Question to be addressed and answered: Can the concept of "digital twin" revolutionize or hurt clinical research?
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Modules will have a one-page perspective which is a written assignment based on the content of the week.
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The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective 2021 COVER OPTION 2 The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective CB Insights helps the world’s leading companies make smarter technology decisions with data, not opinion. Our Technology Insights Platform provides companies with comprehensive data, expert insights and work management tools to drive growth and improve operations with technology. WHAT IS CB INSIGHTS? SIGN UP FOR A FREE TRIAL https://www.cbinsights.com/signup?utm_source=research-pdf&utm_medium=slides&utm_campaign=marketing_future-clinical-trials_2021-03&utm_term=trial-signup&utm_content=trial-signup_futureclinicaltrials 3The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Why faster trials are critical for pharma companies 6 • The current state of clinical trials How AI could change every stage of clinical trials 9 • Finding a clinical trial • Challenges with enrollment • Medication adherence How big tech is disrupting clinical trials 23 • Google’s healthcare data platform • Apple’s and Facebook’s moves Why AI alone isn’t the magic bullet 28 How Covid-19 has affected clinical trial tech adoption 29 • Study design • Virtual trials Table of Contents 4The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Testing new drugs is a slow and expensive process. AI has the potential to disrupt clinical trials — from patient recruitment to adherence monitoring and data collection — and Covid-19 has catalyzed its adoption. In the past year, nearly 5,000 clinical trials were launched to test life-saving treatments and vaccines for the novel coronavirus. Covid-19 clinical trial enrollment is 80% higher than average. However, this is less impressive when considering that for many diseases, such as cancer, less than 10% of eligible patients enroll in a trial. Patients often only enroll in a drug trial when existing forms of treatments have already failed. On top of that, not all diagnosed patients are eligible to participate — determining eligibility alone can be a herculean task. For those that are eligible, participating in a trial is often a cost- and time-intensive endeavor. The process is inefficient for other stakeholders too: drug trials average nearly a decade, costing over $1B on average. The $52B clinical trials market needs a makeover. Startups and big tech are actively developing clinical trial solutions, from IoT for remote monitoring, to machine learning for electronic health record (EHR) processing, to AI-based cybersecurity for data protection. https://app.cbinsights.com/markets/profile?geoIds=11&marketIds=8296 https://www.cbinsights.com/research/startups-ai-healthcare-remote-patient-monitoring/ https://www.cbinsights.com/research/electronic-health-records-top-investors/ https://www.cbinsights.com/research/healthcare-cybersecurity-top-trends-2019-expert-intelligence/ 5The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Below, we map out a patient’s journey through a typical clinical trial process, and explore use cases for emerging technologies like AI at each step. Note: We specifically focus on drug-based trials, although technologies discussed in the final section of the report are applicable to a wider range of clinical studies. 6The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Bringing a drug to market is a long and arduous process. Studies estimate that the clinical trial process — where new drugs are tested on patients before the FDA approves them — lasts 9 years and costs $1.3B on average. Clinical trials are conducted in multiple phases, with cost and complexity increasing from Phase I to Phase III. Despite the time and capital invested in trials, only 1 in 10 drugs that enter Phase I of a clinical trial will be approved by the FDA. Clinical trials fail for a variety of reasons, including failure to recruit enough participants, mid-trial patient drop out, side effects, and inconsistent data. Why faster trials are critical for pharma companies 7The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Naturally, trials that fail at a later stage prove more costly. Switzerland-based Novartis, for instance, attributed a 15% drop in its Q1’17 net income to a failed Phase III drug intended to treat heart failure. The cost of failure is more pronounced for biopharmaceutical startups. With limited cash, a startup’s lead candidate failing during a clinical trial often means the company will not survive. This is because companies rarely IPO until at least one promising drug is in late-stage clinical trials. Recently, the SPAC trend has enabled companies to improve survival probability by accessing public capital at an earlier, higher-risk stage. The high costs associated with clinical trials also have downstream effects on costs for patients. This is because biopharma companies bundle R&D costs of failed trials into the pricing of approved drugs to remain profitable. THE CURRENT STATE OF CLINICAL TRIALS After commercially available treatments have failed, patients must navigate a complicated process to find, enroll, and participate in a clinical trial. https://www.cbinsights.com/company/novartis https://www.cbinsights.com/research/report/what-is-a-spac/ https://www.cbinsights.com/research/spac-pros-cons/ https://www.cbinsights.com/research/spac-pros-cons/ 8The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective In the infographic below, we map out a typical patient journey. Furthermore, many clinical studies use rudimentary data collection and verification methods — which often put the onus on the patient — such as: sending patient medical records via fax, manually counting leftover pills in bottles, and relying on patients’ diary entries to determine medication adherence. This process is ripe for disruption. 9The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Artificial intelligence-powered technology has the potential to change every stage of the clinical trials process, from finding a trial to enrollment to medication adherence. FINDING A CLINICAL TRIAL Matching the right trial with the right patient is a time-consuming and challenging process for both the clinical study team and the patient. “In fact, only 3 percent of cancer patients today are enrolled in clinical trials.” — WHITEHOUSE.GOV, MAY 2018 Roughly 80% of clinical trials fail to meet enrollment timelines, and around one-third of Phase III clinical studies are terminated because of enrollment difficulties. For context, there are over 22,000 clinical studies in the US that are currently recruiting patients. The heatmap below shows the location of 1,000+ active breast cancer studies. How AI could change every stage of clinical trials 10The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Source: ClinicalTrials.gov Patients may occasionally get trial recommendations from their doctors if the physician is aware of an ongoing trial. Otherwise, the onus of scouring through ClinicalTrials.gov — a comprehensive federal database of past and ongoing clinical trials — often falls on the patient. Natural language processing (NLP) can help extract and analyze relevant information from a patient’s EHR records, compare with eligibility criteria for ongoing trials, and recommend matching studies. In fact, extracting information from medical records — including EHRs and lab images — is one of the most sought-after applications of artificial intelligence in healthcare. https://www.cbinsights.com/research/google-patent-clinical-notes/ https://www.cbinsights.com/research/google-patent-clinical-notes/ https://www.cbinsights.com/research/ai-healthcare-startups-market-map-expert-research/ https://www.cbinsights.com/research/ai-healthcare-startups-market-map-expert-research/ 11The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective However, solutions accessing patient data face a number of challenges, including unstructured healthcare data and disparate data sources that don’t communicate with each other. The EHR interoperability challenge Despite a $27B federally funded incentive program to encourage hospitals and providers to adopt EHRs, there is no standard format or centralized repository of patient medical data. In fact, it’s still difficult for patients to access their own records from all the health institutions they’ve visited. Under the Health Insurance Portability and Accountability Act (HIPAA), data sharing is allowed with patient consent. This creates opportunities for AI startups to analyze medical data and suggest eligible patients within minutes — a process that would otherwise take months. https://www.cbinsights.com/research/healthcare-interoperability-startups-expert-intelligence/ https://www.cbinsights.com/research/healthcare-interoperability-startups-expert-intelligence/ 12The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective However, issues with securely sharing health information between institutions and software systems — or interoperability — persist. The Covid-19 pandemic has underscored this issue and has driven investor attention to the EHR ecosystem. News mentions for EHRs have also skyrocketed. Different hospitals and providers treating the same patient may not use the same EHR software to enter data. In many clinical trials, researchers still fax requests for patient records to hospitals, who often send the data back as PDFs or images (including pictures of handwritten notes). This poses a challenge for AI technology. As one study by researchers from MIT, Harvard, Johns Hopkins, and NYU highlights: “Standard natural language processing tasks such as sentiment analysis and word sense disambiguation are difficult in clinical notes, which are misspelled, acronym-laden, and copy-paste heavy.” https://www.cbinsights.com/research/electronic-health-records-top-investors/ 13The Future Of Clinical Trials: How AI, Big Tech, & Covid-19 Could Make Drug Development Cheaper, Faster, & More Effective Health AI company Flatiron Health explains this further in a patent filing: “Structured data can also become unstructured due to transmission methods. For example, a spreadsheet that is faxed or turned into a read-only document (such as PDF) loses much of its structure.” This dated, manual system makes it difficult for clinical trial researchers to collect accurate data needed to determine a patient’s eligibility. Startups are approaching the patient recruitment problem from multiple angles. Deep 6 AI uses NLP to extract clinical data — such as symptoms, diagnoses, and treatments — from patient records. Its software can even identify patients with conditions not explicitly mentioned in EHR data, improving the match rate between patients and clinical trials. Deep 6 AI was valued at over $140M in its latest fundraise. Clinical trial marketplaces, such as the one SubjectWell offers, are another approach. SubjectWell’s platform allows researchers to access pre-screened patients