Improving Clinical Studies for Better Data and Patient Experience by Utilizing the Protocol Considerations for Data Optimization

Featured Solution: Protocol Considerations For Data Optimization

One member company wanted a mindset shift to a data collection approach and process that is fit for purpose. Their aim was to avoid the common pitfalls of bigger studies with more complicated data collection. They felt it was a unique time to take advantage of the regulatory momentum on measured, risk-proportionate data collection. They leveraged the principles from the Protocol Considerations for Data Optimization from TransCelerate as a resource to help their clinical study teams critically assess proposed clinical study-level activities and promote more purposeful designs. They took a two-pronged approach by both piloting an optimized strategy with oncology as well as addressing optimizing data collection at the design stage by adjusting their internal governance through an advisory group.

For the pilot, the team asked themselves: what can we do to bring promising therapies to patients faster? The major challenge was that the probability of success (PoS) in oncology clinical research remains very low (from discovery through to approval) at a 3.4% success rate. However, a recent study found that when a team decides to identify a lead indication it comes with a higher probability of success (11.4% vs all indications) (Wong CH et al. Biostatistics. 2019). Also, a positive patient selection bio marker can lead to greater success. The company took a “less is more” approach for their early oncology clinical trial to increase PoS. Their theory for the pilot was that smaller documents would make it easier for sites to execute and collect high quality data.

They started by identifying their own current challenges with a cross functional working group across three different categories (strategy, tactical plan, and operational execution). Then addressed and mitigated / improvements incorporated into their company’s Oncology first-in-human (FIH) study that designed a pilot that is currently in the clinic.

Their strategy included:

• CHOOSE clear target patient population and plan indication sequencing; START with select lead indication(s)
• HARMONIZE Target Product Profile (TPP), Clinical Development Plan (CDP), and protocol; align with stakeholders across functions / development continuum
• DESIGN clinical studies that estimate a scientific hypothesis to adequately assess safety & anti-tumor activity

Their tactical plans included:

• OPTIMIZE dose and regimen early, align timing of Health Authority (HA) interactions to solicit feedback on Recommended Phase 2 Dose (RP2D) pivotal plans
• SELECT eligibility criteria that adequately represent the target patient population
• SIMPLIFY schedule of assessments in a stage-appropriate way for scientific hypothesis testing
• MAX confidence in early clinical data by using stringent failure/success criteria

Their operational execution included:

• Accelerate dose escalation for speed AND data quality 
• Engage with investigators, sites, Patient Advisory Group (PAG) early and often during the protocol development process 
• Optimize site selection processes 
• Implement scenario planning to avoid last-minute changes to protocol documents

As a result, the team optimized internal workflows to obtain critical data for the cohort and made study decisions more quickly. Current enrollment projections of this design indicate time from first subject dosed (FSD) to proof of concept (POC) in selected lead indications may be shortened by approximately 12 months. The success of the pilot led to an advisory group across the company to help optimize clinical protocol design.

The advisory group was tasked with helping to take the learnings from the pilot and scaling within the organization. They were looking to embed smarter designs for more efficient protocol development which would deliver protocols faster. They wanted to make decisions at the design stage rather than simply after the protocol had been developed. The key challenges they faced were: 

• Key study design decisions were not aligned at the start of protocol authoring (can’t develop detailed content without higher level decisions, significant number of comments during team review cycles)
• They noticed study design changes late in protocol development (many downstream impacts, rewriting of content waste time and resources; risk for document inconsistency, overall delay in protocol delivery, operational impact, delay to study start up)
• There were overly complex study designs = overly complex protocols (excessive endpoints and assessments that do not support the key clinical hypothesis that add protocol content (time and complexity) and more content needing team and cross-functional alignment)

Their approach to address these challenges at an organizational level was the addition of their study design committee which was an internal governance advisory group that preceded protocol development as a mandatory step before teams could move to protocol development. The study design committee worked with teams to:

• Front-load decision making and lock strategy for key study design elements BEFORE protocol writing
• Allow team and stakeholders to focus on high-level scientific concepts rather than document content
• Reconcile differing priorities across internal functions
• Open discussion for input when team is undecided
• Establish consistency across studies and programs

As a result, the team found that their work did reduce the amount of time needed for governance along the way. It also helped with:

• Avoiding revisiting key decisions
• Avoiding last-minute key decisions
• Eliminating or reducing the need for lengthy cross functional scientific alignment at the full protocol stage
• Downstream efficiency 
• Optimized data collection that supports key clinical hypo
• Less future amendments
• Better patient experience