Search

Acknowledgements: The authors wish to thank Dr Maria Saxin and Dr Marie Rydén Landergren of AstraZeneca for acquisition of spectral data., Funder: AstraZeneca; doi: http://dx.doi.org/10.13039/100004325, Vibrational circular dichroism (VCD) spectroscopy can generate the data required for the assignment of absolute configuration, but the spectra are hard to interpret. We have recorded VCD data for thirty pairs of small organic compounds and we use this database to validate a method for the automated analysis of VCD spectra and the assignment of absolute configuration: the Cai•factor (Configuration: absolute information). The analysis of the data demonstrates that the procedure is a reliable and time-efficient method for determination of absolute configuration, which gives both the assignment and a measure of confidence in the outcome, even when the spectra are imperfect. The majority of molecules tested have a high confidence score and all of these have the correct assignment., AstraZeneca

Next-generation risk assessment (NGRA) involves the combination of in vitro and in silico models for more human-relevant, ethical, and sustainable human chemical safety assessment. NGRA requires a quantitative mechanistic understanding of the effects of chemicals across human biology (be they molecular, cellular, organ-level or higher) coupled with a quantitative understanding of the uncertainty in any experimentally measured or predicted values. These values with their uncertainties can then be considered as a probability distribution, which can then be compared to exposure estimates to establish the presence or absence of a margin of safety. We have constructed Bayesian learning neural networks to provide such quantitative predictions and uncertainties for 20 pharmacologically important human molecular initiating events. These models produce high quality quantitative estimates (p(IC50), p(EC50), p(Ki), p(Kd)) of biochemical activity at a molecular initiating event (MIE) with average mean absolute errors (in Log units) of 0.625 ± 0.048 in test data and 0.941 ± 0.215 in external validation data. The key advantage of these models is their ability to also produce standard deviations and credible intervals (CIs) to quantify the uncertainty in these predictions, which we show to be able to distinguish between molecules close to the training data in chemical structure, those less similar to the training data, and decoy compounds drawn from the wider ChEMBL database. These uncertainty values mean that when a prediction is made a user can understand the certainty of the prediction, similar to a quantitative applicability domain, aiding prediction usefulness in NGRA. The ability for in silico methods to produce quantitative predictions with these kinds of probability distributions will be vital to their further use in NGRA, and here clear first steps have been taken.

Whenever a new molecule is made, a chemist will justify the proposed structure by analysing the NMR spectra. The widely-used DP4 algorithm will choose the best match from a series of possibilities, but draws no conclusions from a single candidate structure. Here we present the DP5 probability, a step-change in the quantification of molecular uncertainty: given one structure and one 13C NMR spectra, DP5 gives the probability of the structure being correct. We show the DP5 probability can rapidly differentiate between structure proposals indistinguishable by NMR to an expert chemist. We also show in a number of challenging examples the DP5 probability may prevent incorrect structures being published and later reassigned. DP5 will prove extremely valuable in fields such as discovery-driven automated chemical synthesis and drug development. Alongside the DP4-AI package, DP5 can help guide synthetic chemists when resolving the most subtle structural uncertainty. The DP5 system is available at https://github.com/orgs/Goodman-lab/DP5.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

The IUPAC International Chemical Identifier (InChI) provides a method to generate a unique text descriptor of molecular structures. Building on this work, we report a process to generate a unique text descriptor for reactions, RInChI. By carefully selecting the information that is included and by ordering the data carefully, different scientists studying the same reaction should produce the same RInChI. If differences arise, these are most likely the minor layers of the InChI, and so may be readily handled. RInChI provides a concise description of the key data in a chemical reaction, and will help enable the rapid searching and analysis of reaction databases.

The Takai olefination (or Takai reaction) is a method for the conversion of aldehydes to vinyl iodides, and has seen widespread implementation in organic synthesis. The reaction is usually noted for its high (E)-selectivity; however, herein we report the highly (Z)-selective Takai olefination of salicylaldehyde derivatives. Systematic screening of related substrates led to the identification of key factors responsible for this surprising inversion of selectivity, and enabled the development of a modified mechanistic model to rationalise these observations.

