Clinical Trial Site Distribution MINLP for Enrollment Constraints

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Solution Overview

Problem

Conventional methods for selecting clinical trial sites and countries are inefficient, subjective, and prone to errors due to reliance on manual processes, unsophisticated tools, and incomplete data integration, leading to suboptimal site choices, delays, and compliance issues.

Innovation Solution

A Mixed Integer Non-Linear Program (MINLP) is used to formulate a site distribution model that minimizes the number of sites and countries while meeting operational requirements, using an open-source framework like COIN-OR Branch and Cut (CBC) or Basic Open-source Non-linear Mixed Integer programming (BONMIN) to solve the model and provide a list of sites and countries that satisfy user constraints.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual processes and spreadsheets are used for site selection, then flexibility and ease of operation are maintained, but productivity and time efficiency deteriorate significantly

Engineering Contradiction:

Improveease of operationVSAvoidproductivity

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces manual spreadsheet-based mechanical processes with an automated computational system that uses algorithms and data processing to perform site selection analysis, thereby substituting human manual operations with automated computational mechanisms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically performing data integration, analysis, and site recommendation generation without requiring manual intervention at each step, allowing the system to serve itself in processing and decision-support functions

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If manual iteration processes are used to evaluate site scenarios, then adaptability to constraints is maintained, but loss of time increases dramatically

Engineering Contradiction:

ImproveadaptabilityVSAvoidloss of time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-integrating diverse data sources and pre-processing information before the actual site selection process begins, so that when site evaluation is needed, the foundational analysis is already complete and ready for rapid iteration

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The computational system enables continuous evaluation of multiple site scenarios simultaneously without the interruptions and sequential limitations of manual processes, maintaining continuous useful action throughout the analysis period

Inventive Principle:

Principle #20Continuity of useful action

3Ease of operation

If spreadsheets are used to manage complex clinical trial data, then ease of operation is preserved, but measurement precision and data reliability deteriorate

Engineering Contradiction:

Improveease of operationVSAvoidmeasurement precision

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent merges multiple data sources and validation mechanisms into a unified computational system that processes clinical trial data through consistent algorithms, eliminating the fragmentation and inconsistency inherent in separate spreadsheet files

Inventive Principle:

Principle #5Merging (Combining)

4Device complexity

If conventional manual methods are used for site selection, then device complexity is minimized, but productivity and analytical capability worsen

Engineering Contradiction:

Improvedevice complexityVSAvoidproductivity

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent segments the complex site selection process into distinct computational modules including data integration, constraint validation, scenario evaluation, and recommendation generation, allowing each segment to be optimized independently while maintaining overall system productivity

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250308647A1Systems and methods to produce a clinical trial site distribution model

Publication Date: 2025.10.02 MEDIDATA SOLUTIONS INC

AI summary

A site distribution model identifies a set of sites to satisfy operational requirements of a clinical trial. An objective function is defined as: a first element indicating whether a site is included in the clinical trial; and a second element indicating whether a country is included in the clinical trial. There is a set of constraints including that an estimated total enrollment reaches a defined target enrollment. Computer code is generated to implement a site distribution model, using an optimization modeling language, based on the objective function and the primary set of constraints. The site distribution model is solved, if possible, to produce values of the site decision variable and the country decision variable. Otherwise, it is indicated to a user that a solution is not possible. If solving the site distribution model is possible, a list of clinical trial sites is produced from the site decision variable.