Challenges to AI in Life Sciences
With so many wild predictions being bandied around about AI’s potential to radically transform healthcare for the…
David H. Crean, managing director for Objective Capital Partners, highlights the latest trends in mergers and acquisition (M&A) activities involving companies with artificial intelligence/machine learning platforms and technologies and their application to healthcare companies.
Advances in technologies, an incorporation of entrants from outside the industry and changing consumer expectations are driving a global shift in the healthcare landscape. Powerful forces such as chronic diseases and massive data collection continue to add pressure and are pushing traditional models of research and development, diagnostic and treatment paradigms and healthcare delivery. Companies in the sector must transform to be part of the conversation and relevant or risk being left behind. This transformation requires healthcare leaders to take numerous steps including adopting a consumer-centric focus, shifting to preventive and outcomes-based care models, engaging with nontraditional value chain partners, and driving improvements to access, quality, innovative and affordable care. In addition, these companies must embrace digital and technology enablers that offer artificial intelligence (AI) methods for data application.
AI is terminology that has been used synonymously with machine- and deep-learning. AI often can be described as software platforms comprising complex algorithms to analyze data, reach conclusions and anticipate problems with human-level expertise but without requiring direct human input. Machine-Learning involves continuous and repetitive processes by which machines are capable of integrating new data and improving performance over time without the need for explicitly programmed instructions. Deep Learning is a complex version of machine learning that involves multiple layers of abstract variables, for analysis at a scope and speed far beyond human capability.
AI appears to be infiltrating every industry and sector, such as allowing vehicles to navigate without drivers and mimicking the way humans speak. But for all the authentic and exciting ways it’s transforming the tasks computers can perform, there’s a lot of hype, too. Absence of a cohesive strategy to incorporate cloud platform and data pools, integrating and consolidating data warehouses, data hubs and databases as a single source of data, can easily send organizations into a tailspin. Heterogeneous data sources and diversity of data management technology add to the complexity and can further make it a management challenge. Advanced data quality and analytics governance on data practices are key success factors in its applications, today and tomorrow.
Distinct trends are converging in healthcare, which means AI will come to define our new paradigm.
The possibilities for AI to run the gamut across healthcare is enormous. From improving the rates of drug discovery and clinical successes to reducing the costs associated with incorrect diagnoses and unnecessary treatments to helping patients avoid pricey procedures down the road through predictive monitoring. Life sciences and healthcare companies are moving into spaces and areas involving massive data, high R&D potential detailing the rapid rise in only the last few years involving AI-focused firms and technology. Although not covered in this writing, there is also a flurry of venture capital going into AI for drug development and digital health financings see continued momentum. There’s no shortage of enthusiasm and we are seeing an increase in deal flow associated with AI-based companies and technologies and their incorporation into healthcare companies and systems.
While a number of life science companies have already entered the space through acquisitions, a large percentage of the bigger biopharma companies recognize that they need to do this. The Tufts Center for the Study of Drug Development (TCDD) and the Drug Information Association (DIA), in collaboration with eight pharmaceutical and biotech companies, conducted a survey during the fourth quarter of 2018, which found that 57% of small companies, 74% of mid-sized companies and 88% of large companies are using AI within their organizations. Most were using it for clinical patient selection and recruiting, as well as AI-enhanced literature review, while a smaller percentage were using it for genetic data analysis, target identification, and to generate small molecule leads. Of those not using AI, they cited a lack of skilled staff, regulatory concerns, budget constraints and a mistrust of unvalidated technologies. Although some small- and mid-sized biopharma companies are hanging back from investing in AI technologies, problems of rising costs, complicated diseases, stricter regulations and the pricing debate in Washington, could make partnering or mergers and acquisitions (M&A) a necessity.
Meanwhile, a number of AI companies have already signed deals with large life sciences companies, with some beginning to bear fruit. The successes are leading to more partnerships across the landscape. About 56% of respondents to the Tufts/DIA survey said their AI partnerships were focused on drug discovery/preclinical and 42% were focused on supply chain. Only 6% said AI had contributed to the regulatory approval of a drug at their organization. The hope, however, is that the technology will lead to better development decisions, safer and more cost-effective drugs, and streamlined business operations.
