A phase 2a clinical study on the safety and efficacy of individualized dosed mebendazole in patients with advanced gastrointestinal cancer.

Mebendazole is used extensively for treatment of local gut helminthic and invasive echinococcus infections. Anticancer effects of mebendazole have been shown in experimental cancer models and in case studies in patients with advanced cancer. Given these observations, the aims of this study were to investigate safety and efficacy of individualized dosed mebendazole in the cancer indication. Patients with treatment refractory gastrointestinal cancer were treated with individualized dose adjusted mebendazole up to 4 g/day to target a serum concentration of 300 ng/ml. Efficacy and safety were assessed by CT-scans, clinical surveillance and blood sampling. Eleven patients were included in the study and 10 started the treatment phase. Two patients stopped treatment prior to and the remaining eight after tumour evaluation by CT-scan at 8 weeks, all due to progressive disease. Four patients also fulfilled criteria suggested for hyperprogression. Only five patients reached the target serum-mebendazole concentration. No severe adverse effects were observed. Individualized dose adjusted mebendazole is safe and well tolerated in patients with advanced cancer but all patients experienced rapid progressive disease. New approaches such as prodrug development and combination with other anticancer drugs seem needed for further exploration of mebendazole as an anticancer drug.

In vitro discovery of promising anti-cancer drug combinations using iterative maximisation of a therapeutic index.

In vitro-based search for promising anti-cancer drug combinations may provide important leads to improved cancer therapies. Currently there are no integrated computational-experimental methods specifically designed to search for combinations, maximizing a predefined therapeutic index (TI) defined in terms of appropriate model systems. Here, such a pipeline is presented allowing the search for optimal combinations among an arbitrary number of drugs while also taking experimental variability into account. The TI optimized is the cytotoxicity difference (in vitro) between a target model and an adverse side effect model. Focusing on colorectal carcinoma (CRC), the pipeline provided several combinations that are effective in six different CRC models with limited cytotoxicity in normal cell models. Herein we describe the identification of the combination (Trichostatin A, Afungin, 17-AAG) and present results from subsequent characterisations, including efficacy in primary cultures of tumour cells from CRC patients. We hypothesize that its effect derives from potentiation of the proteotoxic action of 17-AAG by Trichostatin A and Afungin. The discovered drug combinations against CRC are significant findings themselves and also indicate that the proposed strategy has great potential for suggesting drug combination treatments suitable for other cancer types as well as for other complex diseases.

Drug screen in patient cells suggests quinacrine to be repositioned for treatment of acute myeloid leukemia.

To find drugs suitable for repositioning for use against leukemia, samples from patients with chronic lymphocytic, acute myeloid and lymphocytic leukemias as well as peripheral blood mononuclear cells (PBMC) were tested in response to 1266 compounds from the LOPAC(1280) library (Sigma). Twenty-five compounds were defined as hits with activity in all leukemia subgroups (<50% cell survival compared with control) at 10 µM drug concentration. Only one of these compounds, quinacrine, showed low activity in normal PBMCs and was therefore selected for further preclinical evaluation. Mining the NCI-60 and the NextBio databases demonstrated leukemia sensitivity and the ability of quinacrine to reverse myeloid leukemia gene expression. Mechanistic exploration was performed using the NextBio bioinformatic software using gene expression analysis of drug exposed acute myeloid leukemia cultures (HL-60) in the database. Analysis of gene enrichment and drug correlations revealed strong connections to ribosomal biogenesis nucleoli and translation initiation. The highest drug-drug correlation was to ellipticine, a known RNA polymerase I inhibitor. These results were validated by additional gene expression analysis performed in-house. Quinacrine induced early inhibition of protein synthesis supporting these predictions. The results suggest that quinacrine have repositioning potential for treatment of acute myeloid leukemia by targeting of ribosomal biogenesis.

Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors.

OBJECTIVE: Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (CI) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the CI is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice. METHOD AND MATERIAL: It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples. RESULTS: Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets. CONCLUSIONS: An empirically derived ME prior seems promising for improving the Bayesian CI for the unknown error rate of a designed classifier.

Stathmin as a marker for malignancy in pheochromocytomas.

Pheochromocytomas of the adrenal medulla may be life-threatening catecholamine-producing tumors which are malignant in about 10% of cases. Differential diagnosis between malignant and benign tumors is dependent on the development of metastasis or extensive local invasion. A number of genetic aberrations have been described in pheochromocytomas, but no marker associated to malignancy has been reported. We applied an expression microarray containing 7770 cDNA clones and analysed the expression profiles in eleven tumors compared to normal adrenal medulla. Stathmin (STMN1, Op18) was most conspiciously overexpressed among the differentially expressed genes. RT-PCR analysis further confirmed mRNA overexpression, 6 to 8-fold for benign and malignant tumors, and 16-fold for metastases. Stathmin protein overexpression was observed by immunohistochemistry, and distinct differential protein expression between benign and malignant/metastasis specimens was confirmed by Western blot analysis. The results introduce stathmin as a possible diagnostic marker for malignant pheochromocytomas, and further evaluations are warranted.

In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency.

A major concern of chemogenomics is to associate drug activity with biological variables. Several reports have clustered cell line drug activity profiles as well as drug activity-gene expression correlation profiles and noted that the resulting groupings differ but still reflect mechanism of action. The present paper shows that these discrepancies can be viewed as a weighting of drug-drug distances, the weights depending on which cell lines the two drugs differ in.

Identification of molecular mechanisms for cellular drug resistance by combining drug activity and gene expression profiles.

Acquired drug resistance is a major problem in cancer treatment. To explore the genes involved in chemosensitivity and resistance, 10 human tumour cell lines, including parental cells and resistant subtypes selected for resistance against doxorubicin, melphalan, teniposide and vincristine, were profiled for mRNA expression of 7400 genes using cDNA microarray technology. The drug activity of 66 cancer agents was evaluated on the cell lines, and correlations between drug activity and gene expression were calculated and ranked. Hierarchical clustering of drugs based on their drug-gene correlations yielded clusters of drugs with similar mechanism of action. Genes correlated with drug sensitivity and resistance were imported into the PathwayAssist software to identify putative molecular pathways involved. A substantial number of both proapoptotic and antiapoptotic genes such as signal transducer and activator of transcription 1, mitogen-activated protein kinase 1 and focal adhesion kinase were found to be associated to drug resistance, whereas genes linked to cell cycle control and proliferation, such as cell division cycle 25A and signal transducer of activator of transcription 5A, were associated to general drug sensitivity. The results indicate that combined information from drug activity and gene expression in a resistance-based cell line panel may provide new knowledge of the genes involved in anticancer drug resistance and become a useful tool in drug development.