Quantitative Pharmacodynamic Characterization of Resistance versus Heteroresistance of Colistin in E. coli Using a Semimechanistic Modeling of Killing Curves

Mead, Andrew; Toutain, Pierre‐Louis; Richez, Pascal; Pelligand, Ludovic

doi:10.1128/aac.00793-22

Cited by 7 publications

(6 citation statements)

References 44 publications

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“…In terms of parameters, the finding that EC 50 but not E max could be shared between susceptible and resistant subpopulations for CST is consistent with an earlier observation that heteroresistant bacteria differed from the corresponding dominant populations by E max only, i.e. there was no difference in their EC 50 34 . That EC 50 could be a shared parameter for the four different strains is in line with their similar MIC CST values (0.5–0.75 mg/L).…”

Section: Discussionsupporting

confidence: 89%

“…Mechanistically, both heteroresistance and adaptive resistance may however occur for CST. The concurrent presence of these two phenotypic traits in the same strain has earlier been suggested 28,29 , and both model structures have been successfully applied to describe polymyxin effects without a clear pathogen-dependent preference [30][31][32][33][34] . In terms of parameters, the finding that EC 50 but not E max could be shared between susceptible and resistant subpopulations for CST is consistent with an earlier observation that heteroresistant bacteria differed from the corresponding dominant populations by E max only, www.nature.com/scientificreports/ i.e.…”

Section: Discussionmentioning

confidence: 99%

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Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Zhao,

Kristoffersson,

Khan

et al. 2024

Sci Rep

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Co-administering a low dose of colistin (CST) with ciprofloxacin (CIP) may improve the antibacterial effect against resistant Escherichia coli, offering an acceptable benefit-risk balance. This study aimed to quantify the interaction between ciprofloxacin and colistin in an in silico pharmacokinetic-pharmacodynamic model from in vitro static time-kill experiments (using strains with minimum inhibitory concentrations, MICCIP 0.023–1 mg/L and MICCST 0.5–0.75 mg/L). It was also sought to demonstrate an approach of simulating concentrations at the site of infection with population pharmacokinetic and whole-body physiologically based pharmacokinetic models to explore the clinical value of the combination when facing more resistant strains (using extrapolated strains with lower susceptibility). The combined effect in the final model was described as the sum of individual drug effects with a change in drug potency: for ciprofloxacin, concentration at half maximum killing rate (EC50) in combination was 160% of the EC50 in monodrug experiments, while for colistin, the change in EC50 was strain-dependent from 54.1% to 119%. The benefit of co-administrating a lower-than-commonly-administrated colistin dose with ciprofloxacin in terms of drug effect in comparison to either monotherapy was predicted in simulated bloodstream infections and pyelonephritis. The study illustrates the value of pharmacokinetic-pharmacodynamic modelling and simulation in streamlining rational development of antibiotic combinations.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Zhao,

Kristoffersson,

Khan

et al. 2024

Sci Rep

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“…As the strain displayed decline in cell counts due to colistin treatment in the time kill assay (Figure 5), which was resembling trend reported earlier for colistin heteroresistant bacteria [46], we evaluated whether the MDR clinical isolate of E. coli U1007 displays colistin heteroresistance by performing population analysis profile as reported earlier [47]. U1007 strain was serially diluted and spotted on plates containing increasing concentrations of colistin and cell counts were compared with strain plated on antibiotic free plates and if the ratio greater than 0.0001 the tested strain is deemed as colistin heteroresistant.…”

Section: Time Kill Studysupporting

confidence: 86%

Genome Analysis of Three Bacteriophages Targeting Multi Drug Resistant Clinical Isolate of <em>E. coli, </em>Host Receptor Prediction Using Machine Learning Tools and the Evaluation of Monophages and Cocktail In Vitro and In Vivo

