Competition analysis in biotech asks a practical question: why would a physician choose this treatment over the alternatives available when it reaches patients? A drug can produce positive trial results and still offer too little improvement to support ambitious sales forecasts. Conversely, a treatment with modest efficacy gains may have a credible commercial role if it addresses a clearly defined weakness in existing care.
The assessment needs to connect the standard of care, competing development programs and evidence of treatment differentiation. It also needs a date. The relevant comparison is the market at launch, then during the years when sales are expected to grow. Within biotech commercialisation and market economics, competition analysis provides the bridge between clinical promise and a defensible estimate of adoption. Start with the treatment decision, rather than a company’s list of rivals.
Define the Competitive Market Around the Patient
A disease name is usually too broad to define a useful competitive market. Divide the opportunity by disease severity, previous treatment and the circumstances in which a prescribing decision occurs. Two drugs targeting the same biological pathway may serve different patient groups. Drugs with unrelated mechanisms may compete directly if physicians choose between them at the same point in care. Include relevant procedures and non-drug approaches in the comparison, rather than restricting the field to biotechnology companies. The research question should be narrow enough to answer: which alternatives could this patient reasonably receive instead?
Biomarker status can further divide a market. In cancer, some targeted therapies and immunotherapies work only in patients whose cancers have certain biomarkers, making NCI information on biomarker-based treatment selection relevant to the competitive boundary. A broad disease prevalence figure therefore says little about direct commercial overlap. Build the comparison around the population supported by the evidence, and keep possible future expansion separate. The same discipline applies to estimating a drug’s addressable market: patients outside the treatment opportunity should not enter a market-share calculation simply because they share a diagnosis.
Establish the Standard of Care and Its Weaknesses
The standard of care is the clinical benchmark, but a commercial assessment should examine actual treatment pathways as well. Establish what physicians recommend, what patients receive and why those choices differ. Use current professional guidelines and supporting studies for the clinical pathway. Use evidence appropriate to the geography when investigating prescribing patterns or access. An approval announcement establishes an authorised use; it does not establish that a medicine has become the preferred treatment everywhere. Keep those claims separate. Where public evidence on treatment patterns is weak, record the uncertainty rather than substituting a confident market-share assumption.
Then identify the weakness a new treatment is intended to address. Write it as a patient or practice problem: inadequate disease control after an initial response, treatment discontinuation caused by adverse effects, or an administration burden that restricts use. Avoid vague descriptions such as “large unmet need.” State which patients experience the problem, how existing options address it and what evidence would demonstrate improvement. A new mechanism may provide a scientific rationale, but evaluating drug targets and mechanisms of action answers a different question from establishing a competitive advantage. Biological novelty needs a clinical consequence.
The benchmark can change during development. On August 19, 2024, the FDA approved Johnson & Johnson’s lazertinib with amivantamab for a defined first-line population with EGFR-mutated advanced non-small cell lung cancer. The supporting MARIPOSA trial compared the combination directly with osimertinib; median progression-free survival was 23.7 months versus 16.6 months. These are historical findings from the FDA approval assessment of lazertinib with amivantamab, not a complete description of treatment choices today. The investment implication is straightforward: a developer planning to compete in that setting would need to assess the emerging combination evidence, rather than treating an earlier benchmark as permanently sufficient.
Separate Meaningful Differentiation From Product Features
A useful differentiation claim names the advantage, the supporting evidence and the decision it could change. “Oral treatment” describes a product feature. “An oral treatment that preserves disease control while removing recurring clinic administration” describes a testable competitive proposition. Even then, check whether monitoring or other requirements would preserve much of the clinic burden. The purpose is to identify an advantage that survives contact with the full treatment pathway. A shorter dosing schedule has little analytical value if it appears only in a presentation and has not been demonstrated at the dose proposed for commercial use.
| Dimension | Potential advantage | Evidence to examine | Competitive challenge |
|---|---|---|---|
| Efficacy | Better disease control or patient outcomes | Comparator, effect size, follow-up and relevant endpoints | Does the benefit exceed a meaningful clinical threshold? |
| Safety | Fewer serious harms or treatment interruptions | Exposure, adverse events and discontinuations | Is the apparent advantage based on comparable observation? |
| Durability | Longer benefit or less retreatment | Length of follow-up and numbers remaining under observation | Could an early result weaken with more mature data? |
| Administration | Fewer visits or simpler delivery | Complete regimen, monitoring and supporting procedures | Does another treatment requirement cancel the convenience? |
| Patient coverage | A usable option for an underserved group | Eligibility criteria and evidence within that population | Is the opportunity large enough to support the forecast? |
| Treatment economics | Lower total cost or resource use | Drug costs, administration and other care requirements | Who receives the saving, and who makes the decision? |
Rank these dimensions for the intended setting rather than assigning equal weight to every row. A proposed efficacy advantage should use outcomes that matter to the treatment decision; the distinction between disease activity measures and patient benefit belongs in the assessment of clinical endpoints. A safety proposition needs comparable exposure and observation, not a smaller adverse-event percentage lifted from a different study. Examine adverse events and treatment discontinuations separately from the headline efficacy result. The competitive profile must describe the trade-off, including disadvantages that could restrict use.
