Search

Daptomycin, an anti‐methicillin‐resistant Staphylococcus aureus drug, causes exposure‐dependent muscle toxicity and eosinophilic pneumonia. Although the area under the concentration‐time curve (AUC)‐guided dosing is crucial, an optimal blood sampling strategy is lacking. This study aimed to identify an optimal limited sampling strategy using Bayesian forecasting to rapidly achieve the target AUC. Two validated population pharmacokinetic models generated a virtual population of 1000 individuals (models 1 and 2 represent diverse patients and kidney transplant recipients, respectively). The AUC for each blood sample was assessed using the probability of achieving the estimated/reference AUC ratio on the second day (AUC24–48) and at the steady state (AUCss). In Model 1, Bayesian posterior probabilities for AUC24–48 increased from 50.7% (a priori) to 59.4% and for AUCss from 48.9% (a priori) to 61.9%, with one‐point Ctrough sampling at 24 h. With two‐point sampling at 7 and 24 h, the probabilities increased to 73.8% for AUC24–48 and 69.7% for AUCss. In Model 2, the probabilities for both AUC24–48 and AUCss with one‐point Ctrough or two‐point sampling incorporating Ctrough sampling increased compared to a priori probabilities. These results suggest that two‐point sampling incorporating Ctrough during initial dosing enhanced achieving the target AUC24–48 and AUCss rapidly. [ABSTRACT FROM AUTHOR]

Purpose: S-warfarin is used to phenotype cytochrome P450 (CYP) 2C9 activity. This study evaluated S-warfarin limited sampling strategy with a population pharmacokinetic (PK) approach to estimate CYP2C9 activity in healthy adults. Methods: In 6 previously published studies, a single oral dose of warfarin 10 mg was administered alone or with a CYP2C9 inducer to 100 healthy adults. S-warfarin concentrations were obtained from adults during conditions when subjects were not on any prescribed medications. A population PK model was developed using non-linear mixed effects modeling. Limited sampling models (LSMs) using single- or 2-timepoint concentrations were compared with full PK profiles from intense sampling using empiric Bayesian post hoc estimations of S-warfarin AUC derived from the population PK model. Preset criterion for LSM selection and validation were a correlation coefficient (R2) >0.9, relative percent mean prediction error (%MPE) >−5 to <5%, relative percent mean absolute error (%MAE) ≤ 10%, and relative percent root mean squared error (%RMSE) ≤ 15%. Results: S-warfarin concentrations (n=2540) were well described with a two-compartment model. Mean apparent oral clearance was 0.56 L/hr and volume of distribution was 35.5 L. Clearance decreased 33% with the CYP2C9 *3 allele and increased 42% with lopinavir/ritonavir co-administration. During CYP2C9 constitutive conditions, LSMs at 48 hr and at 72 hr as well as 2-timepoint LSMs were within acceptable limits for R2, %MPE, %MAE, and %RMSE. During CYP2C9 induction, S-warfarin LSMs had unacceptable %MPE, %MAE, and %RMSE. Conclusions: Phenotyping studies with S-warfarin in healthy subjects can utilize a single- and/or a 2-timepoint LSM with a population PK approach to estimate constitutive CYP2C9 activity. [ABSTRACT FROM AUTHOR]

