Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures

Evidence for the Role of the Mitochondrial ABC Transporter MDL1 in the Uptake of Clozapine and Related Molecules into the Yeast Saccharomyces cerevisiae

Clozapine is an antipsychotic drug whose accumulation in white cells can sometimes prove toxic; understanding the transporters and alleles responsible is thus highly desirable. We used a strategy in which a yeast (Saccharomyces cerevisiae) CRISPR-Cas9 knock-out library was exposed to cytotoxic concentrations of clozapine to determine those transporters whose absence made it more resistant; we also recognised the structural similarity of the fluorescent dye safranin O (also known as safranin T) to clozapine, allowing it to be used as a surrogate marker. Strains lacking the mitochondrial ABC transporter MDL1 (encoded by YLR188W) showed substantial resistance to clozapine. MDL1 overexpression also conferred extra sensitivity to clozapine and admitted a massive increase in the cellular and mitochondrial uptake of safranin O, as determined using flow cytometry and microscopically. Yeast lacking mitochondria showed no such unusual accumulation. Mitochondrial MDL1 is thus the main means of accumulation of clozapine in S. cerevisiae. The closest human homologue of S. cerevisiae MDL1 is ABCB10.

A preliminary understanding of the relationship between synthetic phenolic antioxidants and early pregnancy loss: Uncovering the potential molecular mechanisms

Mounting evidence suggests that environmental pollutants may affect reproductive health, potentially leading to adverse outcomes like pregnancy loss. However, it remains unclear whether exposure to synthetic phenolic antioxidants (SPAs) correlates with early pregnancy loss (EPL). This study explores SPA exposure's link to EPL and its potential molecular mechanisms. From 2021 to 2022, 265 early pregnant women (136 serum and 129 villus samples) with and without EPL were enrolled. We quantified 17 SPAs in serum and chorionic villus, with AO1010, AO3114, BHT, AO2246, and BHT-Q frequently being detected, suggesting their ability to cross the placental barrier. AO1135 showed a positive relationship with EPL in sera, indicating a significant monotonic dose-response relationship (p-trend <0.001). BHT-Q exhibited a similar relationship with EPL in villi. Inhibitory effects of BHT-Q on estradiol (E2) were observed. Molecular docking revealed SPA-protein interactions involved in E2 synthesis. SPA-induced EPL might occur with specific serum levels of AO1135 and certain villus levels of AO1010, BHT-Q, and AO2246. BHT-Q emerges as a potential biomarker for assessing EPL risk. This study provides insights into understanding of the exposure to SPAs and potential adverse outcomes in pregnant women.

Analysis of metabolites in human gut: illuminating the design of gut-targeted drugs

Gut-targeted drugs provide a new drug modality besides that of oral, systemic molecules, that could tap into the growing knowledge of gut metabolites of bacterial or host origin and their involvement in biological processes and health through their interaction with gut targets (bacterial or host, too). Understanding the properties of gut metabolites can provide guidance for the design of gut-targeted drugs. In the present work we analyze a large set of gut metabolites, both shared with serum or present only in gut, and compare them with oral systemic drugs. We find patterns specific for these two subsets of metabolites that could be used to design drugs targeting the gut. In addition, we develop and openly share a Super Learner model to predict gut permanence, in order to aid in the design of molecules with appropriate profiles to remain in the gut, resulting in molecules with putatively reduced secondary effects and better pharmacokinetics.

MultiDS-MDA: Integrating multiple data sources into heterogeneous network for predicting novel metabolite-drug associations

Metabolic processes in the human body play an important role in maintaining normal life activities, and the abnormal concentration of metabolites is closely related to the occurrence and development of diseases. The use of drugs is considered to have a major impact on metabolism, and drug metabolites can contribute to efficacy, drug toxicity and drug-drug interaction. However, our understanding of metabolite-drug associations is far from complete, and individual data source tends to be incomplete and noisy. Therefore, the integration of various types of data sources for inferring reliable metabolite-drug associations is urgently needed. In this study, we proposed a computational framework, MultiDS-MDA, for identifying metabolite-drug associations by integrating multiple data sources, including chemical structure information of metabolites and drugs, the relationships of metabolite-gene, metabolite-disease, drug-gene and drug-disease, the data of gene ontology (GO) and disease ontology (DO) and known metabolite-drug connections. The performance of MultiDS-MDA was evaluated by 5-fold cross-validation, which achieved an area under the ROC curve (AUROC) of 0.911 and an area under the precision-recall curve (AUPRC) of 0.907. Additionally, MultiDS-MDA showed outstanding performance compared with similar approaches. Case studies for three metabolites (cholesterol, thromboxane B2 and coenzyme Q10) and three drugs (simvastatin, pravastatin and morphine) also demonstrated the reliability and efficiency of MultiDS-MDA, and it is anticipated that MultiDS-MDA will serve as a powerful tool for future exploration of metabolite-drug interactions and contribute to drug development and drug combination.

