scholarly journals PK-DB: pharmacokinetics database for individualized and stratified computational modeling

2020 ◽  
Vol 49 (D1) ◽  
pp. D1358-D1364
Author(s):  
Jan Grzegorzewski ◽  
Janosch Brandhorst ◽  
Kathleen Green ◽  
Dimitra Eleftheriadou ◽  
Yannick Duport ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Smoking Status ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Special Focus ◽  
Biological Ontologies ◽  
Time Courses

Abstract A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

scholarly journals PK-DB: PharmacoKinetics DataBase for Individualized and Stratified Computational Modeling

2019 ◽  
Author(s):  
Jan Grzegorzewski ◽  
Janosch Brandhorst ◽  
Dimitra Eleftheriadou ◽  
Kathleen Green ◽  
Matthias König
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Smoking Status ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Special Focus ◽  
Biological Ontologies ◽  
Time Courses

ABSTRACTA multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials including pre-clinical research. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) measured pharmacokinetic time-courses; (iv) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve). Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/DB), or population pharmacokinetic (pop PK) modeling.

scholarly journals Is the Recommended Once-Daily Dose of Lamivudine Optimal in West African HIV-Infected Children?

2010 ◽  
Vol 54 (8) ◽  
pp. 3280-3286 ◽  
Author(s):  
Naïm Bouazza ◽  
Déborah Hirt ◽  
Christophe Bardin ◽  
Serge Diagbouga ◽  
Boubacar Nacro ◽  
...  
Keyword(s):  
Body Weight ◽  
Area Under The Curve ◽  
Compartment Model ◽  
Individual Characteristics ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Ratio Test ◽  
Once Daily ◽  
Time Courses

ABSTRACT We aimed in this study to describe lamivudine concentration-time courses in treatment-naïve children after once-daily administration, to study the effects of body weight and age on lamivudine pharmacokinetics, and to simulate an optimized administration scheme. For this purpose, lamivudine concentrations were measured in 49 children after at least 2 weeks of didanosine-lamivudine-efavirenz treatment. A total of 148 plasma lamivudine concentrations were measured, and a population pharmacokinetic model was developed with NONMEM. The influence of individual characteristics was tested using a likelihood ratio test. Children were divided into two groups, according to their pharmacokinetic parameters, thanks to tree regression analysis. For each patient, the area under the curve was derived from estimated individual pharmacokinetic parameters. Different once-daily doses were simulated in each group, to obtain the same exposure in children as the mean effective exposure in adults (8.9 mg/liter·h). A two-compartment model in which the slope of distribution is assumed to be equal to the absorption rate constant adequately described the data. Parameter estimates were standardized for a mean standard body weight using an allometric model. Children were then divided into 2 groups according to body weight: CL/F was significantly higher in children weighing less than 17 kg (1.12 liters/h/kg) than in children over 17 kg (0.95 liters/h/kg; P = 0.01). The target mean AUC of 8.9 mg/liters·h was obtained with a 10-mg/kg once-daily lamivudine (3TC) dose for children below 17 kg; the recommended dose of 8 mg/kg seems to be sufficient in children weighing more than 17 kg. These assumptions should be prospectively confirmed.

DO WE HAVE A CONSENSUS TO APPLY MODEL-BASED AMINOGLYCOSIDE THERAPY: A REVIEW OF POPULATION PHARMACOKINETIC MODELS

2015 ◽  
Vol 101 (1) ◽  
pp. e1.51-e1
Author(s):  
Guo-Xiang Hao ◽  
Sophie Teng ◽  
Evelyne Jacqz-Aigrain ◽  
Wei Zhao
Keyword(s):  
Meta Analysis ◽  
Pharmacokinetic Modeling ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
Related Factors ◽  
Model Based ◽  
Aminoglycoside Therapy ◽  
Bibliographic Search ◽  
Adults And Children

Background and ObjectiveAminoglycosides remain the standard antibiotic therapy for Gram-negative infections in both adults and children. The pharmacokinetic modeling approach has been widely used to evaluate aminoglycosides therapy. The aim of the present study is to review the published population pharmacokinetic models of commonly used aminoglycosides (gentamycin, amikacin and tobramycin), in order to determine if there was a consensus to apply model-based personalized aminoglycoside therapy in routine clinical practice.MethodsThe bibliographic search was performed electronically using PubMed on 30th January 2015, following the search strategy: “((population Pharmacokinetics) OR (Pharmacokinetic modeling)) AND (gentamycin OR gentamicin OR amikacin OR tobramycin)”.ResultsA total of 49 articles were identified. Persistent variabilities exist in terms of structure model; typical pharmacokinetic parameters and identified covariates.ConclusionA pharmacokinetic meta-analysis is required to evaluate the study-related factors influencing the pharmacokinetics of aminoglycosides. A clinical evaluation of pharmacokinetic model of aminoglycosides is required to demonstrate its clinical utility.

