Life sciences is on the cusp of a new era of innovation. The convergence of breakthrough technologies, widespread access to data, and deeper insights into complex biological systems is revolutionizing how the industry approaches R&D. For instance, venture capital funding for machine-learning-enabled drug discovery surged more than sevenfold from 2019 to 2022, according to McKinsey analysis. Interest in innovative modalities such as antibody–drug conjugates (ADCs) remains strong, with the top three ADCs expected to bring in $17 billion in global revenue by 2028, and eight of the top ten pharmaceutical companies completing at least one ADC-related acquisition or licensing deal in the past two years.
Yet in this time of rapid transformation and excitement, pressure on the industry continues to rise. For example, R&D pipelines are increasingly crowded, with intensifying competition shortening asset life cycles and compressing time between launches. Remaining competitive will require industry leaders to adopt approaches to optimizing R&D asset strategy. Our analysis of 100 top assets—those with the highest lifetime actual and forecast sales worldwide from 2014 to 2030—identified critical areas to address, including indication breadth and parallelization, trial endpoints, and global trial footprint.
An increasingly challenging environment
There is high pressure on biopharmaceutical companies to drive relentless asset development. We see three primary challenges.
Crowded R&D pipelines
The global industry pipeline has grown significantly: across all therapeutic areas, clinical trial volume increased by 4 percent annually from 2020 to 2024, with the number of compounds in active development doubling in the past decade, according to McKinsey analysis. This surge in activity is intensifying competition and shortening the interval between launches. For instance, the launch gap for the top three oncology targets (HER2, CD20, and BCR-ABL) has shrunk from 6.3 years between the first and second launches to 2.4 years between the second and third launches; this drops to 1.4 years by the fifth launch.1
These shorter launch intervals are linked to asset herding, in which multiple companies pursue the same targets.2 In the past 20 years, the number of assets per target has increased by more than 2.5 times, with oncology leading the trend. In 2000, only 16 percent of top ten pharma pipelines were herded targets (defined as more than five assets pursuing the same target); by 2020, this had risen to 68 percent (Exhibit 1).
Compressed asset life cycles
Rising competitive intensity and accelerated development timelines are steadily shortening asset life cycles, reducing both the time available in which to capture value and the overall value that can be captured from an asset. This life cycle compression—exacerbated by rapidly evolving clinical practice and growing biosimilar adoption—has shortened the time to reach 50 percent of lifetime sales by more than two years in the past two decades.3 Value capture is further constrained by pricing reforms, cost-containment measures, and a shift toward value-based pricing and reimbursement. This is exemplified by the Inflation Reduction Act (IRA), which sets a “maximum fair price” for drugs with the highest Medicare spending and gives small-molecule drugs nine years of protection from price negotiation and biologics 13 years. Given that small-molecule drugs capture 51 percent of lifetime revenue after the protected window and biologics 29 percent, the IRA’s effect on revenue trajectories may reduce follow-on investments and influence portfolio decisions, pressuring companies to capture value within ever-tighter time frames.4
Increasing cost pressures
Drug development is becoming more expensive. R&D spending accounts for an average of 20 percent of biopharma revenue and has continued to rise, reaching $260 billion in 2023.5 But this increase in spending has not translated to an increase in efficacy. Time to launch remains lengthy, averaging ten years from Phase I to launch, while cost per launch has grown by 8 percent annually, reaching $4 billion in 2022.6 Cost drivers include rising trial complexity (due to a shift toward complex modalities and other factors) and decline in enrollment productivity, especially in pivotal Phase III trials. Escalating costs and inefficiencies highlight the need for innovative approaches to improve returns on R&D investment.
Three winning strategies for market success
Across the industry, we have observed a wide range of tactics to optimize asset outcome at both the indication (above-trial) and trial level across study design, conduct, closeout, and post-approval phases (Exhibit 2). Our examination of the top 100 assets in all therapeutic areas identified three areas of strategic focus.
Expanding indication breadth and parallelization
Strategic indication expansion is emerging as a key differentiator, with a clear trend toward more aggressive pursuit of greater indication breadth and parallelization as an asset strategy by top biopharma companies. For example, for two of the most crowded and successful drug classes—anti-VEGF therapies and PD-1 inhibitors7—our analysis showed the top 15 biopharma companies both initiate new trials in other indications more quickly (within 12 months following the first pivotal trial) and proactively launch more trials relative to peers (Exhibit 3).
Beyond these two asset classes, we broadened our analysis to the top 100 assets between 2014 and 2030 and assessed the number of indications initiated within five years of first-in-human (FIH) clinical trials as a marker for early parallelization. Rapid indication expansion was a clear trend in both oncology and non-oncology assets, according to McKinsey analysis. Among anti-PD-1s, trials for Opdivo (FIH 2006) were initiated in six new indications within five years of FIH, followed by Tecentriq (2011) and Imfinzi (2012) with trials in 14 and 18 new indications, respectively. Trials were initiated for Keytruda (FIH 2011), a standout example, in 38 indications within five years of FIH through successful basket trials.8 Among ADCs, Padcev (2014) initiated two indications, while Enhertu (2015) and Datopotamab deruxtecan (2018) initiated 11 and 13 indications within five years of FIH, respectively.
