Current and prospective pharmacotherapies for the treatment of pleural mesothelioma

Introduction: Mesothelioma is a rare asbestos-linked cancer with an expected incidence peak between 2015–2030. Therapies remain ineffective, thus developing and testing novel treatments is important for both oncologists and researchers. Areas covered: After describing mesothelioma and the shortcomings of current therapies, the article discusses numerous therapies in turn such as immunotherapy (passive and active), gene therapy (such as suicide gene therapy) and targeted therapy such as tyrosine kinase inhibitors. The bases for different therapies and clinical trials at different phases are also described. The article concludes by detailing possible reasons for therapy failure. Expert opinion: Despite the many attempts to uncover new therapeutic options, mesothelioma is still an orphan disease, complicated by factors such as the inflammatory microenvironment and low mutational load. Our opinion is that uncovering the biological mechanisms behind mesothelioma development will assist therapy development. The lack of efficacy of tyrosine kinase inhibitors and modest anti-angiogenic activity indicates a less relevant role for tumor cell proliferation and neoangiogenesis, thus the shortcut of treating mesothelioma with therapies from other cancers may be unsound. Conversely, many lines of evidence indicate that focussing on the survival mechanisms that tumor cells exploit may yield better therapeutics, particularly nutrition and cellular machinery

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

<p>Abstract</p> <p>Background</p> <p>Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community.</p> <p>Results</p> <p>This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment.</p> <p>Conclusions</p> <p>AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.</p

Molecular targeted therapies in head and neck cancer - An update of recent developements -

Targeted therapies have made their way into clinical practice during the past decade. They have caused a major impact on the survival of cancer patients in many areas of clinical oncology and hematology. Indeed, in some hematologic malignancies, such as chronic myelogenous leukemia or non-Hodgkin's lymphomas, biologicals and antibodies specifically designed to target tumour-specific proteins have revolutionized treatment standards. In solid tumours, new drugs targeting EGF- or VEGF- receptors are now approved and are entering clinical practise for treatment of colon, lung, kidney and other cancers, either alone or in combination with conventional treatment approaches

Fibrocytes are associated with vascular and parenchymal remodelling in patients with obliterative bronchiolitis

<p>Abstract</p> <p>Background</p> <p>The aim of the present study was to explore the occurrence of fibrocytes in tissue and to investigate whether the appearance of fibrocytes may be linked to structural changes of the parenchyme and vasculature in the lungs of patients with obliterative bronchiolitis (OB) following lung or bone marrow transplantation.</p> <p>Methods</p> <p>Identification of parenchyme, vasculature, and fibrocytes was done by histological methods in lung tissue from bone marrow or lung-transplanted patients with obliterative bronchiolitis, and from controls.</p> <p>Results</p> <p>The transplanted patients had significantly higher amounts of tissue in the alveolar parenchyme (46.5 ± 17.6%) than the controls (21.7 ± 7.6%) (p < 0.05). The patients also had significantly increased numbers of fibrocytes identified by CXCR4/prolyl4-hydroxylase, CD45R0/prolyl4-hydroxylase, and CD34/prolyl4-hydroxylase compared to the controls (p < 0.01). There was a correlation between the number of fibrocytes and the area of alveolar parenchyma; CXCR4/prolyl 4-hydroxylase (p < 0.01), CD45R0/prolyl 4-hydroxylase (p < 0.05) and CD34/prolyl 4-hydroxylase (p < 0.05). In the pulmonary vessels, there was an increase in the endothelial layer in patients (0.31 ± 0.13%) relative to the controls (0.037 ± 0.02%) (p < 0.01). There was a significant correlation between the number of fibrocytes and the total area of the endothelial layer CXCR4/prolyl 4-hydroxylase (p < 0.001), CD45R0/prolyl 4-hydroxylase (p < 0.001) and CD34/prolyl 4-hydroxylase (p < 0.01). The percent areas of the lumen of the vessels were significant (p < 0.001) enlarged in the patient with OB compared to the controls. There was also a correlation between total area of the lumen and number of fibrocytes, CXCR4/prolyl 4-hydroxylase (p < 0.01), CD45R0/prolyl 4-hydroxylase (p < 0.001) and CD34/prolyl 4-hydroxylase (p < 0.01).</p> <p>Conclusion</p> <p>Our results indicate that fibrocytes are associated with pathological remodelling processes in patients with OB and that tissue fibrocytes might be a useful biomarker in these processes.</p

Identification of diagnostic serum protein profiles of glioblastoma patients

Diagnosis of a glioblastoma (GBM) is triggered by the onset of symptoms and is based on cerebral imaging and histological examination. Serum-based biomarkers may support detection of GBM. Here, we explored serum protein concentrations of GBM patients and used data mining to explore profiles of biomarkers and determine whether these are associated with the clinical status of the patients. Gene and protein expression data for astrocytoma and GBM were used to identify secreted proteins differently expressed in tumors and in normal brain tissues. Tumor expression and serum concentrations of 14 candidate proteins were analyzed for 23 GBM patients and nine healthy subjects. Data-mining methods involving all 14 proteins were used as an initial evaluation step to find clinically informative profiles. Data mining identified a serum protein profile formed by BMP2, HSP70, and CXCL10 that enabled correct assignment to the GBM group with specificity and sensitivity of 89 and 96%, respectively (p < 0.0001, Fischer’s exact test). Survival for more than 15 months after tumor resection was associated with a profile formed by TSP1, HSP70, and IGFBP3, enabling correct assignment in all cases (p < 0.0001, Fischer’s exact test). No correlation was found with tumor size or age of the patient. This study shows that robust serum profiles for GBM may be identified by data mining on the basis of a relatively small study cohort. Profiles of more than one biomarker enable more specific assignment to the GBM and survival group than those based on single proteins, confirming earlier attempts to correlate single markers with cancer. These conceptual findings will be a basis for validation in a larger sample size

Cancer Cells Expressing Toll-like Receptors and the Tumor Microenvironment

Toll-like receptors (TLRs) play a crucial role in the innate immune response and the subsequent induction of adaptive immune responses against microbial infection or tissue injury. Recent findings show that functional TLRs are expressed not only on immune cells but also on cancer cells. TLRs play an active role in carcinogenesis and tumor progression during chronic inflammation that involves the tumor microenvironment. Damage-associated molecular patterns (DAMPs) derived from injured normal epithelial cells and necrotic cancer cells appear to be present at significant levels in the tumor microenvironment, and their stimulation of specific TLRs can foster chronic inflammation. This review discusses how carcinogenesis, cancer progression, and site-specific metastasis are related to interactions between cancer cells, immune cells, and DAMPs through TLR activation in the tumor microenvironment

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine