The past three years have seen a boom in venture capital (VC) funding in the biotechnology sector. Our research shows that VC companies invested in 2,200 biotech start-ups worldwide in 2016; by 2021, that number had grown to 3,100.1 We also found that biotech companies raised more than $34 billion globally in 2021, more than doubling the 2020 total of $16 billion.
In the biotech sector, VC funding peaked in the first quarter of 2021 and has declined slightly since (Exhibit 1). Despite recent dips in the valuations of newly public companies and a slight decline in VC funding over the past four quarters, VC companies continue to plow money into biotech.2 The exuberance of these seasoned early-stage investors signals that they see the potential for significant breakthroughs in how drugs are discovered, targeted, and delivered. Start-ups with cutting-edge platform technologies—which constitute a base or infrastructure on which other therapies can be developed—have benefited the most.
We analyzed VC and private-equity funding from seed to series C in privately held biotech companies with deal sizes greater than $10 million from 2019 to 2021. For historical context, we also analyzed seed and series A deals in 2017 and 2018. We categorized companies by their differentiating platform technologies; for biotech companies working in multiple therapeutic areas on various platforms, we categorized them first by platform and then by therapy type. Our analysis excluded medical products, research tools, and contract research and services companies.
Investment in next-generation biotech platforms
From 2019 to 2021, VC companies invested more than $52 billion in therapeutic-based biotech companies globally. Two-thirds of that went to start-ups with platform technologies (Exhibit 2).
VC investors appear focused on emerging technologies that can tailor treatments to individual patients and deliver them to the target site with great accuracy. Six platforms are generating significant investor excitement:
- Cell therapy 2.0 can more precisely address diseased tissues or cells or address a wider range of disease (such as solid tumors).
- Next-generation gene therapies can edit and modulate DNA and RNA and have the potential to cure genetic diseases.
- Precision medicine can diagnose conditions earlier than other diagnostic tools can and tailor therapies to patients’ specific genetic profiles.
- Drug discovery enabled by machine learning (ML) can cut through vast swaths of data to speed the discovery and development of new drugs.
- Strategies are being developed for “undruggable” targets, including hard-to-hit proteins and hard-to-treat diseases.
- New delivery methods can send novel therapies to the entire affected tissue precisely and safely.
Six biotech platform technologies with transformative potential
In each biotech platform that has received investment interest, emerging tools have the potential to address challenges and push current technical boundaries (Exhibit 3).
Cell therapy 2.0
As of 2021, approved cell therapies3 have generated more than $2 billion in sales, and the market is expected to reach $20 billion by 2026. Since the first chimeric antigen receptor (CAR) T cell therapy was approved in 2017,4 they have revolutionized the treatment of hematologic malignancies, achieving unprecedented efficacy. New cell therapy technologies and techniques offer the potential to address diseases with significant unmet needs (such as solid tumors, which represent more than 90 percent of adult cancers5) and nononcological conditions.
However, some significant challenges (including safety) that cell therapies face have hindered broader use. The inflammatory cytokine release syndrome sometimes associated with CAR T cells, for example, can cause side effects ranging from flu-like symptoms to organ failure and death. Current autologous cell therapies (that is, therapies using cells obtained from the patient) require lengthy treatment that can give a disease time to progress. Scientists are studying new ways to harness and modify patients’ cells, and from 2019 to 2021 VC companies stepped up funding of these second-generation efforts.
Three new approaches stood out among cell therapy platforms:
- Innate immune cells. While CAR T cell therapies have shown initial success, some start-ups are shifting their focus from T cells to innate immune cells (such as natural killer [NK] cells and macrophages) because they may better penetrate solid tumors. The growing interest in innate immunity, primarily in NK cells, was evident from 2017 to 2018, with investments expanding to other cells (such as gamma delta T cells and macrophages) once NK cells achieved clinical success.
