scholarly journals Population Pharmacokinetic Modelling of the Complex Release Kinetics of Octreotide LAR: Defining Sub-Populations by Cluster Analysis

Pharmaceutics ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1578
Author(s):  
Iasonas Kapralos ◽  
Aristides Dokoumetzidis
Keyword(s):  
Cluster Analysis ◽  
Goodness Of Fit ◽  
Visual Predictive Check ◽  
Distribution Model ◽  
Population Pharmacokinetic ◽  
Model Parameters ◽  
Octreotide Lar ◽  
Multiple Peak ◽  
Peak Absorption ◽  
Absorption Pattern

The aim of the study is to develop a population pharmacokinetic (PPK) model, of Octreotide long acting repeatable (LAR) formulation in healthy volunteers, which describes the highly variable, multiple peak absorption pattern of the pharmacokinetics of the drug, in individual and population levels. An empirical absorption model, coupled with a one-compartment distribution model with linear elimination was found to describe the data well. Absorption was modelled as a weighted sum of a first order and three transit compartment absorption processes, with delays and appropriately constrained model parameters. Identifiability analysis verified that all twelve parameters of the structural model are identifiable. A machine learning method, i.e., cluster analysis, was performed as pre-processing of the PK profiles, to define subpopulations, before PPK modelling. It revealed that 13% of the patients deviated considerably from the typical absorption pattern and allowed better characterization of the observed heterogeneity and variability of the study, while the approach may have wider applicability in building PPK models. The final model was evaluated by goodness of fit plots, Visual Predictive Check plots and bootstrap. The present model is the first to describe the multiple-peak absorption pattern observed after octreotide LAR administration and may be useful to provide insights and validate hypotheses regarding release from PLGA-based formulations.

scholarly journals Odd Lindley-Kumaraswamy Distribution: Model, Properties and Application

2020 ◽  
pp. 42-58
Author(s):  
Kuje Samson ◽  
Abubakar, Mohammad Auwal ◽  
Asongo, Iorkaa Abraham ◽  
Alhaji, Ismaila Sulaiman
Keyword(s):  
Goodness Of Fit ◽  
Real Life ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Distribution Functions ◽  
Distribution Model ◽  
Model Parameters ◽  
Kumaraswamy Distribution ◽  
Method Of Maximum Likelihood ◽  
Compound Distribution

This article uses the odd Lindley-G family of distributions to propose and study a new compound distribution called “odd Lindley-Kumaraswamy distribution”. In this article, the density and distribution functions of the odd Lindley-Kumaraswamy distribution are defined and studied by deriving and discussing many properties of the distribution such as the ordinary moments, moment generating function, characteristics function, quantile function, reliability functions, order statistics and other useful measures. The unknown model parameters are also estimated by the method of maximum likelihood. The goodness-of-fit of the proposed distribution is demonstrated using two real life datasets. The results show that the proposed distribution outperforms the other fitted compound models selected for this study and hence it is a flexible generalization of the Kumaraswamy distribution.

scholarly journals 1340. Population Pharmacokinetic (PK) Analysis of APX001 Using Phase 1 Data

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S409-S410
Author(s):  
Michael Trang ◽  
Justin C Bader ◽  
Eric A Ople ◽  
William G Kramer ◽  
Michael R Hodges ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Time Course ◽  
Oral Absorption ◽  
Phase 1 ◽  
Visual Predictive Check ◽  
Weight Ratio ◽  
Lag Time ◽  
Population Pharmacokinetic ◽  
Research Support

