Biopharma innovation has brought about dramatic improvements in patient outcomes over the past two decades. Cure rates for hepatitis C have reached 95 percent;1 cancer mortality rates have fallen by 27 percent;2 and COVID-19 vaccines have proved up to 95 percent effective at preventing symptomatic infections.3 Yet in the same period, success rates, speed, and productivity for clinical development have remained stubbornly flat despite global R&D investments increasing sharply—with US R&D spend doubling from $40 billion to $80 billion between 2001 and 20194 (Exhibit 1).
Today, the pressure is even greater to change the trajectory of R&D speed and success. A set of converging trends are putting significant pressure on R&D organizations to boost throughput of innovative medicines. Scientific breakthroughs are creating enormous opportunities to address unmet needs through novel targets and modalities. However, intensifying competition for many of these targets and modalities means that acceleration of asset development to create more time on market becomes even more valuable. This next wave of innovation is focused on smaller patient populations and personalization that create a more diverse and fragmented portfolio as the industry continues its shift away from mass-market blockbusters. At the same time, advances in digital and analytics techniques applied to an expanding array of data open new pathways to innovation in both medical advances and how R&D work gets done.
Combined with these new opportunities are a set of constraints that will require R&D to do more with less. Looming patent cliffs on blockbuster drugs, a constrained pricing environment, plus the prospect of pricing reform (especially in, but not limited to, the United States) means many companies are prioritizing R&D productivity improvement and pushing to get more out of every dollar invested in R&D.
In response to these opportunities and pressures, pharma companies are working to understand and address patients’ unmet needs ahead of their peers to improve lives and maximize revenue potential. They are seeking to boost their new drugs’ chances of success and stepping up the pace at which they bring medicines to patients, sometimes by drawing on lessons learned from COVID-19. They are cutting the cost of core R&D processes through simplification, digitization, and automation. But in our view, all these initiatives need to be brought together in a transformation of the entire R&D operating model if organizations are to unlock substantial and sustainable productivity improvements.
Which transformation approach works best?
Many R&D organizations are already experimenting with new ways of working, rapid asset development, and enhanced capabilities to make them more agile and responsive. Some are embarking on more ambitious transformations that bring together multiple initiatives across the R&D value chain. Among this group, we see three main approaches:
- An asset-centric approach: restructuring R&D around individual assets in the portfolio to create empowered asset teams that operate at the same rapid pace as a small biotech. This approach relies on devolved decision-making authority, an agile culture, and a tailored talent model.
- An operating-model approach: rewiring the R&D operating model to boost efficiency and innovation. This might involve reviewing the role of contract research organizations (CROs) and bringing key capabilities in-house; creating platforms for the rapid development of allogeneic cell therapies, gene therapies, or gene-editing technologies; or reconfiguring the global innovation footprint via new alliances and partnerships, expansion in Asia, or other strategies.
- A functional approach: optimizing select parts of the value chain in key R&D functions for further differentiation and competitive advantage. Target areas might include innovative trial design, digital-enabled site selection, agile study start-up and conduct, submission excellence, and launch planning.
All three approaches have yielded some successes. For example, when one pharma company accelerated planning and decision making, trial execution, and other processes, it shaved two to three years off the time it took to get drugs to patients. However, such benefits often remain at the asset or functional level rather than being extended across the whole of the R&D organization. We believe organizations would do better to pursue a more holistic form of transformation, namely, an outcomes-based approach, which focuses, from the outset, on the handful of critical outcomes that enable sustainable impact at scale. By combining elements from the other three approaches and anchoring them in a long-term perspective, this approach ensures that a company’s investment of resources, energy, and time pays off in a fast, effective, efficient innovation engine and not just a series of successful pilots.
Which outcomes matter most?
Full-scale organizational transformations are difficult, and success rates persistently low. Biopharma R&D is no exception. We see the same few pitfalls occur in case after case: underinvesting in training the whole organization in new ways of working; failing to distinguish standardized activities or processes from those that should be customized; and treating the effort as a once-and-for-all fix rather than a continuous striving to learn and improve. The consequences can be stark: ways of working diverge, efficiencies are lost, new silos emerge, and change stops at a moment, never becoming a movement.
A powerful way to overcome these challenges is to focus on the five outcomes that matter most in an R&D transformation (Exhibit 2).
Outcome one: The R&D organization has a North Star to steer by
To align R&D with its purpose and guide the design of the new operating model, an organization-wide aspiration or “North Star” is needed. It should include specific targets—such as 1,000 days faster from candidate selection to launch—so that progress can be measured. It also needs to be bold, exciting, and personal to overcome organizational inertia, combat conservatism, convince stakeholders that change is needed, and ensure that leaders and teams are wholeheartedly engaged.
