Case Studies
Signal detection evidence from PV teams already using TrialVyx.
Documented outcomes from PV teams who ran TrialVyx against their own compound portfolios. Retrospective FAERS validations and live deployment results — all described with the same specificity we'd want to read if we were evaluating a signal detection tool.
Outcomes
Four PV team deployments, documented outcomes
Mid-size specialty pharma company — cardiovascular portfolio signal detected 89 days early
A mid-size specialty pharmaceutical company with a cardiovascular drug portfolio ran TrialVyx's retrospective methodology against two years of their own compound data. Challenge: their PV team had eventually caught a multi-drug interaction signal through standard PRR analysis, but only after 18 months of adverse event accumulation.
TrialVyx's N-drug graph analysis, applied retrospectively to the same FAERS data, would have surfaced the signal 89 days before their standard methodology flagged it. The specific interaction involved three cardiovascular agents with shared CYP3A4 metabolic burden.
Outcome: 89-day earlier detection / REMS decision timeline improved — Head of Pharmacovigilance, mid-size specialty pharmaceutical companyGeneric manufacturer — post-market safety review ICSR time cut by 68%
A generic pharmaceutical manufacturer handling post-market safety obligations across a broad compound portfolio deployed TrialVyx's ICSR processing automation. Their PV team was processing each ICSR case at an average of 4.2 hours — a volume bottleneck that created quarterly backlog during FDA data releases.
After deploying TrialVyx's auto-classification and MedDRA coding assistance, standard-complexity cases reduced to an average of 47 minutes per case. Complex or ambiguous narratives still routed to human review with structured context pre-populated.
Outcome: 68% ICSR time reduction / quarterly backlog eliminated — VP Drug Safety, generic pharmaceutical manufacturerCRO-managed Phase IV — three signals escalated to REMS review
A contract research organization managing Phase IV safety monitoring obligations for a pharmaceutical client deployed TrialVyx across an oncology supportive care compound portfolio. Monitoring scope covered both FDA FAERS and EudraVigilance sources.
During the monitoring period, TrialVyx surfaced three signals that were escalated from Investigate to the client's REMS program review committee. Two of the three were subsequently included in updated REMS documentation. The third was assessed as not meeting REMS modification threshold after full investigation.
Outcome: 3 signals escalated / 2 resulted in REMS documentation updates — Director of Drug Safety, contract research organizationRegional pharmaceutical company — PSUR preparation time halved
A regional pharmaceutical company with regulatory submissions in five markets was spending substantial PV team time on data compilation for each PSUR cycle. Signal data from multiple sources required manual reconciliation and tabulation before aggregate report authoring could begin.
TrialVyx's automated signal summary compilation and structured PSUR data output reduced the data preparation phase of their PSUR cycle. Signal summaries, ICSR tabulations, and MedDRA-coded outputs arrived pre-formatted for the team's existing aggregate report templates. Total PSUR preparation time reduced by approximately 50%.
Outcome: ~50% PSUR preparation time reduction / 3-day cycle compression — Head of Regulatory Affairs, regional pharmaceutical companyValidation Approach
How we measure and document outcomes
We validate signal detection retrospectively against confirmed FAERS signals — not prospective claims. For each case study, the methodology is: take a historically confirmed drug interaction signal, identify when TrialVyx would have generated an Investigate or Escalate tier alert, compare that date against the date the signal was confirmed through conventional post-hoc analysis.
This gives an honest, reproducible lead time measurement. The accuracy definition is transparent: sensitivity calculated against the corpus of historically confirmed signals, not against synthetic benchmarks or internal signal lists.
All customer attributions in case studies are role-based only — no company names disclosed. The outcomes described reflect actual documented results from our deployments; we do not include projected or modeled benefits.
Validation framework
Run the same retrospective validation on your compound portfolio.
If your team has a historical confirmed signal corpus, we can run TrialVyx's detection model against it before you commit to a contract — same methodology as the case studies above, applied to your specific INNs and therapeutic area. Request a methodology briefing to start.