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs

Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.

Cell Surface Transporters and Novel Drug Developments

Despite the numerous scientific and technological advances made within the last decade the attrition rates for new drug discovery remain as high as 95% for anticancer drugs. Recent drug development has been in part guided by Lipinski's Rule of 5 (Ro5) even though many approved drugs do not comply to these rules. With Covid-19 vaccine development strategy dramatically accelerating drug development perhaps it is timely to question the generic drug development process itself to find a more efficient, cost effective, and successful approach. It is widely believed that drugs permeate cells via two methods: phospholipid bilayer diffusion and carrier mediated transporters. However, emerging evidence suggests that carrier mediated transport may be the primary mechanism of drug uptake and not diffusion as long believed. Computational biology increasingly assists drug design to achieve desirable absorption, distribution, metabolism, elimination and toxicity (ADMET) properties. Perfecting drug entry into target cells as a prerequisite to intracellular drug action is a logical and compelling route and is expected to reduce drug attrition rates, particularly gaining favour amongst chronic lifelong therapeutics. Novel drug development is rapidly expanding from the utilisation of beyond the rule of five (bRo5) to pulsatile drug delivery systems and fragment based drug design. Utilising transporters as drug targets and advocating bRo5 molecules may be the solution to increasing drug specificity, reducing dosage and toxicity and thus revolutionising drug development. This review explores the development of cell surface transporter exploitation in drug development and the relationship with improved therapeutic index.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing "similar" molecules to cluster together in an effective and interpretable way.

The biology of ergothioneine, an antioxidant nutraceutical

Ergothioneine (ERG) is an unusual thio-histidine betaine amino acid that has potent antioxidant activities. It is synthesised by a variety of microbes, especially fungi (including in mushroom fruiting bodies) and actinobacteria, but is not synthesised by plants and animals who acquire it via the soil and their diet, respectively. Animals have evolved a highly selective transporter for it, known as solute carrier family 22, member 4 (SLC22A4) in humans, signifying its importance, and ERG may even have the status of a vitamin. ERG accumulates differentially in various tissues, according to their expression of SLC22A4, favouring those such as erythrocytes that may be subject to oxidative stress. Mushroom or ERG consumption seems to provide significant prevention against oxidative stress in a large variety of systems. ERG seems to have strong cytoprotective status, and its concentration is lowered in a number of chronic inflammatory diseases. It has been passed as safe by regulatory agencies, and may have value as a nutraceutical and antioxidant more generally.

Structural Similarities between Some Common Fluorophores Used in Biology, Marketed Drugs, Endogenous Metabolites, and Natural Products

It is known that at least some fluorophores can act as 'surrogate' substrates for solute carriers (SLCs) involved in pharmaceutical drug uptake, and this promiscuity is taken to reflect at least a certain structural similarity. As part of a comprehensive study seeking the 'natural' substrates of 'orphan' transporters that also serve to take up pharmaceutical drugs into cells, we have noted that many drugs bear structural similarities to natural products. A cursory inspection of common fluorophores indicates that they too are surprisingly 'drug-like', and they also enter at least some cells. Some are also known to be substrates of efflux transporters. Consequently, we sought to assess the structural similarity of common fluorophores to marketed drugs, endogenous mammalian metabolites, and natural products. We used a set of some 150 fluorophores along with standard fingerprinting methods and the Tanimoto similarity metric. Results: The great majority of fluorophores tested exhibited significant similarity (Tanimoto similarity > 0.75) to at least one drug, as judged via descriptor properties (especially their aromaticity, for identifiable reasons that we explain), by molecular fingerprints, by visual inspection, and via the "quantitative estimate of drug likeness" technique. It is concluded that this set of fluorophores does overlap with a significant part of both the drug space and natural products space. Consequently, fluorophores do indeed offer a much wider opportunity than had possibly been realised to be used as surrogate uptake molecules in the competitive or trans-stimulation assay of membrane transporter activities.

