Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models
Abstract
Administration of exogenous progesterone is widely used in hormonal protocols for estrous (re)synchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.
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References
Akaike H, 1973. Information theory as an extension of the maximum likelihood principle. 2nd Int Symp on Information Theory, Kiado, Budapest. pp: 267-281.
Akaike H, 1978. A Bayesian analysis of the minimum AIC procedure. Ann I Stat Math 30: 9-14. http://dx.doi.org/10.1007/BF02480194
Andradóttir S, Bier VM, 2000. Applying Bayesian ideas in simulation. Simulat Pract Theory 8: 253-280. http://dx.doi.org/10.1016/S0928-4869(00)00025-2
Bignardi AB, El Faro L, Cardoso VL, Machado PF, Galvão de Albuquerque L, 2009. Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil Livest Sci 123: 1-7.
Burman P, Nolan D, 1995. A general Akaike-type criterion for model selection in robust regression. Biometrika 82: 877-886. http://dx.doi.org/10.1093/biomet/82.4.877
Burnham KP, Anderson DR, 2002. Model selection and multi-model inference: a practical information-theoretic approach. Springer.
Butler WR, 2000. Nutritional interactions with reproductive performance in dairy cattle. Anim Reprod Sci 60-61: 449-457. http://dx.doi.org/10.1016/S0378-4320(00)00076-2
Butler WR, 2003. Energy balance relationships with follicular development, ovulation and fertility in postpartum dairy cows. Livest Prod Sci 83: 211-218. http://dx.doi.org/10.1016/S0301-6226(03)00112-X
Carlin BP, Louis TA, 2009. Bayesian methods for data analysis. Biometrics 65: 1000-1001. http://dx.doi.org/10.1111/j.1541-0420.2009.01315_15.x
Cristaldi M, Cabrera MI, Martínez E, Grau R, 2011. Finding the simplest mechanistic kinetic model describing the homogeneous catalytic hydrogenation of avermectin to ivermectin. Ind Eng Chem Res 50: 4252-4263. http://dx.doi.org/10.1021/ie101289h
De Martini D, Rapallo F, 2008. On multivariate smoothed bootstrap consistency. J Stat Plan Infer 138: 1828-1835. http://dx.doi.org/10.1016/j.jspi.2007.06.035
Denisov IG, Frank DJ, Sligar SG, 2009. Cooperative properties of cytochromes P450. Pharmacol Therapeut 124: 151-167. http://dx.doi.org/10.1016/j.pharmthera.2009.05.011
Efron B, Tibshirani RJ, 1993. An introduction to the bootstrap. Chapman & Hall, London. http://dx.doi.org/10.1007/978-1-4899-4541-9
Ghavi Hossein-Zadeh N, 2013. Effects of main reproductive and health problems on the performance of dairy cows: a review. Span J Agric Res 11: 718-735. http://dx.doi.org/10.5424/sjar/2013113-4140
Harrison JM, Breeze ML, Harrigan GG, 2011. Introduction to Bayesian statistical approaches to compositional analyses of transgenic crops 1. Model validation and setting the stage. Regul Toxicol Pharm 60: 381-388. http://dx.doi.org/10.1016/j.yrtph.2011.05.006
Joshi M, Seidel-Morgenstern A, Kremling A, 2006. Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems. Metab Eng 8: 447-455. http://dx.doi.org/10.1016/j.ymben.2006.04.003
Kamath KR, Pakkala TPM, 2002. A Bayesian approach to a dynamic inventory model under an unknown demand distribution. Comput Oper Res 29: 403-422. http://dx.doi.org/10.1016/S0305-0548(00)00075-7
Legarra A, López-Romero P, Ugarte E, 2005. Bayesian model selection of contemporary groups for BLUP genetic evaluation in Latxa dairy sheep. Livest Prod Sci 93: 205-212. http://dx.doi.org/10.1016/j.livprodsci.2004.10.008
