Cisplatin is a platinum-based chemotherapeutic that causes acute kidney injury in over 30% of patients. The aim of this study was to develop a population pharmacokinetic/toxicodynamic (PKTD) model of cisplatin-induced kidney injury that incorporated plasma total platinum and urinary kidney injury molecule-1 (KIM-1) concentrations. Cancer patients receiving their first or second round of cisplatin-containing chemotherapy (n=39) were prospectively randomized to a 5-HT3 antagonist (5-HT3A) antiemetic (ondansetron, granisetron, or palonosetron) and had blood and urine collected over 10 days. Plasma concentrations of total platinum and urinary concentrations of KIM-1 were used in the development of a nonlinear mixed effect population PKTD model using Phoenix® NLME (v8.3, Certara Inc.). A stepwise search was used to test potential covariates that influenced PKTD parameters. A two-compartment model best described the plasma total platinum concentration vs. time data and was expanded to an effect compartment PKTD model incorporating urinary KIM-1 concentrations. Significant covariate effects for the PKTD model included previous cisplatin exposure on the volume of the central compartment (V1), 5-HT3A antiemetic treatment on the volume of the peripheral compartment (V2), and baseline urinary KIM-1 levels on the maximum effect (Emax) parameter. The model demonstrated that ondansetron-treated subjects had a 163% increase in exposure to plasma total platinum, a 94% increase in urinary KIM-1 maximum concentrations, and a 235% increase in total urinary KIM-1 excretion compared to palonosetron-treated subjects, suggesting that palonosetron may be a preferred 5-HT3A to reduce the risk of cisplatin-induced kidney injury.

• Cisplatin
• Pharmacokinetics
• Toxicodynamics
• Pharmacodynamics
• Nephrotoxicity
• Kidney Injury
• 5-HT3 Antagonist

Thompson LE, Ghimire A, Wen X, Kim C, Choza J, et al. (2024) A Pharmacokinetic/Toxicodynamic Model of Cisplatin Nephrotoxicity Using the Kidney Injury Molecule-1 Biomarker. J Pharmacol Clin Toxicol 12(1):1184.

AKI: Acute Kidney Injury; 5-HT3A: 5-hydroxytryptamine 3 antagonist; PK: Pharmacokinetic; TD: Toxicodynamic; PD: Pharmacodynamic; PKTD: Pharmacokinetic/toxicodynamic; KIM-1: Kidney Injury Molecule-1; Cr: Creatinine; UCr: Urinary Creatinine; CINV: Chemotherapy-induced nausea and vomiting; OCT2: Organic Cation Transporter 2; MATE1: Multidrug and Toxin Extrusion Protein 1; MRP2: Multidrug Resistance- associated Protein 2

Cisplatin, a platinum-based chemotherapeutic, treats an assortment of solid tumors [1]. However, over 30% of cisplatin- treated patients develop acute kidney injury (AKI) as a major side effect [2-4]. Cisplatin-induced AKI predominantly results from direct injury in proximal tubules, with contributions from oxidative stress and inflammation [5]. Due to the presence of specific uptake transporters on kidney basolateral membranes, such as the organic cation transporter 2 (OCT2), kidney tubular epithelial cells inevitably take up more cisplatin as compared to other tissues, leading to renal concentrations of cisplatin that are up to 5 times higher than plasma concentrations [5,6].

Cisplatin is a highly emetogenic drug which causes chemotherapy-induced nausea and vomiting (CINV) in over 90% of patients, necessitating prophylactic antiemetics including an NK-1 receptor antagonist, a 5-HT3 (5-hydroxytryptamine, or serotonin) antagonist (5-HT3A), dexamethasone, and olanzapine [7, 8]. Previously published studies in both cancer patients and mice demonstrated that ondansetron can alter the pharmacokinetics (PK) of cisplatin and enhance cisplatin- induced AKI [9-11]. Elevated plasma concentrations of cisplatin may reflect decreased renal secretion due to inhibition of renal efflux transporters such as multidrug and toxin extrusion protein 1 (MATE1) by ondansetron [12].

Although previous studies have reported correlations between cisplatin PK parameters and nephrotoxicity [3,13- 20], few population pharmacokinetic/pharmacodynamic (PKPD) or pharmacokinetic/toxicodynamic (PKTD) models of cisplatin-induced kidney injury exist in the literature [21-28]. In these existing models, the toxicodynamic (TD) outcome of nephrotoxicity was measured by the traditional biomarkers serum creatinine (SCr), estimated glomerular filtration rate (eGFR), creatinine clearance (CrCl), and blood urea nitrogen (BUN) [13-18,21-28]. Despite the established importance of incorporating sensitive urinary protein biomarkers (e.g. kidney injury molecule-1 (KIM-1), beta-2 microglobulin (B2M), neutrophil gelatinase-associated lipocalin (NGAL), etc.) into the staging of AKI [29], and as TD markers of renal injury [30-34], there are no existing PKTD models that use these markers. This study sought to fill this gap by utilizing urinary KIM-1 levels as the TD measure of injury, a biomarker well described in cisplatin nephrotoxicity [3,14,19,20,35-39]. Biomarkers have the unique potential to identify early, subclinical kidney injury and clinical injury and can inform the structural location of nephron damage. Few published models have assessed the impact of antiemetic treatments, such as concomitant 5-HT3As, on nephrotoxicity [9]. We conducted the first prospective study of cisplatin- treated cancer patients randomized to a 5-HT3A (ondansetron, granisetron, palonosetron) and explored how co-treatment with these drugs affected the risk for cisplatin-induced kidney injury. A population PKTD approach was used to study the impact of 5-HT3A co-treatment and other patient level covariates on plasma total platinum concentrations and urinary KIM-1 biomarker changes following cisplatin treatment.

Study participants

Study participants receiving their first or second round of cisplatin chemotherapy (≥25 mg/m2; 0.5-2 hour infusion) from the University of Colorado Cancer Center (UCCC) or Memorial Sloan Kettering Cancer Center (MSKCC) were enrolled (NCT03817970). All participants were recruited and studied at UCCC and MSKCC, which are NCI-Designated Consortium Comprehensive Cancer Centers. Study protocols were approved by the Institutional Review Boards at the University of Colorado (18-1752), Memorial Sloan Kettering (19-221), and Rutgers University (Pro2018001802). A full description of the eligibility criteria is listed in the Supplemental Materials. The study adhered to Good Clinical Practice guidelines and principles as described in the Declaration of Helsinki.

Clinical trial design

Participants were prospectively randomized to one FDA- approved 5-HT3A antiemetic drug (ondansetron 8 mg oral, granisetron 2 mg oral, or palonosetron 0.25 mg intravenous; Table S1). 5-HT3As were administered within 1-2 hours before cisplatin and take-home, breakthrough antiemetic medication regimens were standardized (Table S1). Selection of these agents and doses reflect established practices at UCCC and MSKCC and guidelines from the National Comprehensive Cancer Network (NCCN) and American Society of Clinical Oncology (ASCO) [40- 42]. Antiemetic guidelines for high emetic risk anti-cancer agents such as cisplatin contain antiemetic recommendations for delayed CINV (dexamethasone, NK-1 receptor antagonist, and olanzapine), participants received these agents in addition to their randomized 5-HT3A (Table S1). Participants were hydrated with a standardized fluid protocol (saline 1-2 L) pre- and post- cisplatin treatment which included 4 g of magnesium sulfate and 20 mEq of potassium chloride.

