Determining molecular properties with differential mobility spectrometry and machine learning

Towards Generalizable In Silico Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering

Differential mobility spectrometry (DMS), a tool for separating chemically similar species (including isomers), is readily coupled to mass spectrometry to improve selectivity in analytical workflows. DMS dispersion curves, which describe the dynamic mobility experienced by an ion in a gaseous environment, show the maximum ion transmission for an analyte through the DMS instrument as a function of the separation voltage (SV) and compensation voltage (CV) conditions. To date, there exists no fast, general prediction tool for the dispersion behavior of ions. Here, we demonstrate a machine learning (ML) model that achieves generalized dispersion prediction using an in silico feature addition pipeline. We employ a data set containing 1141 dispersion curve measurements of anions and cations recorded in pure N2 environments and in N2 environments doped with 1.5% methanol (MeOH). Our feature addition pipeline can compute 1591 RDKit and Mordred descriptors using only SMILES codes, which are then normalized to sampled molecular distributions (n = 100 000) using cumulative density functions (CDFs). This tool can be thought of as a "learned" feature fingerprint generation pipeline, which could be applied to almost any molecular (bio)cheminformatics tasks. Our best performing model, which for the first time considers solvent-modified environments, has a mean absolute error (MAE) of 2.1 ± 0.2 V for dispersion curve prediction, a significant improvement over the previous state-of-the-art work. We use explainability techniques (e.g., SHAP analysis) to show that this feature addition pipeline is a semideterministic process for feature sets, and we discuss "best practices" to understand feature sets and maximize model performance. We expect that this tool could be used for prescreening to accelerate or even automate the use of DMS in complex analytical workflows (e.g., 2D LC×DMS separation) and perform automated identification of transmission windows and increase the "self-driving" potential of the instrument. We make our models available as a free and accessible tool at https://github.com/HopkinsLaboratory/DispersionCurveGUI.

Insecticidal activities of three recombinant venom proteins of the predatory stink bug, Arma custos

BACKGROUND

Widespread resistance of insect pests to insecticides and transgenic crops in the field is a significant challenge for sustainable agriculture, and calls for the development of novel alternative strategies to control insect pests. One potential resource for the discovery of novel insecticidal molecules is natural toxins, particularly those derived from the venoms of insect predators.

RESULTS

In this study, we identified three insecticidal proteinaceous toxins from the venom glands (VGs) of the predatory stink bug, Arma custos (Hemiptera: Asopinae). Transcriptomic analysis of A. custos VGs revealed 151 potentially secreted VG-rich venom proteins. Three VG-rich venom proteins (designated AcVP1 ~ 3) were produced by overexpression in Escherichia coli. Injection of the recombinant proteins into tobacco cutworm (Spodoptera litura) larvae showed that all of the three recombinant proteins caused paralysis, liquefaction and death. Injection of recombinant proteins into rice brown planthopper (Nilaparvata lugens) nymphs showed higher insecticidal activities, among which a trypsin (AcVP2) caused 100% mortality postinjection at 1.27 pmol mg-1 body weight.

CONCLUSION

A natural toolkit for the discovery of insecticidal toxins from predatory insects has been revealed by the present study. © 2024 Society of Chemical Industry.

Why are nH-perfluoroalkanoate ions more mobile than expected? Implications for identifying an emerging environmental pollutant

nH-Perfluoroalkyl carboxylic acids (nH-PFCAs) are emerging pollutants. Their identification by ion mobility is frustrated by the nH-PFCAs having unexpectedly small collision cross sections (CCS). Theory and experiment agree that this is because nH-PFCA ions undergo internal hydrogen bridging, and this insight will help guide the creation of more accurate methods for pollutant identification.