Judge the Evidence Behind the Competitive Claim
Distinguish evidence that a treatment works from evidence that it compares favourably with another treatment. A placebo-controlled study may establish benefit without resolving the choice between active alternatives. It may also test an addition to background therapy, rather than a replacement for it. A non-inferiority study addresses a different question from a superiority study. The comparator, dose and study population affect the interpretation; the ICH E10 guideline on control groups and fair comparisons sets out these distinctions. For investment research, record what the design can establish before interpreting the result as commercial differentiation.
A direct comparison can sharpen the assessment. In SURMOUNT-5, published in 2025, adults with obesity but without type 2 diabetes were randomly assigned to maximum tolerated weekly doses of tirzepatide or semaglutide. At 72 weeks, the least-squares mean weight reduction was 20.2% with tirzepatide and 13.7% with semaglutide. The published SURMOUNT-5 trial abstract also identifies the study as open label and funded by Eli Lilly. The difference was 6.5 percentage points. That result supports a defined weight-loss comparison under the trial’s conditions; it does not establish superiority across every clinical outcome, dose, patient group or future formulation.
Handle Comparisons Across Separate Trials With Care
A table of results from separate studies can help organise research, but it does not create a randomised comparison. Differences in previous treatment, disease severity, endpoint assessment and follow-up may account for part of an apparent advantage. Compare the control groups where available, then investigate why they performed differently. A formal indirect comparison requires assumptions about comparability across studies. Simply pooling treatment arms and treating them as one trial discards the protection of within-trial randomisation, a problem addressed in the Cochrane handbook chapter on network meta-analysis. A polished chart does not remove that problem.
Use separate-trial comparisons to frame questions and scenarios, with language that matches the evidence. “An encouraging result that requires confirmation against the incumbent” is different from “best in class.” Check whether the highlighted finding comes from the full trial population, a prespecified subgroup or a later exploratory analysis. Also check how much follow-up supports the claimed durability. The broader process of reading clinical trial results for investment research helps separate a useful signal from a claim the available data cannot carry. If no reliable ranking is possible, leave the ranking unresolved.
Build a Pipeline Map That Measures Threat, Not Activity
A competing-pipeline table should distinguish direct threats from adjacent research. Search by disease and treatment setting, then by mechanism and alternative compound names. Searching only for the same target risks missing treatments that could solve the same clinical problem through another approach. Record the study identifier as well as the product name, so that renamed or partnered programs do not become duplicate entries. Treat research-stage projects separately from programs with patient data. Counting every candidate equally turns a useful competitive map into an inflated list, and gives little help in estimating what may reach the market.
For each serious competitor, capture the population, development stage, comparator and principal endpoint. Add the next evidence event and the date on which the information was checked. The ClinicalTrials.gov protocol data definitions distinguish primary completion, when final primary-outcome data are collected, from study completion, which includes other required data collection. Neither date is automatically a public results announcement. An estimated completion date should therefore remain an estimate in the model, not become a firm catalyst date through repetition.
Cross-check trial records against publications, financial filings and dated company disclosures. Use registry histories to investigate changes in enrollment, endpoints or timing; a change deserves examination but does not establish its cause. A sponsor’s explanation should remain identifiable as a sponsor statement unless other evidence supports it. A repeatable process for researching biotech through trial registries and financial filings helps keep these evidence types separate. Record whether a program is a direct substitute, a possible combination partner or an indirect threat to the treatment pathway. That classification is more useful than mechanism alone.
Prioritise the map by asking two independent questions: how much could the competitor change treatment choice, and how credible is its route to market? A late-stage program with relevant comparative evidence deserves different treatment from an early candidate supported by a small uncontrolled study. Do not convert that judgment into an unsupported precise probability. Describe the evidence and remaining hurdles. Also retain a category for threats that could be serious but are poorly characterised. Unknown differentiation is not evidence of weak differentiation, particularly when the focal company’s own forecast assumes competitors will disappoint.
