Optimal starting dosing regimen of intravenous oxytocin for labor induction based on the population kinetic-pharmacodynamic model of uterine contraction frequency.

BACKGROUND: Intravenous oxytocin is commonly used for labor induction. However, a consensus on the initial dosing regimen is lac with conflicting research findings and varying guidelines. This study aimed to develop a population kinetic-pharmacodynamic (K-PD) model for oxytocin-induced uterine contractions considering real-world data and relevant influencing factors to establish an optimal starting dosing regimen for intravenous oxytocin. METHODS: This retrospective study included pregnant women who underwent labor induction with intravenous oxytocin at Peking University Third Hospital in 2020. A  population K-PD model was developed to depict the time course of uterine contraction frequency (UCF), and covariate screening identified significant factors affecting the pharmacokinetics and pharmacodynamics of oxytocin. Model-based simulations were used to optimize the current starting regimen based on specific guidelines. RESULTS: Data from 77 pregnant women with 1095 UCF observations were described well by the K-PD model. Parity, cervical dilation, and membrane integrity are significant factors influencing the effectiveness of oxytocin. Based on the model-based simulations, the current regimens showed prolonged onset times and high infusion rates. This study proposed a revised approach, beginning with a rapid infusion followed by a reduced infusion rate, enabling most women to achieve the target UCF within approximately 30 min with the lowest possible infusion rate. CONCLUSION: The K-PD model of oxytocin effectively described the changes in UCF during labor induction. Furthermore, it revealed that parity, cervical dilation, and membrane integrity are key factors that influence the effectiveness of oxytocin. The optimal starting dosing regimens obtained through model simulations provide valuable clinical references for oxytocin treatment.

Population PK/PD model of tacrolimus for exploring the relationship between accumulated exposure and quantitative scores in myasthenia gravis patients.

Tacrolimus is an important immunosuppressant used in the treatment of myasthenia gravis (MG). However, the population pharmacokinetic (PK) characteristics together with the exposure-response of tacrolimus in the treatment of MG remain largely unknown. In this study, we aimed to develop a population PK/pharmacodynamic (PK/PD) model of tacrolimus in patients with MG, in order to explore the relationships among tacrolimus dose, exposure, and its therapeutic efficacy. The genotype of CYP3A5, Osserman's classification, and status of thymus, as well as demographic characteristics and other biomarkers from laboratory testing were tested as covariate, and simulations were performed based on the final model. The population PK model was described using a one-compartment model with first-order elimination and fixed absorption parameters. CYP3A5 genotype significantly influenced the apparent clearance, and total protein (TP) influenced the apparent volume of distribution as covariates. The quantitative MG scores were characterized by the cumulated area under curve of tacrolimus in a maximum effect function. Osserman's classification was a significant covariate on the initial score of patients with MG. The simulations demonstrated that tacrolimus showed an unsatisfying effect possibly due to insufficient exposure in some patients with MG. A starting dose of 2 mg/d and even higher dose for patients with CYP3A5 *1/*1 and *1/*3 and lower TP level were required for the rapid action of tacrolimus. The population PK/PD model quantitatively described the relationships among tacrolimus dose, exposure, and therapeutic efficacy in patients with MG, which could provide reference for the optimization of tacrolimus dosing regimen at the individual patient level.

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.

Starting dose selection of palbociclib in Chinese patients with breast cancer based on population kinetic-pharmacodynamic model of neutropenia.

Neutropenia is the most common adverse event (AE) of palbociclib, an oral CDK4/6 inhibitor for breast cancer. Neutropenia increases the risk of infection and is even life threatening. Asian patients generally suffer more severe neutropenia from palbociclib treatment, but the label does not recommend a reduction in the starting dose for Asian patients. Therefore, the study aimed to explore the exposure-response (E-R) relationship in Chinese patients and preliminarily generate a scale for starting dose selection of palbociclib in Chinese patients. After comparing the kinetic-pharmacodynamic (K-PD) and the pharmacokinetic/pharmacodynamic (PK/PD) model, a semi-mechanistic K-PD model was selected and developed on the basis of real-world data from 28 patients with breast cancer to describe the time course of longitudinal absolute neutrophil counts (ANC). The longitudinal ANC data were well described by the population K-PD model with reasonable parameters: mean transit time (MTT) of 198 h, feedback parameter (γ) of 0.317, baseline ANC level (Circ0) of 3.36 × 109 L-1, drug effect coefficient (kd) of 0.0349, and drug effect power (ß) of 0.383. No covariate was included in the final model. The model showed that palbociclib dose-dependently reduced ANC levels in a Chinese population, and lower baseline ANC level was associated with more severe neutropenia. The dose selection scale suggested that palbociclib 125 mg daily was appropriate for Chinese patients with Circ0 higher than 3.75 × 109 L-1. In summary, the K-PD model of palbociclib well described the longitudinal ANC in Chinese patients. Besides, the starting dose selection scale may provide reference for clinicians during individualized therapy.

