Literature DB >> 10817724

Use of drug effect interaction modeling with Monte Carlo simulation to examine the impact of dosing interval on the projected antiviral activity of the combination of abacavir and amprenavir.

G L Drusano1, D Z D'Argenio, S L Preston, C Barone, W Symonds, S LaFon, M Rogers, W Prince, A Bye, J A Bilello.   

Abstract

The delineation of optimal regimens for combinations of agents is a difficult problem, in part because, to address it, one needs to (i) have effect relationships between the pathogen in question and the drugs in the combination, (ii) have knowledge of how the drugs interact (synergy, antagonism, and additivity), and (iii) address the issue of true between-patient variability in pharmacokinetics for the drugs in the population. We have developed an approach which employs a fully parametric assessment of drug interaction using the equation of W. R. Greco, G. Bravo, and J. C. Parsons (Pharmacol. Rev. 47:331-385, 1995) to generate an estimate of effects for the two drugs and have linked this approach to a population simulator, using Monte Carlo methods, which produce concentration-time profiles for the drugs in combination. This software automatically integrates the effect over a steady-state dosing interval and produces an estimate of the mean effect over a steady-state interval for each simulated subject. In this way, doses and schedules can be easily evaluated. This software allows for a rational choice of dose and schedule for evaluation in clinical trials. We evaluated different schedules of administration for the combination of the nucleoside analogue abacavir plus the human immunodeficiency virus type 1 protease inhibitor amprenavir. Amprenavir was simulated as either 800 mg every 8 h (q8h) or 1,200 mg q12h, each along with 300 mg q12h of abacavir. Both regimens produced excellent effects over the simulated population of 500 subjects, with average percentages of maximal effect (as determined from the in vitro assays) of 90.9%+/- 11.4% and 80.9%+/-18.6%, respectively. This difference is statistically significant (P<<0.001). In addition, 68.8 and 46.0% of the population had an average percentage of maximal effect which was greater than or equal to 90% for the two regimens. We can conclude that the combination of abacavir plus amprenavir is a potent combination when it is given on either schedule. However, the more fractionated schedule for the protease inhibitor produced significantly better effects in combination. Clinicians need to explicitly balance the improvement in antiviral effect seen with the more fractionated regimen against the loss of compliance attendant to the use of such a regimen. This approach may be helpful in the preclinical evaluation of multidrug anti-infective regimens.

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Year:  2000        PMID: 10817724      PMCID: PMC89928          DOI: 10.1128/AAC.44.6.1655-1659.2000

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


  7 in total

1.  Nucleoside analog 1592U89 and human immunodeficiency virus protease inhibitor 141W94 are synergistic in vitro.

Authors:  G L Drusano; D Z D'Argenio; W Symonds; P A Bilello; J McDowell; B Sadler; A Bye; J A Bilello
Journal:  Antimicrob Agents Chemother       Date:  1998-09       Impact factor: 5.191

Review 2.  The search for synergy: a critical review from a response surface perspective.

Authors:  W R Greco; G Bravo; J C Parsons
Journal:  Pharmacol Rev       Date:  1995-06       Impact factor: 25.468

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5.  The duration of viral suppression during protease inhibitor therapy for HIV-1 infection is predicted by plasma HIV-1 RNA at the nadir.

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6.  Factors influencing the emergence of resistance to indinavir: role of virologic, immunologic, and pharmacologic variables.

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Authors:  M A Wainberg; G Friedland
Journal:  JAMA       Date:  1998-06-24       Impact factor: 56.272

  7 in total
  16 in total

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Authors:  Anouk E Muller; Joost DeJongh; Ymka Bult; Wil H F Goessens; Johan W Mouton; Meindert Danhof; John N van den Anker
Journal:  Antimicrob Agents Chemother       Date:  2007-07-23       Impact factor: 5.191

Review 2.  A long journey from minimum inhibitory concentration testing to clinically predictive breakpoints: deterministic and probabilistic approaches in deriving breakpoints.

Authors:  A Dalhoff; P G Ambrose; J W Mouton
Journal:  Infection       Date:  2009-07-23       Impact factor: 3.553

3.  Antagonism between bacteriostatic and bactericidal antibiotics is prevalent.

Authors:  Paolo S Ocampo; Viktória Lázár; Balázs Papp; Markus Arnoldini; Pia Abel zur Wiesch; Róbert Busa-Fekete; Gergely Fekete; Csaba Pál; Martin Ackermann; Sebastian Bonhoeffer
Journal:  Antimicrob Agents Chemother       Date:  2014-05-27       Impact factor: 5.191

4.  Pharmacokinetics-pharmacodynamics of cefepime and piperacillin-tazobactam against Escherichia coli and Klebsiella pneumoniae strains producing extended-spectrum beta-lactamases: report from the ARREST program.

Authors:  P G Ambrose; S M Bhavnani; R N Jones
Journal:  Antimicrob Agents Chemother       Date:  2003-05       Impact factor: 5.191

5.  Vancomycin dosing assessment in intensive care unit patients based on a population pharmacokinetic/pharmacodynamic simulation.

Authors:  Natalia Revilla; Ana Martín-Suárez; Marta Paz Pérez; Félix Martín González; María Del Mar Fernández de Gatta
Journal:  Br J Clin Pharmacol       Date:  2010-08       Impact factor: 4.335

6.  Pharmacokinetics of clindamycin in pregnant women in the peripartum period.

Authors:  Anouk E Muller; Johan W Mouton; Paul M Oostvogel; P Joep Dörr; Rob A Voskuyl; Joost DeJongh; Eric A P Steegers; Meindert Danhof
Journal:  Antimicrob Agents Chemother       Date:  2010-02-22       Impact factor: 5.191

7.  Pharmacokinetics of aztreonam in healthy subjects and patients with cystic fibrosis and evaluation of dose-exposure relationships using monte carlo simulation.

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9.  The Role of Therapeutic Drug Monitoring in the Management of HIV-infected Patients.

Authors:  Stephen C. Piscitelli
Journal:  Curr Infect Dis Rep       Date:  2002-08       Impact factor: 3.725

10.  Concentration-dependent Mycobacterium tuberculosis killing and prevention of resistance by rifampin.

Authors:  Tawanda Gumbo; Arnold Louie; Mark R Deziel; Weiguo Liu; Linda M Parsons; Max Salfinger; George L Drusano
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