Limited Sampling Strategy Models Research Articles

Limited Sampling Strategies for Estimating Busulfan Area Under the Concentration-Time Curve: Based on Peak and Trough Concentrations in Saliva.

Therapeutic drug monitoring for busulfan is currently performed by multiple plasma sampling. Saliva is considered a noninvasive therapeutic drug monitoring matrix. This study aimed to investigate intravenous busulfan pharmacokinetics (PK) in plasma and saliva, and establish a limited sampling strategy (LSS) for predicting the area under the concentration-time curve from time zero to infinity in plasma (AUC0-∞,p) by using saliva samples. Therefore, the PK of busulfan was studied in 37 Chinese patients. Pearson correlation analysis was used to evaluate the correlation between the AUC of busulfan in plasma and saliva. LSS models were established by the multiple linear regression analysis. The prediction error, the mean prediction error, and the root mean square error were used to evaluate the predictive accuracy. The agreement between the predicted and observed AUC0-∞ in saliva was investigated by the intraclass correlation coefficient and Bland-Altman analysis. The accuracy and robustness of the models were evaluated by using the bootstrap procedure. The result of PK analysis 62.2% of patients (23/37) was within the target range of AUC0-∞,p . A good correlation between saliva and plasma busulfan AUC0-∞ was observed (r = 0.63, p<.01). The bias and precision of the models 7 and 13 were less than 15%. The intraclass correlation coefficient exceeded 0.9, and the limits of agreement were within ±15%. The 2-point LSS model in saliva is a convenient and desirable approach to predict the AUC0-∞ of 4 times daily intravenous busulfan in plasma, which can be used to design personalized dosing for busulfan.

Limited Sampling Strategies to Predict Ganciclovir Exposure after Valganciclovir Administration and to Reduce Monitoring Constraints in Renal Transplant Children.

Valganciclovir, the ganciclovir prodrug, is an antiviral agent used to prevent cytomegalovirus infection in renal transplant children. Therapeutic drug monitoring is still necessary to ensure optimal therapeutic area under the concentration-time curve from 0 to 24 h (AUC0-24) of 40 to 60 μg·h/mL since valganciclovir presents a high pharmacokinetic variability. To calculate ganciclovir AUC0-24 with the trapezoidal method, 7 samples are needed. The objective of this study was to develop and validate a reliable and clinically applicable limited sampling strategy (LSS) for individualizing valganciclovir dose in renal transplant children. Rich pharmacokinetic data from ganciclovir plasmatic dosages measured in renal transplant children who received valganciclovir to prevent cytomegalovirus infection at Robert Debré University Hospital were collected retrospectively. Ganciclovir AUC0-24s were calculated using the trapezoidal method. The LSS was developed using a multilinear regression approach to predict AUC0-24. The patients included were divided into two groups for model development (50 patients) and validation (30 patients). A total of 80 patients were included between February 2005 and November 2018. Multilinear regression models were developed on 50 pharmacokinetic profiles (50 patients) and validated with an independent group of 43 pharmacokinetic profiles (30 patients). Regressions based on samples collected at T1h-T4h-T8h, T2h-T4h-T8h, or T1h-T2h-T8h presented the best AUC0-24 predictive performances with an average difference between reference and predicted AUC0-24 of -0.27, 0.34, and -0.40 μg·h/mL, respectively. In conclusion, valganciclovir dosage adaptation was required in children to achieve the target AUC0-24. Three LSS models using three pharmacokinetic blood samples instead of seven will be useful for individualizing valganciclovir prophylaxis in renal transplant children.

The pharmacokinetics of tacrolimus in peripheral blood mononuclear cells and limited sampling strategy for estimation of exposure in renal transplant recipients.