Toward Part-per-Million Precision in the Determination of an Ion's Collision Cross Section Using Multipass Cyclic Ion Mobility

In cyclic ion mobility (cIMS), ions are permitted to travel multiple passes around the drift cell, increasing the distance traveled and the relative separation between ions. This study tests the hypothesis that multiple passes around the cell can also result in improved precision when measuring an ion's mobility and the collision cross section (TWCCS) derived therefrom. Experiments were performed with a diverse set of compounds, including 16 polycyclic aromatic hydrocarbons using gas chromatographic atmospheric pressure chemical ionization and a set of drug molecules by direct infusion electrospray ionization. The average periodic drift time, viz., the average time required for the ion to travel around the cIMS cell once, shifts dramatically, approaching part-per-million (ppm) precision as the number of passes increases to ∼100. Extrapolation of the precision of the CCS values with respect to the number of passes led to the prediction that the precision will reach 1000 ppm after 50 passes, 100 ppm after 100 passes, and <10 ppm after 150 passes. Experiments wherein the number of passes exceeded 100 produced TWCCS values having within-run precisions ranging between 15 and 117 ppm. The improved precision with an increasing number of passes may be a consequence of mitigating space-charge effects by allowing the ions to occupy a larger region of the cIMS cell. A method is proposed to enable practical measurements of TWCCS with ppm precision and is demonstrated to characterize an unknown drug mixture.

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R² = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R² = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R² = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R² = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

First-Principles Modeling of Preferential Solvation in Mixed-Modifier Differential Mobility Spectrometry

Differential mobility spectrometry (DMS) separates ions based on mobility differences between high and low electric field conditions. To enhance resolution, solvents such as water and acetonitrile are often used to modify the collision environment and take advantage of differing dynamic clustering behavior between analytes that coelute in hard-sphere environments (e.g., N₂). When binary solvent mixtures are used to modify the DMS environment, one solvent can have a dominant influence over the other with respect to ion trajectories. For example, for quinoline derivatives, a 9:1 water:acetonitrile solvent mixture exhibited identical behavior to an environment containing only acetonitrile as a modifier. It was hypothesized that this effect arises due to the significantly different binding strengths of the two solvents. Here, we utilize a first-principles model of DMS to study analytes in single and binary solvent mixtures and explore the effects governing the dominance of one solvent over the other. Computed DMS dispersion curves of quinoline derivatives are in excellent agreement with those measured experimentally. For mixed-modifier environments, the predicted cluster populations show a clear preferential solvation of ions with the stronger binding solvent. The influence of ion-solvent binding energies, solvent concentration, and solvent molecule size is discussed in the context of the observed DMS behavior. This work can guide the usage of binary solvent mixtures for improving ion separations in cases where compounds coelute in pure N₂ and in single-solvent modifier environments. Moreover, our results indicate that binary solvent mixtures can be used to create a relative scale for solvent binding energies.

Determining the gas-phase structures of α-helical peptides from shape, microsolvation, and intramolecular distance data

Mass spectrometry is a powerful technique for the structural and functional characterization of biomolecules. However, it remains challenging to accurately gauge the gas-phase structure of biomolecular ions and assess to what extent native-like structures are maintained. Here we propose a synergistic approach which utilizes Förster resonance energy transfer and two types of ion mobility spectrometry (i.e., traveling wave and differential) to provide multiple constraints (i.e., shape and intramolecular distance) for structure-refinement of gas-phase ions. We add microsolvation calculations to assess the interaction sites and energies between the biomolecular ions and gaseous additives. This combined strategy is employed to distinguish conformers and understand the gas-phase structures of two isomeric α-helical peptides that might differ in helicity. Our work allows more stringent structural characterization of biologically relevant molecules (e.g., peptide drugs) and large biomolecular ions than using only a single structural methodology in the gas phase.

A pattern-recognition-based clustering method for non-invasive diagnosis and classification of various gastric conditions

Conventional endoscopic biopsy tests are not suitable for early detection of the acute onset and progression of peptic ulcer as well as various gastric complications. This also limits its suitability for widespread population-based screening and consequently, many people with complex gastric phenotypes remain undiagnosed. Here, we demonstrate a new non-invasive methodology for accurate diagnosis and classification of various gastric disorders exploiting a pattern-recognition-based cluster analysis of a breathomics dataset generated from a simple residual gas analyzer-mass spectrometry. The clustering approach recognizes unique breathograms and "breathprints" signatures that clearly reflect the specific gastric condition of an individual person. The method can selectively distinguish the breath of peptic ulcer and other gastric dysfunctions like dyspepsia, gastritis, and gastroesophageal reflux disease patients from the exhaled breath of healthy individuals with high diagnostic sensitivity and specificity. Moreover, the clustering method exhibited a reasonable power to selectively classify the early-stage and high-risk gastric conditions with/without ulceration, thus opening a new non-invasive analytical avenue for early detection, follow-up, and fast population-based robust screening strategy of gastric complications in the real-world clinical domain.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R² = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification

Differential mobility spectrometry (DMS)-based detectors are being widely studied to detect chemical warfare agents, explosives, chemicals, drugs and analyze volatile organic compounds (VOCs). The dispersion plots from DMS devices are complex to effectively analyze through visual inspection. In the current work, we adopted machine learning to differentiate pure chemicals and identify chemicals in a mixture. In particular, we observed the convolutional neural network algorithm exhibits excellent accuracy in differentiating chemicals in their pure forms while also identifying chemicals in a mixture. In addition, we propose and validate the magnitude-squared coherence (msc) between the DMS data of known chemical composition and that of an unknown sample can be sufficient to inspect the chemical composition of the unknown sample. We have shown that the msc-based chemical identification requires the least amount of experimental data as opposed to the machine learning approach.

The hitchhiker's guide to dynamic ion-solvent clustering: applications in differential ion mobility spectrometry

This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to understanding an ion's behaviour during differential mobility spectrometry (DMS) experiments. We contrast DMS with static-field ion mobility, where separation is affected by mobility differences under the high-field and low-field conditions of an asymmetric oscillating electric field. Although commonly used in mass spectrometric (MS) workflows to enhance signal-to-noise ratios and remove isobaric contaminants, the chemistry and physics that underpins the phenomenon of differential mobility has yet to be fully fleshed out. Moreover, we are just now making progress towards understanding how the DMS separation waveform creates a dynamic clustering environment when the carrier gas is seeded with the vapour of a volatile solvent molecule (e.g., methanol). Interestingly, one can correlate the dynamic clustering behaviour observed in DMS experiments with gas-phase and solution-phase molecular properties such as hydrophobicity, acidity, and solubility. However, to create a generalized, global model for property determination using DMS data one must employ machine learning. In this article, we provide a first-principles description of differential ion mobility in a dynamic clustering environment. We then discuss the correlation between dynamic clustering propensity and analyte physicochemical properties and demonstrate that analytes exhibiting similar ion-solvent interactions (e.g., charge-dipole) follow well-defined trends with respect to DMS clustering behaviour. Finally, we describe how supervised machine learning can be used to create predictive models of molecular properties using DMS data. We additionally highlight open questions in the field and provide our perspective on future directions that can be explored.

Regulatory perspectives of combination products

Combination products with a wide range of clinical applications represent a unique class of medical products that are composed of more than a singular medical device or drug/biological product. The product research and development, clinical translation as well as regulatory evaluation of combination products are complex and challenging. This review firstly introduced the origin, definition and designation of combination products. Key areas of systematic regulatory review on the safety and efficacy of device-led/supervised combination products were then presented. Preclinical and clinical evaluation of combination products was discussed. Lastly, the research prospect of regulatory science for combination products was described. New tools of computational modeling and simulation, novel technologies such as artificial intelligence, needs of developing new standards, evidence-based research methods, new approaches including the designation of innovative or breakthrough medical products have been developed and could be used to assess the safety, efficacy, quality and performance of combination products. Taken together, the fast development of combination products with great potentials in healthcare provides new opportunities for the advancement of regulatory review as well as regulatory science.

Rapid separation of cannabinoid isomer sets using differential mobility spectrometry and mass spectrometry

With legalization and decriminalization of cannabis in many parts of the world comes the need for rapid separation and quantitation of the psychoactive ingredients.

