Emma Murphy

Efficient and accurate experimental design for enzyme kinetics: Bayesian studies reveal a systematic approach

In areas such as drug development, clinical diagnosis and biotechnology research, acquiring details about the kinetic parameters of enzymes is crucial. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. We demonstrate that a Bayesian approach (the use of prior knowledge) can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian Utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of K(M) and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated.

Effective experimental design: enzyme kinetics in the bioinformatics era.

Acquiring details about the kinetic parameters of enzymes is crucial to both drug development and clinical diagnosis. The correct design of an experiment is crucial to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics now frequently studied, further work is required to estimate parameters of such models with low variance. This review examines the different options available to produce major gains in information, productivity and the accuracy of each experiment.

International variation in classification of dialysis withdrawal: a systematic review

BACKGROUND AND OBJECTIVES In patients with end-stage renal disease (ESRD), the rate of deaths preceded by dialysis withdrawal is high. However, rates vary across studies and national renal registries. This study aimed to (i) determine how dialysis withdrawal mortality is defined in the literature and (ii) whether mortality rates preceded by dialysis withdrawal change over time. METHODS MEDLINE (1946 to March 2012) and EMBASE (1980 to March 2012) databases were searched. We included epidemiological studies that reported data permitting calculation of crude (unadjusted) mortality rates preceded by dialysis withdrawal. Definitions of dialysis withdrawal were also extracted. Crude mortality rates and 95% confidence intervals were calculated using OpenEpi software. Non-English language studies were excluded. RESULTS Twenty-three eligible studies were identified; these included 14 527 885 dialysis patients at risk from six countries. Crude mortality rates preceded by dialysis withdrawal ranged from 3 to 50.2 per 1000 person-years. Seven different definitions of dialysis withdrawal were identified, with no assessment of validity. Crude mortality rates preceded by withdrawal have increased over time across the study period 1966 (3 per 1000 person-years) to 2010 (48.6 per 1000 person-years), although these rates are difficult to interpret because of differences in classification. In the USA crude mortality rates preceded by dialysis withdrawal are higher in the older population and have increased over time in the age group 65+ years. In this age group, the crude mortality rate preceded by dialysis withdrawal was 89.4 per 1000 person-years (2008-10) compared with 26.1 per 1000 person-years in the age group 50-64 years (2008-10). CONCLUSION Mortality rates preceded by dialysis withdrawal over time should be interpreted with caution because of differences in classification. Types of dialysis withdrawal need more careful elucidation, and we propose a unified classification of dialysis withdrawal based on trajectories and causal criteria.

Symptom Burden and Associated Factors in Renal Transplant Patients in the U.K.

CONTEXT Renal transplantation is gold standard care in end-stage kidney disease, but little is known about symptom prevalence in transplanted patients. OBJECTIVES This study assesses symptom prevalence in this population. METHODS This was a U.K.-based, cross-sectional symptom survey of end-stage kidney disease patients transplanted more than one year previously. Patient-reported data were collected using the renal Patient Outcome Scale. Demographic/clinical data also were collected, including estimated glomerular filtration rate (eGFR), renal diagnosis, and comorbidity. RESULTS One hundred ten patients participated; mean age was 47 years (SD 13.6), and mean eGFR was 46 mL/min (SD 16.8, range 14-101). Symptom burden was high, with a mean of seven symptoms, but marked variance (SD 5.2, range 0-22). The most prevalent symptoms were weakness (56%, 95% CI 47-65), difficulty sleeping (46%, 95% CI 37-56), dyspnea (42%, 95% CI 33-51), feeling anxious (36%, 95% CI 28-46), and drowsiness (36%, 95% CI 28-46). Certain symptoms-weakness, difficulty sleeping, dyspnea, and drowsiness-were commonly reported as severe. A significant inverse relationship between renal function, as measured by eGFR, and number of symptoms (P<0.05) emerged. CONCLUSION For renal transplant recipients, symptom burden is similar to dialysis, although with less pain, anorexia, and immobility. Routine symptom assessment should be undertaken in transplant patients to identify these often undisclosed symptoms.

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost‐effectiveness by reducing the necessary amount of experiments and data points measured.

Palliative Nephrology: Time for New Insights

In 1971, Belding Scribner, the father of maintenance dialysis, recognized the complexities of dialysis decision making, anticipating both the importance of deciding not to treat and the difficulty deciding when to stop. As Scribner recounted to Renée Fox in the book The Courage to Fail: A Social View of Organ Transplants and Dialysis, “Selection . will take the form of advising the patient and his family that we don’t think this treatment is right for him. And then for some patients, there will be the usually agonizing decision of whether or not to terminate treatment.” 373) Since the inception of dialysis, decisions about initiating or withdrawing renal replacement therapy have challenged the nephrology community. The issues raised by Scribner more than 40 years ago have become increasingly prominent as the age and comorbid condition burden of the advanced kidney failure population increases. Dialysis treatment has many inherent risks for older frailer patients with comorbid illness. In addition, the burdens of treatment are considerable and may outweigh the benefits for frail older patients. This can result in an accelerated decline in quality of life and independence and/ or high prevalence of depression, symptoms, and cognitive impairment after dialysis therapy initiation. As a result, patients may choose or be advised to not initiate dialysis therapy, and increasing numbers withdraw from dialysis therapy before death. Palliative nephrology initiatives in both the United States and the United Kingdom have influenced how care is delivered toward the end of life, although practice across countries is notably different. In the United States, pioneering work in the 1990s led to developments in palliative care for dialysis patients and a focus of research, policy, and practice on dialysis therapy withdrawal. Conversely, in the United Kingdom, the emphasis has been on developing conservative (nondialytic) management programs. Today, conservative kidney management is one of the fastest growing areas of renal medicine. In palliative nephrology, a creative dynamic among practice, research, and policy has emerged, although there remain important differences in these areas between the United States and the United Kingdom. Rates of dialysis therapy withdrawal range widely from 3 to 50.2/1,000 person-years and have increased over time. Most data for dialysis therapy withdrawal practice originate from the US Renal Data System,

Novel Experimental Design for Steady-state Processes: A Systematic Bayesian Approach for Enzymes, Drug Transport, Receptor Binding, Continuous Culture and Cell Transport Kinetics

We demonstrate that a Bayesian approach (the use of prior knowledge) to the design of steady-state experiments can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. It is equally applicable to enzymes, drug transport, receptor binding, microbial culture and cell transport kinetics.

Information BiotechnologyEffective experimental design: enzyme kinetics in the bioinformatics era

Acquiring details about the kinetic parameters of enzymes is crucial to both drug development and clinical diagnosis. The correct design of an experiment is crucial to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics now frequently studied, further work is required to estimate parameters of such models with low variance. This review examines the different options available to produce major gains in information, productivity and the accuracy of each experiment.

Effective experimental design

Acquiring details about the kinetic parameters of enzymes is crucial to both drug development and clinical diagnosis. The correct design of an experiment is crucial to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics now frequently studied, further work is required to estimate parameters of such models with low variance. This review examines the different options available to produce major gains in information, productivity and the accuracy of each experiment.