Longitudinal and time-to-event modeling for the survival of advanced pancreatic ductal adenocarcinoma patients.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers especially at advanced stage. In order to analyze the dynamics of potential prognostic biomarkers and further quantify their relationships with the overall survival (OS) of advanced PDAC patients, we herein developed a parametric time-to-event (TTE) model integrated with longitudinal submodels. Data from 104 patients receiving standard chemotherapies were retrospectively collected for model development, and other 54 patients were enrolled as external validation. The longitudinal submodels were developed with the time-course data of sum of longest diameters (SLD) of tumors, serum albumin (ALB) and body weight (BW) using nonlinear mixed effect models. The model-derived metrics including model parameters and individual predictions at different time points were further analyzed in the TTE model, together with other baseline information of patients. A linear growth-exponential shrinkage model was employed to describe the dynamics of SLD, while logistic models were used to fit the relationship of time prior to death with ALB and BW. The TTE model estimated the ALB and BW changes at the 9th week after chemotherapies as well as the baseline CA19-9 level that showed most significant impact on the OS, and the model-based simulations could provide individual survival rate predictions for patients with different prognostic factors. This study quantitatively demonstrates the importance of physical status and baseline disease for the OS of advanced PDAC patients, and highlights that timely nutrition support would be helpful to improve the prognosis.

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.

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.

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.

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.

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.

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.

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 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.

Dexamethasone suppresses the growth of human non-small cell lung cancer via inducing estrogen sulfotransferase and inactivating estrogen.

AIM: Dexamethasone (DEX) is a widely used synthetic glucocorticoid, which has shown anti-cancer efficacy and anti-estrogenic activity. In this study we explored the possibility that DEX might be used as an endocrine therapeutic agent to treat human non-small cell lung cancer (NSCLC). METHODS: The viability and proliferation of human NSCLC cell lines A549 and H1299 were assessed in vitro. Anti-tumor action was also evaluated in A549 xenograft nude mice treated with DEX (2 or 4 mg·kg(-1)·d(-1), ig) or the positive control tamoxifen (50 mg·kg(-1)·d(-1), ig) for 32 d. The expression of estrogen sulfotransferase (EST) in tumor cells and tissues was examined. The intratumoral estrogen levels and uterine estrogen responses were measured. RESULTS: DEX displayed mild cytotoxicity to the NSCLC cells (IC50 >500 µmol/L) compared to tamoxifen (IC50 <50 µmol/L), but it was able to inhibit the cell proliferation at low micromolar ranges. Furthermore, DEX (0.1-10 µmol/L) dose-dependently up-regulated EST expression in the cells, and inhibited the cell migration in vitro. Triclosan, a sulfation inhibitor, was able to diminish DEX-caused inhibition on the cell viability. In A549 xenograft nude mice, DEX or tamoxifen administration remarkably suppressed the tumor growth. Moreover, DEX administration dose-dependently increased EST expression in tumor tissues, and reduced intratumoral estrogen levels as well as the volumes and weights of uterine. CONCLUSION: DEX suppresses the growth of A549 xenograft tumors via inducing EST and decreasing estradiol levels in tumor tissues, suggesting that DEX may be used as anti-estrogenic agent for the treatment of NSCLC.

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

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

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

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

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

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

Model-based meta-analysis of the effects of non-selective and α1-selective GABAA receptor agonists in healthy volunteers.

PURPOSE: To quantify pharmacokinetic (PK) and pharmacodynamic (PD) relationships of various classes of GABAA agonists in healthy volunteers, in order to investigate the sensitivity of the biomarker responses due to differing GABAA-subtype selectivity and to explore the correlation between biomarker responses and side effects of these drugs. METHODS: A comprehensive search was conducted for published placebo-controlled clinical studies of non- and α1-selective GABAA drugs in healthy volunteers. PK/PD models were developed for concentrations and biomarker outcomes (saccadic eye movement (SEM), visual analogue scale (VAS), digit symbol substitution task (DSST), and critical flicker fusion test (CFFT)) extracted from included studies. Predicted responses and equivalent doses for biomarkers (based on predicted response) were used to compare drug effects. And the relationship between biomarkers and safety was explored by linear regression. RESULTS: A total of 2237 data from 163 articles were included. Based on PK and placebo effect modeling, linear biomarker-concentration relationships well fit the data. The α1-selective compounds had similar equivalent doses for VAS, DSST, and CFFT (4.7-6.7 mg), which were about three to seven times lower than that for SEM (14.4-35.5 mg), while such difference was less evident for non-selective drugs. DSST had the highest correlations with incidences of somnolence and dizziness. CONCLUSIONS: The integral PK/PD models of GABAA agonists were established in healthy volunteers. SEM was identified as the most sensitive biomarker in differentiating GABAA receptor α1 subtype selective compounds. The exploratory analysis implied that different relationships existed between the drug effects on biomarkers and the adverse event profiles in healthy volunteers.

