Development of a new class of PSMA radioligands comprising ibuprofen as an albumin-binding entity.

Prostate-specific membrane antigen (PSMA)-targeted radioligands have been used for the treatment of metastatic castration-resistant prostate cancer (mCRPC). Recently, albumin-binding PSMA radioligands with enhanced blood circulation were developed to increase the tumor accumulation of activity. The present study aimed at the design, synthesis and preclinical evaluation of a novel class of PSMA-targeting radioligands equipped with ibuprofen as a weak albumin-binding entity in order to improve the pharmacokinetic properties. Methods: Four novel glutamate-urea-based PSMA ligands were synthesized with ibuprofen, conjugated via variable amino acid-based linker entities. The albumin-binding properties of the 177Lu-labeled PSMA ligands were tested in vitro using mouse and human plasma. Affinity of the radioligands to PSMA and cellular uptake and internalization was investigated using PSMA-positive PC-3 PIP and PSMA-negative PC-3 flu tumor cells. The tissue distribution profile of the radioligands was assessed in biodistribution and imaging studies using PC-3 PIP/flu tumor-bearing nude mice. Results: The PSMA ligands were obtained in moderate yields at high purity (>99%). 177Lu-labeling of the ligands was achieved at up to 100 MBq/nmol with >96% radiochemical purity. In vitro assays confirmed high binding of all radioligands to mouse and human plasma proteins and specific uptake and internalization into PSMA-positive PC-3 PIP tumor cells. Biodistribution studies and SPECT/CT scans revealed high accumulation in PC-3 PIP tumors but negligible uptake in PC-3 flu tumor xenografts as well as rapid clearance of activity from background organs and tissues. 177Lu-Ibu-DAB-PSMA, in which ibuprofen was conjugated via a positively-charged diaminobutyric acid (DAB) entity, showed distinguished tumor uptake and the most favorable tumor-to-blood and tumor-to-kidney ratios. Conclusion: The high accumulation of activity in the tumor and fast clearance from background organs was a common favorable characteristic of PSMA radioligands modified with ibuprofen as albumin-binding entity. 177Lu-Ibu-DAB-PSMA emerged as the most promising candidate; hence, more detailed preclinical investigations with this radioligand are warranted in view of a clinical translation.

Behavioral coping strategies predict tumor development and behavioral impairment after chronic social stress in mice.

The aims of this study were to identify behavioral strategies to cope with social defeat, evaluate their impact on tumor development and analyze the contributions of both to changes in physiology and behavior produced by chronic defeat stress. For this purpose, OF1 mice were inoculated with B16F10 melanoma cells and subjected to 18 days of repeated defeat stress in the presence of a resident selected for consistent levels of aggression. Combined cluster and discriminant analyses of behavior that manifested during the first social interaction identified three types of behavioral profiles: active/aggressive (AA), passive/reactive (PR) and an intermediate active/non-aggressive (ANA) profile. Animals that showed a PR coping strategy developed more pulmonary metastases at the end of the social stress period than animals in other groups. The ANA but not AA group also showed higher tumor metastases than non-stressed subjects. In addition, the ANA group differed from the other groups because it displayed the highest corticosterone levels after the first interaction. Chronic stress reduced sucrose consumption, which indicates anhedonia, in all the stressed groups. However, the PR subjects exhibited a longer immobility time and swam for less time than other subjects in the forced swim test (FST), and they travelled a shorter distance in the open field test (OFT). In this test, the ANA group also travelled smaller distances than the non-stressed group, but the difference was more moderate. In contrast, tumor development but not stress increased behaviors associated with anxiety in the OFT (e.g., time in the center) in all tumor-bearing subjects. In summary, although the effects of social stress and tumor development on behavior were rather moderate, the results indicate the importance of behavioral coping strategies in modulating the effects of chronic stress on health.

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models.

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors' drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm3) divided by the time for control TV to reach 600mm3 and therefore not dependent on the chosen final time point tf. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.

A simulation-based sample size calculation method for pre-clinical tumor xenograft experiments.