M&A and leveraged buy-out (LBO) activities over the past 30 months show an increasing appetite for deal completion. According to Cortellis deal database, there were 34 deals completed since 2017 (2019 YTD through August), with increasing deal flow demonstrated each year. Average deal sizes were $438.39m with a median deal size of $64.67m at a range of $70k to $1.9b. Non-control, corporate transactions over the same period involving strategic investments, licensing and partnering also show increasing transactional activities to get access to AI technologies. Twenty-four (24) deals have been completed with average deal sizes of $15.49m with median deal sizes of $10.01m with a range of $100k to $50m. Expect more deal flow to come as companies understand and harness AI better on the regulatory and evidence generation side. AI will be expected to drive actions and efficiencies to help with clinical evidence, speed to market and commercial decisions.
|Company Name||Deal Date||Deal Type||Deal Size||Description||Investors|
|PathAI||24-Jul-19||Corporate||Developer of an AI-powered research platform intended to improve the accuracy and efficiency of cancer diagnosis and treatment. The company’s platform applies machine and deep learning techniques to massive aggregated sets in order to detect cancerous cells separately, enabling pathologists to diagnose cancer patients rapidly and accurately.||Laboratory Corporation of America Holdings (NYS: LH)|
|Quantitative Insights||17-Jul-19||Merger /Acquisition||Provider of patient management services intended to harness the power of research and advanced imaging. The company’s services offers an intuitive computer-aided diagnosis workstation along with a platform incorporating machine learning for the evaluation of breast abnormalities, enabling radiologists to increase both the efficiency and accuracy of breast cancer diagnosis.||Qlarity Imaging|
|Digitize.AI||10-Jul-19||Buyout/LBO||Developer of an intelligence system designed to improve prior healthcare authorizations and utilization management. The company’s intelligence system uses robotic process automation (RPA), artificial intelligence (AI) and machine learning to maximize productivity, reduce waste, protect revenue and improve the member-patient experience, enabling healthcare clients to save time and decrease labor costs by automating back office prior authorization processes.||Bain Capital (Chris Gordon), Waystar Health (Matthew Hawkins)|
|Dr.Brain||05-Jul-19||Corporate||4.36||Developer of an imaging analysis and diagnostic platform designed to diagnose central nervous system disease in early stage. The company’s platform is developed based on the Chinese brain model database, which combines cloud computing artificial intelligence to access brain structures, enabling doctors to provide patients with brain disease diagnosis and treatment programs from neuroimaging data.||Beijing Beilu Pharmaceutical Company (SHE: 300016)|
|Just Biotherapeutics||03-Jul-19||Merger /Acquisition||90||Developer of biotherapeutic technologies intended to dramatically expand global access to biotherapeutics. The company’s offerings including integrating highly synergistic scientific expertise and machine learning driven technologies for design, development and manufacturing of biologics, providing clients with integrated designs that will accelerate the development of biotherapeutics and substantially reduce their manufacturing cost.||Evotec (ETR: EVT)|
|Imago Systems||26-Jun-19||Corporate||0.1||Developer of a medical imaging software designed to reveal structural detail of all tissues to detect cancer and other abnormalities earlier. The company’s software utilizes AI transforms the subtle indistinguishable white-on-white and gray-on-gray pixel density patterns within images, such as mammograms, into clearly defined, visually apparent and qualitatively distinct patterns to detect cancer, enabling clinicians to observe and identify abnormalities at their earliest stages and patients to experience better health, joy and vitality in their lives by gaining a better understanding of their body, transforming their lifestyle and achieving the results they desire.||Mayo Clinic|
|Cytobank||04-Jun-19||Merger /Acquisition||Provider of storage and analysis platform designed to accelerate research productivity. The company’s platform manages, analyzes and enables the sharing of high dimensional single cell biological data, enabling scientists to explore multiple hypotheses simultaneously.||Beckman Coulter Life Sciences (Mario Koksch)|
|BostonGene||24-Apr-19||Corporate||50||Developer of biomedical software designed for advanced patient analysis and personalized therapy. The company’s solution performs analytics for each patient’s individual genetics, tumor and tumor microenvironment, clinical characteristics and disease profile, providing aid to clinicians in their evaluation of viable treatment options.||NEC Corporation (TKS: 6701) (Osamu Fujikawa)|