Intracellular exposure of tacrolimus (TAC) may be a better marker of therapeutic effect than whole blood exposure. We aimed to evaluate the influence of genetic polymorphism on the pharmacokinetics of TAC in peripheral blood mononuclear cells (PBMCs) and develop limited sampling strategy (LSS) models to estimate the area under the curve (AUC0-12h) in the PBMC of Chinese renal transplant patients. Ten blood samples of each of the 23 renal transplant patients were collected 0-12h after 14 (10-18) days of TAC administration. PBMCs were separated and quantified. The TAC level in PBMCs was determined, and pharmacokinetic parameters were estimated by noncompartmental study. The AUC0-12h of TAC in whole blood was estimated by Bayesian approach based on a population pharmacokinetic model established in 65 renal transplant patients. The influence of CYP3A5 and ABCB1 genotypes on exposure was estimated. By applying multiple stepwise linear regression analysis, LSS equations for TAC AUC0-12h in the PMBC of renal transplant patients were established, and the bias and precision of various equations were identified and compared. We found a modest correlation between TAC exposure in whole blood and PBMC (r2 = 0.5260). Patients with the CYP3A5 6986GG genotype had a higher AUC0-12h in PBMCs than those with the 6986 AA or GA genotype (P = 0.026). Conversely, patients with the ABCB1 3435TT genotype had a higher AUC0-12h in PBMC than those with the 3435 CC and CT genotypes (P = 0.046). LSS models with 1-4 blood time points were established (r2 = 0.570-0.989). The best model for predicting TAC AUC0-12h was C2-C4-C6-C10 (r2 = 0.989). The model with C0.5-C6 (r2 = 0.849) can be used for outpatients who need monitoring to be performed in a short period. The CYP3A5 and ABCB1 genotypes impact TAC exposure in PBMCs, which may further alter the effects of TAC. The LSS model consisting of 2-4 time points is an effective approach for estimating full TAC AUC0-12h in Chinese renal transplant patients. This approach may provide convenience and the possibility for clinical monitoring of TAC intracellular exposure.

Limited Sampling Strategy for Estimation of Mycophenolic Acid Exposure in Adult Chinese Heart Transplant Recipients.

Background: With the increasing use of mycophenolic acid (MPA) formulations in organ transplantation, the need for personalized immunosuppressive therapy has become well recognized based on therapeutic drug monitoring (TDM) for avoidance of drug-related toxicity while maintaining efficacy. Few studies have assessed area under the 12 h concentration-time curve of MPA (MPA-AUC0–12h) in heart transplant recipients who received mycophenolate mofetil (MMF) dispersible tablets (MMFdt). The aim of the study was to investigate the pharmacokinetics (PK) of MMFdt combined with tacrolimus and further to develop a practical method for estimation of MPA-AUC0–12h using a limited sampling strategy (LSS). Methods: A prospective study in a single center was performed in patients who continuously administrated with MMFdt or MMF capsule (MMFc) for at least 7 days after cardiac transplantation from 2018 to 2020. A total of 48 Chinese adult heart transplant recipients were enrolled. Blood samples were collected before and 0.5, 1, 1.5, 2, 4, 6, 8, 10 and 12 h after MMF administration. The validated high-performance liquid chromatography combined with tandem mass spectrometry method was used to measure MPA concentrations. Non-compartmental pharmacokinetic (PK) analysis was applied to calculate the data obtained from individual recipients by WinNonlin. LSS models were developed for MPA-AUC0–12h prediction with multivariate stepwise regression analysis. Results: A large inter-individual variability was observed in AUC0–12h, Tmax, Cmax, MRT0–12h, t1/2 and CL/F after multiple dosing of MMFdt. However, no significant differences were observed between main PK parameters of MMFdt and MMFc. The best estimation of MPA-AUC0–12h was achieved with four points: MPA-AUC0–12h = 8.424 + 0.781 × C0.5 + 1.263 × C2 + 1.660 × C4 + 3.022 × C6 (R 2 = 0.844). The mean prediction error (MPE) and mean absolute prediction error (MAPE) of MPA-AUC0–12h were 2.09 ± 14.05% and 11.17 ± 8.52%, respectively. Both internal and external validations showed good applicability for four-point LSS equation. Conclusion: The results provide strong evidence for the use of LSS model other than a single time-point concentration of MPA when performing TDM. A four-point LSS equation using the concentrations at 0.5, 2, 4, 6 h is recommended to estimate MPA-AUC0–12h during early period after transplantation in Chinese adult heart transplant recipients receiving MMFdt or MMFc. However, proper internal and external validations with more patients should be conducted in the future.

Development of a population pharmacokinetic model and Bayesian estimators for isoniazid in Tunisian tuberculosis patients.

This study aimed to develop a population pharmacokinetic model using fullpharmacokinetic (PK) profiles of isoniazid (INH) taking into account demographic and genetic covariates and to develop Bayesian estimators for predicting INH area under the curve (AUC) in Tunisian tuberculosis patients. The INH concentrations in the building data set were fitted using a one- to three-compartment model. The impact of the different covariates was assessed on the PK parameters of the best model. The best limited sampling strategy (LSS) for estimating the INH AUC was selected by comparing the predicted values to an independent data set. INH PK was best described using a three-compartment model with lag-time absorption. The different studied covariates did not have any impact on the PK parameters of the building model. The Bayesian estimation using one-point concentrations gave the lowest values of prediction errors for the C3 LSS model. This model could be sufficient in routine activity for INH monitoring in this population.