Lemley CO, Butler ST, Butler WR, Wilson ME, 2008. Insulin alters hepatic progesterone catabolic enzymes cytochrome P450 2C and 3A in dairy cows. J Dairy Sci 91: 641-645. http://dx.doi.org/10.3168/jds.2007-0636
Lemley CO, Wilmoth TA, Tager LR, Krause KM, Wilson ME, 2010. Effect of a high cornstarch diet on hepatic cytochrome P450 2C and 3A activity and progesterone half-life in dairy cows. J Dairy Sci 93: 1012-1021. http://dx.doi.org/10.3168/jds.2009-2539
Lewis FI, Brülisauer F, Gunn GJ, 2011. Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data. Prev Vet Med 100: 109-115. http://dx.doi.org/10.1016/j.prevetmed.2011.02.003
López-Romero P, Caraba-o MJ, 2003. Comparing alternative random regression models to analyse first lactation daily milk yield data in Holstein-Friesian cattle. Livest Prod Sci 82: 81-96. http://dx.doi.org/10.1016/S0301-6226(03)00003-4
Mariano RN, Turino LN, Cabrera MI, Scándolo DE, Maciel MG, Grau RJA, 2010. A simple pharmacokinetic model linking plasma progesterone concentrations with the hormone released from bovine intravaginal inserts. Res Vet Sci 89: 250-256. http://dx.doi.org/10.1016/j.rvsc.2010.02.015
Marsaglia G, Zaman A, 1991. A new class of random number generators. Ann Appl Probab 1: 462-480. http://dx.doi.org/10.1214/aoap/1177005878
Martínez E, Cristaldi M, Grau R, Lopes J, 2011. Dynamic optimization of bioreactors using probabilistic tendency models and Bayesian active learning. 21st Eur Symp on Computer Aided Process Engineering - ESCAPE 21, Chalkidiki, Greece. pp: 783-787.
Pe-a D, Zamar R, Yan G, 2009. Bayesian likelihood robustness in linear models. J Stat Plan Infer 139: 2196-2207. http://dx.doi.org/10.1016/j.jspi.2008.10.012
Saltelli A, Tarantola S, Campolongo F, 2000. Sensitivity analysis as an ingredient of modeling. Stat Sci 15: 377-395. http://dx.doi.org/10.1214/ss/1009213004
Sangsritavong S, Combs DK, Sartori R, Armentano LE, Wiltbank MC, 2002. High feed intake increases liver blood flow and metabolism of progesterone and estradiol-17ß in dairy cattle. J Dairy Sci 85: 2831-2842. http://dx.doi.org/10.3168/jds.S0022-0302(02)74370-1
Tsuruta S, Misztal I, Lawlor TJ, Klei L, 2004. Modeling final scores in US Holsteins as a function of year of classification using a random regression model. Livest Prod Sci 91: 199-207. http://dx.doi.org/10.1016/j.livprodsci.2003.09.016
Turino LN, Mariano RN, Cabrera MI, Scándolo DE, Maciel MG, Grau RJA, 2010. Pharmacokinetics of progesterone in lactating dairy cows: Gaining some insights into the metabolism from kinetic modeling. J Dairy Sci 93: 988-999. http://dx.doi.org/10.3168/jds.2009-2519
Vasconcelos JLM, Sangsritavong S, Tsai SJ, Wiltbank MC, 2003. Acute reduction in serum progesterone concnetrations after feed intake in dairy cows. Theriogenology 60: 795-807. http://dx.doi.org/10.1016/S0093-691X(03)00102-X
Xu C, Gertner G, 2007. Extending a global sensitivity analysis technique to models with correlated parameters. Comput Stat Data An 51: 5579-5590. http://dx.doi.org/10.1016/j.csda.2007.04.003
Yuen KV, 2010. Recent developments of Bayesian model class selection and applications in civil engineering. Struct Saf 32: 338-346. http://dx.doi.org/10.1016/j.strusafe.2010.03.011
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