Serial blood samples (7.5 mL) were collected into lithium heparin tubes prior to cisplatin administration and at the following time points following the start of the cisplatin infusion: 0.5, 1, 2, 4, 48, 72, and 240 hours. Urine was collected prior to

cisplatin treatment, and at 0-2, 2-4, 4-24, 24-48, 48-72 and 240 hours following the cisplatin infusion for TD assessments. To assess CINV, participants completed the Multinational Association of Supportive Care in Cancer (MASCC) Antiemesis Tool (MAT) 24 hours and 4 days after their cisplatin infusion.

Blood and urine chemistry

Participant blood samples from each visit were evaluated by clinical laboratories at UCCC and MSKCC using comprehensive metabolic and complete blood count (CBC) panels. Laboratory panel results (e.g. SCr, BUN, albumin, etc.) were obtained from participant medical records. eGFR was calculated using the race- free Chronic Kidney Disease Epidemiology Collaboration (CKD- EPI) Creatinine GFR Equation (2021) [43, 44].

KIM-1 was measured in the supernatant of urine samples using the Human KIM-1 DuoSet Enzyme-Linked Immunosorbent Assay (ELISA) Kit (Cat. #DY1750B, R&D Systems, Minneapolis, MN). Values that were above the standard curve were diluted and re-analyzed. Urinary KIM-1 levels (ng/mL) were normalized to 

urinary creatinine (UCr) concentrations to control for variation in urine flow rate [45]. UCr concentrations (mg/dL) were measured using the QuantiChrom Creatinine Assay Kit (Cat. #DICT-500, BioAssay Systems, Hayward, CA). The normalized data are presented as ng KIM-1/mg creatinine (Cr) as previously described [45].

Platinum analysis

Total platinum concentrations were measured in participant plasma samples using inductively coupled plasma mass spectrometry (ICP/MS). The analytical assay is briefly described below, with more detailed information in the Supplemental Materials.

Briefly, to quantify total platinum, 50 µL of plasma was mixed with nitric acid and digested with a CEM Mars X microwave system (CEM Corp, Matthews, NC) and platinum concentrations were quantified using a Nu AttoM ICP/MS.

Population PK model development

Plasma concentrations of total platinum were used in the development of a nonlinear mixed effect PK model using Phoenix® NLME (v8.3, Certara Inc., Princeton, NJ). Model development is described briefly below, with more detailed information in the Supplemental Materials.

Initial  PK  estimates:  To  obtain  initial  estimates for PK modeling, Phoenix® NLME was used to perform noncompartmental analysis (NCA) of plasma total platinum. The linear trapezoidal linear interpolation method was used for area under the concentration versus time curve (AUC) calculations.

Base PK model: Following visual inspection of the plasma total platinum concentration versus time data, one and two- compartment structural models were fitted, along with three different error models (additive, proportional, and combined additive/proportional) to explain residual variability using Phoenix® NLME. Various model engines [first order conditional estimation-extended least squares (FOCE ELS), first order conditional estimation Lindstrom-Bates (FOCE L-B), quasi- random parametric expectation maximization (QRPEM)] were additionally evaluated. The final structural model for total platinum was selected based on inspection of diagnostic plots (goodness of fit plots), the minimum objective function values (OFVs) [-2 log likelihood (-2LL), Akaike information criterion (AIC), Bayesian information criterion (BIC)], and evaluation of parameter and coefficient of variation (CV%) estimates.

Parameter inter-individual variability (IIV) estimates were assessed and removed iteratively to minimize eta shrinkage. The best model was selected based on inspection of diagnostic plots, the minimum OFVs, and evaluation of parameter and CV% estimates.

Covariate effects on the PK model: Covariates were chosen based on observed differences and trends in individual PK parameter estimates vs. covariate plots. Selected covariates were evaluated via a stepwise covariate search using forward addition (decrease in minimum OFV > 3.84, degree of freedom = 1, p<0.05) followed by backward elimination (decrease in minimum OFV > 6.64, degree of freedom = 1, p<0.01). The number of covariates included in the final model was limited by the number of participants.

PK model evaluation and validation: The final model was selected by comparing diagnostic plots, minimum OFVs, and parameter estimations. A visual predictive check (VPC) was used to evaluate the predictability of the model. 200 data sets were simulated for plasma total platinum using the final model. The 5th, 50th, and 95th percentiles of simulated predicted concentrations were compared to observed concentrations for model validation.

Population PKTD model development

The final population PK model of plasma total platinum was used to develop a PKTD model of cisplatin-induced kidney injury using Phoenix® NLME. Urinary KIM-1 concentrations normalized by UCr were used as the TD response marker. Model development is described briefly below, with more detailed information in the Supplemental Materials.

Initial TD estimates: Initial estimates for TD parameters were obtained using NCA and graphs of urinary KIM-1 concentrations over time.

Base TD model: Parameters from the final PK model of plasma total platinum were frozen and urinary KIM-1 concentrations normalized by UCr were added to the model as the TD outcome marker to perform sequential PKTD modeling. Following visual inspection of the plasma total platinum concentration versus time data and the urinary KIM-1/UCr versus time data, multiple TD models were tested including maximum effect (Emax) models, effect compartment models, and indirect response models. The final structural model was chosen based on inspection of diagnostic plots, the minimum OFVs, and evaluation of parameter and CV% estimates.

Parameter IIV estimates were assessed and removed iteratively to minimize eta shrinkage. The best model was chosen based on inspection of diagnostic plots, the minimum OFVs, and evaluation of parameter and CV% estimates.

Covariate effects on the TD model: As in the PK model, covariates were screened to determine the influence on urinary KIM-1 TD parameters (Emax, EC50, Ke0).

TD model evaluation and validation: Selection of the final model was guided by comparing diagnostic plots, minimum OFVs, and parameter estimations. A VPC was used to evaluate the predictability of the model. 200 data sets were simulated for urinary concentrations of KIM-1/UCr using the final model. The 5th, 50th, and 95th percentiles of simulated predicted concentrations were compared to observed concentrations for model validation.

PKTD model simulations: Using the final population PKTD covariate model, plasma total platinum and urinary KIM-1/UCr concentrations following three standard cisplatin dose regimens were simulated and stratified based on the identified significant covariates. 1000 data points were used for each simulation.

Statistical analysis

All statistical analyses were conducted using GraphPad Prism (v10.0.2, San Diego, CA). PK and TD parameter distributions for covariate effects were tested for normality and log-normality with the D’Agostino & Pearson test. If normally distributed, parameters were compared based on covariates using standard linear regression slope analysis, unpaired t-tests, and one-way ANOVAs with Tukey-Kramer post-hoc tests. If log-normally distributed, parameters were log-transformed and compared based on covariates using unpaired t-tests and one-way ANOVAs with Tukey-Kramer post-hoc tests. If not normally or log-normally distributed, parameters were compared based on covariates using the Mann-Whitney test and the Kruskal-Wallis test with Dunn’s multiple comparisons post-hoc test. A p value of <0.05 was considered statistically significant.

Study subjects

Data from 39 subjects were included in the modeling. The baseline demographics of the subjects (Table S2) exhibited that 72% male, 79% Caucasian, and 64% head/neck cancer patients. Across the study subjects, a total of 220 plasma total platinum concentrations and 166 urine KIM-1 concentrations were included in the PK and TD modeling. The concentration/ amount versus time profiles for plasma total platinum, urinary KIM-1 normalized by UCr, and cumulative urinary KIM-1/UCr are shown in Figure 1.

