Antimicrobial Dosing in Specific Populations And Novel Clinical Methodologies: Kidney Function

for more information:ali.ma@wecistanche.com

Manjunath P. Pai

Kidney function is a common parameter used to define antimicrobial drug dosage and frequency of administration. Several methods exist to measure kidney function but for pragmatic reasons rely on estimated kidney function equations based on the endogenous biomarker serum creatinine and common clinical variables. Current regulatory guidance on the design of studies in patients with abnormal kidney function in the United States also recommends consideration of estimated kidney function for this reason. Over the past few decades, alternate endogenous biomarkers, administration of exogenous biomarkers for noninvasive measurement, use of probe substrates to characterize individual kidney drug clearance pathways, modifications to conventional equations to account for time-varying clearance, and improved clinical trial modeling and simulation to factor in these uncertainties have occurred. Furthermore, major changes to kidney replacement therapy delivery in the outpatient, inpatient, and at-home settings are occurring. Antimicrobial drug dose adjustment in this diverse population is complex and in a state of flux due to technical innovations. Over-reliance on kidney function estimates to guide drug dosing in patients with infectious diseases can bias underdosing especially among the acutely ill. A holistic approach to drug dose adjustment in patients with abnormal kidney function is necessary to optimize clinical outcomes.

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Kidney function is a key quantitative figure used by clinicians to stage kidney disease and to inform drug dose selection.1 Several antimicrobials are hydrophilic and eliminated in part as unchanged drugs or as metabolites in urine.1 Of the 41 antibiotics in global clinical development, ~ 40% will likely include recommendations for dose adjustment in patients with abnormal kidney function.2 The regulatory framework on study designs to establish dose adjustments in patients with abnormal kidney function are currently therapeutic indication-agnostic and set from the perspective of achieving bioequivalence in patients with chronic kidney disease (CKD) relative to healthy patients.3 Although patients with CKD constitute a significant portion of individuals with abnormal kidney function, they do not constitute the entire spectrum of potential cases that require antimicrobial therapy. Patients presenting with infectious diseases have a diverse and dynamic set of kidney function phenotypes that may be inadequately accounted for in our conventional approaches to antibiotic dose adjustment.4

When considering these phenotypes in clinical practice, patients with serious infections can have a transient augmentation of kidney function and drug clearance that may necessitate dose adjustment.5,6 In one study, therapeutic failure was observed in 33.3%, 17.4%, and 12.9% when augmentation of kidney function lasted for more than a day, only 1 day, and not at all.7 Alternatively, patients may have transient elevations in the principal biomarkers of kidney function (serum creatinine) that lead to a premature use of lower than standard initial doses.4 Progression of infections to sepsis and septic shock can also lead to acute kidney injury (AKI) that may or may not require dose modifications.8 These dynamic shifts are presently difficult to characterize with serum creatinine (Scr) due to the lag time between the change in this biomarker and the actual improvement or impairment of kidney function.9 The absence of fast turnaround therapeutic drug monitoring for specific antimicrobials exacerbates this uncertainty. Clinical reliance on rapid estimates of kidney function rather than measured is also driven by this pragmatic need for quick decision-making. Our working hypothesis is that equivalent antibiotic pharmacodynamic target exposures in a population yield comparable outcomes.10 However, patients with CKD likely have immunologic and physiologic aberrations from comorbidities that are distinct from patients with AKI.11 Unfortunately, few data have explored these nuances and so this review is limited to the translation of kidney function estimates to drug clearance. In the past few decades, several strategies have advanced to better account for the discordance among estimated kidney function, measured kidney function, and drug clearance. These innovations include the use of alternate endogenous biomarkers, administration of exogenous biomarkers, modifications to conventional equations to account for time-varying clearance, and improved clinical trial modeling and simulation to factor in these uncertainties. The current review explores the uncertainties associated with kidney function estimation and novel clinical methodologies to improve antibiotic dosing in this specific population of patients with abnormal kidney function and augmentation. Nomenclature to harmonize our language and abbreviations surrounding kidney function has recently been detailed and are summarized in Table 1. 12 Patient-centered and precise terminology that align with guidelines are preferred and oblige us to move away from the use of the term “renal impairment” to “abnormal kidney function,” as one example.