A catalyst selection program, BINOPtimal, has been developed. This interactive web tool selects the best performing chiral phosphoric acid catalysts from analysis of the starting materials, imine and nucleophile, on the basis of rules derived from the transformations within its database. This procedure has been applied to an example transformation demonstrating the potential to assist reaction design. The tool is available at www-mmm.ch.cam.ac.uk.

Molecular initiating events (MIEs) are important concepts for in silico predictions. They can be used to link chemical characteristics to biological activity through an adverse outcome pathway (AOP). In this work, we capture chemical characteristics in 2D structural alerts, which are then used as models to predict MIEs. An automated procedure has been used to identify these alerts, and the chemical categories they define have been used to provide quantitative predictions for the activity of molecules that contain them. This has been done across a diverse group of 39 important pharmacological human targets using open source data. The alerts for each target combine into a model for that target, and these models are joined into a tool for MIE prediction with high average model performance (sensitivity = 82%, specificity = 93%, overall quality = 93%, Matthews correlation coefficient = 0.57). The result is substantially improved from our previous study (Allen, T. E. H., Goodman, J. M., Gutsell, S., and Russell, P. J. 2016. A history of the molecular initiating event. Chem. Res. Toxicol. 29, 2060-2070) for which the mean sensitivity for each target was only 58%. This tool provides the first step in an AOP-based risk assessment, linking chemical structure to toxicity endpoint.

Through synthesising both candidate diastereomers of a model C1-C28 fragment of the potent cytotoxic marine polyketide hemicalide, an assignment of the relative configuration between the C1-C15 and C16-C26 regions has been achieved. By detailed NMR comparisons with the natural product, the relative stereochemistry between these two 1,6-related stereoclusters is elucidated as 13,18-syn rather than the previously proposed 13,18-anti relationship. A flexible and modular strategy using an advanced C1-C28 ketone fragment 22 is outlined to elucidate the remaining stereochemical features and achieve a total synthesis.

Using the DP4f GIAO-NMR method, the stereochemistry of hemicalide was computationally analysed, resulting in a reassignment at C18 as supported by improved NMR shift correlations with a model C13-C25 fragment 23. An advanced C16-C28 subunit 6 of this potent anticancer agent was then synthesised with the revised 18,19-syn relationship.

A large-scale optimisation of density functional theory (DFT) conditions for computational NMR structure elucidation has been conducted by systematically screening the DFT functionals and statistical models. The extended PyDP4 workflow was tested on a diverse and challenging set of 42 biologically active and stereochemically rich compounds, including highly flexible molecules. MMFF/mPW1PW91/M06-2X in combination with a 2 Gaussian, 1 region statistical model was capable of identifying the correct diastereomer among up to an upper limit of 32 potential diastereomers. Overall a 2-fold reduction in structural uncertainty and a 7-fold reduction in model overconfidence have been achieved. Tools for rapid set-up and analysis of computational and experimental results, as well as for the statistical model generation, have been developed and are provided. All of this should facilitate rapid and reliable computational NMR structure elucidation, which has become a valuable tool to natural product chemists and synthetic chemists alike.

Choosing the optimal catalyst for a new transformation is challenging because the ideal molecular requirements of the catalyst for one reaction do not always simply translate to another. Large groups at the 3,3′ positions of the binaphthol rings are important for efficient stereoinduction but if they are too large this can lead to unusual or poor results. By applying a quantitative steric assessment of the substituents at the 3,3′ positions of the binaphthol ring, we have systematically studied the effect of modulating this group on enantioselectivity for a wide range of reactions involving imines, and verified this analysis using ONIOM calculations. We have shown that in most reactions, the stereochemical outcome depends on both proximal and remote sterics. Summarising detailed calculations into a simple qualitative model identifies and explains the steric features required for high selectivity. This model is consistent with seventy seven papers reporting reactions (over 1000 transformations in total), and provides a straightforward decision tree for selecting the best catalyst.

