Efficient experimental design for dose response modelling

The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose–response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen–host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose–response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen–immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose–response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined.

Download Full-text

Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation

Viruses ◽

10.3390/v12090955 ◽

2020 ◽

Vol 12 (9) ◽

pp. 955

Author(s):

Ashwin K. Ramesh ◽

Viviana Parreño ◽

Philip J. Schmidt ◽

Shaohua Lei ◽

Weiming Zhong ◽

...

Keyword(s):

Dose Response ◽

Response Models ◽

Infectious Dose ◽

Clinical Data Analysis ◽

Causative Agents ◽

Gnotobiotic Pigs ◽

Gnotobiotic Pig ◽

Response Modelling ◽

Sporadic Acute Gastroenteritis ◽

Endpoint Estimation

Human noroviruses (HuNoVs) are the leading causative agents of epidemic and sporadic acute gastroenteritis that affect people of all ages worldwide. However, very few dose–response studies have been carried out to determine the median infectious dose of HuNoVs. In this study, we evaluated the median infectious dose (ID50) and diarrhea dose (DD50) of the GII.4/2003 variant of HuNoV (Cin-2) in the gnotobiotic pig model of HuNoV infection and disease. Using various mathematical approaches (Reed–Muench, Dragstedt–Behrens, Spearman–Karber, logistic regression, and exponential and approximate beta-Poisson dose–response models), we estimated the ID50 and DD50 to be between 2400–3400 RNA copies, and 21,000–38,000 RNA copies, respectively. Contemporary dose–response models offer greater flexibility and accuracy in estimating ID50. In contrast to classical methods of endpoint estimation, dose–response modelling allows seamless analyses of data that may include inconsistent dilution factors between doses or numbers of subjects per dose group, or small numbers of subjects. Although this investigation is consistent with state-of-the-art ID50 determinations and offers an advancement in clinical data analysis, it is important to underscore that such analyses remain confounded by pathogen aggregation. Regardless, challenging virus strain ID50 determination is crucial for identifying the true infectiousness of HuNoVs and for the accurate evaluation of protective efficacies in pre-clinical studies of therapeutics, vaccines and other prophylactics using this reliable animal model.

Download Full-text

Experimental design strategy for the Weibull dose response model

Environmental Pollution ◽

10.1016/0269-7491(88)90044-9 ◽

1988 ◽

Vol 53 (1-4) ◽

pp. 333-349 ◽

Cited By ~ 4

Author(s):

K.A. Dassel ◽

J.O. Rawlings

Keyword(s):

Experimental Design ◽

Dose Response ◽

Design Strategy ◽

Response Model

Download Full-text

Two-Stage Experimental Design for Dose–Response Modeling in Toxicology Studies

ACS Sustainable Chemistry & Engineering ◽

10.1021/sc4000412 ◽

2013 ◽

Vol 1 (9) ◽

pp. 1119-1128 ◽

Cited By ~ 1

Author(s):

Kai Wang ◽

Feng Yang ◽

Dale W. Porter ◽

Nianqiang Wu

Keyword(s):

Experimental Design ◽

Dose Response ◽

Two Stage ◽

Response Modeling

Download Full-text

Control of glyphosate resistant hairy fleabane (Conyza bonariensis) with dicamba and 2,4-D

Planta Daninha ◽

10.1590/s0100-83582012000200020 ◽

2012 ◽

Vol 30 (2) ◽

pp. 401-406 ◽

Cited By ~ 6

Author(s):

D.J. Soares ◽

W.S. Oliveira ◽

R.F. López-Ovejero ◽

P.J Christoffoleti

Keyword(s):

Experimental Design ◽

Dose Response ◽

Growth Regulator ◽

Response Curve ◽

Poor Control ◽

Response Curves ◽

Conyza Bonariensis ◽

Randomized Experimental Design ◽

Low Sensitivity ◽

Hairy Fleabane

Auxyn type herbicides such as dicamba and 2,4-D are alternative herbicides that can be used to control glyphosate-resistant hairy fleabane. With the forthcoming possibility of releasing dicamba-resistant and 2,4-D-resistant crops, use of these growth regulator herbicides will likely be an alternative that can be applied to the control of glyphosate resistant hairy fleabane (Conyza bonariensis). The objective of this research was to model the efficacy, through dose-response curves, of glyphosate, 2,4-D, isolated dicamba and glyphosatedicamba combinations to control a brazilian hairy fleabane population resistant to glyphosate. The greenhouse dose-response studies were conducted as a completely randomized experimental design, and the rates used for dose response curve construction were 0, 120, 240, 480, 720 and 960 g a.i. ha-1 for 2,4-D, dicamba and the dicamba combination, with glyphosate at 540 g a.e. ha-1. The rates for glyphosate alone were 0, 180, 360, 540, 720 and 960 g a.e. ha-1. Herbicides were applied when the plants were in a vegetative stage with 10 to 12 leaves and height between 12 and 15 cm. Hairy fleabane had low sensitivity to glyphosate, with poor control even at the 960 g a.e. ha-1 rate. Dicamba and 2,4-D were effective in controlling the studied hairy fleabane. Hairy fleabane responds differently to 2,4-D and dicamba. The combination of glyphosate and dicamba was not antagonistic to hairy fleabane control, and glyphosate may cause an additive effect on the control, despite the population resistance.

Download Full-text

Dose–response modelling of infectious animal diseases coupled with computational fluid dynamics: A simulation of airborne porcine reproductive and respiratory syndrome virus

Biosystems Engineering ◽

10.1016/j.biosystemseng.2021.05.005 ◽

2021 ◽

Vol 208 ◽

pp. 58-78

Author(s):

Amy La ◽

Qiang Zhang ◽

Nazim Cicek ◽

David B. Levin ◽

Kevin M. Coombs

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Dose Response ◽

Respiratory Syndrome Virus ◽

Animal Diseases ◽

Response Modelling

Download Full-text

Use of fractional polynomials for dose-response modelling and quantitative risk assessment in developmental toxicity studies

Statistical Modelling ◽

10.1191/1471082x03st051oa ◽

2003 ◽

Vol 3 (2) ◽

pp. 109-125 ◽

Cited By ~ 12

Author(s):

Christel Faes ◽

Helena Geys ◽

Marc Aerts ◽

Geert Molenberghs

Keyword(s):

Risk Assessment ◽

Dose Response ◽

Developmental Toxicity ◽

Quantitative Risk Assessment ◽

Fractional Polynomials ◽

Toxicity Studies ◽

Response Modelling

Download Full-text

Experimental design and statistical analysis for three-drug combination studies

Statistical Methods in Medical Research ◽

10.1177/0962280215574320 ◽

2015 ◽

Vol 26 (3) ◽

pp. 1261-1280 ◽

Cited By ~ 13

Author(s):

Hong-Bin Fang ◽

Xuerong Chen ◽

Xin-Yan Pei ◽

Steven Grant ◽

Ming Tan

Keyword(s):

Experimental Design ◽

Dose Response ◽

Drug Combination ◽

Mathematical Reasoning ◽

Experimental Studies ◽

Drug Combinations ◽

Dose Finding ◽

Nonparametric Model ◽

Response Curves ◽

Interaction Index

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose–response shapes of individual constituent drugs. Thus, different classes of drugs of different dose–response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose–response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose–response surface formed by dose ranges of three drugs.

Download Full-text