swiss pharmaceuticals giant, is caught in a race against time. 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