Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/17818
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dc.contributor.authorMichael, Jacob-
dc.contributor.authorDada, Michael-
dc.contributor.authorYusuf, Shakirudeen-
dc.date.accessioned2023-01-25T14:13:50Z-
dc.date.available2023-01-25T14:13:50Z-
dc.date.issued2017-11-24-
dc.identifier.citationMichael, J., Dada, O. M., Yusuf, S. (2017). Pharmacokinetics/Pharmacodynamics Modelling and Simulation with MATLAB. Nigerian Institute of Physics (NIP) 40th Annual Conference. Federal University of Technology, Minna, Niger State. 20-24 November, 2017.en_US
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/17818-
dc.descriptionNigerian Institute of Physics (NIP) 40th Annual Conference. Federal University of Technology, Minna, Niger State. 20-24 November, 2017.en_US
dc.description.abstractPopulation pharmacokinetics has taken off with an exponential increase in published papers in the last decade. This has revolutionized how data from clinical studies is analyzed. Population pharmacokinetics methods are used almost exclusively for phase II and III studies and to summarize data across a drug development program. Advances in pharmacokinetic and pharmacodynamic modeling will allow fewer, more focused and informative clinical trials, and lead to significant cost savings. However, despite these advances, population methods are not routinely easy to employ. A major hindrance to implementing population methods is that it is mathematically and statistically complex. Hence, in this study, we have used MATLAB computer program to obtain information on effective dosage regimens of regimens by a modeling and simulation approach based on pharmacokinetic (PK)/pharmacodynamic (PD) theory. We have introduced a modification to the PK/PD model which explains in-vitro bactericidal kinetics of regimens for several Pseudomonas aeruginosa strains. Time-course profiles of bacterial counts in patients infected with P. aeruginosa were simulated for typical clinical dosage regimens considering the variability of PK and the patients' backgrounds by a Monte Carlo simulation. Moreover, time-course profiles of probability achieving the criterion (log(CFU/mL) < 0) were predicted for the evaluation of antibacterial efficacy by renal function. The in-vitro bacterial profiles at various dosage regimens could be well explained by the PK/PD model. The simulations suggested the dependence of antibacterial efficacy on the frequency of administration, indicating time-dependent antibacterial activity. It was also shows in two weeks after the start of treatment. Our approach in this study should be useful for determining and drug development.en_US
dc.description.sponsorshipNilen_US
dc.language.isoenen_US
dc.publisherNigerian Institute of Physicsen_US
dc.relation.ispartofseriesCurriculum Vitae;55-
dc.subjectPopulation pharmacokineticsen_US
dc.subjectpharmacodynamic modelingen_US
dc.subjectdrug developmenten_US
dc.subjectstatistically complexen_US
dc.subjectMATLAB computer programen_US
dc.subjectPK/PD modelen_US
dc.titlePharmacokinetics/Pharmacodynamics Modelling and Simulation with MATLABen_US
dc.typeOtheren_US
Appears in Collections:Physics

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