Likelihood of approval, phase transitions, and the crowded-vs-abandoned map of therapeutic areas

Oncology accounts for about a third of all drug-development phase transitions and draws the largest share of big-pharma R&D spend, yet has among the lowest probabilities of moving a Phase I compound to approval at roughly 5.3% in the last decade of industry data we can cleanly measure. Hematology gets a small fraction of that attention and runs around 25%. Urology is the most abandoned therapeutic area in the world, with under 1% of all transitions. The most crowded area in pharma is also among the least productive on a per-program basis. That asymmetry is not a quirk of the data. It is the structure of the industry.

Concept Translation: A phase transition is the binary pass/fail event that advances (or kills) a drug program at the boundary between preclinical, Phase I, Phase II, Phase III, regulatory filing, and approval. Clinical development is a Markov chain over those states, with attrition at every step. The headline numbers in this post are the per-step success rates and the joint probabilities they produce.

Why likelihood of approval matters in 2026

The 2011–2020 BIO/Informa/QLS Clinical Development Success Rates report remains the default citation for industry success rates. In 2026, two updates matter.

First, the headline has moved. For the 2014–2023 ten-year cohort tracked by Citeline (formerly Informa Pharma Intelligence, now part of Norstella), the average Phase I likelihood of approval has fallen to about 6.7%, down from 10.4% for the 2014 cohort alone. Phase-by-phase, the latest transitions are roughly 47% out of Phase I, 28% out of Phase II, 55% out of Phase III, and 92% from filing to approval. Phase II remains the industry’s main loss point. Phase I success fell from over 75% in the 2006–2008 cohort to under 40% in the most recent one. Some failures now surface earlier because biomarker-based efficacy surrogates are measured in Phase I, so programs that once would have failed in Phase II now fail sooner. The target pool has also shifted toward harder, first-in-class biology. The headline 90% overall clinical-failure rate from Phase I to approval still holds. Roughly half of failures are attributable to lack of efficacy and another 30% to safety, with the balance split across biomarker misidentification, trial-design issues, adherence, and unfavorable commercial landscape.

Second, the structure of success remains uneven. Biomarker-stratified programs reach approval at roughly twice the rate of unstratified ones. Stratified means the trial prospectively selects or enriches patients using a measurable marker such as a mutation, expression level, or protein abundance. That finding was striking when the 2011–2020 report first presented it (15.9% vs 7.6% Phase I LOA), and it has held up across methodology refreshes. It remains the strongest program-level effect a team can influence. It is also a target-discovery decision as much as a clinical-development decision, because the choice to pursue a target whose disease biology supports a stratifying biomarker is made upstream. This 2× effect sits alongside a separate, partially overlapping 2× effect from human genetic evidence. Programs whose targets carry general genetic support (broad GWAS signals, common-variant associations) approve at roughly twice the unsupported base rate, and programs supported by high-confidence causal evidence (Mendelian traits, high-impact coding variants) approach 3×. The effects compound when a program’s stratifying biomarker derives directly from its causal-genetics rationale.

Concept Translation: Biomarker stratification is patient pre-selection by a measurable feature, run before enrollment rather than after the trial reads out. Mechanically it is the same move as filtering a training set to a labeled subpopulation where the target effect is expected to be detectable. The trial then asks a sharper question on a smaller cohort, and the design carries less noise per patient. The 2× LOA improvement is the field’s empirical reward for sharper-question trial design.

Two background shifts shape the picture. The GLP-1 receptor agonist wave, the diabetes-and-obesity drug class built around glucagon-like peptide-1 signaling, has rewritten big-pharma revenue rankings since 2023; Eli Lilly’s market capitalization briefly crossed $1 trillion in 2025, and Evaluate Pharma projects Lilly to be the #1 pharma by 2030 revenue at roughly $113B, driven by Mounjaro and Zepbound. What changed is the revenue map; the success-rate map did not move in the same way. Oncology is still the most crowded and lowest-yielding area, hematology is still the highest-yielding. Which disease areas are hard to drug remains a question of biology and trial design that the GLP-1 cycle did not affect.

Concept Translation: GLP-1 receptor agonists mimic the body’s own glucagon-like peptide-1 hormone, which signals satiety and slows gastric emptying. Semaglutide and tirzepatide hit GLP-1 receptors (and in tirzepatide’s case, also GIP receptors) in the gut, brain, and pancreas, blunting appetite and improving glucose control. The class is on track to become the largest in pharma history, which is what makes the cardiometabolic revenue map shift so quickly even though base success rates in chronic disease have not changed.