Figure 1 Total platinum (Pt) and urinary kidney injury molecule-1 (KIM-1) concentrations following cisplatin infusions. (A) – Plasma total platinum concentrations. (B) – Urinary KIM-1 concentrations normalized by urinary creatinine (UCr). (C) – Cumulative urinary KIM-1/UCr amount. (D) – Plasma total platinum compared to urinary KIM-1/UCr concentrations. Each line represents an individual patient profile. For (A-C), ondansetron-randomized patients are represented in blue, granisetron- randomized patients are represented in red, and palonosetron-randomized patients are represented in green. For (D), plasma total platinum concentrations are color coded by 5-HT3A (ondansetron: blue; granisetron: red; palonosetron: green) and urinary KIM-1/UCr concentrations are represented in black.

Population PK model of plasma total platinum

The base population PK model and parameter estimates for plasma total platinum following cisplatin administration are shown in Figure 2A and Table 1. A two-compartment, additive error model with the FOCE-ELS engine was chosen for plasma total platinum based on diagnostic plots, minimum OFV (-2LL = 3237), and parameter estimations (Table 1). The best, final model for total platinum (-2LL = 3228) was the parameter block model for V2 and CL2 and includes IIV estimates for all the parameters (Table 1). The final population PK model parameter estimates for plasma total platinum are presented in Table 1 and the goodness of fit plots are presented in Figure S1.

Table 1: Plasma total platinum population PK model parameter estimates

Abbreviations: PK: pharmacokinetic; SD: standard deviation; CV%: coefficient of variation; V1: volume of central compartment; V2: volume of peripheral compartment; CL1: central clearance; CL2: intercompartmental clearance; dV1dvisit: effect of visit on V1; dV2dAE1: effect of antiemetic (granisetron compared to ondansetron) on V2; dV2dAE2: effect of antiemetic (palonosetron compared to ondansetron) on V2; -2LL: -2 log likelihood; N/A: not applicable.

PK covariate model: An initial covariate search demonstrated that the model for plasma total platinum included visit and concomitant omeprazole on V1 and 5-HT3A antiemetic on V1 and V2 (-2LL = 3186). Visit refers to if the studied dose was a subject’s first or second cisplatin dose. However, the CV% for the covariate parameters were large (>50%). Covariate plots are presented in Figures 2B and S2.

Each indicated covariate was evaluated individually and combined iteratively to find the model with the lowest OFV with reasonable CV% estimates. The final covariate model (-2LL = 3202) included visit on V1 and 5-HT3A antiemetic on V2, with a parameter block model for V2 and CL2, and IIV estimates for all of the parameters except CL1. The parameter estimates for the final covariate model are presented in Table 1 and goodness of fit plots are presented in Figures 2C and S3. The final PK parameters were:

PK covariate effects: The covariate plots suggested that subjects receiving their first dose of cisplatin had significantly higher V1 values (65.2% increase) than patients receiving their second dose (p<0.001, Figure 2B). The covariate plots also suggested that 5-HT3A antiemetic prescription altered V2. Ondansetron-treated subjects had significantly lower V2 values (20.4% decrease) than granisetron-treated subjects (p=0.045; Figure 2B). There were no significant differences in V2 between the ondansetron- and palonosetron-treated subjects (p=0.311) or between granisetron- and palonosetron-treated subjects (p>0.999; Figure 2B).

Figure 2 Population PK model of total platinum. (A) – Base population PK model diagram of plasma total platinum. (B) – Covariate plots for the plasma total platinum model. V1 was significantly influenced by which visit the patient was studied during – first cisplatin dose (1) or second cisplatin dose (2) (p=0.0003). Which 5-HT3A antiemetic the patient was taking significantly altered V2 (O vs. G: p=0.045; O vs. P: p=0.311; G vs. P: p>0.999). Data represented as mean ± SD; *p<0.05, ***p<0.001. (C) – Individual predicted total platinum levels (lines) compared to observed data (red circles) as estimated by the final covariate model. PK: pharmacokinetic; Pt: platinum; V1: volume of central compartment; V2: volume of peripheral compartment; CL1: central clearance; CL2: intercompartmental clearance; SD: standard deviation. Figure 2A was made using BioRender.com.

PK model validation: Diagnostic plots for the final base model are shown in Figure S1 and diagnostic plots for the covariate model are shown in Figures 2C and S3. The individual predicted concentrations are well correlated with the observed plasma data for both the final base and covariate models, suggesting good model performance.

The VPC evaluation for the plasma total platinum model consisted of 200 replicates. The plots of the 5th, 50th, and 95th percentiles of observed plasma concentrations display good superimposition with the equivalent predicted concentration percentiles demonstrating that the final model sufficiently described the data (Figure S4A). The VPC was further split by 5-HT3A antiemetic, which also displayed good superimposition (Figure S4B-D). The effect of previous exposure to cisplatin was tested and the VPC demonstrated good superimposition of observed vs. predicted concentrations, suggesting that the final covariate model sufficiently described the data.

Population PKTD model of cisplatin-induced kidney injury

The Tmax for total platinum (Tcmax) was 0.993 ± 0.227 h, while the Tmax for urinary KIM-1 (Temax) was 101 ± 128 h (Figure 1D). Due to this delayed response, an indirect (turnover) or effect compartment (link) model were evaluated. To evaluate 

correlations between PK and TD parameters, total platinum PK parameter estimates were plotted against urinary KIM-1/UCr TD parameter estimates (Figure 3). Plasma total platinum Cmax levels were significantly correlated with urinary KIM-1/UCr Emax levels (p=0.010; Figure 3A). Plasma total platinum AUCc values were significantly correlated with urinary KIM-1/UCr AUCe values (p<0.0001; Figure 3B).

Figure 3 Total platinum PK parameters are correlated with urinary KIM-1 TD parameters. (A) – Plasma total platinum Cmax levels were significantly correlated with urinary KIM-1/UCr Emax levels (p=0.010, R2=0.178). (B) – Plasma total platinum AUCc values were significantly correlated with urinary KIM-1/UCr AUCe values (p<0.0001, R2=0.688). Ondansetron-randomized patients are represented in blue, granisetron-randomized patients are represented in red, and palonosetron- randomized patients are represented in green. PK: pharmacokinetic; TD: toxicodynamic; Pt: platinum; Cmax: maximum concentration; Emax: maximum effect; KIM- 1: kidney injury molecule-1; UCr: urinary creatinine; AUCc: area under the concentration versus time curve; AUCe: area under the concentration versus effect curve.

The population PKTD model is presented in Figure 4A. The estimates for the total platinum PK parameters were taken from the base model for plasma total platinum and frozen (Table 1). An effect compartment TD model with the FOCE-ELS engine was selected for urinary KIM-1/UCr levels based on diagnostic plots, minimum OFV (-2LL = 3729), and parameter estimations (Table 2). The best, final PKTD model (-2LL = 3720) was the parameter block model for EC50 and Ke0 and includes IIV estimates for all the TD parameters (Table 2). The final population PKTD model parameter estimates for plasma total platinum and urinary KIM- 1/UCr are presented in Table 2 and goodness of fit plots for urinary KIM-1/UCr are presented in Figure S5.

TD covariate model: A covariate search demonstrated that the model for urinary KIM-1/UCr included baseline urinary KIM- 1 normalized by UCr on Emax and serum albumin on Emax (-2LL = 3691). However, the CV% for the covariate effect parameters were large (>50%). Covariate plots are presented in Figures 4B and S6.

Each identified covariate was evaluated individually and combined iteratively to find the model with the lowest OFV with reasonable CV% estimates. The final model (-2LL = 3705) included baseline urinary KIM-1 on Emax, with a parameter block model for V2 and CL2, and IIV estimates for only V1, V2, CL2, and EC50. The parameter estimates for the final covariate model are presented in Table 2 and goodness of fit plots are presented in Figures 4C and S7. The goodness of fit plots for plasma total platinum PK covariate model were previously described (Figure 2C and S3). The final PKTD parameters were:

Table 2: Total platinum population PKTD model parameter estimates. 

