Influences of renal function descriptors on population pharmacokinetic modeling of vancomycin in Chinese adult patients.

Vancomycin, a glycopeptide antibiotic for the treatment of grampositive infections, is mainly eliminated via glomerular filtration. Thus, its therapeutic effects are affected predominantly by renal function. The aim of this study was to develop a population pharmacokinetic model of vancomycin for Chinese adult patients and to investigate the influence of different renal function descriptors on the predictability of the model. A retrospective analysis was performed based on the blood concentrations of vancomycin in 218 Chinese adult patients. Among these patients, the data from 160 were used to establish the population pharmacokinetic model, and the data from the remaining 58 patients were used for external model validation. A simulation was employed to determine the appropriate initial vancomycin dosage regimens in adult Chinese patients for reaching the target steady-state trough concentrations of 10-15 mg/L and 15-20 mg/L. We developed a one-compartment model with first-order absorption to characterize the concentration-time profile of vancomycin. There was a positive correlation between the body clearance of vancomycin and renal function; both creatinine clearance (CLCr) and age were the covariates that influenced the PK of vancomycin, and the excretion of vancomycin decreased as renal function diminishing with age. The typical clearance (CL) value was 2.829 L/h for 75-year-old patients with CLCr values of 80 mL/min, and the rate constant of CL with the CLCr changing at 1 mL/min was 0.00842. The influence coefficient of age on CL was 0.08143. The external validation results revealed that the current different descriptors of renal function behaved similarly to the predicted performance of the models. In conclusion, the developed model is appropriate for Bayesian dose predictions of vancomycin concentrations in the population of Chinese adult patients. Furthermore, the simulation provides a reference for clinical optimized antibacterial therapy with vancomycin.

Population pharmacokinetic models of lamotrigine in different age groups of Chinese children with epilepsy.

PURPOSE: The study aims to establish population pharmacokinetic (PPK) models of lamotrigine (LTG) in different age groups of epileptic children by nonlinear mixed effect modelling(NONMEM), and provide essential tools and theoretical basis for individualised optimal drug dose. METHODS: Cases of 473 epileptic children were divided into infant, toddler and preschool age (≤6 years) (n = 211), school age (6-12 years) (n = 171) and adolescence age (>12 years) (n = 81). A total of 625 steady-state serum trough concentration samples were extracted. The clinical information included demography, medication, serum concentration data and blood biochemical parameters. PPK models of LTG were established by NONMEM program, using first-order absorption and elimination. Demography and drug combination was investigated for influence on apparent clearance (CL) and apparent volume of distribution (Vd). To assess the accuracy and precision of the different ages and whole-age model, the mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were compared. RESULTS: The final model of LTG in different ages stage and whole age was as follows: (1) infant, toddler and preschool age CL = 0.715 × [(WEIG/16.25)0.655] × (0.458VPA) × (1.99IND), V = 10.4; (2) school age CL(L/h) = 1.01 × (WEIG/30)0.399 × 0.465VPA × 1.98IND, Vd(L) = 17.7; (3) adolescence age CL(L/h) = 1.49 × (WEIG/51.5)0.509 × 0.498VPA × 1.7IND, Vd(L) = 23.1; (4) whole age CL = 0.945 × [(WEIG/25)0.645] × (0.463VPA) × (1.94IND), V = 16.7 × (WEIG/25)0.919 (WEIG, total body weight; VPA, combination with valproate, yes = 1, no = 0; IND, combination with enzyme inducer, yes = 1, no = 0). The values of MPE, MAE and RMSE in age-stage-specific models were less than the ones in the whole-age model, which suggests the age-stage-specific models have better precision and accuracy than the whole-age model. CONCLUSION: PPK models of LTG in different age groups of epileptic children were successfully established. Weight and combination therapy were identified as significant covariates on LTG clearance. Compared with the whole-age model, the age-specific models are more reliable.

Dose-biomarker-response modeling of the anticancer effect of ethaselen in a human non-small cell lung cancer xenograft mouse model.

