1,721,067 research outputs found
Comparison of robust criteria for D-optimal designs
No full textThis study compared the performance of a local and three robust optimality criteria in terms of the standard error for a one-parameter and a two-parameter nonlinear model with uncertainty in the parameter values. The designs were also compared in conditions where there was misspecification in the prior parameter distribution. The impact of different correlation between parameters on the optimal design was examined in the two-parameter model. The designs and standard errors were solved analytically whenever possible and numerically otherwise
Evaluation of the pre-posterior distribution of optimized sampling times for the design of pharmacokinetic studies
No full textInformation theoretic methods are often used to design studies that aim to learn about pharmacokinetic and linked pharmacokinetic–pharmacodynamic systems. These design techniques, such as D-optimality, provide the optimum experimental conditions. The performance of the optimum design will depend on the ability of the investigator to comply with the proposed study conditions. However, in clinical settings it is not possible to comply exactly with the optimum design and hence some degree of unplanned suboptimality occurs due to error in the execution of the study. In addition, due to the nonlinear relationship of the parameters of these models to the data, the designs are also locally dependent on an arbitrary choice of a nominal set of parameter values. A design that is robust to both study conditions and uncertainty in the nominal set of parameter values is likely to be of use clinically. We propose an adaptive design strategy to account for both execution error and uncertainty in the parameter values. In this study we investigate designs for a one-compartment first-order pharmacokinetic model. We do this in a Bayesian framework using Markov-chain Monte Carlo (MCMC) methods. We consider log-normal prior distributions on the parameters and investigate several prior distributions on the sampling times. An adaptive design was used to find the sampling window for the current sampling time conditional on the actual times of all previous samples
Model-based Drug Dosing
No full textThe safe and effective use of medicines requires that prescribers use the right drug for the right patient at the right dose. The choice of dosing regimen requires a quantitative understanding of the magnitude of the drug effect for any given dose, the time course over which desired and adverse effects are expected to occur, and how variability between patients will impact dose requirements. The methods used by prescribers to achieve individualised dosing in practice are poorly understood and it is likely that a trial and error process prevails for many drugs. A model-based approach to drug dosing provides a means of predicting drug response in individual patients and, therefore, constitutes a useful tool for designing safe and effective dosing regimens in clinical practice.
A model-based approach was explored for the dosing of warfarin, simvastatin and allopurinol. Each drug presented a different challenge in the clinical setting with regards to safe and effective dosing and this necessitated the use of different pharmacometric methodologies in each case.
For warfarin, a Bayesian forecasting method for dose individualisation was developed. The method required the identification and evaluation of a suitable prior model and the development of a novel optimal International Normalised Ratio (INR) sampling design for Bayesian parameter control. The performance of this design was evaluated using simulation-estimation techniques. It was predicted that this method will result in a substantial improvement in INR control and time-in-the-therapeutic range compared to currently dosing methods.
For simvastatin, a simulation based study using a published pharmacokinetic-pharmacodynamic model was conducted. This analysis addressed a clinical research question related to the practice of dosing simvastatin at bedtime. The model predicted that circadian low-density lipoprotein (LDL) production had a negligible impact on simvastatin effect and that dosing in the morning should be considered for patients who may be less compliant with bedtime dosing.
For allopurinol, a novel population parent-metabolite model was developed. A covariate analysis found that renal function, fat free mass and diuretic use determined the differences in allopurinol and oxypurinol exposure between patients. This pharmacokinetic model provided the basis for an integrated PKPD model, intended to describe the effect of allopurinol on serum urate. While a suitable PD model could not be developed with the data available, optimal design methodologies were used to evaluate future study designs and alternative models for the allopurinol-urate effect.
A PKPD model-based approach to inform rational drug dosing was successfully demonstrated for warfarin, simvastatin, and allopurinol. By quantifying the magnitude and time course of drug effects and by elucidating the patient characteristics which determine drug response between individuals, the model-based approach to drug dosing provides a useful tool to aid the safe and effective use of medicines in clinical practice
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Model-based Drug Dosing
No full textThe safe and effective use of medicines requires that prescribers use the right drug for the right patient at the right dose. The choice of dosing regimen requires a quantitative understanding of the magnitude of the drug effect for any given dose, the time course over which desired and adverse effects are expected to occur, and how variability between patients will impact dose requirements. The methods used by prescribers to achieve individualised dosing in practice are poorly understood and it is likely that a trial and error process prevails for many drugs. A model-based approach to drug dosing provides a means of predicting drug response in individual patients and, therefore, constitutes a useful tool for designing safe and effective dosing regimens in clinical practice.
A model-based approach was explored for the dosing of warfarin, simvastatin and allopurinol. Each drug presented a different challenge in the clinical setting with regards to safe and effective dosing and this necessitated the use of different pharmacometric methodologies in each case.
