Development of a quantitative systems pharmacology model of chronic kidney disease: metabolic bone disorder.

Chronic kidney disease mineral bone disorder (CKD-MBD) is a virtually universal complication of kidney diseases, starting early in the course of disease and resulting in devastating clinical consequences ranging from bone fragility to accelerated atherosclerosis and early cardiovascular death. Guidelines for therapeutic goals for CKD-MBD have been published, and achievement of these guidelines is associated with improved survival. However, the incomplete understanding of CKD-MBD and the individual variability in the manifestations of CKD-MBD have made it difficult to achieve these guidelines. We hypothesized that the progression of MBD through all stages of CKD, including end-stage kidney disease, could be represented by a quantitative systems pharmacology/systems biology (QSP) model. To address this hypothesis, we constructed a QSP model of CKD-MBD, building on an open-source model of calcium and phosphorus metabolism. Specifically, we estimated and validated the model using data from 5,496 patients with CKD enrolled in the Chronic Renal Insufficiency Cohort study. Our model accurately predicted changes in markers of mineral metabolism related to progressing CKD. We demonstrated that the incorporation of fibroblast growth factor 23 and the soft tissue compartment is essential for accurate modeling of the changes in calcium, phosphorus, intact parathyroid hormone, and calcitriol in CKD-MBD. We conclude that our systems biology model accurately represents CKD-MBD disease progression and can be used as a test bench for improving therapeutic interventions.

Proteomic Analysis Identifies Distinct Glomerular Extracellular Matrix in Collapsing Focal Segmental Glomerulosclerosis.

BACKGROUND: The mechanisms leading to extracellular matrix (ECM) replacement of areas of glomerular capillaries in histologic variants of FSGS are unknown. This study used proteomics to test the hypothesis that glomerular ECM composition in collapsing FSGS (cFSGS) differs from that of other variants. METHODS: ECM proteins in glomeruli from biopsy specimens of patients with FSGS not otherwise specified (FSGS-NOS) or cFSGS and from normal controls were distinguished and quantified using mass spectrometry, verified and localized using immunohistochemistry (IHC) and confocal microscopy, and assessed for gene expression. The analysis also quantified urinary excretion of ECM proteins and peptides. RESULTS: Of 58 ECM proteins that differed in abundance between cFSGS and FSGS-NOS, 41 were more abundant in cFSGS and 17 in FSGS-NOS. IHC showed that glomerular tuft staining for cathepsin B, cathepsin C, and annexin A3 in cFSGS was significantly greater than in other FSGS variants, in minimal change disease, or in membranous nephropathy. Annexin A3 colocalized with cathepsin B and C, claudin-1, phosphorylated ERK1/2, and CD44, but not with synaptopodin, in parietal epithelial cells (PECs) infiltrating cFSGS glomeruli. Transcripts for cathepsins B and C were increased in FSGS glomeruli compared with normal controls, and urinary excretion of both cathepsins was significantly greater in cFSGS compared with FSGS-NOS. Urinary excretion of ECM-derived peptides was enhanced in cFSGS, although in silico analysis did not identify enhanced excretion of peptides derived from cathepsin B or C. CONCLUSIONS: ECM differences suggest that glomerular sclerosis in cFSGS differs from that in other FSGS variants. Infiltration of activated PECs may disrupt ECM remodeling in cFSGS. These cells and their cathepsins may be therapeutic targets.