- Precision control of cell therapy. VC investors are betting that companies harnessing synthetic biology and complex logic to control cell therapies precisely may improve their safety profiles. For example, CAR T cells can kill cells they contact, including ones that aren’t cancerous. Engineered networks of genes, called “synthetic gene circuits,” can inhibit the killing of healthy cells, producing better safety outcomes. From 2017 to 2018, we saw seed and series A funding for five companies, all of which succeeded in completing series B or C rounds from 2019 to 2021.
- In vivo cell therapy. The field of in vivo cell therapy saw almost no dealmaking activity between 2017 and 2018. But by 2021, several biotech companies were working on ways for patients to produce CAR T cells in their bodies rather than relying on ex vivo manipulation of extracted cells in distant manufacturing facilities. Delivering genetic material and instructions directly to a patient’s T cells could reduce the logistic and manufacturing complexities that have constrained the uptake of CAR T cell treatments.
Next-generation gene therapies
Gene therapies have transformed patient treatment, offering, in some cases, excellent outcomes for genetic disorders. According to a McKinsey analysis of pharmaceutical industry data from Evaluate, roughly 400 gene therapies are currently in development; by 2025, they could comprise around 20 percent of new product launches. The field continues to evolve, with gene editing enabling permanent and precise genetic deletions and ex vivo modifications.
Obstacles to gene therapy remain. For example, CRISPR-Cas9 gene editing can perform only a limited range of edits on a limited set of genomic targets because of its limited specificity, activity, and deliverability. And safety concerns persist about its potential to inflict permanent DNA damage, mutagenic effects, or cell death.
Biotech companies have increasingly focused on next-generation strategies, including the following, to overcome the challenges of existing gene therapies:
- RNA-based modalities and editing. Several start-ups are developing new RNA-editing tools (such as adenosine deaminase acting on RNA [ADAR] and CRISPR-Cas13) to make transient edits and avoid harmful double-stranded breaks. Another approach is to modulate protein expression with new classes of RNAs (such as transfer and small activating RNAs). Most investments in novel RNAs began in 2019, but the success of messenger-RNA-based COVID-19 vaccines has propelled the entire field.
- Novel nucleases. Enabled by AI and enzyme discovery, several biotech start-ups are exploring novel Cas nucleases or enzymes (such as Cas12 and CasX), which promise improved efficiency and targeting specificity in CRISPR gene editing. Investor excitement has grown exponentially; the average VC funding for nuclease platform companies grew fivefold, from an average of $10 million to $50 million, from the 2017–18 to 2019–21 data sets.
- Non-nuclease editing and modulation. The range of edits possible with conventional CRISPR-Cas9 gene editing is limited. However, novel editing technologies (such as base and prime editors, transposases, and epigenetic modulators) have the potential to overcome this challenge with precise epigenetic modulation or more efficient knockouts and insertions of one to more than a thousand base-pair sequences. While deals from 2017 to 2018 primarily focused on base and epigenetic editing, the wave of start-ups funded from 2019 to 2021 focused on search-and-replace prime editing and transposases, which use an enzyme to catalyze the movement of certain types of genes.
Precision medicine
Precision medicine—an approach to maximizing therapeutics’ effectiveness that uses diagnostics and analytics to consider individual variability in genes, environment, and lifestyle—has already produced breakthroughs, including treatments specific to certain gene mutations. Advances in the ability to process enormous amounts of data combined with AI have allowed the field to explode.
However, there are limits to the broader use of precision medicine to diagnose and treat patients. First-generation precision diagnostics can only detect known biomarkers and mutations. While early precision medicines have affected well-validated disease targets, there remain patient subgroups that don’t respond to treatment, necessitating further identification of unique disease subpopulations. Finally, translating complex genetic data into actionable clinical-care decisions remains challenging.
To advance precision medicine, numerous emerging biotech companies are focusing on cutting-edge technologies, including the following:
- Early disease detection. While existing diagnostics search for a few known mutations, advanced multiomic tools can scan millions of circulating biomarkers (including metabolites and epigenetic markers) to detect early signs of disease. Investors continue to double down on such tools, with significant follow-up funding and series A investment in companies in this space.
