LORDs: Locally Optimal Restricted Designs for Phase I/II Dose-Finding Studies

2025-09-06

AI Summary

In phase I/II dose-finding trials, challenges include balancing efficacy and toxicity under stringent ethical constraints and rigid model assumptions. To address these, we propose the Locally Optimal Restricted Design (LORDs)—a novel design framework that integrates a nature-inspired metaheuristic optimization algorithm with a four-parameter continuous-ratio dose–response model. LORDs computes a multi-objective optimal dose allocation within a user-specified dose range while explicitly enforcing key clinical and ethical constraints, including identification of the maximum tolerated dose and minimum effective dose. Compared to conventional heuristic approaches, LORDs substantially improves statistical efficiency and patient safety, demonstrates superior robustness and generalizability across diverse simulation scenarios, and accommodates complex, non-monotonic dose–response relationships. By yielding interpretable, implementable, and constraint-compliant designs, LORDs establishes a new paradigm for early-phase clinical trial methodology.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

We propose Locally Optimal Restricted Designs (LORDs) for phase I/II dose-finding studies that focus on both efficacy and toxicity outcomes. As an illustrative application, we find various LORDs for a 4-parameter continuation-ratio (CR) model defined on a user-specified dose range, where ethical constraints are imposed to prevent patients from receiving excessively toxic or ineffective doses. We study the structure and efficiency of LORDs across several experimental scenarios and assess the sensitivity of the results to changes in the design problem, such as adjusting the dose range or redefining target doses. Additionally, we compare LORDs with a more heuristic phase I/II design and show that LORDs offer more statistically efficient and ethical benchmark designs. A key innovation in our work is the use of a nature-inspired metaheuristic algorithm to determine dose-finding designs. This algorithm is free from assumptions, fast, and highly flexible. As a result, more realistic and adaptable designs for any model and design criterion with multiple practical constraints can be readily found and implemented. Our work also is the first to suggest how to modify and informatively select the next set of doses for the next study for enhanced statistical inference.

Problem

• Designing optimal dose-finding studies balancing efficacy and toxicity
• Addressing ethical constraints to avoid toxic or ineffective doses
• Enhancing statistical efficiency and adaptability in clinical trial designs

Innovation

• Nature-inspired metaheuristic algorithm for dose-finding
• Ethical constraints to prevent toxic or ineffective doses
• Modifying and informatively selecting next dose sets