|caresyntax||10-Apr-19||Corporate||15||Developer of an IoT technology platform designed to help hospitals reduce surgical risk and improve outcomes. The company’s platform transforms unstructured clinical and operational data into actionable, real-time insights through IoT and data analytics, enabling hospitals and ambulatory surgical centers to identify and manage risk, automate workflows, enhance knowledge sharing and reduce surgical variability.||Barco (BRU: BAR)|
|Jintel Health||09-Apr-19||Corporate||Developer of an innovative precision intelligence platform which captures and structures large clinical and genomic datasets based in Richmond, California. The company applies our advanced analytics and machine learning to enable the practice of precision medicine and the discovery of new patient treatments, allowing advance precision medicine and enabling novel discoveries.|
|Enjo (Mental Wellness)||02-Apr-19||Merger /Acquisition||Provider of artificial intelligence based platform intended to helps the users in making a better understanding of themselves so that they can create stronger relations with their loved ones. The company’s artificial intelligence based platform offers a kind of robot that asks questions to users to make it clearer about how the users feel at the current moment, enabling the user to gain knowledge about their emotional and mental condition in real time.||KRY (Johannes Schildt)|
|ConnectMed||29-Mar-19||Merger /Acquisition||Developer of an online medical consultation platform in Kenya. The company’s platform permits doctors to treat patients for common ailments over video through application or clinic worker intermediaries using machine learning-based tools, enabling people in Africa to access cheaper healthcare and live a healthier life.||Merck (ETR: MRK)|
|KiviHealth||25-Mar-19||Merger /Acquisition||10||Developer of a cloud based healthcare platform created to improve doctor-patient engagement and make healthcare affordable and accessible to everyone. The company’s platform uses AI and machine learning to generate digital prescriptions, offers electronic historical health records storage for the benefit of the patients and the physicians, appointment booking module and a patient engagement tool, along with billing invoicing solutions, providing doctors and patients with digital solution to enhance their relationship.||Netmeds.com (Pradeep Dadha)|
|Wanda||17-Mar-19||Buyout/LBO||Developer of a cloud-based remote patient monitoring platform. The company’s remote monitoring system platform features proactive care management to deliver insights on the pending adverse events to the point of care in real-time before they manifest, enabling medical practitioners to reducing hospitalisation rates.||EMV Capital (Ilian Iliev)|
|Pathway Rx||13-Mar-19||Merger /Acquisition||0.07||Provider of cannabis breeding and research services in Canada. The company’s services are primarily engaged in creating a library of strains for identifying and customizing treatments for specific medical conditions, using machine learning.||Sundial Growers (NAS: SNDL)|
|SimplicityBio||06-Mar-19||Merger /Acquisition||Developer of AI-based algorithms and software intended for development of multi-omic signatures and biomarker assessment. The company’s AI tech is designed to uncover novel combinations of biomarkers using diverse data streams, through a multi-omic agnostic approach, and are utilized by firms specializing in biotech and diagnostics developers along with co-developing diagnostic tests enabling diagnostic companies to discover new accurate and robust biomarker.||QuartzBio|
|Lumedic||11-Feb-19||Merger /Acquisition||Provider of revenue cycle management platform for modern payers and providers associated with the healthcare business. The company’s platform is built on blockchain, machine learning and patient-centric design that offers an end-to-end platform and intelligent network from eligibility to pre-authorization to billing to drive more efficient operations and make smarter data-driven decisions.||Providence St. Joseph Health (Venkat Bhamidipati)|
|Corstem||08-Feb-19||Merger /Acquisition||Developer of AI-based diagnosis software for MRI, CT, and x-ray cardiac imaging modalities. The company’s fully-quantitative, cardiac perfusion analysis uses radiation-free MRI, permitting fast, high-resolution assessment of blood flow through the heart muscle, providing specialists with more accurate cardiac disease diagnosis.||Circle Cardiovascular Imaging (Greg Ogrodnick)|
|Lightning Bolt Solutions||05-Feb-19||Buyout/LBO||Developer of an artificial intelligence optimized physician shift scheduling technology. The company’s software offers advanced analytics and mobile access with secure messaging, enabling hospitals and health systems to align the interests of their physicians and facilities to promote productivity and patient access.||K1 Investment Management, PerfectServe (Terrell Edwards)|
