Approaches for dosage individualisation in critically ill patients

1. Introduction

2. Strategies to optimise dosing

in critically ill patients

3. Conclusions

4. Expert opinion

Review

Approaches for dosageindividualisation in criticallyill patientsMa del Mar Fernandez de Gatta†, Ana Martin-Suarez & Jose M Lanao†University of Salamanca, Institute of Biomedical Research of Salamanca (IBSAL),

Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, Salamanca, Spain

Introduction: Pharmacokinetic variability in critically ill patients is the result

of the overlapping of multiple pathophysiological and clinical factors.

Unpredictable exposure from standard dosage regimens may influence the

outcome of treatment. Therefore, strategies for dosage individualisation are

recommended in this setting.

Areas covered: The authors focus on several approaches for dosage indivi-

dualisation that have been developed, ranging from the well-established

therapeutic drug monitoring (TDM) up to the innovative application of phar-

macogenomics criteria. Furthermore, the authors summarise the specific pop-

ulation pharmacokinetic models for different drugs developed for critically ill

patients to improve the initial dosage selection and the Bayesian forecasting

of serum concentrations. The authors also consider the use of Monte Carlo

simulation for the selection of dosage strategies.

Expert opinion: Pharmacokinetic/pharmacodynamics (PK/PD) modelling and

dosage individualisation methods based on mathematical and statistical crite-

ria will contribute in improving pharmacologic treatment in critically ill

patients. Moreover, substantial effort will be necessary to integrate pharma-

cogenomics criteria into critical care practice. The lack of availability of target

biomarkers for dosage adjustment emphasizes the value of TDMwhich allows

a large part of treatment outcome variability to be controlled.

Keywords: critically ill, Monte Carlo simulation, pharmacogenomics, population

pharmacokinetic/pharmacodynamics, therapeutic drug monitoring

Expert Opin. Drug Metab. Toxicol. (2013) 9(11):1481-1493

1. Introduction

Critically ill patients usually have multiple organ failure and complex medical con-ditions demanding intensive care medicine and efficacious therapy. These clinicalconditions have profound effects on entire body systems, although they are espe-cially relevant with respect to the cardiovascular, pulmonary, renal and hepatic sys-tems and may significantly alter the pharmacokinetic (PK) behaviour of drugs [1,2].As a result, drug dosage individualisation and routine monitoring -- with the aimof avoiding toxicity and ensuring that therapeutic drug concentrations aremaintained -- is necessary to optimise clinical outcomes [3,4].

Compelling evidence points to significant variations in the PK profiles of severalantibiotic drugs in critically ill patients, and dose adjustment is suggested forimproving antibiotherapy in this population [4-7]. Otherwise, very little knowledgein other therapeutic areas and specific patient subpopulations that could help toachieve adequate pharmacotherapy and dose selection is available [8,9]. Applicationof population PK modelling in combination with therapeutic drug monitoring(TDM), Bayesian forecasting and Monte Carlo simulation as tools for dosageoptimisation is the main issue evaluated in the current review.

10.1517/17425255.2013.822486 © 2013 Informa UK, Ltd. ISSN 1742-5255, e-ISSN 1744-7607 1481All rights reserved: reproduction in whole or in part not permitted

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http://informahealthcare.com/journal/EMT

2. Strategies to optimise dosing in criticallyill patients

2.1 General criteriaAs previously reviewed by various authors in recent years[1-3,10-13] PK changes in critical illness can be a result of organdysfunction but can also be a consequence of the acute phaseresponse, drug interactions and therapeutic interventions. As aresult, the use of standard doses may lead to therapeutic fail-ure due to underdosing or toxic accumulation in critically illpatients. The pharmacological effects of many drugs (e.g.,sedatives, vasopressors and antihypertensives) commonlyused in critically ill patients can be readily monitored andthe dose adjusted according to the response. However, forother drugs such as antibiotics, the drug effect cannot bedirectly or immediately observed and therefore dose adjust-ment is not possible based on monitoring clinical parameters.In such cases, dosage individualisation can be accomplishedusing PK criteria to achieve target concentrations.

2.1.1 Route of administrationOwing to the changes in the absorption process, in criticallyill patients, intravenous administration is the preferredadministration route [14]. Oral drug absorption may bedecreased by gastrointestinal dysmotility developed as a resultof different mechanisms that include decreased perfusion ofthe gut, particularly if the patient is taking vasoconstrictivedrugs [2]. In states of shock, blood flow is directed toward vitalorgans and can reduce the systemic absorption of drugs fromthe gut or intramuscular and subcutaneous tissues [1]. Theenteral feeding status of a patient can also affect the oraldrug absorption [15].

2.1.2 Initiation of treatmentThe estimation of the initial or loading dose is performedaccording to the patients’ apparent distribution volume(Vd). The most common mechanisms involved in altered

tissue distribution in critically ill patients are alterations inprotein binding and fluid shifts, which often lead to increasesin the Vd of drugs [16].

