External evaluation of published population pharmacokinetic models of polymyxin B.

OBJECTIVES: Several population pharmacokinetics (popPK) models for polymyxin B have been constructed to optimize therapeutic regimens. However, their predictive performance remains unclear when extrapolated to different clinical centers. Therefore, this study aimed to evaluate the predictive ability of polymyxin B popPK models. METHODS: A literature search was conducted, and the predictive performance was determined for each selected model using an independent dataset of 20 patients (92 concentrations) from the Third Xiangya Hospital. Prediction- and simulation-based diagnostics were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. RESULTS: Eight published studies were evaluated. In prediction-based diagnostics, the prediction error within ± 30% was over 50% in two models. In simulation-based diagnostics, the prediction- and variability-corrected visual predictive check (pvcVPC) showed satisfactory predictivity in three models, while the normalized prediction distribution error (NPDE) tests indicated model misspecification in all models. Bayesian forecasting demonstrated a substantially improvement in the model predictability even with one prior observation. CONCLUSION: Not all published models were satisfactory in prediction- and simulation-based diagnostics; however, Bayesian forecasting improved the predictability considerably with priors, which can be applied to guide polymyxin B dosing recommendations and adjustments for clinicians.

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring.

Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to improve the screening power of SF, a novel approach named EAT-Score was proposed by directly utilizing the energy auxiliary terms (EAT) provided by molecular docking scoring through eXtreme Gradient Boosting (XGBoost). Here, EAT specifically refers to the output of the Molecular Operating Environment (MOE) scoring, including the energy scores of five different classical SFs and the Protein-Ligand Interaction Fingerprint (PLIF) terms. The performance of EAT-Score to discriminate actives from decoys was strictly validated on the DUD-E diverse subset by using different performance metrics. The results showed that EAT-Score performed much better than classical SFs in VS, with its AUC values exhibiting an improvement of around 0.3. Meanwhile, EAT-Score could achieve comparable even better prediction performance compared with other state-of-the-art VS methods, such as some machine learning (ML)-based SFs and classical SFs implemented in docking programs, in terms of AUC, LogAUC, or BEDROC. Furthermore, the EAT-Score model can capture important binding pattern information from protein-ligand complexes by Shapley additive explanations (SHAP) analysis, which may be very helpful in interpreting the ligand binding mechanism for a certain target and thereby guiding drug design.

Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.

Lipophilicity, as evaluated by the n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In this study, we developed several quantitative structure-property relationship (QSPR) models to predict log D7.4 based on a large and structurally diverse data set. Eight popular machine learning algorithms were employed to build the prediction models with 43 molecular descriptors selected by a wrapper feature selection method. The results demonstrated that XGBoost yielded better prediction performance than any other single model (RT2 = 0.906 and RMSET = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance (RT2 = 0.922 and RMSET = 0.359). The robustness, reliability, and generalization ability of the models were strictly evaluated by the Y-randomization test and applicability domain analysis. Moreover, the group contribution model based on 110 atom types and the local models for different ionization states were also established and compared to the global models. The results demonstrated that the descriptor-based consensus model is superior to the group contribution method, and the local models have no advantage over the global models. Finally, matched molecular pair (MMP) analysis and descriptor importance analysis were performed to extract transformation rules and give some explanations related to log D7.4. In conclusion, we believe that the consensus model developed in this study can be used as a reliable and promising tool to evaluate log D7.4 in drug discovery.

Remedial dosing recommendations for delayed or missed doses of valproic acid in patients with epilepsy based on Monte Carlo simulations.

