Comparable Bioavailability and Disposition of Pefloxacin in Patients with Cystic Fibrosis and Healthy Volunteers Assessed via Population Pharmacokinetics

Anti-infectives in Pediatric Patients with Cystic Fibrosis: A Comprehensive Review of Population Pharmacokinetic Analyses

Pulmonary complications are the leading cause of morbidity and mortality in pediatric patients with cystic fibrosis. Altered pharmacokinetic parameters in this population, as well as high inter- and intra-individual variability, complicate the optimization of anti-infective treatments. In this review, we aim to summarize and describe all anti-infective population pharmacokinetic (popPK) models applied to pediatric populations with cystic fibrosis. Our objectives were to identify the most-reported structural models and retained covariates and to compare the dosing regimens used in clinical routine with those recommended in literature and guidelines. A literature search was done through the PubMed database from inception to August 2024. Studies were retained only if they complied with the inclusion and exclusion criteria. The review included 21 popPK models covering the pharmacokinetic profiles of eight different molecules. Among these, five are recommended antibiotics for treating pulmonary infections in patients with cystic fibrosis. All models incorporated body composition and/or renal function measures as covariates in their pharmacokinetic parameter equations. Standard dosing regimens in the studies were consistent with guidelines and literature recommendations. This is the first review summarizing and describing all anti-infective popPK models in pediatric patients with cystic fibrosis. Improved estimation of pharmacokinetic parameters and a clearer understanding of variability sources will enhance the optimization of antibiotic treatment in clinical practice. Finally, the impact of new targeted therapies on the management of this population will have to be closely monitored in the years ahead.

The Pharmacokinetic Changes in Cystic Fibrosis Patients Population: Narrative Review

Cystic fibrosis (CF) is a rare genetic disorder commonly affecting multiple organs such as the lungs, pancreas, liver, kidney, and intestine. Our search focuses on the pathophysiological changes that affect the drugs' absorption, distribution, metabolism, and excretion (ADME). This review aims to identify the ADME data that compares the pharmacokinetics (PK) of different drugs in CF and healthy subjects. The published data highlight multiple factors that affect absorption, such as the bile salt precipitation and the gastrointestinal pH. Changes in CF patients' protein binding and body composition affected the drug distribution. The paper also discusses the factors affecting metabolism and renal elimination, such as drug-protein binding and metabolizing enzyme capacity. The majority of CF patients are on multidrug therapy, which increases the risk of drug-drug interactions (DDI). This is particularly true for those receiving the newly developed transmembrane conductance regulator (CFTR), as they are at a higher risk for CYP-related DDI. Our research highlights the importance of meticulously evaluating PK variations and DDIs in drug development and the therapeutic management of CF patients.

Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Brain Levels of Drug in Rat

One of the main obstacles in neurological disease treatment is the presence of the blood-brain barrier. New predictive high-throughput screening tools are essential to avoid costly failures in the advanced phases of development and to contribute to the 3 Rs policy. The objective of this work was to jointly develop a new in vitro system coupled with a physiological-based pharmacokinetic (PBPK) model able to predict brain concentration levels of different drugs in rats. Data from in vitro tests with three different cells lines (MDCK, MDCK-MDR1 and hCMEC/D3) were used together with PK parameters and three scaling factors for adjusting the model predictions to the brain and plasma profiles of six model drugs. Later, preliminary quantitative structure-property relationships (QSPRs) were constructed between the scaling factors and the lipophilicity of drugs. The predictability of the model was evaluated by internal validation. It was concluded that the PBPK model, incorporating the barrier resistance to transport, the disposition within the brain and the drug-brain binding combined with MDCK data, provided the best predictions for passive diffusion and carrier-mediated transported drugs, while in the other cell lines, active transport influence can bias predictions.

Pharmacokinetics and pharmacodynamics of antibiotics in cystic fibrosis: a narrative review

Cystic fibrosis affects several organs, predisposing patients to severe bacterial respiratory infections, including those caused by methicillin-resistant Staphylococcus aureus. Cystic fibrosis is also associated with a wide spectrum of pathological changes that can significantly affect the absorption, distribution, metabolism, and/or elimination of several drugs, including antibacterial agents. Therefore, awareness of the pharmacokinetic derangements in patients with cystic fibrosis is mandatory for the optimisation of antibiotic therapy. This review discusses the basic principles of pharmacokinetics and the pathophysiology of the pharmacokinetics changes associated with cystic fibrosis; it also provides an update of available data for the most widely used antibiotics. Evidence accumulated in the last few years has clearly shown that a significant number of cystic fibrosis patients treated with conventional dosing schemes have sub-therapeutic antibiotic concentrations, increasing their risk of therapeutic failure and/or the emergence of resistant pathogens. Some proposals to optimise antibiotic therapies in this clinical setting based on therapeutic drug monitoring are also discussed.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.