Accelerating Clinical Development

What is accelerating clinical development?

Accelerating clinical development means cutting the time from protocol to results by removing delays in study startup, data collection and data review, while keeping participant safety and data reliability intact. ICH E6(R3) supports this by asking sponsors to focus effort on what is critical to quality.

Speed matters to sponsors because development time affects cost and the remaining patent life of a product. It matters to patients because a study that enrolls and completes sooner brings an answer, positive or negative, to people waiting for new treatments and to the clinicians who care for them.

Where does time go in clinical development?

Time goes mostly to handoffs and waiting rather than to the work itself. Documents wait for signatures, sites wait for contracts and budgets, data waits to be entered, and queries wait for answers. Each handoff that runs through email and spreadsheets adds days that nobody tracks.

• Feasibility: Repeated questionnaires to sites asking for data they have already provided for similar studies.
• Contracts and budgets: Long negotiation cycles over clinical trial agreements and per-patient budgets.
• Ethics review: Separate submissions to several review boards with different forms and timelines.
• Site activation: Collecting regulatory documents, training records and system access before the first participant.
• Data entry lag: Visit data transcribed from site records into the sponsor's system days or weeks later.
• Query cycles: Data questions that bounce between monitors and coordinators for weeks.
• Database lock: Final cleaning that uncovers problems which could have been fixed months earlier.

What changed with ICH E6(R3)?

ICH E6(R3), the revised guideline for good clinical practice, reached Step 4 on January 6, 2025. FDA published it as final guidance in September 2025. It emphasizes quality by design, proportionate and risk-based approaches, and flexibility in how trials are conducted, including the use of digital tools.

• Quality by design: Identify factors critical to quality during protocol design and build controls around them.
• Proportionality: Match oversight effort to the risks to participants and to the reliability of results.
• Risk-based approaches: Focus monitoring and data review on what matters most rather than checking everything equally.
• Technology neutrality: Accept a range of trial designs and data sources, including remote elements and electronic consent.

For technology teams, the practical effect is that systems should help sponsors define, track and act on critical-to-quality factors, rather than enforcing uniform checks on every data point. Many legacy workflows were built for a 100 percent source verification mindset that E6(R3) no longer assumes.

How does quality by design shorten studies?

Quality by design shortens studies by removing work that does not protect participants or results. Protocols that collect fewer non-essential data points need less entry, fewer queries and simpler monitoring, and sites find them easier to run.

Start with the protocol. Every assessment and data field should trace back to an objective, an endpoint or a safety need. Fields that exist only because a template included them create entry time at sites and cleaning time at the sponsor, and they slow enrollment when visits become long and tiring for participants.

Systems can support this by tagging each data element with its purpose and criticality. Monitoring plans, edit checks and review listings can then concentrate on critical data, while lower-risk fields receive lighter checks.

Protocol amendments are the other target. Each amendment means new review, retraining, system changes and sometimes reconsent, and amendments that could have been avoided with better design input are among the costliest delays in a study. Involving site staff and data managers in protocol review catches many of them before the first site opens.

How can study startup move faster?

Study startup moves faster when steps run in parallel instead of in sequence, when sites reuse information they have already provided, and when everyone can see the status of each site in one place. Most of the gain comes from coordination, not from working harder.

• Site profiles: Keep site capabilities, staff credentials and past performance on file so feasibility asks only study-specific questions.
• Template agreements: Start from pre-negotiated contract language and budget ranges with frequent sites.
• Parallel tracks: Run contracts, budgets, ethics review and document collection at the same time.
• Document exchange: Share and track regulatory documents through a portal rather than email attachments.
• Activation dashboard: Show every site's status and the next blocking item to the whole study team.

Site burden is the hidden cost of slow startup. A coordinator at a busy research site may handle several sponsors, each with its own portal, forms and training requirements. Sponsors that cut duplicate requests and accept training already completed for other studies often see faster activation simply because their study becomes easier to say yes to.