The other shift is regulatory output. The FDA approved 55 novel drugs in 2023, breaking the 2020 record of 53. The counts in 2024 and 2025 were 50 and 46. The ten-year rolling average through 2024 is about 46.5 novel approvals per year, a historical high-water mark. Of those, 16 of 55 (29.1%) in 2023 and 13 of 50 (26.0%) in 2024 were oncology indications, the largest single-therapeutic-area share of CDER’s novel approvals in both years. CDER is the FDA center that handles most standard drug approvals. Add CBER’s cellular-therapy approvals, lifileucel (Amtagvi) for advanced melanoma in February 2024 and afamitresgene autoleucel (Tecelra), the first TCR-T cell therapy for synovial sarcoma, in August 2024, and oncology’s regulatory share is larger still when measured by approved-product count rather than CDER novel-NME count alone. CBER is the FDA center that handles biologics such as cell and gene therapies, TCR-T means T-cell-receptor-engineered T cells, and NME means new molecular entity. The industry is approving more drugs per year on average than ever, oncology remains the largest concentration at the regulatory end, and Phase I LOA has fallen by about a third. Those facts describe the same productivity story from different angles.

What “likelihood of approval” actually is

The term has a specific operational definition. Likelihood of approval (LOA) is the joint probability that a compound currently in a given phase eventually reaches FDA (or equivalent) approval. It is computed as the product of the phase-to-phase transition success probabilities for every phase remaining, across a reference cohort of programs. A worked example. If a drug is currently in Phase II and you observed 50% success for Phase II → III, 50% for Phase III → filing, and 50% for filing → approval across a cohort of ten programs per transition, the LOA for that Phase II drug is 0.5 × 0.5 × 0.5 = 12.5% across n=30 observations. For readers new to clinical development, the shorthand is simple. Phase I asks mainly whether the drug is safe and how the body handles it, Phase II asks whether there is an efficacy signal in patients, Phase III is the larger confirmatory test, and filing is the submission step to the regulator (usually as an NDA for small molecules or a BLA for biologics).

Concept Translation: LOA is the path probability through a Markov chain that allows only forward transitions or termination. The “states” are clinical phases and the “absorbing state” is approval. The headline LOA number is the cohort-average probability of absorption when starting from Phase I, which is a base rate, not a per-program prediction, in the same way base accuracy on a benchmark is not a prediction for a specific input. Conditioning on biomarker stratification, genetic support, or therapeutic area shifts the relevant cohort and therefore the conditional base rate.

A few practical things follow.

• LOA is a cohort statistic, not a program-level probability. It is the rate at which a randomly drawn program from the reference cohort reached approval. Applied to your Phase II compound, it’s only as useful as the reference cohort is relevant.
• “LOA from Phase I” is the standard headline number (industry-wide, 6.7% in the 2014–2023 cohort). It requires four multiplicative transitions and is therefore the most volatile statistic as you slice across disease areas and modalities.
• Transition success rates at each phase matter most if you’re trying to understand where programs die. The 2011–2020 BIO data and the 2014–2023 Citeline data agree on the central point. Phase II is the attrition chokepoint. Phase I is mainly about safety and pharmacokinetics (PK), meaning how the body absorbs, distributes, and clears the drug. Phase III is about powered efficacy, and most programs that reach it have already shown some human proof of concept, an early sign that the mechanism produces the intended effect in patients. Filing-to-approval is high (~92%) because most selection has already happened. Phase II is where target and indication choices meet the biology. AbbVie’s $8.7B acquired schizophrenia asset emraclidine missed both of its Phase II readouts in 2024, a reminder that even at multi-billion-dollar conviction, central nervous system (CNS) Phase II/III failure rates still run around 85%.

Two data framings need to stay distinct. The industry-wide LOA numbers (BIO 2011–2020; Citeline 2014–2023) sample all disclosed programs, including small biotech single-asset programs that fail early and exit quietly. The major-pharma-only numbers tell a different story. A 2025 Drug Discovery Today analysis of 18 leading pharma companies over 2006–2022 found an average LOA of about 14.3%, median 13.8%, range 8–23%. Those estimates are higher because they reflect internal selection. Large organizations kill their weakest programs before they ever appear in public disclosures. When you see LOA numbers in a blog post or investor deck, check which population the author is using. The two framings differ by roughly a factor of two for the same underlying biology.