Irinotecan (CPT-11) is an anticancer agent widely used in the treatment of a variety of adult solid tumors. The objective of this study was to develop an optimal sampling strategy model that accurately estimates pharmacokinetic parameters of CPT-11 and its active metabolite, SN-38. This study included 221 patients with advanced solid tumors or lymphoma receiving CPT-11 single or combination therapy with 5-fluorouracil (5-FU)/leucovorin (LV) (FOLFIRI) plus bevacizumab from 4 separate clinical trials. Population pharmacokinetic analysis of CPT-11 and SN-38 was performed by non-linear mixed effects modeling. The optimal sampling strategy model was developed using D-optimality with expected distribution approach. The pharmacokinetic profiles of CPT-11 and SN-38 were best described by a 3- and 2-compartment model, respectively, with first-order elimination. Body surface area and co-administration with 5-FU/LV plus bevacizumab were significant covariates (p < 0.01) for volumes of the central compartment of CPT-11 and SN-38, and clearance of CPT-11. Pre-treatment total bilirubin and co-administration with 5-FU/LV and bevacizumab were significant covariates (p < 0.01) for clearance of SN-38. Accurate and precise predictive performance (r2 > 0.99, -2 < bias (%ME) < 0, precision (% RMSE) < 12) of both CPT-11 and SN-38 was achieved using: (i) 6 fixed sampling times collected at 1.5, 3.5, 4, 5.75, 22, 23.5 hours post-infusion; or (ii) 1 fixed time and 2 sampling windows collected at 1.5, [3-5.75], [22-23.5] hours post-infusion. The present study demonstrates that an optimal sampling design with three blood samples achieves accurate and precise pharmacokinetic parameter estimates for both CPT-11 and SN-38. [ABSTRACT FROM AUTHOR]

Purpose: Pemetrexed is a widely used cytostatic agent with an established exposure-response relationship. Although dosing is based on body surface area (BSA), large interindividual variability in pemetrexed plasma concentrations is observed. Therapeutic drug monitoring (TDM) can be a feasible strategy to reduce variability in specific cases leading to potentially optimized pemetrexed treatment. The aim of this study was to develop a limited sampling schedule (LSS) for the assessment of pemetrexed pharmacokinetics.Methods: Based on two real-life datasets, several limited sampling designs were evaluated on predicting clearance, using NONMEM, based on mean prediction error (MPE %) and normalized root mean squared error (NRMSE %). The predefined criteria for an acceptable LSS were: a maximum of four sampling time points within 8 h with an MPE and NRMSE ≤ 20%.Results: For an accurate estimation of clearance, only four samples in a convenient window of 8 h were required for accurate and precise prediction (MPE and NRMSE of 3.6% and 5.7% for dataset 1 and of 15.5% and 16.5% for dataset 2). A single sample at t = 24 h performed also within the criteria with MPE and NRMSE of 5.8% and 8.7% for dataset 1 and of 11.5% and 16.4% for dataset 2. Bias increased when patients had lower creatinine clearance.Conclusions: We presented two limited sampling designs for estimation of pemetrexed pharmacokinetics. Either one can be used based on preference and feasibility. [ABSTRACT FROM AUTHOR]

Background: Cytochrome P450 3A is the most relevant drug-metabolizing enzyme in humans as it is involved in the elimination of 50% of marketed drugs. Nothing is known about the activity of cytochrome P450 3A in palliative care patients who have complicated symptoms often associated with a terminal illness. Aim: In order to improve drug dosing in end-of-life care and to avoid drug interactions, cytochrome P450 3A activity was determined in patients of a palliative care unit under real-life clinical conditions. Design: As midazolam is an established marker substance for cytochrome P450 3A activity, this single-arm prospective trial was designed to obtain a 4-h pharmacokinetic profile of midazolam after oral administration of a 10-µg dose from each enrolled patient. Plasma concentrations of midazolam and its primary metabolite 1′-hydroxy-midazolam were quantified by mass spectrometry techniques. Cytochrome P450 3A activity was calculated as partial metabolic clearance from a limited sampling area under the curve. All other drugs taken by the participating patients were considered, as well as recent blood test results and patients' diagnoses. The trial was registered at German Clinical Trials Register (www.drks.de): DRKS00011753. Setting/participants: The trial was carried out at a university palliative care unit under real-life clinical conditions. Every patient admitted to the ward was screened for possible participation, independent of the individual performance status. Results: Partial metabolic clearance of midazolam in palliative care patients was 31.7 ± 32.1 L/h. This was a highly significant 40% reduction (p < 0.0001) in comparison with the cytochrome P450 3A activity of healthy subjects. Conclusion: Dosing of cytochrome P450 3A substrate drugs (e.g. macrolide antibiotics, benzodiazepines, calcium channel blockers) needs to be adjusted in palliative care patients; otherwise, escalation of debilitating symptoms due to drug interactions might occur. [ABSTRACT FROM AUTHOR]

