Isolation of High-Purity Betanin from Red Beet and Elucidation of Its Antioxidant Activity against Peroxynitrite: An in vitro Study.

Reactive oxygen species and reactive nitrogen species (RNS) are damaging for many biomolecules. Peroxynitrite (ONOO-) is the most toxic molecular species among RNS. Betalains are known to possess ONOO- scavenging ability. Betanin, a betalain isolated from red beet, possesses antioxidant, anti-inflammatory, and antitumor activities; however, detailed studies of this isolated pigment have not been conducted, owing to its instability under physiological conditions. This study aimed to isolate highly purified betanin from red beetroots using an improved purification method involving deproteinization and citric acid co-precipitation and evaluated its antioxidant activities. The purified betanin thus obtained had a significantly lower isobetanin content than the commercially available betanin dyes. The antioxidant activity of purified betanin examined in the 2,2-diphenyl-1-picrylhydrazyl assay, the direct ONOO- reaction, ONOO--dependent DNA damage, and lipid peroxidation reactions revealed that betanin possessed higher antioxidant capacity than general antioxidants such as ascorbic acid and quercetin. Furthermore, betanin showed indirect and direct cytoprotective effects against H2O2 and ONOO- cytotoxicity, respectively, in cultured mouse fibroblasts. To the best of our knowledge, this is the first study to demonstrate the cytoprotective effects of betanin against ONOO- toxicity. The highly purified betanin obtained in this study will aid in further exploring its physiological functions.

Insights into the clinical development of regenerative medical products through a comparison of three cell-based products recently approved for limbal stem cell deficiency.

Three regenerative medical products for limbal stem cell deficiency (LSCD), a rare and intractable ocular surface disease, have recently been approved in Japan. To our knowledge, this is the first time multiple stem-cell-based medical products have been approved for the same ocular disease. Development plans and study designs for each product differ, resulting in differences in indications. Since cell-based products have a heterogeneous formulation and often target rare diseases, they require a flexible approach to development. This review article describes the status and prospects of the clinical development of regenerative medical products by summarizing the issues of the three products from the Pharmaceuticals and Medical Devices Agency (PMDA) standpoint. Implementing stem cell-based products is challenging, requiring scientific and flexible review by regulatory authorities. To overcome these issues in the development process, developers and regulatory authorities need to communicate and fully discuss study protocols from the early stage of development.

Regulatory Issues: PMDA - Review of Sakigake Designation Products: Oncolytic Virus Therapy with Delytact Injection (Teserpaturev) for Malignant Glioma.

In June 2021, the Ministry of Health, Labor and Welfare approved Delytact Injection as a regenerative medical product for oncolytic virus therapy. The active substance of Delytact Injection is teserpaturev, a genetically engineered herpes simplex virus type 1 (strain F) in which the α47 gene and both copies of the γ34.5 gene have been deleted and the infected cell protein 6 (ICP6) gene has been inactivated by the insertion of the lacZ gene from Escherichia coli. Delytact Injection, when intratumorally administered to patients with malignant glioma, is expected to exert the following effects: (1) the mutant virus selectively replicates in tumor cells and destroys the infected cells through the replication process, exerting a cytocidal effect, and (2) the administration leads to induction of tumor-responsive T cells, which activates antitumor immunity and thus prolongs the survival of patients with malignant glioma. A Japanese phase II study (Study GD01) was conducted in patients with glioblastoma who had residual or recurrent tumors after radiotherapy with concomitant temozolomide. In Study GD01, however, stable disease continued for an extended period in some patients with glioblastoma. Hence, Delytact Injection is expected to be effective to a certain level. In line with this, Delytact Injection has been approved as an option for the treatment of malignant glioma, with one of the 3 approval conditions including conducting a use-results comparison survey and resubmission of the marketing authorization application within the granted time period of 7 years, under the conditional and time-limited approval scheme described in Article 23-26 of Act on Securing Quality, Efficacy and Safety of Products Including Pharmaceuticals and Medical Devices.

Practical basket design for binary outcomes with control of family-wise error rate.

BACKGROUND: A basket trial is a type of clinical trial in which eligibility is based on the presence of specific molecular characteristics across subpopulations with different cancer types. The existing basket designs with Bayesian hierarchical models often improve the efficiency of evaluating therapeutic effects; however, these models calibrate the type I error rate based on the results of simulation studies under various selected scenarios. The theoretical control of family-wise error rate (FWER) is important for decision-making regarding drug approval. METHODS: In this study, we propose a new Bayesian two-stage design with one interim analysis for controlling FWER at the target level, along with the formulations of type I and II error rates. Since the difficulty lies in the complexity of the theoretical formulation of the type I error rate, we devised the simulation-based method to approximate the type I error rate. RESULTS: The proposed design enabled adjustment of the cutoff value to control the FWER at the target value in the final analysis. The simulation studies demonstrated that the proposed design can be used to control the well-approximated FWER below the target value even in situations where the number of enrolled patients differed among subpopulations. CONCLUSIONS: The accrual number of patients is sometimes unable to reach the pre-defined value; therefore, existing basket designs may not ensure defined operating characteristics before beginning the trial. The proposed design that enables adjustment of the cutoff value to control FWER at the target value based on the results in the final analysis would be a better alternative.

A Bayesian basket trial design accounting for uncertainties of homogeneity and heterogeneity of treatment effect among subpopulations.