Variations in extracellular fluid due, for example, tooedema, ascites, hypoalbuminaemia, fluid therapy or post-surgical drainages can increase Vd. The increased capillarypermeability and decreased oncotic pressure seen in septicstates lead the leakage of large volumes into the interstitialspace. This provides a further compartment into whichhydrophilic drugs may be deposited, thus increasing theirVd. Because the interstitial space is the target site for mostbacterial infections, this issue may be of clinical relevancefor hydrophilic antimicrobial agents [13,17,18]. The expansionof the extracellular water caused by aggressive fluid resuscita-tion and acute renal failure, which is characterised by fluidretention, may also result in an additional increase in Vd forsome drugs used in critically ill patients [16].

An increased unbound fraction is likely to be common forstrongly protein-bound drugs in acutely ill patients whichoften have hypoalbuminaemia [7,19-21]. From the PK pointof view, this means increases in the plasma clearance (Cl)and Vd of drugs. Alterations in drug--protein binding mayalso be caused by another co-administered drug or the accu-mulation of endogenous substances (e.g., bilirubin and freefatty acids) that can compete with the drug for a binding site.

Frequent changes in pH occur in critically ill patients as aresult of numerous conditions (respiratory failure, shockstates, renal failure, etc.) that can also affect protein-bind-ing [1]. Clinically relevant changes have only been proposedfor fentanyl and lidocaine [22].

Accordingly, the use of standard Vd values often leads tounderdosing in critically ill patients, especially for hydro-philic drugs that are strongly bound to plasma proteinsand have low Vd values [13,17,18]. These risks of underdosingwill have special relevance for drugs, such as the aminoglyco-sides, fluoroquinolones or vancomycin, whose effectivenessrequires early high peak drug concentrations or the areaunder the plasma concentration--time curve--minimuminhibitory concentration values (AUC/MIC) [4,23,24]. Recentguidelines have advocated high loading doses in critically illpatients in order to rapidly achieve therapeutic concentra-tions of antibiotic [19,20].

2.1.3 Maintenance dosesDrug accumulation is mainly dependent on Cl. Therefore,maintenance doses must be estimated from this parameter.Drug Cl in critically ill patients may be increased due tohypermetabolic processes in the early stages of sepsis or hypo-albuminaemia. Accordingly, the use of high doses or extendedinfusion time for time-dependent drugs is necessary tomaintain target concentrations and to minimise the likelihoodof therapeutic failure [25]. By contrast, renal or hepaticimpairment may reduce drug Cl, allowing lower doses thanthe standard ones and the persistence of drug concentrationsabove the threshold concentration.

Article highlights.

. Pathophysiological conditions, treatments and medicalinterventions in critically ill patients account for highinter- and intra-individual PK variability.

. Dose requirements in critically ill patients are frequentlyhigher than the standard doses used inother populations.

. TDM and Bayesian forecasting are especially valuable fordosage individualisation in critically ill patients.

. Modelling and simulation are excellent tools for gaugingthe probability of serum concentrations being reachedand for recommending specific dosage regimens.

. Pharmacogenomics has an important role and specificrelevant patient parameters must be considered to makerational therapeutic decisions.

This box summarises key points contained in the article.

M. M. Fernandez de Gatta et al.

1482 Expert Opin. Drug Metab. Toxicol. (2013) 9(11)

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Alterations in drug metabolism can be expected in criti-cally ill patients, especially when Phase I pathways areinvolved [26-28]. Whereas in the hyperdynamic phase of sep-sis, hepatic metabolism may be preserved by blood shuntingto the liver, in patients with advanced sepsis -- since hepaticflow decreases -- the Cl of high-extracted drugs can bereduced. Indeed, the development of a systemic inflamma-tory response to either infection conditions (e.g., sepsis) ornon-infection conditions (e.g., trauma or pancreatitis) hasbeen found to reduce cytochrome P450 (CYP)-related bio-transformation to a significant extent in critically ill patients.Recently, very high plasma concentrations of atorvastatinhave been found in critically ill patients with sepsis, likelydue to CYP3A4-reduced metabolic functionality [8]. Theimpairment of CYP-related drug biotransformation maybecome even more relevant if the patient develops severeliver failure associated with multiple organ failure [29-31].

Unlike renal dysfunction, there is no simple endogenousmarker to predict the specific ability of the liver to metaboliseindividual drugs, and no single test to adjust drug dosage reg-imens in patients with hepatic dysfunction has been devel-oped [30]. However, alfentanil and midazolam, which arerecognised as specific CYP450 3A4 probes, have been usedin the critically ill population to identify alterations in drugmetabolism and the monoethylglycinexylidide formationhas been used as a marker of hepatic dysfunction in patientsfollowing and during liver transplantation [32].

