Characterisation of the pruritus responses and pruritic behaviours in an interleukin 31-induced canine model of pruritus.

BACKGROUND: Intravenous administration of interleukin (IL)-31 in healthy dogs has been used as a model to assess antipruritic drugs. However, there is no known in-depth characterisation of pruritic behaviours, and the repeatability of the IL-31-induced pruritus in the individual dogs is currently unknown. OBJECTIVES: To evaluate the immediate/delayed pruritus responses and the pruritic behaviours observed in the IL-31-induced pruritic model in healthy dogs after repeated IL-31 injections. ANIMALS: Fifteen healthy laboratory beagles. METHODS: All dogs were video-recorded for 270 min after two intravenous recombinant IL-31 injections (1.75 µg/kg) and vehicle (phosphate-buffered saline, control) injections, respectively; interventions were randomised and performed with a 2 week wash-out period. Two blinded investigators reviewed the pruritic behaviours of all video recordings. RESULTS: Both canine IL-31 (IL-31_01, IL-31_02) injections significantly increased pruritic seconds and categorical minutes ('YES'/'NO' behaviour per discrete 1 min interval) in healthy dogs compared with both vehicle groups (Vehicle_01, Vehicle_02). The second intravenous canine IL-31 (IL-31_02) administered 14 days after the first IL-31 injection induced a significant increase in pruritic seconds (p = 0.021) and not pruritic categorical minutes (p = 0.231). An increase in pruritic seconds was observed in both IL-31 groups in the first 30 min post-administration, while there was no significant difference between IL-31 and vehicle groups. CONCLUSIONS AND CLINICAL RELEVANCE: In conclusion, intravenous IL-31 reproducibly induces itch responses in dogs. Future evaluations of the canine IL-31 pruritic model should assess total pruritic behaviours in seconds rather than using a biased 'YES/NO' behaviour per 1 min scoring system.

Uncovering the mechanism of selective stabilization of high-energy diastereoisomers via inclusion.

Supramolecular systems may be used to stabilize otherwise unstable isomers to find alternative synthetic pathways. It has been reported that cucurbit[8]uril can stabilize trans-I and trans-II CuII cyclam, whereas trans-III is the only non-substituted trans CuII cyclam diastereoisomer found outside of the host molecule experimentally. Quantum chemistry methods can provide valuable insight into the intermolecular interactions involved in these inclusion complexes. All five possible trans diastereoisomers of CuII cyclam were studied within the host molecule to calculate the interaction energy and free energy of association for each complex. The relative free energies of the five free cyclams confirm that trans-I and trans-II are the most energetically accessible diastereoisomers from the initial trans-III starting point. Energy decomposition analysis was used to identify the attractive and repulsive interactions between cyclam and cucurbit[8]uril and showed that trans-II encounters repulsive forces almost three times greater than trans-I, which may explain the 7:3 ratio of trans-I to trans-II within cucurbit[8]uril that occurs experimentally. Optimized complex geometries with trans-III, IV, and V show that the cyclams protrude out of cucurbit[8]uril, whereas trans-I and trans-II become more encapsulated and elongate the host, suggesting that the position of the cyclam is extremely important when forming non-covalent interactions. Our results agree with the experimental findings and provide greater insight into why the most stable isolated cyclam diastereoisomer, trans-III, does not form a complex. Supplementary Information: The online version contains supplementary material available at 10.1007/s00214-023-03077-7.

Establishment of an Intradermal Canine IL-31-Induced Pruritus Model to Evaluate Therapeutic Candidates in Atopic Dermatitis.

Pruritic models in healthy dogs utilizing intravenous administration of interleukin 31 (IL-31) bypass the "natural" itch sensation in AD, which is initiated by pruriceptive primary afferent neurons in the skin. This study aimed to evaluate the immediate/delayed pruritus responses and the pruritic behaviors observed in an intradermal IL-31-induced pruritic model of healthy dogs and the anti-pruritic effect of oclacitinib on said model. In Phase 1, all the dogs were randomized and video-recorded for 300 min after intradermal canine recombinant IL-31 injections (1.75 µg/kg) and vehicle (phosphate-buffered saline) injections. In Phase 2, all the dogs received oral oclacitinib (0.4-0.6 mg/kg, twice daily for 4 consecutive days and once daily on day 5), with the intradermal IL-31 injection performed on day 5. Two blinded investigators reviewed the pruritic behaviors in all the video recordings. Intradermal IL-31 administration to healthy dogs caused a significant increase in the total (p = 0.0052) and local (p = 0.0003) seconds of pruritic behavior compared to the vehicle control. Oral oclacitinib administration significantly reduced the total (p = 0.0011) and local (p = 0.0156) intradermal IL-31-induced pruritic seconds; there was no significant difference in pruritic seconds between the vehicle and oclacitinib within the IL-31 groups. Significant delayed pruritic responses at 150-300 min after IL-31 injections were observed, and intradermal IL-31 failed to induce acute itch (first 30 min). Intradermal injection of IL-31 induces delayed itch responses in dogs that are diminished by the effect of oclacitinib, an oral JAK inhibitor.

