Biotech: what are the biggest challenges now?
In our biotechnology market deck, you will find everything you need to understand the market
SUMMARY
Biotech's biggest challenge now is turning increasingly powerful science into medicines that survive human validation, manufacturing, reimbursement and real-world access.
The funding picture is less a shortage of capital than a narrowing of where capital goes. Biopharma can still attract huge rounds, but fewer companies are getting financed and investors increasingly want human evidence before taking the next risk.
The industry's weakest point has moved downstream. Target discovery and molecule design are getting faster, while clinical development still takes roughly a decade and late-stage failures can erase years of apparently convincing biology.
AI may improve biotech productivity, but its biggest test is no longer whether it can design molecules. The harder question is whether AI-originated portfolios can produce better Phase 2 and Phase 3 outcomes across enough programs to change overall R&D economics.
Faster discovery can actually intensify biotech's bottlenecks. More candidates mean more competition for the same clinical sites, specialist investigators, eligible patients, manufacturing capacity and late-stage budgets.
Cell and gene therapies show why scientific success is only half the job. A therapy can work and still struggle commercially because individualized production, specialist treatment centers, conditioning, testing and logistics remain expensive and operationally heavy.
Biomarkers make development smarter, but they can also create false confidence. Pelacarsen's Phase 3 failure showed how a drug can change the intended biological marker and still fail to improve the clinical outcome that matters.
China is compressing the competitive clock. Chinese-origin assets now represent a much larger share of global licensing, which means Western biotechs have less time to turn a good target into differentiated human data before credible rivals appear.
Big Pharma is providing a real exit route, but it is acting as a selective filter rather than a broad rescue mechanism. The strongest demand is for assets that have already removed an important piece of clinical uncertainty.
The deeper pattern is that biotech keeps accelerating the front end of innovation faster than the rest of the system can absorb it. The winners will be the companies that eliminate bad programs earlier, validate good ones faster, manufacture them reliably, and make treatment workable for patients and payers.
This market map, featured in our biotechnology market deck, highlights top companies and startups in the biotechnology market
Why does biotech look so advanced and still feel so fragile today?
Biotech has never had this many powerful technologies, yet the path from good science to a successful medicine is still painfully unreliable.
The contrast has become especially visible lately. Gene editing has reached commercial medicine, AI-designed drugs are moving deeper into human trials, antibody engineering keeps producing new drug formats, and pharmaceutical companies are spending heavily to refill their pipelines. At the same time, clinical development has slowed, weaker startups are struggling to raise money, advanced therapies remain difficult to manufacture, and recent Phase 3 failures have shown how quickly a convincing biological story can fall apart.
IQVIA's latest global R&D analysis found that median end-to-end clinical development had stretched to roughly 10 years in 2025. Intervals between trials alone increased by about three months. That happened while biopharma R&D spending remained well above pre-pandemic levels. We are spending heavily and generating better scientific tools, without getting an equivalent improvement in the speed at which medicines reach patients.
Funding tells a similar story. Silicon Valley Bank counted $12.6 billion of U.S. and European biopharma venture investment in the first half of 2026, but only 618 financings across healthcare, a multi-year low. Investors still have plenty of money for biotech; they are concentrating it into fewer companies that already have unusually strong science, clinical data or commercial evidence.
The weakest point has moved further downstream. Discoveries are arriving quickly. Proving which discoveries deserve ten years of development is where the system keeps struggling.
| What biotech is getting better at | Where progress still gets stuck |
|---|---|
| Finding disease targets | Proving targets matter in humans |
| Designing molecules | Surviving Phase 2 and Phase 3 |
| Editing genes and cells | Manufacturing consistently at scale |
| Generating biological data | Turning data into reliable decisions |
| Creating new therapeutic formats | Making treatment affordable and accessible |
Is the biotech funding crisis actually over?
The biotech funding crisis has eased for the strongest companies, but fundraising is still brutal for everyone sitting below that top tier.
Silicon Valley Bank's latest healthcare data makes the split unusually clear. Biopharma attracted $12.6 billion in venture funding during the first half of 2026, which hardly looks like a sector starved of money. Yet the number of healthcare financings fell to 618, the lowest level in several years.
A few very large rounds also make the total look healthier than the median startup experience. Isomorphic Labs alone raised $2.1 billion. When investors are willing to put billions behind one AI-drug-discovery company while many early biotechs struggle to finance another 18 months of work, aggregate funding stops being a useful description of what most founders are experiencing.
