The clinical trial is a critical stage of drug development workflow, with an estimated average success rate of about 11% for drug candidates moving from Phase 1 towards approval. Even if the drug candidate is safe and efficacious, clinical trials might fail due to the lack of financing, insufficient enrollment, or poor study design.
Artificial Intelligence (AI) is increasingly perceived as a source of opportunities to improve the operational efficiency of clinical trials and minimize clinical development costs. Typically AI vendors offer their services and expertise in the three main areas. AI start-ups in the first area help to unlock information from disparate data sources, such as scientific papers, medical records, disease registries, and even medical claims by applying Natural Language Processing (NLP). This can support patient recruitment and stratification, site selection, and improve clinical study design and understanding of disease mechanisms. As an example, about 18 % of clinical studies fail due to insufficient recruitment, as a 2015 study reported.
Another aspect of success in clinical trials is improved patient stratification. Since trial patients are expensive - the average cost of enrolling one patient was $15,700-26,000 in 2017 -- it is important to be able to predict which patient will have greater benefit or risk from treatment. AI-driven companies operate with multiple data types, such as Electronic Health Records (EHR), omics, and imaging data to reduce population heterogeneity and increase clinical study power. Vendors could use speech biomarkers to identify neurological disease progression, imaging analyses to track treatment progression, or genetic biomarkers to identify patients with more severe symptoms.
AI is also streamlining the operational processes of clinical trials. AI vendors help to track patient health from their homes, monitor treatment response, and patient adherence to the trial procedures. By doing that AI companies decrease the risk of patient dropouts, which accounted for 30% on average. Usually, the Phase 3 clinical study stage requires 1000-3000 participants, with a part of them taking a placebo. That’s why the development of synthetic control arms - AI models that could replace the placebo-control groups of individuals thus reducing the number of individuals required for clinical trials - might become a novel trend.
Below we summarize a list of notable AI vendors providing advanced tools for clinical development.
ConcertAI (formerly, Concerto HealthAI) is a US-based company founded in 2017. The company provides real-world evidence (RWE) services for precision oncology. It has established the broadest clinical network through the partnership and licensing with community oncology networks, thus getting access to Electronic Medical Records, Results of NGS diagnostics, and patient-reported outcomes. Concerto then analyzes such data and generates evidence for new therapeutic approaches.
In 2023, ConcertAI has launched its CTO 2.0 solution to enhance the design and execution of clinical trials by leveraging expanded data assets from public sources, which include site and physician-level trial information. Through collaborations with data partners, the system offers operational trial metrics and site profile data, emphasizing the capabilities and performance of study centers. Furthermore, the CTO 2.0 integrates social determinants of health information at the site, physician, and patient levels, aiming to automate trial site selection using a data-driven methodology. The platform's SaaS technology aligns with FDA mandates, enabling research scalability through advanced clinical informatics and data standards.
The company raised a total of $300 million from a number of investors, with the latest $150 million Series C round from Sixth Street.
Saama is a Silicon Valley-based company that was founded in 1997, but it raised its first venture capital in 2015. The company has raised more than $500 million in venture capital, including the latest mega-round of $430 million from Carlyle and venture funds from Merck, Pfizer, Amgen, McKesson, and others, with a transfer of company control.
Saama is one of the leading players in the AI-driven clinical trial analytics space, offering a diverse suite of solutions: accelerated clinical trials via centralized data analytics and control center, including real-time data processing capabilities; automated data quality capabilities; streamlined regulatory submission capabilities, including pharmacovigilance analytics and submissions.
In 2023, Saama has unveiled a unified SaaS platform powered by AI and advanced analytics intended to streamline clinical development processes and reduce manual tasks. The platform incorporates over 90 AI models specialized for life sciences and is trained on more than 300 million data points. Included in the platform are features such as the Data Hub for data centralization, Operational Insights for an integrated view of trial operations, Patient Insights that employ AI for patient data analysis, Source to Submission (S2S) for automating regulatory submission processes, and Smart Data Quality (SDQ) for efficient data management and cleaning.
Founded in 2015, Boston-based company PathAI is a supplier of AI-enabled image analysis for pathology, including applications to support clinical trials.
In March 2023, PathAI and GSK have entered a collaboration for a randomized Phase IIb trial focusing on non-alcoholic steatohepatitis (NASH). PathAI's AI-powered tool, AIM-NASH, will analyze liver biopsy slides, detecting and quantifying NASH histological features. This trial, which will measure the efficacy of GSK4532990 in liver fibrosis and inflammation against a placebo, intends to recruit 246 pre-cirrhotic NASH patients, using AIM-NASH metrics as exploratory endpoints.
To date, the company raised $255 million from a number of investors, including Merck Global Health Innovation Fund, and Bristol Myers Squibb.
Owkin is a New York and Paris-based AI-driven company founded in 2016. The company uses federated learning to train and develop its machine learning models specifically to increase clinical trial efficiency and the ability to work with data from different sources, without compromising their security and know-how. They built a catalog of advanced models, enabling them to identify new biomarkers from imaging, genomics, and clinical data. As an example, Owkin worked on identifying patients with severe disease progression profiles that might respond best to treatment in development.