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection.

The SARS-CoV-2 coronavirus continues to evolve with scores of mutations of the spike, membrane, envelope, and nucleocapsid structural proteins that impact pathogenesis. Infection data from nasal swabs, nasal PCR assays, upper respiratory samples, ex vivo cell cultures and nasal epithelial organoids reveal extreme variabilities in SARS-CoV-2 RNA titers within and between the variants. Some variabilities are naturally prone to clinical testing protocols and experimental controls. Here we focus on nasal viral load sensitivity arising from the timing of sample collection relative to onset of infection and from heterogeneity in the kinetics of cellular infection, uptake, replication, and shedding of viral RNA copies. The sources of between-variant variability are likely due to SARS-CoV-2 structural protein mutations, whereas within-variant population variability is likely due to heterogeneity in cellular response to that particular variant. With the physiologically faithful, agent-based mechanistic model of inhaled exposure and infection from (Chen et al., 2022), we perform statistical sensitivity analyses of the progression of nasal viral titers in the first 0-48 h post infection, focusing on three kinetic mechanisms. Model simulations reveal shorter latency times of infected cells (including cellular uptake, viral RNA replication, until the onset of viral RNA shedding) exponentially accelerate nasal viral load. Further, the rate of infectious RNA copies shed per day has a proportional influence on nasal viral load. Finally, there is a very weak, negative correlation of viral load with the probability of infection per virus-cell encounter, the model proxy for spike-receptor binding affinity.

Machine Learned Composite Methods for Electronic Structure Theory.

Because of the prohibitive scaling of ab initio techniques for modeling chemical species with high accuracy, they are not generally tractable for large systems. It is therefore of considerable interest to develop high-accuracy computational models with low computational cost that can afford predictions of electronic structure and properties of macromolecular species. Composite methods, as first introduced by Pople [Pople, J. A.; Head-Gordon, M.; Fox, D. J.; Raghavachari, K.; Curtiss, L. A. J. Chem. Phys.1989, 90, 5622.], are an intuitive solution to this problem as they seek to systematically increase accuracy in model chemistries by taking advantage of favorable error cancellation among reasonably low-cost models. By linearly combining a series of carefully chosen model chemistries, the result of a prohibitive-scaling correlated model chemistry with a large basis set may be approximated with relatively good fidelity. However, the full extent to which the choice of low-cost models dictates the predictive accuracy of composite methods is not known, and a full exploration of all model chemistries would be advantageous for the design and validation of a generalizable composite method for widespread application. Here, we show that remarkable accuracy can be generally achieved with composite methods that are more judiciously constructed, leading to increased accuracy with significantly reduced computational cost. By designing a systematic procedure for the automated generation and assessment of over 10 billion unique composite methods, we have extensively explored the space of modern model chemistries to elucidate important design principles in the construction of reliable composite procedures. We anticipate our work to be the starting point in the pursuit of creative approaches to modeling large chemical systems with high accuracy by using novel combinatorial modeling.

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data.

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.

TSNet: predicting transition state structures with tensor field networks and transfer learning.

Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However, transition states are very unstable, typically only existing on the order of femtoseconds. The transient nature of these structures makes them incredibly difficult to study, thus chemists often turn to simulation. Unfortunately, computer simulation of transition states is also challenging, as they are first-order saddle points on highly dimensional mathematical surfaces. Locating these points is resource intensive and unreliable, resulting in methods which can take very long to converge. Machine learning, a relatively novel class of algorithm, has led to radical changes in several fields of computation, including computer vision and natural language processing due to its aptitude for highly accurate function approximation. While machine learning has been widely adopted throughout computational chemistry as a lightweight alternative to costly quantum mechanical calculations, little research has been pursued which utilizes machine learning for transition state structure optimization. In this paper TSNet is presented, a new end-to-end Siamese message-passing neural network based on tensor field networks shown to be capable of predicting transition state geometries. Also presented is a small dataset of SN2 reactions which includes transition state structures - the first of its kind built specifically for machine learning. Finally, transfer learning, a low data remedial technique, is explored to understand the viability of pretraining TSNet on widely available chemical data may provide better starting points during training, faster convergence, and lower loss values. Aspects of the new dataset and model shall be discussed in detail, along with motivations and general outlook on the future of machine learning-based transition state prediction.

A shared-weight neural network architecture for predicting molecular properties.

Quantum chemical methods scale poorly with increasing molecular size and machine learning models have emerged as a promising, computationally-efficient alternative. We present a shared-weight neural network architecture based on modified atom-centered symmetry functions (ACSFs) and show that it performs similarly to the more computationally expensive per-element neural networks of previous work with ACSFs. The model achieves chemically accurate predictions, with a mean absolute error as low as 0.63 kcal mol-1 on energy predictions in the QM9 data set. Additionally, we show that it can reliably predict atomic forces.

Novel Bonding Mode in Phosphine Haloboranes.

We have predicted, using a wide range of theoretical models, the potential energy surfaces of dative bond stretching in some phosphine haloboranes and closely associated analogues. It is shown that these dative complexes demonstrate unusual bond stretching potentials that are characterized by having multiple inflection points and are not able to be fit to any traditional Morse or Lennard-Jones-type curve. Specifically, in the case of Cl3B-PH3, this effect is so pronounced that the surface actually exhibits two distinct minima. To the best of our knowledge, this is the first example of such a unique bonding phenomenon reported for these species and is explained by the competition between the energetic cost of the required pyramidalization of the Lewis acid to form a dative bond and the stabilization from the favorable attraction between the Lewis acid and base. When the cost of pyramidalization of the Lewis acid is high relative to the strength of the interaction between the acid and base, the potential well associated with dative bonding is significantly weakened and the result is a relatively flat potential energy surface that is susceptible to the unusual characteristics described herein.

Revealing Electron-Electron Interactions within Lewis Pairs in Chemical Systems.

The so-called "Lewis pair" is a ubiquitous phenomenon in chemistry and is often used as an intuitive construct to predict and rationalize chemical structure and behavior. Concepts from the very general Valence Shell Electron Pair Repulsion (VSEPR) model to the most esoteric reaction mechanism routinely rely on the notion that electrons tend to exist in pairs and that these pairs can be thought of as being localized to a particular region of space. It is precisely this localization that allows one to intuit how these pairs might behave, generally speaking, so that reasonable predictions may be made regarding molecular structure, intermolecular interactions, property trends, and reaction mechanisms, etc. Of course, it is rather unfortunate that the Lewis model is entirely qualitative and yields no information regarding how any specific electron pair is distributed. Here we demonstrate a novel electronic structure analysis technique that predicts and analyzes precise quantitative details about the relative and absolute distribution of individual electron pairs. This Single Electron Pair Distribution Analysis (SEPDA) reveals quantitative details about the distribution of the well-known Lewis pairs, such as how they are distributed in space and how their relative velocities change in various chemical contexts. We show that these distributions allow one to image the explicitly pairwise electronic behavior of bonds and lone pairs. We further demonstrate how this electronic behavior changes with several conditions to explore the nature of the covalent chemical bond, non-covalent interactions, bond formation, and exotic 3-center-2-electron species. It is shown that indications of the strength of bonded and non-bonded interactions may also be gleaned from such distributions and SEPDA can be used as a tool to differentiate between interaction types. We anticipate that SEPDA will be of broad utility in a wide variety of chemical contexts because it affords a very detailed, visual and intuitive analysis technique that is generally applicable.

Exploring electron pair behaviour in chemical bonds using the extracule density.

