DrugBank 6.0: the DrugBank Knowledgebase for 2024.

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.

HMDB 5.0: the Human Metabolome Database for 2022.

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.

In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries, we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction and also one of the top tools for in silico mass-spectrum-to-compound identification. This work improves CFM-ID's ability to predict ESI-MS/MS spectra from compounds by (1) learning parameters from features based on the molecular topology, (2) adding a new approach to ring cleavage that models such cleavage as a sequence of simple chemical bond dissociations, and (3) expanding its hand-written rule-based predictor to cover more chemical classes, including acylcarnitines, acylcholines, flavonols, flavones, flavanones, and flavonoid glycosides. We demonstrate that this new version of CFM-ID (version 4.0) is significantly more accurate than previous CFM-ID versions in terms of both EI-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a web server and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid.

Quantitative electrospray ionization efficiency scale: 10 years after.

RATIONALE: The first comprehensive quantitative scale of the efficiency of electrospray ionization (ESI) in the positive mode by monoprotonation, containing 62 compounds, was published in 2010. Several trends were found between the compound structure and ionization efficiency (IE) but, possibly because of the limited diversity of the compounds, some questions remained. This work undertakes to align the new data with the originally published IE scale and carry out statistical analysis of the resulting more extensive and diverse data set to derive more grounded relationships and offer a possibility of predicting logIE values. METHODS: Recently, several new IE studies with numerous compounds have been conducted. In several of them, more detailed investigations of the influence of compound structure, solvent properties, or instrument settings have been conducted. IE data from these studies and results from this work were combined, and the multilinear regression method was applied to relate IE to various compound parameters. RESULTS: The most comprehensive IE scale available, containing 334 compounds of highly diverse chemical nature and spanning 6 orders of magnitude of IE, has been compiled. Several useful trends were revealed. CONCLUSIONS: The ESI ionization efficiency of a compound by protonation is mainly affected by three factors: basicity (expressed by pKaH in water), molecular size (expressed by molar volume or surface area), and hydrophobicity of the ion (expressed by charge delocalization in the ion or its partition coefficient between a water-acetonitrile mixture and hexane). The presented models can be used for tentative prediction of logIE of new compounds (under the used conditions) from parameters that can be computed using commercially available software. The root mean square error of prediction is in the range of 0.7-0.8 log units.

Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS.

Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.

Optimization of flow splitting and make-up flow conditions in liquid chromatography/electrospray ionization mass spectrometry.

RATIONALE: In liquid chromatography/mass spectrometry (LC/MS) the LC flow is often split prior to the mass spectrometer, for instance, when collecting fractions of the separated sample for other purposes or when less sensitive parallel detection is applied. The aim of this study is to optimize the actual split ratio and make-up flow composition. METHODS: Different types of splitters were evaluated in combination with a make-up flow. A home-made 1/10 T-piece splitter and commercial 1/10, 1/100 and 1/250 splitters were evaluated by continuous and accurate measurements of the actual split ratio throughout the LC gradient. The make-up flow composition was optimized for maximum electrospray ionization (ESI)-MS sensitivity in the positive mode using ESI efficiency measurements. RESULTS: Altogether 22 different solvent conditions were tested on 20 pharmaceutical compounds with a wide variety of functional groups and physicochemical properties (molecular weight, logP, and pKa ). Methanol/10 mM formic acid in water (90/10) provided on average the best results. CONCLUSIONS: Methanol/10 mM formic acid in water (90/10) proved to be the best make-up flow composition in relation to the average sensitivity obtained. Stronger acidic conditions using oxalic acid or higher formic acid concentrations had a clear positive effect on the sensitivity of compounds with low ionization efficiency. The tested split ratios were relatively stable over the main part of the gradient but showed some variation at very low and very high organic conditions. Differences were larger with methanol compared with acetonitrile containing solvent compositions and when applied without a column or with very long connecting tubing.

Ionization efficiency ladders as tools for choosing ionization mode and solvent in liquid chromatography/mass spectrometry.

