Carbohydrate-based scaffolds in drug discovery

Carbohydrates have been proven as valuable scaffolds to display pharmocophores and the resulting molecules have demonstrated useful biological activity towards various targets including the somatostatin receptors (SSTR), integrins, HIV-1 protease, matrix metalloproteinases (MMP), multidrug resistance-associated protein (MRP), and as RNA binders. Carbohydrate-based compounds have also shown antibacterial and herbicidal activity.

S. cerevisiae as a model for studying mutations in the human gene OPA1 associated with dominant optic atrophy and for drug discovery

L’atrofia ottica dominante (ADOA) è una malattia mitocondriale caratterizzata da difetti visivi, che si manifestano durante l’infanzia, causati da progressiva degenerazione delle cellule gangliari della retina (RGC). ADOA è una malattia genetica associata, nella maggior parte dei casi, a mutazioni nel gene OPA1 che codifica per la GTPasi mitocondriale OPA1, appartenente alla famiglia delle dinamine, principalmente coinvolta nel processo di fusione mitocondriale e nel mantenimento del mtDNA. Finora sono state identificate più di 300 mutazioni patologiche nel gene OPA1. Circa il 50% di queste sono mutazioni missenso, localizzate nel dominio GTPasico, che si pensa agiscano come dominanti negative. Questa classe di mutazioni è associata ad una sindrome più grave nota come “ADOA-plus”. Nel lievito Saccharomyces cerevisiae MGM1 è l’ortologo del gene OPA1: nonostante i due geni abbiano domini funzionali identici le sequenze amminoacidiche sono scarsamente conservate. Questo costituisce una limitazione all’uso del lievito per lo studio e la validazione di mutazioni patologiche nel gene OPA1, infatti solo poche sostituzioni possono essere introdotte e studiate nelle corrispettive posizioni del gene di lievito. Per superare questo ostacolo è stato pertanto costruito un nuovo modello di S. cerevisiae, contenente il gene chimerico MGM1/OPA1, in grado di complementare i difetti OXPHOS del mutante mgm1Δ. Questo gene di fusione contiene una larga parte di sequenza corrispondente al gene OPA1, nella quale è stato inserito un set di nuove mutazioni trovate in pazienti affetti da ADOA e ADOA-plus. La patogenicità di queste mutazioni è stata validata sia caratterizzando i difetti fenotipici associati agli alleli mutati, sia la loro dominanza/recessività nel modello di lievito. A tutt’oggi non è stato identificato alcun trattamento farmacologico per la cura di ADOA e ADOA-plus. Per questa ragione abbiamo utilizzato il nostro modello di lievito per la ricerca di molecole che agiscono come soppressori chimici, ossia composti in grado di ripristinare i difetti fenotipici indotti da mutazioni nel gene OPA1. Attraverso uno screening fenotipico high throughput sono state testate due differenti librerie di composti chimici. Questo approccio, noto con il nome di drug discovery, ha permesso l’identificazione di 23 potenziali molecole attive.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

Molecularly imprinted polymers in the drug discovery process

Since molecularly imprinted polymers (MIPs) are designed to have a memory for their molecular templates it is easy to draw parallels with the affinity between biological receptors and their substrates. Could MIPs take the place of natural receptors in the selection of potential drug molecules from synthetic compound libraries? To answer that question this review discusses the results of MIP studies which attempt to emulate natural receptors. In addition the possible use of MIPs to guide a compound library synthesis towards a desired biological activity is highlighted. © 2005 Elsevier B.V. All rights reserved.

Probabilistic methods for drug discovery

This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large multi-dimensional datasets. The advantage of such a framework to other techniques currently available to the domain experts is that the user is directly involved in the data mining process and advanced machine learning algorithms are employed for better projection. A hierarchical visualisation model guided by a domain expert allows them to obtain an informed segmentation of the input space. Two other components of this thesis exploit properties of these principled probabilistic projection algorithms to develop a guided mixture of local experts algorithm which provides robust prediction and a model to estimate feature saliency simultaneously with the training of a projection algorithm.Local models are useful since a single global model cannot capture the full variability of a heterogeneous data space such as the chemical space. Probabilistic hierarchical visualisation techniques provide an effective soft segmentation of an input space by a visualisation hierarchy whose leaf nodes represent different regions of the input space. We use this soft segmentation to develop a guided mixture of local experts (GME) algorithm which is appropriate for the heterogeneous datasets found in chemoinformatics problems. Moreover, in this approach the domain experts are more involved in the model development process which is suitable for an intuition and domain knowledge driven task such as drug discovery. We also derive a generative topographic mapping (GTM) based data visualisation approach which estimates feature saliency simultaneously with the training of a visualisation model.

Relieving the first bottleneck in the drug discovery pipeline:using array technologies to rationalize membrane protein production

The slow down in the drug discovery pipeline is, in part, owing to a lack of structural and functional information available for new drug targets. Membrane proteins, the targets of well over 50% of marketed pharmaceuticals, present a particular challenge. As they are not naturally abundant, they must be produced recombinantly for the structural biology that is a prerequisite to structure-based drug design. Unfortunately, however, obtaining high yields of functional, recombinant membrane proteins remains a major bottleneck in contemporary bioscience. While repeated rounds of trial-and-error optimization have not (and cannot) reveal mechanistic details of the biology of recombinant protein production, examination of the host response has provided new insights. To this end, we published an early transcriptome analysis that identified genes implicated in high-yielding yeast cell factories, which has enabled the engineering of improved production strains. These advances offer hope that the bottleneck of membrane protein production can be relieved rationally.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Gut hormone GPCRs: structure, function, drug discovery

Crystallization and determination of the high resolution three-dimensional structure of the β2-adrenergic receptor in 2007 was followed by structure elucidation of a number of other receptors, including those for neurotensin and glucagon. These major advances foster the understanding of structure-activity relationship of these receptors and structure-based rational design of new ligands having more predictable activity. At present, structure determination of gut hormone receptors in complex with their ligands (natural, synthetic) and interacting signalling proteins, for example, G-proteins, arrestins, represents a challenge which promises to revolutionize gut hormone endocrinonology.

The conformational landscape of RNA translational regulators and their potential as drug discovery targets

RNA is an underutilized target for drug discovery. Once thought to be a passive carrier of genetic information, RNA is now known to play a critical role in essentially all aspects of biology including signaling, gene regulation, catalysis, and retroviral infection. It is now well-established that RNA does not exist as a single static structure, but instead populates an ensemble of energetic minima along a free-energy landscape. Knowledge of this structural landscape has become an important goal for understanding its diverse biological functions. In this case, NMR spectroscopy has emerged as an important player in the characterization of RNA structural ensembles, with solution-state techniques accounting for almost half of deposited RNA structures in the PDB, yet the rate of RNA structure publication has been stagnant over the past decade. Several bottlenecks limit the pace of RNA structure determination by NMR: the high cost of isotopic labeling, tedious and ambiguous resonance assignment methods, and a limited database of RNA optimized pulse programs. We have addressed some of these challenges to NMR characterization of RNA structure with applications to various RNA-drug targets. These approaches will increasingly become integral to designing new therapeutics targeting RNA.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Development of a novel platform for high-throughput gene design and artificial gene synthesis to produce large libraries of recombinant venom peptides for drug discovery

Tese de Doutoramento em Ciências Veterinárias na Especialidade de Ciências Biológicas e Biomédicas