The challenge: Maintaining clinical trial timelines dependent on vendor performance

A global biotechnology company with seven clinical-phase programs approached QuartzBio with a challenge shared by many organizations in the industry — overall assessment of clinical trial vendors and sites was time-consuming and unreliable, given global site footprints, complex sample lifecycles, and inconsistent naming across sites and vendors.

Tasked with preparing regular executive summaries, the company’s clinical and biomarker operations teams were spending significant time deriving trial-wide key performance indicators (KPIs), lacking visibility into vendor performance – and challenged to translate current experience into future study planning.

This lack of visibility was exacerbated by the absence of standard nomenclature and inconsistent data entry across vendors. Clinical operations teams were spending time manually mapping and reconciling data and metadata from multiple independent sources.

The QuartzBio approach: Continuously updated visibility through consolidated dashboards

The client implemented the virtual Sample Inventory Management solution, powered by the QuartzBio® Data Platform.

Using the platform’s built-in dynamic dashboards (examples shown in Figure 1), augmented by some bespoke reports custom-built by QuartzBio for the client, the clinical operations team was able to easily monitor and report on multiple vendor performance parameters:

  • Turnaround times from sample collection to sample processing to test result generation
  • Completion rate of samples tested within stability window
  • Sample processing quality KPIs by site and vendor
  • Rate of missed/incomplete baseline and on treatment collections
  • Data discrepancies by site and vendor
  • Protocol violations by site and vendor
  • Consent violations by site and vendor

Core to QuartzBio’s approach is rigorous sample data integration. The virtual Sample Inventory Management solution automatically harmonizes sample information from multiple sites, vendors, and other sources, regardless of format or nomenclature. QuartzBio applies an adaptive data dictionary that learns and updates common nomenclatures over time, creating a flexible model. Using this model, it harmonizes the non-standard nomenclature across vendors and studies, enabling clear visibility and eliminating manual processes.

The outcome: Fast, accurate reports, timely decisions, course correction on-study

Empowered by 24/7 access to vendor performance, visibility into expected sample schedules, and full reconciliation oversight, the client’s clinical operations teams started using the QuartzBio Data Platform for generating visualizations to guide executives and inform investors.

These visualizations enabled the client to take action at specific sites and vendors that were underperforming. Armed with more reliable trends in sample schedules and turnaround times, the biomarker operations team could more accurately project data delivery timelines and anticipate when key decisions could be made.

Watch our webinar, “Biomarker Operations KPIs,” to see these dashboards in action, as well as the latest reporting capabilities available to users of QuartzBio’s vSIM solution.

clinical sample KPIs trials

The clinical operations team at a mid-sized oncology biotech client depends on QuartzBio’s virtual Sample Inventory Management solution to gain global visibility into their expected sample schedule, across all collection sites.

They can identify gaps in sample data and sample shipments. Full oversight enables the team to identify data discrepancies, consent violations, and subject exclusions.

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data management KPIs clinical trials

The translational research teams at a clinical-stage biopharma client rely on QuartzBio to report to stakeholders on how their clinical programs are progressing. Together with the clinical data management team, they can monitor vendors, turnaround times, and gaps in contracts – across the entire program.

Using dashboards on the QuartzBio Data Platform, the team can deliver executive summaries and presentations to investors, guiding key decisions and projected timelines.

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sample data multiple trials QuartzBio

One of the core challenges faced by clients in biomarker-informed clinical trials is the integration of key sample and subject metadata derived from multiple independent sources. One case involved multiple immuno-oncology studies, with data coming from multiple sources, in multiple formats.

The QuartzBio® platform was deployed to create a Master Sample Inventory view, yielding answers to key on-study questions, such as:

  • How does the number of available plasma samples compare to the number of available FFPE samples for Study X?
  • What proportion of samples have data available? What’s the breakdown of data availability by sample type?

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Biomarker Data Management in phase 2-3

QuartzBio’s approach to biomarker data management can be seen in the context of a phase 2/3 dose evaluation and expansion oncology study with 5 assays run at 4 different labs. Using QuartzBio’s technology-driven solution, the sponsor could:

  • Pull together and visualize PK, cytokine, and flow cytometry data to inform optimal dosing
  • Integrate mutation, IHC, and cytokine data to develop a multimarker signature to stratify patients

The cross-functional QuartzBio team—including data scientists, biomarker data management programmers, and data managers—delivered a centralized data hub for biomarker data transfer, integration, and collaboration, as well as interactive reports and support for ongoing trial activities.

The client found that our approach transformed their ability to reach key clinical trial objectives.

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flow cytometry data management QuartzBio

Our client’s translational research team needed to be able to monitor clinical outcomes with respect to patients and biomarker-defined subgroups. Within weeks, QuartzBio deployed virtual Sample Inventory Management and Biomarker Data Management solutions for the clinical trial in progress, enabling the team to:

  • Gain centralized visibility into status of samples as they move from sites to central and specialty labs
  • Reveal insights on actual samples collected versus expected samples collected
  • Generate hypotheses based on biomarker assay results linked to patient clinical data and dosage information
  • Eliminate time-consuming and manual data cleaning and cross-referencing processes

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