While clinical trials have made a number of advancements in decentralized design, data processing, and other aspects, one area remains a pervasive challenge: startup time. Outsourcing-Pharma recently spoke with Jae Chung, vice president of product management and strategy with Oracle Health Sciences, about solutions to optimize the process, and to increase overall chances of success.
OSP: Why is shortening the timeline of a study desirable? What are some of the forces contributing to the pressure to shorten the study cycle, end to end?
JC: Thousands of clinical trials for new drugs and therapies are initiated every year around the world. The drug development process is complex, time-consuming, and expensive. Oracle’s study startup solutions track, manage, and accelerate this process to help shorten the time from “bench to bedside”.
If you fail to plan, you are planning to fail. It’s the initialization of clinical trials, more than any other aspect of a clinical trial that has the greatest influence on timelines and budgets.
Sponsors and clinical research organizations (CROs) face intense pressure to speed clinical trials and restrain costs. But the activities involved at the outset of a clinical trial are highly inefficient and prone to bottlenecks and errors.
Current industry processes are woefully inadequate at helping stakeholders spot risk factors and bottlenecks that can disrupt cycle times and budgets. This lack of operational insights has fueled the rescue study industry.
Clinical trials that get off to a good start are more likely to execute well and finish on time and on budget – study startup is the Achilles heel of clinical trials. With unrelenting pressures to rein in budgets and cycle times, the application of modern project management techniques, real-time business intelligence, and innovative capabilities (e.g., machine learning (ML)) to study startup hold the key to optimizing operational efficiencies and compressing timelines.
When the pandemic hit, it impacted all industries in a significant way. The effect on pharmaceutical companies and CROs was profound. For the thousands of active clinical trials, mitigation plans were set. Clinical development teams needed to assess which trials would need to be delayed, others completely stopped, some prematurely ended.
Because many clinical trials are conducted in multiple countries simultaneously and the effects of COVID-19 varied geographically, real-time information and analysis were critical to make this assessment. Oracle’s study startup solutions were able to provide this level of support for our customers.
Additionally, workflows were modified to allow certain tasks to take place simultaneously (i.e., parallelization) resulting in a median cycle time reduction for Essential Docs Sent to Site to IP Release for COVID-specific therapeutic studies of 30 days. This is 76 days more efficient than the industry benchmark, and 34 days in COVID-19 specific vaccine studies 50 days more efficient than the industry benchmark. This agility is unique.
OSP: The study-build process can become bogged down and inefficient as well. What are some of the factors that can lead to that? Why is it a good move to consider ways to build in time-saving considerations and efficiencies at the onset of the process?
JC: Study startup is hindered by inefficient ways of capturing and analyzing operational data. These outdated methods include paper, shared file drives, and Excel—all lacking project and risk management functionality.
Today, many companies still track these study initialization activities (i.e., site identification, feasibility assessment, site selection, and activation) manually using Excel spreadsheets with collaboration done via email siloed in disparate inboxes, reports manually generated based on outdated data, and documents stored on shared drives. With multiple parties making updates, materials rapidly get out of sync; this makes it difficult to track the overall status of the project.
The same is true as it relates to compliance with regulatory and organizational standard operating procedures (SOP) – potentially resulting in downstream audit findings and rescue interventions. This lack of real-time insights prohibits the ability to proactively address issues, trapping the industry in an endless and costly reactive cycle.
Standardized metrics are central to efforts to rein in clinical trials that are either poorly initiated or have incurred unforeseen events, which place the original timelines and/or budgets at risk of overages. There’s no silver bullet when it comes to improving operational efficiencies, but innovative ML techniques show great promise in helping life science companies identify and rectify systemic inefficiencies, allowing them to learn and adapt.
ML is available and ready to prevent the need for rescue studies. Key features needed to do accurate study feasibility (for example, determining which countries and sites are required), are available in the Clinical Development Plan.
ML could be used at the portfolio level of planning to make more detailed study design decisions. By using ML in the planning process, those studies that are more likely to result in failure would not be approved or would be approved with a different study design. For example, a trial might be approved with a decentralized study design as opposed to a traditional study design.
OSP: Could you please share how the start-up time for a typical study has changed over time? Could you please share some of the common missteps that could lead to an inefficient study-build process? Similarly, are there questions that need to be asked and answered—are there any questions that tend to be overlooked?
JC: According to research from the Tufts Center for the Study of Drug Development it now takes approximately six months on average to perform study startup, with recent research indicating that these timelines are getting longer. Despite new technologies making the start-up process easier, 11% of sites are never activated, a figure that has not seen improvement over the past decade.
The reasons for activation failure are many. For both sponsors and CROs, the primary reason is budgeting and contracting.
Protocol complexity is driving these prolonged cycle times. The average study protocol now includes 13 endpoints, 167 procedures, 35 inclusion and exclusion criteria, and requirements for 11 site visits per patient over a 175-day period, resulting in a dramatic increase in study costs.
The rate of technology adoption is also part of the problem. Approximately 80% of respondents who have invested in study startup management technology report time savings. A whopping 51% still use spreadsheets, and another 6% still manage their trials using a paper-based solution.
