Pharmacokinetics, Pharmacodynamics, and Monte Carlo Simulation
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- Bu səhifədəki naviqasiya:
- VARIABILITY IN PLASMA AND TISSUE CONCENTRATIONS ACROSS POPULATIONS
- PHARMACODYNAMICS
- CONTENTS
- STATISTICAL DESCRIPTION OF ANTIMICROBIAL RESISTANCE
- COMPUTER MODELING AND MONTE CARLO SIMULATION
- ANTIMICROBIAL STEWARDSHIP
- FUTURE DIRECTIONS
| C ONCISE
R EVIEWS OF P EDIATRIC
I NFECTIOUS D ISEASES
® Pharmacokinetics, Pharmacodynamics, and Monte Carlo Simulation
antimicrobial, pharmacokinetic, pharmacodynamics (Pediatr Infect Dis J 2010;29: 1043–1046) W hen faced with a neonate, infant, or child with a suspected infection, the clinician must select a specific antimicrobial at a specific dose for a specific duration to treat that infection. Many issues require care- ful consideration, and include knowledge of the suspected pathogens and their suscepti- bility to the antimicrobials under consider- ation, the pharmacokinetic (PK) characteris- tics of the antimicrobials, and the clinician’s assessment of the need to achieve a cure for that particular patient (Table 1). PK and pharmacodynamics (PD) principles together with Monte Carlo simulation can assist the clinician in selecting the appropriate antimi- crobial and dosing regimen. 1 Recent ad- vances in our understanding of antimicrobial PK and PD have lead to important insights in the parameters associated with a successful outcome, and in ways to minimize both drug toxicity and the development of antimicro- bial resistance. VARIABILITY IN PLASMA AND TISSUE CONCENTRATIONS ACROSS POPULATIONS Given the availability of sensitive as- says to measure antibiotic concentrations in plasma and various tissue sites using smaller quantities of blood or tissue fluids, our abil- ity to assess antibiotic exposures at the tissue level, the actual site of infection, has in- creased. As regulatory agencies request more sophisticated antimicrobial exposure data for investigational drugs, these data are fre- quently collected in clinical trials and thus, are becoming more readily available for analysis. As a result, our knowledge of the PK of antimicrobials (ie, concentrations in plasma and in different tissue sites over time) and the variability inherent between patients receiving the same antimicrobial agent is better understood. Both the distribution of antimicrobials within tissue compartments and drug elimination differs by pediatric age group “populations,” from the neonate to the adolescent. Fortunately, antimicrobial PK and variability in each pediatric “population” can also be described. 2 Children with organ dysfunction may not eliminate antimicrobi- als as effectively as those with normal organ function. For example, the PK of vancomy- cin in children with some degree of renal failure will be different than in children with normal renal function. Data from popula- tions with organ failure are becoming more widely available, increasing our knowledge of the variability of drug elimination among those populations. The description of the statistical characteristics of the variability of antimicrobial concentrations across carefully defined populations is known as “population pharmacokinetics.” PHARMACODYNAMICS Our understanding of how antimicro- bial agents eradicate bacteria has also in- creased. The relationship between the anti- microbial concentrations required at the infection site over the dosing interval to eradicate a pathogen and hence, achieve a cure, is known as pharmacodynamics. 3 These defined exposures, indexed to the min- imum inhibitory concentration (MIC) of the antimicrobial to that pathogen, have been used to evaluate the PK-PD measure that best describes antimicrobial activity for that particular antimicrobial/pathogen pair. The 3 most common PK-PD measures associated with efficacy are (1) the percent of the dosing interval that a drug concentration remains above the MIC (%T Ͼ MIC); (2) the ratio of the maximal drug concentration to the MIC (C max :MIC); and (3) the ratio of the area under the drug concentration-versus-time curve (AUC) to the MIC (AUC:MIC). From the *University of California, San Diego, CA; †Rady Children’s Hospital San Diego, San Diego, CA; ‡State University of New York at Buffalo, Buffalo, NY; and §Institute for Clinical Pharma- codynamics, Albany, NY. Copyright © 2010 by Lippincott Williams & Wilkins ISSN: 0891-3668/10/2911-1043 DOI: 10.1097/INF.0b013e3181f42a53
Pharmacokinetics, Pharmacodynamics, and Monte Carlo Simulation EDITORIAL BOARD Co-Editors: Margaret C. Fisher, MD, and Gary D. Overturf, MD Editors for this Issue: John S. Bradley, MD Board Members Michael Cappello, MD Ellen G. Chadwick, MD Janet A. Englund, MD Leonard R. Krilov, MD Charles T. Leach, MD Kathleen McGann, MD Jennifer Read, MD Jeffrey R. Starke, MD Geoffrey A. Weinberg, MD Leonard Weiner, MD Charles R. Woods, MD The Concise Reviews of Pediatric Infectious Diseases (CRPIDS) topics, authors, and contents are chosen and approved indepen- dently by the Editorial Board of CRPIDS.
