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Overview

Case Studies & Success Highlights

Even without referencing specific trial sponsors or exact hospital systems, early adopters of PGxAI in clinical research have reported measurable improvements in patient stratification, adverse event reduction, and overall trial efficiency. Below are hypothetical examples illustrating how CROs and sponsors can leverage AI-driven pharmacogenomics across different therapeutic areas.
Case Study
Mid-Phase Oncology Trial
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Challenge
A Phase II oncology study struggled with slow recruitment and higher-than-expected toxicity in patients receiving a novel targeted therapy.
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Solution
By integrating AI-driven patient stratification, the research team could screen for specific genetic markers linked to treatment responsiveness and lower toxicity risk.
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Results
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30% faster enrollment through refined eligibility criteria
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25% drop in severe adverse events (SAEs) tied to off-target toxicity
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Fewer protocol deviations and a more uniform patient cohort
“PGxAI gave us the confidence to enroll the most responsive patients, helping us meet interim milestones ahead of schedule.”
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Dr. Maria Daniels
Clinical Program Lead, Oncology Trial
Case Study
Multi-Center Cardiovascular Study
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Challenge
A Phase III trial for a new anticoagulant faced frequent dropouts due to bleeding complications and dosing inconsistencies across multiple sites.
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Solution
The research team embedded PGxAI’s AI-driven alerts in their EDC system to tailor dosage strategies based on critical variants (e.g., CYP2C9, VKORC1), while pre-screening high-risk participants.
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Results
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Significant reduction in dropout rates
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Lower hospitalization costs due to fewer bleeding events
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Accelerated timeline to meet primary endpoints without major amendments
“By integrating PGxAI into our workflow, we identified a safer therapeutic window. The trial concluded on time with fewer complications.”
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Sarah Lee
VP of Clinical Development, Cardio Study
Case Study
Late-Phase Psychiatry Program
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Challenge
A late-stage clinical trial for a novel antidepressant struggled with inconsistent patient response, risking the statistical power needed for FDA approval.
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Solution
PGxAI pinpointed specific genetic subgroups likely to respond favorably, refining inclusion criteria and personalizing dosage to poor and ultra-rapid metabolizers.
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Results
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Stronger efficacy signals at interim analysis
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Reduced adverse events due to gene-informed dosing
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Improved sponsor confidence in final outcomes and commercial viability
“PGxAI’s genetic stratification made the difference between a borderline result and a clear success.”
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Dr. Alan Perez
Clinical Operations Director, Psychiatric Trial
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