Public markets are reopening selectively too. Recent biotech IPO conversations have increasingly centered on companies arriving with substantial clinical evidence rather than a platform, a target list and a promising animal study. M&A is much livelier: SVB counted 25 qualifying private biopharma acquisitions in the first half, putting the sector on pace for an unusually active year.
A startup can still raise enormous amounts today. But investors increasingly want evidence earlier, and companies stranded between exciting preclinical science and convincing human data face the hardest part of the market.
If you want more recent data on this point, please see our latest biotechnology market report.
As this chart shows, and as featured in our biotechnology market deck, search interest in biotech has been trending upward
Why do promising biotech drugs still fail so often?
Promising biotech drugs still fail because being right about a molecule is useless when we are wrong about the underlying human biology.
One of the freshest examples came from Novartis. Its Phase 3 Lp(a)HORIZON study tested pelacarsen in people with elevated lipoprotein(a) and cardiovascular disease. Pelacarsen successfully lowered Lp(a), exactly the biological effect the drug was designed to produce. Yet Novartis reported that the study failed its primary endpoint of reducing major cardiovascular events.
That result is unusually revealing because the drug appears to have done its molecular job. The remaining question was whether changing that biological marker would meaningfully reduce heart attacks, strokes and other serious outcomes. In the pivotal study, it did not deliver the expected clinical result.
Biotech repeatedly runs into versions of this problem. A target can look convincing in genetics, cell cultures and animals. A drug can engage the target in people. A biomarker can move in the desired direction. None of those observations guarantees that patients will actually live longer, function better or avoid disease complications.
Complex diseases make prediction even harder. Alzheimer's disease, autoimmune disorders, fibrosis, cardiovascular disease and cancer involve interacting biological pathways and patient populations that can respond very differently to the same intervention.
We can increasingly manipulate biology with precision. Predicting which manipulation will matter enough in a real patient is still much harder.
Is biotech creating too many drug candidates in the same crowded areas?
Biotech can now create potential drug candidates faster than the industry can reliably decide which ones deserve expensive human trials, and too much of that effort piles into the same fashionable diseases and mechanisms.
Genomics, CRISPR screens, single-cell sequencing, proteomics and large biological datasets have dramatically expanded the number of plausible disease targets. AI then makes parts of target prioritization, protein engineering and medicinal chemistry faster still.
The scarce resources come later. Clinical sites do not multiply every time another model identifies a promising target. Neither do eligible patients, experienced investigators, specialist manufacturing facilities or late-stage development budgets.
Crowding makes that selection problem worse. Obesity is the clearest example today. The success of GLP-1 medicines triggered a wave of investment into oral incretins, longer-acting injectables, amylin combinations, muscle-preserving drugs and other next-generation metabolic approaches.
Oncology has lived with this for years. Antibody-drug conjugates, bispecific antibodies and other targeted immunotherapies have attracted dozens of companies toward overlapping targets and patient groups. IQVIA still shows oncology taking the largest share of global clinical research.
Competition can improve medicine when later entrants are safer, easier to administer or substantially more effective. The problem comes when several products are only marginally different. A biotech can run a successful trial and still end up with a weak asset because three competitors arrived earlier or one produced clearly better data.
Big Pharma's behavior shows how valuable it has become to remove uncertainty early. Pharmaceutical companies regularly pay far more for assets that already carry human efficacy data than for scientifically elegant preclinical platforms.
The practical test is harsher now: if this drug works exactly as expected, will anybody still care by the time it launches?
If you want more recent data on this point, please see our latest biotechnology market report.
This chart, featured in our biotechnology market deck, illustrates yearly venture capital funding for biotechnology startups
Are clinical trials now the slowest part of biotech?
Clinical trials have become one of biotech's clearest bottlenecks because drug discovery is speeding up faster than human testing can.
IQVIA's latest R&D analysis puts median end-to-end clinical development at roughly 10 years. Recent productivity improvements have also gone into reverse: trial durations rose overall, and the gaps between successive trials increased by about three months.
Enrollment explains part of the slowdown. A drug needs eligible patients, investigators, hospitals, diagnostic testing and enough observation time to measure an outcome. If several companies target the same narrow patient population, those resources get divided among them.
