Clinical Trial Simulation Platform for Global Design Optimization
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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 resource constraints and site selection challenges.
Innovation Solution
A trial design platform that leverages advanced simulations, distributed computing, and powerful visualizations to evaluate and compare hundreds to millions of design options, optimizing clinical trial designs by identifying optimal or near-optimal configurations through platforms and methods that support collaboration and data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual expertise and heuristics are used for clinical trial design, then resource constraints are reduced, but the number of design options that can be evaluated is limited, leading to suboptimal outcomes
Solution Approach 1:
The patent replaces manual expertise and heuristic methods with an automated computer-based optimization system. The system uses algorithms to evaluate numerous design options automatically, substituting the mechanical manual process with an automated computational approach that can handle large-scale evaluations without human intervention.
Solution Approach 2:
The system changes the parameters of trial design by systematically varying multiple factors such as trial phases, locations, inclusion criteria, and methodologies. By changing these parameters across numerous combinations, the system evaluates a comprehensive set of design options to identify optimal configurations that maximize patient recruitment and trial success.
2Reliability
If more design options are evaluated to find optimal trial designs, then trial performance improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing optimization criteria and constraints before the main evaluation process. It pre-processes data about patient populations, trial methodologies, and success metrics to create a framework that guides the subsequent evaluation, reducing the time needed for comprehensive analysis while maintaining high reliability.
Solution Approach 2:
The patent creates virtual copies or simulations of trial designs to evaluate their potential outcomes without conducting actual trials. By modeling and simulating numerous design scenarios, the system can assess their effectiveness efficiently, reducing the time required for real-world trial execution while maintaining reliable predictions about trial success.
3Duration of action of moving object
If site selection is optimized to maximize patient recruitment, then trial completion time decreases, but the complexity of site evaluation increases
Solution Approach 1:
The system segments the site evaluation process into distinct components, assessing different factors such as patient population characteristics, geographic location, institutional capabilities, and recruitment rates independently. By dividing the complex evaluation into manageable segments, the system can comprehensively assess multiple sites without overwhelming complexity, enabling faster trial completion.
Solution Approach 2:
The patent introduces an intermediary optimization algorithm that mediates between the goal of maximizing patient recruitment and the complexity of site evaluation. This intermediary system processes and synthesizes multiple site parameters automatically, translating complex evaluation data into actionable recommendations that accelerate trial completion without requiring manual analysis of every site detail.
Data Source
AI summary
A method for determining trial designs is provided. The method includes receiving, via at least one processor, one or more trial design criteria and one or more scenarios corresponding to a set of trial designs, and generating, via the at least one processor, simulation data based at least in part on replicating each of the set of trial designs with the one or more trial design criteria and the one or more scenarios. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria. The method further includes determining, via the at least one processor, an optimality criteria for evaluating the trial designs, searching, within the set of trial designs, via the at least one processor, for globally optimum designs based on the optimality criteria, and transmitting, via the at least one processor, globally optimum designs.