Budget negotiation benefits from data. When sponsors can show fair market ranges for common procedures and the site's own past rates, negotiations focus on genuine differences rather than restarting from zero for each study.

What does single IRB review change?

Single IRB review means one institutional review board reviews a multi-site study on behalf of all participating US institutions, rather than each site's board reviewing it separately. Under 45 CFR 46.114(b), US institutions in cooperative research covered by the Common Rule must generally rely on a single IRB.

The benefit is fewer duplicate reviews and consistent consent documents. The cost is coordination: reliance agreements, local context reviews and communication between the reviewing board and each site. Systems that track reliance status, approved document versions and site-specific changes help avoid sites starting with outdated materials.

How should trial data flow?

Trial data should flow from its source into the sponsor's systems with as little retyping as possible. Every transcription step adds delay and errors, so modern studies pull data from EHRs, labs, devices and electronic patient reports through interfaces rather than relying on coordinators to copy it.

• EHR to EDC: Transfer structured data such as labs, vitals and medications from site EHRs into electronic data capture.
• Central labs and imaging: Load results directly from vendors with reconciliation checks.
• Electronic outcomes: Capture patient-reported data on devices or apps, time-stamped at entry.
• Risk signals: Feed data to central monitoring that flags unusual patterns at sites.
• Clean as you go: Run edit checks at entry so issues are fixed during the visit window, not months later.

Data standards make these flows reusable. Mapping site data to consistent formats once, rather than for each study, turns integration from a per-study project into shared infrastructure.

Validation is part of the plan, not an afterthought. Systems that capture or transform trial data must be fit for purpose, with documented requirements, testing and change control, and audit trails that show who changed what and when. Building that evidence alongside the interfaces keeps the data flow from becoming the next bottleneck.

EHR integration has limits worth planning for. Not every site's EHR exposes the data needed, and clinical records often hold values in units or codes that differ from the protocol. A mapping review with each site's informatics team, before first patient in, avoids surprises once data starts arriving.

Which metrics show cycle time improving?

• Protocol final to first site activated: The core study startup measure.
• Site selection to activation: Shows contract, budget and document bottlenecks by site.
• Visit to data entry: Days between a participant visit and data in the sponsor system.
• Query age: Time from query raised to query closed.
• Last visit to database lock: The closeout measure that reflects data quality during the study.

Track these by site and by study, not only as portfolio averages. Patterns show which sites, countries or processes cause delays and where an investment in tools or templates would have the biggest effect.

Capture the timestamps automatically. Metrics assembled from status spreadsheets are only as good as the last update, while dates recorded by the systems that perform each step, such as a signed contract or an approved document, give an accurate picture without extra work for study teams.

Where acceleration efforts stall

• Point tools: Separate systems for feasibility, contracts, documents and data with no shared site record.
• Sequential habits: Teams that wait for one step to finish before starting the next.
• Overloaded protocols: Studies that collect far more data than their objectives require.
• Manual reconciliation: Lab and safety data matched by hand at the end of the study.

Change management matters as much as tooling. Monitors used to visiting every site on a fixed schedule need training and confidence to rely on central signals, and quality teams need to agree how risk-based decisions are documented. Without that, organizations buy risk-based tools and keep running the old process alongside them.

Pick one bottleneck and fix it end to end. A sponsor that cuts contract cycle time in half across its regular sites, and can show the data, earns the internal support and budget needed to tackle the next delay in the chain.

How Agnotic can help

Faster studies depend on connected systems and clear status. Agnotic Technologies builds clinical trial management software that tracks feasibility, contracts, ethics review and site activation in one place, with dashboards that show each site's next blocking item.

Agnotic also builds the data flows that cut entry lag, including EHR-to-EDC interfaces, lab and device integrations, and risk-based monitoring views aligned with ICH E6(R3), validated for use in regulated trials and connected to the cycle time metrics above.

Sources

• ICH E6(R3): Guideline for Good Clinical Practice, Step 4
• Federal Register: E6(R3) Good Clinical Practice, final guidance for industry
• 45 CFR 46.114: Cooperative research