A SAS/IML program for simulating pharmacokinetic data

Prevalence characteristic of BVDV in some large scale dairy farms in Western China

The aim of this study was to analyze the prevalence characteristic of Bovine viral diarrhea virus (BVDV) in some large scale dairy farms in Western China. BVDV was detected in 30 samples of bulk tank milk (BTM) collected from 30 large dairy farms in 7 provinces of western China, 93.33% (28/30) of the farms were infected with BVDV, and S/P ratio was over 0.3 in 28 positive farms. The individual status was further estimated in the dairy farm (No. 10) with the highest positive rate (S/P ratio = 1.37) and the dairy farm (No. 17) with the lowest positive rate (S/P ratio = 0.39). Two hundred cows were, respectively, selected from calf, young cows and lactating cows in farm No. 10 and farm No. 17 and the serum sample of each enrolled cow was collected. The individual positive rate of serum antibody (Ab) was 87.17% (523/600) in farm No. 10 and 31.33% (188/600) in farm No. 17. The individual positive ratio of serum antibody in calves, young cows and lactating cows were 41.75 % (167/400), 58.75% (235/400) and 77.25% (309/400), respectively. BTM Ab of farm No. 10 has an S/P ratio more than 1.0, which indicated there were emergent or persistent infection (PI) cases, and further test showed that PI cases were 0.51% in farm No. 10. Pathogens were positive in 42.34% (163/385) of nasal mucus samples collected from cows with respiratory symptom, and BVDV cases were 57 in 163 positive samples. Three strains of NCP BVDV-1, one strain of CP BVDV-1, one strain of NCP BVDV-2 and one strain of CP BVDV-2 were successfully isolated. Phylogenetic analysis revealed that the subtypes of BVDV currently prevalent in western China were BVDV-1a, BVDV-1m, BVDV-1q and BVDV-2. The findings suggested that the BVDV infection is serious in some Large Scale Dairy Farms in Western China.

Checking distributional assumptions for pharmacokinetic summary statistics based on simulations with compartmental models

Bioequivalence (BE) studies are an essential part of the evaluation of generic drugs. The most common in vivo BE study design is the two-period two-treatment crossover design. AUC (area under the concentration-time curve) and Cmax (maximum concentration) are obtained from the observed concentration-time profiles for each subject from each treatment under each sequence. In the BE evaluation of pharmacokinetic crossover studies, the normality of the univariate response variable, e.g. log(AUC)¹ or log(Cmax), is often assumed in the literature without much evidence. Therefore, we investigate the distributional assumption of the normality of response variables, log(AUC) and log(Cmax), by simulating concentration-time profiles from two-stage pharmacokinetic models (commonly used in pharmacokinetic research) for a wide range of pharmacokinetic parameters and measurement error structures. Our simulations show that, under reasonable distributional assumptions on the pharmacokinetic parameters, log(AUC) has heavy tails and log(Cmax) is skewed. Sensitivity analyses are conducted to investigate how the distribution of the standardized log(AUC) (or the standardized log(Cmax)) for a large number of simulated subjects deviates from normality if distributions of errors in the pharmacokinetic model for plasma concentrations deviate from normality and if the plasma concentration can be described by different compartmental models.

Patient variation in veterinary medicine--part II--influence of physiological variables

In veterinary medicine, the characterization of a drug's pharmacokinetic properties is generally based upon data that are derived from studies that employ small groups of young healthy animals, often of a single breed. In Part I of the series, we focused on the potential influence of disease processes, stress, pregnancy and lactation on drug pharmacokinetics. In this Part II of the series, we consider other covariates, such as gender, heritable traits, age, body composition, and circadian rhythms. The impact of these factors with respect to predicting the relationship between dose and drug exposure characteristics within an animal population is illustrated through the use of Monte Carlo simulations. Ultimately, an appreciation of these potential influences will improve the prediction of situations when dose adjustments may be appropriate.