title: Modeling the mechanism of postantibiotic effect and determining implications for dosing regimens.
abstract: A stochastic model is proposed to explain one possible underlying mechanism of the postantibiotic effect (PAE). This phenomenon, of continued inhibition of bacterial growth after removal of the antibiotic drug, is of high relevance in the context of optimizing dosing regimens. One clinical implication of long PAE lies in the possibility of increasing intervals between drug administrations. The model describes the dynamics of synthesis, saturation and removal of penicillin binding proteins (PBPs). High fractions of saturated PBPs are in the model associated with a lower growth capacity of bacteria. An analytical solution for the bivariate probability of saturated and unsaturated PBPs is used as a basis to explore optimal antibiotic dosing regimens. Our finding that longer PAEs do not necessarily promote for increased intervals between doses, might help for our understanding of data provided from earlier PAE studies and for the determination of the clinical relevance of PAE in future studies.
authors.affiliation: Department of Mathematics, Stockholm University, Stockholm, Sweden. patricia@math.su.se
authors.forename: Patricia
authors.initials: P
authors.lastname: Geli
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000008:administration & dosage* / Q000378:metabolism / Q000494:pharmacology*; D001419:Bacteria / Q000187:drug effects* / Q000254:growth & development* / Q000378:metabolism; D004352:Drug Resistance, Microbial; D006801:Humans; D008954:Models, Biological; D046915:Penicillin-Binding Proteins / Q000378:metabolism; D010406:Penicillins / Q000008:administration & dosage / Q000378:metabolism / Q000494:pharmacology; D013269:Stochastic Processes
chemical_list: D000900:Anti-Bacterial Agents; D046915:Penicillin-Binding Proteins; D010406:Penicillins