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This article considers the nonparametric maximum likelihood estimator (NPMLE) of a joint distribution function when the multivariate failure times of interest are interval-censored. With different types of interval censoring mechanism, the NPMLE's of the multivariate distribution function are studied and the strong consistency for the NPMLEs is obtained in terms of a self-consistency equation. Furthermore, the convergence rate of the estimator is given, which depends on the types of interval censoring mechanism. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Drug combination is a critically important therapeutic approach for complex diseases such as cancer and HIV due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. One of the key issues is to identify which combinations are additive, synergistic, or antagonistic. While the value of multidrug combinations has been well recognized in the cancer research community, to our best knowledge, all existing experimental studies rely on fixing the dose of one drug to reduce the dimensionality, e.g. looking at pairwise two-drug combinations, a suboptimal design. Hence, there is an urgent need to develop experimental design and analysis methods for studying multidrug combinations directly. Because the complexity of the problem increases exponentially with the number of constituent drugs, there has been little progress in the development of methods for the design and analysis of high-dimensional drug combinations. In fact, contrary to common mathematical reasoning, the case of three-drug combinations is fundamentally more difficult than two-drug combinations. Apparently, finding doses of the combination, number of combinations, and replicates needed to detect departures from additivity depends on dose-response shapes of individual constituent drugs. Thus, different classes of drugs of different dose-response shapes need to be treated as a separate case. Our application and case studies develop dose finding and sample size method for detecting departures from additivity with several common (linear and log-linear) classes of single dose-response curves. Furthermore, utilizing the geometric features of the interaction index, we propose a nonparametric model to estimate the interaction index surface by B-spine approximation and derive its asymptotic properties. Utilizing the method, we designed and analyzed a combination study of three anticancer drugs, PD184, HA14-1, and CEP3891 inhibiting myeloma H929 cell line. To our best knowledge, this is the first ever three drug combinations study performed based on the original 4D dose-response surface formed by dose ranges of three drugs.

The design and analysis of drug combination studies continue to be an area requiring further methodological developments. Faessel et al. (1998) studied the joint effects of the combinations of trimetrexate (TMQ) and the GARFT inhibitor AG2034 to inhibit the growth of HCT-8 human ileocecal adenocarcinoma cells. Their experiments provide a rich data resource to validate the performance of new experimental design and analysis methods for future experiments. In this paper, we first re-analyze the same data with a nonparametric model and briefly review the experimental design used in the original paper. By comparing the analysis results, we found that the fixed ratio design and the usage of the parametric model for estimating the interaction index are based on an assumption not supported by the data. We then show how the efficiency of the experiments would be improved had the maximal power experimental design based on uniform measures been used. The usage of the proposed maximal power experimental design is further supported by simulation studies.

Background: Postoperative interferon-alpha (IFN-alpha) therapy improved survival in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). The identification of predictive markers of outcome will help to select patients who are most likely to benefit from treatment., Methods: An immunohistochemical study of P48 was performed on specimens that were collected from patients in a randomized trial who received postoperative IFN-alpha therapy (Group 1; n = 80 patients) and who did not receive postoperative IFN-alpha therapy (Group 2; n = 75 patients). Positive P48 expression was graded as >/=20% positive cells in 1 sample., Results: Eighty-one patients were positive for P48, and 74 patients were negative for P48. The clinicopathologic data were comparable between patients with P48-negative and P48-positive staining. Disease-free survival (DFS) and overall survival (OS) in P48-positive patients were better than that in P48-negative patients in Group 1 (DFS, P = .036; OS, P = .014), however, DFS and OS did not differ between patients with positive and negative P48 in Group 2. OS in P48-positive patients from Group 1 was better than that in patients with P48-positive patients from Group 2 (OS, P = .001) but did not differ when P48 was negative. In Group 1, the risk factors for DFS were cirrhosis and P48 staining, and the risk factors for OS were tumor differentiation and P48 staining. Receiver operating curve analysis indicated that, in the first 2 years of DFS, combined cirrhosis and P48 had good predictive accuracy; and, in the first 4 years of OS, combined tumor differentiation and P48 had good predictive accuracy., Conclusions: P48 was useful as a predictive marker of outcome after postoperative IFN-alpha treatment in patients with HBV-related HCC., ((c) 2006 American Cancer Society.)

To evaluate the function and substrate specificity of human breast cancer resistance protein (BCRP, ABCG2) in the absence of cofactors or heterologous partner proteins, Xenopus laevis oocytes were injected with cRNA of wild-type or mutant (R482T) BCRP. High expression of BCRP was observed on the oocyte surface. Accumulation and efflux assays revealed that oocytes expressing R482T transported daunorubicin (DNR), mitoxantrone (MX), rhodamine 123, and flavopiridol (FLV), whereas wild-type BCRP transported only MX and FLV, in agreement with observations in mammalian and other systems. Transport activity was completely inhibited by fumitremorgin C, a known inhibitor of BCRP. Injection of oocytes with cRNA containing mutations of serine 187 in the ATP-binding cassette signature motif (S187T or S187A) resulted in strong expression of the mutant forms; however, these oocytes were devoid of transporter activity. When oocytes were coinjected with R482T and R482T/S187T, DNR transport was inhibited in a manner dependent on the amount of R482T/S187T cRNA added, consistent with the idea that the active form of BCRP is a homodimer or homomultimer. Substrate interaction studies found that no two substrates reciprocally inhibited the efflux of the other. Although FLV proved to be an effective inhibitor of both MX and DNR transport, and MX inhibited DNR transport, the other substrates tested had only weak or no inhibitory activity, indicating a complex nature of substrate interaction with the BCRP homodimer. We conclude that the X. laevis oocyte heterologous expression system is a valid and effective means of studying BCRP function and substrate specificity.

In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.