Literature DB >> 12384367

Determination of antibiotic effect in an in vitro pharmacodynamic model: comparison with an established animal model of infection.

Charles R Bonapace1, Lawrence V Friedrich, John A Bosso, Roger L White.   

Abstract

Animal infection models have historically been used to study pharmacodynamic relationships. Similar results could theoretically be produced by using an in vitro pharmacodynamic model as an alternative to animal models. We compared the antibiotic effects of ticarcillin administered in various doses and dosing regimens against Pseudomonas aeruginosa ATCC 27853 under conditions analogous to those previously employed in a neutropenic-mouse thigh infection model (B. Vogelman et al., J. Infect. Dis. 158:831-847, 1988). Ticarcillin dosages of either 96, 192, or 384 mg/day were administered at 1-, 2-, 3-, 4-, 8-, 12-, or 24-h intervals into a two-compartment model in order to duplicate the concentration-time profiles of the animal model. Colony counts were enumerated at 0 and 24 h. Linear regression and sigmoidal maximum-effect (Emax) model fitting were used to assess the relationship between the percentage of time that the concentration remained above the MIC (%T>MIC) or above four times the MIC (%T>4xMIC) and the change in the log(10) CFU per milliliter (Deltalog(10) CFU/ml) in the central and peripheral compartments. Statistical analysis of the Deltalog(10) CFU/ml values was performed for matched regimens of the in vitro and animal models based on the %T>MICs. The slopes of the regression equations of %T>MICs relative to Deltalog(10) CFU/ml values were similar for the in vitro and animal models, but the y intercept was greater with the in vitro model. The Deltalog(10) CFU/ml values of the 0- to 24-h colony counts at equivalent %T>MICs in the two models were not statistically different (P = 0.087). Overall, the peripheral compartment of the in vitro model was a better predictor of effect than the central compartment. This study, which compares pharmacodynamic principles between an in vitro and an animal model, demonstrated similar relationships between %T>MICs and effects.

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Year:  2002        PMID: 12384367      PMCID: PMC128701          DOI: 10.1128/AAC.46.11.3574-3579.2002

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


  21 in total

1.  In-vivo assessment of in-vitro killing patterns of Pseudomonas aeruginosa.

Authors:  A U Gerber; C Feller-Segessenmann
Journal:  J Antimicrob Chemother       Date:  1985-01       Impact factor: 5.790

2.  Historical review of in-vitro models.

Authors:  S Grasso
Journal:  J Antimicrob Chemother       Date:  1985-01       Impact factor: 5.790

Review 3.  In vitro models for the study of antibiotic activities.

Authors:  J Blaser; S H Zinner
Journal:  Prog Drug Res       Date:  1987

4.  Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations.

Authors:  K Yamaoka; T Nakagawa; T Uno
Journal:  J Pharmacokinet Biopharm       Date:  1978-04

5.  Effect of the ratio of surface area to volume on the penetration of antibiotics in to extravascular spaces in an in vitro model.

Authors:  L L Van Etta; L R Peterson; C E Fasching; D N Gerding
Journal:  J Infect Dis       Date:  1982-09       Impact factor: 5.226

Review 6.  Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men.

Authors:  W A Craig
Journal:  Clin Infect Dis       Date:  1998-01       Impact factor: 9.079

7.  Comparison study of the kinetics of ceftizoxime penetration into extravascular spaces with known surface area/volume ratio in vitro and in vivo in rabbits.

Authors:  L L Van Etta; C E Fasching; L R Peterson; D N Gerding
Journal:  Antimicrob Agents Chemother       Date:  1983-01       Impact factor: 5.191

8.  Cefmenoxime efficacy, safety, and pharmacokinetics in critical care patients with nosocomial pneumonia.

Authors:  J J Schentag; D P Reitberg; T J Cumbo
Journal:  Am J Med       Date:  1984-12-21       Impact factor: 4.965

9.  Selection of aminoglycoside-resistant variants of Pseudomonas aeruginosa in an in vivo model.

Authors:  A U Gerber; A P Vastola; J Brandel; W A Craig
Journal:  J Infect Dis       Date:  1982-11       Impact factor: 5.226

10.  Kinetics of antimicrobial activity.

Authors:  B Vogelman; W A Craig
Journal:  J Pediatr       Date:  1986-05       Impact factor: 4.406

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  11 in total

1.  Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens.

Authors:  Roland R Regoes; Camilla Wiuff; Renata M Zappala; Kim N Garner; Fernando Baquero; Bruce R Levin
Journal:  Antimicrob Agents Chemother       Date:  2004-10       Impact factor: 5.191

2.  Population dynamics of antibiotic treatment: a mathematical model and hypotheses for time-kill and continuous-culture experiments.

Authors:  Bruce R Levin; Klas I Udekwu
Journal:  Antimicrob Agents Chemother       Date:  2010-06-01       Impact factor: 5.191

3.  Pharmacodynamic model to describe the concentration-dependent selection of cefotaxime-resistant Escherichia coli.

Authors:  Sara K Olofsson; Patricia Geli; Dan I Andersson; Otto Cars
Journal:  Antimicrob Agents Chemother       Date:  2005-12       Impact factor: 5.191

4.  Modeling the mechanism of postantibiotic effect and determining implications for dosing regimens.

Authors:  Patricia Geli
Journal:  J Math Biol       Date:  2009-02-03       Impact factor: 2.259

5.  Pharmacodynamics of non-replicating viruses, bacteriocins and lysins.

Authors:  James J Bull; Roland R Regoes
Journal:  Proc Biol Sci       Date:  2006-11-07       Impact factor: 5.349

6.  Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections.

Authors:  Peter Ankomah; Bruce R Levin
Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-19       Impact factor: 11.205

7.  Once-daily gentamicin administration for community-associated methicillin resistant Staphylococcus aureus in an in vitro pharmacodynamic model: preliminary reports for the advantages for optimizing pharmacodynamic index.

Authors:  Sun Woo Kim; Dong-Gun Lee; Su-Mi Choi; Chulmin Park; Jae-Cheol Kwon; Si-Hyun Kim; Sun Hee Park; Jung-Hyun Choi; Jin-Hong Yoo; Wan-Shik Shin
Journal:  Yonsei Med J       Date:  2010-09       Impact factor: 2.759

8.  Predicting drug resistance evolution: insights from antimicrobial peptides and antibiotics.

Authors:  Guozhi Yu; Desiree Y Baeder; Roland R Regoes; Jens Rolff
Journal:  Proc Biol Sci       Date:  2018-03-14       Impact factor: 5.349

9.  Staphylococcus aureus in continuous culture: a tool for the rational design of antibiotic treatment protocols.

Authors:  Klas I Udekwu; Bruce R Levin
Journal:  PLoS One       Date:  2012-07-20       Impact factor: 3.240

Review 10.  Predicting Antimicrobial Activity at the Target Site: Pharmacokinetic/Pharmacodynamic Indices versus Time-Kill Approaches.

Authors:  Wisse van Os; Markus Zeitlinger
Journal:  Antibiotics (Basel)       Date:  2021-12-04
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