AI meets immune profiling in new Dubai-Vienna healthcare partnership

Two healthcare companies are betting big on the future of immune-based diagnostics. Dubai-based Prognica Labs and Vienna's NextGen Immuno Therapeutics (NGIT) have joined forces to push AI-powered immune profiling into hospitals and clinics across three major markets.

The partnership targets a growing need for precision medicine in cancer care, organ transplants, and autoimmune diseases. By combining Prognica's AI analytics with NGIT's blood-based immune testing platform, the companies want to help doctors make better treatment decisions faster.

Their focus areas include the Gulf countries, India, and the United States - regions where chronic diseases are rising and healthcare systems are hungry for better diagnostic tools.

How will it work?

NGIT has built a platform that analyzes blood samples to create detailed immune profiles for individual patients. Think of it as a snapshot of how someone's immune system is performing at any given moment.

The technology looks for specific immune signatures that can signal disease progression or treatment response before symptoms become obvious. For cancer patients, this could mean catching resistance to immunotherapy early. For transplant recipients, it might predict rejection before traditional tests would spot it.

Prognica brings AI modeling to the mix. Their algorithms can process the complex immune data and turn it into actionable recommendations for doctors. The goal is to move beyond one-size-fits-all treatments toward truly personalized medicine.

"Immune profiling is the missing layer in personalized medicine," says Dr. Ram Vinay Pandey, NGIT's founder and CEO. "Our platform translates complex immunological signals into clinically actionable insights."

Why does it matter?

Current immune monitoring relies heavily on traditional lab tests that often miss early warning signs. By the time problems show up in standard bloodwork, patients may have already developed complications or treatment resistance.

The companies point to several key benefits of their approach. Earlier detection could reduce treatment costs by catching problems before they require intensive interventions. Better patient selection for expensive immunotherapies could improve success rates while avoiding unnecessary side effects.

For healthcare systems dealing with growing cancer rates and transplant programs, having more precise diagnostic tools could help allocate resources more effectively.

"The future of drug discovery lies in precision, not probability," explains Khalid Shaikh, Prognica's founder and CEO. "In markets like GCC, India, and the United States, where oncology burden is rising sharply, we are building not just a diagnostic solution - but an ecosystem for early detection, patient stratification, and cost-efficient care delivery."

The context

The partnership comes as healthcare markets in all three target regions are expanding rapidly. The GCC countries are investing heavily in healthcare modernization, with AI-powered healthcare analytics already representing a $1.2 billion market in the region.

India's pharmaceutical industry is valued at around $50 billion and projected to exceed $130 billion by 2030. The country's large patient population and growing clinical trial activity make it an attractive market for precision diagnostics.

The US remains the largest market for immunotherapy innovations, driving continued investment in cancer treatments and diagnostic technologies. Real-world evidence solutions are becoming increasingly important as drug developers seek faster regulatory approvals.

The global real-world evidence market in the GCC alone is worth $2.5 billion, reflecting strong demand for data-driven healthcare decisions. This suggests healthcare systems are ready to adopt technologies that can provide better patient insights.

Dr. Sujay Patil, Prognica's Chief Scientific Officer, sees the scientific potential: "Immune signatures provide early biological signals long before clinical deterioration becomes evident. By combining high-dimensional immunological data with predictive modeling, we can improve patient stratification, reduce therapeutic trial-and-error, and generate real-world evidence."