|M*Modal (healthcare technology business)||01-Feb-19||Merger /Acquisition||1,000.00||Provider of cloud-based, conversational Artificial Intelligence (AI)-powered systems in Pittsburgh, Pennsylvania. The company’s systems help physicians to efficiently capture and improve the patient narrative, enabling them to spend more time with their patients and provide improved healthcare services.||3M (NYS: MMM) (Michael Vale)|
|Linguamatics||31-Jan-19||Merger /Acquisition||64.67||Developer of a text mining platform built for health sciences. The company’s platform characterizes patient populations with lifestyle factors and social determinant data to improve risk stratification, screens unstructured data to identify early signs of cancer and missed diagnoses, drives problem list reconciliation, fill documentation gaps, and improve clinical insights and extracts pathology data, quality measures and supporting documentation, enabling customers to extract hidden insights and connections, for better healthcare outcomes, improved population health and reduced costs.||IQVIA (NYS: IQV) (Jon Resnick)|
|Exscientia||07-Jan-19||Corporate||26||Developer of artificial intelligence (AI) driven drug discovery technologies designed to address new drugs in areas of complex disease. The company’s technologies actively learns from the preceding experimental results, identify and assimilates multiple subtle and complex compounds and formulations to balance potency, selectivity and pharmacokinetic criteria, enabling pharmaceutical companies to avail new opportunities for treatment of complex disease, where the target mechanisms are often unknown.||Celgene (NAS: CELG) (Rupert Vessey), Evotec (ETR: EVT) (Werner Lanthaler), GT Healthcare Capital Partners (Alan Au)|
|Health[at]Scale Technologies||04-Jan-19||Corporate||16||Provider of machine intelligence technologies intended to transform healthcare outcomes and economics by matching every patient to the right treatment. The company’s Health[at]Scale Interception, Health[at]Scale Steerage and Health[at]Scale Treatment are healthcare-specialized technologies used for identifying, building and delivering personalized predictions of benefit, harm and adherence for members, enabling patients to get the right treatment by the right provider at the right time.||UnitedHealth Group (NYS: UNH)|
The convergence of AI and the healthcare sector was certainly inevitable and continues to change each day. The advent of machine-learning and deep-learning technologies capable of analyzing and synthesizing massive amounts of data with algorithms seems a natural fit for an industry in dire need of greater efficiencies. Successful organizations will likely be those that are willing to reach outside of their own space to transform. Many of these legacy-based companies have been doing things the same way for so long that it’s expected that change will be difficult. Companies that are unable or unwilling to move and participate could find it difficult to stay relevant, grow and transform their businesses.
Organizations in the healthcare sector will continue to lead the way aided by significant improvements and efficiencies from automation, digital, and AI platforms. In the future healthcare paradigm, there will be a greater focus on all areas of wellness/prevention and early detection. Some life sciences companies are already beginning to partner with medtech and other technology organizations around early screening. Rather than looking at an illness alone, life sciences companies will need to consider an entire disease state. They will need to identify potential partners who will help them improve prevention, early detection, precision diagnoses, and treatment. These companies will likely need to partner or merge with multiple players in this ecosystem to demonstrate, and commit to, value.
Objective Capital Partners is a leading investment banking advisory firm whose Principals have collectively engaged in more than 500 successful transactions serving the transaction needs of growth stage and mid-size companies. The executive team has a unique combination of investment banking, private equity, and business ownership experience that enables Objective Capital Partners to provide large enterprise caliber investment banking services to companies with annual revenues up to $500MM. Services include sale transactions, partnering/ licensing, equity and debt capital raises, valuation and comprehensive advisory services. The firm uses a proprietary process to work to achieve maximum company valuation, premium pricing, and high client satisfaction rates post-sale. The firm’s industry expertise is focused on 5 verticals including healthcare, life sciences, business services, technology, and consumer products. Additional information on Objective Capital Partners is available at www.objectivecp.com.
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