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

VANCOUVER, BC / ACCESS Newswire / 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 S1400I¹ (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, comorbidities³, or other factors.