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    Case StudiesCase Study

    CRO SERVICES

    Phase 1 Trial: Protocol to Database Lock in 9-12 months. Trial Cost Under $400K.

    Maxis AI helped an oncology biotech accelerate study startup by 45% through one governed Phase 1 execution model - from protocol through database lock.

    May 2026CRO SERVICESFree to read

    45%

    Faster startup

    2–3×

    Enrollment conversion

    40-50%

    Faster DB lock

    <$400K

    Fixed price vs $2–5M

    Phase 1 Trial: Protocol to Database Lock in 9-12 months. Trial Cost Under $400K.

    WHO IT IS

    The Context

    A Boston-based oncology biotech advancing its first asset into a Phase 1 trial faced a structural execution gap.

    With no internal clinical operations team, execution was fragmented across protocol design, data management, statistical programming, and medical writing without a single point of accountability.

    A traditional CRO proposal of $2-5M+ over 18 months placed significant pressure on cash runway.

    At the same time, ICH E6(R3) requirements required RBQM to be operational and audit-ready before first patient — not treated as a post-hoc compliance exercise.

    Challenges

    Key barriers to trial execution

    Startup

    • Prolonged IRB drafting cycles
    • Fragmented site tracking
    • Late-stage escalation after timeline slips

    Enrollment

    • Only 29.5% patient conversion
    • Low screening-to-enrollment rate
    • Slowed cohort progression

    Data Quality

    • Fragmented data across EDC, eCOA, lab, safety
    • Static KRI thresholds
    • Risks detected too late

    Compliance

    • ICH E6(R3) RBQM requirements active
    • No system in place pre-trial

    Cost & Coordination

    • $2–5M CRO costs impacting runway
    • 4–5 vendors managed separately
    • No single point of accountability

    Operational solution

    Maxis AI agentic workflows — under human oversight throughout

    AI Accelerated Startup & IRB

    • Protocol-aware IRB packages auto-generated
    • Real-time site tracking
    • Deviations flagged within 2–3 days

    Outcome

    45% faster startup

    AI Data Quality & RBQM Hub

    • Unified data layer across all systems
    • Queries resolved under human oversight
    • Predictive risk signals 8–12 weeks early

    Outcome

    40-50% faster DB Lock

    Phase 1 Accelerator

    • End-to-end execution from design to CSR
    • One team, one contract
    • Fixed price under $400K

    Outcome

    2–3× enrollment uplift

    Maxis AI operates as a governed and supervised execution layer within existing systems throughout.

    See it on your study

    Curious how this could work for your Phase 1 protocol?

    We'll review your study workflow, identify execution gaps, and map where AI agents can deliver measurable operational impact in a 30-minute working session.

    Book a working session

    Measured impact

    Quantified outcomes after deploying Maxis AI's agentic workflows

    MetricBefore Maxis AIAfter Maxis AI
    Startup Duration~28 weeks traditional startup cycle~15–16 weeks - up to 45% faster startup execution
    Enrollment Conversion~29.5% screening-to-enrollment60–70% — 2–3× improvement via structured AI patient matching
    Query VolumeManual review consuming team capacityReduced by 60–75%; predictive risk signals 8–12 weeks in advance
    Database LockDelayed in 30–40% of trials40–50% faster - driven by AI-assisted data cleaning; estimated $3–5M in deferred trial value from earlier submission
    Phase 1 Cost & Timeline$2–5M avg CRO cost; 15–18 month protocol-to-DB-lock timelineUnder $400K fixed price; protocol to DB lock delivered in 9–12 months

    CONCLUSION

    Quantified Benefits

    Proof-of-concept delivered in 9–12 months at under $400K versus $2–5M traditional CRO cost — one team, one contract.

    Board confidence maintained through real-time dashboards and proactive safety monitoring

    RBQM audit-ready on first review — fully ICH E6(R3) compliant

    Zero vendor complexity — one team, one contract

    Clear pathway to Phase 2/3 under the same execution model