There are multiple elements to building a study foundation that need to be orchestrated simultaneously to be successful, which amounts to a challenging balancing act. Signals that your trial is not on solid ground may include:
- Poorly selected sites that are struggling with enrollment/retention of trial subjects
- Little oversight or transparency with CRO partners
- Lack of robust risk identification and management processes
- Lack of benchmarking metrics to gauge progress against or upon which to forecast performance
- Inability to ensure SOP/regulatory compliance
- Inability to spot bottlenecks and internal processes that are ripe for optimization
In response to these issues, companies engaged in clinical trials are increasingly implementing study startup applications that are purpose-built to enable sponsors, CROs, and sites to get clinical studies started in the shortest time possible. Study startup applications support communicating, reporting, tracking, oversight, risk mitigation, and data management to speed study teams through activation, while also reducing time spent assembling and discussing status updates.
All stakeholders view information in real-time and have a single view of the truth. In the end, new technologies can be a real time-saver, but only if you’re using them.
OSP: During your recent Connect conference, leaders from Pfizer and Merck gave a glimpse into what the study-build process looks like for their organizations, with similarities and differences. If you could please expand that view, what steps/components does the average, well-executed study-build process look like?
JC: No two clinical studies are identical. Notwithstanding differences in protocol, therapeutic area, indication, INDs, etc. similar studies at different organizations would be implemented differently due to adherence to organization-specific SOPs and regulatory requirements based on the countries in which the study is being conducted.
However, there are a series of steps that all clinical trials go through during study startup, namely: site identification, site engagement, execution of CDAs, feasibility assessment, pre-study visit, initiation, and negotiation on CTA, regulatory document submissions, site initiation visits (SIV), and wrap-up resulting in IP (investigational product) release. Ideally, the protocol for a clinical trial is complete before site identification begins and study startup gets underway. The reality is that oftentimes the protocol is still being finalized as study startup kicks off.
Intelligent country-specific regulatory workflows, benchmarking, and in-depth operational metrics allow organizations to build up institutional knowledge on components for optimization and for reuse in future studies.
The forthcoming ICH E6 (R3) guidance discourages the use of the “standard” checklist approach that is the current practice, in favor of risk-based management approaches which focus the effort on what is truly needed to initialize studies.
OSP: Some aspects can be automated, and some can be manual. Could you please share how to determine which, and some thoughts on why manual intervention/oversight is still necessary even in the face of rapid digital evolution?
JC: ML tools and automaton free clinical project managers from tedious, repetitive activities so they can focus on strategic activities, drive optimal proactive planning in study execution, and aid in-depth internal reviews of organizational processes, resource allocations, study costs, and quality assessments.
Automation can aid in successful forecasting, more accurately and significantly earlier than people do. It is these predictions that can help to drive planning and informed decisions and mitigate mistakes that can impact overall product development costs.
For example, in the case of under-enrollment in studies, an entrenched industry problem, ML using leading indicators contained in the clinical development plan (e.g., indication, phase(s), key inclusion/exclusion criteria, etc.) can be used to help ensure that the required sites can be found, so enrollment success can be predicted, prior to the protocol being written and approved.
Automated study planning tools, like Oracle’s Activate, help to allow sponsors and CROs to fully plan study start-up milestones from protocol approval to activation. Leveraging multi-plan comparison and visualization tools, users can scenario-plan to determine the best countries and sites to activate, based on the characteristics of the study, to hit enrollment goals in the fastest possible time.
In this environment, opportunities abound but none of these systems is a panacea. These tools perform separate functions but serve overlapping user groups and need to be assessed as part of the overall picture of clinical study management, rather than just as one piece of the clinical trial jigsaw puzzle.
Fully optimizing the clinical trials process requires practices and tools that streamline operations, automate processes, increase transparency, and improve stakeholder collaboration.
OSP: Change is inevitable—how can the study-build process be conducted to balance a need for structure, with adaptability? How can technology solutions firms help the process?
JC: Today, clinical operations frequently work in a reactive mode, so adaptability is top of mind. However, recent and forthcoming ICH guidance’s on Quality by Design (QbD) are encouraging more upfront, proactive planning which utilizes modern techniques in quality, risk management, and technology to identify risks before they are incurred and have risk-mitigation plans in place.
With so many multiple aspects to balance across many stakeholders, from finalizing the protocol to coordinating contracts, selecting sites, recruiting patients, tracking information manually, and in a siloed manner, this simply does not work. The stakes are too high and risks too great to have a decision model based on ad-hoc processes and fragmented information.
Fortunately, technology platforms like Oracle Health Sciences Clinical One can help to ensure a smooth process. Clinical One is the only cloud platform built from the ground up to support all the core capabilities required for effective study management throughout the entire drug development lifecycle. Oracle Health Sciences Clinical One Randomization & Supplies Management (ORS) is a self-service, on-demand application, helping to reduce study build time from weeks to days.
Oracle Health Sciences Study Startup solutions help accelerate site identification, feasibility assessment, and selection through to activation with comprehensive metrics to track adherence to timelines and budget.
With the ability to capture data from any source into a single, unified platform, Clinical One is redefining how technology supports clinical research to improve trial efficiency from start to completion and help speed the delivery of breakthrough therapies for people who need them.