We explore explicit electron pair behaviour within the chemical bond (and lone pairs) by calculating the probability distribution for the center-of-mass (extracule) of an electron pair described by single localized orbitals. Using Edmiston-Ruedenberg localized orbitals in a series of 61 chemical systems, we demonstrate the utility of the extracule density as an interpretive tool in chemistry. By accessing localized regions of chemical space we simplify the interpretation of the extracule density and afford a quantum mechanical interpretation of "chemically intuitive" features of electronic structure. Specifically, we describe the localized effects on chemical bonds due to changes in electronegativities of bonded neighbours, bond strain, and non-covalent interactions. We show that the extracule density offers unique insight into electronic structure and allows one to readily quantify the effects of changing the chemical environment.

Assessment and Application of Density Functional Theory for the Prediction of Structure and Reactivity of Vanadium Complexes.

We assess the performance of six density functionals, each paired with one of five basis sets (a total of 30 model chemistries) for the prediction of geometrical parameters in the coordination sphere of nine vanadium complexes (for a total of 270 structural analyses). We find that results are generally consistent over the range of functionals tested and that none fail drastically. For bond lengths, the model chemistry PBE0/QZVP performed the best overall (having a MAD of only 0.02 Å from experiment) yet PBE0/6-31G* provides nearly identical results. For bond angles, PBE0 also performed best overall and, when combined with the 6-31G* basis, produces one of the smallest error distributions of any model chemistry tested. We subsequently applied the PBE0/6-31G* model chemistry to understanding the mechanism of action of a [BIMPY]VCl3 catalyst in the polymerization of styrene (Sty) and vinyl acetate (VAc). Our results indicate that the [BIMPY]VCl3 catalyst operates through a unique, two-step reaction pathway: dehalogenation to form a reactive V(II) intermediate (a highly favorable process) followed by a potentially reversible OMRP to control the polymerization of vinyl acetate. Control over vinyl acetate is facilitated by both the higher reactivity of the radical species and the participation of the ester group in the trapping step. In both the Sty and VAc cases we predict relatively poor control of the polymerization with the vanadium catalyst, which is in good agreement with our experimental results.

Predicting bond strength from a single Hartree-Fock ground state using the localized pair model.

We present an application of the recently introduced Localized Pair Model (LPM) [Z. A. Zielinksi and J. K. Pearson, Comput. Theor. Chem., 2013, 1003, 7990] to characterize and quantify properties of the chemical bond in a series of substituted benzoic acid molecules. By computing interelectronic distribution functions for doubly-occupied Edmiston-Ruedenberg localized molecular orbitals (LMOs), we show that chemically intuitive electron pairs may be uniquely classified and bond strength may be predicted with remarkable accuracy. Specifically, the HF/u6-311G(d,p) level (where u denotes a complete uncontraction of the basis set) is used to generate the relevant LMOs and their respective interelectronic distribution functions can be linearly correlated to the well-known Hammett σp or σm parameters with near-unity correlation coefficients.

Isolation and structure elucidation of satosporin A and B: new polyketides from Kitasatospora griseola.

Satosporins A and B, two novel glucosylated polyketides, were isolated from the actinomycete Kitasatospora griseola MF730-N6. The polyketides possess an unprecedented tricyclic ring system that was fully characterized using a combination of spectroscopic analyses and computational calculations. Satosporin A was quantitatively converted into its aglycon homologue, satosporin C, using a ß-glucosidase. The determination of the absolute stereochemistry was achieved using solution TDDFT/ECD calculations and chemical derivatization methods.

A simultaneous probability density for the intracule and extracule coordinates.

We introduce the intex density X(R,u), which combines both the intracular and extracular coordinates to yield a simultaneous probability density for the position of the center-of-mass radius (R) and relative separation (u) of electron pairs. One of the principle applications of the intex density is to investigate the origin of the recently observed secondary Coulomb hole. The Hartree-Fock (HF) intex densities for the helium atom and heliumlike ions are symmetric functions that may be used to prove the isomorphism 2I(2R)=E(R), where I(u) is the intracule density and E(R) is the extracule density. This is not true of the densities that we have constructed from explicitly correlated wave functions. The difference between these asymmetric functions and their symmetric HF counterparts produces a topologically rich intex correlation hole. From the intex hole distributions (X(exact)(R,u)-X(HF)(R,u)), we conclude that the probability of observing an electron pair with a very large interelectronic separation increases with the inclusion of correlation only when their center-of-mass radius is close to half of their separation.

Isolation, biomimetic synthesis, and cytotoxic activity of bis(pseudopterane) amines.