- Biomarker discovery. Platform technologies that can sort through large integrated multiomic data sets (including those on genomics, proteomics, metabolomics, and so forth) could help companies identify novel biomarkers and genetic targets for patient subpopulations and predict patient responses. Most companies funded in series A from 2017 to 2018 received follow-up series B or C investment and continue to develop their biomarker discovery platforms.
- Precision population health. Several biotech companies are focused on using insights from complex genomic data to guide disease prevention and treatment decisions. These companies simplify and rapidly analyze sequencing data that focus on genomic variations, offering interpretative services for nonspecialized clinicians. Compared with other precision-medicine subsegments, this field has the highest proportion of late-stage funding, with 80 percent of such companies in series C.
Machine learning–enabled drug discovery
Advances in ML promise to accelerate drug discovery and development through computer-modeled simulations that predict molecular behavior. Rapid advances in this field have enabled more effective drug design and optimization. For example, a breakthrough in protein structure prediction by the open-source AI system AlphaFold regularly achieves accuracy competitive with experimentation.6
However, the translatability of ML models is limited. Challenges include an insufficient number of high-quality data sets, a lack of generalizability, and uninterpretable algorithms. This situation creates opportunities for novel ML approaches that use sophisticated algorithms and integrated data sets to increase the efficiency and accuracy of the drug discovery process.
Innovative start-ups have developed novel methods in three areas:
- Target identification. ML is increasingly used for phenotypic screening and understanding disease. Advanced genetic-profiling platforms can identify new genetic variants or use ML to scan the entire genome for hot spots and potential targets. Companies continue to expand the repertoire of identified disease-relevant targets, including proteins, RNA-splicing sites, and biomolecular condensates. This space received roughly 70 percent of series B and C funding, signaling its relative maturity and competitiveness.
- Rational drug design. Start-ups are working to make their ML models more generalizable so that they can apply one predictive model across multiple similar targets. Approaches include robotic platforms that learn from experiments and generative algorithms that can design custom molecules from scratch. Biotech companies advancing such technology are just beginning to form, with almost half at the series A stage.
- Lead validation and optimization. Algorithms can scan libraries of billions of molecules across numerous disease targets to choose those most suited to clinical development. To address the lack of high-quality training and experimental data, ML can generate a list of interactions between proteins and the molecules that bind with them and cross-reference that list to select leading candidates. Biotech companies developing such technology are relatively small, with the median deal size a little more than half that of all ML biotech companies (around $17 million versus $32 million).
Strategies for validated but undruggable targets
Conventional drug modalities (such as small molecules and monoclonal antibodies) target proteins to address disease. However, research suggests that at least 85 percent of disease-associated proteins in the human body are undruggable—conventional modalities can’t produce a therapeutic effect, because they have no binding site. In recent years, the biopharma industry has made progress on this front, and successes in the lab have renewed investors’ interest in delivering drugs to hard-to-target proteins and hard-to-treat diseases.
However, beyond identifying binding pockets in the disease-relevant proteins, problems remain. They include resistance to small-molecule drugs in proteins with known binding pockets, limited disease-modifying effects in targets whose protein functions aren’t easily altered, lack of validated targets, and limited understanding of disease biology.
Biotech start-ups are developing novel platforms to address undruggable targets and diseases. Three promising approaches are as follows:
- New small-molecule binding sites. Many VC-funded companies are focused on advanced techniques in interactions between proteins and small molecules and on computational methods to identify previously unknown binding sites on proteins that small molecules can target. The past three years have seen increased investment in this approach, which constitutes more than 50 percent of all funding in this category.
- Protein degradation. An emerging modality in disease-causing proteins is protein degradation, which circumvents the need to identify elusive small-molecule binding sites. The major class is proteolysis-targeting chimera (PROTAC), which promotes selective protein degradation.
- Novel disease targets. Several biotech companies are advancing the knowledge of disease biology by developing innovative platforms to identify new targets in hard-to-drug illnesses. One approach involves sampling populations that are naturally resistant to those diseases, using advanced analytics to identify protective antibodies, and developing those antibodies as therapeutics.