RATIONALE: The choice of mobile phase components and optimal ion source, mainly electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), is a crucial part in liquid chromatography/mass spectrometry (LC/MS) method development to achieve higher sensitivity and lower detection limits. In this study we demonstrate how to rigorously solve these questions by using ionization efficiency scales. METHODS: Four ionization efficiency scales are used: recorded with both APCI and ESI sources and using both methanol- and acetonitrile-containing mobile phases. Each scale contains altogether more than 50 compounds. In addition, measurements with a chromatographic column were also performed. RESULTS: We observed a correlation between calibration graph slopes under LC conditions and logIE values in ESI (but not APCI) thereby validating the use of logIE values for choosing the ion source. Most of the studied compounds preferred ESI as an ion source and methanol as mobile organic phase. APCI remains the ion source of choice for polycyclic aromatic hydrocarbons. For APCI, both acetonitrile and methanol provide similar ionization efficiencies with few exceptions. CONCLUSIONS: Overall the results of this work give a concise guideline for practitioners in choosing an ion source for LC/MS analysis on the basis of the chemical nature of the analytes.

Think Negative: Finding the Best Electrospray Ionization/MS Mode for Your Analyte.

For the first time, the electrospray ionization efficiency (IE) scales in positive and negative mode are united into a single system enabling direct comparison of IE values across ionization modes. This is made possible by the use of a reference compound that ionizes to a similar extent in both positive and negative modes. Thus, choosing the optimal (i.e., most sensitive) ionization conditions for a given set of analytes is enabled. Ionization efficiencies of 33 compounds ionizing in both modes demonstrate that, contrary to general practice, negative mode allows better sensitivity for 46% of such compounds whereas the positive mode is preferred for only 18%, and for 36%, the results for both modes are comparable.

Establishing Atmospheric Pressure Chemical Ionization Efficiency Scale.

Recent evidence has shown that the atmospheric pressure chemical ionization (APCI) mechanism can be more complex than generally assumed. In order to better understand the processes in the APCI source, for the first time, an ionization efficiency scale for an APCI source has been created. The scale spans over 5 logIE (were IE is ionization efficiency) units and includes 40 compounds with a wide range of chemical and physical properties. The results of the experiments show that for most of the compounds the ionization efficiency order in the APCI source is surprisingly similar to that in the ESI source. Most of the compounds that are best ionized in the APCI source are not small volatile molecules. Large tetraalkylammonium cations are a prominent example. At the same time, low-polarity hydrocarbons pyrene and anthracene are ionized in the APCI source but not in the ESI source. These results strongly imply that in APCI several ionization mechanisms operate in parallel and a mechanism not relying on evaporation of neutral molecules from droplets has significantly higher influence than commonly assumed.

Unified pH values of liquid chromatography mobile phases.

This work introduces a conceptually new approach of measuring pH of mixed-solvent liquid chromatography (LC) mobile phases. Mobile phase pH is very important in LC, but its correct measurement is not straightforward, and all commonly used approaches have deficiencies. The new approach is based on the recently introduced unified pH (pH(abs)) scale, which enables direct comparison of acidities of solutions made in different solvents based on chemical potential of the proton in the solutions. This work represents the first experimental realization of the pH(abs) concept using differential potentiometric measurement for comparison of the chemical potentials of the proton in different solutions (connected by a salt bridge), together with earlier published reference points for obtaining the pH(abs) values (referenced to the gas phase) or pH(abs)(H2O) values (referenced to the aqueous solution). The liquid junction potentials were estimated in the framework of Izutsu's three-component method. pH(abs) values for a number of common LC and LC-MS mobile phases have been determined. The pH(abs) scale enables for the first time direct comparison of acidities of any LC mobile phases, with different organic additives, different buffer components, etc. A possible experimental protocol of putting this new approach into chromatographic practice has been envisaged and its applicability tested. It has been demonstrated that the ionization behavior of bases (cationic acids) in the mobile phases can be better predicted by using the pH(abs)(H2O) values and aqueous pKa values than by using the alternative means of expressing mobile phase acidity. Description of the ionization behavior of acids on the basis of pH(abs)(H2O) values is possible if the change of their pKa values with solvent composition change is taken into account.

Negative electrospray ionization via deprotonation: predicting the ionization efficiency.