Abstract Background APX001 is a novel antifungal agent which is rapidly converted to the active metabolite APX001A. APX001A exhibits in vitro activity against many clinically important yeast and fungi, including echinocandin- and azole-resistant Candida species. Given this activity, intravenous (IV) and oral (PO) formulations of APX001 are being developed for the treatment of patients with candidemia or invasive candidiasis. Phase 1 data were used to develop a population PK (PPK) model to describe the time-course of APX001A in plasma. Methods The PPK model was developed using 3,736 plasma PK samples collected from 128 healthy subjects who received APX001 single and multiple IV and PO doses ranging from 10 to 1,000 mg. Instantaneous conversion was assumed by scaling input doses by the molecular weight ratio of APX001A to APX001. After development of the structural PK model, stepwise forward and backward selection procedures were used to identify significant covariate relationships. Model qualification included standard goodness-of-fit metrics and prediction-corrected visual predictive check (PC-VPC) plots. Results A two-compartment model with zero-order IV input, or first-order PO absorption with lag time to account for the apparent delay in oral absorption, best described APX001A plasma PK. Exponential error models were used to estimate interindividual variability (IIV) for all parameters. Interoccasion variability was estimated for the absorption rate constant, bioavailability, and lag time. Body weight was identified as a statistically significant predictor of the IIV on the volume of the central and peripheral compartments. The PPK model provided an accurate and unbiased fit to the plasma data based on individual- and population-predicted concentrations (r2 = 0.977 and 0.873, respectively). The PC-VPC plots for the final PPK model (Figure 1) demonstrated good alignment between observed concentrations and the model predicted 5th, 50th, and 95th percentiles. Conclusion A PPK model describing APX001A plasma PK following IV or PO doses was successfully developed. This model will be useful for generating simulated APX001A exposures for use in pharmacokinetic–pharmacodynamic target attainment analyses to support APX001 dose selection. Disclosures M. Trang, Amplyx Pharmaceuticals, Inc.: Research Contractor, Research support. J. C. Bader, Amplyx Pharmaceuticals, Inc.: Research Contractor, Research support. E. A. Ople, Amplyx Pharmaceuticals, Inc.: Employee, Salary. W. G. Kramer, Amplyx Pharmaceuticals, Inc.: Scientific Advisor, Consulting fee. M. R. Hodges, Amplyx Pharmaceuticals, Inc.: Employee, Salary. S. M. Bhavnani, Amplyx Pharmaceuticals, Inc.: Research Contractor, Research support. C. M. Rubino, Amplyx Pharmaceuticals, Inc.: Research Contractor, Research support.

scholarly journals Application of Size and Maturation Functions to Population Pharmacokinetic Modeling of Pediatric Patients

Pharmaceutics ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 259 ◽  
Author(s):  
Hyun-moon Back ◽  
Jong Bong Lee ◽  
Nayoung Han ◽  
Sungwoo Goo ◽  
Eben Jung ◽  
...  
Keyword(s):  
Clinical Data ◽  
Goodness Of Fit ◽  
Pediatric Patients ◽  
Visual Predictive Check ◽  
Modeling Method ◽  
Population Pharmacokinetic ◽  
Mixed Effect ◽  
Population Pharmacokinetic Modeling ◽  
Nonlinear Mixed Effect ◽  
One Year

Traditionally, dosage for pediatric patients has been optimized using simple weight-scaled methods, but these methods do not always meet the requirements of children. To overcome this discrepancy, population pharmacokinetic (PK) modeling of size and maturation functions has been proposed. The main objective of the present study was to evaluate a new modeling method for pediatric patients using clinical data from three different clinical studies. To develop the PK models, a nonlinear mixed effect modeling method was employed, and to explore PK differences in pediatric patients, size with allometric and maturation with Michaelis–Menten type functions were evaluated. Goodness of fit plots, visual predictive check and bootstrap were used for model evaluation. Single application of size scaling to PK parameters was statistically significant for the over one year old group. On the other hand, simultaneous use of size and maturation functions was statistically significant for infants younger than one year old. In conclusion, population PK modeling for pediatric patients was successfully performed using clinical data. Size and maturation functions were applied according to established criteria, and single use of size function was applicable for over one year ages, while size and maturation functions were more effective for PK analysis of neonates and infants.

scholarly journals Development of a Population Pharmacokinetic Model Characterizing the Tissue Distribution of Azithromycin in Healthy Subjects

2014 ◽  
Vol 58 (11) ◽  
pp. 6675-6684 ◽  
Author(s):  
Songmao Zheng ◽  
Peter Matzneller ◽  
Markus Zeitlinger ◽  
Stephan Schmidt
Keyword(s):  
Tissue Distribution ◽  
Goodness Of Fit ◽  
Subcutaneous Fat ◽  
Distribution Model ◽  
Tissue Cell ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Ionization State ◽  
Nonparametric Bootstrap ◽  
Bootstrap Analysis

