Predicting the Effect of Renal Function on Systemic Clearance: Is a Simple Scaling Method Sufficient?

Predicting the Effect of Renal Function on Systemic Clearance: Is a simple scaling method sufficient? - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Pharm Sci . Author manuscript; available in PMC: 2026 Apr 22. Published in final edited form as: J Pharm Sci. 2023 Apr 5;112(6):1724–1732. doi: 10.1016/j.xphs.2023.03.008 Search in PMC Search in PubMed View in NLM Catalog Add to search Predicting the Effect of Renal Function on Systemic Clearance: Is a simple scaling method sufficient? Patrick J McNamara Patrick J McNamara , Ph.D. 1 Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 S. Limestone, 361, Lexington, KY 40536-0596 Find articles by Patrick J McNamara 1 , Darius Meiman Darius Meiman , Pharm.D. 2 Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN Find articles by Darius Meiman 2 Author information Article notes Copyright and License information 1 Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 S. Limestone, 361, Lexington, KY 40536-0596 2 Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN Author contribution Patrick McNamara: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing original draft, Writing-reviewing and editing, Visualization Darius Meiman: Methodology, Formal analysis, Data curation, Writing-reviewing and editing, Visualization Issue date 2023 Jun. PMC Copyright notice PMCID: PMC13098391  NIHMSID: NIHMS1889290  PMID: 37023855 The publisher's version of this article is available at J Pharm Sci Abstract Purpose: To employ a simple scaling method to predict systemic or oral clearance for drugs that are primarily renally cleared knowing the fraction eliminated in urine (f e ) and a patient’s renal function relative to healthy controls (S GFR ). Methods: Observations evaluating drug clearance as a function of creatinine clearance for renally cleared drugs (f e >0.3) were obtained from literature sources. The analysis comprised of 82 unique drugs from 124 studies including 31 drugs with replicate studies. A simple scaler for renal function was employed and compared to the linear regression of available data. For drugs in which replicate studies were available, the ability of the linear regression (Cl vs Cl CR ) from one pharmacokinetic study was used to predict observations from an assigned replicate and compared to the scaling approach. Results: For patients categorized as severe kidney disease (Cl CR fixed at 20 ml/min), the scalar tended to over predict some observations, but 92% of the predictions were within 50 – 200% of the observed data. For drugs with available replicates, the scalar was as good or better in predicting the influence of Cl CR on systemic clearance from a separate study when comparing against the linear regression approach. Conclusion: A scaling approach to account for alterations in drug clearance appears to have its advantages and represents a simple and generalizable method for guiding dose adjustments in patients with decreased renal function for drugs that are renally cleared (f e >0.3). In addition to its use in clinical practice, validation of this approach may have implications in facilitating more efficient drug development processes for designing dose-adjusted pharmacokinetic studies in patients with renal disease. Introduction Dose adjustments due to altered renal function is one of the most widely used dose modification strategies in clinical pharmacy. To offset variation in systemic drug exposure, clinicians are tasked with adjusting the amount of drug given. While there is currently no universal standard for adjusting dose due to altered renal function, clinicians often refer to package inserts or clinical databases for recommendations when guiding dose adjustments based on renal function. While these recommendations are good for general guidance, they only offer a broad sense for when changes may be warranted. For instance, recommendations based on renal function categories (i.e.: mild, moderate, severe) make it difficult when evaluating patients around a given cutoff and may limit a clinician’s ability to administer a more personalized dose. Furthermore, recommendations based on categorical approaches are usually derived from a limited number of patients (~6–8 per category) and may not be as generalizable outside of clinical trials. While a categorical approach may be adequate when the risk of administering a supra or subtherapeutic dose is unlikely (i.e.: large therapeutic window), implementation of a universal scaling method for renal function may have its clinical benefits. Traditionally, changes in renal drug clearance are established in standalone trials where drug exposure is assessed in “healthy” control subjects (Cl CR >90 ml/min), as well as patients with mild (Cl CR 89–60 ml/min), moderate (Cl CR 59–30 ml/min) and severe (Cl CR 29–15 ml/min) renal disease, although this range has varied with time. Values of systemic clearance (Cl IV or Cl PO ) are then plotted as a function of measured glomerular filtration, typically creatinine clearance (Cl CR ). While useful, this regression approach is limited to a specific drug and trial observation and cannot always be utilized for more generalizable dose recommendations. Recently, more advanced methods such as physiologically based pharmacokinetic (PBPK) models have been used to simulate the effect of decreased renal function on drug exposure to guide dose adjustments 1 – 4 . However, the utilization of PBPK models in clinical practice has not been established and is limited by its complexity for everyday application. Concerns for scaling clearance based on renal function alone include alterations in non-renal clearance and changes in plasma protein binding. 5 – 6 The FDA has issued guidance 7 around this topic which recommends conducting studies as standalone pharmacokinetic trials for drugs whose fraction eliminated renally (f e ) is 0.3 or greater. Since renal clearance is the predominate pathway of elimination, it is likely for these drugs to be classified as BDDCS Class 3 (high solubility and low permeability). 