AI-driven engine advances our understanding of rare and ultra-rare diseases

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Access to robust, multimodal and longitudinal patient clinical and genetic data is paramount to advancing the understanding of rare and ultra-rare diseases. Learning from this data is the goal of Sema4’s pioneering health intelligence platform, Centrellis®. The AI-led process involves curating a unique RWD to understand the fundamental natural history of diseases, using that data to make predictions about patient subgroups, and developing metrics to guide drug development. then to test these predictions in a retrospective or prospective setting with real patients. and health systems.

In-depth patient data curation using natural language processing (NLP) and expert review

This approach uses machine learning and NLP to collect and store unique patient data to understand the progression of rare and complex diseases. Thanks to an innovative genomic infrastructure, Centrellis® can generate, analyze, and interpret a combination of longitudinal clinical and genetic data (clinicogenomics data) from electronic health records (EHRs), including free-text clinical notes, to create diverse patient profiles to better understand trajectories rare diseases. RWD can also be extracted from lab results, insurance claims, clinical trials, medical reports, omics and safety data, scientific literature, etc.

In addition to leveraging the power of AI, Sema4 also incorporates key insights from scientific opinion leaders from renowned health systems, like Mount Sinai. For example, an expert consultant is an important element in creating an accurate data dictionary.

This combination of expert guidance and AI-powered understanding is key to creating research-ready data. This data provides decision makers with an invaluable tool to achieve concrete results.
information to make data-driven decisions.

Leverage AI-driven insights to make data-driven decisions

The importance of maintaining patient records as a whole from disparate sources provides insight into the rare disease patient journey. For example, Sema4’s clinicogenomic dataset can be used to develop fundamental natural history studies to generate in-depth longitudinal and multimodal insights into rare diseases such as lysosomal storage disorders (LSD).

Insights from this dataset can also be used to predict and test patient outcomes, such as rate of disease progression and therapeutic response, to guide drug development. Additionally, this integrated RWD can be used in combination with advanced molecular profiling to go beyond general full genomic profiling to more targeted analyzes including single-cell RNA analysis and other advanced omics. For example, biobanked samples or those from clinical practice under IRB can be profiled with these technologies to confirm initial results.

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Connecting the dots from R&D to commercialization

Current linear R&D processes are inefficient, resulting in wasted time and costs. AI engines and other innovative technologies that continuously learn are game changers, creating a paradigm shift in R&D by enabling a more cyclical iterative process.

This can help generate real-world insights to fuel informed decision-making across the drug discovery pipeline, accelerating time-to-market progress to ultimately deliver safe and cost-effective therapies to patients who need it the most.

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