Journal of Pharmacokinetics and Pharmacodynamics最新文献

Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models.

There are many challenges with rare diseases drug development and rare oncology indications are not different. To understand the regulatory landscape as it relates to application of clinical pharmacology principles in rare oncology product development, we reviewed publicly available information of 39 approvals by US FDA between January 2019 and March 2023. The objective was to understand the expected clinical pharmacology studies and knowledge base in such approvals. Model informed drug development (MIDD) applications were also reviewed, as such approaches are expected to play a critical role in filling clinical pharmacology gaps in rare oncology, where number of clinical trials and size of these trials will perhaps continue to be small. The findings highlighted how clinical pharmacology contributed to the evidence of effectiveness, dose optimization and elucidation of intrinsic and extrinsic factors affecting drug's behavior. Clinical pharmacology studies were often integrated with modeling in many of the NDAs/BLAs. Of the post marketing requirements (PMR) received, 18% were for dose optimization, 49% for DDI, 8% for QTc, 49% for specific population, and 5% for food effect. Two post marketing commitments (PMC) were issued for immunogenicity of the 11 biologics submissions. 15% (6 of 39) of the submissions used maximum tolerated dose (MTD) to advance their molecule into Phase 2 studies. Of them 3 approvals received PMR for dose optimization. 3 + 3 was the most prevalent Phase 1 design with use in 74% of the New Drug Applications (NDA)/Biologic License Applications (BLA) reviewed. Rest used innovative approaches such as BLRM, BOIN or mTPi, with BLRM being the most common. Seamless clinical pharmacology and MIDD approaches are paramount for rare oncology drug development.

Avalglucosidase alfa (AVAL) was approved in the United States (2021) for patients with late-onset Pompe disease (LOPD), aged ≥ 1 year. In the present study, pharmacokinetic (PK) simulations were conducted to propose alternative dosing regimens for pediatric LOPD patients based on a bodyweight cut-off. Population PK (PopPK) analysis was performed using nonlinear mixed effect modeling approach on pooled data from three clinical trials with LOPD patients, and a phase 2 study (NCT03019406) with infantile-onset Pompe disease (IOPD: 1-12 years) patients. A total of 2257 concentration-time points from 91 patients (LOPD, n = 75; IOPD, n = 16) were included in the analysis. The model was bodyweight dependent allometric scaling with time varying bodyweight included on clearance and distribution volume. Simulations were performed for two dosing regimens (20 mg/kg or 40 mg/kg) with different bodyweight cut-off (25, 30, 35 and 40 kg) by generating virtual pediatric (1-17 years) and adult patients. Corresponding simulated individual exposures (maximal concentration, C_max and area under the curve in the 2-week dosing interval, AUC_2W), and distributions were calculated. It was found that dosing of 40 mg/kg and 20 mg/kg in pediatric patients < 30 kg and ≥ 30 kg, respectively, achieved similar AVAL exposure (based on AUC_2W) to adult patients receiving 20 mg/kg. PK simulations conducted on the basis of this model provided supporting data for the currently approved US labelling for dosing adapted bodyweight in LOPD patients ≥ 1 year by USFDA.