Clinical Trial Screening Using Predicted Disease Progression
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Solution Overview
Problem
Existing technologies have difficulty conducting human clinical trials for diseases with high heterogeneity of disease progression, leading to false negatives and significant financial losses due to undetected drug efficacy.
Innovation Solution
A predictive model is used to screen candidates for clinical trials based on disease progression predictions, identifying subgroups with improved treatment outcomes and establishing screening criteria for enrollment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patients with wide range of disease progression are enrolled in clinical trials, then the trial represents the general population, but the variance in progression rates creates noise that obscures treatment efficacy signals
Solution Approach 1:
The patent segments the heterogeneous patient population into distinct subgroups based on predicted disease progression rates (fast, average, slow progressors). This segmentation allows the trial to separately analyze treatment efficacy within each homogeneous subgroup, thereby reducing noise and improving detection precision while maintaining overall representativeness through multi-arm design.
Solution Approach 2:
The patent applies local quality by tailoring trial criteria and analysis methods to specific patient subgroups with distinct progression characteristics. Each subgroup receives customized enrollment criteria and outcome monitoring approaches optimized for its progression rate, allowing precise efficacy detection within each local population while the overall trial maintains broad applicability.
2Ease of manufacture
If patients with heterogeneous disease progression are included in clinical trials, then the trial can be conducted with simpler enrollment criteria, but false negative results may occur due to high variance
Solution Approach 1:
The patent implements preliminary action by using predictive models to assess disease progression rates for potential trial participants before enrollment. This pre-screening process stratifies candidates into progression subgroups, allowing the trial to maintain simple overall enrollment criteria while ensuring that only patients with appropriate progression characteristics are assigned to specific arms, thereby preventing false negatives.
Solution Approach 2:
The patent employs feedback mechanisms where predictive model outputs are continuously refined based on observed trial data and patient outcomes. This feedback loop improves the accuracy of progression predictions and efficacy assessments over time, enhancing reliability while maintaining operational simplicity through automated decision support systems.
3Productivity
If clinical trials enroll patients without screening based on disease progression predictions, then the trial recruitment process is faster and simpler, but treatment efficacy cannot be reliably detected due to high noise levels
Solution Approach 1:
The patent applies preliminary action by conducting predictive progression assessments during the patient screening phase before formal enrollment. This advance characterization of patient progression rates enables rapid stratification and assignment to appropriate trial arms, maintaining high recruitment speed while pre-filtering for patients most likely to show treatment effects, thereby improving signal-to-noise ratio.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting trial enrollment criteria and analysis parameters based on predicted progression rates. Patients are screened and assigned based on their predicted progression parameters, allowing the trial to optimize the signal-to-noise ratio by focusing resources on patient subgroups with the highest likelihood of demonstrating treatment efficacy while maintaining efficient recruitment through automated parameter-based decision making.
Data Source
AI summary
A method for enrolling patient candidates in a clinical trial includes generating first prediction data indicating predicted progression of a condition for a first group of patients that participated in a first clinical trial using a predictive model and clinical data associated with the first group of patients; grouping clinical trial data into subsets based on the first prediction data; analyzing each subset of clinical trial data to generate a measure of efficacy of the treatment; establishing screening criteria for a second clinical trial by identifying at least one subset that has a measure of efficacy that is higher than a measure of efficacy of the treatment for the full first group of patients; receiving clinical data of a candidate for the second clinical trial; generating second prediction data for the candidate; and enrolling the candidate in the second clinical trial when the second prediction data satisfies the screening criteria.