Clinical Trial Design Platform Using Pareto-Guided Simulated Annealing
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Solution Overview
Problem
Existing clinical trial designs often rely on heuristics and manual expertise, failing to evaluate a sufficient number of design options, leading to suboptimal outcomes in terms of cost, time, and performance, and are hindered by the lack of comprehensive evaluation of site selection and resource optimization.
Innovation Solution
A platform and method for evaluating and comparing millions of clinical trial design options using advanced simulations, distributed computing, and powerful visualizations to identify optimal or near-optimal designs, incorporating site selection and resource optimization platforms to enhance trial efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristics and manual expertise are used for clinical trial design, then the design process is simple and requires less computational resources, but the evaluation of design options is insufficient leading to suboptimal outcomes
Solution Approach 1:
The patent creates virtual copies of clinical trial designs through simulation models that replicate real trial scenarios. These virtual trial models can be extensively evaluated without physical constraints, allowing comprehensive assessment of millions of design options while maintaining the complexity management through computational abstraction rather than physical complexity
Solution Approach 2:
The patent replaces manual heuristic evaluation with computational simulation systems that automatically evaluate trial designs. The mechanical process of manual review is substituted with automated computational models that can process and compare numerous design scenarios, improving evaluation comprehensiveness while the system manages its own complexity through algorithmic efficiency
2Reliability
If a large number of design options are evaluated, then optimal trial designs can be identified, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining evaluation criteria, constraints, and simulation parameters before conducting the actual trial design evaluation. This preliminary setup includes establishing the virtual trial framework, defining success metrics, and preparing computational resources, which enables rapid evaluation of multiple designs without repeating setup procedures for each scenario
Solution Approach 2:
The patent utilizes parameter changes by systematically varying key trial design parameters (sample size, duration, intervention frequency, site locations) to generate diverse design options. The simulation model efficiently processes these parameter variations and their impacts, allowing comprehensive evaluation of design optimality while managing time through automated parameter manipulation rather than manual analysis of each scenario
3Productivity
If site selection is optimized considering multiple factors, then patient recruitment success improves, but the complexity of site evaluation increases
Solution Approach 1:
The patent applies universality by creating a multi-functional site evaluation framework that simultaneously assesses multiple factors including patient population characteristics, geographic accessibility, site infrastructure, regulatory environment, and competitor activity. The virtual trial platform serves multiple purposes: it simulates trial conduct, evaluates site suitability, optimizes recruitment strategies, and predicts outcomes, thereby improving recruitment efficiency while managing complexity through integrated rather than separate evaluation processes
Data Source
AI summary
A method for determining trial designs that includes obtaining simulation data for a set of trial designs that includes combinations of design options for a set of criteria. The simulation data includes performance parameters and performance parameter values associated with each design in the set of trial designs for the set of criteria. The method further includes determining an optimality criteria for evaluating the trial designs. The optimality criteria includes at least one of Pareto optimality or convex hull optimality for clinical trial design performance values. The method further includes: determining at least one of a cooling cycle, a parameter change, or a direction; and searching, within the set of trial designs, for a set of globally optimum designs based on the optimality criteria using simulated annealing. The simulated annealing is based at least in part on at least one of the cooling cycle, the parameter change, or the direction.