Individualized anemia management in a dialysis facility - long-term utility as a single-center quality improvement experience
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BACKGROUND: Standard protocol-based approaches to erythropoiesis stimulating agent (ESA) dosing in anemia management of end-stage renal disease (ESRD) fail to address the inter-individual variability in patient's response to ESA. We conducted a single-center quality improvement project to investigate the long-term performance of a computer-designed dosing system. MATERIALS AND METHODS: The study was a retrospective case-control study with long-term follow-up. All hemodialysis patients who received treatment at University Kidney Center (Louisville, KY, USA) between September 1, 2009, and March 31, 2017, were included. We implemented an individualized ESA dosing algorithm into an electronic health records database software to provide patient-specific ESA dose recommendations to anemia managers at monthly intervals. The primary outcome was the percentage of hemoglobin (Hb) concentrations between 10 and 12 g/dL during the case-control study and 9 and 11 g/dL during follow-up. Secondary outcomes were intra- and inter-individual Hb variability. For the case-control study, we compared outcomes over 12 months before and after implementation of the algorithm. Subjects served as their own controls. We used the last Hb concentration of the month and ESA dose per week. Long-term follow-up examined trends in proportion within the target range, Hb, and ESA dose. RESULTS: Individualized ESA dosing in 56 subjects was associated with a moderate (6.6%) increase of mean Hb maintenance within target over the 12-month observation period (62.7% before vs. 69.3% after, p = 0.063). Intra-individual mean Hb variability decreased (1.1 g/dL before vs. 0.8 g/dL after, p < 0.001), so did inter-individual mean Hb variability (1.2 g/dL before vs. 1.0 g/dL after, p = 0.010). Long-term follow-up in 233 subjects for 42 months demonstrated stability of the achieved Hb despite an increasing ESA resistance in the patient population. CONCLUSION: Implementation of the individualized ESA dosing algorithm facilitates improvement in Hb maintenance within target, decreases Hb variability and reduces the dose of ESA required to achieve Hb target.
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Biomarker enhanced risk prediction for development of AKI after cardiac surgery.

BACKGROUND: Acute kidney injury (AKI) is a common post-cardiac surgery complication and influences patient morbidity and prognosis. This study was designed to identify preoperative candidate urine biomarkers in patients undergoing cardiac surgery. METHODS: A prospective cohort study of adults undergoing cardiac surgery at increased risk for AKI at a single hospital between July 2010 and September 2012 was performed. The primary outcome was the development of AKI, defined as an absolute serum creatinine (SCr) level increase ≥ 0.5 mg/dL or a ≥ 50% relative increase within 72 h of surgery. A secondary outcome was development of AKI defined by Kidney Disease Improving Global Outcomes (KDIGO). Urine collected by voiding within 4 h prior to surgery was used for proteomic analysis and confirmatory enzyme linked immunosorbent assays (ELISAs) studies. Biomarkers were tested for AKI-prediction using Cox and Snell R2, area under the receiver operating curve (AUROC), and percent of corrected classifications. To evaluate the added effect of each candidate biomarker on AKI discrimination, receiver operator characteristic (ROC) curves, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated. RESULTS: Forty-seven of 755 patients met screening criteria including 15 with AKI. Proteomic analysis identified 29 proteins with a significant ≥2-fold change. Confirmatory ELISA measurements of five candidate markers showed urinary complement factor B (CFB) and histidine rich glycoprotein (HRG) concentrations were significantly increased in patients with AKI. By multivariate analysis, NRI, and IDI the addition of CFB and HRG to the standard clinical assessment significantly improved risk prediction for the primary outcome. Only HRG was a significant predictor in the 21 patients with AKI defined by KDIGO criteria. CONCLUSIONS: Pre-operative urine measurement of CFB or HRG significantly enhanced the current post-surgery AKI risk stratification for more restrictive definition of AKI. HRG, but not CFB or clinical risk stratification, predicted AKI defined by KDIGO. The ability of these biomarkers to predict risk for dialysis-requiring AKI or death could not be reliably assessed in our study due to a small number of patients with either outcome.

Artificial intelligence for optimal anemia management in end-stage renal disease.

Computational intelligence for the prediction of hemoglobin to guide the selection of erythropoiesis-stimulating agent dose results in improved anemia management. The models used for the prediction result from the use of individual patient data and help to increase the number of hemoglobin observations within the target range. The benefits of using these modeling techniques appear to be a decrease in erythropoiesis-stimulating agent use and a decrease in the number of transfusions. This study confirms the results of previous smaller studies and suggests that additional beneficial results may be achieved.

Individualized anemia management reduces hemoglobin variability in hemodialysis patients.