Pre-clinical tumor xenograft experiments usually require a small sample size that is rarely greater than 20, and data generated from such experiments very often do not have censored observations. Many statistical tests can be used for analyzing such data, but most of them were developed based on large sample approximation. We demonstrate that the type-I error rates of these tests can substantially deviate from the designated rate, especially when the data to be analyzed has a skewed distribution. Consequently, the sample size calculated based on these tests can be erroneous. We propose a modified signed log-likelihood ratio test (MSLRT) to meet the type-I error rate requirement for analyzing pre-clinical tumor xenograft data. The MSLRT has a consistent and symmetric type-I error rate that is very close to the designated rate for a wide range of sample sizes. By simulation, we generated a series of sample size tables based on scenarios commonly expected in tumor xenograft experiments, and we expect that these tables can be used as guidelines for making decisions on the numbers of mice used in tumor xenograft experiments.

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR.

Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: a Novel Method to Handle the Interval Censoring Caused by Measurement of Smaller Tumors.

The purpose of this study was to explore the interval censoring induced by caliper measurements on smaller tumors during tumor growth experiments in preclinical studies and to show its impact on parameter estimations. A new approach, the so-called interval-M3 method, is proposed to specifically handle this type of data. Thereby, the interval-M3 method was challenged with different methods (including classical methods for handling below quantification limit values) using Stochastic Simulation and Estimation process to take into account the censoring. In this way, 1000 datasets were simulated under the design of a typical of tumor growth study in xenografted mice, and then, each method was used for parameter estimation on the simulated datasets. Relative bias and relative root mean square error (relative RMSE) were consequently computed for comparison purpose. By not considering the censoring, parameter estimations appeared to be biased and particularly the cytotoxic effect parameter, k 2 , which is the parameter of interest to characterize the efficacy of a compound in oncology. The best performance was noted with the interval-M3 method which properly takes into account the interval censoring induced by caliper measurement, giving overall unbiased estimations for all parameters and especially for the antitumor effect parameter (relative bias = 0.49%, and relative RMSE = 4.06%).

Physicochemical Properties and Interfacial Adaptation of Root Canal Sealers

This study compared the physicochemical properties and interfacial adaptation to canal walls of Endo-CPM-Sealer, Sealapex and Activ GP with the well-established AH Plus sealer. The following analyses were performed: radiopacity, pH variation and solubility using samples of each material and scanning electron microscopy of root-filled bovine incisors to evaluate the interfacial adaptation. Data were analyzed by the parametric and no-parametric tests (α=0.05). All materials were in accordance with the ANSI/ADA requirements for radiopacity. Endo-CPM-Sealer presented the lowest radiopacity values and AH Plus was the most radiopaque sealer (p=0.0001). Except for ActiV GP, which was acidic, all other sealers had basic chemical nature and released hydroxyl ions. Regarding solubility, all materials met the ANSI/ADA recommendations, with no statistically significant difference between the sealers (p=0.0834). AH Plus presented the best adaptation to canal walls in the middle (p=0.0023) and apical (p=0.0012) thirds, while the sealers Activ GP and Endo-CPM-Sealer had poor adaptation to the canal walls. All sealers, except for ActiV GP, were alkaline and all of them fulfilled the ANSI/ADA requirements for radiopacity and solubility. Regarding the interfacial adaptation, AH Plus was superior to the others considering the adaptation to the bovine root canal walls.

Este estudo comparou as propriedades físico-químicas e a adaptação interfacial às paredes do canal dos cimentos Endo-CPM-Sealer, Sealapex e Activ GP com o bem estabelecido cimento AH Plus. As seguintes análises foram realizadas: radiopacidade, variação de pH e de solubilidade utilizando amostras de cada material, e microscopia eletrônica de varredura utilizando incisivos bovinos obturados para avaliar a adaptação interfacial. Os dados foram analisados utilizando testes paramétricos e não-paramétricos (α=0,05). Todos os materiais estavam de acordo com os requerimentos da ANSI/ADA para radiopacidade, sendo que o Endo-CPM-Sealer apresentou os menores valores de radiopacidade e o AH Plus foi o cimento mais radiopaco (p=0,0001). Exceto o Activ GP, que foi ácido, todos os outros cimentos apresentaram natureza química básica e liberaram íons hidroxila. Com relação à solubilidade, todos os materiais estavam de acordo com as recomendações da ANSI /ADA, sem diferença significante entre os cimentos (p=0,0834). O AH Plus apresentou a melhor adaptação às paredes do canal nos terços médio (p=0,0023) e apical (p=0,0012), enquanto que os cimentos Activ GP e Endo-CPM-Sealer apresentaram uma pobre adaptação às paredes do canal. Em conclusão, todos os cimentos, exceto o Activ GP, foram alcalinos e todos preencheram os requerimentos da ANSI/ADA para radiopacidade e solubilidade. Com relação à adaptação interfacial, o AH Plus foi superior aos demais para adaptação às paredes do canal radicular de incisivos bovinos.