Aims: Tacrolimus has been associated with notable extrarenal adverse effects (AEs), which are unpredictable and impact patient morbidity. The association between model‐predicted tacrolimus exposure metrics and standardized extrarenal AEs in stable renal transplant recipients was investigated and a limited sampling strategy (LSS) was developed to predict steady‐state tacrolimus area under the curve over a 12‐h dosing period (AUCss,0–12h). Methods: All recipients receiving tacrolimus and mycophenolic acid ≥6 months completed a 12‐h cross‐sectional observational pharmacokinetic–pharmacodynamic study. Patients were evaluated for the presence of individual and composite gastrointestinal, neurological, and aesthetic AEs during the study visit. The associations between AEs and tacrolimus exposure metrics generated from a published population pharmacokinetic model were investigated using a logistic regression analysis in NONMEM 7.3. An LSS was determined using a Bayesian estimation method with the same patients. Results: Dose‐normalized tacrolimus AUCss,0–12h and apparent clearance were independently associated with diarrhoea, dyspepsia, insomnia and neurological AE ratio. Dose‐normalized tacrolimus maximum concentration was significantly correlated with skin changes and acne. No AE associations were found with trough concentrations. Using limited sampling at 0, 2h; 0, 1, 4h; and 0, 1, 2, 4h provided a precise and unbiased prediction of tacrolimus AUC (root mean squared prediction error < 10%), which was not well characterized using trough concentrations only (root mean squared prediction error >15%). Conclusions: Several AEs (i.e. diarrhoea, dyspepsia, insomnia and neurological AE ratio) were associated with tacrolimus dose normalized AUCss,0–12h and clearance. Skin changes and acne were associated with dose‐normalized maximum concentrations. To facilitate clinical implementation, a LSS was developed to predict AUCss,0–12h values using sparse patient data to efficiently assess projected immunosuppressive exposure and potentially minimize AE manifestations. [ABSTRACT FROM AUTHOR]

The aim of this study was to establish a limited sample strategy (LSS) to predict the mycophenolic acid (MPA) area under the curve (AUC)(0-12) in children with systemic lupus erythematosus (SLE). Three months after initiation of mycophenolate mofetil (MMF) 26 children with SLE presented for therapeutic drug monitoring of MPA. On the day of the test, 10 specimens were collected, analyzed, and MPA AUC(0-12) was calculated. Using step-wise regression analysis, LSS equations were developed. Using bootstrap validation, the predictive performance was calculated. The measured mean (standard deviation) for the trough concentration and AUC(0-12) were 2.55 (1.57) µg/ml and 62.6 (21.67) mg.h/L, respectively. The range of trough concentrations and AUC(0-12) were 0.7-5.54 µg/ml and 22.1-104.8 mg.h/L, respectively. The interindividual variability (%CV) for dose normalized AUC(0-12) and dose normalized Ctrough was 46.5% and 61.1%, respectively. The correlation between the concentrations at the different time points and MPAAUC(0-12) ranged from 0.05 (1.5 h) to 0.56 (4 h). Two LSS equations that included 4 or 5 time points up to 3 h were developed and validated. The 4 point LSS had a correlation (R2) of 0.88 and the 5 point LSS an R² of 0.87. With respect to the 4 point and 5 point MPALSS AUC(0-12), the bias was 1.92% and 1.96%, respectively, and the imprecision was 11.24% and 11.28%, respectively. A4 point LSS which concludes within 3 h after the administration of the MMF dose was developed and validated, to determine the MPA AUC(0-12) in children with SLE. [ABSTRACT FROM AUTHOR]

