2018
DOI: 10.1101/369975
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Drug-target binding quantitatively predicts optimal antibiotic dose levels

Abstract: Antibiotic treatment may be compromised by the sporadic appearance and selection of drug resistant mutants during therapy. Identifying optimal dosing strategies for treating bacterial infections that minimize the risk of resistance is difficult, and improving dosing guidelines usually requires long and costly in vitro and in vivo experimentation. Previously, we proposed a mathematical model that links bacterial population biology with chemical reaction kinetics and demonstrated that this model had high predict… Show more

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Cited by 4 publications
(2 citation statements)
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“…The affinity K D of the drug is 125 thus the ratio of off-rate to on-rate, K D = k R /k F . The model assumes that the growth and death 126 rates of a bacterial cell depend on its drug-target occupancy (that is, the number of inactivated 127 drug-target complexes it contains) (Clarelli et al, 2019;Wiesch et al, 2015). We denote drug-128 target occupancy with the index i, which ranges from 0 to N. Cells harboring successively 129 larger numbers of inactivated drug-target complexes have successively faster death rates curves derived from the model for a wild-type (light green) and drug-resistant (dark green) 152 bacterial strain.…”
Section: A Model That Links Bacterial Population Dynamics With Molecumentioning
confidence: 99%
“…The affinity K D of the drug is 125 thus the ratio of off-rate to on-rate, K D = k R /k F . The model assumes that the growth and death 126 rates of a bacterial cell depend on its drug-target occupancy (that is, the number of inactivated 127 drug-target complexes it contains) (Clarelli et al, 2019;Wiesch et al, 2015). We denote drug-128 target occupancy with the index i, which ranges from 0 to N. Cells harboring successively 129 larger numbers of inactivated drug-target complexes have successively faster death rates curves derived from the model for a wild-type (light green) and drug-resistant (dark green) 152 bacterial strain.…”
Section: A Model That Links Bacterial Population Dynamics With Molecumentioning
confidence: 99%
“…Based on the extended model, we have developed an interactive web-based tool, namely vCOMBAT, to allow non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding. In contrast to our previously developed COMBAT modeling framework [18], this tool allows to incorporate antibiotic time-concentration profiles measured in patients. The tool can inform optimal dosing strategies based on antibiotic and bacteria data provided by the users.…”
Section: Introductionmentioning
confidence: 99%