One-size-fits-all protocol-based approaches to anemia management with erythropoiesis-stimulating agents (ESAs) may result in undesired patterns of hemoglobin variability. In this single-center, double-blind, randomized controlled trial, we tested the hypothesis that individualized dosing of ESA improves hemoglobin variability over a standard population-based approach. We enrolled 62 hemodialysis patients and followed them over a 12-month period. Patients were randomly assigned to receive ESA doses guided by the Smart Anemia Manager algorithm (treatment) or by a standard protocol (control). Dose recommendations, performed on a monthly basis, were validated by an expert physician anemia manager. The primary outcome was the percentage of hemoglobin concentrations between 10 and 12 g/dl over the follow-up period. A total of 258 of 356 (72.5%) hemoglobin concentrations were between 10 and 12 g/dl in the treatment group, compared with 208 of 336 (61.9%) in the control group; 42 (11.8%) hemoglobin concentrations were <10 g/dl in the treatment group compared with 88 (24.7%) in the control group; and 56 (15.7%) hemoglobin concentrations were >12 g/dl in the treatment group compared with 46 (13.4%) in the control group. The median ESA dosage per patient was 2000 IU/wk in both groups. Five participants received 6 transfusions (21 U) in the treatment group, compared with 8 participants and 13 transfusions (31 U) in the control group. These results suggest that individualized ESA dosing decreases total hemoglobin variability compared with a population protocol-based approach. As hemoglobin levels are declining in hemodialysis patients, decreasing hemoglobin variability may help reduce the risk of transfusions in this population.

Application of artificial intelligence to chronic kidney disease mineral bone disorder.

The global derangement of mineral metabolism that accompanies chronic kidney disease (CKD-MBD) is a major driver of the accelerated mortality for individuals with kidney disease. Advances in the delivery of dialysis, in the composition of phosphate binders, and in the therapies directed towards secondary hyperparathyroidism have failed to improve the cardiovascular event profile in this population. Many obstacles have prevented progress in this field including the incomplete understanding of pathophysiology, the lack of clinical targets for early stages of chronic kidney disease, and the remarkably wide diversity in clinical manifestations. We describe in this review a novel approach to CKD-MBD combining mathematical modelling of biologic processes with machine learning artificial intelligence techniques as a tool for the generation of new hypotheses and for the development of innovative therapeutic approaches to this syndrome. Clinicians need alternative targets of therapy, tools for risk profile assessment, and new therapies to address complications early in the course of disease and to personalize therapy to each individual. The complexity of CKD-MBD suggests that incorporating artificial intelligence techniques into the diagnostic, therapeutic, and research armamentarium could accelerate the achievement of these goals.

Association of maturation period blood pressure with dialysis access patency.

Problematic dialysis vascular access is a major health issue. The purpose of this study was to evaluate for potentially modifiable factors associated with access patency, particularly, the association of early postoperative, or maturation period, blood pressure with patency. A retrospective review was performed of patients who had undergone placement of an arteriovenous fistula or graft. Demographic, operative, and postoperative factors were evaluated for possible association with access primary patency using univariate and multivariate Cox regression analyses. Seventy-three patients over a 3-year review period were examined. Overall analysis showed a significant association of absence of peripheral vascular disease, aspirin use, and absence of previous permanent dialysis access with higher primary patency rates. Fistula subgroup analysis showed that higher blood pressure during the maturation period relative to preoperative blood pressure was associated with lower patency rates. For grafts, race was significantly associated with patency, with blacks having higher patency rates than whites. Multiple clinical factors were found to have a significant association with dialysis access primary patency. The finding of an association of maturation period blood pressure with fistula patency suggests that the maturation period environment, specifically hemodynamics during this time, may play an important role in dialysis access patency.

Characteristics and outcomes in community-acquired versus hospital-acquired acute kidney injury.

AIM: Relatively little is known about the prevalence of acute kidney injury developing outside a hospital setting (CA-AKI) or the impact of CA-AKI on short-term or long-term clinical outcomes. The objective of this study was to compare the prevalence, causes, severity and outcomes of patients with CA-AKI and hospital-acquired (HA)-AKI. METHODS: A retrospective cohort study of patients with AKI identified by ICD-9 code at a single VA (Veterans Affairs) hospital from September 1999 to May 2007 was performed. AKI was verified by applying the RIFLE criteria, and patients were categorized as CA-AKI if RIFLE criteria were met at admission. Demographic, clinical, and outcome variables were extracted by chart review. RESULTS: Four hundred twenty-two patients met inclusion criteria, of which 335 (79.4%) developed CA-AKI. Patients with CA-AKI were more likely to have volume depletion as the aetiology, had fewer chronic illnesses and hospital complications, had a shorter length of stay, and had a reduced mortality, compared with HA-AKI. Distribution among the three RIFLE classes did not differ between groups, and recovery of renal function was incomplete in both groups. CONCLUSION: We conclude that CA-AKI is a common cause of AKI that is as severe as that seen in HA-AKI. CA-AKI has a significant impact on length of hospital stay, mortality, and the development and/or progression of chronic kidney disease. Strategies to limit the risk of CA-AKI are likely to have a significant impact on healthcare costs and patient care.