ALTERNATE ENDOGENOUS BIOMARKERS FOR ESTIMATED GLOMERULAR FILTRATION RATE

Current approaches to determine estimated glomerular filtration rate (eGFR) in drug development rely almost exclusively on the endogenous biomarker Scr. Routine Scr assay methods had a bias of −0.06 to 0.31 mg/dL at a typical Scr value of 0.90 mg/ dL.13 This bias led to a global effort to reduce interlaboratory variability using isotopic dilution mass spectrometry calibration standards.13,14 However, this standardization does not correct the many limitations of Scr. 15 The ideal endogenous biomarker should be produced at a constant rate, not be bound to plasma proteins, be freely filtered through the glomerulus, have no kidney tubular secretion or metabolism, and have no non-kidney metabolism.16 Unfortunately, Scr fails most of these criteria. The production or input of Scr is not constant and depends on muscle mass and activity, diet, and is impaired in patients with cirrhosis.15 Tubular secretion also accounts for 10–15% of Scr elimination and is impacted by organic cation transporter-mediated drug interactions.17 An almost 50% reduction in glomerular filtration rate (GFR) is necessary before a rise in Scr is observed and the lag time between these events is dependent on the baseline GFR.9 Although kinetic GFR equations have been developed to correct these changes, they cannot overcome all of these flaws.18 Reliance on Scr has also led to the inclusion of a “race factor” in eGFR equations to correct for ethnic differences in average Scr values.19–21 A multitude of alternate equations have been developed to “fix” this bias but the generalizability of this approach between populations is unlikely.22 Although alternatives exist to enhance translation of Scr to eGFR are present, none are ideal and only cystatin C has been evaluated for drug dosing considerations.16

Serum cystatin C (S _cys) is a low molecular weight protein produced at a constant rate by all nucleated body cells that increase the precision of eGFR estimates based on Scr in specific populations.23–25 Although this low molecular weight protein biomarker was discovered over 35 years ago, the novelty of this biomarker exists because its adoption in practice remains slow.23 Current guidelines recommend the use of S cys in patients with an eGFR between 45 and 59 mL/min/1.73 m2 and urine albumin < 30 mg/g creatinine.26 This recommendation overcomes the “creatinineblind range,” where Scr values fail to rise due to the compensatory increase in the tubular secretion of Scr. Warnings for decreased efficacy of certain antibiotics in patients with creatinine clearance (CLcr) estimates of 30–50 mL/minute begs the question of whether inclusion of S cys in future antimicrobial phase III clinical trials could help resolve potential dose adjustment discrepancies.4 Equations that transform S cys measurements to eGFR do not require an adjustment of race as a factor compared with Scr based equations.27 Table 2 provides a summary of the key equations used to determine estimated CLcr (eCLcr) and eGFR and illustrates this use and nonuse of race with adjustment for body surface area (BSA).14,27-31

Over the past few decades, numerous studies have now compared the use of S cys to Scr and the combination of the two to estimate eGFR for drug dosing in acutely ill patients.25,32–34 The use of eGFR using the combination of S cys and Scr improved target concentration achievement from 35% to 54% for vancomycin and the estimate of gentamicin clearance (CL; within 30%) from 77% to 82% when compared with Scr alone.34 Although this improvement is incremental, the use of these biomarkers in combination may be particularly useful in nonambulatory patients with reduced muscle mass.35 Recently, the use of the combination of S cys and Scr has been shown to improve antibiotic dosing for an agent (ceftriaxone) not traditionally adjusted for kidney impairment.36 Sarcopenia or muscle mass loss as a function of aging can be estimated by the ratio of Scr and S cys. 37 Simulations demonstrate that the eGFR based on the combination of these biomarkers better predicts ceftriaxone exposures and doses than body weight.36 Limitations do exist with the use of S cys due to underestimation of eGFR in patients with obesity, malignancy, high-dose corticosteroid use, kidney transplant recipients, and in patients that smoke.38 Production of S cys is dependent on cell turnover, which is disrupted in hypothyroidism and hyperthyroidism, which can lead to overestimation and underestimation of eGFR, respectively.38 Despite these limitations, the international federation for clinical chemistry working group has established human serum certified reference materials and is advancing global standardization.39 The next generation of eGFR based on endogenous biomarkers will require prospective evaluation of S cys and the combination of S cys with Scr. Given the dynamic state of kidney function in patients with serious infectious diseases, eGFR is unlikely to completely close the gap in optimal dose selection in this population.

