The pharmacokinetics of chlortetracycline orally administered to turkeys: influence of citric acid and Pasteurella multocida infection

A brief review on models for birds exposed to chemicals

"A Who's Who of pesticides is therefore of concern to us all. If we are going to live so intimately with these chemicals eating and drinking them, taking them into the very marrow of our bones - we had better know something about their nature and their power."-Rachel Carson, Silent Spring. In her day, Rachel Carson was right: plant protection products (PPP), like all the other chemical substances that humans increasingly release into the environment without further precaution, are among our worst enemies today (Bruhl and Zaller, 2019; Naidu et al., 2021; Tang et al., 2021; Topping et al., 2020). All compartments of the biosphere, air, soil and water, are potential reservoirs within which all species that live there are impaired. Birds are particularly concerned: PPP are recognized as a factor in the decline of their abundance and diversity predominantly in agricultural landscapes. Due to the restrictions on vertebrates testing, in silico-based approaches are an ideal choice alternative given input data are available. This is where the problem lies as we will illustrate in this paper. We performed an extensive literature search covering a long period of time, a wide diversity of bird species, a large range of chemical substances, and as many model types as possible to encompass all our future need to improve environmental risk assessment of chemicals for birds. In the end, we show that poultry species exposed to pesticides are the most studied at the individual level with physiologically based toxicokinetic models. To go beyond, with more species, more chemical types, over several levels of biological organization, we show that observed data are crucially missing (Gilbert, 2011). As a consequence, improving existing models or developing new ones could be like climbing Everest if no additional data can be gathered, especially on chemical effects and toxicodynamic aspects.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 12: Tetracyclines: tetracycline, chlortetracycline, oxytetracycline, and doxycycline

The specific concentrations of tetracycline, chlortetracycline, oxytetracycline and doxycycline in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied. The FARSC for these four tetracyclines was estimated. To address growth promotion, data from scientific publications obtained from an extensive literature review were used. Levels in feed that showed to have an effect on growth promotion/increased yield were reported for tetracycline, chlortetracycline, oxytetracycline, whilst for doxycycline no suitable data for the assessment were available. Uncertainties and data gaps associated with the levels reported were addressed. It was recommended to perform further studies to supply more diverse and complete data related to the requirements for calculation of the FARSC for these antimicrobials.

Development of a physiologically based pharmacokinetic model for flunixin in cattle (Bos taurus)

Frequent violation of flunixin residues in tissues from cattle has been attributed to non-compliance with the USFDA-approved route of administration and withdrawal time. However, the effect of administration route and physiological differences among animals on tissue depletion has not been determined. The objective of this work was to develop a physiologically based pharmacokinetic (PBPK) model to predict plasma, liver and milk concentrations of flunixin in cattle following intravenous (i.v.), intramuscular (i.m.) or subcutaneous (s.c.) administration for use as a tool to determine factors that may affect the withdrawal time. The PBPK model included blood flow-limited distribution in all tissues and elimination in the liver, kidney and milk. Regeneration of parent flunixin due to enterohepatic recirculation and hydrolysis of conjugated metabolites was incorporated in the liver compartment. Values for physiological parameters were obtained from the literature, and partition coefficients for all tissues but liver and kidney were derived empirically. Liver and kidney partition coefficients and elimination parameters were estimated for 14 pharmacokinetic studies (including five crossover studies) from the literature or government sources in which flunixin was administered i.v., i.m. or s.c. Model simulations compared well with data for the matrices following all routes of administration. Influential model parameters included those that may be age or disease-dependent, such as clearance and rate of milk production. Based on the model, route of administration would not affect the estimated days to reach the tolerance concentration (0.125 mg kg(-1)) in the liver of treated cattle. The majority of USDA-reported violative residues in liver were below the upper uncertainty predictions based on estimated parameters, which suggests the need to consider variability due to disease and age in establishing withdrawal intervals for drugs used in food animals. The model predicted that extravascular routes of administration prolonged flunixin concentrations in milk, which could result in violative milk residues in treated cattle.

Plasma disposition and tissue depletion of chlortetracycline in the food producing animals, chickens for fattening

Chickens were used to investigate plasma disposition of chlortetracycline after single IV (15 mg/kg) and multiple oral administration (60 mg/kg, 5 days) and residue depletion of chlortetracycline after multiple oral doses (60 mg/kg, 5 days). Plasma and tissue samples were analyzed by HPLC. Mean elimination half-lives in plasma were 7.96 and 13.15 h after IV and multiple oral administration. Maximum plasma concentration was 4.33 μg/ml and the interval from oral administration until maximal concentration was 1.79 h. Oral bioavailability was 17.76%. After multiple oral dose, mean kidney, liver and muscle tissue concentrations of chlortetracycline+4-epi-chlortetracycline of 835.3, 192.7, and 126.3 μg/kg, respectively, were measured 1 day after administration of the final dose of chlortetracycline. Chlortetracycline residues were detected in kidney and liver (205.4 and 81.7 μg/kg, respectively), but not in muscle, 3 days after the end of chlortetracycline treatment. The mean chlortetracycline+4-epi-chlortetracycline concentrations were below LOQ at 3 and 5 days after cessation of medication in muscle and liver, respectively. A withdrawal time of 3 days was necessary to ensure that the chlortetracycline residues were less than the maximal residue limits (MRLs) established by the European Union (100, 300, and 600 μg/kg in muscle, liver, and kidney, respectively).

Whole body pharmacokinetic models

The aim of the current review is to summarise the present status of physiologically based pharmacokinetic (PBPK) modelling and its applications in drug research, and thus serve as a reference point to people interested in the methodology. The review is structured into three major sections. The first discusses the existing methodologies and techniques of PBPK model development. The second describes some of the most interesting PBPK model implementations published. The final section is devoted to a discussion of the current limitations and the possible future developments of the PBPK modelling approach. The current review is focused on papers dealing with the pharmacokinetics and/or toxicokinetics of medicinal compounds; references discussing PBPK models of environmental compounds are mentioned only if they represent considerable methodological developments or reveal interesting interpretations and/or applications.The major conclusion of the review is that, despite its significant potential, PBPK modelling has not seen the development and implementation it deserves, especially in the drug discovery, research and development processes. The main reason for this is that the successful development and implementation of a PBPK model is seen to require the investment of significant experience, effort, time and resources. Yet, a substantial body of PBPK-related research has been accumulated that can facilitate the PBPK modelling and implementation process. What is probably lagging behind is the expertise component, where the demand for appropriately qualified staff far outreaches availability.