Chiral BINOL-derived phosphoric acids catalyse the transfer hydrogenation of ketimines using Hantszch esters. In many cases the nitrogen on the imine binds to the catalyst through the catalyst hydroxyl group and the nucleophile forms a second hydrogen bond to the phosphoryl oxygen. DFT and ONIOM calculations show that the introduction of an ortho- hydroxyaryl group on the carbon atom of the ketimine leads the reaction to proceed through a 14-membered bifunctional mechanism. The transition states of these reactions involve both hydrogen bonding from the hydroxyl group on the imine and the nucleophile’s proton to the phosphate catalyst. This mechanistic pathway is lower in energy than the conventional route, consistent with the experimentally observed increased rates of reaction relative to imines that are not derived from ortho-hydroxybenzophenone. To complement the high-level calculations, an accessible qualitative model has been developed that predicts the correct sense of stereoinduction for all examples.

The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly stated in the published papers and, hence, not reported in the database entry for those reactions, limiting their usefulness for algorithmic analysis. This paper presents a possible extension to the IUPAC RInChI standard via an auxiliary layer, termed $\textit{ProcAuxInfo}$, which is a standardised, extensible form in which to report certain key reaction parameters such as declaration of all products and reactants as well as auxiliaries known in the reaction, reaction stoichiometry, amounts of substances used, conversion, yield and operating conditions. The standard is demonstrated via creation of the RInChI including the $\textit{ProcAuxInfo}$ layer based on three published reactions and demonstrates accurate data recoverability via reverse translation of the created strings. Implementation of this or another method of reporting process data by the publishing community would ensure that databases, such as Reaxys, would be able to abstract crucial data for big data analysis of their contents., PMJ is grateful to Peterhouse and the Cambridge Trust for scholarships.

The adverse outcome pathway (AOP) framework provides an alternative to traditional $\textit{in vivo}$ experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Molecular initiating events (MIEs) can be boiled down to chemical interactions. Chemicals that interact must have intrinsic properties that allow them to exhibit this behavior, be these properties stereochemical, electronic, or otherwise. In an attempt to discover some of these chemical characteristics, we have constructed structural alert-style structure-activity relationships (SARs) to computationally predict MIEs. This work utilizes chemical informatics approaches, searching the ChEMBL database for molecules that bind to a number of pharmacologically important human toxicology targets, including G-protein coupled receptors, enzymes, ion channels, nuclear receptors, and transporters. By screening these compounds to find common 2D fragments and combining this approach with a good understanding of the literature, bespoke 2D structural alerts have been written. These SARs form the beginning of a tool for screening novel chemicals to establish the kind of interactions that they may be able to make in humans. These SARs have been run through an internal validation to test their quality, and the results of this are also discussed. MIEs have proven to be difficult to find and characterize, but we believe we have taken a key first step with this work.

BINOL-derived phosphoric acids provide effective asymmetric catalysis for many organic reactions. Catalysts based on this scaffold show a large structural diversity, especially in the 3,3' substituents, and little is known about the molecular requirements for high selectivity. As a result, selection of the best catalyst for a particular transformation requires a trial and error screening process, as the size of the 3,3' substituents is not simply related to their efficacy: the right choice is neither too large nor too small. We have developed an approach to identify and quantify structural features on the catalyst that determine selectivity. We show that the application of quantitative steric parameters (a new measure, AREA(θ), and rotation barrier) to an imine hydrogenation reaction allows the identification of catalyst features necessary for efficient stereoinduction, validated by QM/MM hybrid calculations.