scholarly journals Pharmacokinetics of Ganciclovir during Continuous Venovenous Hemodiafiltration in Critically Ill Patients

2013 ◽  
Vol 58 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Thomas Horvatits ◽  
Reinhard Kitzberger ◽  
Andreas Drolz ◽  
Christian Zauner ◽  
Walter Jäger ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
High Performance ◽  
Area Under The Curve ◽  
Antiviral Agent ◽  
Pharmacokinetic Analysis ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Target Area ◽  
Continuous Venovenous Hemodiafiltration

ABSTRACTGanciclovir is an antiviral agent that is frequently used in critically ill patients with cytomegalovirus (CMV) infections. Continuous venovenous hemodiafiltration (CVVHDF) is a common extracorporeal renal replacement therapy in intensive care unit patients. The aim of this study was to investigate the pharmacokinetics of ganciclovir in anuric patients undergoing CVVHDF. Population pharmacokinetic analysis was performed for nine critically ill patients with proven or suspected CMV infection who were undergoing CVVHDF. All patients received a single dose of ganciclovir at 5 mg/kg of body weight intravenously. Serum and ultradiafiltrate concentrations were assessed by high-performance liquid chromatography, and these data were used for pharmacokinetic analysis. Mean peak and trough prefilter ganciclovir concentrations were 11.8 ± 3.5 mg/liter and 2.4 ± 0.7 mg/liter, respectively. The pharmacokinetic parameters elimination half-life (24.2 ± 7.6 h), volume of distribution (81.2 ± 38.3 liters), sieving coefficient (0.76 ± 0.1), total clearance (2.7 ± 1.2 liters/h), and clearance of CVVHDF (1.5 ± 0.2 liters/h) were determined. Based on population pharmacokinetic simulations with respect to a target area under the curve (AUC) of 50 mg · h/liter and a trough level of 2 mg/liter, a ganciclovir dose of 2.5 mg/kg once daily seems to be adequate for anuric critically ill patients during CVVHDF.

scholarly journals Longitudinal Pharmacokinetic-Pharmacodynamic Biomarkers Correlate With Treatment Outcome in Drug-Sensitive Pulmonary Tuberculosis: A Population Pharmacokinetic-Pharmacodynamic Analysis

2020 ◽  
Vol 7 (7) ◽  
Author(s):  
Frank Kloprogge ◽  
Henry C Mwandumba ◽  
Gertrude Banda ◽  
Mercy Kamdolozi ◽  
Doris Shani ◽  
...  
Keyword(s):  
Treatment Failure ◽  
Treatment Success ◽  
Area Under The Curve ◽  
Outcome Data ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Antibiotic Exposure ◽  
Sputum Conversion ◽  
Demographic Covariates ◽  
Pharmacodynamic Biomarkers

Abstract Background This study aims to explore relationships between baseline demographic covariates, plasma antibiotic exposure, sputum bacillary load, and clinical outcome data to help improve future tuberculosis (TB) treatment response predictions. Methods Data were available from a longitudinal cohort study in Malawian drug-sensitive TB patients on standard therapy, including steady-state plasma antibiotic exposure (154 patients), sputum bacillary load (102 patients), final outcome (95 patients), and clinical details. Population pharmacokinetic and pharmacokinetic-pharmacodynamic models were developed in the software package NONMEM. Outcome data were analyzed using univariate logistic regression and Cox proportional hazard models in R, a free software for statistical computing. Results Higher isoniazid exposure correlated with increased bacillary killing in sputum (P < .01). Bacillary killing in sputum remained fast, with later progression to biphasic decline, in patients with higher rifampicin area under the curve (AUC)0-24 (P < .01). Serial sputum colony counting negativity at month 2 (P < .05), isoniazid CMAX (P < .05), isoniazid CMAX/minimum inhibitory concentration ([MIC] P < .01), and isoniazid AUC0-24/MIC (P < .01) correlated with treatment success but not with remaining free of TB. Slower bacillary killing (P < .05) and earlier progression to biphasic bacillary decline (P < .01) both correlate with treatment failure. Posttreatment recurrence only correlated with slower bacillary killing (P < .05). Conclusions Patterns of early bacillary clearance matter. Static measurements such as month 2 sputum conversion and pharmacokinetic parameters such as CMAX/MIC and AUC0-24/MIC were predictive of treatment failure, but modeling of quantitative longitudinal data was required to assess the risk of recurrence. Pooled individual patient data analyses from larger datasets are needed to confirm these findings.