Augmented renal clearance in patients without organ dys-function is increasingly being described in subsets of criticallyill patients [33]. The primary contributors to this process arelikely to be the innate immune response to infection andinflammation (systemic inflammatory response syndrome[SIRS]) and the measures taken to improve cardiovascularfunction in the critically ill, such as the administration ofintravenous resuscitation fluids and the use of vasoactive med-ication. The resulting increase in cardiac output and renalblood flow prompts enhanced glomerular filtration and hencedrug elimination associated with sub-therapeutic concentra-tions. Current evidence suggests that augmented renal clear-ance is most likely in young patients with trauma and lesssevere disease.

Renal dysfunction in critically ill patients can present aspre-existing chronic renal failure, new-onset acute renal fail-ure or tubular necrosis associated with hypoperfusion. Thisresults in decreased renal clearance for drugs with extensiverenal elimination and accumulation of metabolites. The useof renal replacement therapy in acute kidney injury patientshas a significant impact in the PK profile since drug removalfrom the circulation is dependent not only on drug-relatedproperties but also on the technique employed [2,34,35]. Theimportance of using, in this situation, an adequate datasethas been suggested [36].

For drugs eliminated through the kidney, serum creatinineconcentration is routinely used as an index of renal function,allowing Cl estimation and hence dosage individualisation.

Alternatively, creatinine clearance formulas such as that ofCockcroft--Gault and the modification of diet in renal disease(MDRD) equations have also been used [37]. However, theseapproaches may not always be correct in critical illnesses.Conventional equations disregard important effects of diseasepathophysiology and the increase in fluid volume can diluteserum creatinine concentrations, resulting in inappropriatelylow measurements [33]. Urinary creatinine collections takenat 8, 12 and 24 h must be used to determine the glomerularfiltration in critically ill patients, although a 2-h collectionmay be just as accurate [38]. Renal function may dynamicallychange along with patients’ clinical status, so their renalfunction should be frequently monitored [2].

Furthermore, in critically ill patients, it is necessary totake into account other factors that can change medicationneeds, such as drug interactions or (increasingly common)obesity [39-42].

2.1.4 Therapeutic drug monitoringTDM is the measurement and interpretation of drug con-centrations in biological fluids to determine the correctdrug dosage for an individual patient. This approach canreduce potentially preventable adverse end-organ effects andmaximise efficacy.

The technique requires that the dose be correlated withexposure and that such exposure be correlated with the phar-macodynamics (PD) response. The PK parameters commonlyused to characterise drug exposure are AUC, the time abovethe threshold concentration and the maximum, average andtrough concentrations usually obtained at steady state.

The high PK/PD variability and inherent clinical risk ofcritically ill patients justify TDM for setting the optimaldose. In addition, the PK changes that occur with critical ill-ness are by no means static but rather represent a dynamicprocess that changes along with the patient’s clinical status.For example, the eradication of infection, recovery fromend-organ dysfunction, improvement in intravascular volumestatus and enhanced nutritional status are but a few of thechanges that occur in this unique patient population. Alterna-tively, patients may deteriorate and develop worsening ana-sarca, hypotension, new end-organ failure and a progressivedecline in clinical status. Accordingly, these changes must bekept in mind when dosing drugs, with integral control ofthe appropriate therapy. For sedative agents, the control oftherapy based on PD end points should be preferred sincethis may allow the control of PK/PD variability. Target-controlled infusion devices are increasingly used in clinicalpractice to aid in optimisation of dosage for these drugs.The algorithms guiding these pumps are based on sophisti-cated PK/PD modelling to predict the plasma concentrationsof the drug and its effect. However, this approach may be lim-ited by the unavailability of data obtained in critically illpatients [43].

Usually, total drug concentrations are measured in plasma,although the unbound concentration is responsible for drug

Approaches for dosage individualisation in critically ill patients

Expert Opin. Drug Metab. Toxicol. (2013) 9(11) 1483

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efficacy and potential drug toxicity. Changes in the propor-tion of protein-bound drug are common in critically illpatients, and hence it would be more suitable to measurethis free fraction for dose adjustment. Ultracentrifugation,ultrafiltration and equilibrium dialysis are the methods mostwidely used for measuring the unbound drug fraction [44,45].The interpretation of the total drug concentration in the pres-ence of altered protein binding is difficult. Unlike the fractionbound to plasma proteins, the unbound fraction is the onlyfraction available for distribution and clearance from theplasma. Therefore, for many highly bound drugs, an increasedCl as well as Vd are observed in states of reduced proteinbinding such as that commonly observed in critical illness [21].Accordingly, the total drug concentration may decrease with-out the extent and clinical consequences of this alteration nec-essarily being significant. Changes in protein binding may besignificant, particularly for highly protein-bound drugs (e.g.,ceftriaxone, phenytoin), high-clearance drugs -- particularlydrugs predominantly cleared by glomerular filtration -- anddrugs where dosing is not titrated to effect (e.g., antibacteri-als) [19]. Ulldemolins et al. have reviewed the effects of hypo-albuminaemia on optimising antibacterial dosing in criticallyill patients [20]. If there are no concentration data regardingfree drug, it is possible to interpret them as a function of cer-tain criteria. For example, normalised concentration ofphenytoin can be calculated based on the observed totaldrug concentration and the serum protein level [46], or thefree concentrations of ceftriaxone can be estimated from totalconcentrations using in vivo binding parameters [47].In addition, alterations in the ratio of plasma to tissue con-

