Pediatr Nephrol DOI 10.1007/s00467-015-3104-8

REVIEW

Biomarkers and surrogate endpoints in kidney disease Erum A. Hartung 1,2

Received: 13 January 2015 / Revised: 17 March 2015 / Accepted: 19 March 2015 # IPNA 2015

Abstract Kidney disease and its related comorbidities impose a large public health burden. Despite this, the number of clinical trials in nephrology lags behind many other fields. An important factor contributing to the relatively slow pace of nephrology trials is that existing clinical endpoints have significant limitations. BHard^ endpoints for chronic kidney disease, such as progression to endstage renal disease, may not be reached for decades. Traditional biomarkers, such as serum creatinine in acute kidney injury, may lack sensitivity and predictive value. Finding new biomarkers to serve as surrogate endpoints is therefore an important priority in kidney disease research and may help to accelerate nephrology clinical trials. In this paper, I first review key concepts related to the selection of clinical trial endpoints and discuss statistical and regulatory considerations related to the evaluation of biomarkers as surrogate endpoints. This is followed by a discussion of the challenges and opportunities in developing novel biomarkers and surrogate endpoints in three major areas of nephrology research: acute kidney injury, chronic kidney disease, and autosomal dominant polycystic kidney disease.

* Erum A. Hartung [email protected] 1

Division of Nephrology, Children’s Hospital of Philadelphia, 34th and Civic Center Boulevard, Philadelphia, PA 19104, USA

2

Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, 415 Curie Blvd, Philadelphia, PA 19104, USA

Keywords Surrogate endpoints . Biomarkers . Acute kidney injury . Chronic kidney disease . End-stage renal disease . Polycystic kidney disease

Introduction Despite the large public healthcare burden imposed by kidney disease and its related comorbidities, the number of clinical trials in nephrology is relatively low compared to many other fields [1, 2]. An important contributor to the slow pace of nephrology trials is the difficulty posed by existing clinical endpoints. BHard^ endpoints for chronic kidney disease (CKD), such as progression to end-stage renal disease (ESRD), may not be reached for decades. In acute kidney injury (AKI), serum creatinine is a late and often insensitive marker of underlying injury. There is therefore growing interest in defining new biomarkers to serve as surrogate endpoints in kidney disease research. Surrogate endpoints can offer a number of potential advantages over true clinical endpoints and could expand opportunities for nephrology clinical trials. If a surrogate endpoint can be measured earlier in the disease process, it could allow for shorter trial durations, which could improve patient compliance and costeffectiveness [3]. In situations where the true clinical endpoint is severe morbidity or death, surrogate endpoints can allow the recruitment of patients with less severe illness, avoid the ethical dilemmas associated with waiting for a devastating clinical outcome [4], and avoid competing risks on the clinical endpoint from comorbid conditions [5]. In this paper, I first review key concepts related to the selection of clinical trial endpoints and discuss statistical and regulatory considerations related to the use of surrogate

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endpoints. This is followed by a discussion of the challenges and opportunities in developing novel biomarkers and surrogate endpoints in three major areas of nephrology research: AKI, CKD, and autosomal dominant polycystic kidney disease (ADPKD).

Definitions To frame this discussion, I first review the accepted definitions of clinical endpoints, surrogate endpoints, and biomarkers. The following definitions were proposed by the Biomarkers Definitions Working Group of the National Institutes of Health (NIH) in 2001: &

&

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Clinical endpoint: A characteristic or variable that reflects how a patient feels, functions, or survives [4]. An example of a clinical endpoint in CKD research is the onset of ESRD, defined by initiation of maintenance dialysis or kidney transplantation [6]. Biomarker: A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [4]. An example of a biomarker of acute kidney injury is neutrophil gelatinase-associated lipocalin (NGAL), a protein secreted by injured kidney tubule epithelial cells that can be measured in the plasma or urine [7]. Surrogate endpoint: A biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence [4]. An example of an accepted surrogate endpoint for progression of CKD to ESRD is doubling of serum creatinine [8].

Some biomarkers can be considered to be intermediate endpoints, defined as a biomarker that is intermediate in the causal pathway between an intervention and a clinical endpoint [9]. Decline in glomerular filtration rate (GFR) can be considered an intermediate endpoint because it is on the causal pathway to ESRD [9].

