28 years after I graduated from medical school I am a physician-scientist, board-certified surgeon, emergency physician, osteologist, former Professor of Regenerative Medicine, founder, innovator, teacher, key-note speaker, strategic advisor, reviewer, strategic workstream lead, and chair of various boards.
My vision is to develop drugs based on digital twin models, personalized value and outcome-based strategies, leveraging big data analytics, AI based clustering and prediction modeling as well as innovative imaging endpoints.
As a leader in Pharma, Biotech and Academia with experience spanning from basic science, clinical research, academic medicine/clinical practice, early and late stage clinical development, AI-based phenotyping/prediction modeling, digital image analysis, new endpoint development, drug safety, BD&L, medical affairs, medical communication, guideline committees, health authority (FDA, EMA, PMDA) and HTA (GBA) consultations, pre- and post market activities including launch preparation,
I have worked on small molecules, biologics, gene therapies and biosimilars in various therapeutic areas (oncology, immunology, rheumatology, dermatology, cardiovascular, renal): E.g., denosumab, evolocumab, brodalumab, panitumomab, blinatumomab, pegfilgrastim, canakinumab, secukinumab, and compounds addressing key immunology pathways, e.g., IL-1, IL-17, JAK/STAT, inflammasome/NLRP3,
and evaluated multiple external opportunities in immunology, rheumatology, dermatology, neurology, and musculoskeletal diseases.
This panel will explore how a collaborative, cross-sector approach, uniting industry leaders and academic institutions, can unlock the full potential of integrated data and federated learning to transform R&D in complex disease areas. Focusing on osteoarthritis (OA) as a case study, the discussion will highlight how these technologies can overcome long-standing barriers in clinical research, particularly in diseases with high prevalence and socio-economic burden. By embedding disease progression modeling within next-generation analytics such as digital twins, this approach offers a path toward more predictive, efficient, and outcome-driven therapeutic development – bringing new hope to millions of patients worldwide.