The crowded-vs-abandoned map of therapeutic areas

The BIO/Informa/QLS analysis of 2011–2020 bucketed clinical-development transitions into 21 major disease areas covering 623 indications. Oncology, autoimmunity, infectious diseases, neurology, cardiology, hematology, metabolic, endocrine, psychiatry, respiratory, ophthalmology, allergy, gastroenterology (non-IBD), and urology make up the 14 “major” categories, with everything else (dermatology, renal, obstetrics, rheumatology for non-autoimmune indications, ENT/dental, orthopedics) swept into “Other”.

The headline findings from that decade of data still hold directionally after the Citeline refresh, per Item 8 of the 2026 Addendum:

• Oncology is the most crowded. Over 30% of all phase transitions recorded in 2011–2020 were oncology programs. Of the 12,728 total transitions in the dataset, oncology accounted for roughly 33%.
• Oncology is also among the least productive. Phase I LOA for oncology was 5.3%, the third-lowest of the 14 major areas and about 57% of the non-oncology average of 9.3%. Oncology had the lowest success rate at every clinical transition except one.
• The exception is regulatory. The NDA/BLA → approval transition ran at 92.0% for oncology vs 90.2% for non-oncology, a slight oncology advantage. Here, NDA means New Drug Application and BLA means Biologics License Application. One plausible interpretation is that oncology filings that reach the regulator are often supported by large effect sizes in biomarker-defined populations, and the FDA’s oncology center has accumulated substantial experience reviewing this kind of evidence. That reading fits the approval data in 2023 and 2024, where oncology dominated CDER’s novel-NME approvals in both years at 29.1% and 26.0% of total novel approvals respectively.
• Hematology is the highest-yielding major area. Phase I LOA sits close to 25%, roughly five times the oncology rate. Much of that performance comes from hemophilia research, which has unusually tractable underlying biology. The coagulation cascade is one of the best-characterized physiological networks in medicine, and 32 of 34 hemophilia A phase transitions in the dataset were successful. Anemia (multiple sub-indications) also runs well above average.
• Urology is the most abandoned. Less than 1% of all phase transitions.

Concept Translation: The coagulation cascade is a sequence of enzymatic activations that ends in fibrin clot formation. It has been mapped in detail since the 1960s, with most components purified, structures solved, and mutations cataloged. Hemophilia A is a defined deficiency of factor VIII, a single protein in the cascade; hemophilia B is a deficiency of factor IX. The combination of a clear genetic cause, a single missing protein per disease, and a measurable bleeding-time biomarker makes this one of the most ML-friendly subdomains in medicine. The “well-characterized” status pays off as base-rate Phase II success.

Why oncology is crowded and hard is the question most practitioners intuit but rarely state clearly. Commercially, first-in-class and best-in-class oncology wins can command some of the largest addressable markets in medicine, which keeps capital flowing in. Scientifically, cancer is hundreds of diseases collected under one label, many with multi-driver genetics, rapidly evolving resistance, and tissue-of-origin heterogeneity that shows up in Phase II when tumor biology diverges from the preclinical model. The 2024 corpus makes the same point directly. Scientific complexity and competitive dynamics continue to depress oncology success rates.

Hematology shows the opposite pattern. Programs often target a well-characterized protein or cell lineage in a circulating system, without the spatial heterogeneity of solid tumors or the neuroanatomical specificity of CNS disease. Patient populations are also often genetically well defined. A larger share of hematology biology is tractable enough to drug than in fields such as neurology.

Concept Translation: “Spatial heterogeneity of solid tumors” means that within a single tumor, different regions can carry different mutations, different gene expression profiles, and different sensitivity to drugs. A biopsy is one sample; the tumor is many samples that happen to share an organ. Hematological malignancies live in blood and bone marrow, which mix; the sampling problem is comparatively easier. CNS targets add their own tax, because the blood-brain barrier rejects many drugs at the door.

The big commercial players and therapeutic-area concentration. The top 10 pharmaceutal companies by revenue around 2021 all listed oncology as a primary therapeutic area. As of today, the position of oncology-related diseases as the most popular therapeutic areas because they generate the most revenue is only partly true. Cardiometabolic, powered by GLP-1s, has overtaken oncology in revenue growth. Oncology still holds the largest therapeutic-area concentration in R&D investment and CDER novel approvals.

Rare vs chronic, and why rare wins on a per-program basis

The BIO 2011–2020 analysis also split non-oncology programs into rare diseases (affecting fewer than 200,000 people in the US, or 1 in 2,000 people in the EU) and chronic, high-prevalence diseases (identified from the CMS, or Centers for Medicare & Medicaid Services, Chronic Conditions Data Warehouse, with >1 million US-affected patients). Both groups exclude oncology, which is itself 43% rare.