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For aminoglycosides (eg, gentami- cin) and fluoroquinolones (eg, ciprofloxa- cin), the PK-PD measure that is most pre- dictive of efficacy is one for which bactericidal activity is concentration-de- pendent (C max :MIC for gentamicin, and AUC:MIC for ciprofloxacin). In contrast, amoxicillin and other beta-lactam agents demonstrate a time-dependent pattern of bactericidal activity (%T Ͼ MIC). 4
fore, when fluoroquinolone concentrations increase, the rate and extent of bacterial erad- ication will increase. For amoxicillin, maxi- mal bacterial eradication occurs when infec- tion site concentrations exceed the MIC for approximately 40% of the dosing interval. Eradication rates do not further increase as the amoxicillin concentration at the infection site increases or as the percent of time that the amoxicillin concentration is present at the infection site above the MIC, increases beyond 40%. For each antimicrobial/patho- gen pair, the degree of exposure described by the PK-PD measure, that is associated with a positive outcome (eg, cure), is commonly referred to as the “PK-PD target.” This can most easily be evaluated in a nonclinical system (eg, in vitro or animal infection mod- els), but is increasingly being assessed in human clinical trials. In other words, one can now demonstrate for a patient infected by a particular organism and treated with a par- ticular antimicrobial, the magnitude, shape, and duration of antimicrobial exposure that is likely to result in a cure.
Antimicrobial resistance is an increas- ing problem. Certain bacterial species con- tain intrinsic resistance mechanisms that are induced as we apply antimicrobial pressure; other species have the ability to mutate quickly to develop resistance while others have the ability to acquire mechanisms of resistance from other bacteria. For a popula- tion of children who are all treated for the same infection, otitis media for example, a range of susceptibilities of otitis pathogens to each antimicrobial can be described. Pub- lished data are available on the susceptibility of Streptococcus pneumoniae causing ear in- fections in children from Kentucky to Costa Rica, to Israel. 5–7
These data assist us in predicting how likely an infecting pathogen will be susceptible to each of several differ- ent antimicrobials we are considering for therapy. This variability in bacterial suscep- tibility to specific agents can be described and tracked as it changes over time, provid- ing the clinician with an accurate and ongo- ing assessment of the likelihood of drug resistance. The distribution of MIC values for specified pathogens, considered together with the distribution of antimicrobial expo- sure in the population of children all given the same antimicrobial dose, is used in the Monte Carlo simulation to evaluate the prob- ability of achieving a cure at that dose. COMPUTER MODELING AND MONTE CARLO SIMULATION Monte Carlo simulation provides a computer-based mathematical construct that can simultaneously integrate different vari- ables such as tissue concentrations of an antibiotic and antimicrobial susceptibility, each with its own probability distribution, together with information about the PK-PD measure associated with efficacy, to estimate the likelihood of achieving the PK-PD target (and thus, the likelihood of achieving cure). With these data inputs, antimicrobial expo- sures associated with a particular dosing reg- imen for a virtual population of children (often 5000, but any number can be selected) can be simulated, determining the proportion of infected children expected to achieve the PK-PD target. The clinician compares the proportion of simulated children predicted to be cured with the proportion desired to be cured (eg, 95% of children treated for pneu- mococcal pneumonia should be cured when treated). Such an analysis allows the clini- cian to understand, given the variability in the inputs, the statistical likelihood of achieving a cure for a particular child using a particular dosing regimen. To illustrate this concept, one can ex- amine amoxicillin treatment of pneumococ- cal pneumonia. A cure is expected if amoxi- cillin concentrations are present in lung tissue (epithelial lining fluid) at concentra- tions above the MIC of the infecting pneu- mococcus for approximately 40% of the dos- ing interval. 