PLAN

Clinical Trial and Manufacturing Scheduling

Create optimal timelines and resource allocations for clinical trials and pharmaceutical manufacturing under strict regulatory compliance.

Understanding the Problem

Clinical trial and pharmaceutical manufacturing scheduling involves coordinating protocol design, site selection, patient enrollment, and production under cGMP regulations. The pharmaceutical industry faces mounting pressure to reduce timelines and costs while maintaining rigorous quality standards. With 80% of trials experiencing delays due to recruitment issues and only 59% of activated sites meeting enrollment targets, optimization has become critical. Manufacturing must navigate complex constraints including hold times, equipment sterilization expiry, batch release timing, and capacity limitations.

THE CHALLENGE

What Makes it Hard

Pharmaceutical companies must create schedules that coordinate across sponsors, CROs, investigative sites, manufacturing facilities, and regulatory agencies. Every scheduling decision affects trial success rates, manufacturing costs, and ultimately time-to-market for life-saving treatments.

WHO FACES IT

Clinical operations directorsManufacturing plannersQuality assurance managersSupply chain executivesSite coordinators

Multi-stakeholder coordination across sponsors, CROs, sites, vendors, and regulators with different constraints and objectives

cGMP regulations impose hard timing constraints: cleaned vessels expire, batches held beyond limits must be discarded

Trial success is unknown until completion, yet scheduling decisions must be made upfront with uncertain enrollment rates

BUSINESS IMPACT

AI-enhanced scheduling reduces clinical study report timelines by 40%, increases manufacturing throughput by 20%, and cuts trial cycle times by 18%.

Report Timelines

40%

Faster^[1]

AI-enhanced scheduling reduces Clinical Study Report (CSR) timelines by 40%, cutting the process from 8-14 weeks to 5-8 weeks.

Manufacturing Throughput

20%

Increase^[2]

Scheduling optimization increases production output by 20% with batch cycle times reduced from 7 days to under 5 days.

Trial Cycle Time

18%

Reduction^[3]

AI/ML approaches achieve 18% mean cycle time reduction across planning, execution, and regulatory submission phases.

How We Solve It

Mixed-integer linear programming (MILP) with stochastic and multi-objective extensions. Time-indexed formulations with precedence constraints handle GMP compliance. Constraint programming for complex logical constraints around equipment qualification and protocol deviation rules. Rolling horizon algorithms update based on realized outcomes.

Heterogeneous
Hybrid Compute

What We Bring

Integrated trial and manufacturing schedule optimization

GMP-compliant scheduling with hold time constraints

Scenario-based stochastic programming for uncertain trial outcomes

Simulation-optimization with Monte Carlo enrollment modeling

FUTURE POSSIBILITIES

The
Quantum Horizon

Clinical trial site selection and batch sequencing can be formulated as QUBO. Research demonstrates proof-of-concept for 10-20 site instances, but pharmaceutical scheduling involves hundreds of sites and thousands of time periods.

Exploratory Work

Hybrid approaches using quantum for discrete assignment decisions and classical optimization for continuous scheduling variables. Near-term value comes from quantum-inspired metaheuristics integrated with classical MILP for large complex instances.