Cisplatin, gemcitabine, and treosulfan in relapsed stage IV cutaneous malignant melanoma patients

To evaluate the efficacy of cisplatin, gemcitabine, and treosulfan (CGT) in 91 patients with pretreated relapsed AJCC stage IV cutaneous malignant melanoma. Patients in relapse after first-, second-, or third-line therapy received 40 mg m−2 intravenous (i.v.) cisplatin, 1000 mg m−2 i.v. gemcitabine, and 2500 mg m−2 i.v. treosulfan on days 1 and 8. Cisplatin, gemcitabine, and treosulfan therapy was repeated every 5 weeks until progression of disease occurred. A maximum of 11 CGT cycles (mean, two cycles) was administered per patient. Four patients (4%) showed a partial response; 15 (17%) patients had stable disease; and 72 (79%) patients progressed upon first re-evaluation. Overall survival of all 91 patients was 6 months (2-year survival rate, 7%). Patients with partial remission or stable disease exhibited a median overall survival of 11 months (2-year survival rate, 36%), while patients with disease progression upon first re-evaluation had a median overall survival of 5 months (2-year survival rate, 0%). Treatment with CGT was efficient in one-fifth of the pretreated relapsed stage IV melanoma patients achieving disease stabilisation or partial remission with prolonged but limited survival

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans

An Efficient Implementation of Reinforcement Learning Based Routing on Real WSN Hardware

Abstract — Efficient multi-hop data dissemination is a crucial building block to enable mature wireless sensor network (WSN) applications. Exploiting machine learning for these routing problems has received increasing attention in recent years due to its flexibility and localized mechanisms. However, with such an approach the resulting protocols often have additional memory and processing time requirements. Nevertheless, these requirements are within the reach of today’s WSN hardware, however few substantial tests have been performed to clearly demonstrate this. This paper evaluates and discusses the results and experiences gained from implementing our reinforcement learning based multicast routing protocol (FROMS) in a testbed of ScatterWeb nodes. A comparison of our results is made to a well-known WSN routing scheme, namely a multicast variation of Directed Diffusion. Our evaluation includes several minor, but practical modifications to both protocols such as transmission backoffs and the use of acknowledgments. This paper offers three main contributions. First, we demonstrate that machine learning algorithms can be efficiently implemented on resource restricted devices and that they perform very well in multiple network scenarios. Second, we confirm the validity of simulation results obtained in a previous evaluation of FROMS, and at the same time gather delivery rates under realistic settings. Finally, we offer some general observations on properties and pitfalls of WSN implementations along with potential solutions. I

Comparison of Multilabel and Single-Label Classification Applied to the Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Each drug can potentially be metabolized by different CYP450 isoforms. In the development of new drugs, the prediction of the metabolic fate is important to prevent drug-drug interactions. In the present study, a collection of 580 CYP450 substrates is deeply analyzed by applying multi- and single-label classification strategies, after the computation and selection of suitable molecular descriptors. Cross-training with support vector machine, multilabel k-nearest-neighbor and counterpropagation neural network modeling methods were used in the multilabel approach, which allows one to classify the compounds simultaneously in multiple classes. In the single-label models, automatic variable selection was combined with various cross-validation experiments and modeling techniques. Moreover, the reliability of both multi- and single-label models was assessed by the prediction of an external test set. Finally, the predicted results of the best models were compared to show that, even if the models present similar performances, the multilabel approach more coherently reflects the real metabolism information

Rottebehaelter zur mechanischen und biologischen Vorreinigung von haeuslichem Abwasser - Optimierung eines Rottebehaelters Schlussbericht

SIGLEAvailable from TIB Hannover: F03B1239 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung, Berlin (Germany); Arbeitsgemeinschaft Industrieller Forschungsvereinigungen e.V., Koeln (Germany)DEGerman

Exploring potency and selectivity receptor antagonist profiles using a multilabe classification approach: the human adenosine receptor as a key study

Nowadays, in medicinal chemistry adenosine receptors represent some of the most studied targets, and there is growing interest on the different adenosine receptor (AR) subtypes. The AR subtypes selectivity is highly desired in the development of potent ligands to achieve the therapeutic success. So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. In the present study, we have carried out a novel application of the multilabel classification approach by combining our recently reported autocorrelated molecular descriptors encoding for the molecular electrostatic potential (autoMEP) with support vector machines (SVMs). Three valuable models, based on decreasing thresholds of potency, have been generated as in series quantitative sieves for the simultaneous prediction of the hA(1)R, hA(2A)R, hA(2B)R, and hA(3)R subtypes potency profile and selectivity of a large collection, more than 500, of known inverse agonists such as xanthine, pyrazolo-triazolo-pyrimidine, and triazolo-pyrimidine analogues. The robustness and reliability of our multilabel classification models were assessed by predicting an internal test set. Finally, we have applied our strategy to 13 newly synthesized pyrazolo-triazolo-pyrimidine derivatives inferring their full adenosine receptor potency spectrum and hAR subtypes selectivity profile