Breakthroughs in scientific research and increased public and private investments have yet to negate the long timelines and rising costs of clinical trials. Such challenges continue to drag down biopharma R&D productivity and hold back innovative therapies from making it to market. Although clinical development speed increased during the COVID-19 pandemic, an increasing number of pharma candidates in the pipeline, coupled with a decreasing success rate of clinical trials, has put additional pressure on companies to maintain and even pick up the pace. This is on top of the imperative to improve the quality of clinical trials and prioritize patient and site staff experiences.
Critical to these efforts are the IT applications within the clinical development tech stack that are responsible for capturing experimental data and managing trial operations. As the demand for speed and quality grows, the challenges posed by outdated IT applications have become more evident. Legacy architecture, often marked by fragmented, siloed data and cumbersome point-to-point connections, prevents the seamless access, flow, and integration of data across the development cycle.
These challenges also limit the effectiveness of advanced analytical and gen AI tools, which promise to revolutionize trial planning and operations through real-time monitoring, early warnings, risk models, and resource optimization. The application of gen AI, in particular, could generate upward of $50 billion in annual value across the discovery, research, and clinical development phases of the pharma industry value chain.
Capturing the value from AI and digital is only possible with a modernized clinical development application layer paired with a next-gen analytics layer. Our research reveals that this could lead to substantial benefits:
- Faster study start-up and accelerated trial execution. A modern clinical development application landscape provides streamlined workflows for site selection, activation, and training, along with seamless document exchange among sponsors, contract research organizations, and clinical trial sites. Our analysis shows that this can accelerate trial start-up times by 15 to 20 percent. Moreover, companies can leverage AI and real-world data to optimize clinical trial end points, potentially shortening trial length by 15 to 30 percent.
- Higher productivity. Our studies find that seamless and near-real-time information flow enabled by modernized clinical applications and next-gen analytics can increase productivity by 15 to 30 percent through reduced effort in study setup and planning, predictive site monitoring, reporting, payment management, and other trial activities.
- Increased success rates. Seamless access to standardized patient data enhanced by next-gen analytics can help to better identify patient subpopulations with high unmet needs. Per our research, this can lead to improved inclusion and exclusion criteria and a 10 percent increase in trial success rates.
This article offers a structured approach for biopharma leaders to simplify the modernization of a state-of-the-art, fully integrated application architecture and realize its benefits, including the enablement of next-gen analytics.
The challenge of modernizing and scaling the tech stack
Modernized clinical development applications have emerged over the past three to five years as a competitive advantage. For instance, during the trial of the Pfizer–BioNTech COVID-19 vaccine, a modern data integration layer cut the last patient, last visit to database lock time from more than 30 days to fewer than 24 hours, which enabled the rapid launch of the vaccine.
Despite the clear benefits of modernizing the IT architecture of the clinical development application layer, doing so at scale has proven more complex than some companies may have anticipated. Although 40 to 50 percent of the top 20 pharma companies have invested heavily in modernizing their clinical IT applications, we find that many of them have yet to achieve a clear ROI. An additional 30 percent have made limited progress, modernizing only one or two applications, and the rest have yet to get started.
Many companies take a fragmented approach to modernization, which can lead to the neglect of crucial data integration and management systems. Moreover, it is essential to define modernization strategies from a business-led perspective, considering end-to-end processes and required business capabilities, rather than relying too heavily on a tech-first viewpoint. Without a clear plan outlining prioritized capabilities and use cases informed by end-to-end processes, biopharma companies risk accumulating technical debt and increasing the need for future rework.
Visualization of the modern clinical development tech stack
The ecosystem for a next-generation clinical development tech stack is a comprehensive framework that supports the entire life cycle of clinical trials. It can be visualized as four discrete but connected layers: the analytics layer, the application layer—the focus of this article—the data layer, and the infrastructure layer (Exhibit 1).