Compare Timelines and Treatment Sequences
Place the focal drug and its closest competitors on one dated timeline. Include expected results, application timing where supported, and plausible launch windows. Distinguish confirmed events from management targets and analyst assumptions. Then test whether the proposed advantage remains useful at launch and through the expected sales ramp. The relevant biotech catalysts include rivals’ events, even when they occur before the focal company’s next readout. A competitor’s result could change the commercial comparison without changing whether the focal drug’s trial succeeds.
Treatment sequence also matters. Consider a hypothetical drug intended for patients whose disease progresses after an established therapy. If a new first-line regimen becomes widely used, the later-line population could change in size, previous exposure or clinical need. That is a scenario to investigate, not an automatic forecast. Likewise, a candidate may compete by being added to an incumbent rather than replacing it. Map each proposed use separately. Otherwise, a model can assign the same patients to incompatible treatment positions or assume that evidence generated after one regimen applies unchanged after another.
Test Whether the Clinical Advantage Has a Commercial Route
Translate differentiation into a proposed prescribing decision before assigning market share. Identify who would use the treatment, which alternative they would replace and what would justify the change. Keep physician preference separate from access. A clinical advantage does not, by itself, establish broad reimbursement, a particular net price or freedom from treatment restrictions. Those assumptions belong in drug pricing, reimbursement and market-access analysis. Here, the task is to make the link explicit: which supported advantage is expected to change access or choice, and what evidence is still missing?
Distinguish newly treated patients from patients already doing well on an incumbent. A forecast that assumes strong uptake among both groups needs a reason for both decisions. For established patients, examine the evidence relevant to switching and any practical steps that would accompany it. For new patients, examine whether the product fits the normal treatment pathway. Keep this competition assessment separate from a full review of drug launch execution and physician adoption. The commercial model should not compensate for weak differentiation by assuming exceptional execution without evidence.
Time can also change the economic benchmark. Assess the possibility of lower-cost generic or biosimilar competition during the forecast period, rather than comparing a future product only with an incumbent’s current branded economics. Do not infer a reliable competition date from a single patent entry. The research into patent expiry, generics and biosimilars should supply the relevant assumptions. Within the competition model, test what happens if the focal drug must justify its advantage against a cheaper alternative, and keep that scenario distinct from one involving another premium-priced entrant.
Turn the Assessment Into Explicit Forecast Assumptions
Competition analysis becomes useful when it changes the model. Tie each major rival or treatment change to an assumption: eligible population, adoption speed, net price or duration of commercially useful differentiation. Avoid a generic “competition discount” whose meaning is impossible to audit. Keep clinical and regulatory success separate from commercial penetration. A drug can meet its development objectives yet capture less of the market than expected. Equally, a commercially attractive niche does not remove development risk. Mixing these questions makes it difficult to identify which new evidence should change the valuation.
Consider a hypothetical therapy with 10,000 eligible patients annually. A base case that assumes 20% penetration implies 2,000 treated patients. If a rival launches earlier and penetration reaches only 10%, the model falls to 1,000 patients before any pricing change. These figures are illustrative assumptions, not estimates for a real product. Their purpose is to expose what the thesis depends on. Test adoption separately from price, then test them jointly where the competitive scenario justifies both moving. Broader valuation sensitivity analysis should show whether the investment case survives less favourable competitive outcomes.
| Scenario | Evidence or assumption | Forecast response to test |
|---|---|---|
| Clear comparative advantage | Relevant evidence supports better outcomes against the expected benchmark | Faster uptake, subject to access and operational constraints |
| Useful niche | The benefit applies mainly to a defined subgroup | Smaller eligible population with stronger penetration within it |
| Advantage remains uncertain | Differentiation relies on separate-trial comparisons | Conservative uptake until comparative evidence improves |
| Benchmark moves ahead | A rival improves outcomes or changes treatment sequence | Lower penetration, a narrower role or further evidence costs |
Maintain a Dated Competition Decision Log
Keep a short decision log beside the detailed research. For every material assumption, record the supporting evidence, what remains uncertain and the event that would justify a change. Update it after relevant competitor results, approval decisions and revisions to treatment guidance. Preserve the previous version so that a changed thesis remains visible. The log should make it possible to tell whether a forecast changed because evidence improved, a competitor advanced or an unsupported assumption was finally removed. Use direct questions rather than a numerical score that conceals the reasoning:
- Which treatment decision would this drug change, and for which patients?
- What evidence supports the claimed advantage over the expected launch benchmark?
- Which competitor event could remove or narrow that advantage?
- Which sales assumption must change if that event occurs?