Rare diseases push that constraint to an extreme. A condition may affect only a few thousand diagnosed patients worldwide. Some patients are too sick, too young or too old for a protocol. Others live far from specialist hospitals. Another biotech may already have enrolled the same treatment center into a competing trial.
Precision oncology creates the same problem at a larger scale. A common cancer can be divided into molecular subgroups defined by mutations, protein expression, treatment history and other biomarkers. That improves our ability to give the right therapy to the right patient, while shrinking the population eligible for each individual study.
A model can evaluate millions of molecular structures overnight. A six-month patient outcome still takes six months to observe.
Can AI actually make biotech drug development less wasteful?
AI is starting to produce real clinical evidence in biotech, but we still have no convincing proof that it can broadly fix late-stage drug failure.
Insilico Medicine is currently one of the strongest examples. Its AI-enabled TNIK inhibitor rentosertib produced encouraging Phase 2a results in idiopathic pulmonary fibrosis and has now moved into Phase 3. In the 60 mg arm of the Phase 2a study, patients showed a mean 98.4 mL improvement in forced vital capacity after 12 weeks. That is a much more meaningful test of AI drug discovery than showing that an algorithm can generate chemically valid molecules.
IQVIA has also found an early indication that AI-enabled programs from emerging biopharma companies are reaching stronger success rates than comparable programs. The wording "early" matters. The pool of genuinely AI-originated drugs with mature Phase 2 and Phase 3 histories is still small.
Money is arriving well before that proof. Isomorphic Labs raised $2.1 billion. Generate Biomedicines, Xaira, insitro and several other AI-biotech companies have attracted enormous capital because investors expect models to improve target selection, protein design or candidate optimization.
If AI-designed portfolios begin producing materially higher Phase 2 and Phase 3 success rates across dozens of programs, pharmaceutical productivity could change dramatically. Faster molecule design alone is not enough because late-stage failures consume far more capital than early chemistry does.
For now, AI looks genuinely useful and increasingly clinically relevant. Calling it a solution to biotech attrition would still be several steps ahead of the evidence.
| What AI biotech can already show | What still needs proving |
|---|---|
| Faster molecule generation | Higher Phase 3 success |
| Better protein and structure prediction | More correct disease targets |
| Automated literature and data analysis | Lower total R&D cost per approved drug |
| Clinical-stage AI-originated molecules | Better portfolio-wide returns |
| Earlier signs of improved success rates | Reproducible advantage across many programs |
If you want more recent data on this point, please see our latest biotechnology market report.
This chart, featured in our biotechnology market deck, looks at Vertex’s strategy in biotechnology
Can cell and gene therapies actually be manufactured at scale?
Cell and gene therapies can be manufactured commercially today, but the economics and operational complexity remain ugly enough to restrict how widely many treatments can spread.
Autologous cell therapy shows the problem immediately. A conventional tablet can be produced in large batches, tested, stored and shipped. An autologous CAR-T therapy starts with cells collected from one specific patient. Those cells are shipped, processed, engineered, tested, returned and infused into that same person.
More patients therefore mean more individualized manufacturing workflows. Production does gain efficiencies with automation and experience, but it never becomes identical to stamping out another million pills.
Gene therapy has different constraints. Viral-vector manufacturing requires specialized facilities, strict process control and extensive testing. Small changes in cell culture, purification or analytical methods can affect yield and product characteristics. Manufacturing problems can consequently delay trials even when the underlying therapeutic idea is sound.
Regulators are responding directly. The FDA finalized new guidance on CMC flexibility for cell and gene therapy products this year and has since published additional guidance addressing common development problems. The agency is also encouraging genome-editing developers to use prior platform knowledge when scientifically justified.
A biotech company working on gene or cell therapy today needs world-class biology and serious process engineering. Weakness in either one can kill the program.
Can multi-million-dollar biotech therapies ever become mainstream?
Multi-million-dollar therapies can work commercially in small rare-disease populations, but those prices become much harder to sustain as biotech starts treating larger groups of patients.
The arithmetic gets uncomfortable very quickly. A $3 million treatment given to 500 people represents $1.5 billion in gross drug spending. Give the same treatment to 10,000 people and the figure reaches $30 billion before we include hospital care, diagnostics, conditioning therapy or long-term follow-up.
Sickle-cell gene therapy already shows how difficult the delivery model can become. The medicine itself costs millions, while treatment can also require specialist hospitals, chemotherapy conditioning, fertility preservation and extended clinical monitoring.