For warfarin, a Bayesian forecasting method for dose individualisation was developed. The method required the identification and evaluation of a suitable prior model and the development of a novel optimal International Normalised Ratio (INR) sampling design for Bayesian parameter control. The performance of this design was evaluated using simulation-estimation techniques. It was predicted that this method will result in a substantial improvement in INR control and time-in-the-therapeutic range compared to currently dosing methods.
For simvastatin, a simulation based study using a published pharmacokinetic-pharmacodynamic model was conducted. This analysis addressed a clinical research question related to the practice of dosing simvastatin at bedtime. The model predicted that circadian low-density lipoprotein (LDL) production had a negligible impact on simvastatin effect and that dosing in the morning should be considered for patients who may be less compliant with bedtime dosing.
For allopurinol, a novel population parent-metabolite model was developed. A covariate analysis found that renal function, fat free mass and diuretic use determined the differences in allopurinol and oxypurinol exposure between patients. This pharmacokinetic model provided the basis for an integrated PKPD model, intended to describe the effect of allopurinol on serum urate. While a suitable PD model could not be developed with the data available, optimal design methodologies were used to evaluate future study designs and alternative models for the allopurinol-urate effect.
A PKPD model-based approach to inform rational drug dosing was successfully demonstrated for warfarin, simvastatin, and allopurinol. By quantifying the magnitude and time course of drug effects and by elucidating the patient characteristics which determine drug response between individuals, the model-based approach to drug dosing provides a useful tool to aid the safe and effective use of medicines in clinical practice
Understanding and quantifying adherence and its link to therapeutic success
No full textIt is known that increased adherence to appropriately prescribed drugs is associated with better therapeutic outcomes and contributes to lower mortality. Adherence is described by three processes, namely initiation, implementation and discontinuation. The use of electronic monitoring e.g. Medication Event Monitoring System (MEMS) has enabled a quantitative understanding of the three processes of adherence. This includes delayed initiation, early discontinuation and particularly those around implementation including timing variability, random missed doses and drug holidays. There have been many attempts to improve adherence. An alternative approach to assist patients with suboptimal adherence to still attain therapeutic success lies in the choice of forgiving drugs. Forgiveness is a drug specific property that determines how sensitive therapeutic success is under imperfect adherence.
The overarching aim of this thesis was to quantify adherence, influences of factors on adherence and the influence of adherence on therapeutic success. This involved a series of investigations.
Initially, the independent influence of various factors on adherence in two diseases studied, i.e. HIV and hypertension, was determined. The factors included disease, age and dosing regimen. A model-based meta-analysis (MBMA) was adopted in this work to allow for multivariate analyses and continuous dependent variables. It was found that (1) although the influence of disease on adherence was significant, it is likely to be of limited clinical significance (2) increased age positively impacts on adherence and (3) the greater frequency of dosing regimens negatively impacts on adherence.
Various measures of adherence were found to be used in the MEMS literature. Despite the advanced ability of MEMS to record patterns of drug taking, percentage of doses taken was the most commonly used measure. Appropriate summary measures of adherence in relation to adherence patterns are suggested in this thesis. These included percentage of days with correct dosing in conjunction with the number as well as the occurrence of missed doses within a timeframe.
The feasibility of conducting the first MEMS study in New Zealand was undertaken. This study provided suggestions for future MEMS studies in terms of patient identification, recruitment and retention. Collected adherence data were summarised in relation to adherence patterns.
A criterion to quantify the forgiveness of drugs to imperfect adherence was developed. The criterion is described as relative forgiveness (RF). RF is defined as the number of times more likely that target success is attained under perfect adherence compared to imperfect adherence. RF covers the quantification of forgiveness in two scenarios, namely (1) forgiveness of a given drug; and (2) forgiveness between two drugs whose effects can be quantified on the same biomarker of response. Subsequently, RF was illustrated with a hypothetical example and then applied to warfarin as a motivation example. This work was considered at the population level.
The developed relative forgiveness criterion was applied to atorvastatin and omeprazole at the individual patient level. Hence, an individual patient’s clinically observed adherence profile, obtained from the MEMS feasibility study, was used. This study evaluated that RF is generalisable to other drugs of interest. In addition, it can be used at an individual patient level in terms of each patient’s adherence profile. Ultimately, whether or not a drug is forgiving for each patient depends on the individual adherence profile in conjunction with the individual PKPD properties.
In conclusion, better understanding of factors influencing adherence was provided. Adherence data in terms of adherence patterns were described. Ultimately, the time course of drug effects in relation to adherence patterns was quantified. This allows for determining of the forgiveness of drugs under imperfect adherence patterns
- …
