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In this paper, we used CALYPSO program which is conjuncted with DFT to investigate Li doped small-sized neutral Bn clusters. From ground-state and low energy structures, the properties and geometric significance of Li-doped boron clusters are greatly affected. Except for LiB9, which adds a B atom on preceding LiB8 boron wheel, the ground-state clusters of LiBn are formed by replacing the terminal boron atom of the Bn+1 clusters in the same size with a Li atom or by adding a Li atom to the Bn clusters. The transition point from 2 to 3D configuration of LiBn clusters appears at n = 5. The stability of LiBn clusters was analyzed by calculating the average binding energy, Gibbs free energy, first-order differential energy, second-order differential energy, HOMO–LUMO gap (the energy gap between the highest occupied and lowest unoccupied molecular orbital) and charge transfer. The results show that the LiB11 cluster with excellent symmetry and planar structure is the relative most stable structure. Molecular orbital and chemical bonding analysis indicated that the main reason for the stability of the LiB11 cluster is mainly due to the strong s-p bonding of B–B. [ABSTRACT FROM AUTHOR]

In this study, the unbiased crystal structure analysis by particle swarm optimisation (CALYPSO) combined with the density functional theory (DFT) method were used to perform a structure search for neutral Cs2Bn (n = 1–12) clusters, as well as the corresponding global minimum of the potential energy surface were determined. The ground-state structures of the Cs2Bn (n = 1–12) clusters have four types of geometries: planar, arch bridge, double pyramid, and shield shape. Subsequently, the relative stability of the ground-state structures was systematically analysed based on three effective rules, which indicates that the C7V Cs2B8 with a double pyramid structure has unusual stability. The results of the molecular orbitals (MOs) and density of states analysis (DOS) show that the B-2s and 2p atomic orbitals of Cs2B8 are the main components of MOs. In addition, from the results of adaptive natural density partitioning (AdNDP) and localised orbital locator (LOL), it can be seen that the excellent stability of Cs2B8 originates from the delocalised large π bonds with aromaticity and the s–p bonding of B–B. Our results reveal the structural growth mode of Cs2Bn clusters and enrich the chemical bonding properties of boron-based clusters. [ABSTRACT FROM AUTHOR]

In many practical computer engineering projects, will use the ID as a unique identifier. Many methods to generate ID, but it is not easy to choose a cost-effective solution. Through the improved ID method to generate common defects, ID manufacturing is to build a high performance and low cost, for rapid generation of distributed only under the environment of ID.

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 (Ka), 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• We predicted the acute toxicity of organophosphates based on chemical structure. • Large data were collected to develop quantitative structure-toxicity relationship models. • The random forest algorithm was used to develop these models. • Acute toxicity data from different administration routes could not be combined. • QSTR models achieve high predictive performance despite large variance in datasets. 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. [ABSTRACT FROM AUTHOR]

Background and Objectives:Several studies have suggested a difference in clinical features of intellectual ability and psychiatric illness in the Prader–Willi syndrome (PWS) with the 15q11-q13 paternal deletion and maternal uniparental disomy (mUPD). Our objective was to appraise evidence on this association through a meta-analysis. Methods:The electronic records PubMed and EMBASE from 1956 to 2012 were extracted for meta-analysis. Meta-analyses were performed by using fixed effect model. Mean difference, odds ratio, and 95% confidence interval were calculated. Results:We retrieved a total of 744 PWS cases from 13 studies. These include 423 cases with paternal 15q11-q13 deletions and 318 cases of mUPD. Compare to the PWS cases with mUPD, PWS patients with the paternal 15q11-q13 deletion associated with significantly lower full scale IQ (FSIQ) [mean difference (MD), -2.69; 95%CI, -4.86 to -0.52; p=0.02] and verbal IQ (VIQ) (MD, -7.5; 95%CI, -9.75 to -5.26; p<0.00001) but higher performance IQ (PIQ) (MD, 4.02; 95%CI, 1.13 to 6.91; p=0.006). In contrast, PWS patients with mUPD are associated with significantly higher risk of psychiatric illness [odds rate (OR), 0.14; 95%CI, 0.08 to 0.23; p<0.00001] and higher risk of bipolar disorder (OR, 0.04; 95%CI, 0.01 to 0.23; p=0.0002). Conclusions:Significant different clinical features of cognitive development and psychiatric illness are associated with PWS with different molecular defects. These findings provide support for evidence based practice to evaluate and manage the PWS syndrome with different molecular defects. [ABSTRACT FROM AUTHOR]

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. 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. 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. Simulations provide quantitative insight into the remedial regimens for nonadherent patients, and clinicians should select the optimal regimen based on the status of patients. Three remedial strategies identified for a 12-year-old and 40-kg patient administered lamotrigine on 125 mg q12h regimen without an inducer or inhibitor when dose was delayed by 10 h. The dark pink shadow represents the distribution of the 5th–95th percentiles of the simulated concentrations in 90% of the virtual subjects, and the light pink shadows represent the distribution of the simulated concentrations outside the 5th–95th percentiles in the remaining 10% virtual subjects. Red solid line represents median of the simulated concentrations and dotted lines represent 0.5th and 99.5th percentiles of the simulated concentrations, respectively. Black dotted lines represent the individual therapeutic range (2.4–16.8 mg/L). Black solid line represents the deviation time. Unlabelled Image • Monte Carlo simulation was used to develop remedial regimens of nonadherence. • The remedial regimens were dependent on the delayed time and comedications. • Clinicians could select the optimal remedial strategy for individual patients. [ABSTRACT FROM AUTHOR]