OBJECTIVE: Delayed or missed doses are unavoidable in the pharmacotherapy of epilepsy and significantly compromise the efficacy of antiepileptic drug treatment. An inappropriate remedial regimen can cause seizure relapse or serious adverse events. This study investigated the effect of delayed or missed doses on the pharmacokinetics (PK) of valproic acid (VPA) in patients with epilepsy and established remedial dosing recommendations for nonadherent patients. METHODS: Monte Carlo simulations are based on all previous population pharmacokinetic models for pediatric, adult and elderly patients with epilepsy. The following four remedial strategies were investigated for each delayed dose: A) A partial dose or a regular dose is taken immediately; a regular dose is taken at the next scheduled time. B) The delayed dose was administered immediately, followed by a partial dose at the next scheduled time. C) The delayed dose and a partial dose are taken; the next scheduled time is skipped, and the regular regimen is resumed. D) Double doses are taken when missed one dose or two doses, and the regular regimen at the subsequent scheduled time is resumed. RESULTS: The recommended remedial dose was related to the delay duration and daily dose. Remedial dosing strategies A and B were almost equivalent, whereas Strategy C was recommended when the delayed dose was close to the next scheduled dose. Strategy D was only suggested for delayed two doses. CONCLUSION: Simulations provide quantitative insight into the remedial regimens for nonadherent patients, and clinicians should select the optimal regimen for each patient based on the individual's status.

Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery.

Aggregation has been posing a great challenge in drug discovery. Current computational approaches aiming to filter out aggregated molecules based on their similarity to known aggregators, such as Aggregator Advisor, have low prediction accuracy, and therefore development of reliable in silico models to detect aggregators is highly desirable. In this study, we built a data set consisting of 12 119 aggregators and 24 172 drugs or drug candidates and then developed a group of classification models based on the combination of two ensemble learning approaches and five types of molecular representations. The best model yielded an accuracy of 0.950 and an area under the curve (AUC) value of 0.987 for the training set, and an accuracy of 0.937 and an AUC of 0.976 for the test set. The best model also gave reliable predictions to the external validation set with 5681 aggregators since 80% of molecules were predicted to be aggregators with a prediction probability higher than 0.9. More importantly, we explored the relationship between colloidal aggregation and molecular features, and generalized a set of simple rules to detect aggregators. Molecular features, such as log D, the number of hydroxyl groups, the number of aromatic carbons attached to a hydrogen atom, and the number of sulfur atoms in aromatic heterocycles, would be helpful to distinguish aggregators from nonaggregators. A comparison with numerous existing druglikeness and aggregation filtering rules and models used in virtual screening verified the high reliability of the model and rules proposed in this study. We also used the model to screen several curated chemical databases, and almost 20% of molecules in the evaluated databases were predicted as aggregators, highlighting the potential high risk of aggregation in screening. Finally, we developed an online Web server of ChemAGG ( http://admet.scbdd.com/ChemAGG/index ), which offers a freely available tool to detect aggregators.

Remedial dosing recommendations for delayed or missed doses of lamotrigine in pediatric patients with epilepsy using Monte Carlo simulations.

OBJECTIVE: This study investigated the effect of delayed or missed doses on the pharmacokinetics (PK) of lamotrigine (LTG) in children with epilepsy and established remedial dosing recommendations for nonadherent patients. METHODS: The Monte Carlo simulation based on a published LTG population PK model was used to assess the effect of different scenarios of nonadherence and the subsequently administered remedial regimens. The following three remedial approaches were investigated for each delayed dose: A) A partial dose was administered immediately, and the regular dose was administered at the next scheduled time. B) The delayed dose was administered immediately, followed by a partial dose at the next scheduled time. C) The delayed and partial doses were coadministered immediately, the next scheduled dose was skipped, and the regular dosing was resumed at the subsequent scheduled time. The most appropriate remedial regimen was that with the shortest deviation time from the individual therapeutic window. RESULTS: The effect of nonadherence on PK was dependent on the delay duration and daily dose, and the recommended remedial dose was related to the delay duration and concomitant antiepileptic drugs. Remedial dosing strategies A and B were almost equivalent, whereas C showed a larger PK deviation time. If one dose was missed, double doses were not recommended for the next scheduled time. CONCLUSIONS: Simulations provide quantitative insight into the remedial regimens for nonadherent patients, and clinicians should select the optimal regimen based on the status of patients.

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes.

PURPOSE: Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus. METHODS: A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting. RESULTS: Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting. CONCLUSIONS: Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

Influences of CYP2D6*10 polymorphisms on the pharmacokinetics of iloperidone and its metabolites in Chinese patients with schizophrenia: a population pharmacokinetic analysis.