Thioredoxin reductase (TrxR) is a component of several redox-sensitive signaling cascades that mediate important biological processes such as cell survival, maturation, growth, migration and inhibition of apoptosis. The expression levels of TrxR1 in some human carcinoma cell lines are nearly 10 times higher than those in normal cells. Ethaselen is a novel antitumor candidate that exerts potent inhibition on non-small cell lung cancer (NSCLC) by targeting TrxR. In this study we explored the relationship between the ethaselen dose and TrxR activity level and the relationship between TrxR degradation and tumor apoptosis in a human lung carcinoma A549 xenograft model. BALB/c nude mice implanted with human NSCLC cell line A54 were administered ethaselen (36, 72, 108 mg·kg-1·d-1, ig) or vehicle for 10 d. The tumor size and TrxR activity levels in tumor tissues were daily recorded and detected. Based on the experimental data, NONMEM 7.2 was used to develop an integrated dose-biomarker-response model for describing the quantitative relationship between ethaselen dose and tumor eradication effects. The time course of TrxR activity levels was modeled using an indirect response model (IDR model), in which the influence of the tumor growth rates on Kin with the linear correction factor γ1 (0.021 d/mm). The drug binding-inhibition effects on Kout was described using a sigmoidal Emax model with Smax (5.95), SC50 (136 mg/kg) and Hill's coefficient γ2 (2.29). The influence of TrxR activity inhibition on tumor eradication was characterized by an Emax model with an Emax (130 mm3/d) and EC50 (0.0676). This model was further validated using a visual predictive check (VPC) and was used to predict the efficacy of different doses. In conclusion, the properties and characteristics of ethaselen acting on TrxR degradation and subsequently resulting in tumor apoptosis are characterized by the IDR model and integrated dose-biomarker-response model with high goodness-of-fit and great predicative ability. This approach shed new light on the detailed processes and mechanism of ethaselen action and may offer a valuable reference for an appropriate dosing regimen for use in further clinical applications.

Pharmacokinetic-pharmacodynamic modeling of the antitumor effect of TM208 and EGFR-TKI resistance in human breast cancer xenograft mice.

AIM: The novel anticancer compound TM208 is an EGFR tyrosine kinase inhibitor (EGFR-TKI). Since the development of resistance to EGFR-TKIs is a major challenge in their clinical usage, we investigated the profiles of resistance following continuous treatment with TM208 in human breast cancer xenograft mice, and identified the relationship between the tumor pEGFR levels and tumor growth inhibition. METHODS: Female BALB/c nude mice were implanted with human breast cancer MCF-7 cells, and the xenograft mice received TM208 (50 or 150 mg·kg(-1)·d(-1), ig) or vehicle for 18 d. The pharmacokinetics (PK) and pharmacodynamics (PD) of TM208 were evaluated. RESULTS: The PK properties of TM208 were described by a one-compartment model with first-order absorption kinetics. Our study showed the inhibitory effects of TM208 on tumor pEGFR levels gradually reached a maximum effect, after which it became weaker over time, which was characterized by a combined tolerance/indirect response PD model with an estimated EC50 (55.9 µg/L), as well as three parameters ('a' of 27.2%, 'b' of 2730%, 'c' of 0.58 h(-1)) denoting the maximum, extent and rate of resistance, respectively. The relationship between the tumor pEGFR levels and tumor growth inhibition was characterized by a combined logistic tumor growth/transit compartment model with estimated parameters associated with tumor growth characteristics kng (0.282 day(-1)), drug potency kTM208 (0.0499 cm(3)/day) and the kinetics of tumor cell death k1 (0.141 day(-1)), which provided insight into drug mechanisms and behaviors. CONCLUSION: The proposed PK/PD model provides a better understanding of the pharmacological properties of TM208 in the treatment of breast cancer. Furthermore, simulation based on a tolerance model allows prediction of the occurrence of resistance.

Preclinical PK/PD model for combined administration of erlotinib and sunitinib in the treatment of A549 human NSCLC xenograft mice.

AIM: Combined therapy of EGFR TKI and VEGFR TKI may produce a greater therapeutic benefit and overcome EGFR TKI-induced resistance. However, a previous study shows that a combination of EGFR TKI erlotinib (ER) with VEGFR TKI sunitinib (SU) did not improve the overall survival in patients with non-small-cell lung cancer (NSCLC). In this study we examined the anticancer effect of ER, SU and their combination in the treatment of A549 human NSCLC xenograft mice, and conducted PK/PD modeling and simulations to optimize the dose regimen. METHODS: ER (20, 50 mg·kg(-1)·d(-1)) or SU (5, 10, 20 mg·kg(-1)·d(-1)) alone, or their combination were administered to BALB/c nude mice bearing A549 tumors for 22 days. The tumor size and body weight were recorded daily. The experimental data were used to develop PK/PD models describing the quantitative relationship between the plasma concentrations and tumor suppression in different dose regimens. The models were further evaluated and validated, and used to predict the efficacy of different combination regimens and to select the optimal regimen. RESULTS: The in vivo anticancer efficacy of the combination groups was much stronger than that of either drug administered alone. A PK/PD model was developed with a combination index (φ) of 4.4, revealing a strong synergistic effect between ER and SU. The model simulation predicted the tumor growth in different dosage regimens, and showed that the dose of SU played a decisive role in the combination treatment, and suggested that a lower dose of ER (≤5 mg·kg(-1)·d(-1)) and adjusting the dose of SU might yield a better dosage regimen for clinical research. CONCLUSION: The experimental data and modeling confirm synergistic anticancer effect of ER and SU in the treatment of A549 xenograft mice. The optimal dosage regimen determined by the PK/PD modeling and simulation can be used in future preclinical study and provide a reference for clinical application.