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NEW METHODS FOR MEASUREMENT OF GLOMERULAR FILTRATION RATE

The classic method for measured GFR (mGFR) includes administration of a continuous infusion of inulin followed by blood and urine collection at specified time points.40,41 The advantage of the administration of an exogenous compound as a kidney function biomarker is the certainty of the input function. However, the requirement for multiple sample collections and analysis makes this approach cumbersome and not practical for day-to-day clinical use. A systematic review of these methods suggests general agreement between these methods but also bias across the range of GFR.42 This discrepancy between methods suggests that mGFR should not blindly be assumed to be the standard to benchmark drug dosing. From a practical perspective, repeated determination of mGFR may be needed across the duration of antimicrobial therapy because the knowledge gained on the first day of therapy may not be relevant later in the therapy if shifts in kidney biomarkers are observed. Alternatives, such as 8-hour measured CLcr, have been used to determine kidney function but are generally useful in stable patients with indwelling urinary catheters.43 Despite these limitations and complexities, novel techniques have been developed to get real-time mGFR that could support drug dosing decisions among critically ill patients.

A novel bedside approach in clinical development includes the use of visible fluorescent injectate that relies on two fluorescently tagged dextrans that are 5 and 150 kD, respectively.44 After a demonstration of safety in healthy volunteers, a phase IIb clinical trial has been performed with this method.44 The method includes bolus injection (for 30 seconds) of the VFI with blood sampling at 15, 60, and 170 minutes, analysis by standard fluorimeter, and fit by a compartmental model to estimate clearance. A near-perfect correlation of mGFR was documented with this technique in this study (n = 32) compared with iohexol mGFR. The use of the dual marker permits the calculation of the volume of vascular space (150 kD dextran) while simultaneously capturing information of the freely filtered marker (5 kD). Although this approach may not necessarily be ready for day-to-day clinical application, it affords potentially useful technique to ensure appropriate participant allocation in kidney function groups. Previous studies have demonstrated that participants can, on occasion, be miscategorized by eGFR impacting the interpretation of full design kidney impairment studies.45 Although this approach is being advanced for mGFR determination in chronic heart failure, the potential application of this technology in critically ill patients could be particularly meaningful to qualify augmentation or abnormal kidney function that is not well qualified by endogenous biomarkers.

More sophisticated noninvasive technologies are currently in development that may potentially eliminate the need for blood and urine collections to establish mGFR.46 One such technology is transdermal GFR measurement that includes attachment of a sensor to the skin (sternum/manubrium preferred), followed by intravenous administration of a fluorescent tracer that is detected by the sensor. The sensor has been designed to calibrate the individual patient’s skin color and can continuously monitor GFR in a manner that is akin to current pulse oximeters. A phase I trial has been completed with this device but tolerability results have not been made public (NCT03810833). Current and future technologies being developed for mGFR could provide an incremental step toward precision drug dosing in acutely ill patients with infectious diseases. Nonetheless, drug clearance may not be fully characterized by GFR alone. Kidney tubular secretion, reabsorption, and metabolism play variable roles in clearance based on the drug in question. Although innovations have occurred to measure these processes, cost and feasibility have made their practical application difficult. Thus, the translation of kidney function to individual drug CL carries uncertainties that should be recognized by clinicians.

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REBRANDING KIDNEY FUNCTION ESTIMATION

The complexity of kidney function measurement leads to oversimplification by estimation methods. A plethora of equations, modifications to these equations, and caveats exist when estimating kidney function for drug dosing using Scr. 28–30,47–51 These citations include adjustments for body size, unstable Scr, race, and disease conditions, and are by no means a comprehensive list. The jargon surrounding these estimates also leads to their imprecise application and will require action from multiple stakeholders to educate the end-user that these estimates are guideposts.12 The two predominant equations presently in product labels are the Cockcroft-Gault (C-G) equation and the modification of diet in kidney disease (MDRD) equation.45 A recent update to the draft guidance for industry includes the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.3 All three dominant equations rely on Scr and so suffer from the aforementioned limitations of this biomarker.

