The journey to bring new medicines to market remains long and costly, despite recent technological and process improvements in biopharma R&D. It takes about a decade, on average, from candidate nomination to launch, and study and application costs for investigational new drugs (INDs) have soared in recent years by 20–30 percent.
These challenges stem partly from the growing complexity of research and early development, fueled by an explosion of data and insights generated by experiments conducted across diverse therapeutic areas. To assess a drug candidate’s clinical potential, researchers need to efficiently analyze vast amounts of information, considering factors like structure, target binding, modulation efficacy, and manufacturability. The challenges are amplified for research teams working with novel molecular mechanisms or in complex modalities, such as radiopharmaceuticals and cell therapies, especially for niche indications lacking established clinical models. In such instances, the added complexity of experimental design and interpretation forces teams to balance the need for rigorous preclinical evidence with the pressure to deliver more medicines to the market sooner.
To reduce the complexities associated with research and early development, leading pharmaceutical companies are implementing leaner processes and utilizing technological advances in automation and digitization. Some of these have already led to a 40 percent reduction in cycle time from concept to first-in-human (FIH) trials.
In this article—which details the third ingredient (out of eight) for sustained R&D performance improvements introduced in our flagship article, “Making more medicines that matter”—we explore 12 actions biopharma companies have taken to streamline their research and early-development processes and highlight how these actions contribute to the three factors that create value in research and early development: speed, cost, and output and capacity (Exhibit 1).
Speed
From our analysis of and collaborations with some of the top players in the field, we’ve seen that pharmaceutical companies can often cut time to reach FIH trials by 40 percent or more through process improvements, moving from candidate nomination to clinical trials in 12–15 months (Exhibit 2). Even greater gains can be achieved with emerging automation and AI; for example, AI could produce even greater process gains when integrated into broader closed-loop research systems that use data to continually improve each step of preclinical development. For a company seeking to move three to five INDs into FIH studies annually, an acceleration of nine to 12 months applied across its portfolio could yield over $400 million in risk-adjusted net present value.1
To achieve best-in-class timelines, companies can employ the following optimization strategies.
Front-loading investments and using parallel processes
Companies can write sections of the IND application using audited draft reports such as good-laboratory-practice (GLP) toxicology studies. This approach reduces delays, avoiding the five-to-ten-week wait for the final standard for exchange of nonclinical data, and speeds up the completion of IND modules, accelerating downstream tasks such as clinical protocol development.
Simplifying experiment designs and protocols
Research and early-development teams can save significant time by streamlining experiments. For example, early pharmacology experiments can be simplified by using fewer cell lines (as few as three instead of ten or more) and focusing on essential data. Some experiments may even be skipped based on guidance from health authorities. In some cases, if a best-fit experimental model isn’t available, in vivo GLP studies may be waived, potentially saving months in the preclinical phase.
Adopting next-generation technology innovations
Identification of novel targets can be significantly accelerated with next-generation technologies. For example, in silico methods, combined with machine learning and molecular dynamics simulations, can quadruple the speed of lead optimization. These technologies can optimize compound discovery through high-content image analysis and predict assay performance with custom learning algorithms. Advanced cloning technologies, such as those that improve cell line development processes, are other examples of innovations that can also shrink timelines by up to three months.
Improving organizational governance and decision-making
Organizations can accelerate research and early development by systematically mapping key processes such as pharmaceutical sciences, research, writing, and regulatory procedures and benchmarking them against industry standards. This comparative analysis helps identify high-impact initiatives across programs, priorities, modalities, and indications. Some strategies, such as advancing certain “at risk” experiments and investments, may become standard practice for all candidates, while others may apply only to high-priority projects based on the organization’s risk tolerance. Organizations that effectively redesign governance to improve speed often find success by consistently adhering to minimum-viable requirements for each stage or simplifying the templates for milestone presentations. These approaches reduce workload, minimize human error, and can often improve the quality and rigor of decision-making.
Further streamlining can be achieved by reducing the number of decision-makers to a small group of key individuals and by empowering working-level teams to make critical decisions. Many companies also use technology to optimize their governance. Digital recordkeeping systems, such as laboratory information management systems and electronic lab notebooks, feature user-friendly workflow editors that enable digital-process instructions and significantly reduce test-record preparation time. These systems eliminate peer review of handwritten records, minimizing errors. Additionally, live research dashboards, autopopulated with key graphs and metrics, have become invaluable tools for research leaders, giving them immediate access to real-time insights and critical data, which empower them to make faster, more informed decisions.
Cost
The evolving landscape for research and early development presents new challenges in expense management. Novel technologies and modalities demand substantial research investments. Regulatory uncertainty requires dual experimentation with both traditional and new methods. In response, some companies are also shifting toward in-sourcing preclinical technologies. This move may accelerate experimentation, but it can also impose higher fixed costs due to the need for expanded facilities and additional specialized personnel.
Growing pressure to justify research investments and control R&D costs has driven forward-thinking pharma companies to adopt some strategic cost management approaches for research and early development, such as the following.
Pursuing minimum-viable (and maximum efficiency) FIH strategies
One key approach involves aligning with minimum-viable and maximum efficiency FIH strategies. For example, companies can reduce unnecessary experiments by strictly adhering to regulatory requirements and engaging early with regulatory bodies while ensuring compliance. This proactive stance is particularly crucial for novel molecules and complex modalities.