A comparison of laboratory values in pediatric hemodialysis patients: does day of the week matter?

BACKGROUND: The National Kidney Foundation Dialysis Outcomes Quality Initiative (NKF-K/DOQI) guidelines recommend the delivered dose of hemodialysis (HD) be measured no less than monthly by checking Kt/V (K is effective urea clearance, t is minute and V is urea distribution). To date, no studies have explored whether the day of the week for checking maintenance HD laboratory studies impacts dialysis dosing. METHODS: Data were collected at two HD facilities on 19 patients, ages ≤ 21 years receiving maintenance HD thrice weekly over a consecutive 6-month period. Data obtained from the Monday and Wednesday of each full third week of the month included dialysis vintage, ultrafiltration volume, serum electrolytes, hemoglobin and clearances. RESULTS: Kt/V and K+ were significantly different between Monday and Wednesday (P = 0.013 and P = 0.047, respectively). CONCLUSION: Due to variability in values based on the day of laboratory evaluations, the dialysis provider must consider the impact of this on the quality of patient care when prescribing dialysis. Research on a larger scale needs to be conducted to allow for better decision-making capabilities in the chronic HD population.

Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease.

Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with our incomplete understanding of the pathophysiology has significantly hampered the ability of nephrologists to prevent fractures, a leading comorbidity of CKD. Much has been learned from animal models; however, we propose in this review that application of multiple techniques of mathematical modeling and artificial intelligence can accelerate our ability to develop relevant and impactful clinical trials and can lead to better understanding of the osteoporosis of CKD. We highlight the foundational work that informed our current model development and discuss the potential applications of our approach combining principles of quantitative systems pharmacology, model predictive control, and reinforcement learning to deliver individualized precision medical therapy of this highly complex disorder.

Artificial intelligence-guided precision treatment of chronic kidney disease-mineral bone disorder.

Chronic kidney disease (CKD)-mineral bone disorder (MBD) is a complex clinical syndrome that begins early during CKD and evolves into one of the deadliest complications of CKD through its effects on the cardiovascular and skeletal systems. Achievement of treatment goals to decrease the risk of accelerated cardiovascular events and fractures has been challenging. We hypothesized that application of quantitative systems pharmacology (QSP) modeling combined with artificial intelligence techniques could improve the management of CKD-MBD with the goal of improving outcomes for patients with CKD. We present the implementation of a reinforcement learning (RL) approach to achieve the prescribed goals for serum calcium, phosphorus, and parathyroid hormone through concurrent dosing of phosphate binders, vitamin D analogs, and calcimimetics by simulation in 80 subjects in Matlab. In silico simulation results demonstrate that the application of a QSP model coupled with RL more effectively and quickly achieves treatment goals even in the setting of inferior simulated subject compliance with medical therapy and identifies key decision variables for therapeutic recommendations.

Differential Functional Responses of Neutrophil Subsets in Severe COVID-19 Patients.

Neutrophils play a significant role in determining disease severity following SARS-CoV-2 infection. Gene and protein expression defines several neutrophil clusters in COVID-19, including the emergence of low density neutrophils (LDN) that are associated with severe disease. The functional capabilities of these neutrophil clusters and correlation with gene and protein expression are unknown. To define host defense and immunosuppressive functions of normal density neutrophils (NDN) and LDN from COVID-19 patients, we recruited 64 patients with severe COVID-19 and 26 healthy donors (HD). Phagocytosis, respiratory burst activity, degranulation, neutrophil extracellular trap (NET) formation, and T-cell suppression in those neutrophil subsets were measured. NDN from severe/critical COVID-19 patients showed evidence of priming with enhanced phagocytosis, respiratory burst activity, and degranulation of secretory vesicles and gelatinase and specific granules, while NET formation was similar to HD NDN. COVID LDN response was impaired except for enhanced NET formation. A subset of COVID LDN with intermediate CD16 expression (CD16Int LDN) promoted T cell proliferation to a level similar to HD NDN, while COVID NDN and the CD16Hi LDN failed to stimulate T-cell activation. All 3 COVID-19 neutrophil populations suppressed stimulation of IFN-γ production, compared to HD NDN. We conclude that NDN and LDN from COVID-19 patients possess complementary functional capabilities that may act cooperatively to determine disease severity. We predict that global neutrophil responses that induce COVID-19 ARDS will vary depending on the proportion of neutrophil subsets.

