Population modelling to describe pharmacokinetics of amiodarone in rats: Relevance of plasma protein and tissue depot binding

Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment

The aims of this study are (i) to develop a population pharmacokinetic/pharmacodynamic model of daptomycin in patients with normal and impaired renal function, and (ii) to establish the optimal dose recommendation of daptomycin in clinical practice. Several structural PK models including linear and non-linear binding kinetics were evaluated. Monte Carlo simulations were conducted with a fixed combination of creatinine clearance (30–90 mL/min/1.73 m²) and body weight (50–100 kg). The final dataset included 46 patients and 157 daptomycin observations. A two-compartment model with first-order peripheral distribution and elimination kinetics assuming non-linear protein-binding kinetics was selected. The bactericidal effect for Gram+ strains with MIC ≤ 0.5 mg/L could be achieved with 5–12 mg/kg daily daptomycin based on body weight and renal function. The administration of 10–17 mg/kg q48 h daptomycin allows to achieve bactericidal effect for Gram+ strains with MIC ≤ 1 mg/L. Four PK samples were selected as the optimal sampling strategy for an accurate AUC estimation. A quantitative framework has served to characterize the non-linear binding kinetics of daptomycin in patients with normal and impaired renal function. The impact of different dosing regimens on the efficacy and safety outcomes of daptomycin treatment based on the unbound exposure of daptomycin and individual patient characteristics has been evaluated.

Pharmacokinetics and biodistribution of a collagen-targeted peptide amphiphile for cardiovascular applications

Atherosclerosis remains a leading cause of death and disability around the world and a major driver of health care spending. Nanomaterials have gained widespread attention due to their promising potential for clinical translation and use. We have developed a collagen-targeted peptide amphiphile (PA)-based nanofiber for the prevention of neointimal hyperplasia after arterial injury. Our goal was to characterize the pharmacokinetics and biodistribution of the collagen-targeted PA to further its advancement into clinical trials. Collagen-targeted PA was injected into the internal jugular vein of Sprague Dawley rats. PA concentrations in plasma collected at various times after injection (0 to 72 hours) were measured by liquid chromatography-tandem mass spectrometry. Pharmacokinetics of the collagen-targeted PA were characterized by a three-compartment model, with an extremely rapid apparent elimination clearance resulting in a plasma concentration decrease of more than two orders of magnitude within the first hour after injection. This rapid initial decline in plasma concentration was not due to degradation by plasma components, as collagen-targeted PA was stable in plasma ex vivo for up to 3 hours. Indeed, cellular blood components appear to be partly responsible, as only 15% of collagen-targeted PA were recovered following incubation with whole blood. Nanofibers in whole blood also adhered to red blood cells (RBCs) and were engulfed by mononuclear cells. This work highlights the unique pharmacokinetics of our collagen-targeted PA, which differ from pharmacokinetics of small molecules. Because of their targeted nature, these nanomaterials should not require sustained elevated plasma concentrations to achieve a therapeutic effect the way small molecules typically do.

A preclinical study to model taurine pharmokinetics in the undernourished rat

Malnutrition is a common feature of chronic and acute diseases, often associated with a poor prognosis, including worsening of clinical outcome, owing, among other factors, to dysfunction of the most internal organs and systems affecting the absorption, metabolism and elimination of drugs and nutrients. Taurine is involved in numerous biological processes and is required in increased amounts in response to pathological conditions. The aim of this study was to describe the behaviour of taurine in well-nourished (WN) rats and to analyse the influence of protein-energy undernutrition on the pharmacokinetic (PK) parameters of taurine, using a PK model. Wistar rats were randomly distributed into two groups, WN and undernourished (UN), and taurine was administered intravenously or orally at different doses: 1, 10 and 100 mg. Population pharmacokinetic modelling of plasma levels was performed using the NONMEM 7.2 program. Several distribution and absorption models were explored in combination with dose and/or time covariate effects. Covariates such as nutritional status, serum albumin, body weight and score of undernutrition were used. A two-compartment population pharmacokinetic model with zero-order endogenous formation, passive absorption, first-order kinetics distribution and non-linear elimination with parallel Michaelis-Menten excretion and reabsorption processes best described taurine pharmacokinetics. Undernutrition acted as a covariate reducing the V max of the active elimination process. Data analysis showed linear absorption and distribution, and non-linear elimination processes for taurine. Elimination of taurine was reduced in UN animals, suggesting that the reabsorption process via the secretion transporter was modified in that group.

In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part I: Beyond the reduction of animal model use

There is an urgent need for efficient in silico ADME-T prediction tools for the selection of potent therapeutic drugs as well as the elimination of toxic compounds. This is particularly important in view of the high costs and ethical issues inherent to the use of animal models for drugs filtering. To achieve this mission, not only does the accuracy of in silico tools need to be improved, but also new experts in the field with skills in theoretical chemistry, clinical and fundamental biology have to be trained. Similarly, clinical biologists committed to the obligation of means and legally responsible for the results they generate could establish a legal framework that defines legal responsibilities when performing in silico predictions.