Limited sampling strategy for the estimation of the area under the concentration-time curve for ganciclovir in Chinese adult renal allograft recipients.

Valganciclovir (VGCV) treatment is recommended for the prevention of cytomegalovirus (CMV) infection in renal allograft recipients. The aim of the present study is to investigate the pharmacokinetic characteristics of ganciclovir (GCV) after administration of VGCV in Chinese adult renal allograft recipients and estimate the exposure to GCV using limited sampling strategy (LSS). Forty Chinese renal allograft recipients were given 450 mg or 900 mg VGCV daily. Blood samples were drawn before treatment and 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, and 24 h after 5 days of VGCV therapy, and the plasma concentrations of VGCV and GCV were determined using a liquid chromatography-mass spectrometry assay. The major pharmacokinetic parameters for GCV and VGCV were determined using a noncompartmental assay. Multiple stepwise linear regression analysis was conducted to establish a model equation for the estimation of the GCV AUC0-24 h in Chinese patients using LSS. In the 450 and 900 mg groups, the Cmax for VGCV was 0.2 ± 0.10 and 0.4 ± 0.16 mg/L, respectively; the Cmax for GCV was 4.2 ± 1.1 and 8.6 ± 1.6 mg/L, respectively; and the AUC0-24 h for GCV was 28.4 ± 8.4 and 60.7 ± 17.5 mg·h/L, respectively. For the establishment of LSS models, 40 patients were divided into the training group (n = 24) and validation group (n = 16). The model equations used for the calculation of AUC0-24 h for GCV were established in the training group by using multiple linear regression assay. Equations including AUC = 8.1 + 29.7 × C0 + 5.7 × C4 (r2 = 0.91) and AUC = - 0.4 + 11.0 × C0 + 2.1 × C2 + 13.7 × C8 (r2 = 0.98) were acceptable. The %MPE and %MAPE values obtained from the validation group for the two model equations were 5.89 ± 14.5% and 12.1 ± 9.53%, and - 1.30 ± 4.40% and 3.28 ± 3.11%, respectively. The LSS models that included C0 and C4 or C0, C2, and C8 in the estimation of AUC0-24 h for GCV had favorable performance and can be used for therapeutic drug monitoring in the prevention of CMV infection using VGCV in Chinese renal allograft recipients.

Limited sampling strategies for determining the area under the plasma concentration–time curve for isoniazid might be a valuable approach for optimizing treatment in adult patients with tuberculosis

This study aimed to develop clinically feasible models of limited sampling strategy (LSS) for estimation of the area under the concentration–time curve (AUC24h) for isoniazid, that could be applied easily in daily clinical practice for dosage adjustment in adult patients with tuberculosis. Isoniazid plasma concentrations (n = 1665) from 185 adult tuberculous patients were used for the development and validation of LSS models to estimate AUC24h following administration of the standard 5 mg/kg dose of isoniazid. Population pharmacokinetic analysis for appropriate estimation of isoniazid pharmacokinetic parameters was performed in a modelling group (n = 100). The Bayesian estimates of AUC24h (AUCref) obtained for each individual were used as the dependent variable in the regression analysis for the development of various LSS models. The LSS models were validated in a separate cohort (n = 85). Several three and four time point LSS models were built and tested. Model H (AUC24h = −1.88 + 1.05 × C1 + 0.78 × C2 + 9.44 × C5) and Model I (AUC24h = −0.65 + 1.00 × C1 + 1.94 × C2 + 15.45 × C9) had the best performances [adj-R2 = 0.93, median prediction error (MPE) = −0.20, root median squared prediction error (RMSE) = 4.65 for Model H; adj-R2 = 0.96, MPE = −0.05 RMSE = 3.56 for Model I]. The very high R2 values (≥0.94) of these regression equations in the validation cohort confirmed their high reliability. These LSS models could be applied in the context of therapeutic drug monitoring programmes aiming to personalize isoniazid dosing regimens for adult patients with tuberculosis.

