AIkido Pharma
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AIkido Pharma, Cogia BioTech AI Use for Cancer Markers Progresses

Development of Products, Tools, and Technologies to Accelerate Development of Key Marker Panels for the Personalized Treatment of PDA

AIkido Pharma Inc. (Nasdaq: AIKI) today announced that its work with research partner, Cogia BioTech LTD (www.cogia.de), has delivered several key updates on efforts to accelerate the study of PDA (Pancreatic Ductal Adenocarcinoma) treatments. As previously reported, the Company executed a Scientific Research Agreement with The University of Texas Southwestern Medical Center and Cogia to use machine learning to find genetic markers in people that indicate an increased risk of developing pancreatic cancer.

Cogia Biotech, a Big-data, Artificial Intelligence (AI), and Machine Learning (ML) software company committed to using powerful AI engines and algorithms to develop compelling outcomes in drug development, released a comprehensive progress report to AIkido which included:

  • The identification of clusters of markers that interact in signaling pathways affecting PDA survival.
  • The development of products and tools to provide extensive information on PDA markers to guide personalized medicine.
  • The assembly of survival associations of individual markers from ONCOLNC.org into sets that will help identify novel targets in PDA.
  • Information about gene and protein function in human PDA patients and human PDA cancer cell lines to help prioritize individual diagnostic markers.

Anthony Hayes, CEO of AIkido stated, “We are extremely pleased with the progress that Cogia has made in this research project, and are enthusiastic about their continued efforts to develop panels of markers that will rapidly identify patients who have initiated early events in pancreatic cancer. Cogia’s Diagnostic Assay Application and Prognostic Marker Characterization Application products assemble essential information on antibodies specific to the respective proteins for more than 20,000 antibody reagents and utilizes the unsupervised learning protocols of the Cogia AI/ML engine to identify clusters of similarly expressed genes, and their association with PDA subtypes.  These tools establish the building blocks of what we continue to believe will be an even broader set of usable full-service products and tools for ongoing and more expanded PDA diagnostics, prognostics, and therapeutics.

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