centrations suggest that the plasma concentration is not thebest indicator of the response achieved. With antimicrobials,it would be more correct to determine drug concentrationsat the site of infection (e.g., microdialysis), but there are fewstudies available that provide such information and at presentdosing is not adjusted based on such data [48,49].The clinical benefits derived from TDM are well defined

for critical care patients, highlighting their pivotal role inmore favourable outcomes [50-52]. However, in this setting,methodological aspects as well as the ‘treat patient not serumconcentration’ premise are of critical relevance [53]. Forexample, in critically ill patients, the early administration ofappropriate antibiotics in adequate doses to achieve targetconcentrations is necessary in order to reduce treatment fail-ure and the risk for selection of resistant strains. Accordingly,this could mean the administration of higher loading dosesand sampling times at non-steady state. The lack of availa-bility of analytical techniques and undefined samplingconditions are the main methodological obstacles to the rou-tine use of TDM in the care of critically ill patients [14]. Inthis setting, the interpretation of PK data requires an activecomputerised decision support, since one is faced with a com-plex situation. Although the guidelines presuppose a steady-state clinical situation, this is rarely the case in intensive careunits (ICU). In addition, obtaining multiple concentrations

over a single dosing interval to determine AUC could beimpractical in this setting. Prediction of PK parameters bothon a population basis and at the level of individual patientsis hampered by the high inter- and intra-individual variabilityinherent to these patients. Another limitation of TDMimplementation in ICU settings is related to the difficulty ofproviding dosing advice in a timely fashion [54].

Only one author has evaluated the effectiveness of TDM insuch patients using a decision-making algorithm, defining forwhich kind of patients TDM is beneficial [55]. For PD andtoxicodynamic reasons, dose personalisation of antibioticsusing TDM has commonly been used for the aminoglycosidesand glycopeptides [4,51,56-58]. However, only a few studies havereported the usefulness of TDM as a tool to improve the dos-ing of antifungal or b-lactams [5-7,56-61]. For other therapeuticareas, there is a lack of information [8,9,55].

2.2 Modelling and simulation of PK/PD in

critically ill patientsModelling and simulation systems in critical care medicine areconsidered a valuable tool to study the interaction betweenpathophysiology and healthcare delivery [62]. In recent years,much progress has been made in PK/PD modelling andsimulation in critically ill patients.

Through population PK/PD studies, the inter- and intra-individual variability in key PK parameters such as Cl andVd may be evaluated. Furthermore, these models identifythe demographical and clinical covariates explaining the PKvariability in this type of population. These models, whenclinically validated, are essential for the dosage individualisa-tion of drugs with a narrow therapeutic window, some ofwhich are frequently used in these patients. Additionally, sim-ulation methods, and especially Monte Carlo simulation, areincreasingly used to assess dosing strategies and new dosageregimens.

Owing to the importance of antibiotherapy in critically illpatients, most population PK studies conducted in thesepatients are oriented towards the field of antibiotics suchas the aminoglycosides, vancomycin, the quinolones and b-lac-tam antibiotics, among others [56,58,63-65]. Some studies have alsobeen done with other drugs such as anaesthetics [66,67]. Figure 1shows some methodological considerations on population PKmodelling studies in critically ill patients [64-68,70-74,79-92].

When building a population model in critically ill patients,the aim is to identify, with statistical criteria, the covariatesthat are relevant for predicting PK and/or PD changes in thesepatients. The renal function of patients, expressed as creati-nine clearance or through the MDRD value, is a covariateusually used in the prediction of Cl in critically ill patientswith drugs such as vancomycin, tobramycin, ceftazidime andcefpirome, among others [56,65,71]. It should be noted thatthe cretinine clearance (CrCL) estimation needs to be accu-rate, otherwise it introduces a lot of statistical noise into the



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model and future calculations from it. Body weight is anothercovariate often used for the prediction of the Vd [65,68].

The presence of sepsis and/or trauma has been identified asa major source of variability in the PK/PD profile of antibiot-ics in critically ill patients, with a direct impact on the dosageof these drugs [82]. In fact, in critically ill patients, the useful-ness of trauma and sepsis as covariates with predictive capacityfor amikacin Cl and Vd, respectively [68], or polytrauma forthe prediction of the peripheral Vd of ceftazidime, has beenreported [73].

