Netramark launches AI glioblastoma research collab

Mr. Swapan Kakumanu reports NETRAMARK INITIATES AI-DRIVEN COLLABORATION IN GLIOBLASTOMA RESEARCH Netramark Holdings Inc. has formed a new collaboration focused on glioblastoma (GBM) research. The collaboration, supported by a licence to proprietary know-how, grants Netramark access to clinical and biomarker data sets from a leading United States academic medical centre. NetraAI, the company's explainable AI platform, will be applied to these data sets to identify patient subgroups to help build a therapeutic decision support tool that can be used to inform future GBM trials. Glioblastoma is an aggressive and difficult-to-treat cancer, with median survival of just 15 months and fewer than 7 per cent of patients surviving beyond five years. Clinical trials in GBM have a failure rate exceeding 90 per cent, often due to heterogeneous patient populations, limited predictive biomarkers and poorly defined inclusion criteria. Project objectives Netramark's analysis under the collaboration will focus on generating novel insights and hypotheses from longitudinal cerebrospinal fluid (CSF) proteomic data sets. NetraAI will analyze the data into explainable and unexplainable subpopulations, seeking to deliver insights in the form of NetraPersonas -- collections of patients, variables and statistical evidence that can be leveraged to optimize future trial enrichment strategies. The specific objectives include: Glioma versuus non-tumour controls -- identify markers distinguishing GBM samples; Glioma vs. non-glioma brain tumours -- differentiate GBM from supratentorial brain metastases to refine glioma-specific biomarkers; Disease recurrence -- contrast primary and recurrent gliomas to identify molecular markers of recurrence and tumour evolution; Impact of resection -- evaluate paired pre- and postsurgical resection samples to understand molecular changes from surgery; Impact of chemoradiation -- analyze paired pre- and posttreatment samples to assess therapy response and resistance mechanisms; Impact of immunotherapy -- explore pre- and postimmunotherapy lumbar CSF samples to identify molecular effects of immunotherapy. Data and resources The academic partner will provide expanded longitudinal glioblastoma proteomic data sets generated on the SomaLogic platform, along with related deidentified materials. This collaboration represents an important opportunity in applying explainable AI to a significant challenge in oncology, with the potential to help trial sponsors design more efficient, targeted and successful glioblastoma studies. "We are excited to collaborate with a leading academic medical centre on this challenging area of oncology," said Josh Spiegel, president of Netramark. "By applying NetraAI to high-quality glioblastoma data sets, we aim to identify explainable patient subgroups that can guide treatment strategies, accelerate biomarker discovery and support the design of more successful clinical trials." About NetraAI In contrast to other AI-based methods, NetraAI is uniquely engineered to include focus mechanisms that separate small data sets 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) providing the potential to increase the chances of a clinical trial success. Many other AI methods lack these focus mechanisms and assign every patient to a class, often leading to overfitting, which drowns out critical information that could have been used to improve a trial's chance of success. About Netramark Holdings Inc. 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 data sets and accurately segment diseases into different types, as well as accurately classify patients for sensitivity to drugs and/or efficacy of treatment. We seek Safe Harbor.