AIM: Iloperidone is an atypical antipsychotic drug that is mainly metabolized by CYP2D6, CYP3A4, and cytosolic enzymes. Previous studies show that extensive and poor metabolizers of CYP2D6 exhibit different plasma concentrations of iloperidone and its metabolites. The aim of this study was to develop a parent-metabolite population pharmacokinetic (PPK) model to quantify the effects of CYP2D6*10 allele on the pharmacokinetics of iloperidone and its metabolites in Chinese schizophrenia patients. METHODS: Seventy Chinese schizophrenia patients were enrolled, from whom limited blood samples were collected on d 15 (0 h) and d 28 (0, 4 and 12 h after drug administration). The plasma concentrations of iloperidone and its metabolites M1 (P-88) and M2 (P-95) were simultaneously detected using a validated HPLC-MS assay. CYP2D6*10 (rs1065852) genotyping was performed. A PPK model was developed based on data from the patients using the NONMEM software (version 7.2). A one-compartment model with first-order absorption and elimination was used to describe the pharmacokinetic data related to iloperidone and its metabolites. RESULTS: Patients with the CYP2D6*10 T/T genotype had significantly higher concentrations of iloperidone and M1, and lower concentrations of M2 than the patients with C/C or C/T genotypes. The CYP2D6*10 genotype affected the elimination constants for transformation of iloperidone to the metabolites M1 (K23) and M2 (K24). The K23 value of the patients with T/T genotype was 1.34-fold as great as that of the patients with C/C or C/T genotype. The K24 value of the patients with C/T and T/T genotypes was 0.693- and 0.492-fold, respectively, as low as that of the patients with C/C genotype. CONCLUSION: CYP2D6*10 mutations affect the pharmacokinetics of iloperidone and its metabolites in Chinese schizophrenia patients, suggesting that the clinical doses of iloperidone for patients with CYP2D6*10 mutations need to be optimized.

[Effects of multiple-trough sampling design and algorithm on the estimation of population and individual pharmacokinetic parameters].

The purpose of this study is to investigate the effects of multiple-trough sampling design and nonlinear mixed effect modeling (NONMEM) algorithm on the estimation of population and individual pharmacokinetic parameters. Oxcarbazepine and tacrolimus were used as one-compartment and two-compartment model drugs, respectively. Seven sampling designs were investigated using various number of trough concentrations per individual ranging from 1-4. Monte Carlo simulations were performed to produce state-steady trough concentrations. One-compartment model was used to fit simulated data from oxcarbazepine and tacrolimus. The accuracy and precision of the estimated parameters were evaluated using the median prediction error (PE), the median absolute PE and boxplot. The results indicated that trough concentrations could yield reliable estimates of apparent clearance (CL/F). For oxcarbazepine, as the number of trough concentrations per subject increased, the accuracy and precision of CL/F, between-subject variability (BSV) of CL/F and residual variability (RUV) tended to be improved. For tacrolimus, however, although no improvement were observed in the accuracy of CL/F and BSV of CL/F, the PE distribution ranges were significantly narrowed and the RUV estimates were less bias and imprecise. In terms of algorithm, Monte Carlo importance sampling (IMP) and IMP assisted by mode a posteriori estimation (IMPMAP) were consistently better than other methods. Additionally, the sampling design had no significant effects on the individual parameter estimates, which were only depended on the interaction between BSV and RUV in various algorithms. Decreased in BSV and RUV levels can improve the accuracy and precision of the estimation for both population and individual pharmacokinetic parameter estimates.

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre.

Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (R 2) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm3, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https://sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs.

Long-term prognosis of patients with gallbladder carcinoma after curative-intent resection based on changes in the ratio of carbohydrate antigen 19-9 to total bilirubin (CA19-9/TB): a multicenter retrospective cohort study.