Pharma companies are particularly well placed to nurture a collective sense of purpose, with R&D employees finding fulfillment from being part of a cutting-edge scientific endeavor that benefits large numbers of people. Offering patients life-changing and, in some cases, life-saving therapies is a prime example. Evidence shows that employees are more productive, resilient, and committed when they feel their company’s sense of purpose is aligned with their own. If R&D organizations don’t meet this need, they risk losing critical talent to others that do. Several biopharma companies have announced their own North Star in the past few years. For instance, Roche chose “delivering twice as many medical advances to society at half the cost,”5 GSK opted for achieving “top quartile on cycle times,”6 and Pfizer spoke of “delivering up to 25 breakthroughs to patients by the year 2025.”7
Outcome two: A stable R&D backbone supports efficiency and scale
Identifying a North Star is one thing; achieving North Star aspirations is another. R&D organizations need to solve the challenge of being agile and responsive while at the same time maintaining safety, stability, and quality. Best-in-class R&D organizations create a backbone that enables both efficiency and flexibility. For example, developing standardized core processes for study start-up, submission, and so on can deliver impact regardless of the number of assets in the pipeline.
If the R&D backbone is overlooked, creating lasting change at scale will be difficult. One US-based, top ten pharma company trying to reduce cycle times in late-stage development took an asset-led approach. Six months into the transformation, the team had gained valuable experience in processes from agile trial design and execution to submission excellence. But with the R&D system unchanged and no plan for scaling impact across the portfolio, the company was left attempting to replicate the success of a single team. These experiments created new inconsistencies and differences of opinion on how work should get done, increasing the operational burden on the organization.
When designing their backbone, companies should start by identifying where stability is beneficial in the operating model, as opposed to places where creativity and flexibility create more value. For example, streamlining governance to support speed or shifting to an advice-seeking culture to support quality are ways to create predictability without sacrificing agility. The next step is to consider how the organization is structured around key elements such as asset team model, decision-making framework, core operations, capabilities, culture, talent, resources, data, and technology. The design of the new backbone should incorporate lessons learned from experiments and early outcomes from front-runners. Examples might include asset-specific programs to test new team models, therapeutic area-specific initiatives to experiment with new governance mechanisms, or function-specific pilots to test improvements in core operational areas such as study start-up.
Outcome three: An evolution engine enables continuous scale-up of new ways of working
When R&D organizations put their backbone in place and move toward their future state, they are not engaged in a one-off exercise. To maintain a leadership position, they need to keep incorporating new ways of working into their operating model as new opportunities emerge and the portfolio shifts. By building what we call an “evolution engine,” they can identify where innovation is needed in their operating model, manage a portfolio of experiments, measure outcomes with reference to their North Star, and develop a process to scale up successful pilots and integrate them into the R&D backbone.
However, managing a portfolio of operating-model experiments can be as challenging for leaders as managing a pipeline of molecules. One top five global pharma company realized that the organic evolutionary process that enabled it to integrate small, isolated changes (such as a new protocol-review process) was not adequate for larger shifts (such as a new portfolio management approach). It decided to set up a center of excellence for innovation—a kind of R&D “learning lab”—to create an infrastructure for managing larger changes and the scale-up of the new R&D system.
A critical role for an R&D learning lab is to establish repeatable processes for updating the backbone and scaling improvements across the organization. If a team introduces machine learning in site selection to accelerate clinical development, for instance, the new solutions will need to be incorporated into guidelines and standard operating procedures and implemented at scale in new workflows. In addition, the asset team will need to engage with clinical science, operations, and other functions to spread awareness and build capabilities for the changes. The organization may need to make larger changes too, such as increasing direct oversight of CROs and trials or devolving decision making to asset teams. In turn, these changes will require processes for specifying new ways of working, training teams, and adjusting guidelines and procedures.
To create a successful evolution engine, R&D organizations also need to appoint leaders to guide learning, and set up cross-functional teams with decision-making authority and deep expertise across HR, IT, finance, and other functions. If any of these elements are lacking, a transformation can quickly descend into a chaos of chronic pilots that never reach scale impact.
Outcome four: A culture of continuous improvement makes R&D more agile and responsive
When one top 20 pharmaceutical company planned to expand its portfolio and enter a new therapeutic area, it launched a new operating model to improve quality, governance, and capabilities in its asset teams. However, teams struggled to see the benefits the new model would bring, understand how it worked, or figure out how it would affect their role or function. With resistance growing, senior leaders quickly corrected their course. They renewed their focus on change management, developed a compelling transformation story to build excitement, and explained to employees how the new model would affect their work from day to day.
