Phase 1 oncology clinical trials play a key role in the development of cancer therapies. They focus on safety, dose finding, and early signals of activity, and modern designs increasingly incorporate biomarkers, precision medicine, and adaptive approaches. In practice, early decisions made with limited data can shape whether a programme accelerates smoothly or needs costly redesign. This guide focuses on the design choices that shape evidence quality and deliverability.
A useful starting point is understanding what makes oncology Phase 1 distinct in practice, and how targeted therapies and immunotherapy have changed the dose-finding questions.
Why oncology Phase 1 is different in practice
Phase 1 oncology trials evaluate a new therapy for the first time, focusing on safety and tolerability. Unlike early‑phase trials in other therapeutic areas, oncology studies often involve patients with advanced cancer rather than healthy volunteers.
What has changed with a targeted therapies and immunotherapy
Over time, the shift toward targeted therapies and immunotherapies has expanded trial objectives beyond toxicity assessment. Modern designs must account for delayed toxicities, immune‑related events, and variable dose–response patterns, requiring more flexible and data‑driven approaches. As a result, dose escalation decisions must often be made with incomplete toxicity maturation, particularly for immuno-oncology agents.
Dose-limiting toxicity (DLT) window and late-onset toxicities
This also makes practical design choices, such as the length of the dose-limiting toxicity (DLT) window and how teams handle late-onset toxicities, more consequential for escalation pace and patient protection.
Starting dose selection
Where relevant, starting dose selection typically draws on non-clinical data and anticipated pharmacology, aiming to balance safety with a dose range that can meaningfully test exposure and target engagement.
Phase 1 aims to build an evidence package that supports a recommended dose and regimen, not dose alone.
Safety and DLTs
The objective is to determine a safe and effective dose for Phase 2. Evidence generated includes safety, dose‑limiting toxicities (DLTs), pharmacokinetics (PK), pharmacodynamics (PD), and preliminary anti‑tumour activity.
RP2D and schedule
These data inform selection of the recommended Phase 2 dose (RP2D) and schedule, guiding go/no‑go decisions for later phases.
Dose and regimen rationale
Teams aim to build a ‘dose and regimen’ rationale that links tolerability, exposure, and biological effect to the intended schedule, not dose alone.
Interpreting early activity signals
Early activity signals are typically interpreted with the assessment approach in mind (for example, the timing and confirmation of tumour responses and consistency of imaging reads), so that ‘signal’ means the same thing across sites and cohorts.
Dose escalation identifies the dose range and characterises toxicity. Common methods include rule-based designs such as 3+3 and accelerated titration, as well as model-assisted designs such as BOIN and model-based approaches such as CRM and EWOC.
Escalation approaches
Teams may use model-assisted or model-based approaches when seeking greater efficiency or more explicit control of overdose risk.
Anchoring escalation decisions
Regardless of approach, escalation is anchored to the prespecified DLT window and the plan for incorporating late-emerging toxicities into decisions.
Dose expansion: intent and trade-offs
Expansion cohorts further evaluate the chosen dose across specific tumour types, biomarker‑defined groups, and early indicators of efficacy. While expansion cohorts can accelerate signal generation, they also increase patient exposure before dose optimisation is complete, requiring careful safety oversight. Expansions work best when their intent is explicit and stopping principles are clear, so cohort size reflects the decision need.
Combination regimens
Where combination regimens are studied, escalation often needs additional safeguards because overlapping toxicities, drug–drug interactions, and schedule effects can change tolerability and exposure compared with monotherapy.
For many modern therapies, MTD alone is not enough to justify dosing decisions over time.
Why MTD-only can be insufficient
Reliance on maximum tolerated dose (MTD) alone has become insufficient, especially for targeted and immune‑based therapies where toxicity and efficacy may not correlate with dose.
Project Optimus and dose justification
Regulators increasingly expect sponsors to justify dose selection using broader dose-exploration strategies, rather than defaulting to the highest tolerated dose, as seen with initiatives like the FDA’s Project Optimus, encouraging sponsors to evaluate multiple doses, PK/PD relationships, and long‑term tolerability before defining the RP2D.
