Rima A. Mohammad and Gary R. Matzke
Chronic kidney disease (CKD) results in minimal alterations in the absorption or bioavailability of most drugs.
The volume of distribution (VD) of many drugs is increased in the presence of acute and chronic kidney disease as a consequence of volume expansion and/or reduced protein binding.
In addition to the expected decrement in renal clearance, nonrenal clearance (i.e., GI and hepatic drug metabolism) of several drugs is also reduced in patients with CKD.
Individualization of a drug dosage regimen for a patient with reduced kidney function is based on the pharmacodynamic/pharmacokinetic characteristics of the drug and the patient’s degree of residual renal function.
The drug dosing guidelines for patients with CKD in many drug information resources are highly variable and many are not optimal for clinical use.
The effect of hemodialysis or peritoneal dialysis on drug elimination is dependent on the characteristics of the drug and the dialysis prescription.
Hemodialysis clearance data can be used to guide the initial drug dosage regimen recommendation for hemodialysis patients; however, prospective monitoring of serum concentrations is often warranted especially for those with a narrow therapeutic index.
Chronic kidney disease (CKD) is a common condition characterized by the presence of kidney damage, a urine albumin-to-creatinine ratio greater than 30 mg/g (3.4 mg/mmol) or a glomerular filtration rate (GFR) of less than 60 mL/min (1 mL/s) for greater than 3 months. It is estimated that 10% of adults, greater than 20 million people, in the United States have CKD.1 Between 2000 and 2008, the incidence of CKD has more than doubled in adults 65 years old and older.1 The combination of CKD, age-related reductions in renal function, and the high utilization of medications in these patients increases their risk of adverse effects particularly if their drug regimen is not appropriately individualized.2,3 Furthermore a marked reduction in kidney function in adults of any age, whether acute or chronic, has been noted to affect the pharmacokinetics of many medications.4–12 Thus patients with CKD are often prescribed an extensive array of medications for CKD-related and other comorbid conditions (see Chap. 29). These factors necessitate that clinicians individualize drug therapy to maximize therapeutic outcomes and to minimize therapeutic misadventures.
Drug excretion is markedly effected by CKD. Medications which are predominantly renally eliminated unchanged (fe) may accumulate in patients with CKD, which can complicate existing conditions and increase the risk of adverse effects. If 30% or more of a drug is renally eliminated unchanged, it will have a high likelihood, >70%, of requiring dosage regimen adjustment in patients with CKD. The pharmacokinetics of drugs with an fe less than 30% also may be affected and a recent study indicated that 20% to 37.5% of such drugs approved in the United States from 1998 to 2010 had a dosage adjustment for patients in the product labeling.13 If there is no official recommendation in the product labeling a dosage regimen adjustment may be calculated on the basis of the drug’s fe and the ratio of the patient’s residual renal function relative to an age and gender normal value for estimated creatinine clearance (CLcr) or GFR.14 However, for medications that are extensively metabolized or for which dramatic changes in protein binding and/or distribution volume (VD) have been noted, a more complex adjustment strategy may need to be employed.4,15 Furthermore, physiologic and biochemical changes associated with CKD may also independently impact drug dosing and serum drug concentrations.4
Clinicians thus will often need to proactively design individualized therapeutic regimens to optimize achievement of the desired outcomes while minimizing adverse events, if they use basic pharmacokinetic principles combined with the drug’s disposition characteristic and the patient’s clinical status. In this chapter, the influence of CKD on drug pharmacokinetic properties is characterized and the most commonly used medications that are affected are discussed. In addition, a general guide for individualizing drug therapy in patients with CKD is presented along with dosage recommendations for the most commonly used drugs in this patient population. Finally, the impact of chronic renal replacement therapy (i.e., peritoneal dialysis and hemodialysis) on drug disposition is discussed and dosage recommendations for selected drugs are presented. Drug dosage regimen adjustment strategies for patients with acute kidney injury (AKI) including those who are receiving continuous renal replacement therapy are presented in Chapter 28.
EFFECT OF CHRONIC KIDNEY DISEASE ON DRUG DISPOSITION
Prior to the 1990s, there were no regulatory guidelines regarding when and how pharmacokinetic/pharmacodynamic studies of drugs in patients with renal insufficiency should be conducted as part of the new drug application process.16,17 Thus much of the pharmacokinetic/pharmacodynamic data in patients with renal insufficiency published during the 1970s through much of the 1990s were derived from postmarketing studies. Unfortunately, the utility of these studies were often limited by small sample sizes, which led to inconsistent and sometimes conflicting results. Furthermore it was rare that detailed information was incorporated into the official (i.e., FDA) prescribing information. The FDA published the first guidance for industry on renal impairment studies in 1998 and since then, the frequency and quality of renal impairment studies as part of drug development has increased.13,18,19 A proposed revision to the 1998 guidance on renal impairment studies was recently circulated for comment which recommended: (a) conducting studies in nonrenally as well as renally eliminated drugs, (b) conducting studies in patients receiving hemodialysis, (c) conducting studies to evaluate pharmacokinetics of therapeutic proteins in patients with renal insufficiency, (d) categorizing renal function based on estimated GFR (using the modification of diet in renal disease [MDRD] equation) or CLcr (using the Cockcroft–Gault [C–G] equation), and (e) modifications to how the results of renal impairment studies are presented in the official drug label.20–22 Although this revision has not been “officially” implemented by the FDA, it appears to be impacting how the industry considers the design of renal pharmacokinetic/pharmacodynamic studies.
There is little quantitative information regarding the influence of CKD on drug absorption and bioavailability. Many studies evaluating bioavailability of oral medications in CKD patients were not designed to provide an assessment of the drug’s absolute bioavailability (e.g., they did not include a comparison of the area under the concentration–time curve (AUC) after oral and IV administration of the drug). Rather, the principal outcomes that were documented were alterations in the peak concentration (Cmax), time at which the peak concentration was attained (tmax), or in the fractional amount of drug recovered in the urine in a finite time period. Unfortunately, this limited information has been extrapolated by some into a general conclusion that drug absorption is slowed and/or that the extent of absorption is reduced in patients with CKD.23,24
Both CKD and comorbid conditions associated with CKD (e.g., diabetes mellitus and cardiovascular disease) may contribute to changes in drug absorption. Some patients present with changes in GI transit time and gastric pH (e.g., gastroparesis associated with diabetes mellitus), edema of the GI tract, persistent vomiting and diarrhea, and phosphate binder administration have all been proposed as a rationale for alterations in the bioavailability of drugs in CKD patients.2,8 For example, patients with uremic-induced vomiting, now only occasionally observed in patients with severe renal insufficiency, may decrease a medication’s time in the GI tract and thereby limit absorption of that drug. Edema of the GI tract, secondary to cirrhosis or congestive heart failure, can decrease the bioavailability of some medications such as oral furosemide.2,25 A reduction in gastric acidity associated with the concomitant administration of antacids and phosphate binders has been associated with a reduction in bioavailability of several medications including several antibiotics and digoxin.26–29
However, there are only a few drugs (e.g., some β-blockers, dextropropoxyphene, dihydrocodeine, felodipine, sertraline, and cyclosporine) with documented increases in bioavailability in patients with CKD as a result of a reduction in metabolism during the drug’s first pass through the GI tract and liver.4,30–35 Although the bioavailability of several of these compounds is increased in the presence of CKD, clinical consequences (development of excessive or unexpected adverse effects) have only been demonstrated with dextropropoxyphene and dihydrocodeine.31,32 For some agents, those metabolized by cytochrome P450 (CYP) 3A4, the bioavailability may be dramatically increased when the medications are taken along with grapefruit juice: felodipine by 184% and cyclosporine by 20%.33,34
The VD of many drugs is increased in patients with moderate to severe CKD as well as those with preexisting CKD who develop AKI (Table 33-1) and lead to a reduction in serum drug concentrations.4,8–10,36This increase in VDmay be the result of pathophysiologic alterations in body composition, fluid overload secondary to excessive IV fluid administration, decreased protein binding, or increased tissue binding. Decreased tissue binding of drugs in patients with CKD may result in a reduction in VD, which has been reported for only a few medications (e.g., digoxin and pindolol).9,10
TABLE 33-1 Volume of Distribution of Selected Drugs in Patients with End-Stage Renal Disease
Variability in fluid status is a common issue in patients with CKD, especially those that are critically ill. Many of these critically ill patients receive large volumes of IV fluids for various clinical conditions (e.g., resuscitation from shock), and develop edema, pleural effusions, or ascites as a result thereof. These therapeutic interventions, in addition to renal insufficiency that can result in reduced water excretion, can result in an increase in VD and alterations in drug serum concentrations, especially with hydrophilic drugs.3 For example the VD of aminoglycosides has been noted to be increased by 1.34- to 1.66-fold in postsurgical patients with septic shock.37 Interstitial fluid concentration of piperacillin was also reported to be three- to fourfold lower in patients undergoing heart surgery and in patients with sepsis.37 These marked reductions in drug concentration may be due to increased capillary permeability following fluid accumulation in the interstitial space.37
Effect of Altered Protein Binding
The unbound fraction of many acidic drugs increases in CKD patients as the result of a decrease in protein binding and this is associated with an increase in the apparent VD.8 A new equilibrium is ultimately established as a result of increased drug elimination/distribution, such that the unbound concentrations remain comparable to those observed in patients with normal renal function despite the fact that total concentrations are reduced. Thus, the net effect is an alteration in the relationship between total drug concentration and pharmacodynamic effect. For example, protein binding of phenytoin (90% protein-bound, primarily to albumin) is significantly reduced and this change alters the relationship between total phenytoin concentration and desired and toxic effects.8,38 The resulting increase in unbound fraction, from values of 10% in those with normal renal function to approximately 20% or more in those with stage 5 CKD, results in increased hepatic clearance and decreased total concentrations. Thus, in patients with CKD, the therapeutic range based on total phenytoin concentration is shifted downward from normal values of 10 to 20 mg/L (10 to 20 mcg/mL; 40 to 79 mmol/L) to values as low as 4 to 8 mg/L (4 to 8 mcg/mL; 16 to 32 mmol/L). Since the unbound concentration therapeutic range is the same for all patients, 1 to 2 mg/L (1 to 2 mcg/mL; 4 to 8 mmol/L), such a measurement provides the best target for individualizing phenytoin therapy in patients with CKD.