8 Assuming a child is infected by a relatively resistant strain demonstrating an MIC of 2.0 mcg/mL, if the child is treated with 90 mg/kg/d divided into 2 doses, then only 65% of children will achieve the PK-PD target associated with cure; if 90 mg/kg/d is given, but divided into 3 doses (increasing the duration that amoxicillin is present in lung tissue), the chance of achieving the PK-PD target increases to about 90%. To achieve a 95% likelihood of cure in this child, a dose of about 100 mg/kg/d divided into 3 doses will be required. If pneumococ- cal resistance to amoxicillin increases to 4.0 mcg/mL, the percent of children who achieve the desired target will decrease, and the dose of amoxicillin will need to be in- creased further to achieve the goal of 95% cure. On the other hand, if resistance de- creases to 0.5 mcg/mL, then an overwhelm- ing 99.6% of children given 90 mg/kg/d in 2 doses will achieve the PK-PD target associ- ated with cure. The Figure 1 illustrates a Monte Carlo simulation using one of many different software programs (Oracle Crystal Ball, version 11.1.1.30, Oracle Corporation). In this illustration, a 30 mg/kg dose of am- TABLE 1. Considerations in Antimicrobial Management of the Infected Child Patient-specific Site(s) of infection Consideration for how important it is to cure the infection (eg what risk is the clinician willing to accept for treatment failure) Patient-specific plasma antimicrobial concentrations over time Patient-specific tissue site antimicrobial concentrations over time Pathogen-specific Documented or suspected pathogen(s) Susceptibility of pathogen(s) (if cultures are positive) Variability of the susceptibilities of pathogen(s) in specimens collected in the population of children being treated, if therapy is empiric Antibiotic-specific Antibacterial spectrum of activity Antibiotic tissue penetration characteristics Variability of antimicrobial plasma and tissue concentrations over time among children in the population being treated
Antimicrobial pharmacodynamics Treatment-specific Size of dose (mg/kg) Frequency of dosing Duration of dosing
| www.pidj.com picillin is given intravenously to a child in a population of otherwise healthy children. The serum concentrations over time after a single dose are evaluated against a collection of pneumococci isolated during an era of relative resistance, with approximately 20% of strains having an MIC of 2.0 mcg/mL or greater, but most being susceptible at 0.06 mcg/mL. The blue-shaded areas denote the proportion of children who will achieve se- rum concentrations that are above the MICs noted in the population of pneumococci, for 40% or more of an 8 hour dosing interval (3.2 hours). In this simulation of 5000 virtual children, 88% given this dose will achieve this PK-PD target and be expected experi- ence a cure. One can also calculate the dose that would be required to achieve a cure in 95% of children, by either increasing the mg/kg dose, or by dividing the total daily dose into more frequent intervals. Similarly, in treating a child with streptococcal pharyngitis, given the high de- gree of susceptibility of Streptococcus pyo- genes to penicillins, once daily dose of amoxicillin at 50 mg/kg predicted a tonsillar antimicrobial exposure that would result in an approximately 95% of children achieving the targeted exposure for cure. 9,10
In addition to predicting targets for antimicrobial/patho- gen pairs for bacterial infections, population PK and PK-PD relationships have been ex- plored for antimicrobial agents for tubercu- losis, as well as for anti-infective agents for fungal and viral infections, thus permitting similar evaluations of dosing regimens using Monte Carlo simulation, as described above.
Minimizing antimicrobial resistance by using the most appropriate dosing regi- men for the most appropriate duration is a priority. 11 While lowering the dose of an antimicrobial may save a healthcare system funds and decrease dose-dependent toxicity, it may ultimately lead to increased resis- tance, and thus, more difficult-to-treat infec- tions that require more costly and toxic antimicrobials. Prospective data on the dura- tion of therapy required to achieve cure with- out relapse is an area of great importance for future research. Some orally administered beta-lactam antimicrobials are Food and Drug Administration-approved for a 5-day treatment course of streptococcal pharyngi- tis. It seems logical that most beta-lactams that display similar PK-PD measures and tissue penetration characteristics should also be prescribed for no more than 5 days. How- ever, prospectively collected data on the ef- ficacy of a 5-day treatment course for strep- tococcal pharyngitis for each and every penicillin and cephalosporin antimicrobial are not available, and for generic antibiotics, such studies are not likely to be performed. FUTURE DIRECTIONS Human validation of these concepts has lagged far behind their creation. Data from in vitro systems and animal models validate the concepts, and limited retrospec- tive data in adults with invasive infections such as pneumonia have demonstrated that, below a certain drug exposure, patients are more likely to fail treatment, whereas those whose exposures are above a certain PK-PD target, are more likely to be cured. 