Development and Validation of Limited Sampling Strategies for the Estimation of Mycophenolic Acid Area Under the Curve in Adult Kidney and Liver Transplant Recipients Receiving Concomitant Enteric-Coated Mycophenolate Sodium and Tacrolimus

Mycophenolic acid (MPA) is widely used in solid organ transplantation. MPA absorption from enteric-coated mycophenolate sodium (EC-MPS) is delayed, which results in a delayed enterohepatic recirculation and subsequently higher and more variable MPA 12-hour trough concentration and tmax values. Therefore, MPA trough level monitoring cannot be used to monitor MPA exposure in patients who are given EC-MPS. The aim of the study was to develop and validate a limited sampling strategy (LSS) for accurate prediction of the 12-hour area under the concentration-time curve (AUC0-12h) for MPA in patients who receive concomitant EC-MPS and Tacrolimus (Prograf or Advagraf) within 196 months posttransplantation. According to our knowledge, the LSS for MPA AUC estimation using high-performance liquid chromatography to determine MPA concentrations in plasma samples of kidney and liver transplant patients receiving EC-MPS and Tacrolimus (Advagraf) has not been previously evaluated. Seventy-four renal and liver transplant patients receiving EC-MPS and concomitant tacrolimus (either Prograf or Advagraf) provided a total of 74 pharmacokinetic profiles. MPA concentrations were measured using a validated high-performance liquid chromatography method for 9 plasma samples collected at predose and at 0.5, 1, 2, 3, 4, 6, 9, and 12 hours after the morning dose of EC-MPS after an overnight fast. LSS were developed and validated by stepwise multiple regression analysis with the use of a 2-group method (test, n = 37; and validation, n = 37). The 3 and 4 time point equations using C1h, C3h, C9h and C1h, C2h, C3h, C6h, respectively, were found to be superior to all other models tested. When these LSS models were tested in the validation group, the results were acceptable [for 3 time points equation: r = 0.824, percentage of prediction error: 6.32 ± 25.75, 95% confidence interval (CI): -40.71 to 79.76; percentage of absolute prediction error: 27.45 ± 29.89, 95% CI: 0.04-199.92, predictive performance, 71% of estimated AUCs comprised within 85%-115% of the measured full MPA AUC, natural logarithmic residuals (ln) mean ± SD: -0.03 ± 0.24; for 4 time points equation: r = 0.898, percentage of prediction error: 3.32 ± 18.26, 95% CI: -49.35 to 51.06; percentage of absolute prediction error: 14.05 ± 11.89, 95% CI 0.13-49.86, percentage of predictive performance, 83% of estimated AUCs comprised within 85%-115% of the measured full MPA AUC, natural logarithmic residuals (ln) mean ± SD: -0.01 ± 0.19]. LSS equations using concentrations at 1, 3, and 9 hours or 1, 2, 3, and 6 hours time points provided the most reliable and accurate estimations of the MPA AUC in stable renal and liver transplant recipients treated with EC-MPS and tacrolimus. Further studies on independent groups of patients are required to confirm clinical utility of the presented LSS models.

Bioequivalence evaluation of two formulations of pidotimod using a limited sampling strategy

The aim of this study was to develop a limited sampling strategy (LSS) to assess the bioequivalence of two formulations of pidotimod. A randomized, two-way, cross-over study was conducted in healthy Chinese volunteers to compare two formulations of pidotimod. A limited sampling model was established using regression models to estimate the pharmacokinetic parameters and assess the bioequivalence of pidotimod. The model was internally validated by the Jack-knife method and graphical methods. The traditional non-compartmental method was also used to analyze the data and compared with LSS method. The results indicate that following oral administration of a single 800mg dose, the plasma AUC0–12h and Cmax of pidotimod can be predicted accurately using only two to four plasma samples. The bioequivalence assessment based on the LSS models provided results very similar to that obtained using all the observed concentration–time data points and indicate that the two pidotimod formulations were bioequivalent. A LSS method for assessing the bioequivalence of pidotimod formulations was established and proved to be applicable and accurate. This LSS method could be considered appropriate for a pidotimod bioequivalence study, providing an inexpensive cost of sampling acquisition and analysis. And the methodology presented here may also be applicable to bioequivalence evaluation of other medications.

