BackgroundThe 24-h area under the concentration–time curve (AUC24)/minimal inhibitory concentration ratio is the best predictive pharmacokinetic/pharmacodynamic (PK/PD) parameter of the efficacy of first-line anti-tuberculosis (TB) drugs. An optimal sampling strategy (OSS) is useful for accurately estimating AUC24; however, OSS has not been developed in the fed state or in the early phase of treatment for first-line anti-TB drugs.MethodsAn OSS for the prediction of AUC24 of isoniazid, rifampicin, ethambutol and pyrazinamide was developed for TB patients starting treatment. A prospective, randomized, crossover trial was performed during the first 3 days of treatment in which first-line anti-TB drugs were administered either intravenously or in fasting or fed conditions. The PK data were used to develop OSS with best subset selection multiple linear regression. The OSS was internally validated using a jackknife analysis and externally validated with other patients from different ethnicities and in a steady state of treatment.ResultsOSS using time points of 2, 4 and 8 h post-dose performed best. Bias was < 5% and imprecision was < 15% for all drugs except ethambutol in the fed condition. External validation showed that OSS2-4-8 cannot be used for rifampicin in steady state conditions.ConclusionOSS at 2, 4 and 8 h post-dose enabled an accurate and precise prediction of AUC24 values of first-line anti-TB drugs in this population.Trial RegistrationClinicalTrials.gov (NCT02121314).

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Over the past few years, Java has evolved into a mature platform for developing enterprise applications. A critical factor for the commercial success of these applications is end-to-end performance, e.g., in terms of response times, throughput and availability. This raises the need for the development, validation and analysis of performance models to predict performance metrics of interest. To develop and validate performance models, insight in the execution behavior of the application is essential, requiring advanced monitoring capabilities. In this paper we introduce our Java Performance Monitoring Toolkit (JPMT). JPMT represents internal execution behavior of Java applications by event traces. An event represents the occurrence of some activity, such as thread creation, method invocation, and locking contention. Events are annotated by highresolution performance attributes, e.g., duration of locking contention and CPU time usage by method invocations. JPMT is an open toolkit, its event trace API can be used to develop custom performance analysis applications. JPMT comes with an event trace visualizer and a command-line event trace query tool for scripting. JPMT supports event filtering during and after application execution.The instrumentation required for monitoring the application is added transparently to the user during run-time. Overhead is minimized by only instrumenting for events the user is interested in.Furthermore, the instrumentation itself is carefully optimized. This paper discusses the architecture and implementation of the toolkit in detail and reports on our experience in applying the toolkit to model a CORBA implementation.

This paper describes our Java Performance Monitoring Toolkit (JPMT), which is developed for detailed analysis of the behavior and performance of Java applications. JPMT represents internal execution behavior of Java applications by event traces, where each event represents the occurrence of some activity, such as thread creation, method invocation, and locking contention. JPMT supports event filtering during and after application execution. Each event is annotated by high-resolution performance attributes, e.g., duration of locking contention and CPU time usage by method invocations. JPMT is an open toolkit, its event trace API can be used to develop custom performance analysis applications. JPMT comes with an event trace visualizer and a command-line event trace query tool for scripting purposes. The instrumentation required for monitoring the application is added transparently to the user during run-time. Overhead is minimized by only instrumenting for events the user is interested in and by careful implementation of the instrumentation itself.

The tremendous growth of the Internet [42] and the ongoing developments in the hardware and software industry have boosted the development of Information and Communication Technology (ICT) systems. These systems consist of geographically distributed components communicating with each other using networking technology. Such systems are commonly referred to as distributed systems. A key challenge of distributed systems is interoperability: the vast diversity in hardware, operating systems, and programming languages, makes it difficult to build distributed applications. Over the past decade there have been a lot of advances in middleware technology aimed at solving this interoperability problem. Middleware is software that hides architectural and implementation details of an underlying system and offers well-defined interfaces instead. Some of the key advances in middleware include OMG CORBA objectmiddleware and the Sun Java infrastructure middleware. The overall objective of this thesis is to develop and validate quantitative performance models of distributed applications based on middleware technology. We limit the scope of our research toOMG CORBA object middleware and the Java EE platform.