The finding was striking. Rare-disease programs had roughly three times the Phase I LOA of chronic, high-prevalence programs (17.0% vs 5.9%). Rare-disease programs outperformed the overall non-oncology cohort at every transition. The single largest gap was Phase II, at 44.6% for rare diseases vs 28.9% for the overall cohort. Phase I was also markedly better (67.4% vs 52.0%), with Phase III and NDA/BLA broadly comparable.

Three forces usually explain the difference. Rare-disease trials often enroll genetically stratified populations with cleaner disease biology. Effect sizes tend to be larger because untreated baseline disease is more severe. Regulators offer accelerated pathways such as orphan designation (the US rare-disease status that brings tax credits, fee waivers, and post-approval exclusivity), breakthrough therapy designation, and accelerated approval. The 2024 FDA annual report noted that more than 50% of CDER’s novel approvals carried orphan designation, quantitative evidence that the rare-disease pipeline is now a structural majority of new-product output rather than a niche. The claim is narrower than “rare disease is easier.” Rare-disease programs more often begin where the target-disease link is already visible in humans. Target discovery in a rare monogenic disease may start with a causative gene. Target discovery in heart failure with preserved ejection fraction often starts with clinical heterogeneity that has to be resolved before a target becomes legible.

Concept Translation: Orphan designation, breakthrough therapy, and accelerated approval are the FDA’s incentive system for diseases with small populations, severe unmet need, or strong early-phase evidence. Sponsors can stack them. Each one removes a different friction point: orphan removes some regulatory and economic disincentives, breakthrough partners the FDA review team with the sponsor and allows rolling submission, and accelerated approval lets the agency act on a surrogate endpoint with a confirmatory post-market study. The combination matters for the LOA picture because each removed friction point translates into a higher conditional success rate.

For a working drug hunter, rare-disease structure and biomarker enrichment often reinforce each other. A program with both a small, genetically defined population and a prospective enrichment biomarker sits inside the strongest historical success pattern in the data.

Biomarker stratification and likelihood of approval

Across all disease areas in the BIO 2011–2020 data, programs that used a patient-preselection biomarker achieved Phase I LOA of 15.9%, versus 7.6% for programs without one. A patient-preselection biomarker is a measurable feature used before enrollment to select patients more likely to respond, such as a mutation, receptor level, or lab value. This remains one of the clearest findings in the industry success-rate literature, and the 2026 Addendum notes that it still holds through the Citeline refresh.

The phase-by-phase breakdown shows where the difference appears.

• Phase I: no meaningful difference. Both biomarker-enriched and unselected programs clear Phase I at approximately the overall 52.0% rate, because Phase I is about safety and PK, not efficacy.
• Phase II: the gap is large. 46.3% for biomarker-supported programs vs 28.3% for unselected ones. This is the first direct test of whether “this target matters in this disease in these patients” is true.
• Phase III: the advantage persists, though it is smaller. 68.2% vs 57.1%. Programs that survived Phase II with a biomarker-defined cohort have already shown that their effect sizes are real in the relevant population.
• Filing → approval: a small residual difference. By then, Phase III design and data quality determine most of the outcome.

The causal chain begins with target selection. A biomarker is a program-level commitment to a specific subpopulation of patients in whom the target is hypothesized to matter. Finding a target whose biology supports that commitment is the target-discovery problem; the clinical-development problem comes later. When the [target–disease evidence frameworks] → How to tell a drug target matters: evidence frameworks for target–disease linkage work, including driver-vs-passenger distinctions, oncogene addiction, human genetic support, and multi-omics convergence, produce a target with a natural stratifier built in, the LOA doubles. Without that stratifier, the program falls back toward the industry base rate. Human genetic support shows a parallel pattern. Both deliver roughly 2× LOA improvement on average, and the multiplier scales toward 3× for high-confidence causal evidence (Mendelian traits, high-impact coding variants). When the same target carries both a strong genetic rationale and a stratifying biomarker, the gains compound.

Concept Translation: Biomarker-stratified trial design is roughly the clinical analogue of training a model on a curated subpopulation where the predictive signal is known to be present, rather than on the full distribution. The cost is generalizability (the result strictly applies to biomarker-positive patients) and the benefit is statistical power (the effect size is larger in the conditioned cohort). LOA doubling is the empirical reward for accepting that trade-off. ML practitioners will recognize the same logic from any setting where conditional models on labeled subgroups outperform a single model trained on a heterogeneous population.