Chiral phosphoric acids have become powerful catalysts for the stereocontrolled synthesis of a diverse array of organic compounds. Since the initial report, the development of phosphoric acids as catalysts has been rapid, demonstrating the tremendous generality of this catalyst system and advancing the use of phosphoric acids to catalyze a broad range of asymmetric transformations ranging from Mannich reactions to hydrogenations through complementary modes of activation. These powerful applications have been developed without a clear mechanistic understanding of the reasons for the high level of stereocontrol. This Account describes investigations into the mechanism of the phosphoric acid catalyzed addition of nucleophiles to imines, focusing on binaphthol-based systems. In many cases, the hydroxyl phosphoric acid can form a hydrogen bond to the imine while the P═O interacts with the nucleophile. The single catalyst, therefore, activates both the electrophile and the nucleophile, while holding both in the chiral pocket created by the binaphthol and constrained by substituents at the 3 and 3' positions. Detailed geometric and energetic information about the transition states can be gained from calculations using ONIOM methods that combine the advantages of DFT with some of the speed of force fields. These high-level calculations give a quantitative account of the selectivity in many cases, but require substantial computational resources. A simple qualitative model is a useful complement to this complex quantitative model. We summarize our calculations into a working model that can readily be sketched by hand and used to work out the likely sense of selectivity for each reaction. The steric demands of the different parts of the reactants determine how they fit into the chiral cavity and which of the competing pathways is favored. The preferred pathway can be found by considering the size of the substituents on the nitrogen and carbon atoms of the imine electrophile, and the position of the nucleophilic site on the nucleophile in relation to the hydrogen-bond which holds it in the catalyst active site. We present a guide to defining the pathway in operation allowing the fast and easy prediction of the stereochemical outcome and provide an overview of the breadth of reactions that can be explained by these models including the latest examples.

The DP4 parameter, which provides a confidence level for NMR assignment, has been widely used to help assign the structures of many stereochemically-rich molecules. We present an improved version of the procedure, which can be downloaded as Python script instead of running within a web-browser, and which analyses output from open-source molecular modelling programs (TINKER and NWChem) in addition to being able to use output from commercial packages (Schrodinger's Macromodel and Jaguar; Gaussian). The new open-source workflow incorporates a method for the automatic generation of diastereomers using InChI strings and has been tested on a range of new structures. This improved workflow permits the rapid and convenient computational elucidation of structure and relative stereochemistry.

The reaction of tryptamine and (2-oxocyclohexyl)acetic acid can be catalyzed by 3,3'-bis(triphenylsilyl)-1,1'-bi-2-naphthol phosphoric acid to give an asymmetric β-carboline. This reaction was first studied by Holloway et al. ( Org. Lett. 2010 , 12 , 4720 - 4723 ), but their mechanistic work did not explain the high stereoselectivity achieved. This study uses density functional theory and hybrid quantum mechanics/molecular mechanics calculations to investigate this reaction and provide a model to explain its outcome. The step leading to diastereo- and enantioselectivity is an asymmetric Pictet-Spengler reaction involving an N-acyliminium ion bound to the catalyst in a bidentate fashion. This interaction occurs via hydrogen bonds between the two terminal oxygen atoms of the catalyst phosphate group and the hydrogen atoms at N and C2 of the substrate indole group. These bonds hold the transition structure rigidly and thus allow the catalyst triphenylsilyl groups to influence the enantioselectivity.

Density functional theory calculations suggest that asymmetric boronate addition to o-quinone methides proceeds via a Lewis acid catalyzed process through a closed six-membered transition structure. The BINOL-derived catalyst undergoes an exchange process with the original ethoxide boronate ligands. This activation mode successfully accounts for the sense and level of enantioselectivity observed experimentally. A qualitative model which accurately predicts the observed enantioselectivity has been developed and is consistent with results from our study of ketone propargylation demonstrating the reaction model's generality. The effects of replacing the BINOL framework with H8-BINOL have been rationalized.

Two base-mediated cascade rearrangement reactions of diallyl ethers were developed leading to selective [2,3]-Wittig–oxy-Cope and isomerization–Claisen rearrangements. Both diaryl and arylsilyl-substituted 1,3-substituted propenyl substrates were examined, and each exhibits unique reactivity and different reaction pathways. Detailed mechanistic and computational analysis was conducted, which demonstrated that the role of the base and solvent was key to the reactivity and selectivity observed. Crossover experiments also suggest that these reactions proceed with a certain degree of dissociation, and the mechanistic pathway is highly complex with multiple competing routes.

