One of the reasons trials for Autoimmune indications often fail is that endpoints don’t necessarily track patient-related outcomes.
Conditions like Rheumatoid Arthritis, Lupus, T1 Diabetes, and Multiple Sclerosis generate vast evidence - cytokines, imaging, digital measures. They also have significant impact on patient quality of life, as measured by PROs - Patient related outcomes - such as fatigue, pain, mobility, stool frequency.
However, biomarkers and PROs are siloed and inconsistently defined. Translational teams move slowly, protocols churn, and designs lean on incomplete precedent.
Regulators are now recognizing PROs as pivotal endpoints - sometimes even driving approvals or label changes.
In this webinar, we talk about the opportunity of linking PROs with biomarkers to define composite endpoints and cohorts that reflect patient benefit and regulatory expectations.
This webinar will show how Polly Xtract’s agentic AI unifies autoimmune trial evidence into a single, source-linked dataset. By connecting PROs with biological markers, you’ll see how to benchmark across autoimmune diseases, identify composite endpoints, and design trials set up to capture meaningful signal before decisions harden.
One of the reasons trials for Autoimmune indications often fail is that endpoints don’t necessarily track patient-related outcomes.
Conditions like Rheumatoid Arthritis, Lupus, T1 Diabetes, and Multiple Sclerosis generate vast evidence - cytokines, imaging, digital measures. They also have significant impact on patient quality of life, as measured by PROs - Patient related outcomes - such as fatigue, pain, mobility, stool frequency.
However, biomarkers and PROs are siloed and inconsistently defined. Translational teams move slowly, protocols churn, and designs lean on incomplete precedent.
Regulators are now recognizing PROs as pivotal endpoints - sometimes even driving approvals or label changes.
In this webinar, we talk about the opportunity of linking PROs with biomarkers to define composite endpoints and cohorts that reflect patient benefit and regulatory expectations.
This webinar will show how Polly Xtract’s agentic AI unifies autoimmune trial evidence into a single, source-linked dataset. By connecting PROs with biological markers, you’ll see how to benchmark across autoimmune diseases, identify composite endpoints, and design trials set up to capture meaningful signal before decisions harden.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
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If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.
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