clinical trial


Using artificial intelligence in biopharma

#artificialintelligence

Drug discovery is the process of identifying new medicines for treating or curing human diseases.1 Historically, the discovery of new medicines involved extracting ingredients from natural products and basic research to find potential treatments. Progress was generally slow, frustrating and labour-intensive. The majority of drugs discovered during the 20th century were chemically synthesised small molecules, which still make up 90 per cent of drugs on the market today.2 Their advantages include simple manufacturing and administration routes. They also have low specificity and a stable shelf life, meaning they are safe and effective for large groups of people.


AI (Artificial Intelligence): What's The Next Frontier For Healthcare?

#artificialintelligence

Perhaps one of the biggest opportunities for AI (Artificial Intelligence) is the healthcare industry. According to ReportLinker, spending on this category is forecasted to jump from $2.1 billion to $36.1 billion by 2025. This is a hefty 50.2% compound annual growth rate (CAGR). So then what are some of the trends that look most interesting within healthcare AI? Well, to answer this question, I reached out to a variety of experts in the space.


NEC and VAXIMM partner to develop personalized vaccines for cancer

#artificialintelligence

NEC Corporation, a leading firm in network technologies and IT, and VAXIMM AG, a biotech firm focused on the development of oral T-cell immunotherapies, recently announced that both the companies inked a strategic agreement for a clinical trial partnership and an equity investment for the development of innovative personalized neoantigen vaccines for cancer. The terms of this non-exclusive partnership agreement, NEC will be providing funding for the Phase I clinical trial. VAXIMM and NEC will be co-developing personalized vaccines for cancer by using the advanced artificial intelligence (AI) technology of NEC which is used in both of its Neoantigen prediction System and propriety T-cell immunotherapy technology of VAXIMM. Sources informed that the vaccines will be evaluated for several solid tumors in Phase 1 clinical trial. VAXIMM is given the responsibility to conduct the clinical trial that is anticipated to start in 2020.


AI and Clinical Trials

#artificialintelligence

In the United States outcomes-based contracting (OBC) has long been proposed as a measure to reward innovation, based on actual performance of treatments and interventions in patient populations. However, the perceived and actual challenges in implementation have prevented many innovative contracts from leaving the drafting table. Recently, the potential use of artificial intelligence (AI) to predict suitable outcomes for patients to mitigate potential challenges has been discussed.


Data Showing Potential for Machine Learning to Advance Understanding of Nonalcoholic Steatohepatitis (NASH) Presented at the Liver Meeting 2019 BioSpace

#artificialintelligence

"By combining data from across our NASH clinical development program with artificial intelligence (AI)-based tools, we have the opportunity to better characterize this complex disease and understand how potential therapies can impact disease progression," said Mani Subramanian, MD, Senior Vice President, Liver Diseases, Gilead Sciences. "Applying PathAI's deep learning research platform for liver histology assessment will enable a more rigorous review of treatment response and has potential for the exploration of novel biology in patients with advanced fibrosis due to NASH." In a collaboration with PathAI, a leader in AI-powered research in pathology, Gilead is evaluating machine learning approaches to liver histology assessment for use in the diagnosis and staging of NASH and monitoring of treatment response in clinical trials. A study of images from liver biopsies from patients screened for the Phase 3 STELLAR program compared the staging and characterization of liver disease as assessed by experienced pathologists and by the PathAI research platform. The pathologists scored biopsies using the NASH Clinical Research Network (CRN) and Ishak fibrosis classifications, and the PathAI research platform, a convolutional neural network, evaluated these biopsies following training on more than 68,000 annotations from 75 board-certified pathologists.


Data Science: making sense of data

#artificialintelligence

Written by PHASTAR on 01 November 2019. The volume of digital data in healthcare is projected to increase more rapidly in the coming years than any other sector. On a day-to-day basis it is vital that clinical teams ensure they are maximising the value, not only of their own trial data but also of the wealth of external data for example electronic healthcare records, real-world data and peer-reviewed research published in journals. The ability to utilise this data requires not only an understanding of what is available but how to access the data, work with the structure of the data, understand the quality and inherent biases and importantly apply the right methodology to extract value. In addition to the large volume of standard data generated on a clinical trial there can be a raft of other, more specialised data, such as genomics, proteomics, wearables and comprehensive measurements all of which rely on the skills of an experienced data management, programming and statistics team to utilise.


AiCure's adherence, behavior tracker for clinical trials, therapy collects $24.5M

#artificialintelligence

AiCure, a nearly decade-old startup using artificial intelligence to measure medication adherence and other behavior during clinical trials or normal care, has brought in $24.5 million in Series C funding. Palisades Growth Capital led the raise, which also featured new backers Singtel Innov8, Asahi Kasei Corporation, Accelmed Growth Partners, and SpringRock Ventures. The round also included all of the company's existing institutional investors: Baird Capital, New Leaf Venture Partners, the Pritzker Group, Biomatics Capital, Tribeca Venture Partners, and Silicon Valley Bank. AiCure's interactive medical assistant, or IMA, collects visual and audio data from patients to quantify their engagement with a treatment program, allowing the tool to identify patients who are at higher risk of dropout or non-adherence. While care providers can use these capabilities to better target their therapies and cut off preventable medical costs, drug developers can also use these trends to hone in on underperforming trial sites identify enrollees who are deliberately not participating.


Using AI to Understand What Causes Diseases

#artificialintelligence

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.


Using AI to Understand What Causes Diseases

#artificialintelligence

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.


AI in drug development: the FDA needs to set standards - STAT

#artificialintelligence

Artificial intelligence has become a crucial part of our technological infrastructure and the brain underlying many consumer devices. In less than a decade, machine learning algorithms based on deep neural networks evolved from recognizing cats in videos to enabling your smartphone to perform real-time translation between 27 different languages. This progress has sparked the use of AI in drug discovery and development. Artificial intelligence can improve efficiency and outcomes in drug development across therapeutic areas. For example, companies are developing AI technologies that hold the promise of preventing serious adverse events in clinical trials by identifying high-risk individuals before they enroll.