ABSTRACTRecent clinical trials indicate that the use of azithromycin is associated with the emergence of macrolide resistance. The objective of our study was to simultaneously characterize free target site concentrations and correlate them with the MIC90s of clinically relevant pathogens. Azithromycin (500 mg once daily [QD]) was administered orally to 6 healthy male volunteers for 3 days. The free concentrations in the interstitial space fluid (ISF) of muscle and subcutaneous fat tissue as well as the total concentrations in plasma and polymorphonuclear leukocytes (PMLs) were determined on days 1, 3, 5, and 10. All concentrations were modeled simultaneously in NONMEM 7.2 using a tissue distribution model that accounts for nonlinear protein binding and ionization state at physiological pH. The model performance and parameter estimates were evaluated via goodness-of-fit plots and nonparametric bootstrap analysis. The model we developed described the concentrations at all sampling sites reasonably well and showed that the overall pharmacokinetics of azithromycin is driven by the release of the drug from acidic cell/tissue compartments. The model-predicted unionized azithromycin (AZM) concentrations in the cytosol of PMLs (6.0 ± 1.2 ng/ml) were comparable to the measured ISF concentrations in the muscle (8.7 ± 2.9 ng/ml) and subcutis (4.1 ± 2.4 ng/ml) on day 10, whereas the total PML concentrations were >1,000-fold higher (14,217 ± 2,810 ng/ml). The total plasma and free ISF concentrations were insufficient to exceed the MIC90s of the skin pathogens at all times. Our results indicate that the slow release of azithromycin from low pH tissue/cell compartments is responsible for the long terminal half-life of the drug and thus the extended period of time during which free concentrations reside at subinhibitory concentrations.

Amikacin initial dosage in patients with hypoalbuminaemia: an interactive tool based on a population pharmacokinetic approach

2020 ◽  
Vol 75 (8) ◽  
pp. 2222-2231
Author(s):  
Jonás Samuel Pérez-Blanco ◽  
Eva María Sáez Fernández ◽  
M Victoria Calvo ◽  
José M Lanao ◽  
Ana Martín-Suárez
Keyword(s):  
Initial Dose ◽  
Goodness Of Fit ◽  
Plasma Concentrations ◽  
Visual Predictive Check ◽  
Initial Dosage ◽  
Pharmacokinetic Parameters ◽  
Population Pharmacokinetic ◽  
Parameter Estimates ◽  
Interactive Model ◽  
Model Based

Abstract Objectives To characterize amikacin population pharmacokinetics in patients with hypoalbuminaemia and to develop a model-based interactive application for amikacin initial dosage. Methods A population pharmacokinetic model was developed using a non-linear mixed-effects modelling approach (NONMEM) with amikacin concentration–time data collected from clinical practice (75% hypoalbuminaemic patients). Goodness-of-fit plots, minimum objective function value, prediction-corrected visual predictive check, bootstrapping, precision and bias of parameter estimates were used for model evaluation. An interactive model-based simulation tool was developed in R (Shiny and R Markdown). Cmax/MIC ratio, time above MIC and AUC/MIC were used for optimizing amikacin initial dose recommendation. Probabilities of reaching targets were calculated for the dosage proposed. Results A one-compartment model with first-order linear elimination best described the 873 amikacin plasma concentrations available from 294 subjects (model development and external validation groups). Estimated amikacin population pharmacokinetic parameters were CL (L/h) = 0.525 + 4.78 × (CKD-EPI/98) × (0.77 × vancomycin) and V (L) = 26.3 × (albumin/2.9)−0.51 × [1 + 0.006 × (weight − 70)], where CKD-EPI is calculated with the Chronic Kidney Disease Epidemiology Collaboration equation. AMKdose is a useful interactive model-based application for a priori optimization of amikacin dosage, using individual patient and microbiological information together with predefined pharmacokinetic/pharmacodynamic (PKPD) targets. Conclusions Serum albumin, total bodyweight, estimated glomerular filtration rate (using the CKD-EPI equation) and co-medication with vancomycin showed a significant impact on amikacin pharmacokinetics. A powerful interactive initial dose-finding tool has been developed and is freely available online. AMKdose could be useful for guiding initial amikacin dose selection before any individual pharmacokinetic information is available.

