Application of a count data model to evaluate the anti-metastatic efficacy of QAP14 in 4T1 breast cancer allografts.

Dopamine D1 receptor (D1DR) is proved to be a promising target to prevent tumor metastasis, and our previous studies showed that QAP14, a potent anti-cancer agent, exerted inhibitory effect on lung metastasis via D1DR activation. Therefore, the purpose of the study was to establish count data models to quantitatively characterize the disease progression of lung metastasis and assess the anti-metastatic effect of QAP14. Data of metastatic progression were collected in 4T1 tumor-bearing mice. Generalized Poisson distribution best described the variability of metastasis counts among the individuals. An empirical PK/PD model was developed to establish mathematical relationships between steady plasma concentrations of QAP14 and metastasis growth dynamics. The latency period of metastasis was estimated to be 12 days after tumor implantation. Our model structure also fitted well to other D1DR agonists (fenoldopam and l-stepholidine) which had inhibitory impact on breast cancer lung metastasis likewise. QAP14 40 mg/kg showed the best inhibitory efficacy, for it provided the longest prolongation of metastasis-free periods compared with fenoldopam or l-stepholidine. This study provides a quantitative method to describe the lung metastasis progression of 4T1 allografts, as well as an alternative PD model structure to evaluate anti-metastatic efficacy.

An integrated PK/PD model investigating the impact of tumor size and systemic safety on animal survival in SW1990 pancreatic cancer xenograft.

Survival is one of the most important endpoints in cancer therapy, and parametric survival analysis could comprehensively reveal the overall result of disease progression, drug efficacy, toxicity as well as their interactions. In this study we investigated the efficacy and toxicity of dexamethasone (DEX) combined with gemcitabine (GEM) in pancreatic cancer xenograft. Nude mice bearing SW1990 pancreatic cancer cells derived tumor were treated with DEX (4 mg/kg, i.g.) and GEM (15 mg/kg, i.v.) alone or in combination repeatedly (QD, Q3D, Q7D) until the death of animal or the end of study. Tumor volumes and net body weight (NBW) were assessed every other day. Taking NBW as a systemic safety indicator, an integrated pharmacokinetic/pharmacodynamic (PK/PD) model was developed to quantitatively describe the impact of tumor size and systemic safety on animal survival. The PK/PD models with time course data for tumor size and NBW were established, respectively, in a sequential manner; a parametric time-to-event (TTE) model was also developed based on the longitudinal PK/PD models to describe the survival results of the SW1990 tumor-bearing mice. These models were evaluated and externally validated. Only the mice with good tumor growth inhibition and relatively stable NBW had an improved survival result after DEX and GEM combination therapy, and the simulations based on the parametric TTE model showed that NBW played more important role in animals' survival compared with tumor size. The established model in this study demonstrates that tumor size was not always the most important reason for cancer-related death, and parametric survival analysis together with safety issues was also important in the evaluation of oncology therapies in preclinical studies.

QAP14 suppresses breast cancer stemness and metastasis via activation of dopamine D1 receptor.

Breast cancer is the second leading cause of cancer-related mortality in women, mainly due to metastasis, which is strongly associated with cancer stemness. Our previous studies showed that the eradication of cancer stem-like cells (CSCs) may be related to the activation of dopamine D1 receptor (D1DR). This study aimed to explicitly demonstrate the target-role of D1DR activation in antimetastatic therapy and to investigate the potential efficacy and the underlying D1DR-related mechanisms of QAP14, a new oral compound. 4T1, MDA-MB-231, and D1DR-knockout 4T1 (4T1-D1DR) cells were selected for in vitro study, while 4T1 and 4T1-D1DR cells were further used to establish a mouse allograft model for in vivo study. Our results showed that D1DR is abundantly expressed in both 4T1 and MDA-MB-231 cells and that knocking out D1DR in 4T1 cells accelerated migration and invasion in vitro as well as lung metastasis in vivo. QAP14 inhibited colony formation, cell motility, mammosphere formation and CSC frequency, induced CSC apoptosis and D1DR expression, and increased cAMP/cGMP levels. Additionally, QAP14 showed inhibitory effects on tumor growth and lung metastasis with acceptable safety in vivo. Knocking out D1DR almost completely abolished the efficacy, confirming that QAP14 exhibits its anti-CSC and antimetastatic effects through D1DR activation. The underlying mechanisms involved suppression of the nuclear factor κB (NF-κB)/protein kinase B (Akt) pathway and consequent downregulation of both epithelial-to-mesenchymal transition (EMT) process and cancer stemness. In summary, our findings suggest a potential candidate compound, QAP14, as well as a potential target, D1DR, for metastatic breast cancer therapy.

Dexamethasone inhibits pancreatic tumor growth in preclinical models: Involvement of activating glucocorticoid receptor.

Glucocorticoid receptor (GR) modulates extensive biological and pathological processes including tumor progression through diverse mechanisms. The regulatory effects of dexamethasone (DEX), a synthetic glucocorticoid, as well as its interaction with GR have been recognized beyond hematologic cancers. In the present study, we investigated the anti-cancer efficacy of DEX and the correlation with GR in pancreatic cancer, a most aggressive malignancy threatening human health. The differential levels of GR expression were examined in two human pancreatic cancer cell lines, PANC-1 and SW1990, as well as in xenografts and patient tumor tissues. DEX significantly inhibited colony formation, migration, and tumor growth of PANC-1 cells expressing abundant GR. The underlying mechanisms involved suppression of nuclear factor κB (NF-κB) phosphorylation and down-regulation of epithelial-to-mesenchymal transition (EMT), interleukin 6 (IL-6) and vascular endothelial growth factor (VEGF). The anti-cancer effects of DEX were partially reversed by GR silencing or combinational administration of GR antagonist, RU486. The dose-dependent efficacy of DEX in tumor growth inhibition was also demonstrated in a GR-positive patient-derived xenograft model along with safety in mice. DEX was less potent, however, in SW1990 cells with poor GR expression. Our findings suggest that DEX effectively inhibits pancreatic tumor growth partially through GR activation. The potential correlation between GR expression and anti-cancer efficacy of DEX may have some clinical implications.

Longitudinal model-based meta-analysis for survival probabilities in patients with castration-resistant prostate cancer.

PURPOSE: The aims of this longitudinal model-based meta-analysis (MBMA) were to indirectly compare the time courses of survival probabilities and to identify corresponding potential significant covariates across approved drugs in patients with castration-resistant prostate cancer (CRPC). METHODS: A systematic literature review for monotherapy studies in patients with CRPC was conducted up to August 8, 2018. The time courses of progression-free survival (PFS) and overall survival (OS) were fitted with parametric survival models. Covariate analyses were performed to determine the impact of treatment drugs, dosing regimens, and patient characteristics on the survival probabilities. Simulations were carried out to quantify the magnitude of covariate effects. RESULTS: A total of 146 studies including clinical trials and real-world data on longitudinal survival probabilities in 20,712 patients with CRPC were included in our meta-database. The time courses of PFS and OS probabilities were best described by the log-logistic model. There was no significant difference in median OS and PFS between docetaxel, cabazitaxel, abiraterone acetate, and enzalutamide. There was no significant dose-response relationship in PFS or OS for docetaxel at 50 to 120 mg/m2 every 3 weeks (Q3W) and cabazitaxel at 20 to 25 mg/m2 Q3W. Model-based simulations indicated that PFS probability was associated with chemotherapy, Gleason score, and baseline prostate-specific antigen (BLPSA), while OS probability was associated with chemotherapy, Gleason score, visceral metastasis, Eastern Cooperative Oncology Group performance status, and BLPSA. CONCLUSION: Our modeling and simulation framework can be applied to support indirect comparison, dose selection, and go/no-go decision-making for new agents targeting CRPC.

Disease progression model of 4T1 metastatic breast cancer.