KRAS and PIK3CA mutation frequencies in patient-derived xenograft models of pancreatic and colorectal cancer are reflective of patient tumors and stable across passages.

One obstacle in the translation of advances in cancer research into the clinic is a deficiency of adequate preclinical models that recapitulate human disease. Patient-derived xenograft (PDX) models are established by engrafting patient tumor tissue into mice and are advantageous because they capture tumor heterogeneity. One concern with these models is that selective pressure could lead to mutational drift and thus be an inaccurate reflection of patient tumors. Therefore, we evaluated if mutational frequency in PDX models is reflective of patient populations and if crucial mutations are stable across passages. We examined KRAS and PIK3CA gene mutations from pancreatic ductal adenocarcinoma (PDAC) (n = 30) and colorectal cancer (CRC) (n = 37) PDXs for as many as eight passages. DNA was isolated from tumors and target sequences were amplified by polymerase chain reaction. KRAS codons 12/13 and PIK3CA codons 542/545/1047 were examined using pyrosequencing. Twenty-three of 30 (77%) PDAC PDXs had KRAS mutations and one of 30 (3%) had PIK3CA mutations. Fifteen of 37 (41%) CRC PDXs had KRAS mutations and three of 37 (8%) had PIK3CA mutations. Mutations were 100 per cent preserved across passages. We found that the frequency of KRAS (77%) and PIK3CA (3%) mutations in PDAC PDX was similar to frequencies in patient tumors (71 to 100% KRAS, 0 to 11% PIK3CA). Similarly, KRAS (41%) and PIK3CA (8%) mutations in CRC PDX closely paralleled patient tumors (35 to 51% KRAS, 12 to 21% PIK3CA). The accurate mirroring and stability of genetic changes in PDX models compared with patient tumors suggest that these models are good preclinical surrogates for patient tumors.

Joint longitudinal and survival-cure models in tumour xenograft experiments.

In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also, cure data are observed because of a portion of individuals who are completely cured in the experiments. When modelling these data, certain constraints have to be imposed on the parameters in the models to account for the intrinsic growth of the tumour in the absence of treatment. Also, the likely inherent association of longitudinal and survival-cure data has to be taken into account in order to obtain unbiased estimators of parameters. In this paper, we propose such models for the joint modelling of longitudinal and survival-cure data arising in xenograft experiments. Estimators of parameters in the joint models are obtained using a Markov chain Monte Carlo approach. Real data analysis of a xenograft experiment is carried out, and simulation studies are also conducted, showing that the proposed joint modelling approach outperforms the separate modelling methods in the sense of mean squared errors.

Modeling sustained treatment effects in tumor xenograft experiments.

In cancer drug development, demonstrated efficacy in tumor xenograft models is an important step toward bringing a promising compound to human use. A key outcome variable is tumor volume measured over a period of time, while mice are treated with certain treatment regimens. A constrained parametric model has been proposed to account for special features, such as intrinsic tumor growth, or tumor volume truncations due to tumor size being either too large or too small to detect. However, since the drug concentration in the blood of a mouse or its tissues may be stabilized at a certain level and maintained during a period of time, the treatment may have sustained effects. This article extends the constrained parametric model to account for the sustained drug effects. The ECM algorithm for incomplete data is applied to estimating the dose-response relationship in the proposed model. The model selection based on likelihood functions is given and a simulation study is conducted to investigate the performance of the proposed estimator. A real xenograft study on the antitumor agent temozolomide combined with irinotecan against the rhabdomyosarcoma is analyzed using the proposed methods.

Assessing interactions for fixed-dose drug combinations in tumor xenograft studies.

Statistical methods for assessing the joint action of compounds administered in combination have been established for many years. However, there is little literature available on assessing the joint action of fixed-dose drug combinations in tumor xenograft experiments. Here an interaction index for fixed-dose two-drug combinations is proposed. Furthermore, a regression analysis is also discussed. Actual tumor xenograft data were analyzed to illustrate the proposed methods.