The decrease in plasma protein binding of acidic drugs (e.g., phenytoin) has been attributed to qualitative changes in the binding sites, accumulation of endogenous inhibitors of binding, and/or decreased concentrations of albumin. The first two of these mechanisms appear to account for most of the observed changes in binding. In addition, the high concentrations of metabolites of some compounds that accumulate in patients with end-stage renal disease (ESRD) may compete for protein binding sites with the parent compound.39
The principal binding protein for several basic drugs is α1-acid glycoprotein, an acute-phase reactant protein whose plasma concentrations are increased in CKD patients.4,8 As a result of this increase, the unbound fraction of some basic drugs (e.g., bepridil, disopyramide) may be significantly decreased and the VD increased in CKD patients, especially renal transplant and hemodialysis patients.4,8
Effect of Altered Tissue Binding
Altered tissue binding may also result in a reduction of the apparent VD of a drug. For example, the VD of digoxin has been reported to be reduced by 30% to 50% from the normal values in patients with stage 5 CKD, as well as in hemodialysis patients.40 This reduction in VD may be a result of decreased tissue binding, the presence of acidosis, or digoxin-like immunoreactive substances.9,41 In this case, the absolute amount of digoxin bound to the receptor is reduced and the resultant serum digoxin concentration is higher than anticipated. Thus, in patients with renal insufficiency, particularly in those with stage 5 CKD, a “normal” total drug concentration may be associated with either an adverse reaction secondary to elevated unbound drug concentrations, or a subtherapeutic response because of an altered plasma-to-tissue drug concentration ratio. The monitoring of unbound drug concentrations in CKD patients is thus warranted for those drugs that have a narrow therapeutic range, are highly protein bound (free fraction of <20%), and for which marked variability in the free fraction has been reported (e.g., phenytoin and disopyramide).
Effect of VD Calculation Method
Finally, the method used to calculate the volume of distribution may be influenced by renal insufficiency. The three most commonly used volume of distribution terms are: volume of the central compartment (Vc), volume of the terminal phase (Vβ and Varea), and volume of distribution at steady state (Vss). The Vc for many drugs approximates extracellular fluid volume and thus may be increased or decreased by acute changes. Oliguric acute renal failure is often accompanied by fluid overload and a resultant increased Vc for many drugs. The Varea or Vβ represents the proportionality constant between plasma concentrations in the terminal elimination phase and the amount of drug remaining in the body. Vβ is affected by both distribution characteristics, as well as by the terminal elimination rate constant. Vβ and Vsswill often be similar in magnitude, with Vβ being slightly larger. Because Vss has the advantage of being independent of drug elimination, it is the most appropriate volume term to use when one desires to compare drug distribution volumes between patients with renal insufficiency and those with normal renal function.42
Accumulation of Metabolites
There are only a few drugs that are eliminated completely unchanged via the kidneys and thus most drugs are metabolized to some extent. Patients with severe renal insufficiency who are receiving chronic drug therapy may experience accumulation of metabolite(s) as well as the parent compound. Metabolites of several drugs have been reported to have significant pharmacologic and/or toxicologic activity.43However, the pharmacokinetics and pharmacodynamics of metabolites are not often fully elucidated. In a sense, the patient with severe CKD is being exposed to a new pharmacologic entity since the sum of the serum concentrations of the metabolite and the parent compound are markedly different than those reported in patients with normal renal function.
The metabolite may have pharmacologic activity similar to that of the parent drug and thus contribute significantly to clinical response; that is true, for example, of oxypurinol. Alternatively, the metabolite may have qualitatively dissimilar pharmacologic action; for example, normeperidine has CNS stimulatory activity that reportedly produces seizures, whereas meperidine has CNS depressant actions.44,45Because of the multiplicity of potential interactions of compounds that are primarily metabolized, the practical consequences of metabolite accumulation are difficult to predict and are most often identified in those patients at risk by trial and error.
Alterations of CYP 450 Enzyme Activity
A decrease in the renal clearance of drugs in patients with CKD is well appreciated. However, there is now good preclinical and emerging clinical evidence which suggests that CKD may lead to alterations in nonrenal clearance of many medications as the result of alterations in the activities of uptake and efflux transporters as well as CYP enzymes in the liver and other organs8–12,46 (Table 33-2). The effect(s) of renal insufficiency on nonrenal drug clearance appear to depend on whether the reduction in renal function is acute or chronic in nature. For example, higher residual nonrenal clearance for vancomycin, meropenem, and imipenem has been documented in patients with AKI compared to patients with CKD, who have comparable CLcr.47 In humans with renal insufficiency, the activities of CYPs appear to be relatively unaffected. It was reported that CYP3A4 activity was reduced,7,9–12 but recent data indicate that organic anion-transporting polypeptide (OATP) uptake activity is reduced and thus the perceived changes in CYP3A4 activity were likely due to altered transporter activity, not an alteration in CYP activity. The reduction of nonrenal clearance of several drugs that exhibit overlapping CYP and transporter substrate specificity in patients with stage 4 or stage 5 CKD supports this premise (Table 33-3). These studies must be interpreted with caution, however, because concurrent drug intake, age, smoking status, and alcohol intake were often not taken into consideration. Furthermore, pharmacogenetic variations in drug-metabolizing enzymes that may have been present in the individual before the onset of AKI or CKD must be considered, if known.7,9–12 This differential effect on individual enzymes may help to explain some of the conflicting reports of whether drug metabolism is altered in the presence of CKD. Cytochrome P450 enzyme 3A activity as measured by the erythromycin breath test (EBT) is 28% lower in ESRD patients as compared with healthy controls.48 Although baseline CYP3A activity was lower in these patients, the increase in CYP3A activity observed following enzyme induction with rifampin was similar.48 Nolin and colleagues have recently shown that EBT results are reduced more in those ESRD patients with higher blood urea nitrogen concentrations and that hemodialysis is associated with an acute improvement in the patient’s metabolic activity.10 These data indicate that CKD has a detrimental effect on this important pathway of hepatic drug metabolism in humans.