Cancer metastasis is the main cause of death in various types of cancer. However, in the field of pharmacometrics, cancer disease progression models focus on the growth of primary tumors with tumor volume or weight as target values, while the metastasis process is less mentioned. We propose a series of mathematical models to quantitatively describe and predict the disease progression of 4T1 breast cancer in the aspect of primary breast tumor, lung metastasis and white blood cell. The 4T1 cells were injected into breast fat pad of female BALB/c mice to establish an animal model of breast cancer metastasis. The number and volume of lung metastases at different times were measured. Based on the above data, a disease progression model of breast cancer lung metastasis was established and parameter values were estimated. The white blood cell growth and the primary tumor growth of 4T1 mouse are also modeled. The established models can describe the lung metastasis of 4T1 breast cancer in three aspects: (1) the increase in metastasis number; (2) the growth of metastasis volume; (3) metastasis number-size distribution at different time points. Compared with the prior metastasis models based on von Forester equation, our models distinguished the growth rate of primary tumor and metastasis and got parameter values for 4T1 mouse model. And the current models optimized the metastasis number-size distribution model by utilizing logistic function instead of the prior power function. This study provides a comprehensive description of lung metastasis progression for 4T1 breast cancer model, as well as an alternative disease progression model structure for further pharmacodynamics modeling.

Preliminary physiologically based pharmacokinetic modeling of renally cleared drugs in Chinese pregnant women.

AIM: The aim of this study was to build and verify a preliminary physiologically based pharmacokinetic (PBPK) model of Chinese pregnant women. The model was used to predict maternal pharmacokinetics (PK) of 6 predominantly renally cleared drugs. METHOD: Based on SimCYP Caucasian pregnancy population dataset, the preliminary Chinese pregnant population was built by updating several key parameters and equations according to physiological parameters of Chinese (or Japanese) pregnant women. Drug-specific parameters of 6 renally cleared drugs were validated through PBPK modeling of Caucasian non-pregnant, Caucasian pregnant and Chinese non-pregnant population. The preliminary PBPK model of Chinese pregnant population was then developed by integrating the preliminary Chinese pregnant population and the drug-specific parameters. This model was verified by comparing the predicted maternal PK of these 6 drugs with the observed in vivo data from the literature. RESULTS: The preliminary Chinese pregnant population PBPK model successfully predicted the PK of 6 target drugs for different pregnancy stages. The predicted plasma concentrations time profiles fitted the observed data well, and most predicted PK parameters were within 2-fold of observed data. CONCLUSIONS: The preliminary Chinese pregnant population PBPK model provided a useful tool to predict the maternal PK of 6 predominantly renally cleared drugs in Chinese pregnant women.

N-arylpiperazine-containing compound (C2): An enhancer of sunitinib in the treatment of pancreatic cancer, involving D1DR activation.

Previous studies showed that dopamine (DA) significantly reduces the frequency of cancer stem-like cells (CSC) and enhances the efficacy of sunitinib (SUN) in the treatment of breast cancer and non-small cell lung cancer (NSCLC). To overcome the shortcomings of DA in clinical practice, the purpose of this study was to investigate the efficacy as well as the underlying mechanism of an orally available, N-arylpiperazine-containing compound C2, in the treatment of pancreatic cancer when used alone or in combination with SUN. Our results showed that C2 and SUN exerted synergistic effects on inhibiting the growth of SW1990 and PANC-1 pancreatic cancer cells. C2 significantly inhibited colony formation and migration of both cells. SW1990 xenograft and patient-derived xenograft (PDX) models were utilized for pharmacodynamic investigation in vivo. C2 alone showed little inhibition effect on tumor growth but increased the anti-tumor efficacy of SUN in both xenografts. Moreover, C2 down-regulated CSC markers (CD133 and ALDH) of both cancer cells and up-regulated the expression of dopamine receptor D1 (D1DR) in tumor. Besides, the SW1990 tumor growth was dose-dependently inhibited when the cells were pretreated with C2 before implantation. C2 increased intratumoral cAMP level, and the combination with D1DR specific antagonist SCH23390 reversed the above-mentioned effects of C2 both in vitro and in vivo, indicating the activation of D1DR may be involved in the underlying mechanism of C2 action. In summary, C2 could reduce the CSC frequency and enhance the anti-cancer effect of SUN in the treatment of pancreatic cancer, demonstrating its potential in cancer therapy.

Evaluation of potential surrogate endpoints for prediction of overall survival in patients with castration-resistant prostate cancer: trial-level meta-analysis.