CMS built its Cell and Gene Therapy Access Model around this problem. As of now, participating states, the District of Columbia and Puerto Rico cover about 84% of Medicaid beneficiaries with sickle-cell disease. Manufacturers provide outcomes-based arrangements, including rebates when therapies fail to produce promised benefits.
A payer is being asked to absorb a huge cost today for health benefits that may accumulate over many years, potentially after the patient has moved to another insurer.
The model becomes much harder as gene editing moves beyond ultra-rare diseases. Larger patient populations will require lower production costs, more treatment capacity and payment systems that can spread risk over time.
| Treatment situation | How well extreme pricing can work |
|---|---|
| Hundreds of patients with severe rare disease | Often possible |
| Several thousand eligible patients | Budget pressure becomes serious |
| Tens of thousands of patients | Very difficult at current prices |
| Common chronic disease | Current gene-therapy pricing becomes unrealistic |
This chart, featured in our biotechnology market deck, illustrates yearly funding for biotechnology startups
Has China become one of biotech's biggest competitive threats?
China has become a major source of globally competitive biotech drugs, and Western companies can no longer assume the best external assets will come from the U.S. or Europe.
The change is visible in pharmaceutical licensing. IQVIA found that China-linked international R&D deals reached an all-time high in 2025. Chinese-origin assets accounted for roughly 40% of the drugs in-licensed by Big Pharma that year, compared with just under 30% one year earlier.
The individual partnerships are enormous. Bristol Myers Squibb agreed to a multi-program relationship with Hengrui carrying potential payments above $15 billion. AstraZeneca struck a broad metabolic-disease partnership with CSPC worth up to $18.5 billion. Pfizer's oncology agreement with Innovent carried potential economics above $10 billion.
Those headline totals include future milestones that may never be paid, so they exaggerate the cash value of each deal today. The useful observation comes from the repetition. Large Western pharmaceutical companies keep choosing Chinese-origin assets across multiple therapeutic areas.
Chinese biotechs have become especially strong at medicinal chemistry, antibody engineering and rapid early clinical execution. Dense hospital networks and large patient populations can also make some trials faster to recruit.
For a biotech in Boston, Basel or San Diego, the competitive clock has changed. A good target can now attract several credible programs from China before a Western startup has generated human proof of concept.
If you want more recent data on this point, please see our latest biotechnology market report.
Is Big Pharma rescuing biotech right now?
Big Pharma is keeping a lot of biotech alive through acquisitions and licensing, but it is concentrating that money around companies with evidence rather than spreading it across the sector.
SVB counted 25 qualifying private biopharma acquisitions during the first half of 2026, putting the industry on pace for a particularly active M&A year. Licensing activity is also strong, including the wave of large China-related partnerships described above.
Pharmaceutical companies have a straightforward reason to buy. Major products are approaching patent expiry, and replacing billions of dollars of revenue through internal R&D alone is slow and unreliable. Acquiring a biotech with promising Phase 2 data can effectively buy several years of development time.
That demand strongly favors companies that have already crossed an important clinical threshold. A differentiated drug showing human efficacy may attract multiple bidders. A preclinical company with an interesting platform but no proof in patients can still struggle to raise its next private round.
Big Pharma provides a very real exit route. The difficult part is surviving long enough to own something Big Pharma actually wants.
This chart, featured in our biotechnology market deck, compares the main business model options for biotech platform companies
Are biotech biomarkers making companies too confident?
Biomarkers remain essential to modern biotech, but companies still make expensive mistakes when they confuse changing a biological measurement with changing a patient's disease.
The pelacarsen result makes that problem unusually concrete. Novartis reported that the drug lowered lipoprotein(a) in its large Phase 3 cardiovascular study while failing the primary clinical endpoint measuring serious cardiovascular events.
That does not make Lp(a) useless as a biomarker, and other therapeutic approaches targeting the pathway are still being studied. It does show how dangerous it is to treat a biologically plausible surrogate as settled proof of clinical benefit.
Oncology frequently confronts the same issue through tumor response, progression-free survival and molecular-response measures. Neurology uses imaging and protein biomarkers. Metabolic medicine relies on several intermediate measures that correlate to different degrees with the outcomes patients ultimately care about.
Accelerated approval intentionally accepts some uncertainty here. The FDA can approve drugs using surrogate endpoints that are reasonably likely to predict clinical benefit, followed by confirmatory studies. Some indications later verify the expected benefit; others have been withdrawn when follow-up evidence disappoints.