BACKGROUND: The prognostic value of carbohydrate antigen 19-9 (CA19-9) is known to be affected by elevated bilirubin levels in patients with gallbladder carcinoma (GBC). The clinical significance of changes in the ratio of CA19-9 levels to total bilirubin (TB) levels in patients with GBC after curative-intent resection remains unknown. The aim of this study was to determine the prognostic value of changes in preoperative and postoperative CA19-9/TB ratio in these patients. METHODS: Prospectively collected data on consecutive patients who underwent curative-intent resection for GBC between January 2015 and December 2020 stored in a multicenter database from 10 hospitals were analyzed in this retrospective cohort study. Based on the adjusted CA19-9 defined as the ratio of CA19-9 to TB, and using 2×10 3  U/µmol as the upper normal value, patients were divided into a normal group (with normal preoperative and postoperative adjusted CA19-9), a normalization group (with abnormal preoperative but normal postoperative adjusted CA19-9), and a non-normalization group (with abnormal postoperative adjusted CA19-9). The primary outcomes were overall survival (OS) and recurrence-free survival (RFS). The log-rank test was used to compare OS and RFS among the groups. The Cox regression model was used to determine factors independently associated with OS and RFS. RESULTS: The normal group ( n =179 patients) and the normalization group ( n =73 patients) had better OS and RFS than the non-normalization group ( n =65 patients) (the 3-year OS rates 72.0%, 58.4% and 24.2%, respectively; the RFS rates 54.5%, 25.5% and 11.8%, respectively; both P <0.001). There were no significant differences between the normal and the normalization groups in OS and RFS (OS, P =0.255; RFS, P =0.130). Cox regression analysis confirmed that the non-normalization group was independently associated with worse OS and RFS. Subgroup analysis revealed that the non-normalization group of patients who received adjuvant therapy had significantly improved OS and RFS as compared to those who did not receive adjuvant therapy (OS, P =0.025; RFS, P =0.003). CONCLUSIONS: Patients with GBC who underwent curative-intent surgical resection with postoperative abnormal levels of adjusted CA19-9 (the CA19-9/TB ratio) were associated with poorer long-term survival outcomes. Adjuvant therapy after surgery improved the long-term outcomes of these patients.

Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis.

OBJECTIVE: Tacrolimus is used clinically for the long-term treatment of antirejection of transplanted organs in liver and kidney transplant recipients, although dose optimization is poorly managed. The aim of this study was to examine the association between tacrolimus pharmacokinetic variability and CYP3A4 and CYP3A5 genotypes by a population pharmacokinetic analysis based on routine drug monitoring data in adult renal transplant recipients. MATERIALS AND METHODS: Trough tacrolimus concentrations were obtained from 161 adult kidney transplant recipients after transplantation. The population pharmacokinetic analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 7.2. The CYP3A4*1G and CYP3A5*3 genetic polymorphisms from the patients studied were determined by direct sequencing using a validated automated genetic analyzer. RESULTS: A one-compartment model with first-order absorption and elimination adequately described the pharmacokinetics of tacrolimus. Covariates including CYP3A5*3 and CYP3A4*1G alleles and hematocrit were retained in the final model. The apparent clearance of tacrolimus was about two-fold higher in kidney transplant patients with higher enzymatic activity of CYP3A5*1 and CYP3A4*1G (with the CYP3A5*1/*1 or *1/*3 and CYP3A4*1/*1G or CYP3A4*1G/*1G) compared with those with lower enzymatic activity (CYP3A5*3/*3 and CYP3A4*1/*1). CONCLUSION: This is the first study to extensively determine the effect of CYP3A4*1G and CYP3A5*3 genetic polymorphisms and hematocrit value on tacrolimus pharmacokinetics in Chinese renal transplant recipients. The findings suggest that CYP3A5*3 and CYP3A4*1G polymorphisms and hematocrit are determinant factors in the apparent clearance of tacrolimus. The initial dose design is mainly based on CYP3A5 and CYP3A4 genotypes as well as hematocrit. This result may also be useful for maintenance tacrolimus dose optimization and may help to avoid fluctuating tacrolimus levels and improve the efficacy and tolerability of tacrolimus in kidney transplant recipients.

The effect of poor compliance on the pharmacokinetics of carbamazepine and its epoxide metabolite using Monte Carlo simulation.

