ADAPT
Adaptive Trial Design and Supply Planning
Continuously optimize clinical trials and supply plans as enrollment and efficacy data accumulates, reducing waste and accelerating drug development.
Understanding the Problem
Adaptive trial design enables pharmaceutical companies to modify clinical trials based on interim results while maintaining statistical validity. This approach integrates two optimization challenges: adjusting trial parameters like sample sizes and site allocations based on interim results, and dynamically planning supply chain operations to match evolving trial demands. With trials facing a 70%+ failure rate and up to 50% of investigational drug kits wasted due to poor forecasting, adaptive designs address critical inefficiencies through data-driven course corrections during trial execution.
THE CHALLENGE
What Makes it Hard
Pharmaceutical companies must decide how to adjust ongoing clinical trials based on accumulating data while simultaneously managing supply chains that can waste millions in unused drug inventory. Every decision involves statistical trade-offs, regulatory requirements, and cascading supply chain impacts.
WHO FACES IT
Trial design requires pre-specified decision rules for sample size, treatment arm selection, and stopping boundaries while maintaining statistical validity and controlling error rates
Supply chains face cascading uncertainties in enrollment rates, treatment adherence, manufacturing lead times, and production yields across 10+ year horizons
Decisions must simultaneously optimize trial efficiency, statistical power, patient safety, and supply availability with conflicting objectives
BUSINESS IMPACT
Risk-based supply planning reduces drug waste by 41%, cuts per-patient costs by 20-30%, and accelerates FDA submission by 2 months.
Drug Waste Reduction
41%
Reduction[1]
EMD Serono achieved 41% drug waste reduction through kit design optimization using risk-based supply planning.
Cost Per Patient
20-30%
Reduction[2]
Risk-based optimization reduces total supply chain cost per patient by 20-30% compared to traditional approaches.
Time to FDA
2mo
Faster[3]
Better supply management enables trials to reach FDA submission two months faster through improved operational efficiency.
How We Solve It
Multi-stage stochastic mixed-integer programming with endogenous uncertainty. Benders decomposition separates trial design and supply decisions, while scenario-based stochastic programming samples enrollment and outcome trajectories. Rolling-horizon approaches update both components as data accumulates.
Hybrid Compute
What We Bring
Integrated trial design and supply chain optimization
Sequential decision modeling across trial phases with feedback loops
Monte Carlo simulation of enrollment and outcome scenarios
AI-driven demand forecasting with 93% accuracy
FUTURE POSSIBILITIES
The
Quantum Horizon
Combinatorial site selection and patient allocation subproblems map to QAOA, and hybrid approaches with clustering strategies have been demonstrated in research. However, real-world trials involve hundreds of sites and thousands of patients, exceeding current qubit capacity.
Exploratory Work
Proof-of-concept on small instances (10-20 sites) while classical MILP solvers handle production workloads. Practical quantum advantage requires fault-tolerant devices with thousands of logical qubits, estimated 5-10 years away.
Current Research Directions
QAOA approaches with clustering for clinical trial optimization proof-of-concept
Quantum annealing for QUBO-formulated site selection (limited by constraint complexity)
Quantum-inspired algorithms from vendors like Zapata already deployed with pharma partners
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with adaptive trial design and supply planning.
SOURCES