scholarly journals A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Riccardo De Blasis ◽  
Giovanni Batista Masala ◽  
Filippo Petroni
Keyword(s):  
Markov Model ◽  
Electricity Market ◽  
Wind Farm ◽  
Central Italy ◽  
High Order ◽  
Distribution Model ◽  
Spot Price ◽  
Model Parameters ◽  
Price Series ◽  
Cross Correlations

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

scholarly journals Mathematical pattern of Kessler psychological distress distribution in the general population of the U.S. and Japan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shinichiro Tomitaka ◽  
Toshiaki A. Furukawa
Keyword(s):  
United States ◽  
General Population ◽  
National Survey ◽  
Goodness Of Fit ◽  
Quadratic Function ◽  
The United States ◽  
Depression Screening ◽  
Distribution Model ◽  
Epidemiological Surveys ◽  
Screening Scale

Abstract Background Although the 6-item Kessler psychological scale (K6) is a useful depression screening scale in clinical settings and epidemiological surveys, little is known about the distribution model of the K6 score in the general population. Using four major national survey datasets from the United States and Japan, we explored the mathematical pattern of the K6 distributions in the general population. Methods We analyzed four datasets from the National Health Interview Survey, the National Survey on Drug Use and Health, and the Behavioral Risk Factor Surveillance System in the United States, and the Comprehensive Survey of Living Conditions in Japan. We compared the goodness of fit between three models: exponential, power law, and quadratic function models. Graphical and regression analyses were employed to investigate the mathematical patterns of the K6 distributions. Results The exponential function had the best fit among the three models. The K6 distributions exhibited an exponential pattern, except for the lower end of the distribution across the four surveys. The rate parameter of the K6 distributions was similar across all surveys. Conclusions Our results suggest that, regardless of different sample populations and methodologies, the K6 scores exhibit a common mathematical distribution in the general population. Our findings will contribute to the development of the distribution model for such a depression screening scale.

scholarly journals External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab

Pharmaceutics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1191
Author(s):  
Celine Konecki ◽  
Catherine Feliu ◽  
Yoann Cazaubon ◽  
Delphine Giusti ◽  
Marcelle Tonye-Libyh ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Inflammatory Diseases ◽  
Plasma Concentrations ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
External Evaluation ◽  
Immunomodulatory Treatment ◽  
Weighted Residuals ◽  
Prediction Distribution ◽  
Target Plasma

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from −7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of −0.74 to −0.29 mg/L and mean percent error of −16.6 to −0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

Cluster Analysis of Dynamic Parameters of Gene Expression

2003 ◽  
Vol 01 (03) ◽  
pp. 447-458 ◽  
Author(s):  
Xiwei Wu ◽  
T. Gregory Dewey
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Specific Model ◽  
Model Parameters ◽  
Dynamic Parameters ◽  
Clustering Methods ◽  
Transition Matrices ◽  
Great Utility

Cluster analysis has proven to be a valuable statistical method for analyzing whole genome expression data. Although clustering methods have great utility, they do represent a lower level statistical analysis that is not directly tied to a specific model. To extend such methods and to allow for more sophisticated lines of inference, we use cluster analysis in conjunction with a specific model of gene expression dynamics. This model provides phenomenological dynamic parameters on both linear and non-linear responses of the system. This analysis determines the parameters of two different transition matrices (linear and nonlinear) that describe the influence of one gene expression level on another. Using yeast cell cycle microarray data as test set, we calculated the transition matrices and used these dynamic parameters as a metric for cluster analysis. Hierarchical cluster analysis of this transition matrix reveals how a set of genes influence the expression of other genes activated during different cell cycle phases. Most strikingly, genes in different stages of cell cycle preferentially activate or inactivate genes in other stages of cell cycle, and this relationship can be readily visualized in a two-way clustering image. The observation is prior to any knowledge of the chronological characteristics of the cell cycle process. This method shows the utility of using model parameters as a metric in cluster analysis.

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

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