A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients.

AIM: To develop a population pharmacokinetic (PopPK) model of tacrolimus in healthy Chinese volunteers and liver transplant recipients for investigating the difference between the populations, and for potential individualized medication. METHODS: A set of 1100 sparse trough concentration data points from 112 orthotopic liver transplant recipients, as well as 851 dense data points from 40 healthy volunteers receiving a single dose of tacrolimus (2 mg, p.o.) were collected. PopPK model of tacrolimus was constructed using the program NONMEM. Related covariates such as age, hepatic and renal functions that were potentially associated with tacrolimus disposition were evaluated. The final model was validated using bootstrapping and a visual predictive check. RESULTS: A two-compartment model of tacrolimus could best describe the data from the two populations. The final model including two covariates, population (liver transplant recipients or volunteers) and serum ALT (alanine aminotransferase) level, was verified and adequately described the pharmacokinetic characteristics of tacrolimus. The estimates of V2/F, Q/F and V3/F were 22.7 L, 76.3 L/h and 916 L, respectively. The estimated CL/F in the volunteers and liver transplant recipients was 32.8 and 18.4 L/h, respectively. Serum ALT level was inversely related to CL/F, whereas age did not influence CL/F. Thus, the elderly (≥65 years) and adult (<65 years) groups in the liver transplant recipients showed no significant difference in the clearance of tacrolimus. CONCLUSION: Compared with using the sparse data only, the integrating modeling technique combining sparse data from the patients and dense data from the healthy volunteers improved the PopPK analysis of tacrolimus.

Intratumoral estrogen sulfotransferase induction contributes to the anti-breast cancer effects of the dithiocarbamate derivative TM208.

AIM: Sulfotransferase-catalyzed sulfation is the most important pathway for inactivating estrogens. Thus, activation of estrogen sulfotransferase (EST) may be an alternative approach for the treatment of estrogen-dependent breast cancer. In this study we investigated the involvement of EST in anti-breast cancer effects of the dithiocarbamate derivative TM208 in vitro and in vivo. METHODS: The viability of human breast cancer MCF-7 cells was determined using a SBB assay. Nude mice bearing MCF-7 cells were orally administered TM208 (50 and 150 mg·kg(-1)·d(-1)) for 18 days. The xenograft tumors and uteri were collected. The mRNA expression of EST was examined with real-time PCR. EST protein was detected with Western blot, ELISA or immunohistochemical staining assays. A radioactive assay was used to measure the EST activity. Uterotropic bioassay was used to examine the uterine estrogen responses. RESULTS: Treatment with TM208 (10, 15 and 20 µmol/L) concentration-dependently increased EST expression in MCF-7 cells in vitro. Co-treatment with triclosan, an inhibitor of sulfonation, abolished TM208-induced cytotoxicity in MCF-7 cells. TM208 exhibited an apparent anti-estrogenic property: it exerted more potent cytotoxicity in E2-treated MCF-7 cells. In the nude mice bearing MCF-7 cells, TM208 administration time-dependently increased the expression and activity of EST, and blocked the gradual increase of E2 concentration in the xenograft tumors. Furthermore, TM208 administration blocked the estrogens-stimulated uterine enlargement. Tamoxifen, a positive control drug, produced similar effects on the expression and activity of EST in vitro and in vivo. CONCLUSION: The induction of EST and reduction of estrogen concentration contribute to the anti-breast cancer action of TM208 and tamoxifen. TM208 may be developed as anticancer drug for the treatment of estrogen receptor-positive breast cancer.