Considerations in the evaluation of biomarkers as surrogate endpoints It is important to remember that not all biomarkers are intermediate endpoints and that only a subset of biomarkers and intermediate endpoints will qualify as true surrogate endpoints. Before a biomarker can be used as a surrogate endpoint to replace a true clinical endpoint in clinical trials, it must undergo formal evaluation process. An NIH workshop in

2001 reviewed considerations in the selection of surrogate endpoints in clinical trials [10]. Aside from ensuring biological plausibility of the surrogate, a statistical framework must be in place to evaluate candidate biomarkers from exploratory phases through confirmatory clinical trials. The first part of this section is a review of the ideal biologic characteristics of a surrogate endpoint and of the potential pitfalls in surrogate endpoint selection; the second part is a review of statistical concepts and regulatory issues in the evaluation of biomarkers as surrogate endpoints.

Biological plausibility The ideal characteristics of a surrogate endpoint were first defined by Prentice in 1989 as follows: a surrogate should correlate with the true clinical outcome and capture the full relationship between the treatment and the true clinical endpoint [5]. In this ideal scenario, the surrogate endpoint lies in the only causative pathway of the disease process, and the effect of any intervention on the true clinical endpoint is captured entirely by the effect on the surrogate endpoint [11]. This scenario is illustrated in Fig. 1a. In reality, however, the strict criteria proposed by Prentice can be difficult to meet, and many surrogate endpoints can have potential pitfalls. Fleming and DeMets [11] reviewed how surrogate endpoints in clinical trials can be misleading. Importantly, they emphasize that Ba correlate does not a surrogate make^ [11]. This means that a biomarker can be strongly correlated with clinical outcome, yet it can fail as a surrogate endpoint if it is not in the causal pathway of the disease process. Here, the framework outlined by Fleming and DeMets (Fig. 1b–e) is used to review reasons for failure of surrogate endpoints and provide some examples specific to kidney disease research. First, if a surrogate endpoint is not in the causal pathway of disease progression, an intervention targeted at this surrogate endpoint may not affect the true clinical outcome (Fig. 1b). For example, CKD in children is often correlated with poor growth. However, poor growth is not part of the causative biologic process that leads to ESRD. Thus, an intervention targeted at improving the surrogate endpoint (growth) will have no effect on the true clinical outcome (ESRD). Second, a surrogate may represent only one of several potential pathways influencing disease progression (Fig. 1c). Consider the example of using proteinuria as a surrogate endpoint for progression of autosomal dominant polycystic kidney disease (ADPKD) to ESRD. Although increasing proteinuria correlates with worse kidney function in ADPKD [12], interventions to reduce proteinuria [such as angiotensin converting enzyme (ACE) inhibitors] may not delay progression to ESRD due to the multiple other mechanisms causing cyst growth and kidney function decline.

Pediatr Nephrol Fig. 1 Characteristics of an ideal surrogate endpoint (a), and reasons for failure of surrogate endpoints (b–e). See text for examples of each scenario in kidney disease research. (Adapted from Fleming and Demets [11]; used with permission)

Third, an intervention may alter the true clinical outcome in a manner independent of its effect on the surrogate endpoint (Fig. 1d). Consider the example of using hypertension as a surrogate endpoint in CKD caused by glomerulonephritis. An intervention such as steroids may have a beneficial effect on the true clinical outcome (ESRD), but could have a detrimental effect on the surrogate endpoint of hypertension. Finally, an intervention can have effects on the clinical outcome through mechanisms that are independent of the primary disease process (Fig. 1e). A recent study showed that the lipid-lowering agent pravastatin slowed the increase in total kidney volume (TKV) in ADPKD [13], where TKV was considered to be a surrogate endpoint for decline in kidney function. Let us now consider what could happen if a trial of pravastatin in ADPKD used all-cause mortality as the primary clinical outcome. A decline in kidney function contributes to mortality risk in ADPKD; thus, in theory, the drug’s beneficial renal effects could translate to lower mortality risk. However, it is also known that pravastatin can affect mortality risk

through non-renal effects such as lipid lowering, improvement in endothelial dysfunction [14], or lowering of left ventricular mass [15]. Therefore, the effect of the intervention on the clinical outcome can be mediated through multiple mechanisms that may not be fully captured by its effect on the surrogate endpoint. Statistical considerations A number of statistical methods have been described to validate surrogate endpoints, and there is ongoing debate regarding optimal methods. Although a complete review of all these controversies is beyond the scope of this review, some of the key statistical methods that have been described to evaluate surrogate endpoints are worthy of discussion. As mentioned, Prentice [5] published one of the key papers that first outlined a statistical framework to validate surrogate endpoints. However, the Prentice criteria as originally described have been perceived as too stringent and thus not