AIM: To study the effects of delayed and missed doses (poor compliance) on the pharmacokinetics of carbamazepine (CBZ) and its main active metabolite carbamazepine-10,11-epoxide (CBZE) in Chinese epilepsy patients using Monte Carlo simulation. METHODS: CBZ and CBZE time-concentration profiles in various scenarios were generated based on a population pharmacokinetic study in Chinese epilepsy patients using Monte Carlo simulation. The scenarios included patients given multiple doses of CBZ that ranged from 100 to 300 mg three times daily or from 200 to 300 mg every 12 h. The therapeutic range of CBZ and CBZE for each scenario was estimated to assess the effect of delayed or missed doses and to design corresponding rescue regimens. Moreover, the impact of body weight, absorption rate and co-therapy with other antiepileptic drugs (phenytoin, phenobarbital and valproic acid) on the dosage recommendation was investigated in the event of poor compliance. RESULTS: The risk for a sub-therapeutic range of CBZ and CBZE was increased in a dose-dependent manner in both two and three times daily regimens when delayed or missed doses occurred. The effects of poor compliance was less prominent on the lower daily doses compared with those on the higher daily doses. The dose recommendations, in the event of poor compliance, were time related and dose dependent. Patient body weight, absorption rate and co-therapy with phenytoin, phenobarbital and valproic acid had no significant impact on the dose recommendation. CONCLUSION: Patients with epilepsy should take the delayed doses as soon as they remember, and partial missed doses may need to be taken near or at the next scheduled time.

[Estimation of individual pharmacokinetic parameters using maximum a posteriori Bayesian method with D-optimal sampling strategy].

This study was aimed to develop a maximum a posteriori Bayesian (MAPB) estimation method to estimate individual pharmacokinetic parameters based on D-optimal sampling strategy. Meanwhile, the performance of MAPB was compared with the multiple linear regression (MLR) method in terms of accuracy and precision. Pharmacokinetic study of pioglitazone was employed as the example case. The population pharmacokinetics was characterized by nonlinear mixed effects model (NONMEM). The sparse sampling strategy (1-4 points) was identified by D-optimal algorithm using WinPOPT software. The simulated data generated by Monte Carlo method were used to access the performance of MAPB and MLR. As the number of samples per subject decreased, the accuracy and precision of MAPB method tended to get worse. The estimation for CL and Vby MAPB using D-optimal two-point design had less bias with low inter-individual variability, and had more bias and imprecision with high residue variability. The estimation of AUC by MAPB using D-optimal 2 points design had similar accuracy and precision to MLR. However, MAPB estimation was better than MLR while adjusting the sampling time to one hour. Overall, the MAPB method had similar predictive performance as MLR, but MAPB could provide more pharmacokinetic information with higher sampling flexibility.

Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice.

Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure-toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.

How to Handle Delayed or Missed Doses: A Population Pharmacokinetic Perspective.

Delayed or missed doses are unavoidable in clinical practice and remain as a challenge that threatens a patient's health and quality-of-life, especially in the pharmacotherapy of chronic disease treatment. Unfortunately, information or guidance concerning the management of delayed or missed doses is scarce, precluding clinicians or clinical pharmacologists from instructing patients in a precision dosing manner. It is therefore urgent to develop remedial strategies to inform patients of alternative dosing regimens in compensation for the loss of efficacy due to delayed or missed doses and minimize unintended clinical consequences. Studies aiming to establish remedial regimens have been conducted since the 1980s for oral contraceptives and antihypertensive agents, using the controlled substitution of placebos for active medications. However, it appeared to be unethical in many areas of pharmacotherapy due to deliberately discontinuing or restarting the medication. Alternatively, pharmacometric modeling and simulation offers an opportunity to investigate the effect of various non-adherence scenarios on pharmacokinetic profiles and establish the optimum remedial dosing regimen in a time-effective and systematical way. This review provides a general overview of procedures and strategies on how to develop remedial dosing regimens based on pharmacometric approaches through the scrutiny of case examples in the literature.

