Enhancing Carbon Acid pKa Prediction by Augmentation of Sparse Experimental Datasets with Accurate AIBL (QM) Derived Values.

The prediction of the aqueous pKa of carbon acids by Quantitative Structure Property Relationship or cheminformatics-based methods is a rather arduous problem. Primarily, there are insufficient high-quality experimental data points measured in homogeneous conditions to allow for a good global model to be generated. In our computationally efficient pKa prediction method, we generate an atom-type feature vector, called a distance spectrum, from the assigned ionisation atom, and learn coefficients for those atom-types that show the impact each atom-type has on the pKa of the ionisable centre. In the current work, we augment our dataset with pKa values from a series of high performing local models derived from the Ab Initio Bond Lengths method (AIBL). We find that, in distilling the knowledge available from multiple models into one general model, the prediction error for an external test set is reduced compared to that using literature experimental data alone.

Predicting pKa Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods.

The acid dissociation constant (pKa) has an important influence on molecular properties crucial to compound development in synthesis, formulation, and optimization of absorption, distribution, metabolism, and excretion properties. We will present a method that combines quantum mechanical calculations, at a semi-empirical level of theory, with machine learning to accurately predict pKa for a diverse range of mono- and polyprotic compounds. The resulting model has been tested on two external data sets, one specifically used to test pKa prediction methods (SAMPL6) and the second covering known drugs containing basic functionalities. Both sets were predicted with excellent accuracy (root-mean-square errors of 0.7-1.0 log units), comparable to other methodologies using a much higher level of theory and computational cost.

Orai1 Channel Inhibition Preserves Left Ventricular Systolic Function and Normal Ca2+ Handling After Pressure Overload.

BACKGROUND: Orai1 is a critical ion channel subunit, best recognized as a mediator of store-operated Ca2+ entry (SOCE) in nonexcitable cells. SOCE has recently emerged as a key contributor of cardiac hypertrophy and heart failure but the relevance of Orai1 is still unclear. METHODS: To test the role of these Orai1 channels in the cardiac pathophysiology, a transgenic mouse was generated with cardiomyocyte-specific expression of an ion pore-disruptive Orai1R91W mutant (C-dnO1). Synthetic chemistry and channel screening strategies were used to develop 4-(2,5-dimethoxyphenyl)-N-[(pyridin-4-yl)methyl]aniline (hereafter referred to as JPIII), a small-molecule Orai1 channel inhibitor suitable for in vivo delivery. RESULTS: Adult mice subjected to transverse aortic constriction (TAC) developed cardiac hypertrophy and reduced ventricular function associated with increased Orai1 expression and Orai1-dependent SOCE (assessed by Mn2+ influx). C-dnO1 mice displayed normal cardiac electromechanical function and cellular excitation-contraction coupling despite reduced Orai1-dependent SOCE. Five weeks after TAC, C-dnO1 mice were protected from systolic dysfunction (assessed by preserved left ventricular fractional shortening and ejection fraction) even if increased cardiac mass and prohypertrophic markers induction were observed. This is correlated with a protection from TAC-induced cellular Ca2+ signaling alterations (increased SOCE, decreased [Ca2+]i transients amplitude and decay rate, lower SR Ca2+ load and depressed cellular contractility) and SERCA2a downregulation in ventricular cardiomyocytes from C-dnO1 mice, associated with blunted Pyk2 signaling. There was also less fibrosis in heart sections from C-dnO1 mice after TAC. Moreover, 3 weeks treatment with JPIII following 5 weeks of TAC confirmed the translational relevance of an Orai1 inhibition strategy during hypertrophic insult. CONCLUSIONS: The findings suggest a key role of cardiac Orai1 channels and the potential for Orai1 channel inhibitors as inotropic therapies for maintaining contractility reserve after hypertrophic stress.

Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting.

In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.

JPlogP: an improved logP predictor trained using predicted data.

The partition coefficient between octanol and water (logP) has been an important descriptor in QSAR predictions for many years and therefore the prediction of logP has been examined countless times. One of the best performing models is to predict the logP using multiple methods and average the result. We have used those averaged predictions to develop a training-set which was able to distil the information present across the disparate logP methods into one single model. Our model was built using extendable atom-types, where each atom is distilled down into a 6 digit number, and each individual atom is assumed to have a small additive effect on the overall logP of the molecule. Beyond the simple coefficient model a consensus model is evaluated, which uses known compounds as a starting point in the calculation and modifies the experimental logP using the same coefficients as in the first model. We then test the performance of our models against two different datasets, one where many different models routinely perform well against, and another designed to more represent pharmaceutical space. The true strength of the model is represented in the pharmaceutical benchmark set, where both models perform better than any previously developed models.

A Semi-automated Approach to Create Purposeful Mechanistic Datasets from Heterogeneous Data: Data Mining Towards the in silico Predictions for Oestrogen Receptor Modulation and Teratogenicity.

The need to find an alternative to costly animal studies for developmental and reproductive toxicity testing has shifted the focus considerably to the assessment of in vitro developmental toxicology models and the exploitation of pharmacological data for relevant molecular initiating events. We hereby demonstrate how automation can be applied successfully to handle heterogeneous oestrogen receptor data from ChEMBL. Applying expert-derived thresholds to specific bioactivities allowed an activity call to be attributed to each data entry. Human intervention further improved this mechanistic dataset which was mined to develop structure-activity relationship alerts and an expert model covering 45 chemical classes for the prediction of oestrogen receptor modulation. The evaluation of the model using FDA EDKB and Tox21 data was quite encouraging. This model can also provide a teratogenicity prediction along with the additional information it provides relevant to the query compound, all of which will require careful assessment of potential risk by experts.