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achievable under many circumstances [16, 17]. Therefore, a number of authors have proposed alternative approaches for statistical validation. Freedman et al. [18] extended Prentice’s approach by introducing the concept of Bproportion of treatment effect explained^ (PTE), which is the proportion of the treatment effect on the true clinical endpoint that is mediated by the surrogate. Within this framework, the PTE of an ideal surrogate would equal 1. Although the PTE has been viewed by some authors as helpful in certain situations [19, 20], others have raised a number of concerns, such as a high level of variability [21, 22], potential for bias [20], and difficulty in separating intended drug effects from unintended adverse effects [23]. These problems make the PTE useful only in moderate to large studies with moderate to large treatment effects—situations in which there may be little need to establish a surrogate endpoint. Some authors have therefore discouraged the use of the PTE [24]. To overcome problems with the use of the PTE, Buyse and Molenberghs [25] proposed the use of two related quantities: the relative effect, which is the ratio of treatment effects on the clinical and surrogate endpoints, and the adjusted association, which assesses the treatment-adjusted association between the surrogate and clinical endpoints. The use of these quantities in meta-analysis of multiple trials allows both the trial-level and individual-level validity of a surrogate marker to be ascertained [17]. Further work by these authors and others has established a number of meta-analytic methods to assess trial- and individual-level validity of surrogates across a range of endpoint types (e.g., binary, ordinal, continuous, longitudinally measured, time to event) [23, 26–29].

Evaluating the added prognostic impact of a new biomarker When a novel biomarker is being evaluated, an important consideration is how that biomarker adds to existing biomarkers and risk factors in predicting the clinical outcome of interest. A popular method to determine the predictive value of a biomarker (or set of biomarkers) is the area under the receiver-operating-characteristic curve (AUC) [30]. One criticism of AUC analysis, however, is that it can be relatively insensitive to new information, meaning a new biomarker would need to have a large independent association with the clinical outcome to result in a meaningful increase in AUC [31]. To overcome this limitation, Pencina et al. [32] proposed two novel approaches, namely, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), and these methods have become increasingly popular. However, several authors have raised concerns about the reliability of the NRI and IDI [33–35] and urge caution when using these methods.

Regulatory considerations Under the Federal Food, Drug, and Cosmetic Act [36], approval of a drug by the United States (US) Food and Drug Administration (FDA) requires Bsubstantial evidence [consisting of adequate and well-controlled clinical investigations] that the drug will have the effect it … is represented to have under the conditions of use prescribed, recommended, or suggested in the proposed labeling^ [36]. The law does not explicitly address what endpoints provide acceptable evidence of effectiveness. However, given that a drug must also show that it is Bsafe^ (i.e., have a favorable risk–benefit profile), its effects must be of meaningful clinical value [37]. Thus, a drug with effects on a surrogate endpoint that does not correspond to a clinical benefit could not meet the safety standard. The use of surrogate endpoints was addressed in the 1992 FDA Baccelerated approval^ regulation [38], which applies to new drugs Bthat have been studied for their safety and effectiveness in treating serious or life-threatening illnesses and that provide meaningful therapeutic benefit to patients over existing treatments^ [38]. This regulation states that Bthe FDA may grant marketing approval for a new drug product on the basis of adequate and well-controlled clinical trials establishing that the drug product has an effect on a surrogate endpoint that is reasonably likely, based on epidemiologic, therapeutic, pathophysiologic, or other evidence, to predict clinical benefit or on the basis of an effect on a clinical endpoint other than survival or irreversible morbidity.^ Recognizing that there could be Buncertainty as to the relation of the surrogate endpoint to clinical benefit,^ the rule further requires post-marketing studies to Bverify and describe the drug’s clinical benefit^ [38]. The FDA regulations do not specifically address statistical considerations related to the evaluation of a biomarker as a surrogate endpoint. The International Conference on Harmonisation (ICH) Harmonised Tripartite Guideline on Statistical Principles for Clinical Trials E9, adopted by regulatory agencies in the USA, Europe, and Japan, outlines general criteria for validating surrogate endpoints. These include ensuring the Bbiological plausibility of the relationship,^ demonstrating the Bprognostic value of the surrogate for the clinical outcome^ in epidemiological studies, and ensuring that Btreatment effects on the surrogate correspond to effects on the clinical outcome^ in clinical trials [39]. Of note, the E9 Guideline does not advocate specific statistical methods and states that experience with statistical criteria is Brelatively limited^ [39]. Recognizing that biomarkers can play a critical role in accelerating drug development, the Center for Drug Evaluation and Research at the FDA introduced the Biomarker Qualification Program (BQP) [40] in 2009. Part of the FDA’s Critical Path Initiative [41] to spur innovation and facilitate drug development, the BQP provides a defined

