The biopharmaceutical industry stands at a critical juncture, where rapid scientific advancements and increasing competition demand a fresh look at clinical trial delivery. As the industry hurtles toward 2035, the need for a transformative vision has never been more pressing. This article outlines a bold new direction for clinical trials, one that aspires to double trial speed and patient participation while enhancing outcomes and reducing costs. By examining the key drivers of change and the essential elements of a next-generation clinical development engine, we could unlock a future in which clinical trials are more efficient, more accessible, and more patient-centered.
Forces reshaping clinical trial delivery
Biopharmaceutical innovation encompasses an ever-expanding array of therapeutic modalities, mechanisms of action, and markers of disease, which are transforming how patients are treated and how therapeutics are tested. Current pipelines underscore the excitement for innovative modalities, including cell and gene therapies (Exhibit 1), building on the momentum seen in development successes and expansion approaches over the past decade.1
This surge in innovation is underscored by a two-and-a-half times increase in the number of Phase III trials conducted worldwide since 2000. Every major therapeutic area has seen a two- to five-fold rise (Exhibit 2). This has led to fierce competition and compressed asset life cycles that limit value capture and require a heightened focus on asset strategy.2
Innovation and competition are driving pharmaceutical companies to rethink traditional trial designs, leading to the emergence of new, adaptive models (Exhibit 3). These clinical trial designs use approaches such as adaptive randomization and seamless Phase II and III trials to implement prespecified changes to the study’s direction in real time based on emerging data. For example, in an oncology study, an adaptive design might expand enrollment in a treatment arm that demonstrates strong early tumor response while discontinuing less-effective arms, ensuring patients receive the most promising options. These trials reduce operational inefficiencies but also expand patient access. The COVID-19 pandemic reshaped the way we think about the art of the possible in terms of speed and innovation in development and set new benchmarks for what is achievable.3
The geographic distribution of trials and sites is also changing. As the demand for personalized medicine grows, so does the necessity to include diverse patient populations from around the world in clinical trials. Further, companies must consider their trial footprints amid increasing geopolitical complexity and rising cost pressure (Exhibit 4).
Technological advancements are transforming the design, execution, and analysis of clinical trials,4 streamlining them and making them more efficient. For example, large language models can convert unstructured data, such as physicians’ notes, into structured and searchable data. AI can help trial teams select the highest-performing sites and predict patient enrollment. In addition, gen AI can help researchers draft documents more quickly.
Blueprint 2035: Designing the future trial
Achieving a future vision where clinical trials run twice as fast and for twice as many patients with better outcomes will require a model that blends established best practices with significant innovation. The greatest challenge will be to achieve this for multiple modalities, regions, and patient needs while stemming the rising cost of clinical development. This vision is supported by four pillars, which are built on three essential enablers.
The four pillars of the future of clinical trial design
Increasing the productivity of clinical trials starts by advancing the current process in four vital ways.
Trials are part of a seamless care experience
In the future, trials offering cutting-edge medicine could be as accessible as the standard of care regardless of where a patient lives, giving patients access to more options.
The choice of whether to participate would still require the treating physician, patient, and their caregivers to evaluate of the risks and benefits compared with approved therapies but without the need to weigh practical impediments.
Today, trial participation remains a significant logistical burden for many patients. Based on US claims data analysis, only 10 percent of people with schizophrenia in the United States can participate in a trial at the same clinic where they already get care. The remaining number of patients must travel and engage with new doctors; about one-third of patients with schizophrenia in the United States need to drive for more than 60 minutes to access a trial. These barriers can be observed in a variety of disease areas: Among patients with heart failure, ulcerative colitis, sickle cell anemia, and cystic fibrosis, 60 to 80 percent cannot participate in a trial where they currently receive care. While direct access at sites of care is greater for multiple myeloma patients, half of them still need to travel to access a trial (Exhibit 5).
If we map the locations of treatment sites for patients with schizophrenia in the United States versus population concentrations, white spaces in trial site accessibility are evident, calling for a need for more clinical trial access sites in locations such as Chico, California, and Lafayette, Louisiana (Exhibit 6).
Even in areas where trial sites seem to be available, the logistical burden for patients can be a challenge. In the Detroit area, for example, most patients with schizophrenia would likely need a referral and need to travel about 45 minutes from Detroit city center to Ann Arbor (Exhibit 7). These logistical impediments are accepted as normal, but they can impose insurmountable barriers for many patients across multiple disease areas, including heart failure and multiple myeloma.