This “front-load and fail fast” strategy enables companies to rapidly identify the most promising indications to pursue and maximize revenue capture before factors such as competitor entry and loss of exclusivity become pressing. Significant considerations accompany such parallelization, though, including higher risk, operational complexities, and the need for substantial up-front capital and resource investment, factors that lead to differential prevalence of front-loading among biopharma companies. However, should resources permit, this approach can allow companies to establish leadership in competitive markets, especially those in which they may have lost the first-mover advantage.
What does the future hold? The industry is poised for even greater indication breadth and parallelization, especially given the challenges facing R&D today. In particular, the IRA has created a strong incentive to accelerate the development of multiple indications before price controls take effect.
Expansion of indication breadth and parallelization will be enabled by both strategic and operational advancements. On the strategic front, AI-enabled predictive analytics is revolutionizing the way companies identify and prioritize new indications. These advanced tools allow for the early detection of promising therapeutic areas by analyzing vast data sets, including genomic information, real-world evidence, and patient outcomes. (For insight into the opportunity to continue making R&D more inclusive, see sidebar, “Understanding sex-based differences as a catalyst for improved outcomes and growth.”)
Operationally, improvements in trial design and execution are creating synergies that further enhance parallelization. Adaptive trial designs, for example, increase the likelihood of success across multiple indications by enabling modifications to ongoing trials based on interim results. Decentralized trials optimize resource allocation by enabling trials to be conducted more efficiently, as demonstrated by the global enrollment of tens of thousands of participants to support rapid COVID-19 vaccine development. Together, strategic and operational advancements are setting the stage for a new era in drug development, in which indication breadth and parallelization become cornerstones of successful asset strategy.
Increasing the number of trial endpoints
Recent years have seen an increase in the number of endpoints, which are the target outcomes of clinical trials. This approach allows biopharma companies to maximize the value of each trial by gathering data on more metrics beyond primary therapeutic outcomes (for example, quality-of-life measures) but comes at the cost of greater site and patient burden. To quantify this trend, we averaged the number of secondary endpoints across Phase III trials for the 100 top assets.9 Trials initiated between 2015 and 2024 had 12.1 secondary endpoints on average, 25 percent more than trials initiated from 2005 to 2014 (Exhibit 4). In general, oncology trials had the most endpoints, while endocrine and cardiovascular trials showed the broadest spread.
A strategy of increased data collection, when used judiciously, can provide a richer data set to support regulatory submissions, expand labeling options, facilitate broader market access, and ultimately encourage greater uptake among patients and healthcare providers. As an example, within GLP-1 agonists,10 early mover Trulicity had a median of 17.0 secondary endpoints across diabetes trials, while later entrant Cagrisema had 28.8 endpoints. The additional endpoints were tied to patient reported-outcomes (PROs), which could help demonstrate differentiation over current on-market assets.
Notably, one key trade-off with more endpoints is a corresponding increase in protocol burden, which may have a detrimental impact on patient participation. Careful operational design and statistical planning are required to ensure that the trial remains manageable and the data can be meaningfully interpreted.
What does the future hold? Novel classes of endpoints are emerging, especially as personalized medicine becomes more prevalent and precision therapies target patient subgroups. For instance, technologies enabling continuous patient monitoring—such as wearable devices—will support digital biomarkers as secondary endpoints. These will enable monitoring of nuanced, continuous real-time changes in disease progression and treatment response that traditional endpoints might miss.
The scope of secondary endpoints is also likely to expand because of increasing focus on PROs and real-world evidence. In oncology, for example, PROs feature heavily in assessing treatment impact on quality of life and daily functioning. These metrics may be critical differentiators in a crowded market and may be used to support regulatory approval, justify premium pricing, and achieve broader market access.
Beyond secondary endpoints, exploratory endpoints may also play a larger role, supported by advanced analytics enabling hypothesis generation from vast data sets. For example, AI-driven biomarker identification in early-stage trials could be tested as exploratory endpoints in late-stage trials to inform indication expansion strategies.
Broadening the global trial footprint
Expanding a trial footprint enhances the robustness and generalizability of clinical programs. Our analysis found that the total footprint of Phase III trials has doubled in the past two decades (Exhibit 5), consistent with increases in sample size and patient demand. Oncology trials tend to have larger footprints than average, likely reflecting the need for more sites given high protocol complexity, targeting of distinct populations with specific tumor types, or significant patient attrition. Conversely, immunology trials have a more restricted footprint.