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pathway by which an individual or organization can submit a proposal for a biomarker to be evaluated for use in the regulatory process. Biomarker qualification is defined by the FDA as the conclusion that Bwithin the stated context of use, the results of assessment with a biomarker can be relied upon to have a specific interpretation and application in drug development and regulatory review^ [42]. The Bcontext of use^ defines the specific circumstances under which the biomarker is qualified and helps to determine the type of evidence that is needed to support qualification [43]. Once a biomarker receives qualification for a specific context of use, it can be used by drug developers for other applications without needing repeat review [42].

Why do we need new surrogate endpoints in kidney disease research? All existing clinical and surrogate endpoints in kidney disease trials have limitations. In this section, I review the challenges posed by the endpoints currently used in nephrology and discuss the development of new biomarkers and surrogate endpoints, with a focus on three major areas of nephrology research: AKI, CKD, and ADPKD. Table 1 provides an overview of the advantages and disadvantages of the various nephrology endpoints and biomarkers. Acute kidney injury A number of groups have developed consensus clinical criteria defining AKI, including the Risk, Injury, Failure, Loss of kidney function, and End-stage renal disease (RIFLE), Acute Kidney Injury Network (AKIN), and Kidney Disease: Improving Global Outcomes (KDIGO) [44–46]. These AKI staging criteria rely on changes in serum creatinine and urine output, making them easy to apply in most clinical settings. However, serum creatinine has two significant drawbacks as a biomarker of AKI: (1) creatinine rise is delayed relative to the onset of actual injury and (2) creatinine level is influenced by a number of extra-renal factors (such as muscle mass, medications, and hydration status) [47]. Despite this, the RIFLE, AKIN, and KDIGO criteria for AKI have great value in ensuring uniform clinical reporting and in providing consistent endpoints for clinical trials. To try to overcome the limitations of creatinine as an AKI biomarker, there has been tremendous interest in the last decade in developing new serum and urine biomarkers to detect renal injury. Examples of these include NGAL, kidney injury molecule-1, interleukin-18, and liver-type fatty acid binding protein [47]. Most notably, the FDA recently approved the first point-of-care device to use novel biomarkers to assess risk of AKI [48, 49]. Based on measurement of urinary tissue

inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP-7), the device assesses the risk of developing moderate to severe AKI (KDIGO Stage 2 to 3) within 12 h of sample collection [50]. (Note, however, that FDA approval of the testing device does not imply that the measured biomarkers are qualified for use as surrogate endpoints.) Although AKI criteria such as RIFLE, AKIN, and KDIGO and biomarkers such as TIMP-2 and IGFBP-7 can identify renal injury in the short term, it is important to recognize that they may not always translate into clinically meaningful outcomes, such as new dialysis dependence, development of CKD, or death [51]. Therefore, the inclusion of these Bhard^ clinical endpoints has been advocated for clinical trials in AKI. However, these hard endpoints also have their own limitations—timing of dialysis initiation is subject to clinical practice variation, and longer-term or more severe outcomes, such as development of CKD or death, do not provide an opportunity for earlier intervention. In addition, hard endpoints occur less commonly than AKI identified by staging criteria, reducing the statistical power of studies based on hard endpoints. To increase statistical power and provide a more universal indicator of clinical outcome following AKI, Billings and Shaw [51] propose the use of a composite outcome of death, new dialysis, and worsened kidney function (defined as ≥25 % decline in GFR), termed Bmajor adverse kidney events^ (MAKE), for all effectiveness clinical trials in AKI. Clinical endpoints such as MAKE will also be important for any future validation studies of AKI biomarkers, such as TIMP-2 and IGFBP-7. Until novel biomarkers have been validated and have met criteria to qualify as true surrogate endpoints, it will be important for AKI trials to continue to use hard clinical endpoints to assess patient outcomes. Chronic kidney disease Progression of CKD is assessed clinically by a decline in estimated GFR (eGFR) using age-appropriate estimating equations that are most commonly based on serum creatinine. The ultimate Bhard^ clinical endpoint for CKD progression is development of ESRD, which is often defined as a new initiation of renal replacement therapy. However, as discussed earlier, defining ESRD in this manner can lead to inconsistencies due to practice variations in the timing of dialysis initiation and transplantation. A definition of ESRD that also incorporates eGFR (i.e., eGFR<15 mL/min/1.73 m2 for >3 months) [52] can therefore provide greater uniformity. Recognizing that progression to ESRD may occur over years or even decades, the FDA has historically accepted the doubling of serum creatinine [which corresponds to an approximately 57 % decline in eGFR based on the CKD Epidemiology Collaboration (CKD–EPI) equation] as a surrogate endpoint for the development of kidney failure.

Microalbuminuria

Proteinuria

30 % decline in eGFR

Doubling of serum creatinine (57 % eGFR decline based on CKD-EPI equation) 40 % decline in eGFR

NGAL, KIM-1, IL-18, L-FABP, TIMP-2, IGFBP-7, etc.

Proposed surrogate endpoint

Total kidney volume

Autosomal dominant polycystic kidney disease Existing surrogate endpoint GFR decline (various definitions)

Proposed surrogate endpoints

Chronic kidney disease Established surrogate endpoint

Novel biomarkers

Condition-specific biomarkers and surrogate endpoints Acute kidney injury Established surrogate endpoint Serum creatinine and urine output (in KDIGO, RIFLE, AKIN criteria)

Death

BHard^ clinical endpoints (AKI, CKD, or ADPKD) End-stage renal disease

Specific examples of biomarker or endpoint

More sensitive than GFR in detecting changes over short follow-up; correlates with GFR decline and predicts risk of Stage 3 CKD

On causal pathway to ESRD; clinically meaningful

Early marker of renal injury; allows potential for earlier intervention

Earlier endpoint than doubling of creatinine; on causal pathway to ESRD; more common endpoint can increase statistical power of trials Earlier endpoint than 40 % eGFR decline, allowing shorter trials; more common endpoint increases statistical power Easy to measure; useful for certain disease states (e.g., nephrotic syndrome) or specific drugs (e.g., ACE-I/ ARBs)

Easy to measure, on causal pathway to ESRD

Earlier marker of injury than creatinine; opportunity for earlier intervention; more common endpoint than ESRD/death, can increase statistical power; pointof-care device available to test TIMP-2 and IGFBP-7

Widely available; easy to measure and apply

Clinically meaningful outcome Definition based on eGFR<15 mL/min/1.73 m2 is more uniform than one based on dialysis/transplant Clinically meaningful outcome

Advantages

Overview of the advantages and disadvantages of the various nephrology endpoints and biomarkers

Biomarker or endpoint

Table 1

Early ADPKD characterized by hyperfiltration and normal GFR despite underlying cyst progression Unclear if interventions to reduce TKV necessarily improve clinical outcome

Unclear association with clinically relevant outcomes such as ESRD

Provides less direct assessment of ESRD risk; higher Type 1 error rate; shorter trials due to earlier endpoint could miss safety concerns Not part of causal pathway of many diseases; unclear if treatment effects on proteinuria predict effects on renal outcome

May increase Type 1 error rate, especially if large acute effects of intervention

Relatively late event in CKD progression

Influenced by extra-renal factors (e.g., muscle mass, hydration status); change in creatinine lags behind actual injury Less evidence of correlation with clinically meaningful outcomes