Biomarkers let biotech run smarter and faster trials. They become dangerous when an intermediate result starts carrying more confidence than the human outcome it is supposed to predict.
Can regulators speed up biotech without cutting corners?
Regulators are currently pushing biotech development faster by removing duplicated work, especially in cell and gene therapy, while keeping the core requirement for credible safety and efficacy evidence.
The FDA has been unusually active here. Its recent cell and gene therapy guidance gives sponsors more flexibility around chemistry, manufacturing and controls during development. Additional guidance published in August addresses recurring regulatory, clinical, manufacturing and toxicology questions across the sector.
The agency is also proposing that genome-editing developers use established platform knowledge from related products where scientifically appropriate. If the same editing system, delivery technology or manufacturing approach has already generated useful evidence, every subsequent program may no longer need to rebuild the full package independently.
That could make a meaningful difference for rare diseases. A company developing therapies for several mutations with the same technological platform can spend huge amounts repeating similar analytical and nonclinical work for extremely small patient populations.
There is still a hard line around uncertainty that affects patients. Surrogate endpoints need confirmation when long-term benefit remains unclear. Genome editing requires careful off-target and safety assessment. Manufacturing shortcuts that compromise product consistency would simply move risk from development into the clinic.
The most useful regulatory gains will come from reusing knowledge that genuinely transfers and simplifying trials where populations are tiny.
This chart, featured in our biotechnology market deck, breaks down revenue across customer segments in the biotechnology market
Why is biotech still so hard to reproduce reliably?
Biotech remains difficult to reproduce because living systems react to small differences in materials, methods and people, and modern therapies are getting more biologically complicated rather than simpler.
Cell therapies provide an obvious example. Donor biology, cell quality, culture conditions, reagents, processing time, freezing, transport and laboratory handling can influence the final product. Two batches following nominally similar procedures may still contain biologically different cells.
Preclinical science has its own sources of noise. Cell lines drift. Animal models reproduce only part of a human disease. Small studies generate unstable effect sizes. Researchers can test many experimental conditions and eventually find one that appears impressive by chance.
More automation should help. Standardized robotic laboratories can reduce variation in pipetting, timing and protocol execution. Better computational quality control can catch anomalies earlier. Larger datasets can make weak effects easier to distinguish from real ones.
Those tools also require people who understand both the technology and the underlying biology. AI has made this skills gap more obvious. A machine-learning expert may build a sophisticated model without spotting a flawed assay, while an excellent experimental biologist may struggle to detect leakage or bias in a computational dataset.
Drug discovery now requires biology, computation, clinical development and manufacturing teams to catch one another's bad assumptions before they become expensive programs.
Is biotech innovation actually reaching enough patients?
Biotech is producing therapies that would have sounded impossible a decade ago, but access still lags far behind scientific progress.
Approval represents only one step. Patients also need diagnosis, specialist physicians, treatment centers, insurance authorization, manufacturing slots and sometimes the ability to travel or temporarily relocate.
Advanced therapies expose these barriers clearly. Sickle-cell gene therapy can require conditioning chemotherapy, specialist hospital care and long-term follow-up. CMS's national access model even includes support around issues such as fertility preservation because treatment logistics extend well beyond purchasing the drug itself.
Rare-disease patients face an earlier obstacle: many spend years without a correct diagnosis. A highly targeted medicine has little practical value for somebody whose genetic condition has never been identified.
Geography makes the gap wider. IQVIA continues to document substantial differences between countries in clinical-trial activity and access to newly launched medicines. Western Europe, for example, represented 27% of global clinical-trial country use in 2025, but that share had fallen 17% from its 2019 level.
Counting approvals alone therefore gives an incomplete picture. The real test is how many eligible patients can actually be found, treated and paid for.
This chart, featured in our biotechnology market deck, shows how at-home genetic testing technology has evolved over time
What could derail biotech even if the science keeps getting better?
Biotech could keep making spectacular scientific advances and still struggle economically because each breakthrough creates more pressure on the slower parts of the development system.
AI can generate better molecules faster, which means more candidates competing for clinical resources. Genomics can divide diseases more precisely, which means smaller groups of eligible patients. Personalized cell therapy can produce extraordinary responses, while making manufacturing harder. Gene editing can potentially deliver one-time treatments, while forcing healthcare systems to absorb enormous upfront costs.
