Pharma R&D Efficiency: Metrics That Matter

Pharma R&D efficiency is under constant review by executives, investors, and regulators. The cost of bringing a new drug to market has increased, while success rates in clinical trials remain low. At the same time, pressure from pricing reforms, patent expiries, and investor expectations is forcing companies to do more with less.

R&D productivity is made up of two parts:

• Efficiency – the cost of turning research inputs like funding, trials, and talent into approved drugs.

• Effectiveness – the commercial and therapeutic value that each new drug delivers.

According to Clarivate’s 2023 Factbook, global pharma R&D spending is expected to reach $200 billion by 2025, yet only 10% of sales come from drugs launched in the past five years. This gap highlights the importance of tracking the right efficiency metrics.

Importance of R&D Efficiency in Pharma

Pharma R&D is known for high costs, long timelines, and high risk of failure. The industry spends billions of dollars each year on research, but only a small number of projects reach approval. This gap is often called Eroom’s Law, which means R&D productivity tends to decline over time, even as investments increase.

Efficiency matters for three main reasons:

1. Financial returns – Executives and investors want to see strong returns on R&D spend. Poor efficiency reduces ROI and pressures companies to rely on acquisitions or external pipelines.

2. Speed to market – Faster development helps companies launch drugs sooner, which improves revenue and gives patients quicker access to new therapies.

3. Competitiveness – Companies with more efficient R&D models can reinvest savings into innovation, expand into new therapeutic areas, and stay ahead of rivals.

Industry reports (Nature 2024, Elsevier 2023, Clarivate 2023, IQVIA 2025) all highlight the same trend: while total R&D spend has grown steadily, output and ROI have not kept pace. That makes tracking and improving efficiency metrics a top priority for pharma boards and investors.

Strategic Metrics for Pharma R&D Efficiency

Executives and investors look at high-level metrics that show if R&D spending creates enough value. Three of the most used are R&D ROI, cost per approval, and portfolio efficiency.

1. R&D ROI (Return on Investment)

• Deloitte’s 2025 pharma ROI report shows that average R&D returns remain below the cost of capital for many big pharma companies.

• ROI is measured as the expected value of a drug portfolio compared to total R&D spend.

• Even when productivity improves, ROI can stay weak if sales from new drugs are low.

2. Cost per New Drug Approval

• According to Elsevier (2023), the average R&D efficiency for big pharma is $6.16 billion per new drug (2001–2020).

• This reflects the total R&D input divided by the number of FDA approvals.

• The high cost comes from long timelines and high attrition in late-stage trials.

3. Portfolio Efficiency

• Investors also track how well companies allocate R&D across therapy areas.

• McKinsey and IQVIA note that companies with sharper portfolio focus (e.g., oncology, immunology) tend to achieve higher success rates.

• Portfolio rationalization, M&A, and out-licensing are often used to maintain pipeline balance.

Categories of R&D Efficiency Metrics

Pharma R&D efficiency is not measured by a single number. Companies and analysts use different categories of metrics to understand cost, progress, portfolio health, operations, and external impact. Each category answers a different business question.

1. Financial Metrics

These metrics explain how much money it takes to turn research investment into approved products. They are the first set of numbers that boards and investors want to see.

• R&D spend per drug approval – shows the average cost of turning research into one approved drug. It combines both successful and failed projects.

• R&D spend as a percentage of revenue – indicates how much of current sales a company is reinvesting in future products. High reinvestment signals long-term focus, but it must still deliver results.

• R&D ROI (Return on Investment) – compares the commercial value of drugs in the pipeline with the cost of developing them. A falling ROI means more money is spent but the pipeline is not generating enough returns.

Why it matters: Financial metrics allow comparison between companies, and they influence how investors value pharma businesses.

2. Productivity Metrics

These metrics measure how much useful output a company gets from its R&D engine. They link effort with visible progress.

• Number of NMEs (new molecular entities) approved per year – the most basic output measure. It shows how many new products reach the market.

• Clinical trial success rates by phase – track the share of projects that move from one stage to the next (Phase I → II → III → approval). Phase II is especially important because many projects fail at this stage.

• Cycle time from discovery to approval – measures how many years it takes for a drug to move through the full R&D process. Long timelines raise costs and reduce the effective patent life of a drug.

Why it matters: Productivity metrics show whether R&D is producing results in a reasonable time and with manageable risk.