Electrospray ionization (ESI) in the negative ion mode has received less attention in fundamental studies than the positive ion electrospray ionization. In this paper, we study the efficiency of negative ion formation in the ESI source via deprotonation of substituted phenols and benzoic acids and explore correlations of the obtained ionization efficiency values (logIE) with different molecular properties. It is observed that stronger acids (i.e., fully deprotonated in the droplets) yielding anions with highly delocalized charge [quantified by the weighted average positive sigma (WAPS) parameter rooted in the COSMO theory] have higher ionization efficiency and give higher signals in the negative-ion ESI/MS. A linear model was obtained, which equally well describes the logIE of both phenols and benzoic acids (R(2) = 0.83, S = 0.40 log units) and contains only an ionization degree in solution (α) and WAPS as molecular parameters. Both parameters can easily be calculated with the COSMO-RS method. The model was successfully validated using a test set of acids belonging neither to phenols nor to benzoic acids, thereby demonstrating its broad applicability and the universality of the above-described relationships between IE and molecular properties.

Chemical Composition of Commercial Cannabis.

Cannabis is widely used for medicinal and recreational purposes. As a result, there is increased interest in its chemical components and their physiological effects. However, current information on cannabis chemistry is often outdated or scattered across many books and journals. To address this issue, we used modern metabolomics techniques and modern bioinformatics techniques to compile a comprehensive list of >6000 chemical constituents in commercial cannabis. The metabolomics methods included a combination of high- and low-resolution liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and inductively coupled plasma-MS. The bioinformatics methods included computer-aided text mining and computational genome-scale metabolic inference. This information, along with detailed compound descriptions, physicochemical data, known physiological effects, protein targets, and referential compound spectra, has been made available through a publicly accessible database called the Cannabis Compound Database (https://cannabisdatabase.ca). Such a centralized, open-access resource should prove to be quite useful for the cannabis community.

Accurate prediction of isothermal gas chromatographic Kováts retention indices.

We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chromatographic Kováts Retention Indices (RI) using SMILES strings as chemical structure input. RIpred performs RI prediction for three different stationary phases (semi-standard non-polar (SSNP), standard non-polar (SNP), and standard polar (SP)) for both derivatized (trimethylsilyl (TMS) and tert­butyldimethylsilyl (TBDMS) derivatized) and underivatized (base compound) forms of GC-amenable structures. RIpred was developed to address the need for freely available, fast, highly accurate RI predictions for a wide range of derivatized and underivatized chemicals for all common GC stationary phases. RIpred was trained using a Graph Neural Network (GNN) that used compound structures, their extracted features (mostly atom-level features) and the GC-RI data from the National Institute of Standards and Technology databases (NIST 17 and NIST 20). We curated this NIST 17 and NIST 20 GC-RI data, which is available for all three stationary phases, to create appropriate inputs (molecular graphs in this case) needed to enhance our model performance. The performance of different RIpred predictive models was evaluated using 10-fold cross validation (CV). The best performing RIpred models were identified and when tested on hold-out test sets from all stationary phases, achieved a Mean Absolute Error (MAE) of <73 RI units (SSNP: 16.5-29.5, SNP: 38.5-45.9, SP: 46.52-72.53). The Mean Absolute Percentage Error (MAPE) of these models were typically within 3% (SSNP: 0.78-1.62%, SNP: 1.87-2.88%, SP: 2.34-4.05%). When compared to the best performing model by Qu et al., 2021, RIpred performed similarly (MAE of 16.57 RI units [RIpred] vs. 16.84 RI units [Qu et al., 2021 predictor] for derivatized compounds). RIpred also includes ∼5 million predicted RI values for all GC-amenable compounds (∼57,000) in the Human Metabolome Database HMDB 5.0 (Wishart et al., 2022).

Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses.

Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 µg/L), valsartan (14.4 µg/L), and ibuprofen (12.7 µg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.