There are multiple unsettling characteristics of the C-G equation that include the decision to rely on weight and an uninformed adjustment for the female gender as a surrogate for Scr production differences due to muscle mass.28 The C-G equation also predates Scr standardization barring universal re-expression of this equation. Likewise, the MDRD equation and CKD-EPI yield values that require conversion to individual BSA estimates and have an adjustment for a race that is divisive.3,20,21 The eGFR equations have historically quantified BSA using the Dubois and Dubois method, whereas the predominant clinically used method is based on Mosteller’s adaptation.31,52,53 Race is a social construct that is a “lazy” population-level variable that may imprecisely inform individual-level drug dosing decisions.54 Petitions for change, have led some institutions to eliminate this African American (MDRD) or Black (CKD-EPI) race factor.20,55 Arguments about the introduction of bias to eGFR estimation with the elimination of this factor have been presented.56 Race is not a factor when S cys is used alone or when calculating eGFR using Scr in children, which begs the question of its relevance as a factor in adulthood. If a decision is made to eliminate the race factor, then a revision to the entire MDRD or CKD-EPI equation is necessary (not just deletion of the race factor). Ultimately, too many modifications exist to these eCLcr and eGFR that highlight their imprecision.57 Serious concerns about these equations will expand discordance between labeled dosing recommendations and actual clinical practice.58 Healthcare systems cannot easily integrate drug-specific kidney dose modifications and more likely set institutional standards to either use eCLcr or eGFR for all drugs on their formulary. The safety of this clinical decision has not been determined systematically. New methods for reporting these estimates and relaying the uncertainty of these values are critically necessary to inform the dose decision-maker.

From an antimicrobial dosing perspective, the outputs from these equations are snapshots in time that may be useful in stable patients with CKD but not in AKI.59 Acutely ill patients with infectious diseases have dynamic shifts in kidney function that warrant further modeling and simulation considerations. Equations have been developed to define eCLcr and eGFR in these cases but have been included in drug labels.18,47,60 Approximately 20% of patients with an infectious disease condition, such as pneumonia, intrabdominal, and urinary tract infections, have some degree of AKI at clinical presentation.4 Almost half of these individuals can have recovery of kidney function within the first 2–3 days of admission.4 Reductions in the Scr lag behind this recovery and so antibiotic dosing recommendations may be incorrectly lowered early in therapy.9 The bacterial load is expected to be higher at treatment initiation than a few days into therapy. Therefore, ensuring adequate exposures are achieved during this early phase of therapy is paramount. Modeling and simulations to evaluate the risk-benefit of not modifying the antimicrobial dosing regimen during the early phase of therapy should be considered especially given that antibiotics (with few exceptions) tend to have a wide therapeutic index relative to other drug classes.

From a prescriber's perspective, the medico-legal implications of not abiding by the product label designated cutoff values for dose modifications (e.g., reduce dose by 50% if CLcr < 30 mL/ min) force the hand of clinicians in some instances to use these values in unconditional terms. Laboratory values are simply an aid that requires thoughtful interpretation.61 Caution on the reporting and considerations for inclusion of percentile charts by age and sex have been suggested.62 Consensus on whether eGFR or eCLcr is the “best” approach to drug dose selection is unlikely and irrelevant in the long term as new biomarkers and measurement methods develop. Therefore, increased transparency on the population pharmacokinetic models used to generate these drug dosing recommendations is necessary so that adjustments can be made to keep pace with technological and medical advancements. The development and application of physiology-based pharmacokinetic models offer discrete opportunities to help bridge these advancements.63 Ultimately, kidney function is just one of several covariates that should drive decisions on dose adjustment. The complexity of multiple source information integration will likely lead us in the medium term to a path that is informed by machine learning and emerging artificial intelligence methods.64 In the interim, broader education on the nuances of kidney function estimation is necessary along with systems to manage case scenarios where no information is available. This is often the case in patients with kidney failure. A wide array of kidney replacement therapies exist that make it difficult to deliver precise recommendations in current drug labels and beyond the scope of the present review.