Mechanical ventilation has also been described as anotherclinical factor affecting PK in critically ill patients, especiallyat the level of distribution [74]. A population PK model of cef-tazidime has identified mechanical ventilation as a covariateinfluencing the volume of the central compartment [73], andpositive end-expiratory pressure (PEEP) was identified as acovariate with predictive capacity for lorazepam Cl [79]. Otherstatistically significant covariates found in population PKmodels in critically ill patients are catecholamine levels, whichreflect a metabolic response to trauma, and the APACHE IIvalue, which is a prognostic scoring system for mortality.Population PK models developed in the critically ill with anti-biotics such as amikacin show a clear dependence of Cl onrenal function, the use of catecholamines and PEEP and acorrelation between the Vd and the APACHE II score [80].Alternatively, the simplified acute physiology score (SAPS)has been used, together with other covariates, to explain PK

variability of both the distribution and elimination parametersof isepamicin in critically ill patients (Table 1) [81].

A major limitation of population studies in these patients isthat they describe the PK in specific groups of acutely illpatients such as septic shock, trauma or burns patients, whoare not necessarily representatives of the general populationof ICU patients, in which there may be patients with cardio-vascular diseases or other conditions. Consequently, the appli-cation of these models to a general ICU population may beproblematic. Considering the physiological variability andpotential pathological changes in patient covariates, high var-iability in the dosage schedule in this kind of patients isexpected. In addition, no model can be developed that wouldbe perfect because it will always be missing variables likely tocontribute to the alteration in dosing requirements. For thesereasons and the difficulty of implementing models in softwareof clinical use, the potential of population PK modelling indosage individualisation of the critically ill patient is limited.

As in other patient populations, individualised drug dosingin critically ill patients can be based on the use of measuredserum drug concentrations combined with previous popula-tion PK models developed and validated in this type ofpatient with Bayesian forecasting [88-90]. For example, the dos-age individualisation of amikacin using Bayesian forecastingdemands a nearly twofold increase in the daily dose overconventional dosage regimens in critically ill patients withsimultaneous trauma and sepsis [68]. Multiple model Bayesian

Structural models One-compartment [68] Two-compartments [69-71] Three-compartments [65,67] PK/PD models [66,67,72]

Variability models Exponential [64,71,74,76] Exponential-additive [75,76] Constant coefficient of variation [77]

Pharmacostatistical software Parametric (NONMEM) [83] Non-parametric (NPEM) [84]

Estimation methods Parametric: FO and FOCE [83] Non-parametric [85,86]

Validation External validation [68,87] Internal validation [65,87]

Clinical use

Basic model

Final model

Intermediatemodel

Clinical covariatesClcr, Ccr, MDRD [56,65,71]

Plasma albumin concentration [75]Mechanical ventilation [73,74,79]

APACHE II, SAPS [78,80,81]Clinical diagnoses [68,73,82]

Demographic covariatesAge [65,75,78]

Height [71]Body weight [65,68,76]

Figure 1. Methodological considerations on population PK modelling in ICU patients.Clcr, Creatinine clearance; Ccr, Serum creatinine concentration; MDRD, Modification of diet in renal disease formula for estimation of glomerular filtration rate;

APACHE II, Acute physiology and chronic health evaluation; SAPS, Simplified acute physiologic score.



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Table 1. Population PK models of different drugs in critically ill adult patients.

Drug Structural

model

Regression model Inter-individual

variability

Residual

variability

Refs.

Amikacin One compartment Cl (ml/min) = 44.5 + 0.67 * Clcr(ml/min)--1.29 * PEEP (cm H2O)--8.34 * CAT (1 = yes, 0 = no)Vd (l) = 1.5 * APACHE II

sCL = 28%

sVD = 29%

- [80]

Amikacin One compartment Cl (l/h) = (0.93 * Clcr (l/h))(1 + 0.23 * Traumatism(absence = 0 presence = 1))Vd (l) = (0.39 * Weight (kg))(1 + 0.25 * Sepsis(absence = 0 presence = 1))

sCL = 28.2%

sVD = 23.2%

s" = 22% [68]

Vancomycin One compartment Cl (ml/min/kg) = 0.67* Clcr(ml/min/kg) + Age (years)-0.24

Vd (l/kg) = 0.82 + 2.49A

A = 0 or 1 if Ccr (mg/dl) £ 1or Ccr (mg/dl) > 1

sCL = 30.13%

sVD = 22.83%

s" = 4.23 mg/l [56]

Vancomycin One compartment Cl (l/h) = 4.58 * (Clcr/100)V (l) = 1.53 * Weight (kg)

sCL = 38.9%sV = 37.4%

s" = 19.9% [76]