Venoms for all occasions: The functional toxin profiles of different anatomical regions in sea anemones are related to their ecological function

The phylum Cnidaria is the oldest extant venomous group and is defined by the presence of nematocysts, specialized organelles responsible for venom production and delivery. Although toxin peptides and the cells housing nematocysts are distributed across the entire animal, nematocyte and venom profiles have been shown to differ across morphological structures in actiniarians. In this study, we explore the relationship between patterns of toxin expression and the ecological roles of discrete anatomical structures in Telmatactis stephensoni. Specifically, using a combination of proteomic and transcriptomic approaches, we examined whether there is a direct correlation between the functional similarity of regions and the similarity of their associated toxin expression profiles. We report that the regionalization of toxin production is consistent with the partitioning of the ecological roles of venom across envenomating structures, and that three major functional regions are present in T. stephensoni: tentacles, epidermis and gastrodermis. Additionally, we find that most structures that serve similar functions not only have comparable putative toxin profiles but also similar nematocyst types. There was no overlap in the putative toxins identified using proteomics and transcriptomics, but the expression patterns of specific milked venom peptides were conserved across RNA-sequencing and mass spectrometry imaging data sets. Furthermore, based on our data, it appears that acontia of T. stephensoni may be transcriptionally inactive and only mature nematocysts are present in the distal portions of the threads. Overall, we find that the venom profile of different anatomical regions in sea anemones varies according to its ecological functions.

Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database

Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to prevent acute rejection among Black recipients. Methods: We conducted tree-based RF feature importance of Black kidney transplant recipients in United States from 2015 to 2019 in the UNOS database using the number of nodes, accuracy decrease, gini decrease, times_a_root, p value, and mean minimal depth. Feature interaction analysis was also performed to evaluate the most frequent occurrences in the RF classification run between correlated and uncorrelated pairs. Results: A total of 22,687 Black kidney transplant recipients were eligible for analysis. Of these, 1330 (6%) had acute rejection within 1 year after kidney transplant. Important variables in the RF models for acute rejection among Black kidney transplant recipients included recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration. The three most frequent interactions consisted of two numerical variables, including recipient age:donor age, recipient age:serum albumin, and recipient age:BMI, respectively. Conclusions: The application of tree-based RF feature importance and feature interaction network analysis framework identified recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration as important variables in the RF models for acute rejection among Black kidney transplant recipients in the United States.

UVPD Spectroscopy of Differential Mobility-Selected Prototropic Isomers of Rivaroxaban

Two ion populations of protonated Rivaroxaban, [C₁₉H₁₈ClN₃O₅S + H]⁺, are separated under pure N₂ conditions using differential mobility spectrometry prior to characterization in a hybrid triple quadrupole linear ion trap mass spectrometer. These populations are attributed to bare protonated Rivaroxaban and to a proton-bound Rivaroxaban-ammonia complex, which dissociates prior to mass-selecting the parent ion. Ultraviolet photodissociation (UVPD) and collision-induced dissociation (CID) studies indicate that both protonated Rivaroxaban ion populations are comprised of the computed global minimum prototropic isomer. Two ion populations are also observed when the collision environment is modified with 1.5% (v/v) acetonitrile. In this case, the protonated Rivaroxaban ion populations are produced by the dissociation of the ammonium complex and by the dissociation of a proton-bound Rivaroxaban-acetonitrile complex prior to mass selection. Again, both populations exhibit a similar CID behavior; however, UVPD spectra indicate that the two ion populations are associated with different prototropic isomers. The experimentally acquired spectra are compared with computed spectra and are assigned to two prototropic isomers that exhibit proton sharing between distal oxygen centers.

Electronic spectroscopy of differential mobility-selected prototropic isomers of protonated para-aminobenzoic acid

para-Aminobenzoic acid (PABA) was electrosprayed from mixtures of protic and aprotic solvents, leading to formation of two prototropic isomers in the gas phase whose relative populations depended on the composition of the electrospray solvent. The two ion populations were separated in the gas phase using differential mobility spectrometry (DMS) within a nitrogen-only environment at atmospheric pressure. Under high-field conditions, the two prototropic isomers eluted with baseline signal separation with the N-protonated isomer having a more negative CV shift than the O-protonated isomer, in accord with previous DMS studies. The conditions most favorable for formation and separation of each tautomer were used to trap each prototropic isomer in a quadrupole ion trap for photodissociation action spectroscopy experiments. Spectral interrogation of each prototropic isomer in the UV region (3-6 eV) showed good agreement with previously recorded spectra, although a previously reported band (4.8-5.4 eV) was less intense for the O-protonated isomer in our measured spectrum. Without DMS selection, the measured spectra contained features corresponding to both protonated isomers even when solvent conditions were optimised for formation of a single isomer. Interconversion between protonated isomers within the ion trap was observed when protic ESI solvents were employed, leading to spectral cross contamination even with mobility selection. CCSD vertical excitation energies and vertical gradient (VG) Franck-Condon simulations are presented and reproduce the measured spectral features with near-quantitative agreement, providing supporting evidence for spectral assignments.