Change needs to be a movement, not a moment. It takes time and effort to reframe people’s mindsets and behaviors. Pioneers and early adopters may be quick to come on board, but winning hearts and minds across the organization is a different matter. The first step is to define the desired new culture by setting out how mindsets and behaviors should change from today’s norms (such as seeking approval for all decisions, whether strategic or executional) to the target state (such as seeking advice and making some decisions at the team level). Culture is all around us like the air we breathe, but we barely notice it; to change it, we have to make it explicit and seen. In defining desired cultural shifts, organizations should be guided by their North Star, findings from early experiments, and an awareness of organizational barriers, such as deep-rooted “old guard” narratives that may block the adoption of new mindsets.
For senior executives, changing the culture will involve role modeling desired mindsets, providing ongoing coaching for leaders, and engaging teams in capability building. One global top ten pharma company launched its new operating model by convening more than 300 R&D leaders to embark on a journey of exploration. The group talked about why the transformation was needed and how it could affect patients’ lives. They practiced new behaviors, such as seeking feedback from customers, and new mindsets, such as accepting ambiguity and imperfection (neatly summarized as ELMO: Enough. Let’s move on). In a company with a culture oriented around conservatism, hierarchical decision making, and perfection seeking, the session was such an unexpected departure that it set off a ripple effect across the whole organization—one that was subsequently reinforced via coaching, training, and tools.
Outcome 5: A holistic capability-building system equips teams with the right skills at the right time
At times of change, a high turnover of talent can make it hard for R&D organizations to retain their existing technical and institutional expertise, let alone transfer knowledge or build new skills. For new or inexperienced team members, it takes time to learn how an organization works and build critical business, technical, and leadership capabilities, especially in a remote or hybrid working environment. And as new specialty areas emerge and digital and analytical skills come to the fore, the demands on R&D expertise are only intensifying.
Creating a skilled R&D organization means giving employees regular opportunities to learn, sharing best practices quickly, and passing on important lessons in a timely way—for instance, how to accelerate pivotal programs ahead of late-stage investment. This, in turn, calls for a tailored approach to educating, incentivizing, and supporting the leaders and teams who will enable and embody the new ways of working (Exhibit 3).
One European top 20 pharma company invested heavily in capability-building programs to equip asset teams and leaders with the skills they needed at key stages of drug development. It created immersive multiday “cornerstone” experiences in which participants could practice new capabilities in person, reinforcing these sessions with self-paced modules to sustain the lessons learned. To give leaders regular opportunities to reflect and take stock, the company also set up 90-day reviews of the evolving R&D system. These reviews also helped to align expectations between senior leaders and teams and foster a shared culture.
When scaling capability building across the R&D organization, successful companies apply best practices in adult learning, such as content on demand, self-paced learning, “nudges” to prompt new behaviors, and omnichannel delivery. They also ensure that knowledge is constantly refreshed via innovation updates, inspiring stories, and so on. Finally, they build a strong foundation for cross-functional capabilities in digital and analytics, iterative working methods, prioritization, accountability, and other key dimensions.
Start strong
Companies seeking to attain these five outcomes should bear in mind a few helpful guidelines:
- ‘Timebox’ the transformation. Learning as you go has its value, but it should never be interpreted as “We’ll figure this out later.” Any delay in key decisions could mean the design of the new R&D model is never completed, or pilots never reach scale. McKinsey research suggests that successful transformations typically take only 18 months to progress from launch to target state—the point when the changes that affect most people have been implemented. Start with that timeline in mind, and let it guide your approach and pacing.
- Focus on speed and effectiveness, not cost. Though a transformation presents many opportunities for efficiency improvements, cost is the least important driver of impact and value creation. You may even need to spend more on accelerating progress in the near term to reduce overall costs in the long run. Any savings can then be reinvested in R&D, especially if productivity and return on investment are increasing. Finally, if people think a transformation is about cost, they probably won’t think creatively about speed, and may be distracted by fears of job cuts.
- Ensure your R&D organization is all in. Every R&D function and staff member must be committed to the effort. Allowing one function such as clinical operations to dominate can cripple a transformation, frustrate other teams, and create fresh silos, sending everyone back to square one.
- Dedicate full-time resources. No R&D transformation can succeed through a part-time effort. Only a committed full-time agile team can muster the focus, energy, and speed required. Some companies create new executive leadership roles to oversee the transformation. Full-time teams can also act as a natural set of early champions and pioneers to help mobilize change across the organization.
Transforming R&D at scale may seem a daunting prospect. However, leading pharma companies are already experiencing the benefits of faster cycle times, enhanced innovation, higher productivity, and a more patient-centered approach to drug development. As stakeholder needs evolve and the pharma landscape changes, other R&D organizations should consider following their example—or risk getting left behind.