New criteria for analyzing the statistical relationships between biological parameters and therapeutic responses of xenografted tumor models.

Analysis of preclinical studies using human tumors xenografted into rodents is commonly performed with Tumor Growth Index (TGI) and Tumor Growth Delay index (TGDi). To circumvent the limitations of these parameters, two new parameters, Time To Relapse (TTR) and Tumor Growth Speed (TGS), were developed using a mathematical modeling approach based on an exponential tumor growth. TTR is similar to progression free survival used in human clinical trials and TGS characterizes the pattern of tumor cell proliferation. Parameters were estimated for each rodent by the maximum likelihood method and statistical analyses were performed using ANOVA. These criteria can be used when tumor growths are assessed by repeated measures of their volume. As an example, we used data from a previously published study, which aimed to evaluate the relationship between histology, genetic parameters, and response to alkylating agents in a series of twelve gliomas. The group treated with temozolomide was reanalyzed using our criteria. This group presented a significantly longer TTR than the control group. TTR was also different according to tumor type: oligodendrogliomas relapsed later than glioblastomas. The TGS was different according to the tumor type. Loss of heterozygosity (LOH) 1p ± 19q, LOH 10p ± 10q, telomerase activity, PTEN mutation, and EGFR amplification were related to temozolomide efficacy. Our criteria provide additional information to those given by TGI and TGDi. Due to statistical properties of TTR and TGS, some relations between the parameters such as tumor type or genetic alterations can be studied with TTR and TGS and not with TGI or TGDi.

TGI-Simulator: a visual tool to support the preclinical phase of the drug discovery process by assessing in silico the effect of an anticancer drug.

This paper presents TGI-Simulator, a software tool designed to show, through a 2-D graphical animation, the simulated time effect of an anticancer drug on a tumor mass by exploiting the well-known Tumor Growth Inhibition (TGI) model published by Simeoni et al. [1]. Simeoni TGI model is a mathematical model routinely used by pharma companies and researchers during the drug development process. The application is based on a Java graphical user interface (GUI) including a self installing differential equation solver implemented in Matlab together with an optimization algorithm that performs model identification via Weighted Least Squares (WLS). However, it can graphically show also the simulation results obtained within other scientific software tools, if they are preventively stored into a suitable ASCII file. The tool would be a valid support also for researchers with no specific skills in scientific calculations and in pharmacokinetic and pharmacodynamic modeling but daily involved in pharma companies drug development processes at different levels. The availability of a movie with a temporal varying 2-D iconographic representation is an original instrument to communicate results and learn Simeoni TGI model and its potential application in preclinical studies.

Assessment of antitumor activity for tumor xenograft studies using exponential growth models.

In preclinical tumor xenograft experiments, the antitumor activity of the tested agents is often assessed by endpoints such as tumor doubling time, tumor growth delay (TGD), and log10 cell kill (LCK). In tumor xenograft literature, the values of these endpoints are presented without any statistical inference, which ignores the noise in the experimental data. However, using exponential growth models, these endpoints can be quantified by their growth curve parameters, thus allowing parametric inference, such as an interval estimate, to be used to assess the antitumor activity of the treatment.

Interval estimation of quantile ratios applied to anti-cancer drug screening by xenograft experiments.

The current practice in analyzing data from anti-cancer drug screening by xenograft experiments lacks statistical consideration to account for experimental noise, and a sound inference procedure is necessary. A novel confidence bound and interval procedure for estimating quantile ratios developed in this paper fills the void. Justified by rigorous large-sample theory and a simulation study of small-sample performance, the proposed method performs well in a wide range of scenarios involving right-skewed distributions. By providing rigorous inference and much more interpretable statistics that account for experimental noise, the proposed method improves the current practice of analyzing drug activity data in xenograft experiments. The proposed method is fully nonparametric, simple to compute, performs equally well or better than known nonparametric methods, and is applicable to any statistical inference of a 'fold change' that can be formulated as a quantile ratio.

Analysis of multi-arm tumor growth trials in xenograft animals using phase change adaptive piecewise quadratic models.