Limited sampling strategy models for estimating the AUC of gliclazide in Chinese healthy volunteers

The aim of this work is to reduce the cost of required sampling for the estimation of the area under the gliclazide plasma concentration versus time curve within 60h (AUC0-60t ). The limited sampling strategy (LSS) models were established and validated by the multiple regression model within 4 or fewer gliclazide concentration values. Absolute prediction error (APE), root of mean square error (RMSE) and visual prediction check were used as criterion. The results of Jack-Knife validation showed that 10 (25.0%) of the 40 LSS based on the regression analysis were not within an APE of 15% using one concentration-time point. 90.2, 91.5 and 92.4% of the 40 LSS models were capable of prediction using 2, 3 and 4 points, respectively. Limited sampling strategies were developed and validated for estimating AUC0-60t of gliclazide. This study indicates that the implementation of an 80mg dosage regimen enabled accurate predictions of AUC0-60t by the LSS model. This study shows that 12, 6, 4, 2h after administration are the key sampling times. The combination of (12, 2h), (12, 8, 2h) or (12, 8, 4, 2h) can be chosen as sampling hours for predicting AUC0-60t in practical application according to requirement.

Omeprazole limited sampling strategies to predict area under the concentration–time curve ratios: implications for cytochrome P450 2C19 and 3A phenotyping

To develop a limited sampling strategy (LSS) to predict area under the concentration-time curve (AUC) ratios of omeprazole (AUC(OPZ)) to its metabolites 5-hydroxyomeprazole (AUC(5OH)) and omeprazole sulfone (AUC(SUL)) as phenotyping parameters for cytochrome P450 (CYP) 2C19 and 3A. Data were obtained from 37 (4 women) Caucasian, Chinese, and Korean healthy adults from three published studies. The AUC(OPZ), AUC(5OH), and AUC(SUL) were calculated via noncompartmental analysis. Observed AUC(OPZ, OBS)/AUC(5OH, OBS) and AUC(OPZ, OBS)/AUC(SUL, OBS) were determined. Plasma concentrations of omeprazole, 5-hydroxyomeprazole, and omeprazole sulfone at 1, 1.5, 2, 3, 4, 6, and 8 h post-dose were used to generate limited sampling strategy (LSS) models to predict AUC(OPZ,PRE)/AUC(5OH,PRE) and AUC(OPZ,PRE/)AUC(SUL,PRE). Bias and precision were assessed via percentage mean prediction error (%MPE) and percentage mean absolute error (%MAE), with acceptable limits being <15%. For CYP2C19, the AUC(OPZ,OBS)/AUC(5OH,OBS) was [mean ± standard deviation (SD)] 2.10 ± 1.63. Five LSS models of AUC(OPZ,PRE)/AUC(5OH,PRE) were generated, but none met the bias or precision criteria. Upon stratification by CYP2C19 genotype and ethnicity, a three-timepoint (at 1, 2, and 4h) LSS model accurately predicted AUC(OPZ)/AUC(5OH) in Caucasian CYP2C19*1/*1 subjects. For CYP3A, AUC(OPZ,OBS)/AUC(SUL,OBS) (mean ± SD) was 1.79 ± 0.67. All LSS models had unacceptable %MAE, even when stratified by CYP2C19 genotype and ethnicity. A LSS model to predict AUC(OPZ)/AUC(5OH), and thus CYP2C19 activity, was generated for Caucasian CYP2C19*1/*1 subjects. However, additional model validation is needed prior to general use. LSS models to predict AUC(OPZ)/AUC(SUL), and thus CYP3A activity, were not possible, even upon stratification by CYP2C19 genotype and ethnicity.

Bioequivalence assessment of metformin hydrochloride using a limited sampling strategy

The aim of this study was to develop a limited sampling strategy (LSS) that can be used to assess the bioequivalence of two metformin hydrochloride preparations. Healthy subjects (n = 20) enrolled in the bioequivalence study received a single oral tablet of 1,000 mg metformin reference formulation or test formulation. The plasma concentration of metformin was determined using a validated HPLC method. A multiple linear regression analysis of the observed metformin Cmax and AUC0-24 versus the concentration of reference formulation was performed to develop LSS models for estimating these parameters. The models were internally validated by the Jackknife method. The best models were employed to assess the bioequivalence of the two metformin formulations. The linear relationship between pharmacokinetic parameters and a single concentration point was poor. Several models for the estimation of these parameters met the predefined criteria (r2 > 0.9). The Jackknife validation procedure revealed that LSS models based on two sampling times - C1.5 and C2 for Cmax; C4.0 and C10.0 for AUC0-24 - were accurate predictor of Cmax and AUC0-24. Prediction errors (PE) were less than 2%, and absolute prediction errors (AE) were less than 10%. PEs beyond 15% occurred in less than 5% of total samples. The bioequivalence assessment of the two metformin formulations, based on the best LSS models, provided results similar to those obtained using all the observed concentration-time data points, and indicated that the two metformin formulations were bioequivalent. A LSS method for assessing the bioequivalence of metformin formulations was established and proved to be applicable and accurate. This LSS method could be considered appropriate for a metformin bioequivalence study, providing an inexpensive cost of sampling acquisition and analysis.

