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Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2013.1.3-1 | Award Amount: 15.99M | Year: 2013

HeCaToS aims at developing integrative in silico tools for predicting human liver and heart toxicity. The objective is to develop an integrated modeling framework, by combining advances in computational chemistry and systems toxicology, for modelling toxic perturbations in liver and heart across multiple scales. This framework will include vertical integrations of representations from drug(metabolite)-target interactions, through macromolecules/proteins, to (sub-)cellular functionalities and organ physiologies, and even the human whole-body level. In view of the importance of mitochondrial deregulations and of immunological dysfunctions associated with hepatic and cardiac drug-induced injuries, focus will be on these particular Adverse Outcome Pathways. Models will be populated with data from innovative in vitro 3D liver and heart assays challenged with prototypical hepato- or cardiotoxicants; data will be generated by advanced molecular and functional analytical techniques retrieving information on key (sub-)cellular toxic evens. For validating perturbed AOPs in vitro in appropriate human investigations, case studies on patients with liver injuries or cardiomyopathies due to adverse drug effects, will be developed, and biopsies will be subjected to similar analyses. Existing ChEMBL and diXa data infrastructures will be advanced for data gathering, storing and integrated statistical analysis. Model performance in toxicity prediction will be assessed by comparing in silico predictions with experimental results across a multitude of read-out parameters, which in turn will suggest additional experiments for further validating predictions. HeCaToS, organized as a private-public partnership, will generate major socioeconomic impact because it will develop better chemical safety tests leading to safer drugs, but also industrial chemicals, and cosmetics, thereby improving patient and consumer health, and sustaining EUs industrial competitiveness.


Abad-Zapatero C.,University of Illinois at Chicago | Champness E.J.,Optibrium Ltd | Segall M.D.,Optibrium Ltd
Future Medicinal Chemistry | Year: 2014

A number of alternative variables have appeared in the medicinal chemistry literature trying to provide a more rigorous formulation of the guidelines proposed by Lipinski to exclude chemical entities with poor pharmacokinetic properties early in the discovery process. Typically, these variables combine the affinity towards the target with physicochemical properties of the ligand and are named efficiencies or ligand efficiencies. Several formulations have been defined and used by different laboratories with different degrees of success. A unified formulation, ligand efficiency indices, was proposed that included efficiency in two complementary variables (i.e., size and polarity) to map and monitor the drug-discovery process (AtlasCBS). The use of this formulation in combination with an extended multiparameter optimization is presented, with examples, as a promising methodology to optimize the drug-discovery process in the future. Future perspectives and challenges for this approach are also discussed. © 2014 Future Science Ltd.


Obrezanova O.,Optibrium Ltd. | Segall M.D.,Optibrium Ltd.
Journal of Chemical Information and Modeling | Year: 2010

In this article, we extend the application of the Gaussian processes technique to classification quantitative structure-activity relationship modeling problems. We explore two approaches, an intrinsic Gaussian processes classification technique and a probit treatment of the Gaussian processes regression method. Here, we describe the basic concepts of the methods and apply these techniques to building category models of absorption, distribution, metabolism, excretion, toxicity and target activity data. We also compare the performance of Gaussian processes for classification to other known computational methods, namely decision trees, random forest, support vector machines, and probit partial least squares. The results indicate that, while no method consistently generates the best model, the Gaussian processes classifier often produces more predictive models than those of the random forest or support vector machines and was rarely significantly outperformed. © 2010 American Chemical Society.


Segall M.D.,Optibrium Ltd | Barber C.,Lhasa Ltd
Drug Discovery Today | Year: 2014

Prioritising compounds with a lower chance of causing toxicity, early in the drug discovery process, would help to address the high attrition rate in pharmaceutical R&D. Expert knowledge-based prediction of toxicity can alert chemists if their proposed compounds are likely to have an increased likelihood of causing toxicity. We will discuss how multiparameter optimisation approaches can be used to balance the potential for toxicity with other properties required in a high-quality candidate drug, giving appropriate weight to the alert in the selection of compounds. Furthermore, we will describe how information about the region of a compound that triggers a toxicity alert can be interactively visualised to guide the modification of a compound to reduce the likelihood of toxicity. © 2014 Elsevier Ltd.