We compared sites per trial across all trials initiated between 2005 and 2014 and between 2015 and 2024. While oncology has the largest footprint, the number of sites increased by only 10 percent between the two windows; conversely, immunology sites increased by 31 percent, suggesting larger trials over time. Surprisingly, endocrine, cardiovascular, and blood trials showed a 14 percent decrease. This could be due to larger trials for Eliquis and Xarelto in the earlier window (2005–14), which involved ten trials (304 sites per trial) and 26 trials (280 sites per trial), respectively.
Recent years have seen a trend toward diversifying beyond traditional site locations in North America and Western Europe. According to McKinsey analysis, the share of US sites has declined amid a shift to emerging markets (China, Asia–Pacific, and Latin America), which accounted for 65 percent of sites in 2015–24 versus 49 percent in 2005–14. Establishing a broader, more global trial footprint reflects an increasing need to access more diverse patient populations, accelerate recruitment, and meet growing demands for more representative data. This strategy also allows companies to navigate several regulatory environments simultaneously, speeding up approval in multiple markets.
What does the future hold? Expanding trial footprints will remain a key pillar of asset strategy as competition intensifies for patient populations globally. Digital advancements are likely to play a critical role: for example, digital health platforms and remote monitoring technologies could allow for the inclusion of patients from geographically remote or underserved regions, where traditional site infrastructure might be lacking. This expands the pool of eligible participants while ensuring that trials are more representative of the global population.
The rise of precision medicine and biomarker approaches will further contribute to the expansion of the trial footprint due to the need to enroll rare patient subgroups. Beyond broadly increasing the quantity of sites, the use of advanced analytics is also likely to increase site quality. For example, AI-based site selection will help identify and eliminate poor performers, enabling companies to focus resources on high-performing sites.
What does this mean for R&D leaders?
To remain competitive, industry leaders will need to take an approach to asset strategy that balances speed, value, and cost while embracing innovation to optimize for long-term success. For priority assets, it is especially critical to have a comprehensive end-to-end strategy early in development, potentially during FIH trials. This requires gathering insights from other assets, mapping out strategic levers for each development stage, and identifying key winning tactics. None of this should happen in a vacuum—R&D leaders should tailor their strategy to the specific asset context across differentiation and likelihood of disruption. For example, assets with low differentiation and high potential for disruption should prioritize speed to market. Conversely, assets with high differentiation and low potential for disruption should focus on enhancing differentiation, trading off against a longer development timeline to ensure a more robust and defensible market position.
How to get started
For R&D leaders seeking to optimize asset strategy, we recommend adopting a combination of the following strategic approaches.
Conduct a comprehensive asset diagnostic. This diagnostic should scrutinize each detail of the asset plan, leaving no stone unturned in identifying key strategic levers to improve return on investment. For example, targeted refinement of endpoints based on market research could support competitive differentiation in a crowded landscape. Along the same lines, introducing interim analyses could expedite registration filing and improve launch order to enhance market share capture.
The timing of this diagnostic is critical for success. It should be initiated following triggers that influence asset performance, including major competitive landscape shifts, early signs of suboptimal performance, and key life cycle milestones (for example, immediately following the conclusion of early-stage proof-of-concept trials). While important, this diagnostic will be challenging to conduct, given the number of moving variables and the need for both a granular understanding of asset strategy and a synthesized, high-level holistic view. Moreover, every assumption must be rigorously challenged, and external perspectives integrated with internal expertise and asset-specific insights.
Deploy a targeted asset acceleration program. This should focus on operational levers to reduce time to launch, a metric that is becoming increasingly pertinent in a post-IRA landscape. While asset diagnostics are centered on strategic choices, asset accelerations should be highly tactical but similarly tailored to each asset’s unique challenges. The impact of asset acceleration is clear; for example, industry leaders have managed to gain up to one year of acceleration through leveraging data-driven country and site selection to boost enrollment. Operational levers must be matched by executional excellence in the form of disciplined project management and granular oversight to ensure seamless delivery of daily activities and facilitate early risk identification and mitigation. Notably, in a crowded market, strategic differentiation is just as important as operational excellence, and industry leaders must seamlessly integrate both to remain in a position of strength.
Actively embrace AI-enabled approaches. This could involve using AI for indication finding and prioritization, identification of underperforming sites, or support for trial management. Successful digital enablement will require significant investment in people, processes, and technology through capability building, talent acquisition, and upskilling initiatives. For example, this could be achieved by establishing a digital center of excellence, complemented by organization-wide change management. Embracing AI innovation and fostering a culture of digital agility will better position R&D organizations to accelerate development timelines, enhance operational efficiency, and stay ahead in an increasingly competitive landscape.
Life sciences can ride a technology-driven wave of innovation but must simultaneously address the significant challenges the industry faces. We believe this is possible if companies optimize outcomes across the asset R&D life cycle, especially by focusing on strategic opportunities to expand indication breadth and parallelization, number of endpoints, and global trial footprint.