8 Likewise, given their high-water solubility, it is less likely they are appreciably bound to plasma proteins. The goal of this paper is four-fold, centering on a relatively simple approach; (1) to employ a simple scaling method for drug clearance (Cl IV or Cl PO ) accounting for the fraction of a dose eliminated renally (f e ) and a patient’s renal function relative to healthy controls (S GFR ), (2) to assess the scaling approach using literature evidence for drugs eliminated renally (f e >0.30) with limited plasma protein binding (f u >0.40), (3) to assess the same scaling approach using for drugs eliminated renally (f e >0.30) with more extensive plasma protein binding (f u < 0.40) and (4) to compare the use of a simple scalar to a traditional linear regression approach using observations from a standalone pharmacokinetic study of the same drug. Methods Theoretical Framework Drug exposure as calculated by the area under the curve (AUC) is widely accepted as a measure of drug response. Equation (1) describes AUC as a function of bioavailability associated with the absorption site (F A ), the gut wall (F G ), and/or the liver (F H ), administered dose (D) and subsets of clearance usually composed of renal (Cl R ) and hepatic (Cl H ). Many drugs are excreted by multiple pathways and overall systemic clearance can be divided amongst these paths. A U C = F A F G F H D C l R + C l H (1) Frequently, variation is framed in reference to changes in exposure relative to the control condition, when administered an equal dose (e.g., renal disease compared to normal renal function; one genetic variant to another, etc.). Equation (2) illustrates the impact variation has on such a comparison. Parameters with an asterisk represent the condition of interest (e.g., disease, genetics, etc.). A U C ∗ A U C = F A ∗ F G ∗ F H ∗ F A F G F H C l R + C l H C l R ∗ + C l H ∗ (2) Changes in drug clearance are directly reflective to drug exposure. In physiological altered states, as the body’s ability to excrete drug goes down, exposure increases in a proportional manner. Assuming the altered condition yields no change in drug bioavailability and dose is held constant, values of F A , F G , F H, and D are unchanged. A U C ∗ A U C = C l R + C l H C l R ∗ + C l H ∗ (3) Hepatic and renal clearance values may be expressed as the fraction of dose excreted through these pathways multiplied by overall clearance, and one can divide the numerator and denominator in Equation (3) by clearance. A U C ∗ A U C = 1 f e G F R ∗ G F R + 1 − f e (4) Where f e is the fraction of the dose cleared renally and (1-f e ) is the fraction of the dose cleared non-renally, which is assumed to be unaffected except during end stage renal disease. Considerable clinical evidence exists that renal clearance of most drugs correlates with glomerular filtration, irrespective of the renal mechanism by which it is cleared (filtration, secretion, or reabsorption). This assumption is often referred to as the “Intact Nephron” hypothesis 9 . It should be noted that this hypothesis has been questioned using model secretion substrates in a variety of animal models and limited clinical data 10 , 11 . The ratio of GFR values can be represented as a scaling factor, S GFR , which is often represented as the ratio of creatinine clearance values (Cl CR ) in patients with impaired renal function relative to healthy controls. A U C ∗ A U C = 1 f e S G F R + 1 − f e (5) As expected for drugs that are largely removed renally (f e ~1), the AUC ratio approaches the value of the reciprocal of the ratio of the Cl CR values (S GFR ). Putting this in terms of systemic clearance (or apparent oral clearance) results in Equation (6) C l ∗ = f e S G F R + 1 − f e C l (6) With the assumption that non-renal clearance is unaffected by changes in renal function. Additionally, oral dosing would assume that bioavailability is also unaffected by loss of renal function. Figure 1A demonstrates a computer simulation of Equation (5) , illustrating the influence of the changing renal function (i.e., S GFR ) and a given drug’s f e on the relative exposure for drugs administered at the same dose to patients with renal impairment. As expected, this AUC ratio or relative exposure is markedly increased for drugs where the extent of renal clearance is high in patients with decreased renal function. A scaling approach has the advantage over a categorical (mild, moderate, and severe renal disease) approach of being continuous and can be more readily applied across most drugs. Exposure to total drug can be predicted by knowing f e and a patient’s renal function (S GFR ). The relative exposure for a drug with an f e of 0.5 would be less than two-fold at 10% of renal function; whereas the exposure would be close to seven-fold when administered to the same patient for a drug with an f e of 0.95. Figure 1B depicts a similar influence of changing renal function (i.e., S GFR ) on the ratio of systemic clearance (Cl*/Cl). FIGURE 1. Open in a new tab Influence of a scaling factor reflecting changes in GFR (S GFR ) and fraction eliminated GFR in urine (f e ), Panel A on the relative systemic exposure (AUC*/AUC) assuming dose remains the same and Panel B the systemic clearance (Cl*/Cl or oral clearance). Relative dose (D*/D) required to achieve the same exposure relative to normal dosing is identical to the relative clearance pattern (Panel B). To achieve a comparable exposure (AUC) as normal renal function, dose adjustments would be accomplished using the following relationship: D ∗ D = f e S G F R + 1 − f e (7) Equation 7 . simply relates the adjusted dose (D*) compared to the original dose (D) given under normal renal circumstances. It should be noted that this approach to adjustments in daily dose does not take into consideration changes in half-life which may influence dosing interval. The relative dose (D*/D) required to achieve a comparable exposure of total drug as a function of the renal scalar (S GFR ) and f e would be the same as for the ratio of systemic clearance. ( Figure 1B ). Supporting Evidence University of Washington Database Data from clinical trials assessing the impact of renal function on systemic exposure of drugs were obtained through the University of Washington Drug Interactions Database (UWDIDB, Link), accessed February 2022. To assess the influence of renal disease on systemic exposure, the database was screened for drugs (“objects”) for which renal elimination was modest or greater (fe>0.3), with limited plasma protein binding (f u >0.4), obtained in subjects categorized as having mild, moderate, and severe renal disease (no documented hepatic impairment). Furthermore, only those studies containing data from individual patients (typically in the form of a graph of systemic clearance as a function of Cl CR ) were included. Data from subjects whose Cl CR was below 4 ml/min were excluded as the accuracy of measurement becomes unclear. The data from these graphs were manually extracted using a digital overlay process ( https://automeris.io/WebPlotDigitizer/index.html ). Accuracy of the data capture