Definition based on timing of dialysis or transplantation subject to practice variation; late/severe complication Subject to competing risks; devastating outcome, no opportunity for earlier intervention; less common endpoint, reduces statistical power

Disadvantages

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ACE-I, Angiotensin converting enzyme inhibitor; ADPKD, autosomal dominant polycystic kidney disease; AKI, acute kidney injury; AKIN, Acute Kidney Injury Network; ARB, angiotensin receptor blocker; CKD, chronic kidney disease; CKD-EPI, CKD-CKD Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; IGFBP-7, insulin-like growth factor binding protein 7; IL-18, interleukin-18; KDIGO, Kidney Disease: Improving Global Outcomes; KIM-1, kidney injury molecule-1; L-FABP, liver-type fatty acid binding protein; RIFLE, Risk, Injury, Failure, Loss of Kidney Function criteria; M-CSF, macrophage colony stimulating factor; MCP-1, monocyte chemoattractant protein-1; NGAL, neutrophil gelatinase-associated lipocalin; TIMP-2, tissue inhibitor of metalloproteinase-2; TKV, total kidney volume

Unclear association with clinically relevant outcomes Potentially even earlier markers of progression than TKV or GFR Urinary biomarkers (e.g., NGAL, M-CSF, MCP-1); markers of endothelial dysfunction (e.g., pentraxin 3) or vasopressin signaling (e.g., copeptin); urinary proteomic analysis; renal blood flow Novel biomarkers

Biomarker or endpoint

Table 1 (continued)

Specific examples of biomarker or endpoint

Advantages

Disadvantages

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However, this surrogate endpoint is also a relatively late event in the course of CKD progression, and also necessitates trials with large sample sizes and many years of follow-up [53]. Therefore, there has been growing interest in using lower levels of eGFR decline as surrogate endpoints. A number of studies have evaluated eGFR declines of 30 and 40 % as alternative surrogate endpoints [6, 54–56]. In December 2012, the National Kidney Foundation (NKF) and the FDA cosponsored a workshop to evaluate these lower thresholds as potential surrogate endpoints [53]. Using a combination of data from observational cohorts, clinical trials, and simulations, participants in this workshop evaluated how the selection of eGFR declines of 30 or 40 % as surrogate endpoints would affect the Type 1 error rate and power of studies compared to the established eGFR decline endpoint of 57 % [53]. By showing that an endpoint of 40 % eGFR decline improved statistical power without excessive increase in Type 1 error, the conclusion drawn was that this would be an acceptable surrogate endpoint for CKD trials in a range of circumstances. The 30 % decline endpoint also performed well in various conditions, but led to increased Type 1 error rate in situations in which the intervention had acute effects on eGFR [53]. The results of this workshop provide an important illustration of considerations involved in selecting surrogate endpoints and demonstrates how the performance of a particular surrogate endpoint will depend on the context. The 30 and 40 % eGFR decline endpoints can offer the advantage of being earlier and more common markers of deteriorating kidney function, potentially allowing smaller and shorter clinical trials. The relevance of these findings to pediatric nephrology were recently discussed in an editorial commentary by Schnaper et al. [57]. However, the FDA cautions that using these smaller eGFR declines as endpoints, particularly in diabetic nephropathy trials, will provide less direct information on how therapies affect the risk of ESRD [8]. In addition, shorter trials may reduce the power to detect rare safety events and provide less information on the long-term safety and efficacy of interventions [8, 58] . Given the inherent limitations of serum creatinine, there is also greater interest in using other biomarkers of renal damage to assess CKD progression. Proteinuria and microalbuminuria have been the subject of most of the discussion on alternative endpoints. In 2009, the NKF and FDA cosponsored a workshop to examine proteinuria as a surrogate outcome in CKD [9]. Given the heterogeneity of kidney diseases that can result in proteinuria, a major concern is that proteinuria is not necessarily part of the causal biologic pathway in many diseases. Therefore, the participants of this workgroup concluded that proteinuria was only acceptable as a surrogate endpoint for a limited number of disease states (e.g., complete remission of proteinuria in nephrotic syndrome) or for the evaluation of