TABLE 33-2 Impact of ESRD on Nonrenal Clearance of Selected Drugs
TABLE 33-3 Major Pathways of Nonrenal Drug Clearance
Prediction of the effect of renal insufficiency on the metabolism of a particular drug is however difficult and there is currently no quantitative strategy to predict changes for one drug based on data from another even if they are in the same pharmacologic class. However, some qualitative insight can be gained if one knows what enzyme is involved in the metabolism of the drug of interest and how the enzyme(s) or transporter(s) is affected by the presence of CKD.
EFFECT OF CHRONIC KIDNEY DISEASE ON RENAL TRANSPORTERS
Renal clearance (CLR) of a drug is the composite of GFR, tubular secretion, and reabsorption (CLR = [GFR × fu] + [CLsecretion – CLreabsorption]), where fu is the fraction of the drug unbound to plasma proteins. Drug elimination by filtration occurs by diffusion; while tubular secretion and reabsorption are bidirectional processes that involve carrier-mediated renal transport systems.49 Renal transport systems have been broadly classified on the basis of substrate selectivity into the anionic and cationic renal transport systems, which are responsible for the transport of a number of organic acidic and basic drugs, respectively (see eChap. 18 and Table 33-3).8 Several drugs are actively secreted by one or more of these transporter families, which include organic cationic (e.g., famotidine, trimethoprim, and dopamine), organic anionic (e.g., ampicillin, cefazolin, and furosemide), nucleoside (e.g., zidovudine), and P-glycoprotein transporters (e.g., digoxin, vinca alkaloids, and steroids).50,51 Alterations in filtration, secretion, or reabsorption, secondary to CKD may have a dramatic effect on drug disposition: for drugs that are primarily filtered, a reduction in GFR will result in a proportional decrease in renal drug clearance.
ESTIMATION OF RENAL FUNCTION
In the absence of data delineating the contribution of tubular function to renal clearance, the clinical measurement or estimation of CLcr or GFR remains the guiding factor for drug dosage regimen design.4,6,12The importance of an alteration in renal function on drug elimination depends on two factors: the fraction of drug normally eliminated by the kidney unchanged and the degree of renal insufficiency. Quantitation of the patient’s renal function can be accomplished by measurement of CLcr or GFR or estimation of CLcr or GFR based on the stable serum creatinine concentration (see eChap. 18). Because of the time delay involved and problems in obtaining complete urine collections, measured CLcr or GFR values are infrequently used for initial drug dosage regimen design. Therefore the calculation of initial drug dosage regimens relies on the estimation of CLcr or GFR in adults and children from such routinely available clinical data as age, gender, height, weight, and serum creatinine or serum cystatin C. The best method to use for adults remains controversial with many supporters of the C–G equation and others who support the use of the MDRD or chronic kidney disease epidemiology collaboration equation (CKD-EPI) approach52–54 (see eChap. 18).
Recently, there is considerable debate regarding the use of C–G or MDRD equation to guide drug dosing adjustment in patients with CKD. Recommendations as to when to adjust drug dosage regimens for currently approved medications are largely (greater than 95%) based on relationships between drug clearance and CLcr calculated by the C–G equation.55 Several studies have shown that when compared to drug dosing based on C–G, the use of MDRD equation resulted in conflicting drug dosing recommendations in 40% of patients.56–58 A recent systematic comparison between C–G and MDRD equations showed that the classification of renal function with each equation was generally similar.59 However, using the MDRD equation resulted in conflicting drug dosing recommendations (higher or lower dosing categories) in 12% of patients and resulted in higher dosing recommendations in patients with a combination of advanced age (age > 80 years), low weight (<55 kg), and modest elevations of serum creatinine (>0.7 and ≤1.5 mg/dL [>62 and ≤133 μmol/L]).59
The introduction of the 2009 CKD-EPI creatinine equation complicates things further.60 Reports showed that the CKD-EPI creatinine equation was more accurate in those with higher mean measured GFRs (estimated to be >60 mL/min per 1.73 m2); however, the MDRD equation was more accurate in those with GFRs less than 60 mL/min per 1.73 m2.52 In 2012, there were two variations of the CKD-EPI equation (CKD-EPI cystatin C equation and CKD-EPI creatinine–cystatin C equation) reported, which utilize cystatin C with or without serum creatinine to estimate GFR. One study showed that the CKD-EPI creatinine-cystatin C equation performed better and resulted in more accurate estimates of GFR compared to equations that used creatinine or cystatin C alone (CKD-EPI cystatin C or CKD-EPI creatinine equations).54 Although, recent data show improved accuracy of estimating GFR, especially with the CKD-EPI creatinine-cystatin C equation, there is extremely little data regarding the use of these equations to guide drug dosing in CKD patients. As such, drug dosing recommendations should continue to be based on CLcrestimated by C–G for the present.55
DRUG DOSAGE REGIMEN DESIGN FOR PATIENTS WITH CHRONIC KIDNEY DISEASE
The initial or “loading” dose for should be the same for patients with impaired renal function as those with normal renal function unless the volume of distribution is known to be altered in the presence of renal insufficiency or a concomitant disease (Table 33-1). Maintenance dosage regimen guidelines for CKD patients in FDA or European Medicines Agency (EMA) approved product labeling should be the foundation for ongoing therapy. If such information is not available or if there is marked variance between these two agencies’ recommendations the approach depicted in Table 33-4 for designing a dosage regimen for a patient with CKD can be utilized. In either case the design of the optimal dosage regimen is dependent on the availability of an accurate characterization of the relationship between the pharmacokinetic parameters of the drug and renal function and an accurate assessment of the patient’s renal function.
TABLE 33-4 Stepwise Approach to Adjust Drug Dosage Regimens for Patients with Renal Insufficiency
Most dosage-adjustment guidelines have proposed the use of a fixed dose or interval for patients with broad ranges of renal function that are different from those that are the foundation of the CKD staging scheme (see Chap. 29).3,8,15,36,61–63 Indeed, normal renal function has often been ascribed to anyone who has a CLcr >80 to 90 mL/min (>1.34 to 1.50 mL/s), even though the population normal CLcr values range from 115 to 125 mL/min per 1.73 m2(>1.11 to 1.20 mL/s/m2). The recent dosage adjustment guidelines and references often use different ranges to represent mild, moderate, and severe renal insufficiency.55 The predominant ranges for mild, moderate, and severe renal insufficiency can be defined as a CLcr of 60 to 89 mL/min (1 to 1.49 mL/s), CLcr of 30 to 59 mL/min (0.5 to 0.99 mL/s), and CLcrof 10 to 30 mL/min (0.17 to 0.5 mL/s), respectively. ESRD is usually defined as a CLcr of less than 10 mL/min (0.17 mL/s). Each of these categories encompasses a broad range in renal function, and thus the calculated drug regimen may not be optimal for all patients whose renal function lies within the given category of renal function.
Secondary references, such as the American Hospital Formulary Service Drug Information Service,61 Goodman and Gilman’s the Pharmacological Basis of Therapeutics,36 the British National Formulary,62 and Drug Prescribing in Renal Failure,63 are excellent sources of information about a drug’s pharmacokinetic characteristics in subjects with normal and impaired renal function. Marked variation in recommendations along with the paucity of details of the methods used to generate the dosing advice have resulted in some cautioning against their routine clinical use.64 In addition, none of these sources consistently provide the explicit relationships of the kinetic parameters of interest (total body clearance [CL], elimination rate constant [k], and VD) with a continuous index of renal function, such as CLcr. To find this information, one may need to identify the original research study that assessed the drug’s disposition or a comprehensive review article on the class of drugs of interest. This is a time-consuming process that may be difficult to carry out for each drug and patient combination in real time. Ideally, one should be able to identify a relationship between CL and k with an estimated GFR or CLcr, such as those depicted in Table 33-5. This information, along with the patient’s estimated CLcr or GFR, is the foundation upon which one can formulate a therapeutic regimen to attain the desired therapeutic outcome.