Sundaramoorthy,

KU,

Nair

et al. 2023

Preprint

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The objective of the present study is to isolate phages targeting multidrug resistant (MDR), extended spectrum beta lactamase (ESBL) positive clinical isolate of E. coli ( U1007), sequence and analyze the phage genome and use machine learning tools to predict host cell surface receptor and finally evaluate the efficiency of monophage and phage cocktail in vitro and in vivo in a zebrafish model. Phage specific for E. coli U1007 was isolated from Ganges River (designated as U1G), Cuoom River (designated as CR) and Hospital waste water (designated as M phage). The obtained phages were triple purified and enriched. U1G phages had a greater burst size of 124 PFU/cell and a latent time of 25 min.M phage had a burst size of 150 PFU/cell with a shortlatent time of 20min. Similarly CR phage has a short latent 20 min and a burst size of 115 PFU/cell. Based on capsid architecture, U1G phage resembles Podoviridae, CR phage is structurally similar to Myoviridae and M phage has morphology that resembles Siphoviridae. Genome sequencing and analysis revealed that the size of the U1G phage genome is 73275 bp Whereas that of CR phage and M phage are 45236 bp and 45294 bp, respectively. All three genomes were marked by the absence of genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using algorithms such as Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen , LamB or OmpC for U1G FhuA, OmpC for CR phage and FhuA, LamB, TonB or OmpF for the M phage as the host receptor targeted by the receptor binding protein (RBP) of the phages. In vitro time kill assay showed that treatment with monophages alone and along with colistin resulted in regrowth whereas phage combinations significantly reduced the regrowth and by 24h the phage cocktail along with colistin produced a significant 3 log declinein cell counts relative to the untreated control. In vivo intramuscular infection study in zebrafish showed that phages were non toxic and a cocktail of dual (U1G +M) phage along with colsitin resulted in a significant 3.5 log decline in cell counts whereas triple phage combination along with colistin resulted in 3 log decline in cell counts probably due to host receptor competition. Our study highlights the potential of phage cocktail therapy in mitigating MDR clinical isolate of E. coli in vitro and in vivo.

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“…Thus, we concluded that the initial population (starting inoculum) consisted of two subpopulations, representing a heterogenous bacterial population with a proportion (F1) of bacteria being a highly susceptible dominant population (S1) and the remaining sub-dominant population (S2) having a lower susceptibility. F1 was estimated by the model illustrated in Figure 2 and as shown by Mead et al [11].…”

Section: Pre-existing Heterogenous Population Modelmentioning

confidence: 99%

“…Models describe (1) bacterial growth and natural death, (2) drug effects, (3) regrowth and resistance emergence, and (4) antibiotic combinations and their interactions [8]. These models have been implemented in veterinary medicine [7,11,12]. With regard to veterinary fluoroquinolones (FQs) and canine skin pathogens, different studies have evaluated their killing effects in comparison with other antimicrobial classes using standard kill curves without modelling [13,14] or a simple inhibitory I max model using only the final bacterial counts at 24 h [15].…”

Section: Introductionmentioning

confidence: 99%

Time-Kill Analysis of Canine Skin Pathogens: A Comparison of Pradofloxacin and Marbofloxacin

Azzariti,

Mead,

Toutain

et al. 2023

Antibiotics

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Time-kill curves (TKCs) are more informative compared with the use of minimum inhibitory concentration (MIC) as they allow the capture of bacterial growth and the development of drug killing rates over time, which allows to compute key pharmacodynamic (PD) parameters. Our study aimed, using a semi-mechanistic mathematical model, to estimate the best pharmacokinetic/pharmacodynamic (PK/PD) indices (ƒAUC/MIC or %ƒT > MIC) for the prediction of clinical efficacy of veterinary FQs in Staphylococcus pseudintermedius, Staphylococcus aureus, and Escherichia coli collected from canine pyoderma cases with a focus on the comparison between marbofloxacin and pradofloxacin. Eight TCKs for each bacterial species (4 susceptible and 4 resistant) were analysed in duplicate. The best PK/PD index was ƒAUC24h/MIC in both staphylococci and E. coli. For staphylococci, values of 25–40 h were necessary to achieve a bactericidal effect, whereas the calculated values (25–35 h) for E. coli were lower than those predicting a positive clinical outcome (100–120 h) in murine models. Pradofloxacin showed a higher potency (lower EC50) in comparison with marbofloxacin. However, no difference in terms of a maximal possible pharmacological killing rate (Emax) was observed. Taking into account in vivo exposure at the recommended dosage regimen (3 and 2 mg/kg for pradofloxacin and marbofloxacin, respectively), the overall killing rates (Kdrug) computed were also similar in most instances.

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Quantitative Pharmacodynamic Characterization of Resistance versus Heteroresistance of Colistin in E. coli Using a Semimechanistic Modeling of Killing Curves

Abstract: Heteroresistance corresponds to the presence, in a bacterial isolate, of an initial small subpopulation of bacteria characterized by a significant reduction in their sensitivity to a given antibiotic. Mechanisms of heteroresistance versus resistance are poorly understood.

Cited by 7 publications

References 44 publications

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Quantifying combined effects of colistin and ciprofloxacin against Escherichia coli in an in silico pharmacokinetic-pharmacodynamic model

Genome Analysis of Three Bacteriophages Targeting Multi Drug Resistant Clinical Isolate of <em>E. coli, </em>Host Receptor Prediction Using Machine Learning Tools and the Evaluation of Monophages and Cocktail In Vitro and In Vivo

Time-Kill Analysis of Canine Skin Pathogens: A Comparison of Pradofloxacin and Marbofloxacin

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