Validation of Optimal Ampicillin/Sulbactam Ratio in Dosage Forms Using In-Vitro Dynamic Model

Firsov, Alexander A.; Nazarov, Alexey D.; Chernykh, V M; Navashin, S M

doi:10.3109/03639048809152025

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…In vitro dynamic model and operating procedure. A simplified version of the in vitro dynamic model described previously (14,30) was used in the study. Briefly, the model consisted of two connected flasks, one of them containing fresh Mueller-Hinton broth and the other, the central unit, with the same broth containing only a bacterial culture (control growth experiments) or bacterial culture plus antibiotic (killing and regrowth experiments).…”

Section: Methodsmentioning

confidence: 99%

“…Based on these considerations, the term "total killing" was introduced (32), which, for example, may be expressed by the duration of the effect. It was first described as the time interval (T d ) between the zero point (time [t] ϭ 0), when N A is equal to N 0 , and the time to return to the same bacterial numbers in the regrowth phase (25,27) and was later described as the time shift (T E ) between the normal growth and the regrowth curves (14). To express the extent of the reduction of bacterial numbers, another measure describing the number of viable counts (N ) at the end of the observation period that usually mimicked the dosing interval () was introduced (34).…”

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confidence: 99%

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Parameters of bacterial killing and regrowth kinetics and antimicrobial effect examined in terms of area under the concentration-time curve relationships: action of ciprofloxacin against Escherichia coli in an in vitro dynamic model

Firsov¹,

Vostrov²,

Shevchenko³

et al. 1997

Antimicrob Agents Chemother

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Although many parameters have been described to quantitate the killing and regrowth of bacteria, substantial shortcomings are inherent in most of them, such as low sensitivity to pharmacokinetic determinants of the antimicrobial effect, an inability to predict a total effect, insufficient robustness, and uncertain interrelations between the parameters that prevent an ultimate determination of the effect. To examine different parameters, the kinetics of killing and regrowth of Escherichia coli (MIC, 0.013 microg/ml) were studied in vitro by simulating a series of ciprofloxacin monoexponential pharmacokinetic profiles. Initial ciprofloxacin concentrations varied from 0.02 to 19.2 microg/ml, whereas the half-life of 4 h was the same in all experiments. The following parameters were calculated and estimated: the time to reduce the initial inoculum (N0) 10-, 100-, and 1,000-fold (T90%, T99%, and T99.9%, respectively), the rate constant of bacterial elimination (k(elb)), the nadir level (Nmin) in the viable count (N)-versus-time (t) curve, the time to reach Nmin (t(min)), the numbers of bacteria that survived (Ntau) by the end of the observation period (tau), the area under the bacterial killing and regrowth curve (log N(A)-t curve) from the zero point (time zero) to tau (AUBC), the area above this curve (AAC), the area between the control growth curve (log N(C)-t curve) and the bacterial killing and regrowth curve (log N(A)-t curve) from the zero point to tau (ABBC) or to the time point when log N(A) reaches the maximal values observed in the log N(C)-t curve (I(E); intensity of the effect), and the time shift between the control growth and regrowth curves (T(E); duration of the effect). Being highly sensitive to the AUC, I(E), and T(E) showed the most regular AUC relationships: the effect expressed by I(E) or T(E) increased systematically when the AUC or initial concentration of ciprofloxacin rose. Other parameters, especially T90%, T99%, T99.9%, t(min), and log N0 - log Nmin = delta log Nmin, related to the AUC less regularly and were poorly sensitive to the AUC. T(E) proved to be the best predictor and t(min) proved to be the worst predictor of the total antimicrobial effect reflected by I(E). Distinct feedback relationships between the effect determination and the experimental design were demonstrated. It was shown that unjustified shortening of the observation period, i.e., cutting off the log N(A)-t curves, may lead to the degeneration of the AUC-response relationships, as expressed by log N0 - log Ntau = delta log Ntau, AUBC, AAC, or ABBC, to a point where it gives rise to the false idea of an AUC- or concentration-independent effect. Thus, use of I(E) and T(E) provides the most unbiased, robust, and comprehensive means of determining the antimicrobial effect.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Parameters of bacterial killing and regrowth kinetics and antimicrobial effect examined in terms of area under the concentration-time curve relationships: action of ciprofloxacin against Escherichia coli in an in vitro dynamic model

