Despite its clinical promise, photodynamic therapy (PDT) suffers from a key drawback associated with its oxygen-dependent nature, which limits its effective use against hypoxic tumors. Moreover, both PDT-mediated oxygen consumption and microvascular damage further increase tumor hypoxia and, thus, impede therapeutic outcomes. In recent years, numerous investigations have focused on strategies for overcoming this drawback of PDT. These efforts, which are summarized in this review, have produced many innovative methods to avoid the limits of PDT associated with hypoxia.

Maximum likelihood estimate, Randomizing device, Randomized response technique, Sensitive questions, Warner’s model,

Albumin is a promising candidate as a biomarker for potential disease diagnostics and has been extensively used as a drug delivery carrier for decades. In these two directions, many albumin-detecting probes and exogenous albumin-based nanocomposite delivery systems have been developed. However, there are only a few cases demonstrating the specific interactions of exogenous probes with albumin in vivo, and nanocomposite delivery systems usually suffer from tedious fabrication processes and potential toxicity of the complexes. Herein, we demonstrate a facile "one-for-all" switchable nanotheranostic (NanoPcS) for both albumin detection and cancer treatment. In particular, the in vivo specific binding between albumin and PcS, arising from the disassembly of injected NanoPcS, is confirmed using an inducible transgenic mouse system. Fluorescence imaging and antitumor tests on different tumor models suggest that NanoPcS has superior tumor-targeting ability and the potential for time-modulated, activatable photodynamic therapy.

In cancer drug development, xenograft experiments (models) where mice are grafted with human cancer cells are used to elucidate the mechanism of action and/or to assess efficacy of a promising compound. Demonstrated activity in this model is an important step to bring a promising compound to humans. A key outcome variable in these experiments is tumour volumes measured over a period of time, while mice are treated with an anticancer agent following certain schedules. However, a mouse may die during the experiment or may be sacrificed when its tumour volume quadruples and then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumour shrinkage (<0.01 cm3) or random truncation. In addition, if no treatment were given to the tumour-bearing mice, the tumours would keep growing until the mice die or are sacrificed. This intrinsic growth of tumour in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. We develop a maximum likelihood method based on the expectation/conditional maximization (ECM) algorithm to estimate the dose-response relationship while accounting for the informative censoring and the constraints of model parameters. A real xenograft study on a new anti-tumour agent temozolomide combined with irinotecan is analysed using the proposed method.

In anticancer drug development, the combined use of two drugs is an important strategy to achieve greater therapeutic success. Often combination studies are performed in animal (mostly mice) models before clinical trials are conducted. These experiments on mice are costly, especially with combination studies. However, experimental designs and sample size derivations for the joint action of drugs are not currently available except for a few cases where strong model assumptions are made. For example, Abdelbasit and Plackett proposed an optimal design assuming that the dose-response relationship follows some specified linear models. Tallarida et al. derived a design by fixing the mixture ratio and used a t-test to detect the simple similar action. The issue is that in reality we usually do not have enough information on the joint action of the two compounds before experiment and to understand their joint action is exactly our study goal. In this paper, we first propose a novel non-parametric model that does not impose such strong assumptions on the joint action. We then propose an experimental design for the joint action using uniform measure in this non-parametric model. This design is optimal in the sense that it reduces the variability in modelling synergy while allocating the doses to minimize the number of experimental units and to extract maximum information on the joint action of the compounds. Based on this design, we propose a robust F-test to detect departures from the simple similar action of two compounds and a method to determine sample sizes that are economically feasible. We illustrate the method with a study of the joint action of two new anticancer agents: temozolomide and irinotecan.

No abstract

Confidence interval (CI) construction with respect to proportion/rate difference for paired binary data has become a standard procedure in many clinical trials and medical studies. When the sample size is small and incomplete data are present, asymptotic CIs may be dubious and exact CIs are not yet available. In this article, we propose exact and approximate unconditional test-based methods for constructing CI for proportion/rate difference in the presence of incomplete paired binary data. Approaches based on one- and two-sided Wald's tests will be considered. Unlike asymptotic CI estimators, exact unconditional CI estimators always guarantee their coverage probabilities at or above the pre-specified confidence level. Our empirical studies further show that (i) approximate unconditional CI estimators usually yield shorter expected confidence width (ECW) with their coverage probabilities being well controlled around the pre-specified confidence level; and (ii) the ECWs of the unconditional two-sided-test-based CI estimators are generally narrower than those of the unconditional one-sided-test-based CI estimators. Moreover, ECWs of asymptotic CIs may not necessarily be narrower than those of two-sided-based exact unconditional CIs. Two real examples will be used to illustrate our methodologies.

We propose a new non-randomized model for assessing the association of two sensitive questions with binary outcomes. Under the new model, respondents only need to answer a non-sensitive question instead of the original two sensitive questions. As a result, it can protect a respondent's privacy, avoid the usage of any randomizing device, and be applied to both the face-to-face interview and mail questionnaire. We derive the constrained maximum likelihood estimates of the cell probabilities and the odds ratio for two binary variables associated with the sensitive questions via the EM algorithm. The corresponding standard error estimates are then obtained by bootstrap approach. A likelihood ratio test and a chi-squared test are developed for testing association between the two binary variables. We discuss the loss of information due to the introduction of the non-sensitive question, and the design of the co-operative parameters. Simulations are performed to evaluate the empirical type I error rates and powers for the two tests. In addition, a simulation is conducted to study the relationship between the probability of obtaining valid estimates and the sample size for any given cell probability vector. A real data set from an AIDS study is used to illustrate the proposed methodologies.