3. Pipeline Metrics

Pipeline metrics reflect the strength, balance, and risk profile of a company’s portfolio. They show how sustainable R&D output will be in the future.

• Early-stage vs. late-stage balance – a healthy pipeline has enough early projects to feed the future, while also advancing a share of late-stage candidates close to approval.

• Attrition rates by stage or therapy area – high failure rates in specific areas (like oncology) can lower efficiency, while strong performance in others (like immunology) can offset risks.

• First-in-class vs. follow-on drugs – measures how much of the pipeline is focused on novel, breakthrough products compared to drugs that follow existing mechanisms. First-in-class projects bring higher risk but higher potential reward.

4. Operational Metrics

Operational metrics capture how well a company manages clinical development. Even good science can fail if trials are slow or poorly executed.

• Trial recruitment and enrollment speed – delays in finding patients can extend trial timelines and raise costs. Faster enrollment improves efficiency.

• Protocol amendments – frequent changes to study protocols are often a sign of weak planning. Each amendment adds cost, time, and regulatory complexity.

• Use of digital tools, AI, and modern trial designs – adoption of adaptive trials, decentralized models, and predictive analytics shows a company is using new methods to improve trial performance.

Why it matters: Operational excellence reduces waste, shortens cycle times, and increases the probability of regulatory success.

5. External and Impact Metrics

These metrics extend beyond the company to measure how new drugs perform in the real world and how quickly they reach patients.

• Time to patient access – measures the lag between regulatory approval and actual patient availability in major markets. Delays can reduce the value of a new launch.

• Real-world outcomes – tracks whether new drugs deliver meaningful benefits when used outside controlled clinical trials. Strong real-world performance supports pricing and market uptake.

• Regulatory review speed and outcomes – faster approvals and fewer review issues indicate stronger submission quality and more efficient interactions with regulators.

Why it matters: External and impact metrics show whether R&D efficiency translates into real health and business outcomes, not just internal cost savings.

At a glance:

Benchmarking and Industry Standards

Efficiency metrics are more useful when compared against industry benchmarks. These benchmarks set expectations for cost, time, and success, and help companies see if they are performing above or below peers.

1. Cost Benchmarks

• The average cost per new drug approval for large pharma has been estimated at several billion dollars when failures are included.

• Some studies show costs ranging from a few billion up to over ten billion dollars per drug depending on company size, therapeutic focus, and attrition rates.

• Smaller biotechs may have lower upfront R&D costs, but they often rely on partnerships or licensing to complete late-stage development, which shifts part of the cost burden to larger companies.

2. Time Benchmarks

• The average time from discovery to approval is usually 10–15 years across the industry.

• Discovery and preclinical stages take several years, while clinical trials add another 6–9 years.

• Regulatory review times have improved in recent years due to fast-track and priority review programs, but overall development timelines remain long.

3. Success Rate Benchmarks

• The overall probability of success from Phase I to approval is around 4–5%.

• Success rates vary by phase:

• Phase I: highest, often more than half of projects move forward.

• Phase II: lowest, many projects fail due to lack of efficacy.

• Phase III: higher but still at risk from safety or commercial issues.

• Success also varies by therapeutic area. Oncology, for example, often has lower success rates than other areas because of scientific complexity.

4. Large Pharma vs. Small Biotech

• Large pharma: tend to spend more per approval, but have diversified pipelines, global trial infrastructure, and strong regulatory capabilities.

• Small biotech: more focused portfolios and often faster at discovery, but depend heavily on partnerships, licensing, or acquisitions to reach approval.

• Many large pharma companies now rely on biotech partnerships for half or more of their pipeline, showing how efficiency can come from external as well as internal R&D.

5. Investor Perspective

• Investors use these benchmarks to compare performance across companies.

• A company with lower costs per approval, faster cycle times, or stronger success rates than the industry average is seen as more efficient and more competitive.

Trends of Improving R&D Efficiency

Pharma R&D is evolving. Companies are introducing new methods to lower costs, reduce timelines, and improve success rates. Several key trends stand out:

1. Artificial Intelligence and Machine Learning (AI/ML)

AI is becoming central in target identification, drug design, and trial optimization.

• AI tools screen large volumes of biological and chemical data faster than traditional methods.

• Machine learning models improve target validation and help predict drug safety and efficacy earlier.