Tobramycin Two compartments Cl (l/h) = 3.83 + 0.020 * Clcr(ml/min)+ 0.052 * (height: 172)V1 (l) = 25.50Q (l/h) = 4.74V2 (l) = 30.60

sCL = 0.095

sV1 = 0.045sQ, sV2, Fixed

s" = 0.055 [71]

Ceftazidime Two compartments Cl (l/h) = 2.24 (l/h) + 0.024*MDRD (ml/min)V1 (l) = 18.90 withoutmechanical ventilationV1 (l) = 9.02 with mechanicalventilationQ (l/h) = 15.20V2 (l) = 57.10 PolytraumaV2 (l) = 25.70 Post-operativeV2 (l) = 13.60 Aetiology

sCL = 0.09

sV1 = 0.12

sQ = 0.50sV2 = 0.11

s" = 0.05 [73]

Cefpirome Three compartments Cl (l/h) = 7.54 (Clcr/Clcr.standardised)Q2 (l/h) = 1.39Q3 (l/h) = 26.2V1 (l) = 9.04 * (weight/70)V2 (l) = 4.59V3 (l) = 8.63

sCL = 35%

sV1 = 50%

s" = 17% [65]

Isepamicin Two compartments Cl (l/h) = 0.201 + 0.054 * Weight(kg)--0.106 * SAPS + 0.012*Clcr(ml/min)Vc (l) = -2.855 + 0.187*Weight (kg) + 0.411 * SAPSClD (l/h) = 5.65 -- 0.218 * SAPSVt (l) = 4.01 + 0.397 * SAPS

sCL = 67%

sVc = 29%

sCLD = 75%sVt = 25%

- [81]

Propofol Three compartments Cl (ml/min) = 1001V1 (l) = 31.2Vdss (l) = 499

sCL = 15%sV1 = 17%sVdss = 35%

- [67]

Alfentanil Three compartments Cl (ml/min) = 345V1 (l) = 31.9Vdss (l) = 124

sCL = 20%sV1 = 32%sVdss = 33%

- [67]

Dexmedetomidine Two compartments Cl (l/h) = 57 * (1 -- 0.78 *(age: 60/60)) * RCO1,24

Vss (l) = 132 * (1 -- 0.51 *(albumin: 14/14))

sCL = 33.5%sQ = 25.1%

s" = 0.014 ng/ml [75]

s: Variability expressed as variation coefficient or standard deviation; APACHE II: Acute physiology and chronic health evaluation; CAT: Use of catecholamines;

Ccr: Serum creatinine concentration; Clcr: Creatinine clearance; MDRD: Modification of diet in renal disease formula for estimation of glomerular filtration rate;

PEEP: Positive and-expiratory pressure; RCO: Normalised measured cardiac output; SAPS: Simplified acute physiology score.



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algorithms have been proposed to improve the precision inserum drug concentrations and the design of dosage regimensfor patients with clinical instability. The sequential interactingmultiple model (IMM) Bayesian algorithm based on jumpingthe PK parameters when a new dose or a new serum drugconcentration is collected demonstrates the highest precisionfor fitting serum concentrations of antibiotics in patientsundergoing cardiothoracic surgery [91].

In addition, the clinical importance of choice of theright population PK model is remarkable when dosageindividualisation is required in critically ill patients [58].

Monte Carlo simulation and Markov simulation are pow-erful tools for studying processes or events that are difficultto reproduce in the real world and have potential applicationsin critical care medicine [62]. Monte Carlo simulation is fairlyoften used for simulating the probability that an event willoccur in a given clinical setting. This method considers thevariability in PK behaviour to determine the probability of atarget concentration being reached when a specific dosage reg-imen is implemented. Monte Carlo simulation is based onprevious knowledge of the probability distribution of thedemographical and clinical covariates with predictive capacityinvolved in the model. In critical care, different studieshave been performed with antibiotics based on MonteCarlo simulation to establish the probability that serumconcentrations above an observed MIC will be attained incritically ill patients and to recommend specific dosage regi-mens [56,64,71,92]. Through Monte Carlo simulation, peak con-centrations, trough concentrations and the AUC can be

simulated for each kind of regimen in specific critically illpatients with typical covariate values [71,93]. In addition, typi-cal plots of the probability of target serum concentrationsbeing exceeded versus covariate values such as MDRD, thecumulative fraction of response versus drug daily dose or theprobability of target attainment versus MIC may be repre-sented [57,64,65]. Finally, this kind of information may bevery useful to recommend a priori dosing strategies incritically ill patients, as reflected in Figure 2 [56].