30 Years of research on ESI/MS response: Trends, contradictions and applications.

The variation of ionization efficiency for different compounds has puzzled researchers since the invention of the electrospray mass spectrometry (ESI/MS). Ionization depends on the properties of the compound, eluent, matrix, and instrument. Despite significant research, some aspects have remained unclear. For example, research groups have reached contradicting conclusions regarding the ionization processes. One of the best-known is the significance of the logP value for predicting the ionization efficiency. In this tutorial review, we analyse the methodology used for ionization efficiency measurements as well as the most important trends observed in the data. Additionally, we give suggestions regarding the measurement methodology and modelling strategies to yield meaningful and consistent ionization efficiency data. Finally, we have collected a wide range of ionization efficiency values from the literature and evaluated the consistency of these data. We also make this collection available for everyone for downloading as well as for uploading additional and new ionization efficiency data. We hope this GitHub based ionization efficiency repository will allow a joined community effort to collect and unify the current knowledge about the ionization processes.

Standard substances free quantification makes LC/ESI/MS non-targeted screening of pesticides in cereals comparable between labs.

LC/ESI/MS is the technique of choice for qualitative and quantitative food monitoring; however, analysis of a large number of compounds is challenged by the availability of standard substances. The impediment of detection of food contaminants has been overcome by the suspect and non-targeted screening. Still, the results from one laboratory cannot be compared with the results of another laboratory as quantitative results are required for this purpose. Here we show that the results of the suspect and non-targeted screening for pesticides can be made quantitative with the aid of in silico predicted electrospray ionization efficiencies and this allows direct comparison of the results obtained in two different laboratories. For this purpose, six cereal matrices were spiked with 134 pesticides and analysed in two independent labs; a high correlation for the results with the R2 of 0.85.

Quantification for non-targeted LC/MS screening without standard substances.

Non-targeted and suspect analyses with liquid chromatography/electrospray/high-resolution mass spectrometry (LC/ESI/HRMS) are gaining importance as they enable identification of hundreds or even thousands of compounds in a single sample. Here, we present an approach to address the challenge to quantify compounds identified from LC/HRMS data without authentic standards. The approach uses random forest regression to predict the response of the compounds in ESI/HRMS with a mean error of 2.2 and 2.0 times for ESI positive and negative mode, respectively. We observe that the predicted responses can be transferred between different instruments via a regression approach. Furthermore, we applied the predicted responses to estimate the concentration of the compounds without the standard substances. The approach was validated by quantifying pesticides and mycotoxins in six different cereal samples. For applicability, the accuracy of the concentration prediction needs to be compatible with the effect (e.g. toxicology) predictions. We achieved the average quantification error of 5.4 times, which is well compatible with the accuracy of the toxicology predictions.

A systematic approach toward comparing electrospray ionization efficiencies of derivatized and non-derivatized amino acids and biogenic amines.

Ionization efficiency (IE) in mass spectrometry (MS) has been studied for many different compounds, and different IE scales have been constructed in order to quantitatively characterize IE. In the case of MS, derivatization has been used to increase the sensitivity of the method and to lower the limits of detection. However, the influence of derivatization on IE across different compounds and different derivatization reagents has not been thoroughly researched, so that practitioners do not have information on the IE-enhancing abilities of different derivatization reagents. Moreover, measuring IE via direct infusion of compounds cannot be considered fully adequate. Since derivatized compounds are in complex mixtures, a chromatographic method is needed to separate these compounds to minimize potential matrix effects. In this work, an IE measurement system with a chromatographic column was developed for mainly amino acids and some biogenic amines. IE measurements with liquid chromatography electrospray ionization mass spectrometry (LC/ESI/MS) were carried out, and IE scales were constructed with a calibration curve for compounds with and without derivatization reagent diethyl ethoxymethylenemalonate. Additionally, eluent composition effects on ionization were investigated. Results showed that derivatization increases IE for most of the compounds (by average 0.9 and up to 2-2.5 logIE units) and derivatized compounds have more similar logIE values than without derivatization. Mobile phase composition effects on ionization efficiencies were negligible. It was also noted that the use of chromatographic separation instead of flow injection mode slightly increases IE. In this work, for the first time, IE enhancement of derivatization reagents was quantified under real LC/ESI/MS conditions and obtained logIE values of derivatized compounds were linked with the existing scale.