PURPOSE: Overall survival (OS) has traditionally been the primary endpoint to evaluate drug efficiency in oncology but is often limited by the long observation period and high cost. The aims of this study were to perform a comprehensive meta-analysis at a clinical trial level to investigate the potential surrogate endpoints of OS in patients with castration-resistant prostate cancer (CRPC), and to predict OS based on the relationships associated with the potential surrogate endpoints. METHODS: A systematic literature search was conducted in the PubMed database up to August 2018. Correlations between OS and potential surrogate endpoints were determined by linear regression analysis weighted by the square roots of sample size. Simulations were conducted to assess the effect of covariates on the relationships between OS and surrogate endpoints. RESULTS: A total of 233 studies including clinical trials and real-world data were included in our dataset. The correlations between median OS and potential surrogate endpoints for androgen-targeting therapy (R2 = 0.58-0.92) were generally stronger than those for taxane chemotherapy (R2 = 0.37-0.71). Median radiographic progression-free survival (rPFS) showed the strongest correlations with median OS (R2 = 0.94) in patients treated with novel androgen-targeting therapy. CONCLUSION: The meta-analysis demonstrated that rPFS might serve as a potential surrogate endpoint of OS and offer opportunity to facilitate the interim analyses and decision-making during the early stage of clinical trials for androgen-targeting agents.

Preclinical PK/PD model for the combinatorial use of dexamethasone and sulpiride in the treatment of breast cancer.

Previous studies show that dopamine D2-like receptor (D2DR) antagonist sulpiride (SUL) enhances the antitumor efficacy of dexamethasone (DEX) in drug-resistant breast cancer involving cancer stem-like cells (CSCs). In this study, we investigated the pharmacokinetic (PK) properties of SUL in nude mice and developed a semi-mechanism PK/PD model to quantitatively characterize the synergistic effect of DEX and SUL in preclinical breast cancer xenografts. After nude mice received oral administration of a single dose of SUL (50 mg/kg, ig), plasma concentrations were assessed using LC-MS/MS. A two-compartment model with double first-order absorption rate was developed to describe the PK profiles of SUL. The pharmacodynamic (PD) study was conducted in nude mice bearing human breast cancer MCF-7/Adr xenografts, which received oral administration of DEX (1, 8 mg·kg-1·d-1) or SUL (25, 50 mg·kg-1·d-1) alone or in various combination. Tumor volumes were measured every other day. The PK model of SUL as well as that of DEX with a time-dependent clearance were integrated into the final PK/PD model both using Hill's function, where DEX exerted its antitumor efficacy by inhibiting the proliferation of tumor cells, and SUL enhanced DEX responses by decreasing the sensitivity parameter EC50. The PK/PD model was evaluated and subjected external validation. Finally, simulations were performed to predict the antitumor efficacy of DEX combined with SUL under various dose regimens, where changing dosing frequency of SUL had little effect, while the antitumor efficacy was predicted to be improved when DEX was given more frequently. The established PK/PD model in this study quantitatively characterizes the antitumor efficacy of the DEX combined with SUL as well as their synergism, and the simulations could provide reference for dose optimization of the combination in future studies.

Antitumor effect of axitinib combined with dopamine and PK-PD modeling in the treatment of human breast cancer xenograft.

Rising evidence has shown the development of resistance to vascular endothelial growth factor receptor (VEGFR) inhibitors in the practices of cancer therapy. It is reported that the efficacy of axitinib (AX), a VEGFR inhibitor, is limited in the treatment of breast cancer as a single agent or in combination with other chemotherapeutic drugs due to the probability of rising population of cancer stem-like cells (CSCs) caused by AX. The present study evaluated the effect of dopamine (DA) improving AX's efficacy on MCF-7/ADR breast cancer in vitro and in vivo, and developed a pharmacokinetic-pharmacodynamic (PK-PD) model describing the in vivo experimental data and characterizing the interaction of effect between AX and DA. The results showed that AX up-regulated the expression of breast CSC (BCSC) markers (CD44+/CD24-/low) in vivo, and DA significantly synergized the inhibitory effect on tumor growth by deducting the BCSC frequency. The PK-PD model quantitatively confirmed the synergistic interaction with the parameter estimate of interaction factor ψ 2.43. The dose regimen was optimized as 60 mg/kg AX i.g. b.i.d. combined with 50 mg/kg DA i.p. q3d in the simulation study on the basis of the PK-PD model. The model where DA synergistically enhances the effect of AX in an all-or-none manner provides a possible solution in modeling the agents like DA. Moreover, the outcome of AX and DA combination therapy in MCF-7/ADR breast cancer provided further insight of co-administering DA in the treatment of the possible CSC-causing AX-resisting breast cancer. And this combination therapy has the prospect of clinical translation.