Establishing a trial site where there are higher densities of patients could result in thousands of additional patients, where logistics are the main limiting factor for trial participation. To reduce the travel burden further, sponsors can invest in technology for remote monitoring and work with suppliers and providers to incorporate at-home care into trial protocols. These changes can make an invaluable contribution to the seamless care experience that will help increase trial participation, globalize delivery, and achieve long-standing goals for representative patient cohorts.
Trial ecosystems are diversified and scaled
Future clinical trial ecosystems will likely become more diverse and widespread. Trials will expand beyond academic centers to include a wider mix of community hospitals, regional health systems, and local clinics, diversifying the types of sites involved in research. Hub-and-spoke models could link central institutions with geographically dispersed community clinics, improving patient reach and access, particularly for underrepresented and economically disadvantaged communities.
To encourage this, sponsors could promote collaboration between new and established investigators and identify opportunities for smaller practices to participate in trials. They could invest in partnerships with locally based partners, such as pharmacies and health clinics, which will give more patients access.
In 2022, Walgreens expanded its participation in clinical trials by leveraging its real-world data on 130 million patients to identify potential participants living near its stores and inform them about trials they could join. Walgreens established 35 clinical trial agreements and enabled collaborations with 25 partners in biopharma, academia, nonprofits, and government.5
Similarly, Memorial Sloan Kettering joined the US-Australia Cancer Consortium—a public–private partnership—with the goal of pioneering cancer care collaboration. Through enhanced data sharing and integration of novel technologies (for example, liquid biopsy and remote radiation oncology), the consortium was established to address the disparities in cancer burden among indigenous populations and underserved minorities in the United States and Australia.
Having more-extensive collaborations across the healthcare ecosystem could guarantee that eligible patients aren’t excluded, maximize evidence generation, and extend the benefits of new medicines to the broadest possible patient population.6
Sites and sponsors are strategic partners
Tailored site sponsor partnerships could become standard practice by 2035, building on established relationships, shared scientific commitment, and mutual trust in long-term collaboration. Today, these relationships are often transactional and inconsistent, with sponsors sometimes activating new sites for participation in a single clinical trial. To address this issue, sponsors can better utilize data to identify key sites in different therapeutic areas for long-term partnerships.
Sponsors can use predictive analytics and machine learning to identify sites that consistently deliver high-quality results in known therapeutic areas while also predicting those with the potential to succeed in new disease areas. By making smarter site choices, sponsors can strengthen their relationships with these sites. Gen AI can enable more-targeted and effective communication by tailoring messages specifically to each principal investigator. Data-driven feedback on site performance can enhance the efficiency of partnerships, resulting in improved trial outcomes. And sponsors can offer personalized support, training, and recruitment tools to enhance the experience for sites and patients.
Current consolidation across sites, networks, and distributors could help make strategic, targeted partnerships for sponsors more achievable. For example, three large medical supply distributors, McKesson,7 Cencora,8 and Cardinal Health,9 have each made strategic acquisitions or partnerships in oncology over the past five years, consolidating and concentrating physicians and principal investigators. Sponsors may be able to target these types of vendors, leveraging this acquisition activity to their advantage and partnering with selected vendors through a single contracting process to expand patient access.
Sponsors can also continue to develop long-term networks with strategic partners to further scientific understanding and facilitate the exchange of knowledge. An example of such a collaboration is the Bloomberg New Economy International Cancer Coalition, which brings together academia, industry, government, patient advocacy, and policy think tanks to accelerate cancer cures and prevention globally (see sidebar, “The Bloomberg New Economy International Cancer Coalition: Driving global health equity in cancer care”). Even encouraging individual research sites to work together and form their own networks can lead to improved resourcing and more exchanges of ideas.
Innovation is ingrained in trial design
To increase speed and efficiency in clinical development, there will be tradeoffs in the design of clinical trials, balancing the volume and range of data collected versus patient and site burdens. By 2035, clinical development organizations are likely to frequently leverage nonstandard designs such as adaptive protocols and digital twins in therapeutic areas beyond oncology.
Further, the way individual trial design components are selected is evolving. Trial teams can use AI to optimize endpoint selection, better define patient populations through enhanced decision-making on inclusion and exclusion criteria, and incorporate real-world data into evidence packages.10 This design evolution could lead to faster and more-efficient trials that are targeted to indications and patients where they are expected to have a higher chance of success. Ways of working could shift, with clinical science and clinical operations teams collaborating more closely throughout the trial design and operationalization phase. This could ensure patient and site experience is consistently at the core of decision-making from the outset.