A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces.

Dermal contact with chemicals may lead to an inflammatory reaction known as allergic contact dermatitis. Consequently, it is important to assess new and existing chemicals for their skin sensitizing potential and to mitigate exposure accordingly. There is an urgent need to develop quantitative non-animal methods to better predict the potency of potential sensitizers, driven largely by European Union (EU) Regulation 1223/2009, which forbids the use of animal tests for cosmetic ingredients sold in the EU. A Nearest Neighbours in silico model was developed using an in-house dataset of 1096 murine local lymph node (LLNA) studies. The EC3 value (the effective concentration of the test substance producing a threefold increase in the stimulation index compared to controls) of a given chemical was predicted using the weighted average of EC3 values of up to 10 most similar compounds within the same mechanistic space (as defined by activating the same Derek skin sensitization alert). The model was validated using previously unseen internal (n = 45) and external (n = 103) data and accuracy of predictions assessed using a threefold error, fivefold error, European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) and Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classifications. In particular, the model predicts the GHS skin sensitization category of compounds well, predicting 64% of chemicals in an external test set within the correct category. Of the remaining chemicals in the previously unseen dataset, 25% were over-predicted (GHS 1A predicted: GHS 1B experimentally) and 11% were under-predicted (GHS 1B predicted: GHS 1A experimentally). Copyright © 2017 John Wiley & Sons, Ltd.

Trivalent Gd-DOTA reagents for modification of proteins.

The development of novel protein-targeted MRI contrast agents crucially depends on the ability to derivatise suitable targeting moieties with a high payload of relaxation enhancer (e.g., gadolinium(iii) complexes such as Gd-DOTA), without losing affinity for the target proteins. Here, we report robust synthetic procedures for the preparation of trivalent Gd-DOTA reagents with various chemical handles for site-specific modification of biomolecules. The reagents were shown to successfully label proteins through isothiocyanate ligation or through site-specific thiol-maleimide ligation and strain-promoted azide-alkyne cycloaddition.

Solid-phase methodology for synthesis of O-alkylated aromatic oligoamide inhibitors of α-helix-mediated protein-protein interactions.

Rapid access to rigid rods: A method is described for the synthesis of 3-O-alkylated aromatic oligobenzamide foldamers that could be used for assembly of libraries of α-helix mimetic inhibitors of protein-protein interactions (see scheme; Fmoc=9-fluorenylmethoxycarbonyl).

N-alkylated oligoamide alpha-helical proteomimetics.

Generic approaches for the design and synthesis of small molecule inhibitors of protein-protein interactions (PPIs) represent a key objective in modern chemical biology. Within this context, the alpha-helix mediated PPIs have received considerable attention as targets for inhibition using small molecules, foldamers and proteomimetics. This manuscript describes a novel N-alkylated aromatic oligoamide proteomimetic scaffold and its solid-phase synthesis--the first time such an approach has been used for proteomimetics. The utility of these scaffolds as proteomimetics is exemplified through the identification of potent microM inhibitors of the p53-hDM2 helix mediated PPI--a key oncogenic target.

Oligobenzamide proteomimetic inhibitors of the p53-hDM2 protein-protein interaction.

Oligobenzamide inhibitors of the p53-hDM2 protein-protein interaction are described.

Trifluoromethyldiazirine: an effective photo-induced cross-linking probe for exploring amyloid formation.

The separative and analytical power of ion mobility spectrometry-mass spectrometry combined with photo-induced cross-linking of site-specifically incorporated trifluoromethyldiazirine provides a powerful approach towards structural characterisation of amyloid fibrils.

Synthesis of functionalised aromatic oligamide rods.

A current goal in synthetic chemistry is the design and synthesis of molecules that adopt well defined conformations-so called foldamers. In this manuscript we describe a modular approach for construction of rod shaped para-oligobenzamide molecules. Our approach permits regiospecific incorporation of side chains through a phenolic ether linkage on the scaffold; a feature that partly restricts the conformation of the rod through intramolecular hydrogen-bonding.

Macrocyclic scaffolds derived from p-aminobenzoic acid.

The regiospecific synthesis of C3 macrocyclic scaffolds possessing multiple different functional groups is described.

Shape-selective fluorescent sensing ensemble using a tweezer-type metalloreceptor.

[reaction: see text] A fluorescent sensing ensemble for pyridine-derived compounds is described. The receptor portion of the ensemble is prepared from a bisimidazole pyridine which coordinates copper to form a well-defined cavity. Small heteroaromatic guests such as adenine bind strongly in the cavity. The fluorescent response is provided by a dye which is coordinated to the receptor and quenched by the metal ion. The dye is released upon guest binding providing up to 25-fold fluorescence increases.

A rigid cavity containing tetra-cobalt(III) [2 x 2] grid complex.

The synthesis and structure of a rigid, cavity containing tetra-cobalt(III) [2 x 2] grid complex using an unusual bis(bipyridine)dimethoxynaphthyridine ligand is described.