Sustainability over multiple cycles
This is partly operational. A dose that is ‘tolerated’ over a short DLT window may still be difficult to sustain over multiple cycles, leading to interruptions, reductions, and reduced time on treatment that undermines the intended exposure. Dose optimisation therefore considers sustained tolerability and whether patients can remain on the planned regimen.
Operating characteristics and simulation help teams judge how a design is likely to behave before enrolment begins.
Using operating characteristics to compare designs
Operating characteristics, such as the probability of correct dose selection and patient allocation efficiency, allow sponsors to compare design options objectively. Simulation assessments help determine whether a given approach supports safe escalation, adequate DLT observation, and high‑quality decision‑making.
Seamless Phase 1/2 and governance
Seamless Phase 1/2 designs may be used when early efficacy signals are essential but require strong statistical and operational governance.
What poor operating characteristics can mean in practice
Poorly performing operating characteristics can result in excessive patient exposure to sub-therapeutic or overly toxic doses. Teams commonly simulate questions such as, ‘If toxicity is delayed by one cycle, how often does this design escalate too quickly, and how many patients are exposed before the signal is recognised?’
Biomarkers can sharpen the signal, but they also affect feasibility through eligibility, assay readiness, and specimen logistics.
How biomarkers shape eligibility and cohorts
Biomarkers increasingly guide eligibility, cohort selection, and dose‑expansion strategy.
Assay readiness and turnaround time
Sponsors must ensure assay readiness, including analytical validation, sample handling processes, and turnaround time. Companion diagnostics may be required when a biomarker is essential for therapy selection.
ctDNA and MRD
Emerging technologies, such as circulating tumour DNA (ctDNA) and minimal residual disease (MRD) assessment, offer earlier signal detection but require careful interpretation and operational planning.
Feasibility and governance
Biomarker-driven eligibility can improve signal clarity but may significantly narrow the eligible population, affecting recruitment timelines. Teams also need clarity on whether a biomarker is exploratory or used for treatment assignment, and a realistic view of prevalence and result turnaround times within screening windows.
Specimen logistics
Specimen logistics such as collection timing, shipping, storage and chain of custody, as well as alignment between local and central testing can also materially affect data interpretability.
Master protocols can accelerate learning across cohorts, but they depend on coordination.
Where master protocols fit
Master‑protocol designs support efficient testing of multiple therapies, tumour types, or biomarker groups. Basket trials test one therapy across multiple tumour types; umbrella trials evaluate multiple therapies within a single tumour type; platform trials enable ongoing, adaptive evaluation of new treatments.
Governance and decision rules
These designs expand the evidence base but demand infrastructure and statistical frameworks. These approaches are most effective when supported by coordination and clear decision rules for cohort continuation or closure.
When they are most used in early oncology
Because multiple cohorts can open, pause, or close over time, version control becomes central to maintaining consistent eligibility, assessments, and decision criteria across the master protocol.
Cadence and cycle time often determine whether dose-finding and expansion progress smoothly in practice.
Cadence and safety monitoring
Early phase oncology trials require close coordination across sites, with intensive visit schedules, frequent safety monitoring, and rapid cohort‑review cycles. Efficient cycle‑time management is critical for timely dose escalation. Cohort review meetings often determine escalation pace, particularly where dose decisions rely on timely safety and PK/PD review.
Expansion delivery considerations
Expansion cohorts require additional tumour‑specific expertise, imaging assessments, and central review processes to ensure data consistency and safety oversight. Regular safety review committee meetings are essential to support timely escalation decisions while maintaining patient protection.
Cycle time levers
Cycle time is often driven by practical levers such as timely data entry and cleaning for key safety fields, agreed data cut-offs for review, rapid query resolution for escalation-critical variables, and a predictable committee cadence with clear decision documentation.
Escalation vs expansion visit intensity
Visit intensity often differs by stage. For example, escalation may concentrate around first-cycle safety and intensive PK/PD sampling, while expansion can add tumour-specific assessments and repeated imaging that increase coordination load and subject burden. This can affect recruitment when intensive schedules reduce willingness to participate or site capacity.