Amikacin and kanamycin are considered important and effective drugs in the treatment of multidrug-resistant tuberculosis (MDR-TB). Unfortunately, the incidence of toxicity is high and is related to elevated drug exposure. In order to achieve a balance between efficacy and toxicity, a population pharmacokinetic (PPK) model may help to optimise drug exposure. Patients with MDR-TB who had received amikacin or kanamycin as part of their treatment and who had routinely received therapeutic drug monitoring were evaluated. A PPK model was developed and subsequently validated. Using this model, a limited sampling model was developed. Eleven patients receiving amikacin and nine patients receiving kanamycin were included in this study. The median observed 24-h area under the concentration–time curve (AUC 0–24h) was 77.2 mg h/L [interquartile range (IQR) 64.7–96.2 mg h/L] for amikacin and 64.1 mg h/L (IQR 55.6–92.1 mg h/L) for kanamycin. The PPK model was developed and validated using n −1 cross-validation. A robust population model was developed that is suitable for predicting the AUC 0–24h of amikacin and kanamycin. This model, in combination with the limited sampling strategy developed, can be used in daily routine to guide dosing but also to assess AUC 0–24h in phase 3 studies. [ABSTRACT FROM AUTHOR]

Understanding the relationship between drug dose and exposure (pharmacokinetics, PK) and the relationship between exposure and effect (pharmacodynamics) is an important component of pharmacology when attempting to predict clinical effects of anticancer drugs. PK studies can provide a better understanding of these relationships; however, they often involve intensive sampling over an extended period of time, resulting in increased cost and decreased compliance. Doxorubicin ( DOX), one of the most widely used antineoplastic agents in veterinary cancer therapy, is characterized by large interpatient variability in overall drug exposure and the development and degree of myelosuppression following equivalent dosages. We have developed and validated a limited-sampling strategy for DOX, in which three blood samples are obtained over 1 h post-treatment, that accurately predicts patient exposure. This strategy could allow for refining of dosing variables and utilization of therapeutic drug monitoring to ensure optimized dosing. [ABSTRACT FROM AUTHOR]

Abstract: This paper illustrates the control strategies of an Electric Arc Furnace. It involves the prediction of the control action which aids in reduction of carbon, manganese and other impurities from the in-process molten steel. Predictive models using Artificial Neural Networks (ANN) with Bayesian Regularization and Adaptive Neuro Fuzzy Inference System (ANFIS) were developed. The control action is the amount of oxygen to be lanced at different sampling instants. The predictive models were constructed based on the values of the individual chemical constituents of the collected molten samples. Two control strategies were devised: one with full sampling and the other with limited or reduced sampling. For the full sampling case two predictive models were devised separately with ANN with Bayesian Regularization and ANFIS. For the limited sampling strategy a combination of ANN with Bayesian Regularization and ANFIS were employed. For full sampling strategy, ANFIS model performs better than ANN. The application of the limited sampling strategy gave satisfactory Mean Percentage Error (MPE) thereby justifying its practical implementation. The main advantage of reduced or limited sampling is that it helps in the reduction of cost, time and manpower associated the sample collection and its subsequent analysis. [Copyright &y& Elsevier]

The Hsp90-directed anticancer agent 17-(allylamino)-17-demethoxygeldanamycin (17-AAG) is currently undergoing phase I and phase II clinical investigation. Our goal was to develop a simple limited sampling model (LSM) for AUC of 17-AAG and its active metabolite, 17-(amino)-17-demethoxygeldanomycin (17-AG) using drug concentrations from a few time points. Pharmacokinetic data from 34 patients treated at 11 dose levels on a Mayo Clinic Cancer Center phase I clinical trial of 17-AAG was utilized. Blood samples were collected at 11 different time points, spanning 25 h. Graphical methods and correlations were used to assess functional forms and univariate relationships. Multivariate linear regression and bootstrap resampling were used to develop the LSM. Using log-transformed data, the two and three time point 17-AAG LSMs are log-AUC (17-AAG) = 0.869 + 0.653*(C55min) +0.469*(C5h) and log-AUC (17-AAG) = 2.449 + 0.400*(C55min) +0.441*(C5h) +0.142*(C9h). The two and three time point LSMs for 17-AG are log-AUC (17-AG) = 3.590 + 0.747*(C5h) +0.169*(C17h), and log-AUC (17-AG) = 3.797 + 0.650*(C5h) +0.111*(C9h) +0.122*(C17h). Ninety-seven percent and 94% of the predicted log-AUC values were within 5% of the observed log-AUC for the two and three time point models for 17-AAG and 17-AG respectively. The precise calculation of AUC is cumbersome and expensive in terms of patient and clinical resources. The LSM developed using a multivariate regression approach is clinically and statistically meaningful. Prospective validation is underway. [ABSTRACT FROM AUTHOR]

