— New offering uses NetraMark's NetraAI to analyze customer's clinical data and identify key variables that drive efficacy, toxicity and placebo responses in only four weeks —
— Recommendations based on NetraAI generated insights can enhance drug effect size and statistical significance, providing a validated approach to overcoming the industry's 88% clinical trial failure rate —
"Our mission at NetraMark is to have a meaningful impact on the clinical trial process. Our singular focus has been on building advanced AI solutions that leverage clinical study data and affect the quality, accuracy, speed and safety of late stage trials. Our proven NetraAI has been leveraged to "see insights" that de-risk clinical trials by providing advanced personas that are important for various stages of clinical trial planning and execution with the goal to accelerate drug development, and bring safer and more effective treatments to market" said NetraMark CEO,
"We are pleased to unveil our
Key features and benefits of the
- NetraAl analysis for a single clinical stage asset
- Defined categories of inquiry (e.g. response, placebo, toxicity)
- Dataset/data sources discovery (auditing /inventory)
- Next phase study simulation with regards to effect size and p-values
- Four-week turnaround
- Integrated project team
- NetraAl project lead, senior data scientist, bioinformatician, clinical scientist
- Sponsor development team
- High-value deliverables/recommendations
- Mid-stage meeting with initial findings and hypotheses testing
- Final recommendations meeting
- Presentation with detailed variables and simulation impact on effect size/p-values
- Enrichment strategies and protocol design considerations
In contrast with other AI-based clinical trial solutions, NetraAI is uniquely engineered to include focus mechanisms that separate small datasets into explainable and unexplainable subsets. Unexplainable subsets are collections of patients that can lead to suboptimal overfit models and inaccurate insights due to poor correlations with the variables involved. The NetraAI uses the explainable subsets to derive insights and hypotheses (including factors that influence treatment and placebo responses, as well as adverse events) that can increase the chances of a clinical trial success. Typical AI methods lack these focus mechanisms and assign every patient to a class, even when this leads to "overfitting" which drowns out critical information that could be used to improve a trial's chance of success.
Additionally, the "black box" approach of other AI solutions makes it difficult to understand the recommendations provided. In contrast, NetraAI is a "glass house" in which every variable identified is associated with a biologic rationale and testable hypothesis that customers' subject matter experts can evaluate and assess.
The power of NetraAI has been validated in a recent publication in Frontiers in Computational Neuroscience, which described its use in discovering novel amyotrophic lateral sclerosis (ALS) drug targets and unique ALS patient subpopulations that may substantially improve clinical trial success rates.
"There is a growing understanding about the benefits of personalized therapies tailored to each patient's specific biology and health factors. Other AI-based clinical trial solutions provide homogenized recommendations based on hundreds of thousands of patients," said Dr.
By launching the
For additional information about the
NetraMark is a company focused on being a leader in the development of Generative Artificial Intelligence (Gen AI)/Machine Learning (ML) solutions targeted at the Pharmaceutical industry. Its product offering uses a novel topology-based algorithm that has the ability to parse patient data sets into subsets of people that are strongly related according to several variables simultaneously. This allows NetraMark to use a variety of ML methods, depending on the character and size of the data, to transform the data into powerfully intelligent data that activates traditional AI/ML methods. The result is that NetraMark can work with much smaller datasets and accurately segment diseases into different types, as well as accurately classify patients for sensitivity to drugs and/or efficacy of treatment.
For further details on the Company please see the Company's publicly available documents filed on the System for Electronic Document Analysis and Retrieval (SEDAR) at sedarplus.ca.
This press release contains "forward-looking information" within the meaning of applicable Canadian securities legislation including statements regarding the Company's objectives with NetraAI and the
When considering these forward-looking statements, readers should keep in mind the risk factors and other cautionary statements as set out in the materials we file with applicable Canadian securities regulatory authorities on SEDAR at www.sedarplus.ca including our Management's Discussion and Analysis for the year ended
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