Longitudinal and time-to-event modeling for prognostic implications of radical surgery in retroperitoneal sarcoma.

Retroperitoneal sarcoma (RPS) is a rare malignancy which can be difficult to manage due to the variety of clinical behaviors. In this study, we aimed to develop a parametric modeling framework to quantify the relationship between postoperative dynamics of several biomarkers and overall/progression-free survival of RPS. One hundred seventy-four patients with RPS who received surgical resection with curative intent at the Peking University Cancer Hospital Sarcoma Center were retrospectively included. Potential prognostic factors were preliminarily identified. Longitudinal analyses of body mass index (BMI), serum total protein (TP), and white blood cells (WBCs) were performed using nonlinear mixed effects models. The impacts of time-varying and time-invariant predictors on survival were investigated by parametric time-to-event (TTE) models. The majority of patients experienced decline in BMI, recovery of TP, as well as transient elevation in WBC counts after surgery, which significantly correlated with survival. An indirect-response model incorporating surgery effect described the fluctuation in percentage BMI. The recovery of TP was captured by a modified Gompertz model, and a semimechanistic model was selected for WBCs. TTE models estimated that the daily cumulative average of predicted BMI and WBC, the seventh-day TP, as well as certain baseline variables, were significant predictors of survival. Model-based simulations were performed to examine the clinical significance of prognostic factors. The current work quantified the individual trajectories of prognostic biomarkers in response to surgery and predicted clinical outcomes, which would constitute an additional strategy for disease monitoring and intervention in postoperative RPS.

A model-based meta-analysis of immune-related adverse events during immune checkpoint inhibitors treatment for NSCLC.

Immune checkpoint inhibitors (ICIs) have become a vital part of the therapeutic landscape for non-small cell lung cancer (NSCLC) in recent years benefiting from their remarkable efficacy. However, ICIs are associated with potentially life-threatening immune-related adverse events (irAEs). This study aims to quantify dose dependence and additional influencing factors of both any grade and grade greater than or equal to 3 irAEs in patients with NSCLC treated by ICIs. The trial-level irAE data was collected and pooled from 129 cohorts in 81 clinical studies. A logit-transformed meta-regression model was applied to derive the quantitative relationship of irAE rate and ICI exposure. Programmed cell death-1 (PD-1) or programmed cell death ligand-1 (PD-L1) inhibitors showed no dose dependence in patients with NSCLC, whereas cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) inhibitors exhibited a statistically significant dose dependence when used alone or combined with PD-1 or PD-L1 inhibitors. Besides, therapy line and combination of ICIs with chemotherapy or target therapy were significant covariates. Hopefully, the results of this study can improve clinicians' awareness of irAEs and be helpful for clinical decisions during ICI treatment for NSCLC.

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.

Mechanistic prediction of first-in-human dose for bispecific CD3/EpCAM T-cell engager antibody M701, using an integrated PK/PD modeling method.

AIM: M701 is a bispecific CD3/EpCAM T-cell engager antibody to treat malignant ascites. This study aimed to predict in vivo exposure-cytotoxicity relationship and human pharmacokinetics (PK) characteristics of M701, as well as to design optimal starting dose and effective dose for M701 first-in-human (FIH) study. METHOD: Mechanistic in vitro PK/PD model was firstly developed based on in vitro data of M701's cytotoxicity and binding affinities with targeting receptors. The cell killing effect of M701 in vitro was driven by tri-molecular synapse, which formed by binding drug to both CD3 receptor on T cells and EpCAM receptor on tumor cells. Human exposure-response (E-R) curve in ascites was estimated using the same model structure with clinical systemic model parameters. Human PK was predicted by allometrically scaling monkey PK data, which was characterized using a two compartment model. Human PK model was integrated into in vivo synapse-based cell killing model to provide human PK/PD characteristics. Integrated human PK/PD model was applied in FIH dose design. Clinical starting dose and effective dose were suggested as the simulated drug concentration in human ascites that achieved the estimated in vivo minimally anticipated biological effect level (MABEL) and pharmacologically active level. Other approaches including PK-driven and receptor occupancy calculation were also employed in this study to verify the starting dose prediction. RESULTS: In vitro M701 cytotoxicity curves under 24, 48, 72 h incubations were well captured by mechanistic synapse-based cell killing model. Human E-R curve in ascites was obtained based on in vitro model structure and clinical systematic parameters. We defined 10~20% and 80% of maximum cytotoxicity effect as in vivo MABEL and pharmacologically active level. Human E-R curve indicated in vivo EC10, EC20 and EC80 were 0.56, 1.26 and 31.6 ng/mL. For human PK model, clearance (CL, CLd), distribution volumes (Vc, Vp) and absorption rate were allometrically scaled using exponent of 0.9, 1 and -0.25. Predicted clearance and volume were 0.53- and 1.19-fold of observed data. Simulated average ascites M701 concentrations (calculated as Cave_ ascites = AUCτ/τ) were 0.81 and 32.5 ng/mL under dose of 5 and 200 µg within 2-hour i.p. infusion. By integrating human E-R curve and the simulated PK profile in ascites, we suggested 5 and 200 µg within 2-hour i.p. infusion as MABEL dose and pharmacologically active dose (PAD) for M701 FIH study. PK-driven approach predicted a starting dose of 5 µg, which was comparable to that predicted via PK/PD-driven approach. CONCLUSIONS: This study predicted human ascites PK and E-R curve by integrating human PK model into in vivo synapse-based cell killing model. Optimal clinical MABEL dose and PAD of bispecific T cell engager antibody M701 were suggested based on current integrated PK/PD approach.