Population pharmacokinetic modelling for enterohepatic circulation of mycophenolic acid in healthy Chinese and the influence of polymorphisms in UGT1A9.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: Mycophenolic acid (MPA) undergoes enterohepatic circulation (EHC) in the body and several population models have been proposed to describe this process using sparse data. Recent studies in Whites have found that polymorphism in UGT1A9 could partly explain the large interindividual variability associated with the pharmacokinetics of MPA. WHAT THIS STUDY ADDS: A new population pharmacokinetic model for EHC combining MPA and its main glucuronide metabolite (MPAG) simultaneously was established based on physiological aspects of biliary excretion using intensive sampling data. Pharmacokinetic profiles of MPA and MPAG with the UGT1A9 polymorphism in healthy Chinese were characterized. AIMS To establish a population pharmacokinetic model that describes enterohepatic circulation (EHC) of mycophenolic acid (MPA) based on physiological considerations and to investigate the influence of polymorphisms of UGT1A9 on the pharmacokinetics of MPA. METHODS: Pharmacokinetic data were obtained from two comparative bioavailability studies of oral mycophenolic mofetil formulations. Nonlinear mixed effects modelling was employed to develop an EHC model including both MPA and its main glucuronide metabolite (MPAG) simultaneously. Demographic characteristics and UGT1A9 polymorphisms were screened as covariates. RESULTS: In total, 590 MPA and 589 MPAG concentration-time points from 42 healthy male volunteers were employed in this study. The chain compartment model included an intestinal compartment, a gallbladder compartment, a central and a peripheral compartment for MPA and a central compartment for MPAG. The typical population clearance (CL/F) estimates with its relative standard error for MPA and MPAG were 10.2 l h(-1) (5.7%) and 1.38 l h(-1) (6.9%), respectively. The amount of MPA recycled in the body was estimated to be 29.1% of the total amount absorbed. Covariate analysis showed that body weight was positively correlated with CL/F of MPA, intercompartment CL/F of MPA and distribution volume of MPA peripheral compartment. Polymorphisms of UGT1A9 did not show any effect on the pharmacokinetics of MPA and MPAG. The model evaluation tests indicated that the proposed model can describe the pharmacokinetic profiles of MPA and MPAG in healthy Chinese subjects. CONCLUSIONS: The proposed model may provide a valuable approach for planning future pharmacokinetic-pharmacodynamic studies and for designing proper dosage regimens of MPA.

Population Pharmacokinetic Modeling of Diltiazem in Chinese Renal Transplant Recipients.

BACKGROUND AND OBJECTIVES: Diltiazem is a benzothiazepine calcium blocker and widely used in renal transplant patients since it improves the level of tacrolimus or cyclosporine A concentration. Several population pharmacokinetic (PopPK) models had been established for cyclosporine A and tacrolimus but no specific PopPK model was established for diltiazem. The aim of the study is to develop a PopPK model for diltiazem in renal transplant recipients and provide relevant pharmacokinetic parameters of diltiazem for further pharmacokinetic interaction study. METHODS: Patients received tacrolimus as primary immunosuppressant agent after renal transplant and started administration of diltiazem 90 mg twice daily on 5th day. The concentration of diltiazem at 0, 0.5, 1, 2, 8, and 12 h was measured by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Genotyping for CYP3A4*1G, CYP3A5*3, and MDR1 3435 was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). 25 covariates were considered in the stepwise covariate model (SCM) building procedure. RESULTS: One-compartment structural pharmacokinetic model with first-order absorption and elimination was used to describe the pharmacokinetic characteristics of diltiazem. Total bilirubin (TBIL) influenced apparent volume of distribution (V/F) of diltiazem in the forward selection. The absorption rate constant (K a), V/F, and apparent oral clearance (CL/F) of the final population pharmacokinetic (PopPK) model of diltiazem were 1.96/h, 3550 L, and 92.4 L/h, respectively. CONCLUSION: A PopPK model of diltiazem is established in Chinese renal transplant recipients and it will provide relevant pharmacokinetic parameters of diltiazem for further pharmacokinetic interaction study.

[Development and identifiability analysis of parent-metabolite pharmacokinetic model for risperidone and its main active metabolite 9-hydroxyrisperidone].

