Optimal Weibull Distribution I for Dose-Response Modelling of Theophylline Drug

Author(s)

Kupolusi, Joseph A. , AKINDOLANI, Toluwalope ,

Download Full PDF Pages: 01-09 | Views: 18 | Downloads: 9 | DOI: 10.5281/zenodo.10577598

Volume 13 - January 2024 (01)

Abstract

Dose-response model for complex systems is crucial for the treatment of diseases and drug discovery. Understanding the mechanism of drug action has become increasingly important due to the growth of large-scale biological data obtained through computational modelling. This study compared four Dose-response models namely; four parameters log-logistic model, Brain-Cousens hormesis model, Cedergreen-Ritz-Streibig modified log-logistic model, and Weibull distribution I to predict the best model for theophylline dosage and its corresponding physiological properties through sensitivity analysis and Bayesian information criteria (BIC). The findings revealed that Weibull distribution 1 outperformed other models with the least BIC value of 294.4214. Therefore, Weibull distribution 1 is the best model for modelling theophylline drug. Also, a sensitivity analysis was carried out that shows the robustness and optimality of the model. Weibull I model shows a significant variation of the model fit with a sharp decline at high dose. Therefore, Weibull I model is more sensitive to model Theophylline drug data.

Keywords

Dose-Response model, Theophylline drug, Sensitivity Analysis, four parameters log-logistic model, Brain-Cousens hormesis model, Cedergreen-Ritz-Streibig modified log-logistic model, and Weibull distribution 1.

References

Agathokleous, E., & Calabrese, E. J. (2019). Hormesis: The dose response for the 21st century: The future has arrived. Elsevier, . doi:https://doi.org/10.1016/j.tox.2019.152249

Arnich, N., & Thébault, A. (2018). Dose-Response Modelling of Paralytic Shellfish Poisoning (PSP) in Humans. Toxins, 10(4), 141. doi:doi.org/10.3390/toxins10040141

Belz, R. G., & Cedergreen, N. (2010). Environmental and Experimental Botany, 69(3), 293-301. doi:doi.org/10.1016/j.envexpbot.2010.04.010

Belz, R. G., Cedergreen, N., & Sørensen, H. (2008). Hormesis in mixtures—Can it be predicted? Science of the Total Environment, 404(1), 77-87. doi:doi.org/10.1016/j.scitotenv.2008.06.008

Calabrese, E. J. (2008). Hormesis and medicine. BJCP, . doi:doi.org/10.1111/j.1365-2125.2008.03243.x

CEDERGREEN, N. (2008). Herbicides can stimulate plant growth. Weed Research, 48(5), 429-438. doi:doi.org/10.1111/j.1365-3180.2008.00646.x

Cedergreen, N., & Olesen, C. F. (2010). Pesticide Biochemistry and Physiology, 96(3), 140-148. doi:doi.org/10.1016/j.pestbp.2009.11.002

Cedergreen, N., Felby, C., Porter, J. R., & Streibig, J. C. (2009). Chemical stress can increase crop yield. Field crops research, 114(1), 54-57. doi:doi.org/10.1016/j.fcr.2009.07.003

Cedergreen, N., Ritz, C., & Streibig, J. C. (2005). mproved empirical models describing hormesis. Environmental Toxicology and Chemistry. An International Journal, 24(12), 3166-3172. doi:doi.org/10.1897/05-014R.1

Cedergreen, N., Streibig, J. C., Kudsk, P., Mathiassen, S. K., & Duke, S. O. (2007). The occurrence of hormesis in plants and algae. Dose-response. dose-response, 5(2). doi:doi.org/10.2203/dose-response.06-008.Cedergreen

Culpepper, S. A. (2016). Revisiting the 4-parameter item response model: Bayesian estimation and application. Psychometrika, 81(4), 1142-1163. doi:doi.10.1007/s11336-015-9477-6

Duncan, F., Kimler, B., & Briley, S. (2016). Combating radiation. Future Oncology, 12(14), 1687-1690. doi:doi.org/10.2217/fon-2016-0121

Loken, E., & Rulison, K. L. (2010). Estimation of a four-parameter item response theory model. British Journal of Mathematical and Statistical Psychology, 63(3), 509-525. doi:doi.org/10.1348/000711009X474502

Meng, X., Xu, G., Zhang, J., & Tao, J. (2020). Marginalized maximum a posteriori estimation for the four‐parameter logistic model under a mixture modelling framework. British Journal of Mathematical and Statistical Psychology, 73, 51-82. doi:doi.org/10.1111/bmsp.12185

Mould, D. R., Walz, A. C., Lave, T., Gibbs, J. P., & Frame, B. (2015). Developing Exposure/Response Models for Anticancer. CPT: pharmacometrics & systems phamacology, 4(1), 12-27. doi:doi:10.1002/psp4.16

Reise, S. P., & Revicki, D. A. (2014). Applications of typical performance assessment. Routledge.

Reise, S. P., & Waller, N. G. (2003). Psychological methods,, 8(2), 164-184. doi:doi/10.1037/1082-989X.8.2.164

Rulison, K. L., & Loken, E. (2009). I’ve Fallen and I Can’t Get Up: Can High-Ability Students Recover From Early. Applied Psychological Measurement, 33(2), 83-101. doi:doi.org/10.11177/0146621608324023

Schabenberger, O., & Birch, J. B. (2001). Statistical Dose-Response Models with Hormetic Effects. Human and Ecological Risk Assessment, 7(4), 891-908. doi:doi.org/10.1080/20018091094718

Schabenberger, O., Tharp, B. E., Kells, J. J., & Penner, D. (1999). Statistical tests for hormesis and effective dosages in herbicide dose response. Agronomy Journal, 91(4), 713-721. doi:doi.org/10.2134/agronj1999.914713x

Strachana, J., Michael, P., & Fumiko, K. (2005). Dose response modelling of Escherichia coli O157 incorporating. Elsevier, 103(1), 35-47. doi:doi:10.1016/j.ijfoodmicro.2004.11.023

Waller, N. G., & Feuerstahler, L. (2017). Multivariate behavioral research, 52(3), 350-370. doi:doi.org/10.1080/00273171.2017.1292893

Waller, N. G., & Reise, S. P. (2010). Measuring psychopathology with nonstandard item response theory models: Fitting the four-parameter model to the Minnesota Multiphasic Personality Inventory. psycent.apa, 147-173. doi:doi/10.1037/12074-007

Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of applied mechanics,  

Cite this Article: