Clinical Development

Clinical Development Director

Clinical development is where biotech value is created or destroyed. A well-designed trial with the right endpoint, enriched patient population, and robust biomarker strategy can turn a marginal molecule into a blockbuster approval. A poorly designed trial can kill a genuinely effective drug. This director routes clinical program questions to the right specialist and sequences multi-skill analyses so that each decision builds on the one before it.

Child Skills

| Skill | Type | When to Use | |-------|------|-------------| | trial-design-optimizer | action | Designing or critiquing a trial protocol — adaptive designs, randomization schemes, control arm selection, sample size justification, interim analysis plans | | endpoint-selection | knowledge | Choosing primary/secondary/exploratory endpoints, understanding regulatory endpoint precedent by indication, surrogate vs clinical endpoints, composite endpoints | | biomarker-enrichment | action | Designing biomarker-driven enrollment strategies, predictive vs prognostic biomarker analysis, companion diagnostic requirements, subgroup pre-specification | | patient-population-sizer | action | Estimating addressable patient populations, prevalence/incidence modeling, screen failure rate estimation, enrollment feasibility by geography |

Routing Logic

| Question Signal | Route To | Examples | |-----------------|----------|----------| | Trial design, protocol, adaptive, randomization, control arm, sample size, interim analysis | trial-design-optimizer | "Should this be an adaptive design?" / "What sample size do we need for 90% power?" | | Endpoint, primary endpoint, surrogate, composite endpoint, clinical outcome, OS vs PFS | endpoint-selection | "Is PFS an acceptable primary endpoint in this indication?" / "Should we use a composite endpoint?" | | Biomarker, enrichment, companion diagnostic, predictive biomarker, subgroup, patient selection | biomarker-enrichment | "Should we enrich for biomarker-positive patients?" / "What CDx strategy makes sense here?" | | Patient population, prevalence, incidence, addressable patients, enrollment, screen failure | patient-population-sizer | "How many patients can we realistically enroll?" / "What is the addressable population for this rare disease?" | | Trial design + endpoint together | trial-design-optimizer then endpoint-selection | "Design a registrational trial for this asset" | | Biomarker + population together | biomarker-enrichment then patient-population-sizer | "If we enrich for HER2-low, what does that do to our enrollment timeline?" | | Full clinical program review | All four in sequence | "Evaluate this Phase 3 protocol" |

Multi-Skill Questions

Most clinical development questions span multiple children. Common combinations:

1. Trial Design + Endpoint: "What is the optimal registrational strategy for this asset?"

• Load endpoint-selection first to establish regulatory precedent for acceptable endpoints in the indication
• Then load trial-design-optimizer to build the protocol around the chosen endpoint
• Synthesize: The endpoint drives the design. An OS endpoint requires a larger, longer trial than a surrogate. An adaptive design may allow a surrogate-to-OS transition.

1. Biomarker + Population: "Should we enrich, and what does that do to feasibility?"

• Load biomarker-enrichment to assess whether a predictive biomarker exists and what enrichment ratio is achievable
• Then load patient-population-sizer to model the impact on addressable population and enrollment timelines
• Synthesize: Enrichment increases effect size but shrinks the denominator. The net impact on sample size and enrollment duration determines whether enrichment is operationally viable.

1. Full Protocol Review: "Critique this Phase 3 design"

• Sequence all four: endpoint-selection (is the endpoint right?) then trial-design-optimizer (is the design right?) then biomarker-enrichment (is patient selection right?) then patient-population-sizer (is enrollment feasible?)
• Each analysis feeds the next. Endpoint choice constrains design. Design constrains biomarker strategy. Biomarker strategy constrains population size.

Curriculum Order

1. endpoint-selection — Foundation. You cannot design a trial until you know what you are measuring. Endpoint literacy is the prerequisite for everything else.
2. trial-design-optimizer — Second. Once the endpoint is chosen, the trial design follows. Adaptive designs, randomization, control arms, interim analyses — all depend on the endpoint.
3. biomarker-enrichment — Third. Enrichment is a design modifier. It changes who enters the trial and how the treatment effect manifests. Requires understanding of trial design to appreciate the tradeoffs.
4. patient-population-sizer — Fourth. Population sizing is the operational reality check. It takes the designed trial and asks: can we actually run this? Requires all prior skills as inputs.

Conflict Resolution

| Conflict | Resolution | Reason | |----------|------------|--------| | Endpoint selection favors a surrogate but trial design recommends powering for OS | endpoint-selection leads if there is regulatory precedent for the surrogate; trial-design-optimizer leads if the indication lacks surrogate acceptance | Regulatory precedent is the binding constraint. If FDA has accepted the surrogate in prior approvals, use it. If not, powering for OS is safer despite longer timelines. | | Biomarker enrichment improves effect size but patient-population-sizer shows enrollment becomes infeasible | Quantify the tradeoff explicitly — compare total patients needed (enriched vs all-comers) and enrollment duration | Often enrichment reduces total patients needed despite a smaller eligible pool. If the math does not work, consider a biomarker-stratified (not enriched) design. | | Trial design recommends adaptive but endpoint selection shows the endpoint requires fixed-duration follow-up | endpoint-selection takes priority — the regulatory endpoint constrains the design, not the reverse | An adaptive design with a time-to-event endpoint works differently than one with a fixed-timepoint responder endpoint. The endpoint dictates which adaptive elements are feasible. |

Scope Boundaries

This director handles: All questions about clinical trial design, protocol optimization, endpoint selection, biomarker enrichment strategy, patient population sizing, enrollment feasibility, adaptive trial methodology, and clinical development strategy for therapeutic assets.

Route to Asclepius when:

• The question requires translating clinical design into probability of success (route to probability-of-success)
• The question involves regulatory pathway strategy beyond endpoint precedent (route to regulatory-strategy)
• The question involves competitive differentiation based on clinical design choices (route to competitive-intelligence)
• The question requires cost estimation for the clinical program (route to asset-valuation)
• The question spans multiple diligence pillars and needs orchestrator-level coordination

Cross-Domain Connections

• Biotech-venture/endpoint-selection, trial-design-optimizer, biomarker-enrichment, patient-population-sizer: Child skills that execute specialist clinical development analyses
• Data-science/statistical-testing: Biomarker enrichment power calculations and sample size estimation rely on statistical testing foundations
• Research/spelunker: Deep research on clinical trial design precedent, regulatory endpoint acceptance history, and adaptive design methodology

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: npbuilds
• Source: npbuilds/skill-library
• License: MIT
• Homepage: https://skill-library-prod.web.app/

Install and usage instructions live in the source repository linked above.