Abbreviations: PKTD: pharmacokinetic/toxicodynamic; SD: standard deviation; CV%: coefficient of variation; V1: volume of central compartment; V2: volume of peripheral compartment; CL1: central clearance; CL2: intercompartmental clearance; Emax: maximum effect; EC50: half maximum effective concentration; Ke0: effect-site equilibrium rate constant; dCL1dsplit: effect of split dosing on CL1; dEmaxdKIM1: effect of baseline urinary KIM-1 normalized by UCr on Emax; -2LL: -2 log likelihood; N/A: not applicable; KIM-1: kidney injury molecule-1; UCr: urinary creatinine.

TD covariate effects: The covariate plots suggested that subjects with higher baseline urinary KIM-1/UCr concentrations had higher Emax values (p<0.0001; Figure 4B).

.

Figure 4 Population PKTD model of cisplatin-induced kidney injury. (A) – Effect compartment population PKTD model diagram of cisplatin-induced kidney injury. (B) – Covariate plot for the PKTD model. Emax was significantly influenced by baseline urinary KIM-1/UCr concentrations. Emax estimates from the base PKTD model (p<0.0001, R2=0.415). (C) – Individual predicted urinary KIM-1/UCr levels (lines) compared to observed data (red circles) as estimated by the final covariate model. PKTD: pharmacokinetic/toxicodynamic; Pt: platinum; Cmpt: compartment; V1: volume of central compartment of total Pt; V2: volume of peripheral compartment of total Pt; CL1: central clearance of total Pt; CL2: intercompartmental clearance of total Pt; Ke0: effect-site equilibrium rate constant; Emax: maximum effect; Ce: effect compartment concentration; EC50: half maximum effective concentration; KIM-1: kidney injury molecule-1; UCr: urinary creatinine. Figure 4A was made using BioRender.com.

TD model validation: Diagnostic plots for the final base model are shown in Figure S5 and diagnostic plots for the covariate model are shown in Figures 4C and S7. The diagnostic plots (Figure S1), covariate models (Figure 2C and S3), and VPC plots (Figure S4) for plasma total platinum were previously described. The individual predicted concentrations are well correlated with the observed urinary KIM-1/UCr data for both the final base and covariate models, suggesting good model performance.

The VPC evaluation for the urinary KIM-1/UCr PKTD model consisted of 200 replicates. The plots of the 5th, 50th, and 95th percentiles of observed urinary concentrations display good superimposition with the equivalent predicted concentration percentiles demonstrating that the final model sufficiently described the data, with the exception of an outlier skewing the observed 5th percentile at later time points (Figure S8).

PKTD model simulations: Using the final model, plasma total platinum and urinary KIM-1/UCr concentrations following standard cisplatin dose regimens were simulated. Plasma total platinum concentrations following a single 50 mg cisplatin dose, once weekly 50 mg cisplatin doses, and once daily 50 mg cisplatin doses were simulated and stratified based on the identified significant PK covariates: 1st or 2nd cisplatin dose and 5-HT3A antiemetic prescription (Figure 5). The single 50 mg dose led to an increase in plasma total platinum levels after a first dose vs. second dose of cisplatin in ondansetron- and granisetron- treated subjects, but a reduction in plasma total platinum levels after a first dose vs. second dose in palonosetron-treated subjects (Figure 5A). Ondansetron-treated subjects exhibited the highest plasma total platinum Cmax values in both the weekly and daily cisplatin dosing simulations (Figures 5B-C).

Figure 5 Simulated plasma total platinum (Pt) concentrations over time based on the final plasma total platinum covariate model. (A) – A single 50 mg cisplatin dose stratified based on 1st or 2nd cisplatin dose and 5-HT3A antiemetic prescription. (B) – 50 mg cisplatin dosed once per week for six weeks stratified based on 5-HT3A antiemetic prescription. (C) – 50 mg cisplatin dosed once per day for five days stratified based on 5-HT3A antiemetic prescription. Each simulation included 1000 simulated data points.

Urinary KIM-1/UCr concentrations following the standard cisplatin dose regimens from above were also simulated and stratified based on the identified significant TD covariate: baseline urinary KIM-1 concentrations (Figure 6). All three simulations demonstrated that subjects with high baseline urinary KIM-1 concentrations (>1.0 ng/mg) were predicted to exhibit the highest levels of urinary KIM-1 and subjects with low baseline urinary KIM-1 concentrations (<0.5 ng/mg) were predicted to exhibit the lowest levels of urinary KIM-1 following cisplatin treatment (Figure 6).

Figure 6 Simulated urinary KIM-1/UCr concentrations over time based on the final PKTD covariate model. (A) – A single 50 mg cisplatin dose stratified based on baseline urinary KIM-1/UCr (mean ± SD: 0.49 ± 0.51 ng/mg; low: <0.5 ng/ mg, average: 0.5-1 ng/mg, high: >1 ng/mg). (B) – 50 mg cisplatin dosed once per week for six weeks stratified based on baseline urinary KIM-1/UCr. (C) – 50 mg cisplatin dosed once per day for five days stratified based on baseline urinary KIM-1/UCr. Each simulation included 1000 simulated data points. PKTD: pharmacokinetic/toxicodynamic; KIM-1: kidney injury molecule-1; UCr: urinary creatinine; SD: standard deviation.

Comparison by 5-HT3A antiemetic drug

Population parameter estimates for plasma total platinum and urinary KIM-1/UCr grouped by 5-HT3A treatment group are presented in Table 3. Ondansetron-treated subjects had a 163% increase in plasma total platinum AUCc (p=0.036; Figure 7A) and a 116% increase in AUCc_d (p=0.042; Figure 7B) values compared to palonosetron-treated subjects. Ondansetron- treated subjects had a 94% increase in urinary KIM-1/UCr Emax levels compared to granisetron-treated subjects (p=0.047; Figure 7C). Ondansetron-treated subjects also had a 235% increase in urinary KIM-1/UCr AUCe (p=0.045; Figure 7D) and a 232% increase in AUCe_d (p=0.019; Figure 7E), compared to palonosetron-treated subjects. Differences in SCr, eGFR, and BUN over the course of the study were also assessed between the 5-HT3A treatment groups, but no significant differences were observed.

Figure 7 Total platinum PK and urinary KIM-1 TD parameters by 5-HT3A antiemetic. (A) – AUCc for plasma total platinum. (B) –AUCc_d for plasma total platinum. (C) – Emax of urinary KIM-1 normalized by UCr. (D) – AUCe for urinary KIM-1/UCr. (E) – AUCe_d for urinary KIM-1/UCr. Data represented as mean ± SD; *p<0.05. O: ondansetron; G: granisetron; P: palonosetron; PK: pharmacokinetic; TD: toxicodynamic; Pt: platinum; KIM-1: kidney injury molecule-1; AUCc: area under the concentration versus time curve; AUCc_d: AUCc normalized to a 100 mg cisplatin dose; Emax: maximum effect; UCr: urinary creatinine; AUCe: area under the concentration versus effect curve; AUCe_d: AUCe normalized to a 100 mg cisplatin dose; SD: standard deviation.

Table 3: Population PKTD parameter estimates for total platinum and urinary KIM- 1 by 5-HT3A antiemetic.

*p<0.05 compared to ondansetron; ^p<0.05 compared to granisetron.

