ArticleFuture microbiology2011
Relationship between pharmacodynamic indices and killing patterns in vitro.
Article in Future microbiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Antimicrobial agents are conventionally categorized in three classes based on their pharmacokinetic/pharmacodynamic properties, based on their exposure response relationship in vivo with either area under the concentration-time curve, C(max) or T>MIC. Alternatively, they are often categorized as 'concentration dependent' and 'concentration independent' (or time dependent), based on their in vitro kill kinetics. However, both of these classifications are arbitrary at best. Various classes of drugs display different modes of action and in reality there is a whole spectrum of pharmacokinetic/pharmacodynamic characteristics. Whereas the relationship between in vitro kill kinetics and in vivo exposure response relationships has been demonstrated in the past, methods were cumbersome and the results of such studies were not always easy to grasp. In the study, Tam and Nikolaou develop a framework by defining the average kill rate D during a dosing interval based on in vitro kill kinetics and correlate that with dosing regimen design as well as dose. They show, in an easy-to-grasp manner, what these correlations entail and the impact of killing characteristics on pharmacodynamic behavior in vivo. They conclude that their framework could be used in drug development to predict efficacy in vivo and the effect of changing doses and dosing regimens can be used as a tool in decision support.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.