Core roles and coverage
Sites must have experienced investigators, oncology pharmacists, and nursing teams capable of managing complex sampling schedules, investigational product preparation, and rapid management of adverse events.
Capabilities that influence feasibility
Radiation safety, tumour‑specific diagnostic capabilities, and biomarker processing capacity often influence feasibility during site selection. Prior experience with early phase oncology trials is often critical to managing protocol complexity and minimising protocol deviations.
Out-of-hours response and sample logistics
Many programmes also depend on reliable out-of-hours coverage for urgent safety events and protocol-defined interventions, plus practical logistics for time-critical samples. This will often include weekend shipping, central laboratory cut-offs, and controlled temperature handling where required. Clear role coverage across clinic, pharmacy, labs, and data teams helps prevent avoidable deviations in high-intensity visit windows.
Approvals and oversight need to keep pace with complexity, especially where expansion, biomarkers, or adaptation are planned.
Approvals and amendments
Phase 1 oncology trials require regulatory approvals, ethics review, and robust SOPs for safety reporting, dose escalation, and adaptive decision‑making. Expansion cohorts may require substantial protocol amendments and oversight. This typically includes appropriate institutional review board or ethics committee (IRB/EC) review, depending on jurisdiction.
Governance for adaptive and biomarker-led designs
Expectations may vary across jurisdictions, with agencies such as the FDA and EMA placing increased scrutiny on dose justification and adaptive decision-making. Teams often pay additional attention to consent content and how risks are reviewed and communicated as the trial evolves.
Quality, data integrity, and privacy
Across jurisdictions, teams typically also plan for core GCP expectations, data integrity controls for electronic systems, and privacy requirements for sensitive health and genomic data, keeping documentation proportionate to risk and complexity.
Traceable dose decisions
Safety governance may sit with an internal committee, a sponsor-led safety review committee, or an independent body, but the essential requirement is that escalation and adaptation decisions are traceable, timely, and based on the agreed data set.
Phase 1 oncology trial design continues to evolve, balancing safety, scientific depth, and operational feasibility. In practice, the strongest designs make the trade-offs explicit. This includes what evidence is needed for dose and regimen decisions, what the biomarker strategy is intended to prove, and what the programme can realistically deliver at site level.
A Phase 1 oncology trial tests a new cancer therapy in people for the first time, focusing on safety, tolerability, and dose-finding, usually in patients with advanced cancer. It establishes how the therapy behaves in the body and identifies the dose for Phase 2. It also often looks at PK/PD and early signals of activity to support next step decisions.
Phase 1 oncology trials focus on safety, tolerability, and selecting a recommended dose and regimen (RP2D). Phase 2 typically evaluates activity at the selected dose, although expansion cohorts can blur the boundary between Phase 1 and 2 in practice.
Unlike many first-in-human trials that enrol healthy volunteers, Phase 1 oncology trials usually enrol patients with advanced cancer. They often involve intensive safety monitoring and may incorporate biomarkers to guide eligibility or expansion cohorts.
Dose escalation explores increasing dose levels to understand tolerability and define a dose range. Dose expansion studies the selected dose in larger or more specific cohorts (for example, tumour types or biomarker-defined groups) to strengthen the evidence package.
RP2D is the recommended Phase 2 dose, often considered alongside regimen elements such as schedule and supportive measures. Teams typically support it using safety, PK/PD, and early activity signals.
3+3 is a rule‑based dose-escalation approach with fixed escalation rules. BOIN is a model‑assisted design that uses statistical boundaries to support dose-escalation decisions.
Phase 1 oncology studies often include dose‑escalation and dose‑expansion, and may include first‑in‑human studies and biomarker‑driven cohorts. Some programmes also run additional early clinical assessments, depending on the therapy and development plan.
Quanticate’s statistical consultancy team supports sponsors with dose-finding design, simulation, and operating characteristic evaluation to help ensure early-phase studies generate reliable, decision-ready evidence. If you’re planning an early oncology programme, request a consultation to discuss how our statisticians can support your Phase 1 trial design.