In May 2023, Owkin received endorsement from the European Medicines Agency (EMA) for its AI-based methods applied to oncology trial analysis, specifically using deep learning prognosis variables derived from histological slides. Owkin's two proprietary models, MesoNet and HCCnet, use deep learning to predict overall survival in mesothelioma and hepatocellular carcinoma (HCC) patients. These predictions aim to adjust efficacy analyses on the overall survival of life-prolonging drugs in randomized phase 2 and 3 clinical trials. Although the EMA acknowledges the advancements these models bring, they advise Owkin to validate the AI approach in future trials, given the limited evidence base compared to traditional methods.
Owkin has raised a total of $304 million from a number of investors, including Sanofi, Bpifrance, and Mubadala Capital Ventures.
Lantern Pharma Inc. (NASDAQ: LTRN) integrates artificial intelligence (AI) with oncology to enhance the efficiency of clinical trials via biomarker-led clinical trial design. Their RADR® platform employs a combination of AI and machine learning techniques to analyze over 25 billion oncology-specific data points. Lantern's portfolio now includes four drug candidates across two Phase 2 programs, an antibody-drug conjugate program across 12 cancer indications, and several partner programs, illustrating the platform's potential in enhancing clinical therapeutic pipelines in the industry.
Through this data-driven method, Lantern has also streamlined their preclinical drug development process, managing to reduce the average time from AI-derived insights to first-in-human clinical trials to 2-3 years at a budget of $1.0-2.0 million per program.
Founded in 2013 in Dallas, US, Lantern Pharma went public in 2020, and had post-IPO fundraising, all totalling in $95 million.
Neuroute (formerly Neucruit)
Neuroute, previously recognized as Neucruit, offers a comprehensive software-as-a-service platform aimed at streamlining clinical trials. Drawing from an expansive database, Neuroute's platform harnesses data from over 1,500 hospital sites, 7,000 disease areas, and an impressive 115 million patient-reported outcomes, spanning 125 countries. This is further enriched by an integration of "five trillion consumer insights". Since its inception in 2019, Neuroute has garnered over $1 million in investments from prominent backers such as PharmStars and Nina Capital. Its functionality, covering aspects like patient feasibility, market research, and patient recruitment, has attracted a diverse clientele. Among its users are teams from biotech, medtech, and the pharmaceutical sector, with notable customers including Motto, Modus TX, and Flow.
Neuroute's Copilot positions itself as an analytical tool for clinical trials, boasting a training dataset that includes over 700,000 clinical trials, patents, and publications. The platform utilizes real-world evidence to offer predictions regarding potential barriers in trial participation and aims to cut down study time by as much as 30%. Additionally, Neuroute emphasizes its utility in protocol development, promising optimized designs and performance predictions through protocol simulations. In patient recruitment, it provides a pre-screening solution and study materials in multiple languages, ensuring wider reach.
AICure is a US-based company, founded in 2010. AiCure Patient Connect™ is a suite of HIPAA and GDPR-compliant tools built within a mobile application to improve patient engagement, improve the relationship between the site and the patient, and achieve a deeper understanding of individual and population-wide disease symptomology for improved health and trial outcomes. AiCure Data Intelligence is highly configurable data ingestion and visualization platform that offers sponsors real-time and predictive insights for advanced visibility into each trial’s and site’s performance.
The company’s AI platform as a service (PaaS) allows for the dynamic collection of disparate data sources to correlate previously unrelated endpoints and translate them into meaningful, actionable insights to be deployed at scale.
AICure raised a total of $52.8 million from a number of investors, including Palisades Growth Capital.
Unlearn.AI is a San-Francisco-based company founded in 2017 by a former principal scientist at Pfizer. Unlearn.AI developed the TwinRCTs™ platform, which combines AI, Digital Twins, and novel statistical methods to enable smaller, more efficient trials. The concept of “Digital Twins” includes AI-driven integration of multiple data types from real-world patients. Unlearn.AI is creating these profiles through their DiGenesis platform with the aim to replace real patients in placebo control groups. TwinRCTs™ incorporates prognostic information from Digital Twins into randomized controlled trials to enable smaller control groups while maintaining power and generating evidence suitable for supporting regulatory decisions.
The company’s activities included work on Alzheimer’s Disease and Multiple Sclerosis. Unlearn.AI raised a total of $84.9 million from a number of investors, including BVC and DCVC Bio.
The growing impact of AI in clinical research
AI vendors are believed to provide a tangible impact on the improvement of the clinical study process. Today, there is evidence that AI may accelerate patient enrollment, one study reported reduced patient screening time by 34% and improved patient enrollment by 11.1%. In the other example, IQVIA reported a 20% increase in clinical trial patient enrollment. On the other hand, AICure reported that using their platform increased the rate of taking prescribed medication from 72% to 90%.
The adoption of AI for clinical trial design, patient enrollment, and stratification, optimizing regulatory filings, and predicting clinical trial outcomes -- are among the most beneficial use cases for the AI application in pharmaceutical research, since clinical trials are the most expensive and demanding part of the whole drug discovery journey of a novel therapeutics.
Topics: Biotech Companies