The analytics layer includes tools and systems that enable the analysis and extraction of insights from clinical development data. It requires robust data architecture and high-performance computing capabilities. The data layer is responsible for the integration and transformation of clinical and operational data from different sources. It builds a harmonized data model that enables both primary and secondary data use. The infrastructure layer provides the cloud-based foundation necessary for high performance, scalability, and security and supports the system’s overall functionality.
Wedged between the analytics layer and the data layer is the application layer, which includes core clinical systems, such as electronic data capture, clinical trial management, electronic clinical-outcome assessment, and electronic master files from trials. These applications have integrated workflows and advanced features that are tailored to meet the specific needs of clinical development. If supported by robust analytics, data, and infrastructure layers, they enable seamless trial execution from planning to submission, end-to-end data collection, and real-time insight generation (Exhibit 2).
Benefits of modernizing the clinical development application layer
A modernized clinical development application layer offers several major benefits. We identified four that stand out: enabling business agility, accelerating trial timelines and cost reduction, supporting AI, and attracting and retaining top talent.
Business agility
A modern ecosystem for clinical development applications must be flexible enough to manage decentralized trials. In addition, it needs to support integration with advanced analytical tools (see sidebar, “Enabling the integration of novel tools for more patient-friendly and inclusive trials”).
Business agility is especially critical during M&A, when the integration of disparate IT ecosystems becomes a strategic priority.
Companies that have modernized their application layer find it easier to innovate and adapt to market shifts, such as increasing competition for recruitment of the same patients and competition for being the preferred partners for trial sites, as well as regulatory changes and technological advancements. This agility ensures that they remain at the forefront of the industry.
Trial efficiency and cost
Modernizing the core IT landscape, particularly through upgrades to critical applications, has a direct impact on the efficiency of clinical trials. Our analysis finds that companies that have invested in these upgrades have reported, in addition to accelerated trial execution, achieving database lock within two to three weeks after last subject, last visit. They also achieved a 20 to 30 percent reduction in IT cost through decreased maintenance and the efficient use of the cloud and software as a service.
AI support
The rise of gen AI and machine learning is transforming clinical development, offering both opportunities and challenges. As a result, the life science industry is shifting from isolated analytics use cases to integrated end-to-end solutions. Advancements include near-time operational-decision support, with predictive analytics, centralized platforms for trial management and data analysis, and flexible architectures that allow for tailored implementation. These integrated solutions enable more precise trial designs and provide a unified, real-time view of trial data, enhancing efficiency and decision-making throughout the clinical process.
Top talent
The competition for digital talent has intensified across the industry. Organizations that modernize their clinical development tech stacks are in a better position to attract and retain such talent. Technologists are skilled professionals needed to drive innovation and success in clinical development.
Structured approach to modernizing IT application architecture
Life science leaders are increasingly adopting a platform-focused approach to the clinical development application layer, with minimal configuration. They favor platforms over best-of-breed solutions, commercial off-the-shelf solutions over in-house builds, and out-of-the-box configurations over highly customized solutions (Exhibit 3).
To achieve a fit-for-purpose, fully integrated architecture for clinical development IT applications, leaders can adopt a structured approach that addresses five strategic questions:
- What is the scope of our modernization?
- Where do we want to differentiate?
- Which clinical development application archetype should we select?
- Which vendor combinations best meet our needs?
- How can we guarantee joint ownership between the business and IT teams?
Scope of modernization
Leaders will need to define the extent of their modernization efforts within the overall tech stack. They should consider the domain of interest (clinical development versus broader R&D), depth of the tech stack (upgrading only the application layer versus embedding it into the data or infrastructure layers), data strategy (primary data analysis versus a holistic data strategy, including secondary data use cases), and desired level of value delivery (building the foundation only versus enabling decentralized trials or scaling advanced analytics and AI use cases). Additionally, they must assess their maturity across end-to-end clinical development, including nontechnical aspects, such as partnerships, vendor management, talent strategy, and operating model.