This is the strongest quantitative support for the argument made in the [front-of-funnel post] → Target discovery: the front-of-funnel decision behind most Phase II failures. The biomarker-LOA finding is the number that argument rests on.

Biomarker stratification improves the chance of an apparent Phase II win. It does not guarantee that the biomarker is measuring the biology you think it is measuring. The PARP-inhibitor reversal is the clearest recent case study. Niraparib, olaparib, and rucaparib achieved early FDA approvals in advanced ovarian cancer based on strong progression-free-survival (PFS) signals in BRCA-mutant and HRD-positive populations, that is, patient groups selected for DNA-repair-related genomic features, and between late 2022 and late 2023 the FDA narrowed or withdrew six of those indications because mature randomized controlled trial data showed an overall survival detriment despite the earlier PFS gains. PFS is friendly to biomarker-enriched trial design; overall survival is the outcome that matters most to patients. The advantage is real in expectation, but the endpoint still has to track the disease.

Concept Translation: PARP inhibitors block poly(ADP-ribose) polymerase, an enzyme that helps cells repair single-strand DNA breaks. Cells with BRCA1/BRCA2 mutations or other homologous-recombination-deficiency (HRD) defects already cannot repair certain double-strand breaks well, so blocking PARP forces them into “synthetic lethality.” Losing two repair routes at once is fatal. PFS (progression-free survival) is time until the tumor grows or the patient dies; OS (overall survival) is time until death from any cause. PFS reads out faster and is easier to power, which makes it attractive for biomarker-enriched Phase II/III. The PARP reversal is what happens when the biomarker correctly selects responders on the proxy outcome (PFS) but the proxy fails to track the outcome that actually matters (OS).

Indication prioritization: the strategic companion to target selection

A good target is only half the program. The other half is indication prioritization, the choice of which disease to run the first trial in when a target is relevant to several. The course notes frame it plainly. “If the first indication is chosen incorrectly and clinical trials fail, subsequent trials with the next indication become difficult to launch”. A failed Phase II costs more than the trial itself. It can also stigmatize the asset when the team tries to relaunch it in a different indication.

The course gives four criteria for indication prioritization:

• Pathway relevance. Does the target play a key role in the pathogenesis of the candidate indication? Analyze omics and experimental data. Animal models may or may not recapitulate the human disease biology; treat them as useful but biased training labels.
• Competitive landscape. What does the existing-therapy and competitor-pipeline density look like? Diseases with no effective treatments may be high priority even if the science is harder; diseases with five approved comparators raise the efficacy bar.
• Unmet medical need and societal impact. Disability-adjusted life years (DALYs) are the standard unit for quantifying which diseases are most important to address. They roll prevalence, mortality, and morbidity into one number.
• Commercial effectiveness. Patient-population size, willingness-to-pay, time to market. Rare-disease indications with small populations can support high prices and accelerated paths; chronic-disease indications need volume and price to clear the hurdle.

Concept Translation: A DALY (disability-adjusted life year) is one year of healthy life lost. It combines years of life lost to premature death with years lived with disability, weighted by severity. The WHO and the Global Burden of Disease project use it as a single scalar for comparing burden across very different conditions. One death from leukemia at age 40 is many DALYs; a hundred years lived with mild migraines is fewer DALYs than the population count would suggest. Indication prioritization uses DALYs as a public-health weight on top of the commercial and scientific weights.

Those four criteria define a multi-criteria ranking problem over a set of candidate indications, with noisy partial signal in each criterion. Rank the N indications a target might plausibly treat by a learned weighting of DALY burden, pathway-relevance score, competitive density, and commercial headroom. The training signal is weak and slow. You get a label every five to seven years when a Phase II reads out. In practice, ML indication-prioritization tools in 2026 are decision supports rather than decision makers. They surface hypotheses the human strategy team should weigh, at a breadth a human team could not otherwise cover.

Indication expansion is the other lifecycle move. As of 2020, roughly 52% of the global drug pipeline was indication-expansion entries (new indications for already-approved drugs) versus 48% new molecular entities. In 2014 the split was roughly 50-50; by 2018–2019 expansion had risen to 65–67% of total entries. That trend fits directly into the LOA story. Indication expansion starts with a compound that has already cleared safety and PK, so the expansion-indication program inherits everything except the efficacy question. The 2014 BIO data has off-patent therapies running a Phase I LOA of 14.7% versus novel agents at 6.8%, roughly double, for the same reason.