China adds competitive pressure to the same system. Western biotech companies now face credible programs that may move through discovery and early clinical testing more quickly and cheaply. Big Pharma can shop globally for the best asset rather than waiting for a familiar U.S. startup to mature.
The recent clinical failures matter here too. Novartis's pelacarsen setback came only days before another Novartis Phase 3 program, delpacibart etedesiran in myotonic dystrophy type 1, also missed its primary endpoint. Two very different late-stage programs failing within days of each other are a useful reminder of how much uncertainty survives all the way to pivotal trials.
If discovery accelerates much faster than clinical validation, manufacturing and commercialization, biotech may spend more money pushing more programs into the same narrow downstream bottlenecks.
| Scientific progress | Pressure created elsewhere |
|---|---|
| Faster AI drug discovery | More candidates competing for trials |
| Better genomic segmentation | Smaller eligible patient populations |
| Personalized cell therapies | Harder manufacturing and logistics |
| One-time gene therapies | Huge upfront reimbursement costs |
| More sensitive biomarkers | More risk of over-trusting surrogate outcomes |
| Faster global development | Less time before competitors arrive |
So what is biotech's biggest challenge now?
Biotech's biggest challenge today is figuring out which brilliant scientific ideas can survive the much harsher test of becoming reliable, manufacturable and economically viable medicines.
Funding hurts, but $12.6 billion of first-half biopharma venture investment shows that capital still appears quickly when investors believe the evidence. AI is improving discovery, and rentosertib's move into Phase 3 gives the field a genuine clinical milestone. Regulators are also becoming more flexible in advanced therapies, while pharmaceutical companies remain hungry for external drugs.
The stubborn problem sits between discovery and widespread use.
IQVIA's roughly 10-year median clinical-development timeline shows how slowly human proof still arrives. Pelacarsen shows that successfully changing a major disease biomarker can collapse at the clinical-outcome stage. Cell and gene therapies show that a working medicine can run into manufacturing and access problems. China's rise shows that companies also have less time to solve those issues before somebody else develops a competing asset.
The biotech industry is generating possibilities faster than it can eliminate the bad ones and industrialize the good ones.
Better target validation would prevent weak programs from entering expensive trials. Faster clinical designs would reduce the years spent waiting for answers. Reusable platform knowledge could cut redundant regulatory work, while more automated manufacturing could make advanced therapies cheaper and more consistent.
For now, biotech has plenty of invention. What it still lacks is an equally powerful way to turn invention into dependable medicine.
If you want more recent data on this point, please see our latest biotechnology market report.
In our biotechnology market deck, we identify pain points entrepreneurs should prioritize
OUR METHODOLOGY
We broke the biotech question into the parts that determine whether scientific progress can actually become successful medicine: funding and capital allocation, biological validation and clinical attrition, trial execution, AI-driven R&D, manufacturing, pricing and patient access, regulation, reproducibility, and global competition.
We looked at each dimension separately before bringing the evidence together. We prioritized outcomes over promises: human clinical results over preclinical excitement, development timelines over claims of faster discovery, financing breadth alongside headline funding totals, and repeated licensing behavior alongside the maximum theoretical value of individual deals.
We used broad datasets from IQVIA and Silicon Valley Bank to establish sector-level patterns, then checked specific questions against primary sources. Those included FDA and CMS material for regulation and access, ClinicalTrials.gov for trial records, NIH material for reproducibility, and direct company disclosures from Novartis, Insilico Medicine, Isomorphic Labs, Bristol Myers Squibb, AstraZeneca and Pfizer for trial results, financings and licensing agreements.
Company examples were used to illustrate or test a broader pattern, rather than to carry an industry-wide conclusion by themselves. We gave the most weight to constraints that appeared across several parts of the analysis and repeatedly affected the passage from discovery to human validation, manufacturing, commercialization or patient access.
Key sources include IQVIA's Global R&D Trends 2026, Silicon Valley Bank's Healthcare Industry Trends, Novartis on the Phase 3 Lp(a)HORIZON result, ClinicalTrials.gov on rentosertib, Insilico Medicine on rentosertib's Phase 3 progression, FDA guidance on CMC flexibility for cell and gene therapies, CMS on the Cell and Gene Therapy Access Model, and NIH on replication and reproducibility.
This chart, featured in our biotechnology market deck, breaks down revenue across Europe, Asia, North America, Africa, and South America in the biotechnology market
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