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FUTURE DIRECTIONS

Patients with kidney disease are a complex specific population that is mathematically reduced to a single value, such as eGFR and eCLcr to inform drug dosing. Although this simplification is necessary to ensure exposure matching in this population relative to patients without abnormal kidney function, it can bias underdosing of antimicrobials in this population. This is especially true because clear distinctions between patients with CKD and AKI are unaccounted for when dosing antimicrobials. Techniques that improve the precision of kidney function estimation through measurement, such as transdermal GFR, are emerging and may better detect shifts in kidney function to improve antibiotic dosing. Likewise, newer biomarkers, such as S cys, have the potential to be adopted and create an incremental improvement over Scr alone in the near term. Ultimately, increased education of clinicians that uncertainty exists with these estimates coupled with better integration of information sources can help to improve antimicrobial dosing. Increased data transparency and the creation of information warehouses within this space will allow researchers an opportunity to create newer user-friendly drug dosing models adaptive to kidney function.

FUNDING

No funding was received for this work.

CONFLICTS OF INTEREST

The author declared no competing interests for this work

From: ' Antimicrobial Dosing in Specific Populations and Novel Clinical Methodologies: Kidney Function' by Manjunath P. Pai

--- CLINICAL PHARMACOLOGY & THERAPEUTICS | VOLUME 109 NUMBER 4 | April 2021 © 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics

REFERENCES:

1. Lea-Henry, T.N., Carland, J.E., Stocker, S.L., Sevastos, J. & Roberts, D.M. Clinical pharmacokinetics in kidney disease: fundamental principles. Clin. J. Am. Soc. Nephrol. 13, 1085–1095 (2018).

2. Pew Trusts. Antibiotics Currently in Global Clinical Development. <https://www.pewtrusts.org/en/research-and-analysis/datavisualizations/2014/antibiotics-currently-in-clinical-development> (2020).
3. US Food and Drug Administration (FDA). Pharmacokinetics in Patients with Impaired Renal Function — Study Design, Data Analysis, and Impact on Dosing and Labeling. (2020).
4. Crass, R.L., Rodvold, K.A., Mueller, B.A. & Pai, M.P. Renal dosing of antibiotics: are we jumping the gun? Clin. Infect. Dis. 68, 1596–1602 (2019).
5. Udy, A.A., Roberts, J.A., Boots, R.J., Paterson, D.L. & Lipman, J. Augmented renal clearance: implications for antibacterial dosing in the critically ill. Clin. Pharmacokinet. 49, 1–16 (2010).
6. Udy, A.A. et al. Augmented renal clearance in the ICU: results of a multicenter observational study of renal function in critically ill patients with normal plasma creatinine concentrations*. Crit. Care Med. 42, 520–527 (2014).
7. Claus, B.O. et al. Augmented renal clearance is a common finding with worse clinical outcomes in critically ill patients receiving antimicrobial therapy. J. Crit. Care 28, 695–700 (2013).
8. Matzke, G.R. 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. 80, 1122–1137 (2011).
9. Waikar, S.S. & Bonventre, J.V. Creatinine kinetics and the definition of acute kidney injury. J. Am. Soc. Nephrol. 20, 672–679 (2009).
10. Rizk, M.L. et al. Considerations for dose selection and clinical pharmacokinetics/pharmacodynamics for the development of antibacterial agents. Antimicrob. Agents Chemother. 63(5), e02309-18 (2019).
11. Sato, Y. & Yanagita, M. Immune cells and inflammation in AKI to CKD progression. Am. J. Physiol. Renal. Physiol. 315, F1501–F1512 (2018).
12. Levey, A.S. et al. Nomenclature for kidney function and disease: executive summary and glossary from a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney Res. Clin. Pract. 39, 151–161 (2020).
13. Myers, G.L. et al. Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin. Chem. 52, 5–18 (2006).
14. Levey, A.S. et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin. Chem. 53, 766–772 (2007).
15. Delanaye, P., Cavalier, E. & Pottel, H. Serum creatinine: not so simple! Nephron 136, 302–308 (2017). 16. Ebert, N. & Schaeffner, E. New biomarkers for estimating glomerular filtration rate. J. Lab. Prec. Med. 3, 75 (2018).
17. Ciarimboli, G. et al. Proximal tubular secretion of creatinine by organic cation transporter OCT2 in cancer patients. Clin. Cancer Res. 18, 1101–1108 (2012).

