Juanjuan Jiang, Li Xu, Lin Chai, Li Zhang, Hong Liu, Yan Yan, Xiaoyuan Guan, Hui Sun, Lei Tian
{"title":"Population pharmacokinetic/pharmacodynamic modeling of nifekalant injection with varies dosing plan in Chinese volunteers: a randomized, blind, placebo-controlled study.","authors":"Juanjuan Jiang, Li Xu, Lin Chai, Li Zhang, Hong Liu, Yan Yan, Xiaoyuan Guan, Hui Sun, Lei Tian","doi":"10.1007/s10928-023-09882-8","DOIUrl":"10.1007/s10928-023-09882-8","url":null,"abstract":"<p><p>Nifekalant hydrochloride is a class III antiarrhythmic agent which could increase the duration of the action potential and the effective refractory period of ventricular and atrial myocytes by blocking the K<sup>+</sup> current. Nifekalant is used to prevent ventricular tachycardia/ventricular fibrillation. QT interval prolongation is the main measurable drug effect. However, due to the complicated dosing plan in clinic, the relationship among dosage, time, drug concentration and efficacy is not fully understood. In this study, a single-center, randomized, blind, dose-ascending, placebo-controlled study was conducted to explore the intrinsic characteristics of nifekalant injection in healthy Chinese volunteers by a population pharmacokinetic (PK)-pharmacodynamic (PD) model approach. 42 subjects were enrolled in this study and received one of three dose plans (loading dose on Day 1 (0.15, 0.3 or 0.5 mg/kg), loading dose followed by maintenance dose (0.2, 0.4 or 0.8 mg/kg/h) on Day 4) or vehicle. Blood samples were drawn for PK evaluation, and ECGs were recorded for QTc calculation at the designed timepoints. No Torsades de Pointes occurred during the study. The popPK model of nifekalant injection could be described by a two-compartment model with first-order elimination. The population mean clearance (CL) was 53.8 L/h. The population mean distribution volume of the central (V<sub>c</sub>) and peripheral (V<sub>p</sub>) compartments was 8.27 L and 45.6 L, respectively. A nonlinear dose-response (E<sub>max</sub>) model well described the pharmacodynamic effect (QTc interval prolongation) of nifekalant. The E<sub>max</sub> and EC<sub>50</sub> from current study were 101 ms and 342 ng/mL, respectively.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"77-87"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10328050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Wang, Tak Hung, Noelyn Hung, Paul Glue, Chris Jackson, Stephen Duffull
{"title":"Optimal sample selection applied to information rich, dense data.","authors":"David Wang, Tak Hung, Noelyn Hung, Paul Glue, Chris Jackson, Stephen Duffull","doi":"10.1007/s10928-023-09883-7","DOIUrl":"10.1007/s10928-023-09883-7","url":null,"abstract":"<p><p>Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"33-37"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9967411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter L Bonate, Jeffrey S Barrett, Sihem Ait-Oudhia, Richard Brundage, Brian Corrigan, Stephen Duffull, Marc Gastonguay, Mats O Karlsson, Shinichi Kijima, Andreas Krause, Mark Lovern, Matthew M Riggs, Michael Neely, Daniele Ouellet, Elodie L Plan, Gauri G Rao, Joseph Standing, Justin Wilkins, Hao Zhu
{"title":"Correction to: Training the next generation of pharmacometric modelers: a multisector perspective.","authors":"Peter L Bonate, Jeffrey S Barrett, Sihem Ait-Oudhia, Richard Brundage, Brian Corrigan, Stephen Duffull, Marc Gastonguay, Mats O Karlsson, Shinichi Kijima, Andreas Krause, Mark Lovern, Matthew M Riggs, Michael Neely, Daniele Ouellet, Elodie L Plan, Gauri G Rao, Joseph Standing, Justin Wilkins, Hao Zhu","doi":"10.1007/s10928-023-09885-5","DOIUrl":"10.1007/s10928-023-09885-5","url":null,"abstract":"","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"89"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10164395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ari Brekkan, Rocío Lledo-Garcia, Brigitte Lacroix, Siv Jönsson, Mats O Karlsson, Elodie L Plan
{"title":"Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach.","authors":"Ari Brekkan, Rocío Lledo-Garcia, Brigitte Lacroix, Siv Jönsson, Mats O Karlsson, Elodie L Plan","doi":"10.1007/s10928-023-09890-8","DOIUrl":"10.1007/s10928-023-09890-8","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"65-75"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10884144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71521891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter L Bonate, Jeffrey S Barrett, Sihem Ait-Oudhia, Richard Brundage, Brian Corrigan, Stephen Duffull, Marc Gastonguay, Mats O Karlsson, Shinichi Kijima, Andreas Krause, Mark Lovern, Matthew M Riggs, Michael Neely, Daniele Ouellet, Elodie L Plan, Gauri G Rao, Joseph Standing, Justin Wilkins, Hao Zhu
{"title":"Training the next generation of pharmacometric modelers: a multisector perspective.","authors":"Peter L Bonate, Jeffrey S Barrett, Sihem Ait-Oudhia, Richard Brundage, Brian Corrigan, Stephen Duffull, Marc Gastonguay, Mats O Karlsson, Shinichi Kijima, Andreas Krause, Mark Lovern, Matthew M Riggs, Michael Neely, Daniele Ouellet, Elodie L Plan, Gauri G Rao, Joseph Standing, Justin Wilkins, Hao Zhu","doi":"10.1007/s10928-023-09878-4","DOIUrl":"10.1007/s10928-023-09878-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"5-31"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10531805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Thoughts on plagiarism and the case against Claudine Gay.","authors":"Peter L Bonate","doi":"10.1007/s10928-024-09904-z","DOIUrl":"10.1007/s10928-024-09904-z","url":null,"abstract":"","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"1-4"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139741300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Correction to: Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.","authors":"Carla White, Vivi Rottschäfer, Lloyd Bridge","doi":"10.1007/s10928-023-09879-3","DOIUrl":"10.1007/s10928-023-09879-3","url":null,"abstract":"","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"91"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10884089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9873732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artak Khachatryan, Stephanie H Read, Terri Madison
{"title":"Correction: External control arms for rare diseases: building a body of supporting evidence.","authors":"Artak Khachatryan, Stephanie H Read, Terri Madison","doi":"10.1007/s10928-024-09900-3","DOIUrl":"10.1007/s10928-024-09900-3","url":null,"abstract":"","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"93"},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10884162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139502487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.","authors":"Carla White, Vivi Rottschäfer, Lloyd Bridge","doi":"10.1007/s10928-023-09870-y","DOIUrl":"10.1007/s10928-023-09870-y","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"39-63"},"PeriodicalIF":2.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10884104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10250235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Special issue: Model-informed drug development in rare diseases: connecting the dots in an information rich ecosystem.","authors":"Rajesh Krishna","doi":"10.1007/s10928-023-09862-y","DOIUrl":"10.1007/s10928-023-09862-y","url":null,"abstract":"","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"425-427"},"PeriodicalIF":2.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9722150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}