Abbreviations: PKTD: pharmacokinetic/toxicodynamic; KIM-1: kidney injury molecule-1; SD: standard deviation; 5-HT3A: 5-HT3 antagonist; Ondan: ondansetron; Gran: granisetron; Palo: palonosetron; Pt: platinum; Cmax: maximum concentration; Cmax_d: Cmax normalized to a 100 mg cisplatin dose; Tcmax: time to maximum total platinum concentration; AUCc: area under the concentration versus time curve; AUCc_d: AUCc normalized to a 100 mg cisplatin dose; V1: volume of distribution of first compartment; V2: volume of distribution of second compartment; CL1: clearance of first compartment; CL2: clearance of second compartment; Emax: maximum effect; Emax_d: Emax normalized to a 100 mg cisplatin dose; Emax_fc: Emax fold change from baseline; Temax: time to maximum effect of urinary KIM-1/ UCr; AUCe: area under the effect versus time curve; AUCe_d: AUCe normalized to a 100 mg cisplatin dose; Emax_m: Emax estimated by PKTD covariate model; EC50: half maximum effective concentration; Ke0: effect-site equilibrium rate constant; UCr: urinary creatinine.

The current study led to the development of a population PKTD model to study the influence of covariates, including patient parameters and 5-HT3A co-treatment, on plasma total platinum concentrations and urinary KIM-1 biomarker changes following cisplatin treatment. Hence, this is the first reported PKTD model of cisplatin-induced nephrotoxicity that used a novel urinary biomarker of kidney injury (KIM-1) instead of a traditional clinical biomarker such as SCr, eGFR, or BUN. This approach enables the detection of injury earlier than clinical biomarkers and has enhanced sensitivity for the location of the injury along the nephron [29,33,34,46-54]. Urinary KIM- 1 has been well-studied in both animal and human cisplatin nephrotoxicity and served as the prototype biomarker for the current study [3,20,37,38,55]. The developed PKTD model performed well and was used to simulate PK and TD outcomes given common cisplatin dosing regimens in humans. Important PK (visit and 5-HT3A) and TD (baseline urinary KIM-1) covariates were identified. The Cmax and especially AUC of plasma total platinum correlated well with these respective parameters for KIM-1. The study supports the development of PKTD models that use the KIM-1 urinary biomarker and has the potential to be expanded to additional urinary biomarkers to best inform relationships between platinum PK and toxicity.

Population PK model of plasma total platinum

Plasma total platinum was best modeled by a two- compartment, additive error model with the FOCE-ELS model engine (-2LL = 3228), which is consistent with published models 

[56,57]. The model performed well with CV% under 20%, good diagnostic plots, and parameter estimates that were similar to those previously reported (Table 1) [56,57]. A published model that used a similar study design (n=43 subjects), but only 24 hours of blood collection, reported parameter estimates for V1, V2, CL1, and CL2 of 21.1 L, 42.6 L, 0.68 L/h, and 22.2 L/h, respectively [57]. The estimates were similar to the current model (Table 1), and the parameter CV% were somewhat improved over the published model [57].

Published PK models of cisplatin/platinum have identified the following significant covariate effects: increased CL with increased body surface area (BSA) [16,26,56-58], increased V with increased BSA and age [56-59], decreased CL with increased length of infusion [26], and decreased CL with reductions in CrCl [57,59]. BSA, age, and infusion duration were all tested in the covariate search in the current model along with other relevant covariates, but none of the three were associated with significant PK parameter effects.

The covariate search of the current PK model identified visit effect on V1 and 5-HT3A antiemetic effect on V2. The covariate plots suggested that patients receiving their first dose of cisplatin had significantly higher volume of the central compartment of total platinum (V1) values than patients receiving their second dose. As the volume of distribution of a drug is characterized by the dose administered and the resultant plasma concentration of the drug, subjects receiving their first dose of cisplatin had reduced plasma platinum concentrations compared to subjects receiving their second dose who still had residual platinum onboard from the first dose. Other PK models of cisplatin/ platinum have not described the effect of previous exposure to cisplatin as most have studied only one dose of cisplatin.

5-HT3A antiemetic co-treatments influenced V2. Ondansetron- treated subjects had a 20% decrease in V2 compared to granisetron-treated subjects and a 15% decrease in V2 compared to palonosetron-treated subjects. Inhibition of efflux transporters in the renal tubules generally leads to an increase in the volume of distribution [60]. The observed decrease in V2 may be explained by the fact that the central compartment volume is slightly increased in ondansetron-treated subjects, which may be playing a larger role and confounding the V2 results. No previous publications have evaluated the influence of 5-HT A co-treatment on platinum PK.

Population PKTD model of cisplatin-induced kidney injury

The published PKTD models of cisplatin-induced nephrotoxicity are limited by their use of traditional clinical biomarkers of kidney injury (e.g. SCr, eGFR, BUN, and CrCl) [21- 28]. This approach limits assessment to patients that exhibit overt kidney injury. There is a lack of cisplatin PKTD models that utilize sensitive and early urinary biomarkers as TD markers of renal toxicity. The advantage of using these novel urinary biomarkers is that they are more sensitive to structural kidney injury compared to traditional functional markers. Correlation analyses have assessed several novel urinary biomarkers (e.g. KIM-1, cystatin C, albumin, calbindin, clusterin, B2M, NGAL, and TFF3) and cisplatin PK parameters [3,14,15,19,20]. For example, the Cmax of total platinum in plasma and urine is correlated with urinary biomarker levels such as KIM-1 and TFF3 [14]. Studies have found that urinary KIM-1 levels increase after just one cisplatin dose and these increases precede increases in SCr [38,39,61]. Also, there have been discrete trends in biomarker changes observed after single and multiple exposure to cisplatin [3,19,20,35].

PKTD models of cisplatin nephrotoxicity typically have employed either a maximum effect (Emax) model or a signal transduction-indirect response model and used SCr, BUN, and/or CrCl as the TD outcome [21-23,25,26,28]. Multiple PKTD models of cisplatin-induced kidney injury in rats have used plasma creatinine or BUN as a marker of nephrotoxicity [21-25]. These studies reported that kidney, urine, and plasma platinum levels were correlated with cisplatin-induced nephrotoxicity [24], and maximum BUN levels were related to plasma concentrations of intact cisplatin (but not total platinum) [21], AUC of cisplatin in plasma [23], and AUC of cisplatin in the kidneys [21].

The current study assessed plasma total platinum and urinary KIM-1 to build a PKTD model of cisplatin-induced nephrotoxicity. Plasma total platinum Cmax and AUCc values were significantly correlated with urinary KIM-1/UCr Emax and AUCe values, respectively (Figure 3). This suggests that increased exposure to platinum resulted in increased kidney injury.

The delayed response (100 h) between plasma total platinum levels and urinary KIM-1/UCr concentrations suggested that an indirect or effect compartment model should be used for the population PKTD model. The PK parameter estimates were taken The final covariate PK model performed reasonably well with a decreased OFV (-2LL = 3215) compared to the base model, good diagnostic plots, but with one CV% over 20% (Table 1). The VPC for the total platinum covariate PK model adequately described the data with some scattering of data at the early time points (0 – 2 h). The observed variability may be a result of insufficient flushing of the infusion line when drawing blood samples. Although the infusion lines were flushed multiple times with saline before blood was collected, a possibility exists that residual cisplatin resulted in inaccurately measured platinum levels during the first hour of treatment.

from the plasma total platinum PK model and frozen. The TD modeling of urine KIM-1/UCr resulted in an effect compartment model using the FOCE-ELS model engine (-2LL = 3720). The model performed well with CV% under 20% and good diagnostic plots (Table 2). Effect compartment models represent a delay in drug response secondary to slow distribution of the drug to the site of action and/or a delay in the emergence of KIM-1 in urine. In an effect compartment model, the drug effect is driven by the drug concentration in the effect compartment. In this model, the effect compartment represents the kidneys as urinary KIM-1 is distinct to the proximal tubules of the kidney and the levels are a direct indicator of kidney injury. The small effect-site equilibrium rate constant (Ke0) value suggests that the effect compartment platinum concentration equilibrates slowly and hence, the subsequent effect is delayed relative to plasma concentrations [62].