Pharmacokinetic/Pharmacodynamic Modeling of Schedule-Dependent Interaction between Docetaxel and Cabozantinib in Human Prostate Cancer Xenograft Models.

In this work, a semimechanistic pharmacokinetic/pharmacodynamic (PK/PD) model to quantitatively describe the antitumor activity of docetaxel (Doc) and cabozantinib (Cab) under monotherapy, concurrent therapy, interval therapy, and different sequential therapy in mouse xenograft models of castration-resistant prostate cancer was developed and evaluated. The pharmacokinetics (PK) of Doc and Cab when administered separately and simultaneously were investigated in nude mice, and PD study was conducted in tumor-bearing mice treated with different dosing schedules. The PK interaction between Doc and Cab was expressed by adding the effect of Cab on the clearance of Doc in the PK model. And the PD interaction between the two drugs was demonstrated by the developed PK/PD model through the combination index "φ" Our results showed that the concurrent therapy and Doc followed by Cab (Doc ∼ Cab) sequential therapy exhibited better tumor inhibitory efficacy than monotherapy. The Cab followed by Doc (Cab ∼ Doc) sequential schedule was less effective than monotherapy, and the interval therapy did not enhance the antitumor efficacy compared with the concurrent therapy. The parameter φ estimated from the PK/PD model quantitatively characterized the action between Doc and Cab. There was no significant PD interaction between Doc and Cab in both the concurrent schedule and the interval schedule, whereas the effect of the two drugs in the "Doc ∼ Cab" and "Cab ∼ Doc" sequential schedule was synergistic and antagonistic, respectively. The proposed model properly described the antitumor effects of Doc and Cab under different treatment schedules and could be used for dose optimization through model-based simulation.

Time-dependent pharmacokinetics of dexamethasone and its efficacy in human breast cancer xenograft mice: a semi-mechanism-based pharmacokinetic/pharmacodynamic model.

Dexamethasone (DEX) is the substrate of CYP3A. However, the activity of CYP3A could be induced by DEX when DEX was persistently administered, resulting in auto-induction and time-dependent pharmacokinetics (pharmacokinetics with time-dependent clearance) of DEX. In this study we investigated the pharmacokinetic profiles of DEX after single or multiple doses in human breast cancer xenograft nude mice and established a semi-mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for characterizing the time-dependent PK of DEX as well as its anti-cancer effect. The mice were orally given a single or multiple doses (8 mg/kg) of DEX, and the plasma concentrations of DEX were assessed using LC-MS/MS. Tumor volumes were recorded daily. Based on the experimental data, a two-compartment model with first order absorption and time-dependent clearance was established, and the time-dependence of clearance was modeled by a sigmoid Emax equation. Moreover, a semi-mechanism-based PK/PD model was developed, in which the auto-induction effect of DEX on its metabolizing enzyme CYP3A was integrated and drug potency was described using an Emax equation. The PK/PD model was further used to predict the drug efficacy when the auto-induction effect was or was not considered, which further revealed the necessity of adding the auto-induction effect into the final PK/PD model. This study established a semi-mechanism-based PK/PD model for characterizing the time-dependent pharmacokinetics of DEX and its anti-cancer effect in breast cancer xenograft mice. The model may serve as a reference for DEX dose adjustments or optimization in future preclinical or clinical studies.

Integrated pharmacokinetic/viral dynamic model for daclatasvir/asunaprevir in treatment of patients with genotype 1 chronic hepatitis C.