Anchoring planning in clinical development
By involving clinical development teams earlier in the research phase, organizations can better direct critical outputs such as dose projection and optimize investments in areas such as drug product volume (that is, having sufficient drug product material available) for initial clinical trials, among others. This approach brings a valuable clinical perspective to preclinical work, enhancing overall efficiency and costs.
Replacing in vivo experiments with in vitro or in silico methods
As noted earlier, some companies are also exploring the replacement of traditional in vivo experiments with in vitro or in silico alternatives. While this shift requires capital investments in new platforms and regulatory validation, it has the potential to reduce costs significantly in the long run. In silico techniques are proving particularly useful for tasks like target identification and antibody design. Some emerging in vitro methods, such as organoid models, also offer additional avenues for savings.
Utilizing lab resources effectively
Companies can dramatically improve the cost efficiency of their lab operations through multiple avenues. Integrated digital management systems and connected equipment are two potential paths to better tracking, understanding, and managing resource utilization. Regular reassessment of internal capacity and access to flexible, on-demand services (such as cloud-based labs) further enhance this optimization.
Output and capacity
Research and early-development teams aim to actively increase throughput and optimize the use of their capacity to boost output and productivity. They can achieve these goals by pursuing strategies that effectively manage the increasing complexity of research processes. Despite the shift in research portfolios toward more intricate modalities and novel indications, organizations are pursuing efficiencies through a wide array of new experimental tools, designs, and processes. Injecting automation and digitization into research and early development opens new opportunities for more effective management and enhanced reproducibility.
Teams that embrace this vision have utilized the following strategies.
Employing miniaturization and high-throughput screening
Organizations are turning to advanced technologies such as acoustic liquid handling and droplet microfluidics to boost output. These techniques, often used in lab-on-a-chip devices, miniaturize and automate laboratory processes, enabling researchers to conduct experiments with tiny droplets of liquid. This approach can increase throughput by up to 100 times compared with traditional methods and enable more precise handling of materials. These technologies can also reduce the volume of sample material and reagents needed by up to 90 percent, which expands the number of experiments that can be done from limited patient samples.
Applying lab automation
Each candidate screening process can easily involve analyzing up to 10,000 compounds to find a molecule that effectively hits the desired target. Reducing the screening workload of scientists managing the screening equipment opens up the possibility of 24-hour automation that eliminates the need for overnight staffing. Continuous automation can triple throughput on the same equipment and, when combined with automated data analysis, can also enable AI-driven lead discovery and optimization.
Adopting in silico experimentation to increase throughput
Computational models can increase throughput by rapidly screening thousands of compounds to help researchers prioritize the most promising candidates for physical testing.
Using strategic external resourcing
To overcome resource constraints, organizations are turning to specialized contract research organizations (CROs). For example, CROs with advanced high-throughput screening technologies can execute large-scale compound testing with relative ease. Partnering with external resources addresses skilled-scientist and lab space shortages while providing access to cutting-edge capabilities without a substantial upfront investment.
What it takes to capture value
Achieving operational excellence in research and early development requires a coordinated effort across all relevant functions that drives progress simultaneously across these five critical steps:
- Setting the ambition. A realistic and clearly articulated ambition serves as a constant North Star for prioritizing where to focus efforts across portfolios and individual programs, guiding leaders in selecting the best approach for each opportunity.
- Leveraging digitalization, automation, and AI. Achieving high ambitions will require companies to embed these innovations and align them with specific goals. Companies aiming to increase the probability of success and throughput may leverage AI for more effective target identification and selection. Those looking to improve biologics development might explore in silico protein engineering. Organizations should balance the risks and benefits of each solution, recognizing that more mature methods carry lower risks than those that still require significant improvements.
- Creating a culture of innovation. Piloting new ideas requires aligning dedicated preclinical teams, clear practices, and a systematic approach and cultivating exceptional talent across all functions. Training talent to be innovative should extend beyond core skill sets to enable sound judgment for trade-off decisions. Asset teams should prioritize activities by evaluating risk–benefit trade-offs, and talent should know when and how to engage relevant experts across the organization.
- Designing a future-ready research model. To adapt to increasing portfolio complexity, evolving regulatory landscapes, and new technologies, pharmaceutical companies should evolve their research and early-development operating models. This transformation requires both structural and process innovations, as well as a cross-functional approach. Adopting nimble governance and agile project teams can facilitate the passage of drug candidates through critical early-stage gates. Asset teams and functional leaders that are empowered to make decisions based on predefined criteria can reduce internal governance burdens and free senior executives to focus on strategic issues.
- Selecting the optimal delivery model. A range of options are available to research and early-development leaders to deliver on their ambitions, including internal capabilities and resources, new talent, vendor outsourcing, or third-party partnerships, such as with academia or biotech start-ups. Companies may choose to insource or outsource based on which option delivers faster results or provides a greater competitive edge. For example, outsourcing medical writing for straightforward programs (for example, programs such as those that the company has successfully developed before) can save resources for writing more complex, novel programs in-house. Partnerships offer access to key datasets, while insourcing analytics enables organizations to identify differentiating biological insights.
The pursuit of operational excellence in research and early development is a challenging yet rewarding endeavor. It requires a systematic approach and an unwavering commitment to innovation and efficiency. By proactively embracing these principles, companies can gain a competitive edge while accelerating the delivery of groundbreaking therapies to patients.