Both prioritization and expansion are places where target-discovery knowledge reaches directly into commercial strategy. If you know which subnetworks of the human disease graph a given target modulates, you know which indications it could plausibly address, and you know where the strongest target-disease evidence exists to guide the first-indication choice. That is the rationale for the [knowledge graphs for target discovery] → Knowledge graphs and case studies in AI-driven target discovery¹ covered in C6.

Worked example: Keytruda as indication prioritization done right

Pembrolizumab (Keytruda, Merck) is the most cited indication-expansion case in the course notes, and its trajectory follows the four criteria above in sequence.

First indication, 2014: advanced melanoma. At the time, advanced melanoma met two conditions that made it a sensible first indication. Profound unmet need existed (median survival of roughly a year or less for advanced metastatic disease in the pre-checkpoint era), and the competitive landscape had limited options even after ipilimumab’s 2011 approval. Melanoma is also immunogenically “hot,” meaning more visible to the immune system than many other tumors; tumor mutational burden is on average high. That matters for a drug whose mechanism (blocking PD-1 to unleash anti-tumor T cells) depends on there being a T-cell response to unleash. PD-1 and PD-L1 form an immune checkpoint, a receptor-ligand pair that dampens T-cell activity. Pathway relevance was strong, unmet need was severe, and the competitive landscape was manageable. Those were the right priorities.

Concept Translation: PD-1 sits on T cells. PD-L1 sits on many normal cells and on a fraction of tumor cells. When PD-L1 binds PD-1, the T cell pulls back its attack, which is how the body normally stops T cells from attacking healthy tissue. Tumors that learn to display PD-L1 hijack this brake to evade immune destruction. Pembrolizumab is a monoclonal antibody that physically blocks PD-1, releasing the brake and letting T cells attack. The mechanism only works on tumors the immune system can already “see,” which is why tumor mutational burden (the count of non-self-looking mutations per tumor genome) and PD-L1 expression both predict response. From an ML angle: the drug is a feature mask on a learned pathway, but only useful when the pathway has signal to mask in the first place.

Second indication, NSCLC. Non-small cell lung cancer became, per the course, the single largest contributor to Keytruda revenue and the dominant indication share in U.S. sales. NSCLC shows how expansion changes the economics. The patient population is an order of magnitude larger than advanced melanoma, the PD-L1 expression biomarker gives a way to enrich for likely responders before treatment, and the checkpoint-responsive fraction is substantial. The same mechanism entered a much larger market with a built-in biomarker.

Expansion footprint. Keytruda has been approved for more than 40 indications and tested in over 1,500 clinical trials as of early 2026. The 2026 Addendum notes that Keytruda is the most combined drug in clinical oncology history and places the looming 2028 composition-of-matter loss of exclusivity (the point at which the core patent protection runs out) at the center of Merck’s strategy. Three pieces define that strategy. Aggressive perioperative-indication expansion (movement into treatment settings before and after surgery), the subcutaneous Qlex formulation (given under the skin rather than by intravenous infusion, approved September 2025 across 38 indications), and biosimilar defense via formulation patents extending to 2039.

What Keytruda illustrates for the LOA story. Keytruda succeeded because its target had deep pathway relevance across many tumor types, its first indication offered both severe unmet need and a biomarker-defined responder population, and later expansions followed tumor types whose biology suggested they would respond to immune-checkpoint blockade. That is indication prioritization done well. It explains why Keytruda’s revenue trajectory looks so different from the median oncology asset even though oncology as a field carries a 5.3% industry-wide Phase I LOA. Program-level LOA depends on target quality and indication choice. Crowded therapeutic areas can still produce standout programs; they are unforgiving to average ones.

The non-oncology mirror is botulinum toxin (Botox, Dysport), a smaller revenue story but a long-arc example of the same logic. It was initially approved for narrow neuromuscular indications, then systematically expanded into migraine, overactive bladder, and cosmetic indications over two decades. One mechanism, blocking acetylcholine release at neuromuscular junctions, supported a widening set of indications. Pharmacology and indication prioritization stayed aligned.

Concept Translation: Acetylcholine is the chemical messenger that motor neurons release at the synapse where they meet a muscle fiber (the neuromuscular junction). The release tells the muscle to contract. Botulinum toxin cleaves the proteins that allow that release, and the muscle stops getting the contract signal. In facial cosmetics, this smooths wrinkles by paralyzing small muscles. In migraine, it interferes with sensory-nerve signaling. In overactive bladder, it relaxes the detrusor muscle. Same mechanism, different muscle or nerve, different indication.