scholarly journals Population Pharmacokinetics of Clozapine: A Systematic Review

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Orwa Albitar ◽  
Sabariah Noor Harun ◽  
Hadzliana Zainal ◽  
Baharudin Ibrahim ◽  
Siti Maisharah Sheikh Ghadzi
Keyword(s):  
Systematic Review ◽  
Animal Studies ◽  
Smoking Status ◽  
External Validation ◽  
Sampling Strategy ◽  
Predictive Performance ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Interindividual Variations

Background and Objective. Clozapine is a second-generation antipsychotic drug that is considered the most effective treatment for refractory schizophrenia. Several clozapine population pharmacokinetic models have been introduced in the last decades. Thus, a systematic review was performed (i) to compare published pharmacokinetics models and (ii) to summarize and explore identified covariates influencing the clozapine pharmacokinetics models. Methods. A search of publications for population pharmacokinetic analyses of clozapine either in healthy volunteers or patients from inception to April 2019 was conducted in PubMed and SCOPUS databases. Reviews, methodology articles, in vitro and animal studies, and noncompartmental analysis were excluded. Results. Twelve studies were included in this review. Clozapine pharmacokinetics was described as one-compartment with first-order absorption and elimination in most of the studies. Significant interindividual variations of clozapine pharmacokinetic parameters were found in most of the included studies. Age, sex, smoking status, and cytochrome P450 1A2 were found to be the most common identified covariates affecting these parameters. External validation was only performed in one study to determine the predictive performance of the models. Conclusions. Large pharmacokinetic variability remains despite the inclusion of several covariates. This can be improved by including other potential factors such as genetic polymorphisms, metabolic factors, and significant drug-drug interactions in a well-designed population pharmacokinetic model in the future, taking into account the incorporation of larger sample size and more stringent sampling strategy. External validation should also be performed to the previously published models to compare their predictive performances.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals The Potential Diagnostic Accuracy of Circulating MicroRNAs for Leukemia: A Meta-Analysis

2021 ◽  
Vol 20 ◽  
pp. 153303382110119
Author(s):  
Wen-Ting Zhang ◽  
Guo-Xun Zhang ◽  
Shuai-Shuai Gao
Keyword(s):  
Diagnostic Accuracy ◽  
Likelihood Ratio ◽  
Subgroup Analysis ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Diagnostic Odds Ratio ◽  
Circulating Micrornas ◽  
Sensitivity Specificity

Background: Leukemia is a common malignant disease in the human blood system. Many researchers have proposed circulating microRNAs as biomarkers for the diagnosis of leukemia. We conducted a meta-analysis to evaluate the diagnostic accuracy of circulating miRNAs in the diagnosis of leukemia. Methods: A comprehensive literature search (updated to October 13, 2020) in PubMed, EMBASE, Web of Science, Cochrane Library, Wanfang database and China National Knowledge Infrastructure (CNKI) was performed to identify eligible studies. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) for diagnosing leukemia were pooled for both overall and subgroup analysis. The meta-regression and subgroup analysis were performed to explore heterogeneity and Deeks’ funnel plot was used to assess publication bias. Results: 49 studies from 22 publications with a total of 3,489 leukemia patients and 2,756 healthy controls were included in this meta-analysis. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the curve were 0.83, 0.92, 10.8, 0.18, 59 and 0.94, respectively. Subgroup analysis shows that the microRNA clusters of plasma type could carry out a better diagnostic accuracy of leukemia patients. In addition, publication bias was not found. Conclusions: Circulating microRNAs can be used as a promising noninvasive biomarker in the early diagnosis of leukemia.

scholarly journals A Population Pharmacokinetic Meta‐analysis of Veliparib, a PARP Inhibitor, Across Phase I/II/III Trials in Cancer Patients

2021 ◽  
Author(s):  
Sven Stodtmann ◽  
Silpa Nuthalapati ◽  
Doerthe Eckert ◽  
Sreeneeranj Kasichayanula ◽  
Rujuta Joshi ◽  
...  
Keyword(s):  
Phase I ◽  
Cancer Patients ◽  
Meta Analysis ◽  
Parp Inhibitor ◽  
Population Pharmacokinetic

scholarly journals Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Lei Xi ◽  
Chunqing Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Value ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity ◽  
Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

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