TABLE 33-5 Relationship Between Creatinine Clearance and Total Body Clearance and Terminal Elimination Rate Constant of Selected Drugs
If specific literature recommendations and/or the relationship of kinetic parameters to estimated GFR or CLcr are not available, then one can estimate the CL or k of the patient with the method of Rowland and Tozer,14 provided the fraction of the drug that is eliminated renally unchanged (fe) in subjects with normal renal function is known.65 This approach assumes that the change in CL and k are proportional to CLcr, that the renal disease does not alter the drug’s metabolism, that the metabolites, if formed, are inactive and nontoxic, that the drug obeys first-order (linear) kinetic principles, and that it is adequately described by a one-compartment model. If these assumptions are true, then the kinetic parameter/dosage-adjustment factor (Q) can be calculated as:
where KF is the ratio of the patient’s CLcr or GFR to the assumed normal value of 120 mL/min (equivalent to 2 mL/s). Thus for a drug that is 85% eliminated renally unchanged in a patient who has a CLcr of 10 mL/min (0.17 mL/s), the Q factor would be:
The best method for dosage regimen adjustment must then be selected. Specifically, one must determine whether the desired goal is the maintenance of a similar peak, trough, or average steady-state drug concentration or if there is a clearly defined pharmacodynamics endpoint such as the time above the minimum inhibitory concentration (MIC) or the ratio of the AUC relative to the MIC. If there is a significant relationship between peak concentration and clinical response66 (e.g., aminoglycosides) or toxicity67 (e.g., phenobarbital and phenytoin), then attainment of the specific target values is critical. If, however, no specific target values for peak or trough concentrations have been reported (e.g., antihypertensive agents and benzodiazepines), then a regimen goal of attaining the same average steady-state concentration is likely to be appropriate.
Although several methods have been proposed to attain the desired average steady-state concentration profile, the principal choices are to decrease the dose or prolong the dosing interval. If the size of the dose is reduced while the dosing interval remains unchanged, the desired average steady-state concentration will be similar; however, the peak will be lower and the trough higher (Fig. 33-1). Alternatively, if the dosing interval is increased and the dose size remains unchanged, the peak and trough concentrations in the patient with reduced renal function will be similar to those in the patient with normal renal function. This dosage adjustment method is often recommended because it is likely to yield cost savings as a result of a reduction in nursing and pharmacy time, as well as a reduction in the supplies associated with frequent drug administration. Finally, the dose and dosing interval may both need to be changed to allow the administration of a clinically feasible dose (500 mg vs. a calculated value of 487 mg) or a practical dosing interval, for example, 12 hours instead of 17 hours.
FIGURE 33-1 Although the average steady-state concentrations (Cave) are identical regardless of which dosage-adjustment strategy one decides to implement, the concentration–time profile will be markedly different if one changes the dose and maintains the dosing interval (τ) constant (Scenario A), versus changing the dosing interval and maintaining the dose constant (Scenario B) or changing both (Scenario C).
If the relationship between the pharmacokinetic parameters of the drug and renal function are known, the first step in the process is to estimate the drug disposition parameters in the patient with renal insufficiency. The dosage-adjustment factor (Q) can then be calculated as the ratio of the estimated k or CL of the patient relative to subjects with normal renal function defined as a CLcr = 120 mL/min (2 mL/s) by Rowland and Tozer.14 The dosage adjustment factor is then used to determine the dose or dosing interval alterations necessary for the patient. Table 33-6 provides an example application of the stepwise approach of calculating a dosage regimen based on pharmacokinetic characteristics of the drug and the patient’s renal function.
TABLE 33-6 Stepwise Approach to Calculating a Dosage Regimen Based on Drug’s Pharmacokinetic Characteristics and Patient’s Renal Function
The relationship between drug clearance and CLcr (expressed in conventional units of mL/min) has been reported for several drugs (Table 33-5). How one can apply the relationship between a patient’s renal function and pharmacokinetic characteristics of cefazolin, a commonly used antibiotic for the treatment of infections in CKD and dialysis patients to develop and individualized dosage recommendation are illustrated in Table 33-6 and briefly highlighted here. The first step is to calculate the CL of cefazolin for a subject with normal renal function (CLnorm) and CL for the patient with renal insufficiency (CLfail) to obtain the ratio of the predicted clearance values (Q) which can be used to calculate the new dosing regimen.
It is also important to consider other characteristics of cefazolin, such as MICs and concentrations associated with toxicities and adverse events, before modifying a dosage regimen. For cefazolin, the rate of cell kill is optimized by maintaining the concentration of drug above the MIC of the organism for at least 40% of the dosing interval. In general, this means that lower doses given more frequently would be expected to achieve target attainment to a greater degree than high doses given less often. The intermittent dose method may also achieve high concentrations of cefazolin which could result in dose-dependent toxicities such as seizures.68 Therefore, in the example shown in Table 33-6, the maintenance dose (Df) for a patient with renal insufficiency and the adjusted dosing interval (τf) are calculated from the relationships between the Q value and the normal dose (Dn) and normal dosing interval (τn).
If the VD of a drug is significantly altered in CKD patients or in whom one desires to attain a specific maximum or minimum concentration, the estimation of a dosage regimen becomes more complex. If the relationship between VD and CLcr has been characterized, then VD may be estimated. If one assumes that a one-compartment linear model can describe the drug, the predicted VD may then be used with the predicted k of the drug to yield an adjusted-dosing interval and IV dose.
For orally administered drugs, the τf can be calculated and the dose can be approximated as:
where F equals bioavailability, Ctp equals the desired plasma concentration at time t, and ka is the absorption rate constant. This approach allows for the individualization of an oral dosage regimen for attainment of specific peak and trough serum concentrations. If the drug is absorbed extremely rapidly, one can approximate the τf and the dose using equations originally proposed for IV dosing as:
These principles have been used by many investigators to derive dosage recommendations for commonly used drugs for patients with CKD (Tables 33-7 and 33-8).61,63,68–75
TABLE 33-7 Drug Dosing Guidelines for Nonantibiotics Commonly Used by CKD Patients
TABLE 33-8 Antibiotic Drug Dosing Recommendations
DRUG DOSAGE REGIMEN DESIGN FOR PATIENTS RECEIVING RENAL REPLACEMENT THERAPY
Continuous renal replacement therapies are used for the management of fluid overload and the removal of uremic toxins in patients with AKI and other conditions.69 Several forms of continuous renal replacement therapy in clinical use today are extensively described in Chapter 28 and several dosage regimen individualization approaches are presented and critiqued. Which of these therapies will be optimal for a given patient is dependent on several factors, including bleeding risk, degree of hypercatabolism, acid–base balance, and experience of the healthcare provider team. The rationale for and approaches for delivery of renal replacement therapy on an intermittent and occasionally continuous basis for those with ESRD are described in Chapter 30.
Peritoneal dialysis, like other dialysis modalities, has the potential to affect drug disposition; however, drug therapy individualization is often less complicated in these patients as a result of the limited drug clearances achieved with the variants of this procedure (see Chap. 30). In general, hemodialysis is more effective in removing drugs than peritoneal dialysis such that if a drug is not removed by hemodialysis, it is unlikely to be significantly removed by peritoneal dialysis. Many of the factors that are important in determining drug dialyzability for other treatment modalities pertain to peritoneal dialysis as well.76,77Factors that influence drug dialyzability by peritoneal dialysis include drug-specific characteristics such as molecular weight, solubility, degree of ionization, protein binding, and VD. The intrinsic properties of the peritoneal membrane that affect drug removal include blood flow and peritoneal membrane surface area, which is approximately equal to the body surface area. There is an inverse relationship between peritoneal drug clearance and molecular weight, protein binding, and VD. In addition, drug compounds that are ionized at physiologic pH will diffuse across the membrane more slowly than unionized compounds. Detailed reviews of the disposition of several drugs in chronic peritoneal dialysis patients are reported elsewhere.76,78 Antiinfective agents are the most commonly studied drugs because of their primary role in the treatment of peritonitis.76,79 The treatment priorities for peritoneal dialysis peritonitis and the recommended drug regimens are presented in detail in Chapter 30 (see Tables 30-10 and 30-11).