What likelihood of approval means for an ML practitioner

For practitioners entering drug discovery from an ML background, LOA and therapeutic-area economics matter in three operational ways.

First: it defines the loss function at the program level. Approval is a rare, slow, expensive label: 6.7% base rate, 5–10 year lag, and per-asset cost estimates whose means span roughly $0.95B to $2.23B depending on whether high-cost outliers are excluded, with medians closer to $0.7B–$1.0B (RAND 2025 median: $708M; JAMA 2020 median: $985M; Deloitte 2024 mean for the top-20 biopharma cohort: $2.23B; Tufts CSDD’s widely cited $2.8B mean is heavily weighted by ~$1.16B in foregone-investor returns over the 10–15 year development window plus ~$312M in post-approval R&D). You cannot optimize directly against that outcome the way you optimize a docking score against a binding-affinity benchmark. You work instead with proxies that correlate with approval: human genetic support, target-disease evidence quality, druggability scores, and biomarker-enrichability. The biomarker-stratification result is the clearest single proxy in the literature. Any target-prioritization pipeline that scores candidate targets on whether a target has a plausible patient-stratification biomarker is, in effect, modeling the 15.9%-vs-7.6% split. The [quantifying drug target novelty] → Novel vs repurposed targets: quantifying novelty and extending drug-repurposing methods² is a second proxy axis. The [target–disease evidence frameworks] → How to tell a drug target matters: evidence frameworks for target–disease linkage systematize the first.

Second: it sets the prior on therapeutic-area choice. If your platform is modality-agnostic or pathway-focused, therapeutic-area choice becomes a modeling decision. Oncology is data-rich (TCGA, The Cancer Genome Atlas; ICGC, the International Cancer Genome Consortium; GEO oncology collections, the Gene Expression Omnibus; and the Cancer Dependency Map, a large set of gene-essentiality screens) and low base rate. Rare genetic disease is data-poor (small patient cohorts, narrow genomic catalogues for any single disease) and high base rate, with accelerated regulatory paths. The ML-productivity question is where the data sits in a therapeutic area whose LOA can justify the modeling work. Many 2024–2026 platform biotechs that started in oncology have explicitly added rare-disease indications for exactly this base-rate reason.

Third: it quantifies the cost of being wrong. Funnel cost estimates put numbers behind upstream decision quality. Target validation absorbs roughly $353M over 2.5 years; lead optimization runs over half a billion; Phase II/III adds roughly half a billion more. Phase 2 completion, successful or unsuccessful, absorbs around 20% of the per-drug discovery pipeline spend. The directional point about Phase II cost concentration is consistent with the contemporary RAND/JAMA/Deloitte cost-distribution work even where the exact magnitudes differ. A target-assessment tool that trims Phase II failure by a few percentage points can pay for itself in industry-average units. The biomarker-stratification result shows the scale of the upside when upstream decisions improve.

Chapter 1 of Build AI Drug Discovery Pipelines frames the scale problem: 10⁶³ drug-like molecules, ~10⁵ potential human protein targets, a $1–3B and 10–15 year cost to move from ideation to market, and Eroom’s Law as the secular productivity decline that this book’s methods are intended to push against. The LOA economics in this post are the companion to the chemical search space. Even if chemistry were solved, the pipeline’s output rate would still be constrained by target and indication choice. Part of the productivity gap that Eroom’s Law names sits at the front of the funnel.

LOA numbers to watch through 2027

Three developments are worth watching over the next twelve to twenty-four months.

The Citeline numbers will refresh. The 2014–2023 cohort is the current industry reference; a 2024 or 2015–2024 update will arrive. Watch whether the biomarker-stratification doubling holds up. The front-of-funnel ML argument leans heavily on that result. Also watch whether Phase II transition rates continue to slide or whether the tentative post-2018 recovery reported in the 2025 Nature Communications analysis (cited but contested in the Citeline coverage) proves to be signal rather than artifact.