Firsov¹,

Vostrov²,

Shevchenko³

et al. 1997

Antimicrob Agents Chemother

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“…In a recent study, the predictive value of 12 currently relevant endpoint parameters was investigated, using time-kill curves with Escherichia coli exposed to ciprofloxacin over a 1000-fold range of the area under the curve (AUC)/MIC ratio [7]. The endpoints studied included ( Figure 1) the times to 10-fold, 100-fold and 1000-fold reductions of the initial inoculum (N 0 ), ie, T 90% [8], T 99% [9], and T 99.9% , respectively [10]; the slope of time-dependent changes in the difference between logarithms of viable counts with antibiotic (N A ) and without antibiotic (N C -control growth), ie, the "elimination-rate constant" of bacteria (k elb ) [11]; the minimal number of bacteria resulting from exposure to antibiotic (N min ) [12] and the time to reach this nadir (t min ) [12]; the time shift between control growth and the regrowth curves after antibiotic exposure ie, the duration of the effect (T E ) [13]; the viable bacteria count (N ) at the end of the observation period that mimics the usual dosing interval () [14]; the area under log N A -time curve (AUBC) [15,16]; the area above this curve and under the baseline drawn at the level of N A = N 0 (AAC) [17] that is the algebraic sum of the areas around the N 0 level; the area between the control-growth and bacterial killing/regrowth curves (ABBC) [18,19] over the dosing interval, ; and ABBC, determined to the end of the regrowth phase, which is also referred to as the intensity of the antimicrobial effect (I E ) [20].…”

Section: Quantification Of the Antimicrobial Effectmentioning

confidence: 99%

Use of modeling techniques to aid in antibiotic selection

Firsov¹,

Zinner

2001

Curr Infect Dis Rep

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Over the past decade, the use of modeling techniques in the development of novel antibiotics has been primarily associated with in vitro dynamic models. These models allow comparisons among different antibiotics by simulating human pharmacokinetics. Although dynamic models have been used extensively, their full potential has not been achieved because of inadequate experimental design and/or suboptimal quantitation of bacterial killing/regrowth curves inherent in many studies. These issues are discussed in this review, which is based on recent pharmacodynamic findings with novel fluoroquinolones.

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“…no. 275,1987), the AME of sisomicin on various bacterial strains (8), the AME of various ampicillin-sulbactam combinations (9), the influence of different routes (8,9) or regimens of antibiotic administration (7), and, of course, evaluation of AME-concentration dependences (6; A. A. Firsov, Proc.…”

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of antimicrobial effect kinetics in an in vitro dynamic model

Firsov

Chernykh

Navashin

1990

Antimicrob Agents Chemother

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Variants of the available methods for estimating antimicrobial effect kinetics in an in vitro dynamic model were analyzed. Two integral parameters characterizing antimicrobial effect duration (TE) and intensity (IE) are suggested to define and analyze the concentration-effect relationships in these models, irrespective of the method of recording. TE is defined by the time from the moment of antibiotic administration to the moment when the bacterial count again reaches its initial level. IE is defined by the area between the microbial growth curves in the presence and absence of an antibiotic. TE and IE were used to quantify the antimicrobial effects of sisomicin on Pseudomonas aeruginosa 58, Escherichia coli 93, and Klebsiella pneumoniae 5056, simulating the pharmacokinetic profiles of the drugs observed following intramuscular administration in therapeutic doses, including the variability of aminoglycoside concentrations in human blood.The development of dynamic models that allow the simulation of antibiotic pharmacokinetic profiles for the study of the kinetics of antimicrobial effect (AME) has provided a means for evaluating the role of the pharmacokinetic factor in the development of AME (5,8,10) and has stimulated the in vitro assessment of the relative effectiveness of various antibiotics and dosage regimens. However, despite strictly controlled conditions for experiments with dynamic models, conclusions about the preference of one drug or dosage regimen over another are essentially qualitative and not always unequivocal. This results to a considerable degree from deficiencies in the available methods for quantifying AME.The analysis of AME kinetics or, strictly speaking, the kinetics of microbial growth in the presence of an antibiotic traditionally employs models that describe the initial phase of cell growth inhibition under the action of the drug and compare microbial growth rates in the presence and absence of the antibiotic.It has been proposed that four parameters be evaluated to characterize more completely the quantitative change in microbial counts in the presence of an antibiotic. These include the time required for microbial count to be reduced by 99% (Tgg), the time required to reach the minimum count (Tmin), the minimum bacterial cell concentration (Bmin), and the time required for a subsequent 10-fold (1-log) increase in bacterial count (tulg) (19). Unfortunately, the last three parameters can be estimated only by counting CFU after samples are grown on solid nutrient media or with the microcalorimetric technique for Tgg (4). Since the high sensitivity of colony counts is "compensated" for by its insufficient accuracy, the estimates for the parameters Tgg, tjug, and especially Tmin may be only approximate. Moreover, the use of multiple parameters often does not allow the definitive assessment of AME, because of conflicting values of Tg, Bmin, and tjg. A comprehensive analysis of these and other AME parameters is presented elsewhere (8).In view of the above-mentioned considerations, we hav...

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Validation of Optimal Ampicillin/Sulbactam Ratio in Dosage Forms Using In-Vitro Dynamic Model

Cited by 8 publications

References 9 publications

Parameters of bacterial killing and regrowth kinetics and antimicrobial effect examined in terms of area under the concentration-time curve relationships: action of ciprofloxacin against Escherichia coli in an in vitro dynamic model

Parameters of bacterial killing and regrowth kinetics and antimicrobial effect examined in terms of area under the concentration-time curve relationships: action of ciprofloxacin against Escherichia coli in an in vitro dynamic model

Use of modeling techniques to aid in antibiotic selection

Quantitative analysis of antimicrobial effect kinetics in an in vitro dynamic model

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