2.3 Application of pharmacogenomics for drug

dosage in critically ill patientsIt is increasingly apparent that genetic variations interact toproduce drug-related phenotypes such as drug response ortoxicity. Accordingly, pharmacogenomics offers significantpotential to individualise therapy and to improve outcomesin individual patients [94]. However, the challenges in thedevelopment and implementation of pharmacogenomics inclinical practice are substantial. The difficulty in quantifyingthe contribution of genetics to drug response is further con-founded in critically ill patients by numerous other factorsthat influence drug disposition, such as the existence of organdysfunction, the stress response to acute illness, the presenceof comorbid disease or interactions with concomitantlyadministered medications [95]. In fact, most studies examiningthe influence of genetic variation on drug effects or diseasepredispositions have focused on patients outside an ICU envi-ronment. However, such research would be useful in ICU

Table 1. Population PK models of different drugs in critically ill adult patients (continued).

Drug Structural

model

Regression model Inter-individual

variability

Residual

variability

Refs.

Q (l/h) = 183V1 (l) = 12.3

sV1 = 53.4%sVdss = 65%

Lorazepam Two compartments Cl (l/h) = 4.13 -- (PEEP-5) * 0.417No alcohol abuseCl (l/h) = 0.74Alcohol abuseV (l) = 0.743Vss (l) = 1.56 -- (age: 58) * 2.07Q (l/h) = 36.3

sCL = 63%

sCL = 389%

sV = 124%sVdss = 117%sQ = 75%

s" = 15.4% [78]

Midazolam Two compartments Cl (l/h) = 11.3 -- (age: 57) * 0.145No alcohol abuseCl (l/h) = 7.3 -- (age: 57) * 0.145Alcohol abuseV (l) = 7.15Vss (l) = 431Q (l/h) = 40.8 -- (APACHE-26) * 2.75

sCL = 77%

sCL = 77%

sV = 225%sVdss = 355%sQ = 54%

s" = 30.9% [78]

Midazolam Two compartments Cl (l/h) = 12.6V1 (l) = 19.1V2 (l) = 108Q (l/h) = 18.4

sCL = 12%sV1 = 92%sV2 = 47%sQ = 68%

s" = 7 ng/ml [69]

s: Variability expressed as variation coefficient or standard deviation; APACHE II: Acute physiology and chronic health evaluation; CAT: Use of catecholamines;

Ccr: Serum creatinine concentration; Clcr: Creatinine clearance; MDRD: Modification of diet in renal disease formula for estimation of glomerular filtration rate;

PEEP: Positive and-expiratory pressure; RCO: Normalised measured cardiac output; SAPS: Simplified acute physiology score.



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scenarios because of the frequency with which many of thesesame drugs are prescribed and the particular conditions ofacutely ill patients. Genetic predisposition to adverse drugreactions in the ICU has also been analysed, focusing on genesthat are particularly relevant to drug therapy in the criticallyill [96]. However, the strength of associations between geno-type and the clinical effects is variable, rarely includes datafrom critically ill patients, and has rarely been implementedin clinical practice. In fact, the paucity of pharmacogenomicresearch involving critically ill patients is astonishing and itsclinical implementation is very limited. To our knowledge,no population models that include genetic information havebeen published in critically ill patients. To determine whetherpharmacogenomics will be important as a contributing factorto dosing alterations in patients receiving drugs in the ICU, itis critical that the genotype never be interpreted in the absenceof phenotype data.Some biomarkers are currently being investigated for their

prognostic predictive value in sepsis but the turnaroundtime remains a major limiting factor for clinical use. Rapidtests now under investigation and refined methods of geneticprofiling will afford an accurate means to stratify the risks ofmortality and develop targeted treatment options in patientswith sepsis [97].

3. Conclusions

The coexistence of several underlying pathophysiological,treatments and medical interventions in critically ill patientscan lead to broad inter- and intra-individual PK/PD variabil-ity. Hence, the lack of predictability of drug disposition incritically ill patients supports the major role of TDM as a

tool for controlling pharmacological treatments and dosageindividualisation, especially when drugs with a narrow thera-peutic window are used. The lack of availability of analyticaltechniques and undefined sampling conditions are the mainmethodological obstacles to the routine use of TDM in thecare of critically ill patients. For non-antibiotic drugs, a lackof population PK studies in these patients hinders the initialdosage selection and subsequent individualisation using theBayesian approach. In critical care, modelling and simulationPK/PD can be used to establish the likelihood of serum con-centrations being reached and to recommend specific dosageregimens. Efforts must also be made to demonstrate thepotential of pharmacogenomics as a means of improving thecare provided to critically ill patients. However, the clinicalapplications of this kind of research are still limited. Despitethe several barriers to effective dosing in critically ill patients,evidence-based dosing can offer a valuable tool to achievetreatment efficacy and the optimisation of patient outcomes.