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specific drugs (e.g., reduction in mild to moderate proteinuria to assess effects of ACE inhibitors or angiotensin receptor blockers) [9, 59]. The use of microalbuminuria as an endpoint has also been controversial. Microalbuminuria has been associated with cardiovascular and renal risk in diabetes and hypertension [60]. By virtue of being an early marker of end-organ damage, microalbuminuria may offer the advantage of being able to assess the effects of early interventions to delay CKD progression [61]. However, it remains unclear whether therapies to decrease microalbuminuria necessarily improve important clinical outcomes, such as the development of ESRD [62]. Further studies will therefore be needed before microalbuminuria can be established as a surrogate endpoint in CKD. Autosomal dominant polycystic kidney disease Autosomal dominant polycystic kidney disease causes bilateral, progressively enlarging kidney cysts, as well as a number of extrarenal manifestations, including liver cysts and intracranial aneurysms. Kidney function declines over the course of decades and ultimately leads to ESRD in a significant proportion of patients. Despite progressive growth of the kidney cysts over patients’ lifetimes, the early course of ADPKD is actually characterized by hyperfiltration and relatively normal GFR for many decades [63, 64]. This makes GFR an insensitive marker of underlying renal parenchymal damage in ADPKD. To address the limitations of GFR as an endpoint in ADPK D trials, the National Institute of Diabetes and Digestive and Kidney Diseases sponsored the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP). Using magnetic resonance imaging (MRI), the CRISP studies established that TKV increases as eGFR declines in ADPKD [65, 66], and that baseline TKV predicts risk of developing Stage 3 CKD (GFR<60 mL/min/1.73 m2) [64]. These correlations with GFR decline, and the ability to detect changes in TKV over relatively short follow-up periods, have led some authors to propose TKV as a surrogate endpoint for renal disease progression in ADPKD [64]. Acceptance of this endpoint would allow trials to assess potential therapies earlier in the disease course, before extensive renal parenchymal damage has occurred. However, from a regulatory perspective, the FDA has expressed concern about the use of TKV as a surrogate endpoint. When change in TKV was proposed as the primary endpoint for the TEMPO 3/4 trial of tolvaptan for ADPKD [67, 68], the FDA cautioned that Bthere is no intervention to alter renal volume that is known to affect renal function, so it is hard to accept renal volume as a surrogate^ [69]. Indeed, the FDA determined that the key efficacy endpoint it would consider for approval would be the composite secondary endpoint

consisting of clinical factors (hypertension, renal pain, albuminuria, and renal function), rather than change in TKV [67]. It is therefore important that ongoing trials in ADPKD continue to assess treatment efficacy based on key clinical endpoints in addition to TKV. A number of other biomarkers have also been evaluated in ADPKD. These include urinary biomarkers such as NGAL, macrophage colony stimulating factor, monocyte chemoattractant protein-1 [70]; markers of endothelial dysfunction such as pentraxin 3 [71]; markers of vasopressin signaling such as copeptin [72]; urinary proteomic biomarkers [73]; renal blood flow measured by MRI [74]. There is hope that some of these biomarkers will provide more sensitive indicators of ADPKD progression than TKV or eGFR, particularly if they measure factors related to underlying pathophysiology, such as tubular damage, inflammation, or cyst growth [75]. However, further studies are needed to determine if any of these biomarkers predict clinically meaningful endpoints, and TKV remains the most well-characterized surrogate endpoint for ADPKD progression.

Summary Improving the pace of clinical trials in nephrology will require the development of new biomarkers and surrogate endpoints to overcome the limitations posed by existing clinical endpoints. Validating a new biomarker as a surrogate endpoint requires a comprehensive biological, statistical, and regulatory framework for evaluation. Recent developments in nephrology research include novel biomarkers of renal parenchymal damage in AKI (e.g., TIMP-2 and IGFBP-7), acceptance of lower levels of eGFR decline in CKD, and use of TKV to assess progression of ADPKD. Although many of these biomarkers appear to be promising candidates to serve as surrogate endpoints, further studies will be needed to validate them against key clinical outcomes. Acknowledgments Dr. Hartung is supported by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) under Award Number KL2TR000139. The content is solely the responsibility of the author and does not necessarily represent the official view of NCATS or the NIH. Conflict of interest There are no conflicts of interest.

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