was made by comparing regression analysis, as well as reported group means for Cl CR , Cl (or AUC, Cl R ) of the original to that of the recovered data. After excluding several drugs (prodrugs, drugs undergoing interconversion, etc.), the resultant data set contained a total of 64 unique drugs with 100 studies (25 drugs had replicate studies). A second screening for more extensively bound drugs yielded a total of 18 unique drugs with 24 studies (6 drugs had replicate studies). The scalar prediction of patient Cl IV (or Cl PO ) was based on the average Cl IV (or Cl PO ) from the study subjects with normal renal function (e.g., Cl CR > 80 ml/min) and the fraction excreted (f e ) [ Supplemental Table 1 ]. The range of normal was expanded since several studies contained few subjects whose Cl CR >90 ml/min, thus limiting information on normal Cl IV (or Cl PO ). A weighted average of f e was used, derived from available literature for intravenously administered drug. For a limited number of drugs this intravenous data was not available, and in such cases an f e estimate derived from oral studies or taken from the drug product label was utilized. The Cl IV (or Cl PO ) prediction of the scalar approach was compared with that of the linear regression for the same study at a clinically relevant Cl CR of 20 ml/min. For the 31 drugs with replicate data sets, the ability of the scalar approach to predict Cl IV (or Cl PO ) in a test data set was compared to the prediction using the regression line of a separate (replicate) study from the literature (prior data set). For drugs with FDA labeled dosing recommendations based on renal function, equation 7 was employed to predict daily dose adjustments based on the fraction eliminated renally (f e ) listed in Supplemental Table 2 using the upper and lower bounds of Cl CR as specified on the product label relative to normal (Cl CR =120mL/min). As outlined by Li and coworkers 12 , the assessment of f e can be pooled from various sources by using a weighted mean, computed as follows: W X = ∑ j = 1 J n j x j ∑ j = 1 J n j (8) where n j represents the number of participants in the j th study and x j denotes the average value of the parameter in question in the j th study. The WX value for f e associated with normal renal function patients / volunteers in several studies was then applied to scaling for renal function. Evaluation of Performance Accuracy Consistent with the process outlined by Li and coworkers 12 , the predictive accuracy of the model was assessed using predictive error (PE) 13 , average fold error (AFE) 14 , 15 , and absolute AFE (AAFE) 14 , 15 . Predefined fold errors of deviation were established. These accuracy estimates were determined as follows: P E ( % ) = X p r e d − X o b s X o b s × 100 (9) A F E = 10 1 n ∑ i = 1 n L o g 10 X p r e d , i X o b s , i (10) A A F E = 10 1 n ∑ i = 1 n | L o g 10 x p r e d , i x o b s , i | (11) where X pred, i and X obs, i refer to the ith predicted and observed value, respectively, and n refers to the size of the data set. Precision The precision was estimated from the root-mean squared error (RMSE) 15 by R M S E = 1 n ∑ i = 1 n L o g 10 X o b s , i − L o g 10 X p r e d , i 2 (12) Correlation In addition to the typical correlation coefficient (r 2 ), the concordance correlation coefficient (CCC) was calculated as follows: C C C = 2 s x y s x 2 + s y 2 + ( x ¯ − y ¯ ) 2 (13) where x i and y i are the observed and predicted values for f u for the ith drug/situation, respectively, x and y are the mean values of the observed and predicted f u , respectively, and s and s 2 are the covariance and variance, respectively. In addition the following estimates were obtained. s x 2 = 1 n ∑ i = 1 n x i − x ¯ 2 ; s y 2 = 1 n ∑ i = 1 n y i − y ¯ 2 (14) s x y = 1 n ∑ i = 1 n x i − x ¯ y i − y ¯ (15) Concordance correlation coefficient (CCC) is a global measure of the accuracy and evaluates the degree to which pairs of predicted and observed data fall on the line of unity passing through the origin 14 . Results Clearance (Cl IV or Cl PO ) due to changes in renal function are presented in Figure 2 . Ampicillin (Panel A ) and cefepime (Panel B ) represent studies for which the scalar approach appears to visually mimic the observed clearance for drugs with limited binding across all Cl CR values (~70% of studies). Meropenem (Panel C ) and sulbactam (Panel D ) represent studies for which the scalar approach appears to visually overpredict observations at lower Cl CR values (~30% of studies). Azapropazone (Panel E ) and ibandronate (Panel F ) represent studies for more extensively bound drugs. Graphs for all 124 studies can be found in the supplemental information . FIGURE 2. Open in a new tab Influence of renal function (Cl CR or equivalent) on the systemic or apparent clearance (solid blue circles) for ampicillin 39 (Panel A), cefepime 40 (Panel B), meropenem 41 (Panel C), sulbactam 42 (Panel D), azapropazone 43 (Panel E) and ibandronate 44 (Panel F) from published clinical references. Panels A-D are representative figures of an analysis of 100 studies encompassing 64 drugs, possessing low protein binding (f u > 0.40) and significant renal elimination (f e > 0.3) found in Supplemental Table 1 . Panels E and F are representative figures of an analysis of 24 studies encompassing 18 drugs, possessing more extensive protein binding (f u < 0.40) and significant renal elimination (f e > 0.3) found in Supplemental Table 1 The blue solid line is the linear regression of the observations. The red interrupted line is that predicted by Equation 6 , using the control clearance values (red circle), the literature fraction eliminated in the urine (f e ) from Supplemental Table 2 , and the scaling factor for renal function ) . A more complete analysis for all 124 studies can be found in Supplemental Figures S1 and S3 . Figure 3 represents the predictive error (%PE) of the scalar approach for systemic clearance (Cl IV or Cl PO ) as a function of the fraction of the dose excreted renally (f e ) in patients categorized as having severe kidney damage (Cl CR fixed at 20 ml/min). Consistent with the visual assessment, the scalar approach tends to overpredict some drug observations, however 92% of predictions lie within 50 – 200% of the observed data. There is no apparent influence of the value of f e , route of administration ( Figure 3A ), renal clearance mechanism ( Figure 3B ) or extent of binding ( Figure 3C ) on the accuracy of predictions. Ibandronate is the drug for which the scalar approach is over 4-fold higher for all three panels. FIGURE 3. Open in a new tab Predictive error (%PE) for the scalar model estimating the Cl (or Cl/F) for 124 studies (82 drugs) as a function of the fraction eliminated renally (f e ) when renal function is fixed at 20 ml/min compared to the linear regression from the same study. In Panel A the closed circles are derived from studies in which the drug was administered intravenously (n=85), the open circles are from studies in which the drug was administered orally (n=37), the closed triangles are from studies in which the drug was administered intramuscularly (n=2). In Panel B the circles are derived from intravenous studies in which the drug is removed largely by glomerular filtration (GFR, n=43), the squares from studies in which the drug’s renal elimination involves tubular secretion as well as GFR (unbound Cl R is greater than 1.5 times GFR, n=40), the triangles from studies in which the drug’s renal elimination involves tubular reabsorption and GFR (unbound Cl R is less than half of GFR, n=2). In Panel C the closed circles are derived from studies in which the drug is less extensively bound (f u > 0.4) to plasma proteins (n=100) and the open circles are derived from studies in which the drug is more extensively bound (f u < 0.4) to plasma proteins (n=24). For drugs with available replicate studies, the prediction of the scaling approach was compared to the linear regression from a separate, standalone study ( Figure 4 and Table 1 ). On average the scalar approach was slightly biased (higher average AFE, Table 1 ), but was comparable to the replicate regression approach with respect to average AAFE, RMSE, and observations within specified fold errors (1.25, 1.5 and 2-fold error). CCC values ( Table 1 ), a global measure of the accuracy, indicated that the scalar approach was more accurate than the replicate regression approach. For bisoprolol ( Figure 4 , Panel A ), ceftazidime 3 (Panel B ) meropenem 2 (Panel C ) and pemetrexed (Panel D ), as well as 18 additional replicates ( Supplementary information ), the scalar approach was more accurate (CCC, Supplemental Table 3 ). For lamivudine 2 (Panel E ), and imipenem 2 (Panel F ) the regression line prediction from another study better represented the observed data established from the test study. Tazobactam (Panel G ) and teicoplanin (Panel H ) represent twelve other replicate examples in which the two approaches had similar CCC values ( Supplemental Table 3 ). Table 1 presents the statistical analysis averages of the scalar approach compared to the linear regression line from a parallel study for the 36 replicate cases representing 31 unique drugs. A comparison analysis of individual drug replicates ( Supplemental Table 3 ) revealed that the scaling method was equal or better than using the replicate linear regression from a separate study (69% AFE, 94% CCC). FIGURE 4. Open in a new tab Influence of renal function (Cl CR or equivalent) on the systemic or apparent clearance (solid blue circles) for bisoprolol 45 (Panel A), ceftazidime 46 (Panel B), meropenem 41 (Panel C), pemetrexed 47 (Panel D), lamivudine 48 (Panel E), imipenem 49 (Panel E), tazobactam 50 (Panel G) and teicoplanin 51 (Panel H) from published clinical references. Panels A - F are representative figures of an analysis of 30 replicate studies encompassing 25 drugs, possessing low protein binding (f u > 0.40) and significant renal elimination (fe > 0.3) found in Supplemental Table 1 . Panels G and H are representative figures of an analysis of 6 replicate studies encompassing 6 drugs, possessing more extensive protein binding (f u < 0.40) and significant renal elimination (fe > 0.3) found in Supplemental Table 1 .The solid green line is the linear regression of the observations from a separate study of the same drugs [bisoprolol 52 (Panel A), ceftazidime 53 (Panel B), meropenem 54 (Panel C), pemetrexed 55 (Panel D), lamivudine 56 (Panel E), imipenem 57 (Panel F), tazobactam 58 (Panel G) and teicoplanin 51 (Panel H). The red interrupted line is that predicted by Equation 6 , using the control clearance values (red circle), the literature fraction eliminated in the urine (f e ) from Supplemental Table S2 , and the scaling factor for renal function. Representations of all the studies and more complete analysis can be found in Supplemental Figures S2 and S4 as well as Supplemental Table S3 . TABLE 1. Predictive analysis of two approaches to predict drug clearance as a function of renal status (Cl CR ) for 36 replicate studies assessing 31 unique drugs found in literature. Scalar Approach ( Equation 6 ) uses the mean normal clearance and estimates of f e ( Supplemental Table 3 ). The Prior Regression uses the regression line from a replicate study of the same drug. Average fold error (AFE) – measure of the extent to which a particular method under/over predicts the observed values (goal = 1.00). Absolute average fold error (AAFE) – absolute of plus and minus data quantifying the magnitude from the true value (goal = 1.00). Root-mean squared error (RMSE) – how close the predicted values are compared to observed (desired goal = 0). Concordance correlation coefficient (CCC) - agreement between two variables, e.g., to evaluate reproducibility (goal = 1.00). Error within Specified Fold Error Model AFE AAFE RMSE CCC <1.25-fold <1.5- fold <2-fold Average Scalar Approach 1.28 1.45 0.197 0.743 51% 70% 96% Prior Regression 1.10 1.48 0.198 0.615 45% 74% 98% StDev Scalar Approach 0.33 0.29 0.111 0.174 22% 22% 10% Prior Regression 0.41 0.29 0.091 0.222 22% 25% 7% Open in a new tab Figure 5 illustrates the influence of using age matched controls. Figure 5A shows the same bias in over estimating clearance values of famotidine at low Cl CR when young healthy controls were used in the scalar approach. Figure 5B illustrates a superior prediction when elderly controls were used. FIGURE 5. Open in a new tab Influence of renal function (Cl CR ) on the systemic clearance for famotidine (solid blue circles) in a study that included young as well as age-matched controls 59 . Panel A includes values from the young control (23–32 yo, Cl CR =110 ml/min) population. Panel B includes values from the elderly control (65–74 yo, Cl CR =108 ml/min) population. The blue solid line is the linear regression of the observations. The red interrupted line is that predicted by Equation 6 , using the control clearance values (red circle) from young controls (Panel A) and elderly controls in (Panel B). The ability of the scalar approach to predict daily dose adjustments found in drug product labels is depicted in Figure 6 . Fifty-five of the 82 studied drugs had FDA labeled dosing recommendations (n=113) for various Cl CR ranges and are listed in Supplemental Table 4 . The scalar approach was reasonably accurate in predicting daily dose recommendations as listed on FDA drug labels (AFE = 1.01, AAFE = 1.31, RMSE = 0.154, CCC = 0.942). Several drugs had predictions above (clavulanic acid, lamivudine and meropenem) or below (lisinopril) the 50 – 200% range of the recommended daily dose. FIGURE 6. Open in a new tab Utility of scalar approach to predict daily dose (mg/d) for 55 drugs compared to FDA label recommendations (n=113). Prediction was set as the average scaled daily dose based on Cl CR ranges as specified in the product label. The solid line is unity, with the interrupted line representing a 50–200% prediction error. Clavulanic acid, lamivudine, lisinopril and meropenem dosing predictions fell outside the 50–200% error range. Details of these comparisons can be found in Supplemental Table 4 . Discussion A key tenet of precision medicine is to offset variation in clearance by adjusting dose to achieve a similar exposure as an otherwise normal individual. Variability in clearance can arise due to changes in either renal or non-renal pathways, occurring from a loss of organ mass, blood flow (i.e.: shunting), changes in capacity or affinity underlying pharmacokinetic processes (i.e. nephron or hepatocyte), or alterations in plasma protein binding. Variability can also arise due to changes in the capacity or affinity of transporters and enzymes due to inhibition by concomitant drugs or endogenous ligands. The goal of pharmacokinetic scaling is to provide a simple conceptual framework for understanding how variation impacts drug exposure and to provide an integrated strategy for which dose adjustments can be made clinically. The use of this type of scaling for drug-drug interactions in the inhibition of hepatic clearance was explored previously 16 – 23 . A similar approach using translational informatic methodologies was employed to facilitate drug research 24 . Dose adjustments for liver 25 and renal disease 26 have also used a similar type of scaling approach. The scaling approach for dose adjustment outlined herein ( Eq. 7 ) would greatly simplify dose adjustments for renally cleared drugs. The most common approach to assess the influence of loss of renal function on a specific drug’s pharmacokinetics is typically a plot of systemic (or renal) clearance as a function of creatinine clearance. While this application is the most direct, it does require specific measurements of these parameters in patients. Moreover, it is difficult to extrapolate specific drug features more broadly since the slopes and intercepts of such plots vary with each drug. The value of the current approach ( Figure 2 ) is that it allows for a general prediction of a change in clearance [hence, exposure (AUC*/AUC)] based solely on an estimate of a drug’s f e in subjects with normal renal function and a patient’s renal function status (S GFR or Cl CR ). The drug selection criteria used in this analysis [i.e., small molecules with significant renal elimination (f e >0.3)], resulted in a dataset of predominately (86%) Class 3 drugs (BDDCS). Class 3 drugs are highly soluble, with poor membrane permeability, resulting in poorly metabolized drugs whose intestinal absorption and hepatic entry may require uptake transporters 8 . It is not surprising that scaling for renal function status would work best for these types of drugs. In addition to the drug selection criteria, the limitations of this generalized approach to predicting exposure in patients with diminished renal function are several. First, it requires an accurate assessment of a drug’s f e . This is of particular importance as f e approaches 1, as it may be difficult to accurately measure the difference between 0.9 and 0.95, but such a difference will have a major impact on changes in exposure. Second, it requires an accurate assessment of renal function. Most clinical studies utilize creatinine clearance, measured directly, or estimated using an established formula (e.g., Cockcroft and Gault). As demonstrated by several investigators 27 – 30 , creatinine clearance can overestimate glomerular filtration as measured by other means (e.g., iothalamate clearance). There is some inconsistency regarding the extent to which creatinine clearance overestimates GFR, with estimates ranging from 10 to 60% 27 – 30 . An important consideration is the implicit assumption that these patients with renal disease are relatively homogeneous. Clearly this is unlikely to be the case for all the cited studies. Patients undoubtedly vary with respect to acute versus chronic disease, extent of uremia, concomitant medications, and the presence of other disease conditions. The cited references rarely explicitly addressed these factors. It is likely that some of these variations will be more impactful for some drugs on the ability of the scalar approach to predict clearance. Another factor to consider is that of age-matched controls. Many of the cited studies ( Supplemental Table 1 ) used healthy younger controls compared to patients with various degrees of renal failure. As illustrated by the case of famotidine ( Figure 5 ), the use of younger controls may bias the results. Clearly the use of age matched controls is most appropriate as comorbidity, polypharmacy, and age-related changes in pharmacokinetics is well established. 31 , 32 Yeung et all 5 described the unpredictable nature of pharmacokinetics of non-renally cleared drugs in patients with chronic renal failure. They speculated that this variability may arise from altered expression and/or activity of drug-metabolizing enzymes and transporters, primarily localized in the liver and intestine. Other literature reports indicate that renal failure can alter drug transport and metabolism pathways 33 34 . More recently, investigators 35 6 have examined the influence of renal function on cytochrome P450 enzymes for drugs largely cleared non-renally suggesting that OATP, CYP2C8, and CYP2D6-mediated clearance decreased in parallel with the severity of renal disease 35 6 . There was no apparent relationship between the severity of renal disease and CYP1A2, CYP2C9, CYP2C19 and CYP3A4/5-mediated clearance 35 6 . The current analysis appeared to work irrespective of the route of administration, the mechanism of renal clearance or the extent of binding to plasma proteins. The later observation is somewhat surprising given plasma protein binding changes which occur in renal disease. Most of the more extensively bound drugs examined herein are thought to be bound to human serum