Where to differentiate
Beyond defining the architecture, leaders should set ambitious goals for integrating analytics and AI use cases, which can address every part of the clinical development process. This might include AI-based indication finding, digital biomarkers, digital twins, and patient dropout prediction. Identifying current gaps and determining the organization’s appetite for differentiation will help pinpoint high-value use cases that align with strategic goals.
Selection of archetype
Leaders have the option of choosing among platform, best-of-breed, or hybrid architectures for their application layer modernization. The platform approach involves implementing ready-to-use solutions from a limited number of vendors, supplemented with self-built “bolt-ons” where necessary. The best-of-breed approach selects the best solutions from a wider selection of vendors according to their fit for specific use cases. The hybrid approach supplements a primary platform with best-of-breed solutions for areas selected for differentiation.
Vendor combinations
Choosing the right vendor combination will depend on the desired set of clinical capabilities that the organization is seeking. The organization can tailor its vendors to its distinctive business needs (Exhibit 4). This decision should consider four crucial factors: interoperability across the four tech stack layers and with adjacent business units (such as research), the current skill set and drive to achieve differentiation through self-built applications, the desired balance between picking a user-friendly solution or high-quality specialized solutions, and potential concerns about inflexible vendor agreements.
Joint ownership
Successful modernization of the IT application architecture requires collaboration between the chief information officer and head of R&D, as well as strong support from the entire C-suite. It is essential that the business side owns and drives the modernization effort. Otherwise, the initiative could struggle to gain broad acceptance and face challenges in being effectively embedded within the organization. By extension, the IT side should position itself as an enabler, translating business goals into precise technical requirements and use cases that can be tested during the vendor selection phase.
How to get it right
To bring the vision of an end-to-end clinical development application landscape to life in the biopharma industry, leaders in the field should follow five principles of success:
- Transform process, people, and tech. End-to-end, cross-functionally defined and optimized clinical development processes, as well as clear roles and responsibilities, should accompany tech implementation. Train future users along the way. Communicate the why early and often.
- Embrace complexity without getting stuck. Recognize the complexity of the application landscape and then set clear, value-driven, business-backed priorities. These priorities should align with the pipeline—for example, trial design first and filings first. Drop the tech-focused lens, such as prioritizing a clinical-trial-management system, and go across functions.
- Prioritize interoperability. Clinical development applications should mesh seamlessly with both the internal data infrastructure and the external data infrastructures—for example, those used when working with contract development and manufacturing organizations. Successful trial design and execution depend on integrating data from across the value chain, such as using disease pathway analysis to inform trial criteria.
- Clearly define value up front, and continually track success. Recognition of a successfully implemented application landscape requires a set of financial and nonfinancial metrics. Examples include the acceleration of clinical trials, higher efficiency of conducting trials, enhanced user and customer experience, and enablement of AI use cases. Regularly track and monitor these metrics and translate them to profit-and-loss metrics and classic project execution KPIs.
- Choose vendors with long-term goals in mind. Base vendor selection on the long-term platform strategy in clinical IT. Avoid point solutions, which are likely to become commodities during the implementation phase. Be cautious about codeveloping solutions with vendors. For the vendors (including the chosen system integrator), define long-term performance metrics to secure support in both current and future operations.
To get the most out of modernizing clinical trials, biopharma R&D leaders need a clear action plan that connects their business goals with their IT strategy. This plan should build on areas to improve, outline how future trials will work, evaluate the current technology, and decide what tech changes are needed. Such alignment is critical for releasing the potential of advanced applications to accelerate the development of life-saving therapies.
“Our firm is designed to operate as one—a single global partnership united by a strong set of values. We are equally committed to both sides of our mission: attracting and developing a talented and diverse group of colleagues and helping our clients create meaningful and lasting change.
From the C-suite to the front line, we partner with clients to help them innovate more sustainably, achieve lasting gains in performance, and build workforces that will thrive for this generation and the next.”
Please visit the firm link to site