Peritoneal dialysis, in current practice, is often prescribed to attain a urea clearance of approximately 10 mL/min (0.17 mL/s), so it is unlikely to significantly impact the CL of any drug by more than 10 mL/min (0.17 mL/s).6 In addition, since most medications have a larger molecular size than urea, their resultant CL will likely be even lower: probably between 5 and 7.5 mL/min (0.08 to 0.13 mL/s). Therefore, drug dosing recommendations for the management of conditions other than peritonitis, reported for patients with estimated CLcr or GFR of 10 to 15 mL/min (0.17 to 0.25 mL/s), are likely suitable for patients receiving peritoneal dialysis.63
Although many new hemodialyzers have been introduced in the last 20 years and more than 100 different ones were available in the United States in 2013, the effect of hemodialysis on drug disposition is rarely reevaluated after it is initially reported. Thus, most of the literature, especially for older medications, probably represents an underestimation of the impact of hemodialysis on its disposition.80,81
The impact of hemodialysis on a patient’s drug therapy is dependent on several factors, including the characteristics of the drug, the dialysis conditions, and the clinical situation for which dialysis is performed. Drug-related factors that affect dialyzability include the molecular weight or size, degree of protein binding, and VD.4 The vast majority of dialysis filters in use in the United States up until the mid-1990s were composed of cellulose, cellulose acetate, or regenerated cellulose (cuprophane), and they were generally impermeable to drugs with a molecular weight greater than 1,000 Da.82 Drugs that are small but highly protein bound also are not well dialyzed because both of the principal binding proteins, α1-acid glycoprotein and albumin, have a very high molecular weight. Finally, those drugs that are widely distributed, VD greater than 2 L/kg, are poorly removed by hemodialysis.
The hemodialysis procedure, be it acute for the management of AKI, intermittent three times a week or daily for an extended period or some combination thereof for the management of ESRD can dramatically affect the dialysis clearance of a medication.81 The primary factors that vary between patients are the composition of the dialysis filter, the filter surface area, the blood, dialysate and ultrafiltration flow rates, and whether or not the dialysis unit reuses the dialysis filter. Dialysis membranes in the 21st century are predominantly composed of semisynthetic or synthetic materials (e.g., polysulfone, polymethylmethacrylate, or polyacrylonitrile). High-flux dialysis membranes have larger pore sizes and more closely mimic the filtration characteristics of the human kidney. This allows the passage of most solutes, including drugs (e.g., vancomycin) that have a molecular weight of 20,000 Da or less.80,82 An increase in removal has also been reported with several other drugs that have lower molecular weights (Table 33-9).80
TABLE 33-9 Drug Disposition during Dialysis Depends on Dialyzer Characteristics
Overall, the impact of hemodialysis on drug therapy is highly variable and thus one cannot assume that a certain percentage of a drug is removed with each dialysis session; neither should a “yes” answer regarding the dialyzability of drugs be considered sufficient information to make therapeutic decisions, since this provides no quantification of the impact of hemodialysis. Characteristics of the dialysis procedure that was utilized in the drug study, such as membrane composition and surface area and blood and dialysis flow rates, are thus critical data that should be known before one uses the hemodialysis clearance data to prospectively design a drug dosing regimen for a hemodialysis patient.
The quantitative impact of hemodialysis on drug disposition can be calculated in several ways.4 The most commonly utilized means for assessing the effect of hemodialysis is to calculate the dialyzer clearance (CLD) of the drug. The CLD can be calculated by several approaches. The from blood can be calculated as , where Qb is the blood flow through the dialyzer, Ab is the concentration of drug in blood going into the dialyzer, and Vb is the blood concentration of the drug leaving the dialyzer. This equation, also known as the “A–V difference method,” is valid only if the drug concentrations are measured in whole blood and if the drug rapidly and completely distributes into red blood cells. Because drug concentrations are generally determined in plasma, the previous equation is usually modified to , where p represents plasma and Qp is the plasma flow, which equals Qb (1 – hematocrit). This clearance calculation most accurately reflects dialysis drug clearance as most drugs do not significantly penetrate red blood cells or bind to formed blood elements. However, for drugs that readily partition into and out of erythrocytes, this equation could likely underestimate hemodialysis clearance. Furthermore, one must keep in mind that venous plasma concentrations may be artificially high and will be low if plasma water is removed from the blood at a faster rate than the drug. This tends to occur when extensive ultrafiltration is performed simultaneously with diffusion during dialysis.6
The recovery clearance approach remains the benchmark for the determination of dialyzer clearance and it can be calculated as:4
where, R is the total amount of drug recovered unchanged in the dialysate and AUC0–t is the area under the predialyzer plasma concentration–time curve during the period of time that the dialysate was collected. To determine the AUC0–t, at least two and preferably three to four plasma concentrations should be obtained during dialysis.
The hemodialysis clearance values reported in the literature may vary significantly depending on which of these methods were used to calculate CLD. The principal reason for this is that for most medications we do not know the degree and rapidity with which the drug crosses the red blood cell membrane. Because the method incorporates no assumption of the degree of red blood cell permeability, it can be reliably used as the benchmark value. The primary limitation of this calculation is that the concentrations of the drug in the dialysate may be below the sensitivity limits of the assay.
The following principles may be used to generate a drug dosage regimen recommendation for hemodialysis patients by using a value of CLD that is reported in the literature.4,80 Because clearance terms are additive, the total clearance during dialysis can be calculated as the sum of the patient’s residual renal and nonrenal clearance during the interdialytic period (CLRES) and dialyzer clearance (CLD):
The half-life during the period between dialysis treatments and during dialysis can then be calculated from the following relationships using an estimate of the drug’s VD, which can be obtained from the literature:65
Once the key pharmacokinetic parameters have been estimated/calculated, they may be used to simulate the plasma concentration–time profile of the drug for the individual patient and then one can ascertain how much drug to administer and when. This approach to drug therapy individualization can be accomplished in a stepwise fashion assuming first-order elimination of the drug and a one-compartment model.