[Using the cumulative odds Logit model to explore the application of the questionnaire derived from PANSS].

Currently, aripiprazole, olanzapine and risperidone are three anti-psychiatry agents commonly used in the treatment of schizophrenia. Although the efficacy of these drugs is good, schizophrenia cannot be completely cured yet. Patients need long-term medication. The family members of patients may play a key role to understand the disease status of patients after patient discharge from hospital. PANSS is a commonly used scale in the clinic to evaluate the disease status and drug effects of anti-psychiatry agents. It was professionally written, and is not user friendly to amateurs. In the previous study, we developed a questionnaire for patient's family members to monitor the disease status. In this study, we explored the correlations between the results of questionnaire and 5 kinds of disease state corresponding to different PANSS score interval using the cumulative odds Logit model. The final results show that the model had relatively good prediction ability for aripiprazole, olanzapine and risperidone, suggesting that the questionnaire has an extensive prospect of clinical applications.

[Population pharmacokinetics of vancomycin and prediction of pharmacodynamics in the Chinese people].

Population pharmacokinetics of vancomycin (VAN) in the Chinese patients was described by using nonlinear mixed-effects modeling (NONMEM). 619 VAN serum concentrations data from 260 patients including 177 males and 83 females were collected separately from two centers. A one-compartment model was used to describe this sparse data. No significant difference was observed between two center datasets by introducing SID covariate. The final model was as CL= (θ (base0+ θ(max) x(1 -e(-θ(Age)(Age/72) and V = θ x θ (Age)(Age/72). The creatinine clearance (CL(Cr)) and Age were identified as the most significant covariate in the final model. Typical values of clearance (CL) and volume of distribution (V) in the final model were 2.91 L x h(-1) and 54.76 L, respectively. Internal model validation by Bootstrap and NPDE were performed to evaluate the robustness and prediction of the final model. The median and 95% confidence intervals for the final model parameters were based on 1000 Bootstraps. External model evaluation was conducted using an independent dataset that consisted of 34 patients to predict model performance. Pharmacodynamic assessment for VAN by AUC (0-24 h) to MIC ratios of over 400 was considered to be the best to predict treatment outcomes for patients. AUC (0-24 h) was calculated by clearance based on the above population model. The results indicate that the conventional dosing regimen probably being suboptimal concentrations in aged patients. The approach via population pharmacokinetic of VAN combined with the relationship of MIC, Age, CL(Cr) and AUC(0-24 h)/MIC can predict the rational dose for attaining efficacy.

Population pharmacokinetics of clozapine and its primary metabolite norclozapine in Chinese patients with schizophrenia.

AIM: To develop a combined population pharmacokinetic model (PPK) to assess the magnitude and variability of exposure to both clozapine and its primary metabolite norclozapine in Chinese patients with refractory schizophrenia via sparse sampling with a focus on the effects of covariates on the pharmacokinetic parameters. METHODS: Relevant patient concentration data (eg, demographic data, medication history, dosage regimen, time of last dose, sampling time, concentrations of clozapine and norclozapine, etc) were collected using a standardized data collection form. The demographic characteristics of the patients, including sex, age, weight, body surface area, smoking status, and information on concomitant medications as well as biochemical and hematological test results were recorded. Persons who had smoked 5 or more cigarettes per day within the last week were defined as smokers. The concentrations of clozapine and norclozapine were measured using a HPLC system equipped with a UV detector. PPK analysis was performed using NONMEM. Age, weight, sex, and smoking status were evaluated as main covariates. The model was internally validated using normalized prediction distribution errors. RESULTS: A total of 809 clozapine concentration data sets and 808 norclozapine concentration data sets from 162 inpatients (74 males, 88 females) at multiple mental health sites in China were included. The one-compartment pharmacokinetic model with mixture error could best describe the concentration-time profiles of clozapine and norclozapine. The population-predicted clearance of clozapine and norclozapine in female nonsmokers were 21.9 and 32.7 L/h, respectively. The population-predicted volumes of distribution for clozapine and norclozapine were 526 and 624 L, respectively. Smoking was significantly associated with increases in the clearance (clozapine by 45%; norclozapine by 54.3%). The clearance was significantly greater in males than in females (clozapine by 20.8%; norclozapine by 24.2%). The clearance of clozapine and norclozapine did not differ significantly between Chinese patients and American patients. CONCLUSION: Smoking and male were significantly associated with a lower exposure to clozapine and norclozapine due to higher clearance. This model can be used in individualized drug dosing and therapeutic drug monitoring.