The covariate search yielded a final covariate model that included baseline urinary KIM-1/UCr effect on the parameter Emax. As expected, subjects with higher baseline urinary KIM-1/ UCr levels have higher Emax values, which suggests that subjects with increased baseline kidney injury are predisposed to an increased risk of additional injury.

The final covariate model performed reasonably well with a decreased OFV (-2LL = 3670) compared to the final model without covariates, good diagnostic plots, and CV% around or less than 20% (Table 2). The VPC for the PKTD covariate model adequately described the data with the exception of an outlier skewing the observed 5th percentile at later time points.

Simulations of expected concentrations with common cisplatin doses

Several simulations of common cisplatin regimens were created using the final PKTD model. These included a single 50 mg dose, six weekly doses of 50 mg, and five daily doses of 50 mg. The single 50 mg dose simulation demonstrated increased predicted plasma total platinum concentrations after the second dose of cisplatin vs. after the first dose (Figure 5A). This is likely due to residual plasma platinum from the first dose. Granisetron- treated subjects exhibited lower plasma platinum levels compared to ondansetron- or palonosetron-treated subjects in the single 50 mg dose simulation (Figure 5A). Concomitant treatment with ondansetron predicted an increased peak plasma total platinum level in both the weekly and daily cisplatin dosing simulations compared to concomitant treatment with granisetron or palonosetron (Figure 5B-C). All three simulations demonstrated that increased baseline urinary KIM-1 concentrations were associated with increased levels of urinary KIM-1 following cisplatin treatment (Figure 6).

Comparison by 5-HT3A antiemetic drug

Ondansetron-treated subjects had a 163% increase in plasma total platinum AUCc and a 116% increase in dose-normalized AUCc (AUCc_d) compared to palonosetron-treated subjects. This suggests that ondansetron-treated subjects experience increased exposure to circulating total platinum compared to palonosetron-treated subjects. This increased exposure has the potential of causing increased risk of cisplatin toxicity secondary to the kidneys being a highly perfused body organ (receive 20- 25% of cardiac output) [63].

Ondansetron-treated subjects had a 94% increase in urinary KIM-1/UCr Emax levels compared to granisetron-treated subjects. Ondansetron-treated subjects also had a 235% increase in urinary KIM-1/UCr AUCe and a 232% increase in AUCe_d values compared to palonosetron-treated subjects. This suggests that ondansetron-treated subjects are predicted to exhibit the most kidney injury, as measured by urinary KIM-1, compared to the other treatment groups. These results are important as other studies have had difficulty in understanding the potential role of 5-HT3As in cisplatin nephrotoxicity given they were reliant on clinical measures such as SCr. The use of a sensitive and specific urinary biomarker such as KIM-1 enables the sensitive and early detection of subclinical injury. The current model demonstrates the utility of using a urinary biomarker to model a PKTD response of early kidney injury.

MATE1 is an efflux transporter located on the apical membrane of renal tubule cells that secretes cisplatin and glutathione conjugates into the urine [64]. The 5-HT3As have variable potency as MATE1 inhibitors [10-12], with ondansetron exhibiting the strongest inhibition of MATE1 (IC50 = 0.1 µM) by over ten-fold compared to granisetron (IC50 = 5.0 µM) or palonosetron (IC50 = 1.6 µM) using a prototypical cationic substrate, suggesting that ondansetron therapy results in enhanced intracellular cisplatin levels in the renal tubules, leading to an increased risk of nephrotoxicity (Figure 8) [12].

Increased exposure to platinum and increased kidney injury in ondansetron-treated subjects is likely a result of reduced renal secretion and enhanced accumulation of cisplatin in the renal proximal tubule cells due to inhibition of MATE1 by ondansetron (Figure 8) [64]. Previously published studies in both cancer patients and mice have shown that ondansetron can increase kidney and blood levels of cisplatin, increase the AUC of cisplatin, and enhance cisplatin-induced kidney injury [9-11]. Together, these results suggest that granisetron or palonosetron co-treatment may be preferred over ondansetron as the 5-HT3A antiemetic co-treatment as they were associated with less extensive cisplatin-induced kidney injury.

Figure 8 Proposed mechanism of altered cisplatin toxicity by 5-HT3As. Proposed mechanism for cisplatin toxicity by 5-HT3As due to preferential inhibition of MATE1 by ondansetron and subsequent reduced secretion of the highly electrophilic parent and aquated cisplatin species. GSH: glutathione; MATE1: multidrug and toxin extrusion protein 1; MRP2: multidrug resistance-associated protein 2; OCT2: organic cation transporter 2. Figure was made using BioRender.com.

A population PKTD approach was used to model cisplatin- induced nephrotoxicity using plasma total platinum levels and urinary KIM-1 concentrations. Significant covariates for the two- compartment PK model included a previous dose of cisplatin and 5-HT3A antiemetic treatment, and significant covariates for the effect compartment TD model included baseline urinary KIM-1 levels. The model suggested that ondansetron-treated subjects had increased exposures to platinum and increased kidney injury as measured by urinary KIM-1 compared to granisetron- and palonosetron-treated subjects. These findings suggest that antiemetic treatment with granisetron or palonosetron may be preferential over ondansetron to avoid an increased risk of nephrotoxicity in cisplatin-treated cancer patients.

This work was supported by the National Institutes of Diabetes and Digestive and Kidney Disorders [Grant R21 DK093903] and General Medical Sciences [Grant R01 GM123330] components of the National Institutes of Health (NIH). This work was additionally supported by the following institutions which are funded by the NIH: University of Colorado Cancer Center [Grant P30 CA046934], Colorado Clinical and Translational Sciences Institute [Grant UM1 TR004399], Memorial Sloan Kettering Cancer Center [Grant P30 CA008748], Rutgers University [Grant P30 ES005022], NJ Alliance for Clinical and Translational Science [Grant UL1 TR003017], and Cancer Institute of New Jersey [Grant P30 CA072720]. L. E. Thompson was supported by predoctoral fellowships from the NIH National Center for Advancing Translational Sciences/Colorado Clinical and Translational Sciences Institute [Grant TL1 TR002533] and NIH National Institute of Environmental Health Sciences [Grant T32 ES029074]. C. Kim was supported by a predoctoral fellowship from the NIH National Institute of Environmental Health Sciences [Grant T32 ES007148].

The authors would like to thank Jordan N. Palan (UCCC), Sarah Asby (UCCC), Justine Jorgensen (MSKCC), and Arianna Mohan (MSKCC) for their help with recruiting patients and collecting samples as Clinical Research Coordinators. The University of Colorado is a Certara Center of Excellence. The Center of Excellence program supports leading institutions with Certara’s state-of-the-art model-informed drug development software. Contents are the authors’ sole responsibility and do not necessarily represent official NIH views.

The authors declare no conflicts of interest. Lauren M. Aleksunes is a member of the scientific advisory board of RenaSym® Simulations Plus.

