Exploring new labelling strategies for boronated compounds: towards fast development and efficient assessment of BNCT drug candidates

208 p.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

In a multi-target complex network, the links (L-ij) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K-i, K-m, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Mugilid Fish Are Sentinels of Exposure to Endocrine Disrupting Compounds in Coastal and Estuarine Environments

Effects on fish reproduction can result from a variety of toxicity mechanisms first operating at the molecular level. Notably, the presence in the environment of some compounds termed endocrine disrupting chemicals (EDCs) can cause adverse effects on reproduction by interfering with the endocrine system. In some cases, exposure to EDCs leads to the animal feminization and male fish may develop oocytes in testis (intersex condition). Mugilid fish are well suited sentinel organisms to study the effects of reproductive EDCs in the monitoring of estuarine/marine environments. Up-regulation of aromatases and vitellogenins in males and juveniles and the presence of intersex individuals have been described in a wide array of mullet species worldwide. There is a need to develop new molecular markers to identify early feminization responses and intersex condition in fish populations, studying mechanisms that regulate gonad differentiation under exposure to xenoestrogens. Interestingly, an electrophoresis of gonad RNA, shows a strong expression of 5S rRNA in oocytes, indicating the potential of 5S rRNA and its regulating proteins to become useful molecular makers of oocyte presence in testis. Therefore, the use of these oocyte markers to sex and identify intersex mullets could constitute powerful molecular biomarkers to assess xenoestrogenicity in field conditions.