Planarians in pharmacology: parthenolide is a specific behavioral antagonist of cocaine in the planarian Girardia tigrina

Planarians are traditional animal models in developmental and regeneration biology. Recently, these organisms are arising as vertebrate-relevant animal models in neuropharmacology. Using an adaptation of published behavioral protocols, we have described the alleviation of cocaine-induced planarian seizure-like movements (pSLM) by a naturally-occurring sesquiterpene lactone, parthenolide. Interestingly, parthenolide does not prevent the expression of pSLM induced by amphetamines; in vertebrates, amphetamines interact with the same protein target as cocaine. Parthenolide is also unable to prevent pSLM elicited by the cholinergic compounds nicotine and cytisine or by the glutamatergic agents L- or D- glutamic acid or NMDA. Thus, we conclude that parthenolide is a specific anti-cocaine agent in this experimental organism

UFSRAT:Ultra-fast shape recognition with atom types -The discovery of novel bioactive small molecular scaffolds for FKBP12 and 11βHSD1

MOTIVATION:Using molecular similarity to discover bioactive small molecules with novel chemical scaffolds can be computationally demanding. We describe Ultra-fast Shape Recognition with Atom Types (UFSRAT), an efficient algorithm that considers both the 3D distribution (shape) and electrostatics of atoms to score and retrieve molecules capable of making similar interactions to those of the supplied query. RESULTS:Computational optimization and pre-calculation of molecular descriptors enables a query molecule to be run against a database containing 3.8 million molecules and results returned in under 10 seconds on modest hardware. UFSRAT has been used in pipelines to identify bioactive molecules for two clinically relevant drug targets; FK506-Binding Protein 12 and 11β-hydroxysteroid dehydrogenase type 1. In the case of FK506-Binding Protein 12, UFSRAT was used as the first step in a structure-based virtual screening pipeline, yielding many actives, of which the most active shows a KD, app of 281 µM and contains a substructure present in the query compound. Success was also achieved running solely the UFSRAT technique to identify new actives for 11β-hydroxysteroid dehydrogenase type 1, for which the most active displays an IC50 of 67 nM in a cell based assay and contains a substructure radically different to the query. This demonstrates the valuable ability of the UFSRAT algorithm to perform scaffold hops. AVAILABILITY AND IMPLEMENTATION:A web-based implementation of the algorithm is freely available at http://opus.bch.ed.ac.uk/ufsrat/

Development of New Methods for the (Q)SAR Applicability Domain Assessment: Using Structural Information in a Statistical Study of the Errors in Prediction

The main aim of (Q)SAR is to build models to evaluate and predict properties of molecules, such as biological and environmental effects, and physicochemical properties. These models are built using available experimental data, whose quality and quantity heavily affect their capability of obtaining reliable predictions for new chemicals. A dataset can be viewed as a "sampling" of the whole chemical space, if a sample is too small and / or too homogeneous, the model will inevitably have limitations in the type of chemicals it can predict. From the point of view of protecting the human health and the environment, it is preferable that a model is able to predict even a small number of chemicals, but with the highest possible reliability. The "coverage" issue can be overcome by integrating results from different models. In this perspective the importance of clearly defining the model's applicability domain is crucial to identify which model is most suitable for each chemical to assess. The definition of the applicability domain (AD) of (Q)SAR models is still an open research field. Several approaches have been proposed and implemented through years, including the use of structural features such as functional groups and atom-centered fragments. These features have also proven to be useful for an a priori definition of AD, making it independent from the specific algorithm chosen to develop the model. Within this study, the definition of (Q)SAR models' applicability domain has been investigated using structural features of different complexity: thresholds for chemical composition and molecular weight, chemical classes related to commonly well and badly predicted molecules, and statistically-extracted structural fragments to model the error in prediction. In the case studies considered, these approaches improved the AD definition provided by the model developers, supporting their integration within the definition of the models' applicability domain

A Transferable Infochemical for Multiple Crustaceans and Mechanistic Assessment of its pH Dependency

Many organisms rely on their sense of smell to explore and interact with their environment. Meeting another organism, infochemicals help them decide whether to eat it, flee, fight or mate with it. The infochemical 2-phenylethylamine (PEA) has previously been associated with predator avoidance in mammals and feeding deterrence in algae. This thesis explores the hypothesis that PEA also induces a comparable avoidance behaviour in crustaceans. In behaviour assays with shore crabs and hermit crabs, movement patterns and behavioural displays were explored in current average marine pH conditions and end-of-the-century average pH, as associated with climate change. Furthermore, using liquid chromatography with tandem mass-spectrometry, shore crab urine was analysed for PEA. Results indicate that PEA attracts both species in reduced pH conditions, rather than deterring them as expected for a predator cue. Agonistic displays in response to PEA and evidence for PEA in shore crab urine suggest that PEA mediates agonistic interactions in crustaceans. Excreted with urine, PEA elicits the full behavioural response in crustaceans only in decreased pH conditions. Therefore, this thesis tested the hypothesis that the pH-dependent response hinges on pH-dependent changes to the infochemical itself. Quantum chemical computations validated by nuclear magnetic resonance (NMR) spectroscopy allowed calculating PEA's different protonation states, and its interaction with a model receptor in different protonation states. Results confirm that the pH-dependent response to PEA could be attributed to changes in charge distribution in the infochemical, potentially leading to altered receptor-ligand affinity. A comparison with the neurotransmitter dopamine reveals that the exact chemical structure and charge distribution of PEA matters for its biological function. Although dopamine and PEA are broadly chemically similar, their biological role for crustaceans appears different. In summary, PEA is an infochemical mediating agonistic interactions in crustaceans in a pH-dependent manner