Health Informatics

How do we analyse complex diseases? Are computer-based solutions such as AI and machine learning the best solution direction? What is their potential and limitations? And what future steps do we need in R&D to actualise such solutions?

Harsha Rajasimha
Global Head, Life Sciences R&D, NTT DATA Inc.
Washington, USA

Patient Centricity in The Era of Data-Driven Value-Based Precision Medicine

The growing number of FDA approved targeted therapies on one-side and an increasing number of pay-for-performance deals between big pharma and payers on the other, indicates that the life science industry is heading towards a data-driven, value-based, precision medicine. In order to adapt to this new paradigm, life science companies need access to RWD (Real World Data) and the ability to draw insights for clinical R&D, value-based pricing negotiations and better reimbursements. Insights drawn from RWD are expected to enable biopharma companies to better engage with the patients directly eliminating the traditional bottlenecks. Harsha Rajasimha will present his point of view on how Biopharmaceutical companies are adapt to these and other major trends to sail through the storm and present potential solutions to address use cases in R&D, commercial and value-based reimbursements.

Sahar Gelfman
Institute for Genomic Medicine, Columbia University Medical Center
New York, USA

Identifying Non-Coding Variants that Cause Rare Disorders: Opening a Window to the Rest of the Genome in Variant Detection Analyses

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods successfully capture the effect of coding variants, but ineffectively capture pathogenic non-coding variants in genic regions. For this reason, synonymous and intronic variants are mostly overlooked when searching for disease risk, although they may infer severe damage to the protein by damaging the final transcript. Here we present  the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP’s strong advantage in finally allowing for the inclusion of these sites in gene discovery and diagnostic sequencing efforts is already being utilized at institutes around the world to perform genetic diagnosis and identify risk factors in large disease cohorts such as ALS, epilepsy, Parkinson’s disease and schizophrenia.

Edit Buzas
Professor and Chair at Semmelweis University, Department of Genetics, Cell- and Immunobiology
Budapest, Hungary

Extracellular Vesicles: Next Generation Tools in Precision Medicine?

Extracellular vesicles such as exosomes, microvesicles and apoptotic bodies are recently recognized subcellular structures of cell-cell communication. Their potential exploitation as novel biomarkers and/or therapeutic tools place them into the focus of intense current research. The talk will summarize the strengths and limitations of extracellular vesicles with respect to their potential use in precision medicine.

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