Oncostatin M receptor ß and cysteine/histidine-rich 1 are biomarkers of the response to erythropoietin in hemodialysis patients.

Biomarkers that evaluate the response to erythropoietic-stimulating agents largely measure inflammation and iron availability. While these are important factors in modifying an individual's response to these agents, they do not address all aspects of a poor response. To clarify this, we isolated peptides in the serum of good and poor responders to erythropoietin in order to identify biomarkers of stimulating agent response. Ninety-one candidate biomarker targets were identified and characterized using mass spectrometry, of which tandem mass spectroscopy provided partial amino-acid sequence information of 17 different peptides for 16 peptide masses whose abundance significantly differed between poor and good responders. The analysis concluded that three peptides associated with a poor response were derived from oncostatin M receptor ß (OSMRß). The 13 serum peptides associated with a good response were derived from fibrinogen α and ß, coagulation factor XIII, complement C3, and cysteine/histidine rich 1(CYHR1). Poor response was most strongly associated with the OSMRß fragment with the largest molecular weight, while a good response was most strongly associated with CYHR1. Immunoblots found the abundance of intact OSMRß and CYHR1 significantly differed between good and poor responders. Thus, two measurable biomarkers of the response to erythropoietic-stimulating agents are present in the serum of treated patients.

Predictive modeling for improved anemia management in dialysis patients.

PURPOSE OF REVIEW: This review will explore the basic assumptions needed to perform predictive modeling of hemoglobin response to erythropoiesis stimulating agents (ESAs) and summarize the current literature in the area so that the practitioner can incorporate these tools as part of an improved anemia management process. RECENT FINDINGS: During the last year, several publications have demonstrated some advances in the field that may improve anemia management. The first of these was the publication of a randomized, controlled clinical trial of model predictive control in the dosing of erythropoietin. This work showed that hemoglobin variability can be decreased using predictive models of hemoglobin response. The second publication is potentially more interesting in the long run, as new markers of erythropoietin response were identified in a well-defined population of patients. SUMMARY: Predictive models of hemoglobin response improve anemia management by decreasing hemoglobin variability. This will result in more patients within the target range. Coupling these tools with new biomarkers of hemoglobin response has the potential to dramatically improve anemia management.

Adaptive Individualized Drug-Dose Response Modeling from a Limited Clinical Data: Case of Warfarin Management.

Administration of drugs requires sophisticated methods to determine the drug quantity for optimal results, and it has been a challenging task for the number of diseases. To solve these challenges, in this paper, we present the semi-blind robust model identification technique to find individualized patient models using the minimum number of clinically acquired patient-specific data to determine optimal drug dosage. To ensure the usability of these models for dosage predictability and controller design, the model (In)validation technique is also investigated. As a case study, the patients treated with warfarin are studied to demonstrate the semi-blind robust identification and model (In)validation techniques. The performance of models is assessed by calculating minimum means squared error (MMSE).Clinical Relevance- This work establishes a general framework for adaptive individualized drug-dose response models from a limited number of clinical patient-specific data. This work will help clinicians in decision-making for improved drug dosing, patient care, and limiting patient exposure to agents with a narrow therapeutic range.

Plasma Cytokine Profiling to Predict Steroid Resistance in Pediatric Nephrotic Syndrome.