1. Dasari S, Tchounwou PB. Cisplatin in cancer therapy: molecular mechanisms of action. Eur J Pharmacol. 2014; 740: 364-378.
2. Akcay A, Turkmen K, Lee D, Edelstein CL. Update on the diagnosis and management of acute kidney injury. Int J Nephrol Renovasc Dis. 2010; 3: 129-140.
3. George B, Joy MS, Aleksunes LM. Urinary protein biomarkers of kidney injury in patients receiving cisplatin chemotherapy. Exp Biol Med (Maywood). 2018; 243: 272-282.
4. Einhorn LH. Curing metastatic testicular cancer. Proc Natl Acad Sci U S A. 2002; 99: 4592-4595.
5. Ozkok A, Edelstein CL. Pathophysiology of cisplatin-induced acute kidney injury. Biomed Res Int. 2014; 2014: 967826.
6. Ciarimboli G, Ludwig T, Lang D, Pavenstadt H, Koepsell H, Piechota HJ, et al. Cisplatin nephrotoxicity is critically mediated via the human organic cation transporter 2. Am J Pathol. 2005; 167: 1477-1484.
7. Kris MG, Gralla RJ, Clark RA, Tyson LB, O’Connell JP, Wertheim MS, et al. Incidence, course, and severity of delayed nausea and vomiting following the administration of high-dose cisplatin. J Clin Oncol. 1985; 3: 1379-1384.
8. Hesketh PJ, Kris MG, Basch E, Bohlke K, Barbour SY, Clark-Snow RA, et al. Antiemetics: American Society of Clinical Oncology Clinical Practice Guideline Update. J Clin Oncol. 2017; 35: 3240-3261.
9. Cagnoni PJ, Matthes S, Day TC, Bearman SI, Shpall EJ, Jones RB. Modification of the pharmacokinetics of high-dose cyclophosphamide and cisplatin by antiemetics. Bone Marrow Transplant. 1999; 24: 1-4.
10. Goda M, Kanda M, Yoshioka T, Yoshida A, Murai Y, Zamami Y, et al. Effects of 5-HT((3)) receptor antagonists on cisplatin-induced kidney injury. Clin Transl Sci. 2021; 14: 1906-1916.
11. Li Q, Guo D, Dong Z, Zhang W, Zhang L, Huang SM, et al. Ondansetron can enhance cisplatin-induced nephrotoxicity via inhibition of multiple toxin and extrusion proteins (MATEs). Toxicol Appl Pharmacol. 2013; 273: 100-109.
12. George B, Wen X, Jaimes EA, Joy MS, Aleksunes LM. In Vitro Inhibitionof Renal OCT2 and MATE1 Secretion by Antiemetic Drugs. Int J Mol Sci. 2021; 22.
13. Nagai N, Kinoshita M, Ogata H, Tsujino D, Wada Y, Someya K, et al. Relationship between pharmacokinetics of unchanged cisplatin and nephrotoxicity after intravenous infusions of cisplatin to cancer patients. Cancer Chemother Pharmacol. 1996; 39: 131-137.
14. Ibrahim ME, Chang C, Hu Y, Hogan SL, Mercke N, Gomez M, et al. Pharmacokinetic determinants of cisplatin-induced subclinical kidney injury in oncology patients. Eur J Clin Pharmacol. 2019; 75: 51-57.
15. Erdlenbruch B, Nier M, Kern W, Hiddemann W, Pekrun A, LakomekM. Pharmacokinetics of cisplatin and relation to nephrotoxicity in paediatric patients. Eur J Clin Pharmacol. 2001; 57: 393-402.
16. Morita-Ogawa T, Sugita H, Minami H, Yamaguchi T, Hanada K. Population pharmacokinetics and renal toxicity of cisplatin in cancer patients with renal dysfunction. Cancer Chemother Pharmacol. 2020; 86: 559-566.
17. Reece PA, Stafford I, Russell J, Khan M, Gill PG. Creatinine clearance as a predictor of ultrafilterable platinum disposition in cancer patients treated with cisplatin: relationship between peak ultrafilterable platinum plasma levels and nephrotoxicity. J Clin Oncol. 1987; 5: 304-309.
18. Campbell AB, Kalman SM, Jacobs C. Plasma platinum levels: relationship to cisplatin dose and nephrotoxicity. Cancer Treat Rep. 1983; 67: 169-172.
19. George B, Wen X, Mercke N, Gomez M, O’Bryant C, Bowles DW, et al. Profiling of Kidney Injury Biomarkers in Patients Receiving Cisplatin: Time-dependent Changes in the Absence of Clinical Nephrotoxicity. Clin Pharmacol Ther. 2017; 101: 510-518.
20. George B, Wen X, Mercke N, Gomez M, O’Bryant C, Bowles DW, et al. Time-dependent changes in kidney injury biomarkers in patients receiving multiple cycles of cisplatin chemotherapy. Toxicol Rep. 2020; 7: 571-576.
21. Hanada K, Ninomiya K, Ogata H. Pharmacokinetics and toxicodynamics of cisplatin and its metabolites in rats: relationship between renal handling and nephrotoxicity of cisplatin. J Pharm Pharmacol. 2000; 52: 1345-1353.
22. Hanada K, Mukasa Y, Nomizo Y, Ogata H. Effect of buthionine sulphoximine, glutathione and methimazole on the renal disposition of cisplatin and on cisplatin-induced nephrotoxicity in rats: pharmacokinetic-toxicodynamic analysis. J Pharm Pharmacol. 2000; 52: 1483-1490.
23. Hanada K, Asano K, Nishimura T, Chimata T, Matsuo Y, Tsuchiya M, et al. Use of a toxicity factor to explain differences in nephrotoxicity and myelosuppression among the platinum antitumour derivatives cisplatin, carboplatin and nedaplatin in rats. J Pharm Pharmacol. 2008; 60: 317-322.
24. Fukushima K, Okada A, Oe H, Hirasaki M, Hamori M, Nishimura A, et al. Pharmacokinetic-Pharmacodynamic Analysis of Cisplatin with Hydration and Mannitol Diuresis: The Contribution of Urine Cisplatin Concentration to Nephrotoxicity. Eur J Drug Metab Pharmacokinet. 2018; 43: 193-203.
25. Fukushima K, Okada A, Sasaki K, Kishimoto S, Fukushima S, Hamori M, et al. Population Pharmacokinetic-Toxicodynamic Modeling and Simulation of Cisplatin-Induced Acute Renal Injury in Rats: Effect of Dosing Rate on Nephrotoxicity. J Pharm Sci. 2016; 105: 324-332.
26. Nagai N, Ogata H, Wada Y, Tsujino D, Someya K, Ohno T, et al. Population pharmacokinetics and pharmacodynamics of cisplatin in patients with cancer: analysis with the NONMEM program. J Clin Pharmacol. 1998; 38: 1025-1034.
27. Xie F, Van Bocxlaer J, Colin P, Carlier C, Van Kerschaver O, Weerts J, et al. PKPD Modeling and Dosing Considerations in Advanced Ovarian Cancer Patients Treated with Cisplatin-Based Intraoperative Intraperitoneal Chemotherapy. AAPS J. 2020; 22: 96.
28. Nagai N, Ogata H. Quantitative relationship between pharmacokinetics of unchanged cisplatin and nephrotoxicity in rats: importance of area under the concentration-time curve (AUC) as the major toxicodynamic determinant in vivo. Cancer Chemother Pharmacol. 1997; 40: 11-18.
29. Ostermann M, Zarbock A, Goldstein S, Kashani K, Macedo E, Murugan R, et al. Recommendations on Acute Kidney Injury Biomarkers From the Acute Disease Quality Initiative Consensus Conference: A Consensus Statement. JAMA Network Open. 2020; 3: e2019209-e2019209.
