OBJECTIVES: This research describes tools and methodologies to fill an existing data gap between randomized phase 3 clinical studies (RCTs), Real World Evidence (RWE) data and clinical practice when a drug is near to be/already approved, but yet to be reimbursed with an assigned price for a rare or orphan kidney disease.

METHODS: This original research advocates the urgent use of new methodologies to predict when patients suffering from rare and orphan kidney diseases, can achieve a more adequate benefit/risk assessment profile of an approved/to be approved drug. For the scope, an ongoing extensive literature review from main available sources, including PUBMED, ENBASE, COCHRANE, CLINICALTRIALS.GOV, DR EVIDENCE and similarly available platforms with posted clinical study results will be completed.

RESULTS: Expected outcomes:

• Developing a novel methodology to fill the highlighted clinical gap, such as a risk assessment tool to assist Health Care Professionals (HCP) to support patient treatment algorithms.
• Developing a predictive/treatment algorithm based on both RCTs, RWE data from available registry platforms.
• Conducting a subsequent clinical study for verification and validation of clinical and laboratory results for improving therapeutic appropriateness of available and new medicines.
• Producing HCP feedback, truly patient-focused data, regulatory and payers’ feedback.
• Incorporating new clinical evidence in current scientific management and treatment guidelines.

CONCLUSIONS: Results of this ongoing qualitative research are expected in the first half of 2023. The main objectives are 1) to generate meaningful clinical data and an assessment tool based on new technologies to achieve a more adequate patient-focused benefit/risk drug profile in this diseases area. 2) to further improve quality of available data for future pharmacological treatments. Ultimately the research is expected to generate a truly patient-focused self-management approach through increased disease awareness and education and personalized treatment/risk disease algorithms. The generated results are expected to better reflect clinical practice and patient-focused disease management.