albumin. Altered drug binding due to lower plasma concentration and the accumulation of metabolic waste products which compete for drug binding has been established in patients with renal disease 36 37 38 . Hence it was anticipated that the decrease in renal clearance as Cl CR decreased, there would be an offsetting increase in f u resulting in a more muted decrease in overall clearance. A larger sample size may yet support this conjecture. That being said changes in binding do not impact unbound drug concentration (compared to total) for drugs undergoing primarily renal elimination.” The scalar approach to adjust dose would result in comparable unbound drug exposure and clinical response. The tendency of the scalar approach to overpredict clearance at lower Cl CR values for some drugs is likely due to one or a combination of factors: (1) a decrease in nonrenal elimination as Cl CR decreases; (2) an underestimation of f e in a control population, (3) inappropriate control patients (young volunteers vs elderly renal disease patient) (4) varied measurement of renal function (measured / estimated Cl CR , inulin, EDTA, etc.), (5) patient variation beyond a simple measurement of renal function and (6) limited number of control and patient data. The FDA Draft Guidance on pharmacokinetics in patients with impaired renal function 7 currently recommends standalone pharmacokinetic studies for drugs (or active metabolites) excreted primarily renally (f e ≥ 0.3). If renal clearance is the predominate pathway of elimination it is likely that these are largely BDDCS Class 3 drugs (high solubility and low permeability). 8 Our analysis ( Figure 4 and Table 1 ) suggests that the scaling approach is at least as good, if not better than using the regression line obtained from a replicate study for these drugs. An alternate (i.e., best practice) to the standalone pharmacokinetic study in renal disease would be to gather robust data on systemic clearance, protein binding, and f e (n~32) in both young and elderly control subjects utilizing the scalar approach ( Equation 7 ) to guide dose adjustments based on changes in renal function (S GFR ). If standalone trials are still required, a dose-normalized strategy could be used and confirmed as opposed to administering a similar dose across categories (mild, moderate, severe). In lieu of standalone trials, predictions would be validated once larger trial (Phase 2/3) datasets become available including information on individuals with varying renal function. The goal of standalone pharmacokinetic studies is to provide dosing recommendations in patients with varied renal function. The scalar approach appears reasonable ( Figure 6 ), although it should be noted that log-log plots visually minimize error. Only clavulanic acid, lamivudine, lisinopril and meropenem dosing predictions lie outside the range of 50–200% of the FDA product label dosing recommendations. Although the scalar approach works well for most drugs, the precision of this approach is dependent upon an accurate and robust value for both the fraction eliminated in urine (f e ) and clearance (systemic or apparent) in an age matched control population. Changes in nonrenal elimination pathways in severe renal disease may occur for some drugs, hence it is important to fully understand the mechanism of nonrenal elimination for a given drug when applying this scalar approach. While it is possible to adjust dose on a continuous scale for drugs given intravenously for patients with severe renal disease, we recognize adjusting dose may be limited for certain routes of administration and available dosage forms (e.g., oral products). It should also be noted that several drugs (e.g., clavulanic acid, cilastatin and sulbactam) are marketed as combination products and dosing recommendations may be based on the other component. Again, the scalar approach offers an opportunity to independently assess the impact of renal disease on both drugs assuming the necessary information to employ the scalar approach is collected. Conclusion The implementation of a scaling approach to account for alterations in drug clearance appears to have its advantages and represents a relatively simple and generalizable method for guiding dose adjustments in patients with decreased renal function for small molecule drugs that are renally cleared (f e >0.3). In addition to its application in clinical practice, validation of this approach may have implications in facilitating more efficient drug development processes as standalone pharmacokinetic studies for drugs meeting these characteristics may not be needed, or modified using a dose-normalized design strategy. Supplementary Material 1 NIHMS1889290-supplement-1.pdf (2.2MB, pdf) 2 NIHMS1889290-supplement-2.docx (3.5MB, docx) Acknowledgments DM was supported by the NIH/NIGMS Grant T32-GM008425. Footnotes Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. References 1. De Sousa Mendes M, Chetty M 2019. Are Standard Doses of Renally-Excreted Antiretrovirals in Older Patients Appropriate: A PBPK Study Comparing Exposures in the Elderly Population With Those in Renal Impairment. Drugs in R&D 19(4):339–350. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Franchetti Y, Nolin TD 2020. Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation. J Clin Pharmacol 60 Suppl 1:S36–S51. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Rowland Yeo K, Gil Berglund E 2021. An Integrated Approach for Assessing the Impact of Renal Impairment on Pharmacokinetics of Drugs in Development: Pivotal Role of PBPK Modelling. Clinical pharmacology and therapeutics 110(5):1168–1171. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Zhuang X, Lu C 2016. PBPK modeling and simulation in drug research and development. Acta Pharm Sin B 6(5):430–440. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Yeung CK, Shen DD, Thummel KE, Himmelfarb J 2014. Effects of chronic kidney disease and uremia on hepatic drug metabolism and transport. Kidney Int 85(3):522–528. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Tan ML, Yoshida K, Zhao P, Zhang L, Nolin TD, Piquette-Miller M, Galetin A, Huang SM 2018. Effect of Chronic Kidney Disease on Nonrenal Elimination Pathways: A Systematic Assessment of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and OATP. Clinical pharmacology and therapeutics 103(5):854–867. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. 2020. Guidance for Industry Pharmacokinetics in Patients with Impaired Renal Function – Study Design, Data Analysis, and Impact on Dosing. In Researchs UFaDACfDEa, editor, ed., Federal registry: U.S. Department of Health and Human Services. [ Google Scholar ] 8. Benet LZ, Broccatelli F, Oprea TI 2011. BDDCS applied to over 900 drugs. Aaps J 13(4):519–547. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Bricker NS, Morrin PA, Kime SW Jr. 1960. The pathologic physiology of chronic Bright’s disease. An exposition of the “intact nephron hypothesis”. The American journal of medicine 28:77–98. [ DOI ] [ PubMed ] [ Google Scholar ] 10. Wright DFB, Duffull SB 2017. A general empirical model for renal drug handling in pharmacokinetic analyses. Br J Clin Pharmacol 83(9):1869–1872. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Pradhan S, Duffull SB, Walker RJ, Wright DFB 2019. The intact nephron hypothesis as a model for renal drug handling. European journal of clinical pharmacology 75(2):147–156. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Li GF, Yu G, Li Y, Zheng Y, Zheng QS, Derendorf H 2018. Quantitative Estimation of Plasma Free Drug Fraction in Patients With Varying Degrees of Hepatic Impairment: A Methodological Evaluation. Journal of pharmaceutical sciences 107(7):1948–1956. [ DOI ] [ PubMed ] [ Google Scholar ] 13. Wu G, Baraldo M, Furlanut M 1995. Calculating percentage prediction error: a user’s note. Pharmacol Res 32(4):241–248. [ DOI ] [ PubMed ] [ Google Scholar ] 14. Poulin P, Theil FP 2009. Development of a novel method for predicting human volume of distribution at steady-state of basic drugs and comparative assessment with existing methods. Journal of pharmaceutical sciences 98(12):4941–4961. [ DOI ] [ PubMed ] [ Google Scholar ] 15. Poulin P 2015. Drug Distribution to Human Tissues: Prediction and Examination of the Basic Assumption in In Vivo Pharmacokinetics-Pharmacodynamics (PK/PD) Research. Journal of pharmaceutical sciences 104(6):2110–2118. [ DOI ] [ PubMed ] [ Google Scholar ] 16. von Moltke LL, Greenblatt DJ, Cotreau-Bibbo MM, Duan SX, Harmatz JS, Shader RI 1994. Inhibition of desipramine hydroxylation in vitro by serotonin-reuptake-inhibitor antidepressants, and by quinidine and ketoconazole: a model system to predict drug interactions in vivo. The Journal of pharmacology and experimental therapeutics 268(3):1278–1283. [ PubMed ] [ Google Scholar ] 17. Ito K, Iwatsubo T, Kanamitsu S, Ueda K, Suzuki H, Sugiyama Y 1998. Prediction of pharmacokinetic alterations caused by drug-drug interactions: metabolic interaction in the liver. Pharmacol Rev 50(3):387–412. [ PubMed ] [ Google Scholar ] 18. Davit B, Reynolds K, Yuan R, Ajayi F, Conner D, Fadiran E, Gillespie B, Sahajwalla C, Huang SM, Lesko LJ 1999. FDA evaluations using in vitro metabolism to predict and interpret in vivo metabolic drug-drug interactions: impact on labeling. J Clin Pharmacol 39(9):899–910. [ DOI ] [ PubMed ] [ Google Scholar ] 19. Rodrigues AD, Winchell GA, Dobrinska MR 2001. Use of in vitro drug metabolism data to evaluate metabolic drug-drug interactions in man: the need for quantitative databases. J Clin Pharmacol 41(4):368–373. [ DOI ] [ PubMed ] [ Google Scholar ] 20. Tucker GT, Houston JB, Huang SM 2001. EUFEPS conference report. Optimising drug development: strategies to assess drug metabolism/transporter interaction potential - towards a consensus. European Federation of Pharmaceutical Sciences. Eur J Pharm Sci 13(4):417–428. [ DOI ] [ PubMed ] [ Google Scholar ] 21. Yao C, Levy RH 2002. Inhibition-based metabolic drug-drug interactions: predictions from in vitro data. Journal of pharmaceutical sciences 91(9):1923–1935. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Ito K, Brown HS, Houston JB 2004. Database analyses for the prediction of in vivo drug-drug interactions from in vitro data. Br J Clin Pharmacol 57(4):473–486. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Houston JB, Galetin A. 2010. In Vitro Techniques to Study Drug–Drug Interactions of Drug Metabolism: Cytochrome P450. In Pang KS, Rodrigues AD, Peter RM, editors. Enzyme and Transporter Based Drug-Drug Interactions, Progress and Future Challenges, ed., New York: Springer. p 169–215. [ Google Scholar ] 24. Zhang P, Wu HY, Chiang CW, Wang L, Binkheder S, Wang X, Zeng D, Quinney SK, Li L 2018. Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research. CPT: pharmacometrics & systems pharmacology 7(2):90–102. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Verbeeck RK 2008. Pharmacokinetics and dosage adjustment in patients with hepatic dysfunction. European journal of clinical pharmacology 64(12):1147–1161. [ DOI ] [ PubMed ] [ Google Scholar ] 26. Verbeeck RK, Musuamba FT 2009. Pharmacokinetics and dosage adjustment in patients with renal dysfunction. European journal of clinical pharmacology 65(8):757–773. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Zhang X, Rule AD, McCulloch CE, Lieske JC, Ku E, Hsu CY 2020. Tubular secretion of creatinine and kidney function: an observational study. BMC Nephrol 21(1):108. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Walser M 1998. Assessing renal function from creatinine measurements in adults with chronic renal failure. Am J Kidney Dis 32(1):23–31. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Schwartz GJ, Furth SL 2007. Glomerular filtration rate measurement and estimation in chronic kidney disease. Pediatr Nephrol 22(11):1839–1848. [ DOI ] [ PubMed ] [ Google Scholar ] 30. Shemesh O, Golbetz H, Kriss JP, Myers BD 1985. Limitations of creatinine as a filtration marker in glomerulopathic patients. Kidney Int 28(5):830–838. [ DOI ] [ PubMed ] [ Google Scholar ] 31. Mangoni AA, Jackson SH 2004. Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol 57(1):6–14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Shi S, Klotz U 2011. Age-related changes in pharmacokinetics. Curr Drug Metab 12(7):601–610. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Sun H, Frassetto L, Benet LZ 2006. Effects of renal failure on drug transport and metabolism. Pharmacology & therapeutics 109(1–2):1–11. [ DOI ] [ PubMed ] [ Google Scholar ] 34. Nolin TD, Naud J, Leblond FA, Pichette V 2008. Emerging evidence of the impact of kidney disease on drug metabolism and transport. Clinical pharmacology and therapeutics 83(6):898–903. [ DOI ] [ PubMed ] [ Google Scholar ] 35. Yoshida K, Sun B, Zhang L, Zhao P, Abernethy DR, Nolin TD, Rostami-Hodjegan A, Zineh I, Huang SM 2016. Systematic and quantitative assessment of the effect of chronic kidney disease on CYP2D6 and CYP3A4/5. Clinical pharmacology and therapeutics 100(1):75–87. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. McNamara PJ, Meiman D 2019. Predicting Drug Binding to Human Serum Albumin and Alpha One Acid Glycoprotein in Diseased and Age Patient Populations. Journal of pharmaceutical sciences 108(8):2737–2747. [ DOI ] [ PubMed ] [ Google Scholar ] 37. McNamara PJ, Lalka D, Gibaldi M 1981. Endogenous accumulation products and serum protein binding in uremia. J Lab Clin Med 98(5):730–740. [ PubMed ] [ Google Scholar ] 38. Craig WA, Evenson MA, Sarver KP, Wagnild JP 1976. Correction of protein binding defect in uremic sera by charcoal treatment. J Lab Clin Med 87(4):637–647. [ PubMed ] [ Google Scholar ] 39. Yokoyama Y, Matsumoto K, Yamamoto H, Iguro Y, Imoto Y, Ikawa K, Morikawa N, Ishida S, Okano Y, Watanabe E, Shimodozono Y, Yamada K, Takeda Y 2012. Pharmacokinetics of ampicillin-sulbactam and the renal function-based optimization of dosing regimens for prophylaxis in patients undergoing cardiovascular surgery. Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy 18(6):878–882. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Preston RA, Mamikonyan G, DeGraff S, Chiou J, Kemper CJ, Xu A, Mastim M, Yeole R, Chavan R, Patel A, Friedland HD, Bhatia A 2019. Single-Center Evaluation of the Pharmacokinetics of WCK 5222 (Cefepime-Zidebactam Combination) in Subjects with Renal Impairment. Antimicrob Agents Chemother 63(1). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Leroy A, Fillastre JP, Borsa-Lebas F, Etienne I, Humbert G 1992. Pharmacokinetics of meropenem (ICI 194,660) and its metabolite (ICI 213,689) in healthy subjects and in patients with renal impairment. Antimicrob Agents Chemother 36(12):2794–2798. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Reitberg DP, Marble DA, Schultz RW, Whall TJ, Schentag JJ 1988. Pharmacokinetics of cefoperazone (2.0 g) and sulbactam (1.0 g) coadministered to subjects with normal renal function, patients with decreased renal function, and patients with end-stage renal disease on hemodialysis. Antimicrob Agents Chemother 32(4):503–509. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Breuing KH, Gilfrich HJ, Meinertz T, Wiegand UW, Jahnchen E 1981. Disposition of azapropazone in chronic renal and hepatic failure. European journal of clinical pharmacology 20(2):147–155. [ DOI ] [ PubMed ] [ Google Scholar ] 44. Bergner R, Henrich DM, Hoffmann M, Honecker A, Mikus G, Nauth B, Nagel D, Uppenkamp M 2007. Renal safety and pharmacokinetics of ibandronate in multiple myeloma patients with or without impaired renal function. J Clin Pharmacol 47(8):942–950. [ DOI ] [ PubMed ] [ Google Scholar ] 45. Kirch W, Rose I, Demers HG, Leopold G, Pabst J, Ohnhaus EE 1987. Pharmacokinetics of bisoprolol during repeated oral administration to healthy volunteers and patients with kidney or liver disease. Clin Pharmacokinet 13(2):110–117. [ DOI ] [ PubMed ] [ Google Scholar ] 46. Norrby SR, Burman LA, Linderholm H, Trollfors B 1982. Ceftazidime: pharmacokinetics in patients and effects on the renal function. J Antimicrob Chemother 10(3):199–206. [ DOI ] [ PubMed ] [ Google Scholar ] 47. Mita AC, Sweeney CJ, Baker SD, Goetz A, Hammond LA, Patnaik A, Tolcher AW, Villalona-Calero M, Sandler A, Chaudhuri T, Molpus K, Latz JE, Simms L, Chaudhary AK, Johnson RD, Rowinsky EK, Takimoto CH 2006. Phase I and pharmacokinetic study of pemetrexed administered every 3 weeks to advanced cancer patients with normal and impaired renal function. J Clin Oncol 24(4):552–562. [ DOI ] [ PubMed ] [ Google Scholar ] 48. Johnson MA, Verpooten GA, Daniel MJ, Plumb R, Moss J, Van Caesbroeck D, De Broe ME 1998. Single dose pharmacokinetics of lamivudine in subjects with impaired renal function and the effect of haemodialysis. Br J Clin Pharmacol 46(1):21–27. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Gibson TP, Demetriades JL, Bland JA 1985. Imipenem/cilastatin: pharmacokinetic profile in renal insufficiency. The American journal of medicine 78(6A):54–61. [ DOI ] [ PubMed ] [ Google Scholar ] 50. Johnson CA, Halstenson CE, Kelloway JS, Shapiro BE, Zimmerman SW, Tonelli A, Faulkner R, Dutta A, Haynes J, Greene DS, et al. 1992. Single-dose pharmacokinetics of piperacillin and tazobactam in patients with renal disease. Clinical pharmacology and therapeutics 51(1):32–41. [ DOI ] [ PubMed ] [ Google Scholar ] 51. Derbyshire N, Webb DB, Roberts D, Glew D, Williams JD 1989. Pharmacokinetics of teicoplanin in subjects with varying degrees of renal function. J Antimicrob Chemother 23(6):869–876. [ DOI ] [ PubMed ] [ Google Scholar ] 52. Payton CD, Fox JG, Pauleau NF, Boulton-Jones JM, Ioannides C, Johnston A, Thomas P 1987. The single dose pharmacokinetics of bisoprolol (10 mg) in renal insufficiency: the clinical significance of balanced clearance. Eur Heart J 8 Suppl M:15–22. [ DOI ] [ PubMed ] [ Google Scholar ] 53. Welage LS, Schultz RW, Schentag JJ 1984. Pharmacokinetics of ceftazidime in patients with renal insufficiency. Antimicrob Agents Chemother 25(2):201–204. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Rubino CM, Bhavnani SM, Loutit JS, Lohse B, Dudley MN, Griffith DC 2018. Single-Dose Pharmacokinetics and Safety of Meropenem-Vaborbactam in Subjects with Chronic Renal Impairment. Antimicrob Agents Chemother 62(3). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Latz JE, Chaudhary A, Ghosh A, Johnson RD 2006. Population pharmacokinetic analysis of ten phase II clinical trials of pemetrexed in cancer patients. Cancer Chemother Pharmacol 57(4):401–411. [ DOI ] [ PubMed ] [ Google Scholar ] 56. Heald AE, Hsyu PH, Yuen GJ, Robinson P, Mydlow P, Bartlett JA 1996. Pharmacokinetics of lamivudine in human immunodeficiency virus-infected patients with renal dysfunction. Antimicrob Agents Chemother 40(6):1514–1519. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 57. Verpooten GA, Verbist L, Buntinx AP, Entwistle LA, Jones KH, De Broe ME 1984. The pharmacokinetics of imipenem (thienamycin-formamidine) and the renal dehydropeptidase inhibitor cilastatin sodium in normal subjects and patients with renal failure. Br J Clin Pharmacol 18(2):183–193. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Derendorf H, Dalla Costa T 1996. Pharmacokinetics of piperacillin, tazobactam and its metabolite in renal impairment. Int J Clin Pharmacol Ther 34(11):482–488. [ PubMed ] [ Google Scholar ] 59. Lin JH, Chremos AN, Yeh KC, Antonello J, Hessey GA 2nd 1988. Effects of age and chronic renal failure on the urinary excretion kinetics of famotidine in man. European journal of clinical pharmacology 34(1):41–46. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials 1 NIHMS1889290-supplement-1.pdf (2.2MB, pdf) 2 NIHMS1889290-supplement-2.docx (3.5MB, docx) ACTIONS View on publisher site PDF (1.4 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top