Xenograft trials allow tumor growth in human cell lines to be monitored over time in a mouse model. We consider the problem of inferring the effect of treatment combinations on tumor growth. A piecewise quadratic model with flexible phase change locations is proposed to model the effect of change in therapy over time. Each piece represents a growth phase, with phase changes in response to change in treatment. Piecewise slopes represent phase-specific (log) linear growth rates and curvature parameters represent departure from linear growth. Trial data are analyzed in two stages: (i) subject-specific curve fitting (ii) analysis of slope and curvature estimates across subjects. A least-squares approach with penalty for phase change point location is proposed for curve fitting. In simulation studies, the method is shown to give consistent estimates of slope and curvature parameters under independent and AR (1) measurement error. The piecewise quadratic model is shown to give excellent fit (median R(2)=0.98) to growth data from a six armed xenograft trial on a lung carcinoma cell line.

Interval approach to assessing antitumor activity for tumor xenograft studies.

In preclinical cancer drug screening tumor xenograft experiments, the tumor growth inhibition ratio (T/C) is commonly used to assess the antitumor activity of the agents. Unfortunately, this measurement can discard useful data and result in a high false-negative rate. Furthermore, the degree of antitumor activity based on the T/C ratio is assessed on the basis of an arbitrary cutoff point that does not reflect variations in different tumor lines. To overcome these drawbacks, we propose an adjusted area-under-the-curve (aAUC) ratio to quantify tumor growth inhibition. A nonparametric bootstrap t-interval of the aAUC ratio is also proposed for assessing the significance of the antitumor activity of the agents. The proposed method is then applied to a real tumor xenograft study.

Synthesis and antitumor activities of 3-modified 2-methoxyestradiol analogs.

The syntheses of 2-methoxyestradiol analogs with modifications at the 3-position are described. The analogs were assessed for their antiproliferative, antiangiogenic, and estrogenic activities. Several lead substituents were identified with similar or improved antitumor activities and reduced metabolic liability compared to 2-methoxyestradiol.

The rapamycin-regulated gene expression signature determines prognosis for breast cancer.

BACKGROUND: Mammalian target of rapamycin (mTOR) is a serine/threonine kinase involved in multiple intracellular signaling pathways promoting tumor growth. mTOR is aberrantly activated in a significant portion of breast cancers and is a promising target for treatment. Rapamycin and its analogues are in clinical trials for breast cancer treatment. Patterns of gene expression (metagenes) may also be used to simulate a biologic process or effects of a drug treatment. In this study, we tested the hypothesis that the gene-expression signature regulated by rapamycin could predict disease outcome for patients with breast cancer. RESULTS: Colony formation and sulforhodamine B (IC50 < 1 nM) assays, and xenograft animals showed that MDA-MB-468 cells were sensitive to treatment with rapamycin. The comparison of in vitro and in vivo gene expression data identified a signature, termed rapamycin metagene index (RMI), of 31 genes upregulated by rapamycin treatment in vitro as well as in vivo (false discovery rate of 10%). In the Miller dataset, RMI did not correlate with tumor size or lymph node status. High (>75th percentile) RMI was significantly associated with longer survival (P = 0.015). On multivariate analysis, RMI (P = 0.029), tumor size (P = 0.015) and lymph node status (P = 0.001) were prognostic. In van 't Veer study, RMI was not associated with the time to develop distant metastasis (P = 0.41). In the Wang dataset, RMI predicted time to disease relapse (P = 0.009). CONCLUSION: Rapamycin-regulated gene expression signature predicts clinical outcome in breast cancer. This supports the central role of mTOR signaling in breast cancer biology and provides further impetus to pursue mTOR-targeted therapies for breast cancer treatment.

Assessing cytotoxic treatment effects in preclinical tumor xenograft models.

In preclinical solid tumor xenograft experiments, tumor response to cytotoxic agents is often assessed by tumor cell kill. Log10 cell kill (LCK) is commonly used to quantify the tumor cell kill in such experiments. For comparisons of antitumor activity between tumor lines, the LCK values are converted to an arbitrary rating; for example, the treatment effect is considered significant if the LCK > 0.7 (Corbett et al., 2003). The drawback of using such a predefined cutoff point is that it does not account for the true variation of the experiments. In this article, a nonparametric bootstrap percentile interval of the LCK is proposed. The cytotoxic treatment effect can be assessed by the confidence limits of the LCK. Monte Carlo simulations are conducted to study the coverage probabilities of the proposed interval for small samples. Tumor xenograft data from a real experiment are analyzed to illustrate the proposed method.