Is Biopharma Doing Enough to Advance Novel Targets?

R&D leaders devote substantial effort to selecting therapeutic targets, carefully assessing their associated risk profiles. A key strategic trade-off often emerges: develop drugs against well-established targets — with typically lower risk in early development but potentially higher commercial risk due to crowded competition — or pursue novel biological targets, which carry greater scientific uncertainty but offer stronger differentiation and the potential for first-to-market advantage.

In recent years, the biopharma industry appears to have leaned toward known targets, limiting investment in novel mechanisms. In this edition of Executive Insights, L.E.K. Consulting examines whether the industry is striking the right balance between refining known pathways and exploring uncharted biology to address unmet patient needs. We share findings that point to target crowding, outline limitations in recent target innovation and assess the strategic implications for future pipeline planning.

By the end of 2024, approximately 10,000 drugs with known target activity were in the preclinical and clinical R&D pipeline. Accounting for drugs with multiple targets (e.g., bispecifics), this corresponded to around 13,600 unique drug-target pairs. To understand how these biological targets shape pharmaceutical R&D activity, all unique targets were extracted and categorized based on underlying drug activity.

About 2% of active R&D targets — 38 targets in total — were associated with 50 or more drugs. Despite representing a small number of total targets, the 38 highly developed targets account for roughly one quarter of the entire preclinical and clinical R&D pipeline — highlighting substantial crowding among a limited set of biological mechanisms (see Figures 1a and 1b).

The concentration of drug development around a small number of established targets is likely driven by the high scientific risk of pursuing novel targets, coupled with the lower risk and greater clinical familiarity of known targets. These well-characterized targets serve as a foundation for continued innovation through scientific and clinical enhancements. As a result, the ecosystem around known targets has become increasingly crowded through various entry and expansion strategies, including:

1. Alternative target modulation
   Using similar compounds to engage different regions of a protein (e.g., targeting distinct epitopes or developing allosteric vs. competitive inhibitors) to optimize efficacy and safety
2. New modalities or delivery systems
   Developing a new therapeutic agent to more successfully modulate a disease target, leading to a stronger clinical outcome (e.g., using a new modality to perturb a disease target or pathology at a different macromolecular or cellular stage, developing a more patient-friendly route of administration, or developing a fixed-dose combination that reaches multiple targets in one therapy)
3. Precision medicine approaches
   Applying detailed patient stratification based on genetic or biomarker profiles to match therapies with those most likely to benefit, increasing efficacy and reducing risk
4. Indication and therapeutic area expansion
   Extending known targets into new indications or disease areas to meet additional unmet needs, whether through life cycle management of existing drugs or development of new ones in novel settings
    

The annual rate at which novel targets enter the pipeline has dropped significantly — from around 100 a decade ago to just 30 in 2024. This decline in early-stage innovation isn’t due to a lack of new drugs in development or reduced early-stage venture capital funding. In fact, the overall R&D pipeline has nearly doubled in size, growing from approximately 11,000 active drug programs in 2015 to about 21,000 by the end of 2024, even after accounting for product launches, program pauses and terminations.

At the same time, Series A investment in early-stage life sciences companies has grown steadily, averaging around 18% annual growth over the past 10 years, with increasing average investment across a smaller number of companies being funded (see Figure 2).
 

Roughly 350 novel targets entered the R&D pipeline between 2020 and 2024, with most being pursued in oncology, immunology, metabolism and neuroscience. A closer look reveals six core mechanistic categories driving this wave of biological innovation:

1. Cell fate and differentiation
2. Cell metabolism and clearance
3. Enzymatic modification
4. Immune cell balance
5. Neuron plasticity and activation
6. Protein catabolism

These mechanisms span diverse biological functions, but the targets associated with them remain largely early-stage — about 70% are still in preclinical development, including examples such as ALKBH5 and YTHDC1. The remaining approximately 30% have advanced to the clinic, primarily in Phase 1 trials, with targets such as LY6G6D and NEK7. As this biology continues to mature, deeper scientific assessment of these mechanistic areas is warranted to uncover high-potential innovation opportunities (see Figure 3).

Our data shows that the biopharma industry is becoming increasingly cautious in its clinical target selection. While refining known biology remains valuable, the current focus on a narrow set of well-characterized targets is leading to inefficient capital deployment. This crowding signals a broader imbalance — prioritizing familiar, lower-risk mechanisms over novel approaches that may offer greater long-term potential. As a result, even technically strong programs often struggle to differentiate clinically or commercially, with true differentiation emerging only after significant late-stage investment — raising the risk of redundancy.

The upside? There’s still significant untapped potential in novel and underexplored targets. Despite persistent unmet needs, around 55% of the 4,500 druggable proteins in the human genome remain untouched by drug development (Finan et al., 2017). While not all will prove viable, scientific advances are steadily expanding the boundaries of druggable space.

Realizing this potential will require rigorous scientific vetting and targeted investment. Emerging technologies — such as artificial intelligence-driven discovery and in silico experimentation — provide powerful tools for derisking novel biology earlier and more cost-effectively. Equally critical is strategic collaboration among leading biopharma companies, emerging biotechs and academic institutions to foster smarter risk-taking and increase pipeline momentum around novel, first-in-class targets.

To remain competitive and deliver meaningful innovation, the industry must rebalance its approach — embracing bold science, advanced technologies and collaborative models that unlock the next wave of high-impact targets and transformative therapies.
 

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