12 While limited data exist for children treated for otitis media, 13,14 no data yet exist for inva- sive infections such as pneumonia, meningi- tis, or osteomyelitis. As one might expect, prospective studies would require appropri- ate plasma and tissue site antimicrobial con- centration profiling for the population stud- ied, together with confirmation of bacterial etiology and antimicrobial susceptibility test- ing. Furthermore, a study design using as- cending doses, in which doses and resulting exposures straddle the drug exposure “break- point” associated with efficacy (eg, exposures below which patients fail and above which, they are cured), is inherently unethical to per- form in children. Pediatric investigators and human research committees would not know- ingly administer a dose of antimicrobial to a child that is likely to fail. For the moment, animal studies remain the most widely avail- able in vivo support to validate the outcomes of Monte Carlo simulation. Additional PK data that provide rele- vant tissue concentration values are needed for a wide variety of infections. For some infections such as staphylococcal pneumo- nia, multiple tissue sites of infection require step-wise modeling, with each subsequent step integrating PK data from each of the different potential intrathoracic infection sites (eg, pneumonia, empyema, lung ab- scess, and necrotic pulmonary tissue) to ac- count for all the sites of antimicrobial expo- sure. Another complexity that is difficult to FIGURE 1. Distribution of free-drug % T Ͼ MIC for ampicillin 30 mg/kg administered IV every 8 hours among 5000 simulated pediatric patients infected by a relatively resistant population of pneumococci.
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account for in Monte Carlo simulation is the polymicrobial nature of certain infections, such as complicated appendicitis, in which multiple pathogens are involved, as well as multiple tissue sites.
The use of PK-PD concepts and tools such as Monte Carlo simulation provide the best opportunity to gain insight about the most appropriate dose required to treat an infection and prevent antimicrobial resis- tance, while minimizing drug toxicity. As new antimicrobial agents are developed, PK-PD concepts represent a critical compo- nent for appropriate dose selection for clini- cal trials in different patient populations, and for pathogens of differing susceptibilities. Ongoing evaluation of the “correct dose” should be conducted in the context of chang- ing susceptibilities of bacterial pathogens of interest. REFERENCES 1. Bradley JS, et al. Predicting efficacy of antiinfec- tives . . .. Pediatr Infect Dis J. 2003;22:982–992. 2. Kearns GL, et al. Developmental pharmacology— drug disposition . . .. N Engl J Med. 2003;349: 1157–1167. 3. Ambrose PG,
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Pharmacokinetics- pharmacodynamics of antimicrobial therapy . . .. Clin Infect Dis. 2007;44:79 – 86. 4. Drusano GL. Pharmacokinetics and pharmacody- namics of antimicrobials. Clin Infect Dis. 2007; 45:S89 –S95. 5. Block SL, et al. Pneumococcal serotypes from acute . . .. Pediatr Infect Dis J. 2002;21:859 – 865. 6. Aguilar L, et al. Microbiology of the middle ear fluid . . .. Int J Pediatr Otorhinolaryngol. 2009; 73:1407–1411. 7. Porat N, et al. Increasing importance of multi- drug-resistant . . .. Pediatr Infect Dis J. 2010;29: 126 –130. 8. Craig WA. Basic pharmacodynamics of antibac- terials . . .. Infect Dis Clin North Am. 2003;17: 479 –501. 9. Lee J, et al. Once daily dosing of amo- xicillin . . .. Paper presented at: the Western States Conference; May 24 –25, 2004; Asilo- mar, CA. 10. Lennon DR, et al. Once-daily amoxicillin versus twice-daily penicillin V . . .. Arch Dis Child. 2008;93:474 – 478. 11. Dellit TH, et al. Infectious Diseases Society of America and the Society for Healthcare Epidemi- ology of America guidelines . . .. Clin Infect Dis. 2007;44:159 –177. 12. Bhavnani SM,
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Pharmacokinetics- pharmacodynamics of quinolones . . .. Diagn Mi- crobiol Infect Dis. 2008;62:99 –101. 13. Craig WA, et al. Pharmacokinetics and pharma- codynamics of antibiotics in otitis media. Pediatr
14. Dagan R. The use of pharmacokinetic/pharmaco- dynamic principles . . .. Int J Antimicrob Agents. 2007;30:S127–S130. Concise Reviews The Pediatric Infectious Disease Journal • Volume 29, Number 11, November 2010 © 2010 Lippincott Williams & Wilkins 1046 | www.pidj.com Yüklə 379,09 Kb. Dostları ilə paylaş:
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