Inka Health Co-Founders Publish New Study Advancing Real-World Applicability of Lung Cancer Clinical Trial Outcomes

Calgary, Canada – June 20, 2025 – Onco-Innovations Limited (CBOE CA:  ONCO) (OTCQB: ONNVF) (Frankfurt: W1H, WKN: A3EKSZ) (“Onco“ or the “Company”) is pleased to announce that the Co-Founders of its wholly-owned subsidiary, Inka Health Corp. (“Inka Health”), have authored a significant new study titled Global Transportability of Clinical Trial Outcomes to Real-World Lung Cancer Populations: A Case Study using Lung-MAP S1400I[1] (the “Study”), published in medRxiv in June 2025. The Study examines a key challenge in global cancer research by exploring how clinical trial results can be made more applicable to the diverse patient populations treated in routine clinical practice across different countries and healthcare systems. In this case, the approach demonstrated strong predictive performance, matching real-world outcomes within less than one percent over a 30-month period. [2]

Practical Implications and Strategic Value of the Study:

By simulating patient outcomes in advance to optimize trial design and define more relevant populations, these methods can de-risk development pipelines. These approaches directly inform SynoGraph, Inka Health’s next-generation causal AI platform, which is designed to support faster, more transparent, and globally applicable drug development through advanced real-world analytics.
About the Study:

The Study presents a novel approach to improving the global applicability of clinical trial outcomes by assessing how well results from controlled trials translate to real-world patient populations. The research specifically examined whether findings from Lung-MAP S1400I, a leading randomized clinical trial for advanced non-small cell lung cancer (NSCLC), could accurately predict outcomes for patients receiving routine care in the United States, Germany, and France.

Clinical trials often have strict eligibility criteria, meaning many patients seen in daily practice would not qualify to participate. This can limit the ability to apply trial results to broader, more diverse patient populations. In this Study, the researchers used advanced modeling techniques and external clinical knowledge to bridge the gap between trial participants and real-world patients—including those typically excluded from trials due to age, comorbidities[3], or other factors.

The results demonstrated meaningful progress in aligning clinical trial findings with real-world outcomes. When adjusting only for measured clinical factors, the alignment between trial and real-world results improved but remained incomplete. Incorporating additional external knowledge about patient groups excluded from the original trial further enhanced the model, resulting in predicted outcomes that closely mirrored actual survival observed among real-world patients in the United States, Germany, and France, with an average discrepancy of just 0.27 months (8.2 days) over a 30-month period. In other words, the model was able to match real-world outcomes with an error of less than one percent over the full timeframe, underscoring its potential to improve the relevance of trial findings for everyday clinical practice.

“This work represents a significant step forward in making clinical trial results more relevant to real-world cancer care globally. By bringing in external data and expert knowledge, we can better account for the diversity of patients who receive cancer treatments every day, helping to improve clinical decision-making, informing regulatory approvals, and ultimately expanding patient access to innovative therapies,” said Paul Arora, Co-Founder of Inka Health.

The ability to robustly translate clinical trial results to diverse real-world populations is increasingly critical as regulators, payers, and clinicians seek evidence that reflects actual patient outcomes. Methodologies such as those demonstrated in this Study offer a scalable, scientifically rigorous path toward this goal especially in cancers like NSCLC where patient populations are highly heterogeneous.

Among the Study’s notable co-authors is Dr. Vivek Subbiah, an oncologist and leader in early-phase drug development. Dr. Subbiah currently serves as Chief of Early-Phase Drug Development at the Sarah Cannon Research Institute, where he oversees one of the largest early oncology clinical trial networks globally.[4] He has served as principal investigator on more than 100 Phase I and Phase II clinical trials and has played a pivotal role in several tissue-agnostic drug approvals, including therapies targeting BRAF[5] and RET[6] genetic alterations[7]. His work has contributed directly to regulatory approvals by both the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA).[8] Dr. Subbiah has authored over 400 peer-reviewed publications in leading journals such as The New England Journal of Medicine (NEJM), Nature Medicine, and JAMA Oncology.

“The ability to model outcomes for patient groups who wouldn’t typically be included in clinical trials is not just a technical achievement—it’s a clinical and strategic one. It opens the door to faster trial design, smarter expansion decisions, and better evidence for patients who are often overlooked. These kinds of tools will be essential as we push toward more globally inclusive and data-driven drug development,” said Dr. Subbiah.

About Inka Health 

Inka Health is an AI-driven analytics company revolutionizing oncology research and drug development through advanced causal AI. Its proprietary platform, SynoGraph, leverages AI-powered causal inference to identify which cancer patients are most likely to respond to specific treatments, advancing precision medicine. By integrating diverse multimodal medical data—including genomics, transcriptomics, and proteomics—SynoGraph uncovers hidden insights that can optimize treatment decisions and clinical trial design. With this cutting-edge technology, Inka Health aims to help pharmaceutical companies accelerate drug development, reduce trial failures, and bring life-saving therapies to market faster.

About Onco-Innovations Limited

Onco-Innovations is a Canadian-based company dedicated to cancer research and treatment, specializing in oncology. Onco’s mission is to pursue the prevention and treatment of cancer through pioneering research and innovative solutions. The company has secured an exclusive worldwide license to patented technology that targets solid tumours.

ON BEHALF OF ONCO-INNOVATIONS LIMITED,

“Thomas O’Shaughnessy”

Chief Executive Officer

For more information, please contact:

Thomas O’Shaughnessy

Chief Executive Officer

Tel: + 1 888 261 8055

investors@oncoinnovations.com

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[1] https://doi.org/10.1101/2025.05.30.25328679

[2] https://doi.org/10.1101/2025.05.30.25328679

[3] Comorbidities refer to additional medical conditions or diseases that a patient may have alongside their primary illness. In the context of cancer, common comorbidities might include conditions such as diabetes, heart disease, or chronic respiratory illnesses, which can affect treatment choices and outcomes. (see https://my.clevelandclinic.org/health/articles/comorbidities for more)

[4] https://sarahcannon.com/about/newsroom/vivek-subbiah-md-joins-scri-to-advance-early-phase-clinical-research?

[5] BRAF is a gene that, when mutated, can drive cancer growth. Targeted therapies for BRAF mutations are approved for several cancers, including melanoma and lung cancer. (see https://www.cancer.gov/publications/dictionaries/cancer-terms/def/braf-gene for more)

[6] RET is a gene involved in cell signaling. Abnormal RET gene changes can lead to cancer, and targeted RET inhibitors have been approved for cancers such as lung and thyroid cancer. (See https://www.cancer.gov/news-events/cancer-currents-blog/2023/selpercatinib-ret-lung-medullary-thyroid#:~:text=Selpercatinib%20is%20approved%20for%20treating,proteins%20involving%20parts%20of%20RET. for more)

[7] https://oncodaily.com/drugs/45235?

[8] https://www.nursingcenter.com/journalarticle?Article_ID=6696238&Issue_ID=6696154&Journal_ID=401957&