We discuss a method for combining different but related longitudinal studies to improve predictive precision. The motivation is to borrow strength across clinical studies in which the same measurements are collected at different frequencies. Key features of the data are heterogeneous populations and an unbalanced design across three studies of interest. The first two studies are phase I studies with very detailed observations on a relatively small number of patients. The third study is a large phase III study with over 1500 enrolled patients, but with relatively few measurements on each patient. Patients receive different doses of several drugs in the studies, with the phase III study containing significantly less toxic treatments. Thus, the main challenges for the analysis are to accommodate heterogeneous population distributions and to formalize borrowing strength across the studies and across the various treatment levels. We describe a hierarchical extension over suitable semiparametric longitudinal data models to achieve the inferential goal. A nonparametric random-effects model accommodates the heterogeneity of the population of patients. A hierarchical extension allows borrowing strength across different studies and different levels of treatment by introducing dependence across these nonparametric random-effects distributions. Dependence is introduced by building an analysis of variance (ANOVA) like structure over the random-effects distributions for different studies and treatment combinations. Model structure and parameter interpretation are similar to standard ANOVA models. Instead of the unknown normal means as in standard ANOVA models, however, the basic objects of inference are random distributions, namely the unknown population distributions under each study. The analysis is based on a mixture of Dirichlet processes model as the underlying semiparametric model. [ABSTRACT FROM AUTHOR]

To develop a limited sampling strategy for estimation of epirubicin clearance. The data set comprised 1051 concentrations measured in 105 patients with advanced or metastatic breast cancer treated with epirubicin alone. Ten limited sampling designs comprising two or three blood samples were proposed, taken at times identified by D-optimality from population pharmacokinetic parameter estimates. The data set was then truncated to include the sampling times for each of the designs. MAP Bayesian estimates of clearance were generated for each design and compared with clearance estimates obtained using all the data. The limited sampling designs were also validated using a separate data set obtained from 18 patients with either breast cancer or hepatocellular carcinoma. The sensitivity of the best limited sampling designs to sample time recording errors of 0–10% or 10–20% was then assessed using a simulated data set including 200 patients. The optimum sampling times were: end of the injection and 18 min, 40 min, 3 h, 10 h and 48 h after the start of the injection. The best three-sample design included samples at 40 min, 3 h and 48 h and gave unbiased estimates of clearance with an imprecision of 9.1% (95% CI 7.3, 10.5). The best two sample design included samples at 3 and 48 h and gave unbiased estimates of clearance with an imprecision of 12.4% (95% CI 9.6, 14.6). Using the validation data set, these two and three sample designs gave unbiased estimates of clearance with an imprecision of 5.6% (95% CI 3.7, 7.0) and 4.2% (95% CI 2.6, 5.3), respectively. Simulations that included 0–10% or 10–20% errors in the recording of the blood sampling times had negligible effects on the bias and imprecision of clearance estimates. Limited sampling designs have been identified and validated that estimate epirubicin clearance with adequate precision and without bias from two or three blood samples. These designs also allow flexibility in blood sample collection and are robust with regard to sample time recording errors. [ABSTRACT FROM AUTHOR]

Aims E7070 is a novel, sulphonamide anticancer agent currently under clinical development for the treatment of solid tumours. The aim of this study was to develop and validate limited sampling strategies for the prediction of E7070 exposure in two different treatment schedules for phase II studies using the Bayesian estimation approach. Methods Data from two phase I dose finding studies were used in which E7070 was administered either as a single 1 h infusion or as a daily 1 h infusion for 5 days. Plasma concentration-time data from 75 patients were randomly divided into an index data set, used for the development of the strategies, and a validation data set. Population pharmacokinetic parameters were derived on the basis of the index data set. The D-optimality algorithm was used for the selection of optimal time points for both treatment schedules. The developed strategies were compared by assessment of their predictive performance of exposure, expressed as AUC (area under the plasma concentration vs time curve), in the validation data set. Results The developed population pharmacokinetic model comprised three compartments, with saturable distribution to one peripheral compartment and both linear and saturable elimination from the central compartment. For the 1 h infusion, a four sample strategy was selected which resulted in unbiased and accurate predictions of AUC (bias 0.74%, precision 13%). A five sample strategy was generated for the daily times five schedule yielding unbiased (bias 3.2%) and precise (12% precision) predictions of AUC. Conclusions Optimal sampling strategies were developed and validated for estimation of E7070 exposure in two different treatment schedules. Both schedules enabled accurate and unbiased predictions of AUC. [ABSTRACT FROM AUTHOR]

