The Control Tower for Clinical Development

What makes oncology trials unique? They are long, multifactorial, and cohort-intensive — with adaptive designs, high volumes of protocol amendments, frequent safety and efficacy assessments, and multiple overlapping endpoints (e.g., PFS, ORR, OS).

‍Vivo was built to handle the complexity of cancer trials, delivering:
→  Real-Time Cohort Monitoring: Aligns imaging, labs, and EDC data to track progression, response, and toxicity endpoints without waiting for static listings.
→ Early Signal Detection: Surfaces changes in enrollment patterns, site behaviors, and safety signals before they become bottlenecks.
→ Adaptive Oversight Across Protocol Amendments: Maintains insight continuity across protocol amendments and re-baselines expectations automatically, preserving context and enabling ongoing analysis across versions.

Why this matters: You can’t wait for monthly listings to realize a cohort is underperforming or a protocol amendment introduced site confusion. Vivo helps oncology teams anticipate risks to act earlier.

What makes I&I trials different? They capture frequent, episodic symptoms and rely on real-time lab-based biomarker shifts, wearable outputs, patient diaries, and centralized imaging that often live outside traditional EDC systems.

‍Vivo was built to harmonize diverse data to enable:

→ Continuous, cross-system time alignment: Synchronizes high-frequency data from all sources to ensure symptom trends, biomarker changes, and treatment exposure are correctly ordered and interpreted.
→ Flare and adherence detection across data sources: Detect flares that may compromise primary endpoint interpretation, even when not explicitly reported.
→ Population Subgroup Insights: Aligns biologic marker changes, lab data, and symptom self-reporting to understand treatment response and uncover heterogeneity in response across biomarker- and treatment-defined subgroups.

‍Why this matters: Fluctuating symptoms and large, complex datasets can bury signals. Vivo lets I&I teams see through the noise, enabling faster, more confident decisions.

What makes rare disease trials different? In rare disease studies, sample sizes are small, site networks are limited, and patient data is invaluable. There is little room for error.

‍Vivo makes every datapoint count, bringing hypersensitive, patient-level intelligence to low-N recruitment environments.
→ Multi-platform Data Surveillance: Harmonizes data across bespoke systems, registries, and patient-sourced platforms into a single portal to enable automated monitoring and flagging of unusual patterns.
→ Insights to Optimize Performance: Delivers immediate alerts on missing, inconsistent, or outlier data and site performance trends to help sponsors adapt quickly and prevent downstream delays in analysis or regulatory readiness.
→ Optimize Enrollment Yield: Highlights site performance and screen failure trends to help sponsors adapt quickly when every subject matters.
→ Aggregate Across Global Sources: Harmonizes data across CROs, geographies, and niche data platforms to ensure no information is lost in translation.

‍Why this matters: When one subject can shift the entire trial outcome, you need oversight that’s immediate and precise. Vivo gives rare disease sponsors that control.

What makes obesity trials challenging? They require long timelines to demonstrate durable weight loss, face high placebo responses due to lifestyle interventions, and struggle with dropout and adherence erosion. Outcomes must integrate weight-based endpoints (e.g., % body weight change) with metabolic markers (A1C, lipids, blood pressure), behavioral data (diet, activity), and safety monitoring (notably cardiovascular outcomes).What makes rare disease trials different? In rare disease studies, sample sizes are small, site networks are limited, and patient data is invaluable. There is little room for error.

Vivo was built to bring real-time clarity and foresight, enabling:
→ Cross-System Data Harmonization: Integrates EDC, labs, ePROs, COAs, wearable data, and CV safety outcomes into a single, harmonized stream for continuous analysis.
→Adherence and Engagement Risk Detection: Identifies patients or sites trending toward disengagement (e.g., missed visits, declining eDiary compliance) and surfaces actionable insights to prevent dropout.
→Placebo and Response Trend Analysis: Monitors weight/BMI trajectories and metabolic responses across arms in real time, flagging unexpected placebo trends or regional anomalies.
→Enrollment and Timeline Forecasting: Projects recruitment, screen failures, and retention risks, giving teams the foresight to address bottlenecks before they impact primary endpoint delivery.

Why this matters: In obesity trials, behavioral confounders and high dropout can erode statistical power. Vivo helps sponsors detect risks earlier, ensure adherence, and accelerate time-to-insight while maintaining oversight.