Assessment of oral midazolam limited sampling strategies to predict area under the concentration time curve (AUC) during cytochrome P450 (CYP) 3A baseline, inhibition and induction or activation

A previous study reported a 2- and 3-timepoint limited sampling strategy (LSS) model accurately predicted oral midazolam area under the concentration time curve (AUC), and thus cytochrome P450 (CYP) 3A activity. This study evaluated whether the LSS models predict midazolam AUC during CYP3A baseline, inhibition and induction/activation. Plasma midazolam concentrations from 106 healthy adults from 6 published studies were obtained where oral midazolam was co-administered alone or with ketoconazole, double-strength grapefruit juice, Ginkgo biloba extract, pleconaril, or rifampin. Observed and predicted midazolam AUCs were determined. Bias and precision of the LSS models were determined. Contrasting results were observed for the 2- and 3-timepoint LSS models in accurately predicting midazolam AUC during baseline CYP3A conditions. With the exception of 1 study (single dose, double-strength grapefruit juice), the 2- and 3-timepoint LSS models did not accurately predict midazolam AUC during conditions of CYP3A inhibition and induction/activation. The previously reported 2- and 3-timepoint oral midazolam LSS models are not applicable to the evaluated conditions of CYP3A baseline, inhibition, and induction/ activation.

Carbamazepine: a bioequivalence study and limited sampling modeling

To assess the bioequivalence of 2 formulations of carbamazepine and to develop and validate limited sampling strategy (LSS) models for estimating the area under the plasma concentration-time curve (AUC0-infinity) and the peak plasma concentration (Cmax) of carbamazepine. Twenty-four (12 men, 12 women) healthy volunteers received single oral doses (400 mg) of carbamazepine, as reference and test conventional-release formulations, in a standard 2-sequence, 2-period crossover design. Bioequivalence assessment was based on the individual ratios of log-transformed values of AUC0-infinity and Cmax LSS modeling was developed in a training set of 12 randomly assigned volunteers and was validated on the other 12 subjects (validation set). Carbamazepine AUC0-infinity and Cmax can be accurately predicted (R2 = 0.89 - 0.95, precision = 2.6 - 7.2%) by single-point (72 h) and 2-point LSS models (6, 32 h), respectively. Bioequivalence assessments based on LSS-derived AUC0-infinity and Cmax provided results similar to those obtained using all the concentration-in-plasma data points, and indicated that the 2 formulations are bioequivalent. One-and 2-point LSS models provided accurate estimates of carbamazepine's AUC0-infinity and Cmax, and allowed correct assessment of bioequivalence between the formulations studied.

Limited-sampling strategy models for estimating the pharmacokinetic parameters of 4-methylaminoantipyrine, an active metabolite of dipyrone

Bioanalytical data from a bioequivalence study were used to develop limited-sampling strategy (LSS) models for estimating the area under the plasma concentration versus time curve (AUC) and the peak plasma concentration (Cmax) of 4-methylaminoantipyrine (MAA), an active metabolite of dipyrone. Twelve healthy adult male volunteers received single 600 mg oral doses of dipyrone in two formulations at a 7-day interval in a randomized, crossover protocol. Plasma concentrations of MAA (N = 336), measured by HPLC, were used to develop LSS models. Linear regression analysis and a "jack-knife" validation procedure revealed that the AUC(0-infinity) and the Cmax of MAA can be accurately predicted (R2>0.95, bias <1.5%, precision between 3.1 and 8.3%) by LSS models based on two sampling times. Validation tests indicate that the most informative 2-point LSS models developed for one formulation provide good estimates (R2>0.85) of the AUC(0-infinity) or Cmax for the other formulation. LSS models based on three sampling points (1.5, 4 and 24 h), but using different coefficients for AUC(0-infinity) and Cmax, predicted the individual values of both parameters for the enrolled volunteers (R2>0.88, bias = -0.65 and -0.37%, precision = 4.3 and 7.4%) as well as for plasma concentration data sets generated by simulation (R2>0.88, bias = -1.9 and 8.5%, precision = 5.2 and 8.7%). Bioequivalence assessment of the dipyrone formulations based on the 90% confidence interval of log-transformed AUC(0-infinity) and Cmax provided similar results when either the best-estimated or the LSS-derived metrics were used.