For example, a 54-year-old critically ill female with ESRD was transferred to a medical intensive care unit from the general medical unit, where she was febrile with a temperature of 39°C (102.2°F). Her weight was 64 kg (141 lb) and her height was 65 inches (165 cm). She had a residual CLcr of 3 mL/min (0.05 mL/s), and was receiving high-flux dialysis (F80 polysulfone dialyzer) for 4 hours on Mondays, Wednesdays, and Fridays. She was started on vancomycin for a methicillin-resistant Staphylococcus aureus (MRSA) catheter-associated bacteremia and her first dose of 1,000 mg was administered at the end of her hemodialysis treatment at the referring hospital. The first step is to estimate this patient’s pharmacokinetic parameters of vancomycin on the basis of published population data.83 The VD in this patient can be estimated to be 54.4 L (0.85 L/kg × 64 kg), and her residual total body clearance (CLRES) estimated from the relationship between CL and CLcr [CLRES = (0.69 × CLcr) + 3.7] is 7.15 mL/min (0.12 mL/s) or 0.43 L/h. The k can be approximated as:
The hemodialysis clearance of vancomycin (CLD) is dependent on the dialyzer and a value of 120 mL/min (2 mL/s; 7.2 L/h) is a reasonable estimate for this dialyzer.84
One now can predict what the plasma concentrations of vancomycin will be over the next 24 to 48 hours, assuming the infusion time for the drug (t′) was 1 hour. The concentration at the end of the 1-hour infusion (Cmax) would be:
The plasma concentration prior to the next dialysis session (CbD), which is 44 hours away, and the concentration 4 hours later after dialysis (CaD) can be calculated as:
On the basis of these data, the second dose which should be administered after the second dialysis session should be increased as one generally desires to maintain vancomycin trough concentrations between 15 and 20 mg/L (10 to 14 μmol/L) for a MRSA catheter-associated bacteremia.70,85 The patient received a vancomycin dose of 1,500 mg 4 hours after the end of the second dialysis session. The increase in serum concentration at the end of this 1-hour infusion (Cchange) would be:
Thus the Cmax would be approximately 34 mg/L (24 μmol/L), the sum of the residual concentration from the first dose of approximately 7 mg/L (5 μmol/L) and the Cchange. The plasma concentration prior to the third dialysis session (CbD), which is 40 hours away, and the concentration 4 hours later after the third dialysis (CaD) can be estimated as:
This higher dose would be considered by many to have achieved too high of concentrations since the lowest value during the majority of the dosing interval exceeded 24.8 mg/L (17.1 μmol/L). Thus the serum concentration data from the several blood samples, which were collected to characterize this patient’s residual vancomycin clearance, VD, and the clearance of vancomycin during dialysis should be analyzed to generate a new dose estimate for administration after the second dialysis session. Blood samples were collected at the following times after the first dose of 1,000 mg so that the patient’s pharmacokinetic parameters for vancomycin could be determined:
The elimination rate during the interdialytic period (kID) and during dialysis (kDD), and the VD can be calculated as:
where Δt is the time in hours between the two measured concentrations and Cmin the vancomycin concentration in plasma prior to the administration of the first dose is zero. The patient’s residual clearance (CLRES) and dialyzer clearance (CLD) of vancomycin can then be calculated as:
Analysis of the measured serum concentrations obtained after the first dose yielded pharmacokinetic parameters that were significantly less than those projected based on population data: the patient’s residual clearance was 12.5% lower and the volume of distribution was 26.5% lower. Thus the serum peak concentration was 6.9 mg/L (4.8 μmol/L) higher than desired and the concentration prior to the second dialysis session was almost 30% higher than desired. These measured values in clinical practice could now be utilized to plan the dosage to be administered after the third dialysis session.
For medications with a narrow therapeutic index (e.g., vancomycin and gentamicin), therapeutic drug monitoring (e.g., plasma concentration measurements and dialyzer clearance estimation) should be utilized to guide drug dosing.6 The ultimate reason for measuring the plasma concentrations of aminoglycosides, vancomycin, and several other antibacterial agents is to individualize the patient’s dosage regimen to achieve a bacteriologic cure while preserving residual renal function. Thus there remains one important step in our evaluation: the calculation of the dose this patient should receive after the second dialysis session. The two factors that enter into this decision are the desired peak and trough concentrations. Vancomycin dosing is primarily based on attaining desired trough concentrations, usually between 15 and 20 mg/L (10 to 14 μmol/L). Peak concentrations are rarely used and not recommended to derive dosing recommendations and adjustments; however, for this patient example, a desired peak concentration [20 to 40 mg/L (14 to 28 μmol/L)] could be utilized to calculate a dose.70 In addition to considering desired vancomycin concentrations, it is important to consider the timing of vancomycin sample collections relative to the time of dialysis. If vancomycin levels are obtained after hemodialysis, it is recommended to wait at least 3 hours to check a level because vancomycin concentrations have been noted to reach the maximum rebounded concentration within 3 hours after the end of hemodialysis.86
Assuming the desired peak concentration was 30 mg/L (21 μmol/L) and trough concentration was 15 mg/L (10 micromol/L), the postdialysis dose this patient would need can then be calculated using the simplified approach below, because the t1/2 is extremely prolonged relative to the infusion time, and thus minimal drug is eliminated during the infusion period:
It is common practice in most hemodialysis units to administer drugs after the patient has received dialysis on the premise that it is desirable to minimize the loss of drug that would result from the additional clearance during hemodialysis. Certainly, administration of antihypertensive agents and vasoactive drugs should be avoided in the hours prior to a hemodialysis session to minimize the likelihood of hypotension. However, emerging pharmacokinetic and pharmacodynamic considerations suggest that this may not be the optimal approach for several other agents, such as aminoglycosides87–89 and vancomycin.90–92 Two evaluations of predialysis and one of intradialytic dosing of aminoglycosides indicate that similar peak concentrations, a prime indicator of efficacy, can be obtained in these scenarios relative to those observed with postdialysis dosing.87,88 The AUC during the dosing interval and the subsequent predialysis concentrations were noted to be significantly reduced and thus the risk of ototoxicity and further renal injury may be minimized. The best dosing schedule, a dose roughly twice that traditionally employed for postdialysis administration, in the 26 patients evaluated by Teigen et al., resulted in the achievement of the desired peak and AUC in approximately 90% of patients.88 The administration of traditional doses of tobramycin (1.5 mg/kg) or vancomycin (1,000 mg) during dialysis has been associated with markedly lower areas under the concentration–time curve than those observed when the same dose was administered postdialysis; consequently, higher dosage regimens are usually necessary to compensate for the additional loss of drug during the dialysis procedure. It is highly recommended that aminoglycoside and vancomycin concentrations in hemodialysis patients should be measured after the first dose and dialysis session and so that the dosage regimen can be individualized accordingly using Bayesian methodology whenever possible.
CLINICAL BOTTOM LINE
Subtherapeutic or supratherapeutic responses to drugs in patients with renal insufficiency are often misinterpreted and not recognized. The adverse outcomes associated with inappropriate drug dosing have not been quantified but do warrant future investigations. The utilization of FDA or EMA drug dosage recommendations in official prescribing information should be used for the initiation of therapy in most clinical situations. Critically ill individuals especially those with renal insufficiency likely have marked pharmacokinetic variability and may require the use of sound pharmacokinetic principles in concert with reliable population pharmacokinetic estimates to project the optimal approach to drug dosage regimen design. Individualization of all drugs with a narrow therapeutic index for AKI and CKD patients should be undertaken whenever clinical therapeutic monitoring tools are available. The key action step is to use the knowledge we have to improve patient outcomes. The recent study of van Dijk et al. is an unfortunate reminder of how far we still have to go to optimize the therapy of patients with renal insufficiency.93 They observed that although dosage adjustments based on renal function were warranted in 24% of the prescriptions of the patients with CLcr less than 51 mL/min (0.85 mL/s), such adjustments were only performed in 59% of cases.
1. National Kidney and Urologic Diseases Information Clearinghouse (NKUDIC). Kidney Disease Statistics for the United States. U.S. Department of Health and Human Services, New Release, November 2012, http://kidney.niddk.nih.gov/kudiseases/pubs/kustats/
2. Olyaei AJ, Steffl JL. A quantitative approach to drug dosing in chronic kidney disease. Blood Purif 2011;31:138–145.
3. Olyaei AJ, Bennett WM. Drug dosing in the elderly patients with chronic kidney disease. Clin Geriatr Med 2009;25:459–527.
4. Matzke GR, Comstock TJ. Influence of renal disease and dialysis on pharmacokinetics. In: Evans WE, Schentag JJ, Burton ME, eds. Applied Pharmacokinetics: Principles of Therapeutic Drug Monitoring, 4th ed. Baltimore, MD: Lippincott Williams & Wilkins, 2005:187–212.
5. Ritschel WA, Denson DD. Influence of disease on bioavailability. In: Ritschel WA, ed. Pharmacokinetics: Regulatory, Industrial, Academic Perspectives. New York, NY: Marcel Dekker, 1995.
6. Matzke GR, Aronoff GR, Atkinson AJ Jr, et al. Drug dosing consideration in patients with acute and chronic kidney disease—A clinical update from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int 2011;80:1122–1137.
7. Naud J, Nolin TD, Leblond FA, Pichette V. Current understanding of drug disposition in kidney disease. J Clin Pharmacol 2012;52:10S–22S.
8. Verbeeck RK, Musuamba FT. Pharmacokinetics and dosage adjustment in patients with renal dysfunction. Eur J Clin Pharmacol 2009;65:757–773.
9. Dreisbach AW. The influence of chronic renal failure on drug metabolism and transport. Clin Pharmacol Ther 2009;86:553–556.