1-Azatricyclo[3.3.1.1(3,7)]decan-2-one (3), the parent compound of a rare class of 90°-twisted amides, has finally been synthesized, using an unprecedented transformation. These compounds are of special interest as transition-state mimics for the enzyme-catalyzed cis-trans rotamer interconversion of amides involved in peptide and protein folding and function. The stabilization of the amide group in its high energy, perpendicular conformation common to both systems is shown for the rigid tricyclic system to depend, as predicted by calculation, on its methyl group substitution pattern, making 3 by some way the most reactive known "amide".

Consumer and environmental safety decisions are based on exposure and hazard data, interpreted using risk assessment approaches. The adverse outcome pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine current risk assessment practices in ecotoxicology. This framework can also be applied to human toxicology and is explored on the basis of investigating the molecular initiating events (MIEs) of compounds. The precise definition of the MIE has yet to reach general acceptance. In this work we present a unified MIE definition: an MIE is the initial interaction between a molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway. Case studies are presented, and issues with current definitions are addressed. With the development of a unified MIE definition, the field can look toward defining, classifying, and characterizing more MIEs and using knowledge of the chemistry of these processes to aid AOP research and toxicity risk assessment. We also present the role of MIE research in the development of in vitro and in silico toxicology and suggest how, by using a combination of biological and chemical approaches, MIEs can be identified and characterized despite a lack of detailed reports, even for some of the most studied molecules in toxicology.

Polymorphism of drugs has been the subject of intense interest in the pharmaceutical industry for over forty years. Although identical in chemical composition, polymorphs differ in bioavailability, solubility, dissolution rate, chemical and physical stability, melting point, colour, filterability, density, flow properties, and many other properties. The difference in solubility is particularly important for pharmaceuticals, as it can affect drug efficacy, bioavailability and safety. Despite significant investment in processes to find all the possible polymorphs of active pharmaceutical ingredients (APIs), new polymorphs can suddenly appear without warning. Polymorphs tend to convert spontaneously from less stable to more stable forms, and, therefore, it is best to discover and characterize the stable form as early as possible. Ideally the most stable polymorph will be found while the drug candidate is still in the discovery process, so that this is the form used for subsequent testing. The most stable polymorph will be the least soluble and solubility may be a limiting factor in the efficacy of the API. Despite the huge importance of polymorphism in the properties of materials, however, there is no method that can produce all the stable polymorphs of a compound, or even one that can provide confidence that the most stable polymorph has been obtained. Here we describe a new method, `potentiometric cycling for polymorph creation (PC)2', which is able to generate the most stable polymorph in aqueous solution. This new method has been applied to sulindac, a non-steroidal anti-inflammatory drug, which also shows promise in anticancer treatment, producing two polymorphs of this API, including a new more stable one. By adjusting the conditions, this method is able to produce either polymorph exclusively.

An open-access software for creating a unique, text-based identifier for reactions (RInChI) was developed by the Goodman group at the University of Cambridge, based on the IUPAC International Chemical Identifier (InChI) standard. RInChIs describe the substances (reactants, products, reagents and solvents) participating in a reaction with their respective InChIs. The structure of RInChIs is analogous to that of InChIs. In addition to generate RInChIs from widely used Rxnfiles and RDfiles, the software also includes the generation of long- and short-form, hashed representations - RInChIKeys. Furthermore, the software allows to reversibly convert between CT-files and RInChIs, to search for specific substances and their specific roles in reactions and to analyze databases. All these functions are available through web-based tools. An easy-to-use website has been developed at the University of Cambridge and is freely accessible at http://www-rinchi.ch.cam.ac.uk/. We will discuss details of the program and the status of the RInChI project.