Predicting differential ion mobility behaviour in silico using machine learning

Although there has been a surge in popularity of differential mobility spectrometry (DMS) within analytical workflows, determining separation conditions within the DMS parameter space still requires manual optimization. A means of accurately predicting differential ion mobility would benefit practitioners by significantly reducing the time associated with method development. Here, we report a machine learning (ML) approach that predicts dispersion curves in an N2 environment, which are the compensation voltages (CVs) required for optimal ion transmission across a range of separation voltages (SVs) between 1500 to 4000 V. After training a random-forest based model using the DMS information of 409 cationic analytes, dispersion curves were reproduced with a mean absolute error (MAE) of ≤ 2.4 V, approaching typical experimental peak FWHMs of ±1.5 V. The predictive ML model was trained using only m/z and ion-neutral collision cross section (CCS) as inputs, both of which can be obtained from experimental databases before being extensively validated. By updating the model via inclusion of two CV datapoints at lower SVs (1500 V and 2000 V) accuracy was further improved to MAE ≤ 1.2 V. This improvement stems from the ability of the "guided" ML routine to accurately capture Type A and B behaviour, which was exhibited by only 2% and 17% of ions, respectively, within the dataset. Dispersion curve predictions of the database's most common Type C ions (81%) using the unguided and guided approaches exhibited average errors of 0.6 V and 0.1 V, respectively.

Determining Collision Cross Sections from Differential Ion Mobility Spectrometry

The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of ion trajectories to CCS in other non-linear IMS techniques that employ dynamic electric fields, such as differential mobility spectrometry (DMS), has remained a challenge since its inception. Here, we describe how an ion's CCS can be measured from DMS experiments using a machine learning (ML)-based calibration. The differential mobility of 409 molecular cations (m/z: 86-683 Da and CCS 110-236 Å2) was measured in a N2 environment to train the ML framework. Several open-source ML routines were tested and trained using DMS-MS data in the form of the parent ion's m/z and the compensation voltage required for elution at specific separation voltages between 1500 and 4000 V. The best performing ML model, random forest regression, predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for analytes excluded from the training set (i.e., out-of-the-bag external validation). This accuracy approaches the inherent statistical error of ∼2.2% for the MobCal-MPI CCS calculations employed for training purposes and the <2% threshold for matching literature CCSs with those obtained on a TWIMS platform.

Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles

The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA) and accurately predict the pulmonary immune responses and lung burden of NPs, with the correlation coefficient of all training sets >0.9 and half of the test sets >0.75. This framework overcomes the feature importance bias brought by small datasets through a multiway importance analysis. TBRFA also builds feature interaction networks, boosts model interpretability, and reveals hidden interactional factors (e.g., various NP properties and exposure conditions). TBRFA provides guidance for the design and application of ideal NPs and discovers the feature interaction networks that contribute to complex systems with small-size data in various fields.

Separating chiral isomers of amphetamine and methamphetamine using chemical derivatization and differential mobility spectrometry

The separation and analysis of chiral compounds, especially enantiomers, presents a great challenge to modern analytical chemistry, particularly to mass spectrometry (MS). As a result, integrated orthogonal separations, such as chiral liquid chromatography (chiral LC), gas chromatography (GC), or capillary electrophoresis (CE), are often employed to separate enantiomers prior to MS analysis. Here, we combine chemical derivatization with differential mobility spectrometry (DMS) and MS to separate and quantitate the transformed enantiomeric pairs R- and S-amphetamine, as well as R- and S-methamphetamine. We also demonstrate separation of these drugs by using reverse-phase LC. However, while the LC method requires ∼5 min to provide separation, we have developed a flow-injection analysis (FIA) method using DMS as the exclusive mode of separation (FIA-DMS), requiring only ∼1.5 min with equivalent quantitative metrics (1-1000 ng/mL range) to the LC method. The DMS-based separation of each diastereomeric pair is driven by differences in binding energies between the analyte ions and the chemical modifier molecules (acetonitrile) added to the DMS environment.