• Companies are forming billion-dollar partnerships with AI-driven firms to speed up discovery.

2. Real-World Data (RWD) and Digital Biomarkers

The use of real-world data from electronic health records, wearables, and patient apps is growing.

• Regulators now accept RWD as supportive evidence in some approvals.

• Digital biomarkers make patient monitoring more accurate, allowing faster trial endpoints.

• These approaches reduce the need for very large, traditional trial populations.

3. Novel Clinical Trial Designs

New designs aim to make trials more efficient and flexible.

• Adaptive trials allow protocol changes mid-study, reducing wasted time and resources.

• Basket and umbrella trials test multiple therapies or indications under one framework, saving cost and improving speed.

• Decentralized trials use digital platforms and remote monitoring to reduce site burden and improve patient access.

4. Outsourcing and Partnerships

Efficiency is no longer only about in-house R&D.

• Many pharma companies outsource parts of discovery or development to contract research organizations (CROs).

• Partnerships with biotechs provide access to novel science without building everything internally.

• Mergers and acquisitions remain a common way to strengthen pipelines when internal productivity is weak.

5. Portfolio Focus and Strategic Discipline

Companies are narrowing their focus to therapeutic areas where they have expertise.

• Dropping low-productivity areas helps concentrate investment and expertise.

• Stronger portfolio governance ensures projects are terminated earlier if risks are high.

• Companies with disciplined portfolio management often report stronger R&D efficiency gains.

In summary:
AI, real-world evidence, innovative trial models, external partnerships, and tighter portfolio discipline are all reshaping R&D. Together, these trends help pharma companies reduce cost-per-approval, shorten timelines, and improve overall productivity.

Challenges in Measuring Efficiency

While efficiency metrics are valuable, they are not always easy to measure or compare. Different companies, therapeutic areas, and data sources make benchmarking complex. Several challenges stand out.

Data Availability and Consistency

R&D data is often fragmented across discovery labs, clinical trial sites, and external partners. Some companies report detailed metrics, while others keep them private. This lack of consistent data makes industry-wide comparisons difficult. Even when benchmarks are available, they often reflect averages that hide big differences between companies.

Variability by Therapeutic Area

Not all therapeutic areas face the same risks or timelines. Oncology projects often have lower success rates because of scientific complexity, while infectious disease programs may move faster due to urgent public health needs. Comparing efficiency across these areas can be misleading if the context is not considered.

Balancing Speed and Safety

Companies face pressure to shorten timelines, but faster development can create risks. If attrition is pushed too late in the process, costs rise because failures happen in expensive phases. If trials are rushed, safety and regulatory concerns can emerge. Finding the balance between speed and rigorous science is a constant challenge.

Snapshot vs. Long-Term View

Efficiency metrics can look very different depending on the timeframe. A company might appear efficient in a short snapshot because of recent approvals, but the long-term ROI may still be weak if earlier investments have not delivered value. Analysts need to consider both short-term and historical data to get a realistic picture.

Impact of External Factors

Regulatory changes, pricing reforms, and global health events can all affect R&D outcomes. For example, faster FDA reviews or new pricing rules can change how efficiency is measured. These external shifts make it difficult to rely on one standard definition of efficiency.

Conclusion

R&D efficiency is one of the most important drivers of long-term success in the pharmaceutical industry. It affects financial returns for investors, the pace of innovation for companies, and the speed at which patients gain access to new treatments. Measuring efficiency through financial, productivity, pipeline, operational, and impact metrics gives decision-makers a balanced view of how well resources are being used.

The benchmarks show that developing a single drug often takes more than a decade and requires billions of dollars of investment, with only a small fraction of projects reaching approval. These challenges make efficiency improvements essential. Companies that manage attrition early, adopt AI and real-world data, run smarter trials, and maintain disciplined portfolios are already seeing gains in productivity.

At the same time, measuring efficiency remains difficult. Data gaps, therapeutic variability, and external pressures mean that no single metric tells the full story. Efficiency must be viewed as a balance between cost, time, and patient value, not as a race to reduce one measure at the expense of others.

Looking ahead, pharma R&D is likely to become more data-driven and technology-enabled. Companies that combine strong science with efficient operations will be better positioned to deliver both shareholder returns and meaningful patient outcomes. Efficiency, therefore, is not just a measure of cost control but a foundation for sustainable innovation in the years to come.