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.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.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).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. [ABSTRACT FROM AUTHOR]

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/cm 3 , 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., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

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., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Lipophilicity, as evaluated by the n -octanol/buffer solution distribution coefficient at pH = 7.4 (log  D 7.4 ), 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  D 7.4 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 ( R T 2 = 0.906 and RMSE T = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance ( R T 2 = 0.922 and RMSE T = 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  D 7.4 . In conclusion, we believe that the consensus model developed in this study can be used as a reliable and promising tool to evaluate log  D 7.4 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.

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 M 1 (P-88) and M 2 (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 M 1 , and lower concentrations of M 2 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 M 1 (K 23 ) and M 2 (K 24 ). The K 23 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 K 24 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.

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.

Background: This study aimed to evaluate the effect of UGT1A8*2, SLCO1B3 T334G, ABCC2 C-24T and ABCG2 C421A polymorphisms on the pharmacokinetics (PKs) of mycophenolic acid (MPA) and its phenolic glucuronide (MPAG) in healthy Chinese volunteers and in stable renal transplant patients., Methods: The data were extracted from comparative bioavailability studies conducted in 42 healthy individuals and 37 renal transplant patients. A complete PK profile was obtained over 48 h for healthy volunteers and over 12h for the transplant patients. The MPA/MPAG plasma concentrations were measured by HPLC. The genotypes were determined using either the Taqman probe technique or direct sequencing. A multivariate analysis was used to assess the effect of the genotypes (UGT1A8*2, SLCO1B3 T334G, ABCC2 C-24T and ABCG2 C421A) and other covariates (age, weight, height, calculated creatinine clearance, serum albumin, haemoglobin and drug comedication) on the AUC(4-12) and AUC(0-12) for MPA and MPAG in the healthy volunteers and patients., Results: In the healthy volunteers, the dose-adjusted geometric means (GM) of the MPA AUC(4-12) in individuals with the SLCO1B3 334T allele were 30.4% lower than those values in the 334G homozygote carriers (P<0.05); in the transplant patients, the steroid dose was associated with a negative effect on the AUC of MPAG (P<0.03) and weight was associated with a negative effect on the AUC for MPA in the healthy volunteers and patients (P<0.03). No other significant effect of genotype or of the other studied variables on AUC(4-12) or AUC(0-12) of MPA/MPAG was found in the healthy volunteers or patients., Conclusions: The PKs of MPA is affected by the SLCO1B3 polymorphism in healthy Chinese individuals. The absence of an effect of SLCO1B3 polymorphisms in transplant patients may be due to the co-administration of cyclosporine (CsA). Concomitant steroid dose and weight are two important covariates of the AUC of MPA and MPAG, which should be taken into account in clinical use. Further confirmatory in vivo studies are needed., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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.

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.

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

Aim: To establish a new amino acid structure descriptor that can be applied to polypeptide QSAR studies., Methods: The new amino acid structure descriptor c-scales were derived from a principal components analysis of 167 amino acid structure descriptor indexes by theoretic calculation. The c1,c2,c3-scales were related to 3D structural features of amino acid such as steric, electronic and conformation properties etc. G/PLS regression method was used to find out the relationship between the c-scales and the biological activity and developed QSAR models of the polypeptides., Results: Using the established method, we developed accordingly QSAR models of Bitter tasting dipeptide, ACE inhibitors and bradykinin-potentiating pentapeptides and their r2 and XV-r2 were more than 0.70., Conclusion: The c-scales can quantitatively describe the 3D structural features of any coded and non-coded amino acid and can be used to establish a QSAR model of good predictability.

Aim: To determine the relationship between C3435T mutation in exon 26 of the human multidrug resistant 1 gene and cyclosporine (CsA) pharmacokinetic (PK) parameters among healthy Chinese volunteers by nonlinear mixed effect model (NONMEM)., Methods: Twenty healthy subjects were given orally a single dose of 500 mg CsA in microemulsion solution. Blood CsA concentrations were measured with HPLC and the genotype for the C3435T polymorphism of MDR1 gene was determined with the PCR and restriction fragment length polymorphism. The results were further confirmed by sequencing. NONMEM was performed to assess the effect of genotype on CsA PK profile., Results: MDR1 C3435T genotype was identified as the best predictor of CsA systemic exposure. The relative bioavailability of CsA was 40% higher in subjects who carried at least one 3435C allele compared to that of TT type individuals in the study population., Conclusion: The MDR1 C3435T genotype offers a potential basis of mechanism to explain inter-subject differences in CsA oral bioavailability.