Basket trials are a recent and innovative approach in oncological clinical trial design. A basket trial is a type of clinical trial for which eligibility is based on the presence of a specific genomic alteration, irrespective of cancer type. Additionally, basket trials are often used to evaluate the response rate of an investigational therapy across several types of cancer. Recently developed statistical methods for evaluating the response rate in basket trials can be generally categorized into two groups: (a) those that account for the degrees of homogeneity/heterogeneity of response rates among subpopulations, and (b) those using borrowed response rate information across subpopulations to improve the statistical efficiency using Bayesian hierarchical models. In this study, we developed a new basket trial design that accounts for the uncertainties of homogeneity and heterogeneity of response rates among subpopulations using the Bayesian model averaging approach. We demonstrated the utility of the proposed method by comparing our approach against other methods for the two methodological groups using simulated and actual data. On an average, the proposed methods offered an intermediate performance between the BHM-weak and BHM-strong methods. The proposed method would be useful for "signal-finding" basket trials without prior information on the treatment effect of an investigational drug, in part because the proposed method does not require specifications regarding prior distributions of homogeneity response rates among subpopulations.

Master protocol trials in oncology: Review and new trial designs.

In oncology, next generation sequencing and comprehensive genomic profiling have enabled the detailed classification of tumors using molecular biology. However, it is unrealistic to conduct phase I-III trials according to each sub-population based on patient molecular subtypes. Common protocols that assess the combination of several molecular markers and their targeted therapies by means of multiple sub-studies are required. These protocols are called "master protocols," and are drawing attention as a next-generation clinical trial design. Recently, several reviews of clinical trials based on the master protocol design have been published, but their definitions of these such trials, including basket, umbrella, and platform trials, were not consistent. Concurrently, the acceleration of the development of new statistical designs for master protocol trials has been underway. This article provides an overview of recent reviews for master protocols, including their statistical design methodologies in Oncology. We also introduce several examples of previous and on-going master protocol trials along with their classifications by some recent studies.

Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data.

The Cox proportional hazards cure model is a survival model incorporating a cure rate with the assumption that the population contains both uncured and cured individuals. It contains a logistic regression for the cure rate, and a Cox regression to estimate the hazard for uncured patients. A single predictive model for both the cure and hazard can be developed by using a cure model that simultaneously predicts the cure rate and hazards for uncured patients; however, model selection is a challenge because of the lack of a measure for quantifying the predictive accuracy of a cure model. Recently, we developed an area under the receiver operating characteristic curve (AUC) for determining the cure rate in a cure model (Asano et al., 2014), but the hazards measure for uncured patients was not resolved. In this article, we propose novel C-statistics that are weighted by the patients' cure status (i.e., cured, uncured, or censored cases) for the cure model. The operating characteristics of the proposed C-statistics and their confidence interval were examined by simulation analyses. We also illustrate methods for predictive model selection and for further interpretation of variables using the proposed AUCs and C-statistics via application to breast cancer data.

Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data.

A cure rate model is a survival model incorporating the cure rate with the assumption that the population contains both uncured and cured individuals. It is a powerful statistical tool for prognostic studies, especially in cancer. The cure rate is important for making treatment decisions in clinical practice. The proportional hazards (PH) cure model can predict the cure rate for each patient. This contains a logistic regression component for the cure rate and a Cox regression component to estimate the hazard for uncured patients. A measure for quantifying the predictive accuracy of the cure rate estimated by the Cox PH cure model is required, as there has been a lack of previous research in this area. We used the Cox PH cure model for the breast cancer data; however, the area under the receiver operating characteristic curve (AUC) could not be estimated because many patients were censored. In this study, we used imputation-based AUCs to assess the predictive accuracy of the cure rate from the PH cure model. We examined the precision of these AUCs using simulation studies. The results demonstrated that the imputation-based AUCs were estimable and their biases were negligibly small in many cases, although ordinary AUC could not be estimated. Additionally, we introduced the bias-correction method of imputation-based AUCs and found that the bias-corrected estimate successfully compensated the overestimation in the simulation studies. We also illustrated the estimation of the imputation-based AUCs using breast cancer data.

Use of Cox's Cure Model to Establish Clinical Determinants of Long-Term Disease-Free Survival in Neoadjuvant-Chemotherapy-Treated Breast Cancer Patients without Pathologic Complete Response.

In prognostic studies for breast cancer patients treated with neoadjuvant chemotherapy (NAC), the ordinary Cox proportional-hazards (PH) model has been often used to identify prognostic factors for disease-free survival (DFS). This model assumes that all patients eventually experience relapse or death. However, a subset of NAC-treated breast cancer patients never experience these events during long-term follow-up (>10 years) and may be considered clinically "cured." Clinical factors associated with cure have not been studied adequately. Because the ordinary Cox PH model cannot be used to identify such clinical factors, we used the Cox PH cure model, a recently developed statistical method. This model includes both a logistic regression component for the cure rate and a Cox regression component for the hazard for uncured patients. The purpose of this study was to identify the clinical factors associated with cure and the variables associated with the time to recurrence or death in NAC-treated breast cancer patients without a pathologic complete response, by using the Cox PH cure model. We found that hormone receptor status, clinical response, human epidermal growth factor receptor 2 status, histological grade, and the number of lymph node metastases were associated with cure.