New delivery methods
Drug delivery has seen significant advances as more therapies rely on robust vehicles to target disease-specific cells. The lipid nanoparticles used in messenger-RNA-based COVID-19 vaccines are among the most promising of such vehicles. This market is growing exponentially—more than 400 RNA-based therapies in the development pipeline will require targeted delivery mechanisms.
Delivery is one of the biggest challenges for novel drug modalities, and significant scientific and technical advances will be necessary to realize their full potential. For example, the ability of adenoassociated viruses to deliver large cargo (such as CRISPR nucleases) in vivo is limited. Additionally, currently validated vehicles can access only a limited set of tissues. Intravenous lipid nanoparticles, for example, primarily target the liver. Some delivery methods can also trigger the immune system, leading to adverse events and blocking the efficacy of the therapy.
Most drug delivery start-ups are using one of three main types of cutting-edge bioengineering:
- Improved capsids. Biotech companies are using rational design and directed evolution powered by ML to improve knowledge of existing adenoassociated virus vectors and discover new vector capsids (or protective protein shells). While novel-vector companies won the majority of drug delivery funding from 2017 to 2018, the share of deals in the space has declined since 2019.
- Biological vehicles. To improve safety, biotech companies are engineering delivery methods using the body’s natural signaling system (such as by using exosomes—extracellular bubbles of liquid or cytoplasm enclosed by a double layer of lipids—which have the potential to reach almost all tissues). This technology is still young, as shown by the limited number of investing deals for it from 2017 to 2018. Most deals from 2019 to 2021 were seed and series A rounds.
- Enhanced nanoparticles. Nanoparticle enhancements (such as lipid composition optimization) can expand the range of tissues that a drug can reach. Nanoparticle companies have had relatively small funding rounds of around $25 million each, around half of the $45 million average deal size in the drug delivery sector.
Achieving long-term benefit from biotech platform technologies
Our analysis highlights increasing investment in several platform technologies that could have a significant long-term impact on drug development. The convergence of AI and ML technologies with a greater understanding of biology could make drug discovery faster and more efficient. However, to benefit from the innovations that VC companies are funding, the biotech industry has several existential risks to address, including:
- Differentiating incremental innovations. Given continuous innovation, many start-ups are improving incrementally on competitor technologies. CasX, for example, is smaller than Cas9 for easier delivery and is believed to be less likely to trigger an immune response. To compete successfully, pioneers will need to generate evidence that will differentiate them and show the value of their products. They can also advocate for a regulatory framework that can assess new technologies even when head-to-head comparisons in clinical trials aren’t feasible.
- Addressing more intractable diseases. Because of the risks inherent in innovation, many biotech companies validate their technologies in conditions with well-understood mechanisms (such as sickle cell disease). However, if many companies with new platform technologies pursue this strategy, new therapies may outstrip need in diseases with limited patient populations. For example, with more than 30 products in development to treat the approximately 20,000 children around the world who are diagnosed with Duchenne muscular dystrophy every year,7 companies struggle to recruit patients for trials. Companies can benefit from enhancing their understanding of disease biology and innovate to design unique products that can target underaddressed diseases.
- Demonstrating value for cost-constrained healthcare systems. The pharma industry has transitioned toward a model that produces innovative drugs at premium prices to treat small subsets of patients. For example, while the top ten blockbuster drugs in 2010 treated more than 40 million patients, the top ten in 2020 treated 12 million. The prevailing model will no longer be sustainable if the top ten drugs in 2030 treat even fewer patients. Companies may need to pursue a different model, pushing the boundaries of emerging platforms to address larger populations. To create products that can help more patients at lower costs, manufacturing processes may need to evolve, and R&D may need to become more efficient.
With substantial VC investment fueling innovative therapies that address unmet needs, the biotech industry is poised to make lasting changes that can overcome the challenges it faces. In the future, biopharma may be able not only to treat but also to cure more common diseases and prevent others, touching much larger patient populations at lower costs. Technological innovations, coupled with ecosystem solutions, could enable cheaper, safer, and more efficacious therapies.