The AI-discovered-drug cohort will produce its first registrational readouts. Registrational readouts are late-stage trial results intended to support approval. Rentosertib (Insilico Medicine’s INS018_055 / ISM001-055), the first widely recognized drug whose biological target and molecular structure were both nominated by generative-AI systems, is the clearest signal program. Insilico’s platform identified TNIK (TRAF2 and NCK-interacting protein kinase) as a master regulator of fibrosis and inflammation, then designed a selective small-molecule inhibitor; the discovery-to-clinical-trial timeline ran under 30 months versus a 4.5–6 year traditional industry average. Phase IIa results published in Nature Medicine in June 2025 showed a +98.4 mL change in forced vital capacity (FVC), a standard lung-function measure, in the 60 mg arm versus a −20.3 mL placebo decline at week 12 (n=71 patients, 12 weeks, not powered for pivotal efficacy). More than 173 AI-discovered drug programs are tracked in clinical development as of early 2026, with 15–20 expected to reach pivotal Phase III this year (industry tracker reporting; the cohort is not yet mature enough to compute its own LOA). The field will be judged on cohort LOA. Ask again in 2028.

The therapeutic-area revenue-vs-success-rate gap may widen. GLP-1 economics are rewriting cardiometabolic R&D priorities, and cardiometabolic remains a large, chronic, high-prevalence area where, per the BIO rare-vs-chronic analysis, Phase I LOA runs around 5.9%. The next wave of GLP-1-adjacent targets (GLP-1/GIP dual agonists, amylin analogs, triple agonists, oral formulations) will be crowded and, on historical base rates, hard. Oncology, meanwhile, is structurally unchanged. Whether cardiometabolic’s post-GLP-1 base rate improves because GLP-1-like programs offer clearer mechanisms and better stratification, or stays near the chronic-disease average, is an open empirical question for 2027–2030 cohort readouts.

The LOA picture will not get simpler. Every additional year of data makes the cohorts denser, more modality-heterogeneous (small molecules, biologics, ADCs, cell therapies, gene therapies, oligonucleotides, each with its own base rates), and more biomarker-stratified on average. Approval counts are at historical highs while Phase I success is at historical lows, and interpreting both trends depends on selection into Phase I. Both possibilities matter (sharper programs entering, noisier programs entering), and in 2026 the most defensible answer is that both effects are present, with biomarker-stratified programs accounting for much of the productive output.

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Further reading

• BIO, Informa Pharma Intelligence, and QLS Advisors. (2021). Clinical Development Success Rates and Contributing Factors 2011–2020. [URL: https://www.bio.org/clinical-development-success-rates-and-contributing-factors-2011-2020]. The foundational industry-wide analysis covering 12,728 phase transitions across 21 major disease areas. Source for disease-area rankings, the biomarker-stratified LOA doubling (15.9% vs 7.6%), and the rare-vs-chronic disease comparison (17.0% vs 5.9% Phase I LOA). The analytical framework the industry still uses a decade after the data cutoff, and the specific report this post’s crowded-vs-abandoned map section is built on.

• Citeline / Norstella. (2024). Why are clinical development success rates falling? Industry analysis. [URL: https://www.norstella.com/why-clinical-development-success-rates-falling/; accessible coverage at https://pharmaphorum.com/rd/clinical-development-rates-are-falling-its-not-all-bad-news]. The current industry reference for headline LOA figures, covering 2014–2023. The primary source for the 6.7% Phase I LOA figure, the phase-by-phase transition rates (47%/28%/55%/92%), and the Phase I success-rate decline from >75% (2006–2008) to <40% (most recent cohort). Citeline is the successor entity to Informa Pharma Intelligence, now part of Norstella; cite Citeline/Norstella for 2026-current blog posts and BIO for the 2011–2020 historical baseline.

• FDA Center for Drug Evaluation and Research. (Annual). Novel Drug Approvals [year]. [URL: https://www.fda.gov/drugs/novel-drug-approvals-fda]. Annual summaries link from this page. The canonical per-year approval counts, orphan and expedited-program breakdowns, and PDUFA-goal-date statistics that the post’s 2023/2024/2025 approval-count figures (55, 50, 46 respectively) reference, along with the 29.1% (2023) and 26.0% (2024) oncology share of CDER novel approvals discussed in the crowded-vs-abandoned section. Pair with the Nature Reviews Drug Discovery annual FDA approvals analysis; search “Nature Reviews Drug Discovery FDA approvals annual analysis [year]” for readable per-approval commentary.

• Lai, R., et al. (2025). Productivity of the top 18 pharmaceutical companies measured by likelihood of approval of their compounds 2006–2022. Drug Discovery Today. [URL: https://www.sciencedirect.com/science/article/pii/S1359644625000042]. Empirical analysis that produced the major-pharma-only LOA figure (mean 14.3%, median 13.8%, range 8–23%) the post cites for contrast against the industry-wide Citeline numbers. Essential reading for the “industry-wide vs major-pharma-only” distinction, the factor-of-two framing the post uses to warn against mixing cohorts in LOA comparisons.

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