4. Expert opinion

The broad spectrum of different critically ill admission diag-noses, the overlap between the pathophysiological changesthat cause PK alterations in this type of patient populationand the effect of different levels of organ function or druginteractions do not allow specific dosing recommendationsto be implemented for each potential patient. Although it ispossible to obtain an initial estimation of drug dosage needsby considering the most likely factors affecting absortion,distribution, metabolism, excretion and toxicity (ADMET)processes, this approach may be impractical in this clinical

Vancomycin daily dosage (g)

Cu

mu

lati

ve f

ract

ion

of

resp

on

se (

%)

20

01 2 3 4 5

40

60

80

100

S. aureus

Vancomycin daily dosage (g)

Cu

mu

lati

ve f

ract

ion

of

resp

on

se (

%)

20

01 2 3 4 5

40

60

80

100

A.VISA

B.

Figure 2. Cumulative fraction of response (proportion of the population that achieved an AUC: MIC ratio ‡ 400) against

Staphylococcus aureus for several vancomycin daily doses in different ICU population subgroups: (A) for susceptible S. aureus;

(B) for VISA (vancomycin intermediate susceptibility) strains. Clcr: Creatinine clearance measured in the ICU setting (ml min-1).

Clcr £ 60 ml/min and age > 65 years (--); Clcr £ 60 ml/min and age £ 65 years (----); Clcr > 60 ml/min and age > 65 years (--);

Clcr > 60 ml/min and age £ 65 years (----).Reproduced from [56] with permission of John Wiley and Sons.



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setting and may lack generalisability. This weakness is espe-cially noticeable for non-antibiotic drugs.

Dosage guidelines derived from representative populationPK modelling in patient subpopulations could be a usefultool for guiding initial dosing decisions. In addition, dosesshould be adjusted as patients’ clinical status change andTDM should subsequently be used whenever possible.Emphasis should also be placed on the importance of multi-disciplinary team work, including the expertise of a clinicalpharmacologist to maximise treatment efficacy or safety foreach individual patient. Future advances in population PK/PD modelling in critically ill patients and dosage individuali-sation methods based on mathematical and statistical criteriawill contribute in improving pharmacological treatment incritically ill patients. Physiology-based simulations of patho-logical conditions with a view to predict PK and drug dosagerequirements may become useful to simulate likely outcomesin defined subpopulations of critically ill patients. Thisapproach using Simcyp (population-based ADME simulator,Simcyp Ltd.) has already been applied in liver cirrhosis [98].

Recently, the concept and methodology of personalisedprescription has been proposed: the selection of a giventherapy for a patient is based on patient characteristics, thescientific evidence and pharmacogenomic information whenavailable [99]. The prescriber has an important role and mustconsider the relevant specific patient parameters in order tomake a rational therapeutic decision. In addition, personalisedprescription requires access to knowledge of pharmacologyand evidence-based medicine. The databases that provideuseful information in the area of pharmacogenetics have

previously been reviewed [100]. As the numbers of useful bio-markers for predicting drug response increases, such informa-tion becomes a powerful tool for both personalisedprescription and personalised medicine. Some peer-reviewedguidelines for dosing based on specific pharmacogenomictests have already been published in such databases [101]. It isconceivable that such progress could result in more effectivecare for the critically ill owing to the inherent high risk statusof this vulnerable population. The challenge for critical careinvestigators will be to design gene association studies thatare both appropriately controlled and of sufficient power forthe genetic determinants of disease susceptibility and drugeffects to be measured accurately. Moreover, substantialefforts will be necessary to integrate this approach into criticalcare practice.

The lack of availability of target biomarkers for dosageadjustment emphasizes the undeniable value of TDM, whichallows a major part of treatment outcome variability to becontrolled. In fact, as stated, exposure is the best biomarkerfor response, and personalised medicine should involve morethan dosing a patient with the right drug; it should involvedosing a patient with the right drug at the dose required toachieve the right exposure [53].

Declaration of interest

The authors declare that they have no conflict of interestand have received no payment in the preparation of theirmanuscript.

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http://dx.doi.org/10.1016/j.ijantimicag.2013.04.021

http://dx.doi.org/10.1016/j.ijantimicag.2013.04.021























AffiliationMa del Mar Fernandez de Gatta†1 PhD,

Ana Martin-Suarez2 PhD & Jose M Lanao3 PhD†Author for correspondence1Associate Professor of Pharmacy and

Pharmaceutical Technology,

University of Salamanca,

Institute of Biomedical Research of Salamanca

(IBSAL), Department of Pharmacy and

Pharmaceutical Technology, Faculty of

Pharmacy, Avda. Licenciado Mendez N�unez,

37007 Salamanca, Spain

Tel: +0034 923 294 536;

Fax: +0034 923 294 515;

E-mail: [email protected] Professor of Pharmacy and


University of Salamanca,



Pharmaceutical Technology, Salamanca, Spain3Professor of Pharmacy and Pharmaceutical

Technology,

Professor of Pharmacy and Pharmaceutical

Technology, University of Salamanca,




Salamanca, Spain



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mailto:[email protected]


Documents

Approaches for dosage individualisation in critically ill patients