The three enablers of the 2035 vision
The four pillars of our 2035 vision are supported by three enablers that sponsors can invest in and rethink now for the future.
Fit-for-portfolio clinical operations model
As trial designs evolve, sponsors can assess their R&D portfolio to determine which clinical operations models they will need to deploy over the next ten years. This assessment may lead to a spectrum of operational configurations, with the model for rare diseases on one end and chronic diseases on the other.
Trials for rare and chronic diseases require different sponsor strategies. For patient recruitment, sponsors can choose between broad approaches for large chronic disease populations and targeted ones for smaller, dispersed rare-disease groups. This decision affects resources, outreach strategies, and the need for specialized networks. The approaches to building site relationships also differ. Chronic disease trials benefit from widescale partnerships with high-volume sites, which, due to their abundance, often require less oversight and site engagement. Conversely, rare-disease trials often require deep, long-term engagement with a select group of expert centers that possess specialized knowledge and infrastructure.11 These differences may require clinical operations groups to be both global and local.
AI- and technology-driven decision-making engine
While AI for site selection, trial forecasting, and scenario modeling is increasingly used in clinical development today, end-to-end integration of gen AI and agentic AI could be essential to remain competitive in 2035. Clinical operations teams will want to be proficient in leveraging AI to support a vast range of activities, such as identifying suitable patients, predicting trial trajectories, guiding operational decision-making and risk benefit adjustments, and powering patient-facing support tools.
Agentic AI has the potential to further revolutionize how trials are run. AI agents can plan and execute complex end-to-end workflows, due to their ability to collaborate, use tools, and learn from experience and outcomes. This enables a wide variety of use cases along the trial delivery value chain, such as success-optimized trial design, next-best-action site engagement, and integrated trial management.
For instance, AI agents could autonomously manage the critical path through country and site activation by managing site interactions, tracking documents, and flagging pending items, thereby accelerating the start-up process. AI agents built into electronic medical record workflows could identify potential candidates the instant a crucial decision is made. Over the next decade, development organizations can integrate these technologies into their trial delivery pipeline to stay at the forefront of innovation. Effectively managing the vast and diverse datasets these AI applications generate will be crucial to ensuring data quality, security, and integrity through the trial life cycle.
To fully realize the potential of these advanced capabilities, they must be integrated into a cohesive enterprise vision and contemporary technology architecture. Such architecture is vital for fostering coordination between digital platforms and development functions. A modernized core system supports the adoption of new technologies, facilitating innovative trial designs and effective trial management. The ability to incorporate emerging forms of data is equally important, including real-world evidence, wearable-device metrics, and patient-reported outcomes, which can enrich trial insights and support adaptive approaches. By integrating these diverse data streams into the technology backbone, organizations can strengthen trial design and unlock more-predictive decision-making. Collectively, these integrated technological capabilities, including well-managed architecture, can lead to shorter development timelines while maintaining high quality standards.
Upskilled, customer-centric talent model
An effective future talent model for clinical operations emphasizes roles that require analytical skills and data-driven decision-making as fundamental competencies. For example, McKinsey analysis has identified a distinct shift in responsibilities among clinical research associates (CRAs) over the past ten years. Monitoring has largely become centralized, so CRAs are now responsible for managing site relationships and managing two to three times as many protocol sites. Other clinical operations roles are transitioning from executing tasks to managing strategic relationships and are better leveraging data to support decision-making.
In addition, various specialized roles will be necessary based on the specific operations model a sponsor adopts. For example, in ultra-rare-disease trials, a patient liaison may work closely with the medical team as a patient advocate within a center of excellence. For new clinical research sites, a dedicated set-up team could be assigned to facilitate a smooth trial setup. In chronic disease trials, a digital recruitment team can play a crucial role in supporting site enrollment by utilizing real-world data strategies to identify potential patients.
Regardless of the specific role, a customer-centric mindset is essential for making site sponsor partnerships a reality. Strong interpersonal and engagement skills are critical for building site relationships, for example, through dedicated key account managers.
To achieve the ambitious goals of doubling trial speed and patient participation by 2035, significant changes will be necessary in clinical development. If successful, this transformation could lead to more-accessible treatments for patients, broader community impact with more clinical trial sites, and more-efficient evidence generation for sponsors. Progress toward this vision will require adopting the outlined pillars and enablers and making a decisive shift toward more-patient-centered approaches in drug development.