Toward Greater Insights on Applications of Modeling and Simulation in Pregnancy.

Pregnant women are often excluded from routine clinical trials. Consequently, appropriate dosing regimens for majority of drugs are unknown in this population, which may lead to unexpected safety issue or insufficient efficacy in this un-studied population. Establishing evidence through the conduct of clinical studies in pregnancy is still a challenge. In recent decades, physiologically-based pharmacokinetic (PBPK) modeling has proven to be useful to support dose selection under various clinical scenarios, such as renal and/or liver impairment, drug-drug interactions, and extrapolation from adult to children. By integrating gestational-dependent physiological characteristics and drug-specific information, PBPK models can be used to predict PK during pregnancy. Population pharmacokinetic (PopPK) modeling approach also could complement pregnancy clinical studies by its ability to analyze sparse sampling data. In the past five years, PBPK and PopPK approaches for pregnancy have made significant progress. We reviewed recent progress, challenges and potential solutions for the application of PBPK, PopPK, and exposure-response analysis in clinical drug development for pregnancy.

Development and validation of a highly sensitive HPLC-MS/MS method for the QAP14, a novel potential anti-cancer agent, in rat plasma and its application to a pharmacokinetic study.

QAP14 is a novel anti-cancer compound exhibiting good efficacy and safety in preclinical study. To investigate its pharmacokinetic properties, a rapid and sensitive HPLC-MS/MS method was developed and validated to quantify the concentration of QAP14 in rat plasma. QAP14 was separated on ZORBAX Eclipse XDB-C8 column with a gradient elution. Erlotinib was selected as internal standard and plasma samples were prepared by precipitation with acetonitrile. In the pharmacokinetic study, rats were treated with QAP14 at 2.4 mg/kg (i.v.) or 6 mg/kg (p.o.). This method provided good linearity in the range of 0.5-1000 ng/mL in rat plasma. The accuracy, precision, matrix effect, recovery, stability and carryover fulfilled the criteria. The pharmacokinetic profiles of QAP14 in rats were described by non-compartmental analysis. The oral bioavailability was 50.29 %. The assay is reliable and requires only little sample volume, and the pharmacokinetic properties of QAP14 in rats may provide reference for future studies.

Important roles of estrogen receptor alpha in tumor progression and anti-estrogen therapy of pancreatic ductal adenocarcinoma.

AIMS: The roles of estrogen receptors (ERs) and the efficacy of anti-estrogen (E2) therapies in pancreatic cancer stay controversial. The main objectives of this study were to investigate the potential roles of ERs in tumor progression and endocrine therapies. MAIN METHODS: The ER expression status in PANC-1 and SW1990 pancreatic cancer cell lines was determined. SRB assay, colony formation assay and proliferation assay were used to investigate the responses of these cells to E2. ERα-selective agonist propylpyrazoletriol (PPT), ERß-selective agonist diarylpropionitrile (DPN), ERα over-expressed SW1990 cells, ERα knock-out PANC-1 cells and patient-derived xenografts (PDX) were applied to investigate the potential roles of ERα in pancreatic cancer. The phosphorylation of ERα-related signaling molecules extracellular regulated protein kinases (ERK1/2) and protein kinase B (AKT) were investigated. The in vivo anti-tumor efficacy and safety of letrozole (LTZ) combined with leuprorelin acetate (LA) and gemcitabine (GEM) were also preliminarily studied. KEY FINDINGS: PANC-1 cells expressed much more ERα than SW1990 cells, and ERß level was with less diversity. Accordingly, the proliferation of PANC-1 rather than SW1990 cells could be stimulated by E2, and only PANC-1 could respond to LTZ endocrine therapy in female but not male mice. The phosphorylation of ERK1/2 but not AKT was altered by over-expressed or knocking out of ERα with or without the addition of E2 and LTZ. The combination therapy of LTZ and GEM showed acceptable efficacy and safety. SIGNIFICANCE: This study showed the important roles of ERα in tumor progression and endocrine therapies of pancreatic cancer in women.