Segall M.D.,Optibrium Ltd | Champness E.J.,Optibrium Ltd
Journal of Computer-Aided Molecular Design | Year: 2015

All of the experimental compound data with which we work have significant uncertainties, due to imperfect correlations between experimental systems and the ultimate in vivo properties of compounds and the inherent variability in experimental conditions. When using these data to make decisions, it is essential that these uncertainties are taken into account to avoid making inappropriate decisions in the selection of compounds, which can lead to wasted effort and missed opportunities. In this paper we will consider approaches to rigorously account for uncertainties when selecting between compounds or assessing compounds against a property criterion; first for an individual measurement of a single property and then for multiple measurements of a property for the same compound. We will then explore how uncertainties in multiple properties can be combined when assessing compounds against a profile of criteria, a process known as multi-parameter optimisation. This guides rigorous decision-making using complex, uncertain data to focus on compounds with the best chance of success, while avoiding missed opportunities by inappropriately rejecting compounds. © 2015 Springer International Publishing Switzerland.


Segall M.,Optibrium Ltd
Expert Opinion on Drug Discovery | Year: 2014

Introduction: A high-quality drug must achieve a balance of physicochemical and absorption, distribution, metabolism and elimination properties, safety and potency against its therapeutic target(s). Multiparameter optimization (MPO) methods guide the simultaneous optimization of multiple factors to quickly target compounds with the highest chance of downstream success. MPO can be combined with 'de novo design' methods to automatically generate and assess a large number of diverse structures and identify strategies to optimize a compound's overall balance of properties.Areas covered: The article provides a review of MPO methods and recent developments in the methods and opinions in the field. It also provides a description of advances in de novo design that improve the relevance of automatically generated compound structures and integrate MPO. Finally, the article provides discussion of a recent case study of the automatic design of ligands to polypharmacological profiles.Expert opinion: Recent developments have reduced the generation of chemically infeasible structures and improved the quality of compounds generated by de novo design methods. There are concerns about the ability of simple drug-like properties and ligand efficiency indices to effectively guide the detailed optimization of compounds. De novo design methods cannot identify a perfect compound for synthesis, but it can identify high-quality ideas for detailed consideration by an expert scientist. © Informa UK, Ltd.


Segall M.,Optibrium Ltd
Journal of Computer-Aided Molecular Design | Year: 2012

In this article, we discuss what we mean by 'design' and contrast this with the application of computational methods in drug discovery. We suggest that the predictivity of the computational models currently applied in drug discovery is not yet sufficient to permit a true design paradigm, as demonstrated by the large number of compounds that must currently be synthesised and tested to identify a successful drug. However, despite the uncertainties in predictions, computational methods have enormous potential value in narrowing the range of compounds to consider, by eliminating those that have negligible chance of being a successful drug, while focussing efforts on chemistries with the best likelihood of success. Applied appropriately, computational approaches can support decision-makers in achieving multi-parameter optimisation to guide the selection and design of compounds with the best chance of achieving an appropriate balance of properties for a drug discovery project's objectives. Finally, we consider some approaches that may contribute over the next 25 years to improve the accuracy and transferability of computational models in drug discovery and move towards a genuine design process. © 2011 Springer Science+Business Media B.V.


Patent
Optibrium Ltd | Date: 2014-01-10

Methods, software, products and systems used to support decision making in complex multidimensional problem environments. Methods, software, products and systems to prioritize solutions for selection based upon selection criteria and available data regarding the possible solutions. The methods achieve a robust approach to determine the sensitivity of a selection to a multi-parameter profile of selection criteria and the importance of such criteria.


Trademark
Optibrium Ltd. | Date: 2015-08-10

Computer software application used to process chemical and pharmaceutical compounds, molecules, molecular structures, physical property data, and biological property data, and which software application has a graphical user interface, for use in the fields of chemistry and pharmaceutical research and development. Computer software application used for data processing, analytics, information processing, data analysis, compound design and data visualization in the fields of chemistry and pharmaceutical research and development; providing a website featuring on-line downloadable software that enables users to perform data visualization, analysis and compound design. Business consultation services regarding research and development methodology in the fields of chemistry and pharmaceutical research and development. Educational services, namely, providing training in the use and operation of computer software and applications in the fields of chemistry and pharmaceutical research and development. Providing scientific and technological consulting services regarding the utilization of software technology in the fields of chemistry and pharmaceutical research and development; technical support services, namely, providing software installation and software operational support to users for computer software and applications in the fields of chemistry and pharmaceutical research and development.


Patent
Optibrium Ltd | Date: 2015-10-28

Methods, software, products and systems used to support decision making in complex multidimensional problem environments. Methods, software, products and systems to prioritize solutions for selection based upon selection criteria and available data regarding the possible solutions. The methods achieve a robust approach to determine the sensitivity of a selection to a multi-parameter profile of selection criteria and the importance of such criteria.

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