To develop a parent-metabolite pharmacokinetic model for risperidone (RIP) and its major active metabolite (9-hydroxyrisperidone) and investigate their pharmacokinetics characteristics in healthy male volunteers, twenty-two healthy volunteers were orally given a single dose of 2 mg RIP. Plasma samples were collected in the period of 96 hours and concentrations of RIP and 9-hydroxyrisperidone were measured by a validated HPLC/MS method. CYP2D6 phenotypes were identified by the T1/2 of RIP and 9-hydroxyrisperidone according to the literature. Model structure identifiability analysis was performed by the similarity transformation approach to investigate whether the unknown parameters of the proposed model could be estimated from the designed experiment. Pharmacokinetics parameters were estimated using weighted least squares method, and the final pharmacokinetics model were tested and evaluated by Monte Carlo simulation. Eighteen volunteers were phenotyped as extensive metabolizers (EM) and four volunteers were identified as intermediate metabolizers (IM). The final model included central and peripheral compartment for both parent (RIP) and metabolite (9-hydroxyrisperidone) respectively. Model structure identifiability analysis indicated that the proposed model was local identifiable. However, if the ratio of RIP converted to 9-hydroxyrisperidone was assumed to be 32% in EM, and 22% in IM, the model could be globally identifiable. The predicted time-concentration curve and AUC(0-t), C(max), T(max) of RIP and 9-hydroxyrisperidone estimated by the established model were in agreement with the observations and noncompartment analysis. Rate constant of RIP conversion to 9-hydroxyrisperidone was (0.12 +/- 0.08) h(-1) and (0.014 +/- 0.007) h(-1) for EM and IM, respectively. Elimination rate constants of RIP were (0.25 +/- 0.18) and (0.05 +/- 0.23) h(-1) for EM and IM, respectively. Model validation result showed that all parameters derived from the concentration data fitted well with the theoretical value, with mean prediction error of most PK parameter within +/- 15%. The established model well defined the disposition of RIP and 9-hydroxyrisperidone simultaneously and showed large inter-individual pharmacokinetics variation in different CYP2D6 phenotype. The model also provide a useful approach to characterize pharmacokinetics of other parent-metabolite drugs.

[Bioequivalence assessment of pioglitazone hydrochloride oral preparation by limited sampling strategy].

AIM: To develop limited sampling strategy (LSS) for estimation of C(max) and AUC(0-t) and assessing the bioequivalence of two pioglitazone hydrochloride (PGT) preparations. METHODS: Healthy subjects (n = 20), enrolled in a bioequivalence study, were received 30 mg PGT po of reference or test formulation. The plasma concentration of PGT was determined by the validated HPLC method. A multiple linear regression analysis of the Cmax and AUC(0-t) against the PGT concentration for the reference formulation was carried out to develop LSS models to estimate these parameters. The models were internally validated by the Jackknife method and externally validated using simulated sets generated by Monte Carlo method. The best model was employed to assess bioequivalence of the two PGT formulations. RESULTS: The linear relationship between pharmacokinetics parameters and single concentration point was poor. Several models for these parameters estimation met the predefined criteria (r2 > 0.9). The Jackknife validation procedure revealed that LSS models based on two sampling times (C1, C2.5 and C1.5, C2.5 for C(max); C1.5, C9 and C2.5, C9 for AUC(0-t) predict accurately. Mean prediction errors (MPE) were less than 3%, and mean absolute prediction error (MAE) were less than 9%. The prediction error (PE) beyond 20% was less than 5% of total samples. Model external validation by Monte Carlo simulated data indicated that the most informative sampling combinations were C1.5, C2.5 for C(max), and C1.5, C9 for AUC(0-t), respectively. MPE and MAE of the proposed models were less than 5% , and 9% respectively. The PE beyond 20% was less than 5% of the total. Bioequivalence assessment of the two PGT 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 PGT formulations were bioequivalent. CONCLUSION: The LSS method for bioequivalence assessment of PGT formulations was established and proved to be applicable and accurate. Thus, it could be considered appropriate for PGT bioequivalence study with inexpensive cost of sampling acquisition and analysis. Key words: pioglitazone hydrochloride; limited sampling strategy; Monte Carlo simulation; bioequivalence