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.

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.

Prediction of Human Pharmacokinetics and Clinical Effective Dose of SI-B001, an EGFR/HER3 Bi-specific Monoclonal Antibody.

SI-B001 is a new EGFR/HER3 bi-specific antibody showing encouraging anti-tumor efficacy in the preclinical studies and was ready for further clinical research. To help with the dose design, human pharmacokinetics (PK) and clinical effective doses of SI-B001 were predicted by PK and PK/PD modeling and simulation. A Michaels-Menten (M-M) PK model was first used to describe the PK of SI-B001 in cynomolgus monkeys, whose parameters were allometrically scaled to humans for the simulation of human PK profiles. Besides, the anti-tumor efficacy of SI-B001 on different xenografts in tumor-bearing mice was quantitatively described by PK/PD models. The clinical effective doses were predicted by comparing the effective exposure (AUCs) in mice with simulated human AUCs. The clinical effective doses of SI-B001 were predicted to be over 16 mg/kg, 5-7 mg/kg or 5-6 mg/kg per week for colon cancer, head and neck cancer or esophageal cancer, respectively, which may help with the optimization of dose escalation schemes and the selection of indications for SI-B001.

Mechanistic Pharmacokinetic/Pharmacodynamic Model of Sunitinib and Dopamine in MCF-7/Adr Xenografts: Linking Cellular Heterogeneity to Tumour Burden.

The self-renewal and differentiation of cancer stem-like cells (CSCs) leads to cellular heterogeneity, causing one of the greatest challenges in cancer therapy. Growing evidence suggests that CSC-targeting therapy enhances the effect of concomitant antitumour therapy. To gain an in-depth understanding of this enhanced effect, the kinetic profile of estimated CSC frequency (the fraction of CSCs in tumour) was evaluated for in vivo characterization of cellular heterogeneity using sunitinib and dopamine as a paradigm combination therapy. Female MCF-7/Adr xenografted Balb/c nude mice were treated with sunitinib (p.o., 20 mg/kg) and dopamine (i.p., 50 mg/kg), alone or in combination. Estimated CSC frequency and tumour size were measured over time. Mechanistic PK/PD modelling was performed to quantitatively describe the relationship between drug concentration, estimated CSC frequency and tumour size. Sunitinib reduced tumour size by inducing apoptosis of differentiated tumour cells (DTCs) and enriched CSCs by stimulating its proliferation. Dopamine exhibited anti-CSC effects by suppressing the capacity of CSCs and inducing its differentiation. Simulation and animal studies indicated that concurrent administration was superior to sequential administration under current experimental conditions. Alongside tumour size, the current study provides mechanistic insights into the estimation of CSC frequency as an indicator for cellular heterogeneity. This forms the conceptual basis for in vivo characterization of other combination therapies in preclinical cancer studies.

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.

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.

Pharmacokinetic/Pharmacodynamic Modeling of the Anti-Cancer Effect of Dexamethasone in Pancreatic Cancer Xenografts and Anticipation of Human Efficacious Doses.

Dexamethasone (DEX), a synthetic glucocorticoid, exhibited anti-cancer efficacy in pancreatic xenografts derived from patient tumor tissue or cancer cell lines. The aim of this study was to establish pharmacokinetic/pharmacodynamic (PK/PD) models to quantitatively characterize the inhibitory effect of DEX on tumor growth as well as its discrepancy among 3 xenograft models. Data of tumor growth profiles were collected from a patient-derived xenograft (PDX) model in NOD/SCID mice and 2 cell line-derived (PANC-1 and SW1990) xenograft models in BALB/c nude mice. Empirical PK/PD models were developed to establish mathematical relationships between plasma concentration of DEX and tumor growth dynamics after integrating PK parameters extracted from literature. Drug effect in each model was well described by a linear inhibitory function with a potency factor of 4.67, 3.14, and 2.35 L/mg for PDX, PANC-1, and SW1990 xenograft, respectively. Human efficacious doses of DEX were preliminarily predicted through model-based simulation, and 60% tumor growth inhibition at clinical exposure corresponded to a daily dose range of 26-52 mg intravenously. This modeling work quantified the preclinical anti-cancer effect of DEX and demonstrated the feasibility of its medication in pancreatic cancer, which would be conductive to future translational research.