Before you enter Phase 1 clinical trials, your biggest lever is knowing where not to go. Currently, integrating Phase 1 evidence is a messy and slow challenge, so translational teams lack a simple, reliable way to benchmark their drug candidate - and often compare against an incomplete picture and hence risk losing out on time.
Across therapeutic areas, drug development still faces a big gap, early evidence sits across many trials and formats, making it hard to compare a new candidate with what’s already been tried. Without a clear benchmark, choices on patient cohorts, biomarkers, and trial design recommendations take longer and lean on subjective judgment - and once an IND is set, changing course is costly.
This webinar shows a practical way to de-risk your plan. We’ll use Inflammatory Bowel Disease (IBD) clinical trials as a live use case to walk through the whole journey. You’ll see how Polly Xtract’s agentic AI workflow to turn comparator drugs’ Phase 1 reports into a single, source-linked dataset. You’ll see apples-to-apples benchmarks and simple tables that highlight which subtypes to avoid, which to prioritize, and how to design a Phase 1 that’s set up to show signal - before decisions harden.
Before you enter Phase 1 clinical trials, your biggest lever is knowing where not to go. Currently, integrating Phase 1 evidence is a messy and slow challenge, so translational teams lack a simple, reliable way to benchmark their drug candidate - and often compare against an incomplete picture and hence risk losing out on time.
Across therapeutic areas, drug development still faces a big gap, early evidence sits across many trials and formats, making it hard to compare a new candidate with what’s already been tried. Without a clear benchmark, choices on patient cohorts, biomarkers, and trial design recommendations take longer and lean on subjective judgment - and once an IND is set, changing course is costly.
This webinar shows a practical way to de-risk your plan. We’ll use Inflammatory Bowel Disease (IBD) clinical trials as a live use case to walk through the whole journey. You’ll see how Polly Xtract’s agentic AI workflow to turn comparator drugs’ Phase 1 reports into a single, source-linked dataset. You’ll see apples-to-apples benchmarks and simple tables that highlight which subtypes to avoid, which to prioritize, and how to design a Phase 1 that’s set up to show 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%.
%201.svg){kind=icon}
.png)
.png)
.png)
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.
.png)
.svg)
.svg)
%201%20(1).svg)
.svg)
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.webp)
.webp)
.webp){kind=spacer}
.webp){kind=spacer}
.webp)
.webp)
.webp)
.webp){kind=spacer}
.webp)
.webp)
.webp)
.webp)
.webp)
.svg)
%20(1).svg)