30. Allinovi M, Sessa F, Villa G, Cocci A, Innocenti S, Zanazzi M, et al. Novel Biomarkers for Early Detection of Acute Kidney Injury and Prediction of Long-Term Kidney Function Decline after Partial Nephrectomy. Biomedicines. 2023; 11.
31. Dieterle F, Perentes E, Cordier A, Roth DR, Verdes P, Grenet O, et al. Urinary clusterin, cystatin C, beta2-microglobulin and total protein as markers to detect drug-induced kidney injury. Nat Biotechnol. 2010; 28: 463-469.
32. Dieterle F, Sistare F, Goodsaid F, Papaluca M, Ozer JS, Webb CP, et al. Renal biomarker qualification submission: a dialog between the FDA- EMEA and Predictive Safety Testing Consortium. Nat Biotechnol. 2010; 28: 455-462.
33. Vaidya VS, Ozer JS, Dieterle F, Collings FB, Ramirez V, Troth S, et al. Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies. Nat Biotechnol. 2010; 28: 478-485.
34. Griffin BR, Faubel S, Edelstein CL. Biomarkers of Drug-Induced Kidney Toxicity. Ther Drug Monit. 2019; 41: 213-226.
35. Chang C, Hu Y, Hogan SL, Mercke N, Gomez M, O’Bryant C, et al. Pharmacogenomic Variants May Influence the Urinary Excretion of Novel Kidney Injury Biomarkers in Patients Receiving Cisplatin. Int J Mol Sci. 2017; 18.
36. Bonventre JV. Kidney Injury Molecule-1 (KIM-1): a specific and sensitive biomarker of kidney injury. Scand J Clin Lab Invest Suppl. 2008; 241: 78-83.
37. McMahon KR, Chui H, Rassekh SR, Schultz KR, Blydt-Hansen TD, Mammen C, et al. Urine Neutrophil Gelatinase-Associated Lipocalin and Kidney Injury Molecule-1 to Detect Pediatric Cisplatin-Associated Acute Kidney Injury. Kidney360. 2022; 3: 37-50.
38. Tanase DM, Gosav EM, Radu S, Costea CF, Ciocoiu M, Carauleanu A, et al. The Predictive Role of the Biomarker Kidney Molecule-1 (KIM-1) in Acute Kidney Injury (AKI) Cisplatin-Induced Nephrotoxicity. Int J Mol Sci. 2019; 20.
39. Tekce BK, Uyeturk U, Tekce H, Uyeturk U, Aktas G, Akkaya A. Does the kidney injury molecule-1 predict cisplatin-induced kidney injury in early stage? Ann Clin Biochem. 2015; 52: 88-94.
40. NCI. NCI: Nausea and Vomiting. NCI. 2016.
41. NCI. NCCN: Antiemesis. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). NCI. 2014.
42. Hesketh PJ, Bohlke K, Lyman GH, Basch E, Chesney M, Clark-Snow RA, et al. Antiemetics: American Society of Clinical Oncology Focused Guideline Update. J Clin Oncol. 2016; 34: 381-386.
43. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N Engl J Med. 2021; 385: 1737-1749.
44. Delgado C, Baweja M, Crews DC, Eneanya ND, Gadegbeku CA, Inker LA, et al. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. J Am Soc Nephrol. 2021.
45. Waikar SS, Sabbisetti VS, Bonventre JV. Normalization of urinary biomarkers to creatinine during changes in glomerular filtration rate. Kidney Int. 2010; 78: 486-494.
46. Vaidya VS, Ferguson MA, Bonventre JV. Biomarkers of acute kidney injury. Annu Rev Pharmacol Toxicol. 2008; 48: 463-493.
47. Vaidya VS, Waikar SS, Ferguson MA, Collings FB, Sunderland K, Gioules C, et al. Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans. Clin Transl Sci. 2008; 1: 200-208.
48. Bufkin KB, Karim ZA, Silva J. Review of the limitations of current biomarkers in acute kidney injury clinical practices. SAGE Open Med. 2024; 12: 20503121241228446.
49. Devarajan P. Emerging urinary biomarkers in the diagnosis of acute kidney injury. Expert Opin Med Diagn. 2008; 2: 387-398.
50. Fuchs TC, Hewitt P. Biomarkers for drug-induced renal damage and nephrotoxicity-an overview for applied toxicology. AAPS J. 2011; 13: 615-631.
51. Rizvi MS, Kashani KB. Biomarkers for Early Detection of Acute Kidney Injury. J Appl Lab Med. 2017; 2: 386-399.
52. van Meer L, Moerland M, Cohen AF, Burggraaf J. Urinary kidney biomarkers for early detection of nephrotoxicity in clinical drug development. Br J Clin Pharmacol. 2014; 77: 947-957.
53. Vanmassenhove J, Vanholder R, Nagler E, Van Biesen W. Urinary and serum biomarkers for the diagnosis of acute kidney injury: an in-depth review of the literature. Nephrol Dial Transplant. 2013; 28: 254-273.
54. Wen Y, Parikh CR. Current concepts and advances in biomarkers of acute kidney injury. Crit Rev Clin Lab Sci. 2021; 58: 354-368.
55. Shinke H, Masuda S, Togashi Y, Ikemi Y, Ozawa A, Sato T, et al. Urinary kidney injury molecule-1 and monocyte chemotactic protein-1 are noninvasive biomarkers of cisplatin-induced nephrotoxicity in lung  cancer patients. Cancer Chemother Pharmacol. 2015; 76: 989-996.
56. Hanada K, Nishijima K, Ogata H, Atagi S, Kawahara M. Population pharmacokinetic analysis of cisplatin and its metabolites in cancer patients: possible misinterpretation of covariates for pharmacokinetic parameters calculated from the concentrations of unchanged cisplatin, ultrafiltered platinum and total platinum. Jpn J Clin Oncol. 2001; 31: 179-184.
57. Urien S, Lokiec F. Population pharmacokinetics of total and unbound plasma cisplatin in adult patients. Br J Clin Pharmacol. 2004; 57: 756- 763.
58. de Jongh FE, Gallo JM, Shen M, Verweij J, Sparreboom A. Population pharmacokinetics of cisplatin in adult cancer patients. Cancer Chemother Pharmacol. 2004; 54: 105-112.
59. Kaushik HK, Kumar VS, Kumar SB, Reddy NY, Raghavaiah VK, Devarakonda KR. Population pharmacokinetics of cisplatin in Asian Indian cancer patients. Clinical Research and Regulatory Affairs. 2009; 26: 84-92.
60. Grover A, Benet LZ. Effects of drug transporters on volume ofdistribution. AAPS J. 2009; 11: 250-261.
61. Bonventre JV, Vaidya VS, Schmouder R, Feig P, Dieterle F. Next- generation biomarkers for detecting kidney toxicity. Nat Biotechnol. 2010; 28: 436-440.
62. Upton RN, Mould DR. Basic concepts in population modeling, simulation, and model-based drug development: part 3-introduction to pharmacodynamic modeling methods. CPT Pharmacometrics Syst Pharmacol. 2014; 3: e88.
63. Kaufman DP, Basit H, Knohl SJ. Physiology, Glomerular Filtration Rate. StatPearls. Treasure Island (FL). 2024.
64. Nakamura T, Yonezawa A, Hashimoto S, Katsura T, Inui K. Disruption of multidrug and toxin extrusion MATE1 potentiates cisplatin- induced nephrotoxicity. Biochem Pharmacol. 2010; 80: 1762-1767.
65. FDA. Clinical Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions. FDA; 2020.
66. FDA. Zofran Prescribing Information. FDA. 2016.