The impact of pharmaceutical policy measures: a structural-break approach

Pharmaceutical spending in many other countries has had a steep increase in the last decade. The Portuguese Government has adopted several measures to reduce pharmaceutical expenditure growth, ranging from increased co-payments to price decreases determined administratively. Promotion of generic consumption has also ranked high in political priorities. We assess the overall impact of the several policy measures on total pharmaceutical spending, using monthly data over the period January 1995 – August 2008. Endogenous structural breaks (time-series) methods were employed. Our findings suggest that policy measures aimed at controlling pharmaceutical expenditure have been, in general, unsuccessful. Two breaks were identified. Both coincide with administratively determined price decreases. Measures aimed at increasing competition in the market had no visible effect on the dynamics of Government spending in pharmaceutical products. In particular, the introduction of reference pricing had only a transitory effect of less than one year, with historical growth resuming quickly. The consequence of it is a transfer of financial burden from the Government to the patients, with no apparent effect on the dynamics of pharmaceutical spending. This strongly suggests that pharmaceutical companies have been able to adjust to policy measures, in order to sustain their sales. It remains a challenge for the future to identify firms’ strategies that supported continued growth of sales, despite the several policy measures adop

Structural investigation of the Bacillus subtilis morphogenic factor RodZ

A thesis to obtain a Master degree in Structural and Functional Biochemistry

Transport mechanism in ionic liquid membranes and the study of thiomersal biodegradation

The initial goal of this work was the development of a supported liquid membrane (SLM) bioreactor for the remediation of vaccine production effluents contaminated with a highly toxic organomercurial – thiomersal. Therefore, two main aspects were focused on: 1) the development of a stable supported liquid membrane – using room temperature ionic liquids (RTILs) – for the selective transport of thiomersal from the wastewater to a biological compartment, 2) study of the biodegradation kinetics of thiomersal to metallic mercury by a Pseudomonas putida strain. The first part of the work focused on the evaluation of the physicochemical properties of ionic liquids and on the SLMs’ operational stability. The results obtained showed that, although it is possible to obtain a SLM with a high stability, water possesses nonnegligible solubility in the RTILs studied. The formation of water clusters inside the hydrophobic ionic liquid was identified and found to regulate the transport of water and small ions. In practical terms, this meant that, although it was possible to transport thiomersal from the vaccine effluent to the biological compartment, complete isolation of the microbial culture could not be guaranteed and the membrane might ultimately be permeable to other species present in the aqueous vaccine wastewater. It was therefore decided not to operate the initially targeted integrated system but, instead, the biological system by itself. Additionally, attention was given to the development of a thorough understanding of the transport mechanisms involved in the solubilisation and transport of water through supported liquid membranes with RTILs as well as to the evaluation of the effect of water uptake by the SLM in the transport mechanisms of water-soluble solutes and its effect on SLM performance. The results obtained highlighted the determinant role played by water – solubilised inside the ionic liquids – on the transport mechanism. It became clear that the transport mechanism of water and water-soluble solutes through SLMs with [CnMIM][PF6] RTILs was regulated by the dynamics of water clusters inside the RTIL, rather than by molecular diffusion through the bulk of the ionic liquid. Although the stability tests vi performed showed that there were no significant losses of organic phase from the membrane pores, the formation of water clusters inside the ionic liquid, which constitute new, non-selective environments for solute transport, leads to a clear deterioration of SLM performance and selectivity. Nevertheless, electrical impedance spectroscopy characterisation of the SLMs showed that the formation of water clusters did not seem to have a detrimental effect on the SLMs’ electrical characteristics and highlighted the potential of using this type of membranes in electrochemical applications with low resistance requirements. The second part of the work studied the kinetics of thiomersal degradation by a pure culture of P. putida spi3 strain, in batch culture and using a synthe tic wastewater. A continuous ly stirred tank reactor fed with the synthetic wastewater was also operated and the bioreactor’s performance and robustness, when exposed to thiomersal shock loads, were evaluated. Finally, a bioreactor for the biological treatment of a real va ccine production effluent was set up and operated at different dilution rates. Thus it was possible to treat a real thiomersal-contaminated effluent, lowering the outlet mercury concentration to values below the European limit for mercury effluent discharges.

Structural stability of adenylate kinase from the sulfate-reducing bacteria Desulfovibrio gigas

Biophysical Chemistry 110 (2004) 83–92

Synechocystis ferredoxin/ferredoxin-NADP+-reductase/NADP+ complex: Structural model obtained by NMR-restrained docking

FEBS Letters 579 (2005) 4585–4590

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Structural and mechanistic studies of iron containing proteins

Dissertação apresentada para obtenção do Grau de Doutor em Bioquímica, ramo de Bioquímica-Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Periplasmic nitrate reductases: structural and spectroscopic studies

Dissertação apresentada para obtenção do grau de Doutor em Bioquímica, especialidade Bioquímica-Física, pela Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa

Functional role of macrobenthos in estuarine sediment dynamics

Thesis submitted to the Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

Comments on ‘‘Modeling fractional stochastic systems as non-random fractional dynamics driven Brownian motions

Applied Mathematical Modelling, Vol.33