Purpose: Ecteinascidin 743 (ET-743) is a novel, marine-derived anticancer agent currently under clinical development for the treatment of solid tumors. The aim of this study was to develop and validate limited sampling strategies for the prediction of ET-743 clearance in phase II studies, using two techniques: the stepwise linear regression approach and the Bayesian estimation approach.Methods: Data from a phase I dose-finding study were used with ET-743 administered as a 24-h infusion. Plasma concentration time data from 34 patients treated with 1200. 1500 or 1800 microg/m2 ET-743 were randomly divided into an index data set, used for the development of the strategies, and a validation data set. With the linear regression approach, clearance (obtained by non-compartmental analysis) was correlated with the ratios of dose to the observed concentrations. For the Bayesian approach a three-compartment population pharmacokinetic model was developed; optimal time-points were selected using the D-optimality algorithm. The strategies were compared by assessment of their predictive performance of CL in the validation data set.Results: The linear regression method yielded a single-point sampling schedule with no significant bias and acceptable precision (-0.03% and 21%, respectively). With the Bayesian approach, a three-sample strategy was selected which resulted in less-accurate, but unbiased, predictions (bias 13%, precision 34%).Conclusions: Optimal sampling strategies were developed and validated for estimation of ET-743 clearance. Although the linear regression approach showed slightly better predictive performance, the Bayesian approach is preferred for the current phase II studies as it is more robust and flexible and allows the description of the full pharmacokinetic profile. [ABSTRACT FROM AUTHOR]

A limited sampling model for the estimation of the carboplatin area under the concentration versus time curve (AUC), as developed by Sørensen et al., was validated prospectively for the use in a high-dose combination chemotherapy schedule. The model allows an estimation of the AUC on the basis of only one timed plasma drug concentration, sampled at exactly 2.75 h after a 1-h carboplatin infusion. Pharmacokinetic curves were obtained from nine patients receiving carboplatin (400 mg/m per day) combined with cyclophosphamide (1500 mg/m per day), thiotepa (120 mg/m per day), and mesna (3 g/day) for 4 consecutive days. Peripheral blood stem-cell transplantation (PBSCT) was performed 3 days later to restore hematopoiesis. Using this combination of high doses, the model proved to be unbiased (MPE −3.40%; SE, 1.22%) and highly precise [root mean squared prediction error (RMSE), 5.15%; SE, 0.17%] for estimation of the AUC during 4 consecutive days. The validated limited sampling model provides a starting point for future pharmacokinetic studies in a larger population of patients, which might lead to more insight into the relationships with the pharmacodynamic outcome of carboplatin and may help in achieving more rational dosing of patients on the basis of an AUC determination. [ABSTRACT FROM AUTHOR]

Purpose Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Methods Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects’ serial plasma concentration data up to 72 hours and corresponding AUC 0−t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration−time points’ correlation with AUC 0−t of saroglitazar. Only models with regression coefficients ( R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0−t of saroglitazar and verification of precision and bias using Bland−Altman plot were carried out. Findings None of the evaluated 1- and 2-concentration−time points models achieved R 2 > 0.90. Among the various 3-concentration−time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours ( R 2 = 0.9323) and 0.75, 2, and 8 hours ( R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation ( r ) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0−t prediction of saroglitazar. The same models, when applied to the AUC 0−t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation ( r ) was at least 0.9339 in the same pool of healthy subjects. Implications A 3-concentration−time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0−t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0−∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. [ABSTRACT FROM AUTHOR]