INTRODUCTION: Glucocorticoids (GCs) are the primary treatment for nephrotic syndrome (NS), although ∼10% to 20% of children develop steroid-resistant NS (SRNS). Unfortunately, there are no validated biomarkers able to predict SRNS at initial disease presentation. We hypothesized that a plasma cytokine panel could predict SRNS at disease presentation, and identify potential pathways regulating SRNS pathogenesis. METHODS: Paired plasma samples were collected from 26 children with steroid-sensitive NS (SSNS) and 14 with SRNS at NS presentation and after ∼7 weeks of GC therapy, when SSNS versus SRNS was clinically determined. Plasma cytokine profiling was performed with a panel of 27 cytokines. RESULTS: We identified 13 cytokines significantly different in Pretreatment SSNS versus SRNS samples. Statistical modeling identified a cytokine panel (interleukin [IL]-7, IL-9, monocyte chemoattractant protein-1 [MCP-1]) able to discriminate between SSNS and SRNS at disease presentation (receiver operating characteristic [ROC] value = 0.846; sensitivity = 0.643; specificity = 0.846). Furthermore, GC treatment resulted in significant decreases in plasma interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), IL-7, IL-13, and IL-5 in both SSNS and SRNS patients. CONCLUSIONS: These studies suggest that initial GC treatment of NS reduces the plasma cytokines secreted by both CD4+ TH1 cells and TH2 cells, as well as CD8+ T cells. Importantly, a panel of 3 cytokines (IL-7, IL-9, and MCP-1) was able to predict SRNS prior to GC treatment at disease presentation. Although these findings will benefit from validation in a larger cohort, the ability to identify SRNS at disease presentation could greatly benefit patients by enabling both avoidance of unnecessary GC-induced toxicity and earlier transition to more effective alternative treatments.

Precise Warfarin Management through Personalized Modeling and Control with Limited Clinical Data.

Warfarin belongs to a medication class called anticoagulants or blood thinners. It is used for the treatment to prevent blood clots from forming or growing larger. Patients with venous thrombosis, pulmonary embolism, or who have suffered a heart attack, have an irregular heartbeat, or prosthetic heart valves are prescribed with warfarin. It is challenging to find optimal doses due to inter-patient and intra-patient variabilities and narrow therapeutic index. This work presents an individualized warfarin dosing method by utilizing the individual patient model generated using limited clinical data of the patients with chronic conditions under warfarin anticoagulation treatment. Then, the individual precise warfarin dosing is formalized as an optimal control problem, which is solved using the DORBF control approach. The efficiency of the proposed approach is compared with results obtained from practiced clinical protocol.

Na/H Exchange Regulatory Factor 1 Deficient Mice Show Evidence of Oxidative Stress and Altered Cisplatin Pharmacokinetics.

(1) Background: One third of patients who receive cisplatin develop an acute kidney injury. We previously demonstrated the Na/H Exchange Regulatory Factor 1 (NHERF1) loss resulted in increased kidney enzyme activity of the pentose phosphate pathway and was associated with more severe cisplatin nephrotoxicity. We hypothesized that changes in proximal tubule biochemical pathways associated with NHERF1 loss alters renal metabolism of cisplatin or response to cisplatin, resulting in exacerbated nephrotoxicity. (2) Methods: 2-4 month-old male wild-type and NHERF1 knock out littermate mice were treated with either vehicle or cisplatin (20 mg/kg dose IP), with samples taken at either 4, 24, or 72 h. Kidney injury was determined by urinary neutrophil gelatinase-associated lipocalin and histology. Glutathione metabolites were measured by HPLC and genes involved in glutathione synthesis were measured by qPCR. Kidney handling of cisplatin was assessed by a kidney cortex measurement of γ-glutamyl transferase activity, Western blot for γ-glutamyl transferase and cysteine S-conjugate beta lyase, and ICP-MS for platinum content. (3) Results: At 24 h knock out kidneys show evidence of greater tubular injury after cisplatin and exhibit a decreased reduced/oxidized glutathione ratio under baseline conditions in comparison to wild-type. KO kidneys fail to show an increase in γ-glutamyl transferase activity and experience a more rapid decline in tissue platinum when compared to wild-type. (4) Conclusions: Knock out kidneys show evidence of greater oxidative stress than wild-type accompanied by a greater degree of early injury in response to cisplatin. NHERF1 loss has no effect on the initial accumulation of cisplatin in the kidney cortex but is associated with an altered redox status which may alter the activity of enzymes involved in cisplatin metabolism.