In order to develop an integrated pharmacokinetic/viral dynamic (PK/VD) model to predict long-term virological response rates to daclatasvir (DCV) and asunaprevir (ASV) combination therapy in patients infected with genotype 1 (GT1) chronic hepatitis C virus (HCV), a systematic publication search was conducted for DCV and ASV administered alone and/or in combination in healthy subjects or patients with GT1 HCV infection. On the basis of a constructed meta-database, an integrated PK/VD model was developed, which adequately described both DCV and ASV PK profiles and viral load time curves. The IC50 values of DCV and ASV were estimated to be 0.041 and 2.45 µg/L, respectively, in GT1A patients. A sigmoid Emax function was applied to describe the antiviral effects of DCV and ASV, depending on the drug concentrations in the effect compartment. An empirical exponential function revealed that IC50 changing over time described drug resistance in HCV GT1A patients during DCV or ASV monotherapy. Finally, the PK/VD model was evaluated externally by comparing the expected and observed virological response rates during and post-treatment with DCV and ASV combination therapy in HCV GT1B patients. Both the rates were in general agreement. Our PK/VD model provides a useful platform for the characterization of pharmacokinetic/pharmacodynamic relationships and the prediction of long-term virological response rates to aid future development of direct acting antiviral drugs.

Pharmacokinetic-Pharmacodynamic Modeling of the Anti-Tumor Effect of Sunitinib Combined with Dopamine in the Human Non-Small Cell Lung Cancer Xenograft.

PURPOSE: To investigate the anti-tumor effect of sunitinib in combination with dopamine in the treatment of nu/nu nude mice bearing non-small cell lung cancer (NSCLC) A549 cells and to develop the combination PK/PD model. Further, simulations were conducted to optimize the administration regimens. METHODS: A PK/PD model was developed based on our preclinical experiment to explore the relationship between plasma concentration and drug effect quantitatively. Further, the model was evaluated and validated. By fixing the parameters obtained from the PK/PD model, simulations were built to predict the tumor suppression under various regimens. RESULTS: The synergistic effect was observed between sunitinib and dopamine in the study, which was confirmed by the effect constant (GAMA, estimated as 2.49). The enhanced potency of dopamine on sunitinib was exerted by on/off effect in the PK/PD model. The optimal dose regimen was selected as sunitinib (120 mg/kg, q3d) in combination with dopamine (2 mg/kg, q3d) based on the simulation study. CONCLUSIONS: The synergistic effect of sunitinib and dopamine was demonstrated by the preclinical experiment and confirmed by the developed PK/PD model. In addition, the regimens were optimized by means of modeling as well as simulation, which may be conducive to clinical study.

Population pharmacokinetic analysis of tacrolimus in Chinese myasthenia gravis patients.

The importance of tacrolimus in the treatment of myasthenia gravis (MG) as a substitute for corticosteroid-dependent immunosuppressive therapy is increasing. Thus far, however, no population pharmacokinetic (PopPK) analysis of tacrolimus in treating MG patients has been published. This article aimed to construct a PopPK model of tacrolimus for Chinese MG patients with the goal of improving its performance in MG treatment. A total of 253 trough concentration records were obtained from 83 Chinese MG patients. The effects of demographics, lifestyle and health status, biochemical test data, disease progression and treatment-related information (including co-administered medications) as covariates on the various parameters were investigated. The covariate selection was based on biological plausibility, clinical significance, statistical significance and reduction in inter-individual variability (IIV). Bootstrap and normalized prediction distribution error (NPDE) analysis were performed to validate the final model. A one-compartment PopPK model with first-order elimination and a fixed absorption phase was constructed. The estimated apparent oral clearance (CL/F) and apparent oral volume of distribution (V/F) were 3.6 L/h and 1700 L, respectively, in the MG patients. Hematocrit and blood urea nitrogen were identified as two covariates that significantly influenced the CL/F. Immunoglobulin treatment (PRO) also had the potential to influence V/F, which was consistent with the clinical observations and the high protein-binding property of tacrolimus. Other covariates including age, weight, gender and co-administered medications had no obvious influence on CL/F or V/F. The first PopPK model of tacrolimus in MG patients was established. The identified covariates were of biological plausibility and clinical importance to help individualize the dosing schedule in MG patients.

Dopamine D2 receptor antagonist sulpiride enhances dexamethasone responses in the treatment of drug-resistant and metastatic breast cancer.

