A Universal Delayed Difference Model Fitting Dose-response Curves. Issue 4 (6th December 2021)

• Record Type: Journal Article
• Authors: Yang, Linqian; Wang, Jiaying; Cheke, Robert A.; Tang, Sanyi
• Is Part Of: Dose-response. Volume 19:Issue 4(2021)
• Publication Date: 2021-12-06
• Subjects: dose-response curves -- dynamic delayed Ricker difference model (DRDM) -- curve fitting -- data analysis -- hormesis; Dose-response relationship (Biochemistry) -- Periodicals; Drugs -- Dose-response relationship -- Periodicals; Drugs -- Physiological effect -- Periodicals; Hormesis -- Periodicals; Dose-Response Relationship, Drug -- Periodicals; Dose-response relationship (Biochemistry); Drugs -- Dose-response relationship; Drugs -- Physiological effect; Periodicals; 571.634
• DOI: 10.1177/15593258211062785
• Languages: English
• ISSNs: 1559-3258

Abstract

Purpose: Dose-response curves, which fit a multitude of experimental data derived from toxicology, are widely used in physics, chemistry, biology, and other fields. Although there are many dose-response models for fitting dose-response curves, the application of these models is limited by many restrictions and lacks universality, so there is a need for a novel, universal dynamical model that can improve fits to various types of dose-response curves. Methods: We expand the hormetic Ricker model, taking the delay inherent in the dose-response into account, and develop a novel and dynamic delayed Ricker difference model (DRDM) to fit various types of dose-response curves. Furthermore, we compare the DRDM with other dose-response models to confirm that it can mimic different types of dose-response curves. Data analysis: By fitting various types of dose-response data sets derived from drug applications, disease treatment, pest control, and plant management, and comparing the imitative effect of the DRDM with other models, we find that the DRDM fits monotonic dose-response data well and, in most circumstances, the DRDM has a better imitative effect to non-monotonic dose-response data with hormesis than other models do. Results: The MSE of fits of the DRDM to S-shaped dose-response data (DS2-G) is not lower than those for four other models, but the MSE of fits to U-shaped (DS7) and inverted U-shaped dose-response data (DS10) were lower than for two other models. This means that the imitative effect of the DRDM is comparable to other models of monotonic dose-response data, but is a significant improvement compared to traditional models of non-monotonic dose-response data with hormesis. Conclusion: We propose a novel dynamic model (DRDM) for fitting to various types of dose-response curves, which can reflect the dynamic trend of the population growth compared with traditional static dose-response models. By analyzing data, we have confirmed that the DRDM provides an ideal description of various dose-response observations and it can be used to fit a wide range of dose-response data sets, especially for hormetic data sets. Therefore, we conclude that the DRDM has a good universality for dose-response curve fitting.