LC-MS/MS-based screening of the dichloromethane extract of the gorgonian coral Pseudopterogorgia acerosa led to the isolation of a novel bis(pseudopterane) amine (1). The structural assignment of 1 was achieved by 1D and 2D NMR and mass spectrometry analysis. A biomimetic synthesis of 1 and the known symmetrical diterpene 2 from pseudopterolide (3) is described in this report. Bis(pseudopterane) amine showed selective growth inhibition activity against cancer cell lines with IC(50) values of 4.2 microM (HCT116) and 42 microM (HeLa).

Intracule functional models. IV. Basis set effects.

We have calculated position and dot intracules for a series of atomic and molecular systems, starting from an unrestricted Hartree-Fock wave function, expanded using the STO-3G, 6-31G, 6-311G, 6-311++G, 6-311++G(d,p), 6-311++G(3d,3p), and 6-311++G(3df,3pd) basis sets as well as the nonpolarized part of Dunning's cc-pV5Z basis. We find that the basis set effects on the intracules are small and that correlation energies from the dot intracule ansatz are remarkably insensitive to the basis set quality. Mean absolute errors in correlation energies across the G1 data set agree to within 2 mE(h) for all basis sets tested.

Understanding the electronic structure, reactivity, and hydrogen bonding for a 1,2-diphosphonium dication.

The experimental charge density for hexamethyldiphosphonium ditriflate has been determined from low-temperature high-resolution X-ray diffraction data. These results have been compared with theoretically calculated values for the isolated gas-phase compound. Analysis of the topological and atomic basin properties has provided insight into the exact nature of the P-P bond in both the crystalline and the gas-phase structures. The rho(b)(r) and nabla2rho(b)(r) values highlight the covalent nature of the P-P bond, while the atomic charges indicate a localization of the positive charges on the two phosphorus atoms. This seems to indicate that a covalent bond is formed despite a strong electrostatic repulsion between these two heteroatoms. The topological properties and electrostatic potentials have also been shown to provide significant insight into the chemical reactivity of the title compound. A topological analysis of P2Me4, P2Me5(+), and P2Me6(+2) species has provided information about the progression of the P-P bond in the synthesis of the title compound. An investigation of the different hydrogen-bonding networks present in the crystalline and gas-phase structures, along with their affect on the electronic structure of the title compound has also been investigated. This has all led to significant new insight into the electronic structure, reactivity, and weak hydrogen bonding in prototypical 1,2-diphosphonium dications.

Effect of substituents on the GPx-like activity of ebselen: steric versus electronic.

The direct oxidation of ebselen and several derivatives by hydrogen peroxide is investigated using the B3LYP/6-31G(d,p) method to elucidate the effects of substituents on GPx-like activity. While previous studies have attributed the differences in GPx activity of substituted ebselen compounds to the electronic nature of the substituents, the influence of functional groups is poorly understood. The effects of various solvents are incorporated by employing the CPCM method. It is shown that a substituent in the ortho position to the selenium atom sterically hinders attack of a nucleophile at selenium and thus increases the barrier to reaction. The observed increase in GPx-like activity of an ebselen derivative with an ortho substituent is explained by the fact that the steric hindrance prevents thiol exchange reactions.

Density functional theory study of the reaction mechanism and energetics of the reduction of hydrogen peroxide by ebselen, ebselen diselenide, and ebselen selenol.

Density functional theory calculations at the B3LYP/6-311++G(3df,3pd)//B3LYP/6-31G(d,p) level have been performed to elucidate the mechanism and reaction energetics for the reduction of hydrogen peroxide by ebselen, ebselen diselenide, ebselen selenol, and their sulfur analogues. The effects of solvation have been included with the CPCM model, and in the case of the selenol anion reaction, diffuse functions were used on heavy atoms for the geometry optimizations and thermochemical calculations. The topology of the electron density in each system was investigated using the quantum theory of atoms in molecules, and a detailed interpretation of the electronic charge and population data as well as the atomic energies is presented. Reaction free energy barriers for the oxidation of ebselen, ebselen diselenide, and ebselen selenol are 36.8, 38.4, and 32.5 kcal/mol, respectively, in good qualitative agreement with experiment. It is demonstrated that the oxidized selenium atom is significantly destabilized in all cases and that the exothermicity of the reactions is attributed to the peroxide oxygen atoms via reduction. The lower barrier to oxidation exhibited by the selenol is largely due to entropic effects in the reactant complex.