18. Chen, S. Retooling the creatinine clearance equation to estimate kinetic GFR when the plasma creatinine is changing acutely. J. Am. Soc. Nephrol. 24, 877–888 (2013).

19. Delanaye, P., Mariat, C., Maillard, N., Krzesinski, J.M. & Cavalier, E. Are the creatinine-based equations accurate to estimate glomerular filtration rate in African American populations? Clin. J. Am. Soc. Nephrol. 6, 906–912 (2011).

20. Eneanya, N.D., Yang, W. & Reese, P.P. Reconsidering the consequences of using race to estimate kidney function. JAMA 322, 113–114 (2019).
21. Levey, A.S., Titan, S.M., Powe, N.R., Coresh, J. & Inker, L.A. Kidney disease, race, and GFR estimation. Clin. J. Am. Soc. Nephrol. 15, 1203–1212 (2020).
22. Stevens, L.A. et al. Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int. 79, 555–562 (2011).
23. Grubb, A., Simonsen, O., Sturfelt, G., Truedsson, L. & Thysell, H. Serum concentration of cystatin C, factor D, and beta 2-microglobulin as a measure of glomerular filtration rate. Acta Med. Scand. 218, 499–503 (1985).

24. Newman, D.J. Cystatin C. Ann. Clin. Biochem. 39, 89–104 (2002).

25. Barreto, E.F., Rule, A.D., Voils, S.A. & Kane-Gill, S.L. Innovative use of novel biomarkers to improve the safety of really eliminated and nephrotoxic medications. Pharmacotherapy 38, 794–803 (2018).

