BioPorto (BIOPOR) investor relations material

Current state and challenges in kidney care

• Acute kidney injury (AKI) and chronic kidney disease (CKD) are rising globally, now reaching epidemic levels and affecting a significant portion of hospitalized patients, with many cases going undiagnosed until late stages.

• Advances in critical care have improved survival rates for children and adults with kidney issues, but many survivors live with chronic conditions, increasing the burden on healthcare systems.

• There is a severe shortage of nephrologists, especially in pediatrics, making it essential for primary care and other specialists to be involved in early detection and management.

• Access to new therapies is limited by cost and insurance coverage, creating disparities in care, particularly in underserved populations.

• Care coordination is hampered by resource constraints, especially for non-physician team members critical to comprehensive kidney care.

Innovations and advances in diagnostics and treatment

• The NGAL biomarker, developed over 25 years, enables early detection of AKI and is now FDA-cleared for pediatric use, with ongoing trials in adults.

• NGAL and other biomarkers help differentiate between functional and structural kidney injury, guide treatment decisions, and are used in drug development for nephrotoxicity assessment.

• New medications, including SGLT2 inhibitors, GLP-1 agonists, and MRAs, have expanded treatment options for CKD, but access remains a challenge.

• Pediatric innovations like the Carpe Diem dialysis machine have transformed care for infants with severe kidney disease, enabling earlier and safer interventions.

Role of artificial intelligence and data-driven care

• AI and machine learning models are being integrated into nephrology to predict AKI risk, optimize dialysis, and analyze complex patient data in real time.

• Combining AI-generated risk scores with biomarkers aims to improve prediction accuracy and personalize interventions.

• Successful implementation of AI requires collaboration between clinical experts and data scientists, with attention to data quality and cybersecurity.