Abstract
A precision medicine ecosystem for Alkaptonuria through clinical and experimental knowledge management and sharing system for an integrated approach Alkaptonuria (AKU) is a rare, debilitating genetic metabolic disorder caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. The condition leads to the accumulation of homogentisic acid (HGA), resulting in ochronosis and progressive damage to multiple organs, particularly joints, heart, and kidneys. Although recently approved therapies such as nitisinone (NTBC) can lower HGA levels, they fail to halt disease progression, address underlying inflammation, or reverse established tissue damage. Moreover, AKU lacks prognostic biomarkers, disease severity indicators, and effective combination therapies. Recent evidence also challenges the traditional view of AKU as a non-neurological disease by indicating potential involvement of the central and peripheral nervous systems. This project proposes an innovative precision medicine approach to address the multifaceted nature of AKU, combining preclinical biomedical research, patient data, and artificial intelligence (AI) tools. The project is structured around four main objectives: 1. Understanding the molecular mechanisms of AKU, utilizing patient-derived samples and in vitro disease models to investigate key factors such as inflammation, oxidative stress, lysosomal dysfunction, and DNA damage. 2. Exploring neurological involvement in AKU, particularly HGA-induced neuroinflammation and neurotoxicity using co-cultures of neuronal and microglial cells, along with blood-brain barrier (BBB) models. 3. Preclinical evaluation of novel therapeutic strategies, focusing on antioxidant molecules (e.g., N-acetylcysteine, ascorbic acid) and anti-inflammatory agents (e.g., methotrexate), with the aim of identifying more effective combination therapies. 4. Enhancing the ApreciseKUre digital ecosystem—a unique integrated AKU patient database—by incorporating clinical, molecular, and AI-assisted predictive tools to support patient stratification, biomarker discovery, and treatment personalization. The research integrates molecular biology, biochemistry, bioinformatics, and AI across five work packages (WPs). Key methodologies include: • Metabolomics (HPLC/NMR) to analyze patient blood samples; • Proteomic analyses (LC-MS/MS) of treated cellular models; • Imaging, Western blotting, immunofluorescence, RT-PCR, and ELISA for the analysis of oxidative and inflammatory markers; • Development of a Clinical Knowledge Portal (CKP) and AI tools to analyze integrated data from the ApreciseKUre platform. Two research units (Università di Siena and Università di Bologna) will collaborate to share expertise in experimental biochemistry, neurobiology, pharmacology, and computational biology. The expected results are: • New insights into AKU pathophysiology, particularly concerning brain involvement and lysosomal dysfunction; • Discovery of prognostic biomarkers and indicators of disease severity and progression; • Preclinical validation of effective drug combinations, addressing oxidative stress and inflammation in AKU; • Enhanced ApreciseKUre database, functioning as a dynamic clinical knowledge tool integrating patient data, in vitro results, and AI-driven models; • Publications in peer-reviewed journals, workshops, and engagement with AKU patient associations for knowledge dissemination. The deliverables include experimental reports, updated datasets for ApreciseKUre, AI-driven prediction modules, and at least four scientific publications. A kick-off and final scientific meeting are also planned, with interim workshops as necessary. This project addresses strategic goals within the PNRR thematic cluster “Human Wellbeing” and aligns with Italy’s national research strategies in biomedicine, biotechnology, and digital health. It contributes directly to the United Nations 2030 Agenda for Sustainable Development, particularly the 2021 UN Resolution on rare diseases, by promoting inclusive healthcare and research for persons living with rare diseases (PLWRD). Expected long-term results include: • Improved quality of life for AKU patients through personalized therapeutic strategies; • Cost savings in healthcare, potentially reducing surgical interventions and long-term complications; • Paradigm model for rare disease research, applicable to other monogenic and rheumatologic conditions; • Strengthening of international collaborations, with ties to global AKU research networks and patient advocacy groups such as AKU Society UK, and others. The project is distinguished by its integration of experimental, clinical, and digital domains within a single precision medicine framework. The use of AI to refine diagnostics and therapeutic decision-making, combined with dynamic in vitro models and a sophisticated patient database, offers a model for addressing complexity in rare diseases. The multidisciplinary consortium brings together decades of expertise, a strong publication record, international partnerships, and a proven track record in patient-centric research and data sharing.
Dettagli del progetto
Responsabile scientifico: Cristina Angeloni
Strutture Unibo coinvolte:
Dipartimento di Scienze per la Qualità della Vita
Coordinatore:
Università di Siena(Italy)
Contributo totale Unibo: Euro (EUR) 95.540,00
Durata del progetto in mesi: 24
Data di inizio
30/11/2023
Data di fine:
28/02/2026