26. Shlipak, M.G., Mattes, M.D. & Peralta, C.A. Update on cystatin C: incorporation into clinical practice. Am. J. Kidney Dis. 62, 595–603 (2013).
27. Inker, L.A. et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N. Engl. J. Med. 367, 20–29 (2012).
28. Cockcroft, D.W. & Gault, M.H. Prediction of creatinine clearance from serum creatinine. Nephron 16, 31–41 (1976).
29. Schwartz, G.J. et al. New equations to estimate GFR in children with CKD. J. Am. Soc. Nephrol. 20, 629–637 (2009).
30. Levey, A.S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009).
31. Mosteller, R.D. Simplified calculation of body-surface area. N. Engl. J. Med. 317, 1098 (1987).
32. Ebert, N. & Shlipak, M.G. Cystatin C is ready for clinical use. Curr. Opin. Nephrol. Hypertens. 29, 591–598 (2020).
33. Hudson, J.Q. & Nolin, T.D. Estimated GFR and cystatin C for drug dosing: moving beyond proof of concept to clinical translation? Am. J. Kidney Dis. 65, 534–536 (2015).
34. Barreto, E.F. et al. Prediction of the renal elimination of drugs with cystatin C vs creatinine: a systematic review. Mayo Clin. Proc. 94, 500–514 (2019).
35. Mirahmadi, M.K. et al. Prediction of creatinine clearance from serum creatinine in spinal cord injury patients. Paraplegia 21, 23–29 (1983).
36. Tan, S.J. et al. Population pharmacokinetic study of ceftriaxone in elderly patients, using Cystatin C-based estimates of renal function to account for frailty. Antimicrob. Agents Chemother. 64, e00874-20 (2020).
37. Kashani, K.B. et al. Evaluating muscle mass by using markers of kidney function: development of the Sarcopenia Index. Crit. Care Med. 45, e23–e29 (2017).
38. Teaford, H.R., Barreto, J.N., Vollmer, K.J., Rule, A.D. & Barreto, E.F. Cystatin C: a primer for pharmacists. Pharmacy (Basel) 8, 35 (2020).
39. Grubb, A. et al. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin. Chem. Lab. Med. 48, 1619–1621 (2010).
40. Shannon, J.A. & Smith, H.W. The excretion of inulin, xylose, and urea by normal and phlorizinized man. J. Clin. Invest. 14, 393–401 (1935).
41. Berger, E.Y., Farber, S.J., Earle, D.P. & Jackenthal, R. Comparison of the constant infusion and urine collection techniques for the measurement of renal function. J. Clin. Invest. 27, 710–716 (1948).
42. Hsu, C.Y. & Bansal, N. Measured GFR as "gold standard"–all that glitters is not gold? Clin. J. Am. Soc. Nephrol. 6, 1813–1814 (2011).
43. O’Connell, M.B., Wong, M.O., Bannick-Mohrland, S.D. & Dwinell, A.M. Accuracy of 2- and 8-hour urine collections for measuring creatinine clearance in the hospitalized elderly. Pharmacotherapy 13, 135–142 (1993).
44. Rizk, D.V. et al. A novel method for rapid bedside measurement of GFR. J. Am. Soc. Nephrol. 29, 1609–1613 (2018).
45. Crass, R.L. & Pai, M.P. Estimating renal function in drug development: time to take the fork in the road. J. Clin. Pharmacol. 59, 159–167 (2019).
46. Solomon, R. & Goldstein, S. Real-time measurement of glomerular filtration rate. Curr. Opin. Crit. Care 23, 470–474 (2017).
47. Chiou, W.L. & Hsu, F.H. A new simple and rapid method to monitor the renal function based on pharmacokinetic consideration of endogenous creatinine. Res. Commun. Chem. Pathol. Pharmacol. 10, 315–330 (1975).
48. Jelliffe, R.W. Letter: Creatinine clearance: bedside estimate. Ann. Intern. Med. 79, 604–605 (1973).
49. Levey, A.S. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 130, 461–470 (1999).
50. Levey, A.S. et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann. Intern. Med. 145, 247–254 (2006).
51. Salazar, D.E. & Corcoran, G.B. Predicting creatinine clearance and renal drug clearance in obese patients from estimated fat-free body mass. Am. J. Med. 84, 1053–1060 (1988).
52. Du Bois, D. & Du Bois, E.F.A formula to estimate the approximate surface area if height and weight be known. Arch. Intern. Med. 17, 863–871 (1916).
53. Pai, M.P. Drug dosing based on weight and body surface area: mathematical assumptions and limitations in obese adults. Pharmacotherapy 32, 856–868 (2012).
54. Adigbli, G. Race, science, and (im)precision medicine. Nat. Med. 26, 1675–1676 (2020).
55. Vyas, D.A., Eisenstein, L.G. & Jones, D.S. Hidden in plain sight - reconsidering the use of race correction in clinical algorithms. N. Engl. J. Med. 383, 874–882 (2020).
56. Levey, A.S., Tighiouart, H., Titan, S.M. & Inker, L.A. Estimation of glomerular filtration rate with vs without including patient race. JAMA Intern. Med. 180, 793–795 (2020).
57. Porrini, E. et al. Estimated GFR: time for a critical appraisal. Nat. Rev. Nephrol. 15, 177–190 (2019).
58. Dowling, T.C., Matzke, G.R., Murphy, J.E. & Burckart, G.J. Evaluation of renal drug dosing: prescribing information and clinical pharmacist approaches. Pharmacotherapy 30, 776–786 (2010).
59. Awdishu, L. et al. Use of estimating equations for dosing antimicrobials in patients with acute kidney injury not receiving renal replacement therapy. J. Clin. Med. 7, 211 (2018).
60. Jelliffe, R.W. & Jelliffe, S.M. A computer program for estimation of creatinine clearance from unstable serum creatinine levels, age, sex, and weight. Math. Biosci. 14, 17–24 (1972).
61. Wians, F.H. Clinical laboratory tests: which, why, and what do the results mean? Lab. Med. 40, 105–113 (2009).
62. Glassock, R.J. & Winearls, C.G. Routine reporting of estimated glomerular filtration rate: not ready for prime time. Nat. Clin. Pract. Nephrol. 4, 422–423 (2008).
63. Hsueh, C.H. et al. PBPK modeling of the effect of reduced kidney function on the pharmacokinetics of drugs excreted renally by organic anion transporters. Clin. Pharmacol. Ther. 103, 485–492 (2018).
64. Challen, R. et al. Artificial intelligence, bias and clinical safety. BMJ Qual. Saf. 28, 231–237 (2019).