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<p>Evaluating the repellency of essential oils against <i>Tetranychus urticae</i> requires robust methodologies to detect time-dependent changes in their efficacy, due to differential efficacy, volatility, and sustained effectiveness. This study introduces the Pole-dance bioassay, a novel no-choice method optimized for high-throughput screening of time-dependent repellency, demonstrated through tests on twenty botanical volatiles against <i>T. urticae</i>. Four statistical models, probit, two-parameter Hill, four-parameter Hill, and Gaussian Process (GP) regression, were compared for analyzing time-response landing data, employing both experimental results and scenario-based synthetic datasets reflecting diverse curve shapes. GP regression often provided superior model fits, especially for biphasic or incomplete landing trajectories. While repellency rankings based on median effective time (ET<sub>50</sub>) and area under the curve (AUC) were highly correlated within respective models, the choice of model significantly influenced parameter estimates. AUC proved essential for quantifying activity for highly potent compounds where ET<sub>50</sub> was inestimable (e.g., t-cinnamaldehyde, 1,8-cineole, (-)-terpinen-4-ol, thymol) and offered complementary insights into repellent effects. The Pole-dance bioassay combined with GP modeling establishes an optimized framework for statistically rigorous in vivo high-throughput screening against <i>T. urticae</i>.</p>

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Comparison of statistical models for time-dependent repellency using the novel Pole-dance bioassay against Tetranychus urticae Koch

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Junho Yoon

摘要

Evaluating the repellency of essential oils against Tetranychus urticae requires robust methodologies to detect time-dependent changes in their efficacy, due to differential efficacy, volatility, and sustained effectiveness. This study introduces the Pole-dance bioassay, a novel no-choice method optimized for high-throughput screening of time-dependent repellency, demonstrated through tests on twenty botanical volatiles against T. urticae. Four statistical models, probit, two-parameter Hill, four-parameter Hill, and Gaussian Process (GP) regression, were compared for analyzing time-response landing data, employing both experimental results and scenario-based synthetic datasets reflecting diverse curve shapes. GP regression often provided superior model fits, especially for biphasic or incomplete landing trajectories. While repellency rankings based on median effective time (ET50) and area under the curve (AUC) were highly correlated within respective models, the choice of model significantly influenced parameter estimates. AUC proved essential for quantifying activity for highly potent compounds where ET50 was inestimable (e.g., t-cinnamaldehyde, 1,8-cineole, (-)-terpinen-4-ol, thymol) and offered complementary insights into repellent effects. The Pole-dance bioassay combined with GP modeling establishes an optimized framework for statistically rigorous in vivo high-throughput screening against T. urticae.