10. Nolin TD. Altered nonrenal drug clearance in ESRD. Curr Opin Nephrol Hypertens 2008;17:555–559.
11. Momper JD, Venkataramanan R, Nolin TD. Nonrenal drug clearance in CKD: Searching for the path less traveled. Adv Chronic Kidney Dis 2010;17:384–391.
12. Nolin TD, Unruh ML. Clinical relevance of impaired nonrenal drug clearance in ESRD. Semin Dial 2010;23:482–485.
13. Matzke GR, Marks SA, Dowling TC, Murphy JE, Burckart GJ. Influence of Kidney Disease on Drug Pharmacokinetics: An assessment of industry studies submitted to FDA for New Molecular Entities 1999–2010. Presented at 45th Annual Meeting of The American Society of Nephrology, November 3, 2012, San Diego, CA.
14. Rowland M, Tozer TN. Clinical Pharmacokinetics: Concepts and Applications, 3rd ed. Philadelphia, PA: Lea & Febiger, 1995:156–183.
15. Matzke GR, Dowling TD. Dosing concepts in renal dysfunction. In: Murphy JE, ed. Clinical Pharmacokinetics Pocket Reference, 5th ed. Bethesda, MD: American Society of Health-System Pharmacists, 2011:427–443.
16. Ibrahim S, Honig P, Huang SM, et al. Clinical pharmacology studies in patients with renal impairment: Past experience and regulatory perspectives. J Clin Pharmacol 2000;40(1):31–38.
17. Huang SM, Temple R, Xiao S, et al. When to conduct a renal impairment study during drug development: US Food and Drug Administration perspective. Clin Pharmacol Ther 2009:86:475–479.
18. Anonymous. Characterization of the relationship between the pharmacokinetics and pharmacodynamics of a drug and renal function. U.S. Department of Health and Human Services, FDA Guidance, May 1998, http://www.fda.gov/cber/guidelines.htm.
19. Zhang Y, Zhang L, Abraham S, et al. Assessment of the impact of renal impairment on systematic exposure of new molecular entities: Evaluation of recent new drug applications. Clin Pharmacol Ther 2009;85:305–311.
20. Anonymous. Pharmacokinetics in patients with impaired renal function—Study design, data analysis, and impact on dosing and labeling. U.S. Department of Health and Human Services, Draft FDA Guidance, March 2010, http://www.fda.gov/cber/guidelines.htm.
21. Zhang L, Xu N, Xiao S, et al. Regulatory perspective on designing pharmacokinetic studies and optimizing labeling recommendations for patients with chronic kidney disease. J Clin Pharmacol 2012;52:79S–90S.
22. Tortorici MA, Cutler D, Zhang L, Pfister M. Design, conduct, analysis, and interpretation of clinical studies in patients with impaired kidney function. J Clin Pharmacol 2012;52:109S–118S.
23. Cusack BJ. Pharmacokinetics in older persons. Am J Geriatr Pharmacother 2004;2(4):274–302.
24. Ritschel WA, Denson DD. Influence of disease on bioavailability. In: Ritschel WA, ed. Pharmacokinetics: Regulatory, Industrial, Academic Perspectives. New York, NY: Marcel Dekker, 1995.
25. Bellomo R, Prowle JR, Echeverri JE. Diuretic therapy in fluid-overloaded and heart failure patients. Contrib Nephrol 2010;164:153–163.
26. Hurwitz A. Antacid therapy and drug kinetics. Clin Pharmacokinet 1977;2:269–280.
27. Maton PN, Burton ME. Antacids revisited: A review of their clinical pharmacology and recommended therapeutic use. Drugs 1999;57:855–870.
28. Craig RM, Murphy P, Gibson TP, et al. Kinetic analysis of d-xylose absorption in normal subjects and in patients with chronic renal failure. J Lab Clin Med 1983;101:496–506.
29. Craig RM, Carlson S, Ehrenpreis ED. d-xylose kinetics and hydrogen breath tests in functionally anephric patients using the 15-gram dose. J Clin Gastroenterol 2000;31:55–59.
30. Matzke GR, Frye RF. Drug administration in patients with renal insufficiency: Minimizing renal and extrarenal toxicity. Drug Saf 1997;16:205–231.
31. Gibson TP, Giacomini KM, Briggs WA, et al. Propoxyphene and norpropoxyphene plasma concentrations in the anephric patient. Clin Pharmacol Ther 1980;27:665–670.
32. Barnes JN, Williams AJ, Tomson MJ, et al. Dihydrocodeine in renal failure: Further evidence for an important role of the kidney in the handling of opioid drugs. BMJ 1985;290:740–742.
33. Bailey DG, Arnold JM, Munoz C, Spence JD. Grapefruit juice—Felodipine interaction; mechanism, predictability, and effect of naringin. Clin Pharmacol Ther 1993;53(6):637–642.
34. Min Dl, Ku YM, Perry PJ, et al. Effect of grapefruit juice on cyclosporine pharmacokinetics in renal transplant patients. Transplantation 1996;62:123–125.
35. Ueda N, Yoshimura R, Umene-Nakano W, et al. Grapefruit juice alters plasma sertraline levels after single ingestion of sertraline in healthy volunteers. World J Biol Psychiatry 2009;10:832–835.
36. Thummel KE, Shen DD, Isoherranen N. Appendix II. Design and optimization of dosage regimens: Pharmacokinetic data. In: Brunton LL, Chabner BA, Knollmann BC, eds. Goodman & Gilman’s The Pharmacological Basis of Therapeutics, 12th ed. New York, NY: McGraw-Hill, 2011, http://www.accessmedicine.com/content.aspx?aID=16683174.
37. Alvarez-Lerma F, Grau S. Management of antimicrobial use in the intensive care unit. Drugs 2012;72:447–470.
38. Winter ME. Phenytoin and fosphenytoin. In: Murphy JE, ed. Clinical Pharmacokinetics Pocket Reference, 5th ed. Bethesda, MD: American Society of Health-System Pharmacists, 2011:247–259.
39. Meijers BKI, Bemmers B, Verbeke B, et al. A review of albumin binding in CKD. Am J Kidney Dis 2008;51:839–850.
40. Job ML. Digoxin. In: Murphy JE, ed. Clinical Pharmacokinetics Pocket Reference, 5th ed. Bethesda, MD: American Society of Health-System Pharmacists, 2011:139–147.
41. Malini PL, Strocchi E, Feliciangeli G, et al. Digitalis receptors and digoxin sensitivity in renal failure. Clin Exp Pharmacol Physiol 1985;12:115–120.
42. Koup J. Disease states and drug pharmacokinetics. J Clin Pharmacol 1989;29:674–679.
43. Yuan R, Venitz J. Effect of chronic renal failure on the disposition of highly hepatically metabolized drugs. Int J Clin Pharmacol Ther 2000;38:245–253.
44. Szeto HH, Inturrisi CE, Houde R, et al. Accumulation of normeperidine, an active metabolite of meperidine, in patients with renal failure of cancer. Ann Intern Med 1977;86:738–741.
45. Murphy EJ. Acute pain management for the patient with concurrent renal or hepatic disease. Anaesth Intensive Care 2005;33:311–322.
46. Nolin DT, Frye RF, Le P, et al. ESRD impairs nonrenal clearance of fexofenadine but not midazolam. J Am Soc Nephrol 2009;20:2269–2276.
47. Vilay AM, Churchwell MD, Mueller BA. Drug metabolism and clearance in acute kidney injury. Crit Care 2008;12:235.
48. Dowling TC, Briglia AE, Fink JC, et al. Characterization of hepatic cytochrome P4503 A activity in patients with end-stage renal disease. Clin Pharmacol Ther 2003;73:427–434.
49. Lee W, Kim RB. Transporters and renal drug elimination. Annu Rev Pharmacol Toxicol 2004;44:137–166.
50. Sun H, Frassetto L, Benet LZ. Effects of renal failure on drug transport and metabolism. Pharmacol Ther 2006;109:1–11.