Recent evidence shows that dopamine D2-like receptor (D2DR) antagonists, such as trifluoperazine and thioridazine, are effective for cancer therapy and inhibition of cancer stem-like cells (CSCs). In this study, we investigated the anti-cancer effects of combination therapy of dexamethasone (DEX) and sulpiride (SUL), an atypical antipsychotic, against drug-resistant and metastatic breast cancers and further explored the underlying mechanisms. Oral administration of SUL (25, 100 mg·kg-1·d-1) alone did not inhibit the tumor growth in human breast cancer MCF-7/Adr xenograft model, but dose-dependently decreased the proportion of CSCs in vitro and in vivo. In contrast, combination therapy of SUL (50 mg·kg-1·d-1) and DEX (8 mg·kg-1·d-1) markedly suppressed the tumor growth in MCF-7/Adr xenograft model with little systemic toxicity and lung metastasis in murine metastatic breast cancer 4T1 xenograft model. Among the metastasis-associated biomarkers analyzed, the combination therapy significantly decreased the levels of MMP-2, but increased E-cadherin levels in 4T1 xenograft tumors. Moreover, the combination therapy significantly inhibited the cell colony formation, migration and invasion of 4T1 and human breast cancer MDA-MB-231 cells in vitro. Addition of a specific D2DR agonist 7-OH-DPAT to the combination therapy reversed the enhanced anti-cancer effects in vivo and CSC population loss in tumor tissues. Our data demonstrate that SUL remarkably enhances the efficacy of DEX in the treatment of drug-resistant and metastatic breast cancer via the antagonism of D2DR, which might result from the eradication of CSCs.

Population pharmacokinetics of vancomycin in Chinese pediatric patients
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OBJECTIVE: This study was conducted to develop a population pharmacokinetic (PopPK) model for vancomycin and to detect the significant covariates that influence the PopPKs to facilitate individualized therapy for Chinese pediatric patients. METHODS: Patients ≤ 10 years old who received vancomycin for ≥ 72 hours between 2007 and 2010 were analyzed using a nonlinear mixed-effects modeling approach (-NONMEM). A one-compartment model with first-order elimination was chosen to depict the data. Stepwise covariate modeling (SCM) was employed to detect significant covariates and obtain a final model. Internal validation methods, including bootstrapping and visual predictive checks (VPC), were applied to evaluate the robustness and predictive power of the final model. RESULTS: The analysis included 54 pediatric inpatients with 128 serum concentration samples. The mean age (range) was 124.30 (1.29 - 541.4) weeks, the mean weight was 10.36 (1.4 - 33.5) kg, and the mean baseline serum creatinine (Scr) level was 0.39 (0.15 - 1.32) mg/dL. Pneumonia was the most common indication for vancomycin therapy (33.33%), followed by bacteremia (25.93%), and meningitis (22.22%). A PopPK model of vancomycin in Chinese pediatric patients was developed. Postnatal age (PNA) significantly affected the vancomycin clearance (CL) of pediatric patients, and the influence was described using the sigmoid maximum effect (Emax) model. The effect of body weight (WT) on CL and volume of distribution (V) was investigated using an allometric scaling equation. The final model parameters were CL(L/h) = 11.75×[PNA0.4672/(PNA0.4672+33.30.4672)]×(WT/70)0.75×e0.362 and V(L) = 54.49×WT/70×e0.6711. The model evaluation results suggested robustness and good predictability of the final model. CONCLUSION: A PopPK model of vancomycin for Chinese pediatric patients was developed in this study. The significant covariates distinguished in the final model can provide helpful information to facilitate individualized therapy for Chinese pediatric patients.
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[Population pharmacokinetics and its application in new drug research].

Population pharmacokinetics is an emerging discipline developed from the combination of classical pharmacokinetic compartment model and statistics principles, which has been received more and more attention in recent years. Population pharmacokinetics plays important roles in all stages of new drug research. In the early preclinical phase, population pharmacokinetic analysis can help to achieve the preliminary prediction of parameters from animal to human, optimize clinical trial designs, and shorten the time required for new drugs from laboratory to clinical trials. In clinical trials and applications stage, population pharmacokinetic research can help researchers investigate the related covariates that affecting pharmacokinetic behavior of patients comprehensively, and find potential drug-drug interactions in clinical. In addition, population pharmacokinetics has a unique advantage in pediatric drug development due to its strong analysis ability of sparse data. This paper provides a summary on the history and methods of population pharmacokinetics, and the application in new drug discovery and development.