Facts, Patterns, and Principles in Drug Discovery: Appraising the Rule of 5 with Measured Physicochemical Data

The rule of 5 was designed to estimate the likelihood of poor absorption or permeation, noting the impact of poor solubility. This Perspective explores the impact of various physicochemical descriptors and contemporary lipophilicity measurements on permeability and solubility, showing that the distribution coefficient log D_7.4 (rather than log P) is the most impactful parameter. Molecular weight, almost invariably the defining characteristic of "beyond the rule of 5" compounds, has little impact on solubility when log D_7.4 measurements and aromaticity are considered. Predicting permeation is more complex, given passive and carrier transport mechanisms; however, notable patterns of behavior are apparent, giving insight even "beyond the rule of 5". Recommended best practices should involve using the facts (measurements) and the patterns they reveal to establish informative principles rather than fastidious rules.

Understanding Nontraditional Differential Mobility Behavior: A Case Study of the Tricarbastannatrane Cation, N(CH2CH2CH2)3Sn. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020;31:796-802. [PMID: 32129991 DOI: 10.1021/jasms.9b00042]

The effect of strong ion-solvent interactions on the differential mobility behavior of the tricarbastannatrane cation, N(CH₂CH₂CH₂)₃Sn⁺, has been investigated. Exotic "type D" dispersion behavior, which is intermediate to the more common types C and A behavior, is observed for gaseous N₂ environments that are seeded with acetone and acetonitrile vapor. Quantum chemical calculations and first-principles modeling show that under low-field conditions [N(CH₂CH₂CH₂)₃Sn + solvent]⁺ complexes containing a single solvent molecule survive the entire separation waveform duty cycle and interact weakly with the chemically modified environment. However, at high separation voltages, the ion-solvent bond dissociates and dynamic clustering ensues.

Preferential Ion Microsolvation in Mixed-Modifier Environments Observed Using Differential Mobility Spectrometry

The preferential solvation behavior for eight different derivatives of protonated quinoline was measured in a tandem differential mobility spectrometer mass spectrometer (DMS-MS). Ion-solvent cluster formation was induced in the DMS by the addition of chemical modifiers (i.e., solvent vapors) to the N₂ buffer gas. To determine the effect of more than one modifier in the DMS environment, we performed DMS experiments with varying mixtures of water, acetonitrile, and isopropyl alcohol solvent vapors. The results show that doping the buffer gas with a binary mixture of modifiers leads to the ions binding preferentially to one modifier over another. We used density functional theory to calculate the ion-solvent binding energies, and in all cases, calculations show that the quinolinium ions bind most strongly with acetonitrile, then isopropyl alcohol, and most weakly with water. Computational results support the hypothesis that the quinolinium ions bind exclusively to whichever solvent they have the strongest interaction with, regardless of the presence of other modifier gases.

Separation of Sialylated Glycan Isomers by Differential Mobility Spectrometry

Mass spectrometry has proven itself to be an important technology for characterizing intact glycoproteins, glycopeptides, and released glycans. However, these molecules often present significant challenges during analysis. For example, glycans of identical molecular weights can be present in many isomeric forms, with one form having dramatically more biological activity than the others. Discriminating among these isomeric forms using mass spectrometry alone can be daunting, which is why orthogonal techniques, such as ion mobility spectrometry, have been explored. Here, we demonstrate the use of differential mobility spectrometry (DMS) to separate isomeric glycans differing only in the linkages of sialic acid groups (e.g., α 2,3 versus α 2,6). This ability extends from a small trisaccharide species to larger biantennary systems and is driven, in part, by the role of intramolecular solvation of the charge site(s) on these ions within the DMS environment.