Effects of age on the pharmacokinetics of single dose sulfamethazine after intravenous administration in cattle

U.S. nationwide reconnaissance of ten infrequently monitored antibiotics in municipal biosolids

Ten infrequently monitored antibiotics in biosolids were examined in archived American sewage sludges (n = 79) collected as part of the 2006/2007 U.S. Environmental Protection Agency (EPA) Targeted National Sewage Sludge Survey. This study inspected the occurrence of amoxicillin, ampicillin, erythromycin, furazolidone [proxy metabolite: 3-(2-nitrobenzylidenamino)-2-oxazolidinone (NP-AOZ)], nalidixic acid, oxolinic acid, oxytetracycline, spiramycin, sulfadimidine, and sulfadimethoxine in sewage sludges after nearly a decade in frozen storage. Six antibiotics were detected at the following average concentrations (ng/g dry weight): amoxicillin (1.0), nalidixic acid (19.1), oxolinic acid (2.7), erythromycin (0.6), oxytetracycline (4.5), and ampicillin (14.8). The remaining four were not detected in any samples (<method detection limit, ng/g dry weight): sulfadimethoxine (<0.5), sulfadimidine (<1.0), spiramycin (<2.0), and NP-AOZ (<20.0). This study provides the first data on spiramycin, NP-AOZ, and nalidixic acid in U.S. sewage sludges. This study also provides new data on the losses of 5 antibiotics during long term frozen storage (-20 °C) in comparison to the 2006/2007 U.S. EPA Targeted National Sewage Sludge Survey.

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.