51. Masereeuw R, Russel FGM. Therapeutic implications of renal anionic drug transporters. Pharmacol Ther 2010;126:200–216.
52. Earley A, Miskulin D, Lamb EJ, et al. Estimating equations for glomerular filtration rate in the era of creatinine standardization. Ann Intern Med 2012;156:785–795.
53. Steffl JL, Bennett W, Olyaei AJ. The old and new methods of assessing kidney function. J Clin Pharmacol 2012;52:63S–71S.
54. Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012;367:20–29.
55. Dowling TC, Matzke GR, Murphy JE, Burckart GJ. Evaluation of renal drug dosing: Prescribing information and clinical pharmacist approaches. Pharmacotherapy 2010;30:776–786.
56. Wargo KA, Eiland EH, Hamm W, et al. Comparison of the modification of diet in renal disease and Cockcroft–Gault equations for antimicrobial dosing adjustments. Ann Pharmacother 2006;40:1248–1253.
57. Golik MV, Lawrence KR. Comparison of dosing recommendations for antimicrobial drugs based on two methods for assessing kidney function: Cockcroft–Gault and modification of diet in renal disease. Pharmacotherapy 2008;28:1125–1132.
58. Hermsen ED, Maiefski M, Florescu MC, et al. Comparison of the modification of diet in renal disease and Cockcroft–Gault equations for dosing antimicrobials. Pharmacotherapy 2009;29:649–655.
59. Park EJ, Wu K, Mi Z, Dong T, et al. A systematic comparison of Cockcroft–Gault and modification of diet in renal disease equations for classification of kidney dysfunction and dosage adjustment. Ann Pharmacother 2012;46:1174–1187.
60. Levey AS, Stevens LA, Schmid CH, et al. CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–612.
61. McEvoy GK, Snow EK, Miller J, et al. American Hospital Formulary Service, Drug Information. Bethesda, MD: American Society of Health-System Pharmacists, 2012.
62. Joint Formulary Committee. British National Formulary, 64th ed. London: British Medical Association and Royal Pharmaceutical Society of Great Britain, 2004. http://www.bnf.org/bnf/.
63. Aronoff GR, Bennett WM, Berns JS, et al. Drug Prescribing in Renal Failure: Dosing Guidelines for Adults and Children, 5th ed. Philadelphia, PA: American College of Physicians-American Society of Internal Medicine, 2007.
64. Vidal L, Shavit M, Fraser A, et al. Systematic comparison of four sources of drug information regarding adjustment of dose for renal function. BMJ 2005;331:263–266.
65. Matzke GR, Clermont G. Clinical pharmacology and therapeutics. In: Murray PT, Brady HR, Hall JB, eds. Intensive Care in Nephrology. Boca Raton, FL: Taylor & Francis, 2006:245–265.
66. Craig WA. Pharmacokinetic/pharmacodynamic parameters: Rationale for antibacterial dosing of mice and men. Clin Infect Dis 1998;26:1–12.
67. Murphy JE. Clinical Pharmacokinetics Pocket Reference, 5th ed. Bethesda, MD: American Society of Health-System Pharmacists, 2011.
68. Drusano GL. Pharmacokinetic optimisation of β-lactams for the treatment of ventilator-associated pneumonia. Eur Respir Rev 2007;16:45–49.
69. Heintz BH, Matzke GR, Dager WE. Antimicrobial dosing concepts and recommendations for critically ill adult patients receiving continuous renal replacement therapy or intermittent hemodialysis. Pharmacotherapy 2009;29:562–577.
70. Rybak MJ, Lomaestro BM, Rotschafer JC, Moellering RC Jr, et al. Therapeutic monitoring of vancomycin in adults summary of consensus recommendations from the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, and the Society of Infectious Diseases Pharmacists. Pharmacotherapy 2009;29:1275–1279.
71. Munar MY, Singh H. Drug dosing adjustments in patients with chronic kidney disease. Am Fam Physician 2007;75:1487–1496.
72. Micromedex® Healthcare Series. Thomson Reuters (Healthcare) Inc., 2012, http://www.thomsonhc.com.
73. Mandell GL, Bennett JE, Dolin R, eds. Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Disease, 7th ed. Philadelphia, PA: Churchill Livingstone Elsevier, 2010.
74. Shah A, Lettieri J, Blum R, et al. Pharmacokinetics of intravenous ciprofloxacin in normal and renally impaired subjects. J Antimicrob Chemother 1996;38:103–116.
75. Gilbert B, Robbins P, Livornese LL Jr. Use of antibacterial agents in renal failure. Infect Dis Clin North Am 2009;23:899–924.
76. Manley HJ, Bailie GR. Treatment of peritonitis in APD: Pharmacokinetic principles. Semin Dial 2002;15:418–421.
77. Veltri MA, Neu AM, Fivush BA, et al. Drug dosing during intermittent hemodialysis and continuous renal replacement therapy: Special considerations in pediatric patients. Pediatr Drugs 2004;6:45–65.
78. Taylor CA, Abdel-Rahman E, Zimmerman SW, Johnson CA. Clinical pharmacokinetics during continuous ambulatory peritoneal dialysis. Clin Pharmacokinet 1996;31:293–308.
79. Li PKT, Szeto CC, Piraino B, et al. Peritoneal dialysis-related infections recommendations: 2010 update. Perit Dial Int 2010;30:393–423.
80. Matzke GR. Status of hemodialysis of drugs in 2002. J Pharm Pract 2002;15:405–418.
81. Secker BS, Mueller BA, Sowinski KM. Drug dosing considerations in alternative hemodialysis. Adv Chronic Kidney Dis 2007;14:e17–e26.
82. Cheung AK. Hemodialysis and hemofiltration. In: Greenberg A, Cheung AK, Coffman TM, Falk RJ, Jennette JC, eds. Primer on Kidney Disease, 5th ed. Philadelphia, PA: WB Saunders, 2008.
83. Matzke GR, Buby J. Vancomycin. In: Murphy JE, ed. Clinical Pharmacokinetics Pocket Reference, 5th ed. Bethesda, MD: American Society of Hospital Pharmacists, 2011.
84. Launay-Vacher V, Izzedine H, Mercadal L, Deray G. Clinical review: Use of vancomycin in haemodialysis patients. Crit Care 2002;6:313–316.
85. Liu C, Bayer A, Cosgrove SE, et al. Clinical practice guidelines by the infectious diseases society of America for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children: Executive summary. Clin Infect Dis 2011;52(3):285–292.
86. Welage LS, Mason NA, Hoffman EJ, et al. Influence of cellulose triacetate hemodialyzers on vancomycin pharmacokinetics. J Am Soc Nephrol 1995;6:1284–1290.
87. Matsuo H, Hayashi J, Ono K, et al. Administration of aminoglycosides to hemodialysis patients immediately before dialysis: A new dosing modality. Antimicrob Agents Chemother 1997;41:2597–2601.
88. Teigen MMB, Duffull S, Dang L, Johnson DW. Dosing of gentamicin in patients with end-stage renal disease receiving hemodialysis. J Clin Pharmacol 2006;46:1259–1267.
89. Mohamed OHK, Wahba IM, Watnick S, et al. Administration of tobramycin in the beginning of the hemodialysis session: A novel intradialytic dosing regimen. Clin J Am Soc Nephrol 2007;2:694–699.
90. Scott MK, Macias WL, Kraus MA, et al. Effects of dialysis membrane on intradialytic vancomycin administration. Pharmacotherapy 1997;17(2):256–262.
91. Ariano RE, Fine A, Sitar DS, et al. Adequacy of a vancomycin dosing regimen in patients receiving high-flux hemodialysis. Am J Kidney Dis 2005:46;681–687.
92. Crawford BS, Largen RF, Walton T, Doran JJ. Once-weekly vancomycin for patients receiving high-flux hemodialysis. Am J Health-Syst Phar 2008;65:1248–1253.
93. van Dijk EA, Drabbe NRG, Kruijtbosch M, De Smet PAGM. Drug dosage adjustments according to renal function at hospital discharge. Ann Pharmacother 2006;40:1254–1260.