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

Molecular similarity is an elusive but core "unsupervised" cheminformatics concept, yet different "fingerprint" encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are "better" than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a "bowtie"-shaped artificial neural network. In the middle is a "bottleneck layer" or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.

Unique metabolite preferences of the drug transporters OAT1 and OAT3 analyzed by machine learning

The multispecific organic anion transporters, OAT1 (SLC22A6) and OAT3 (SLC22A8), the main kidney elimination pathways for many common drugs, are often considered to have largely-redundant roles. However, whereas examination of metabolomics data from Oat-knockout mice (Oat1 and Oat3KO) revealed considerable overlap, over a hundred metabolites were increased in the plasma of one or the other of these knockout mice. Many of these relatively unique metabolites are components of distinct biochemical and signaling pathways, including those involving amino acids, lipids, bile acids, and uremic toxins. Cheminformatics, together with a "logical" statistical and machine learning-based approach, identified a number of molecular features distinguishing these unique endogenous substrates. Compared with OAT1, OAT3 tends to interact with more complex substrates possessing more rings and chiral centers. An independent "brute force" approach, analyzing all possible combinations of molecular features, supported the logical approach. Together, the results suggest the potential molecular basis by which OAT1 and OAT3 modulate distinct metabolic and signaling pathways in vivo As suggested by the Remote Sensing and Signaling Theory, the analysis provides a potential mechanism by which "multispecific" kidney proximal tubule transporters exert distinct physiological effects. Furthermore, a strong metabolite-based machine-learning classifier was able to successfully predict unique OAT1 versus OAT3 drugs; this suggests the feasibility of drug design based on knockout metabolomics of drug transporters. The approach can be applied to other SLC and ATP-binding cassette drug transporters to define their nonredundant physiological roles and for analyzing the potential impact of drug-metabolite interactions.

Prioritizing candidate diseases-related metabolites based on literature and functional similarity

Background

As the terminal products of cellular regulatory process, functional related metabolites have a close relationship with complex diseases, and are often associated with the same or similar diseases. Therefore, identification of disease related metabolites play a critical role in understanding comprehensively pathogenesis of disease, aiming at improving the clinical medicine. Considering that a large number of metabolic markers of diseases need to be explored, we propose a computational model to identify potential disease-related metabolites based on functional relationships and scores of referred literatures between metabolites. First, obtaining associations between metabolites and diseases from the Human Metabolome database, we calculate the similarities of metabolites based on modified recommendation strategy of collaborative filtering utilizing the similarities between diseases. Next, a disease-associated metabolite network (DMN) is built with similarities between metabolites as weight. To improve the ability of identifying disease-related metabolites, we introduce scores of text mining from the existing database of chemicals and proteins into DMN and build a new disease-associated metabolite network (FLDMN) by fusing functional associations and scores of literatures. Finally, we utilize random walking with restart (RWR) in this network to predict candidate metabolites related to diseases.

Results

We construct the disease-associated metabolite network and its improved network (FLDMN) with 245 diseases, 587 metabolites and 28,715 disease-metabolite associations. Subsequently, we extract training sets and testing sets from two different versions of the Human Metabolome database and assess the performance of DMN and FLDMN on 19 diseases, respectively. As a result, the average AUC (area under the receiver operating characteristic curve) of DMN is 64.35%. As a further improved network, FLDMN is proven to be successful in predicting potential metabolic signatures for 19 diseases with an average AUC value of 76.03%.

Conclusion

In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. This result suggests that integrating literature and functional associations can be an effective way to construct disease associated metabolite network for prioritizing candidate diseases-related metabolites.

Generation of a Small Library of Natural Products Designed to Cover Chemical Space Inexpensively

Natural products space includes at least 200,000 compounds and the structures of most of these compounds are available in digital format. Previous analyses showed (i) that although they were capable of taking up synthetic pharmaceutical drugs, such exogenous molecules were likely the chief ‘natural’ substrates in the evolution of the transporters used to gain cellular entry by pharmaceutical drugs, and (ii) that a relatively simple but rapid clustering algorithm could produce clusters from which individual elements might serve to form a representative library covering natural products space. This exploited the fact that the larger clusters were likely to be formed early in evolution (and hence to have been accompanied by suitable transporters), so that very small clusters, including singletons, could be ignored. In the latter work, we assumed that the molecule chosen might be that in the middle of the cluster. However, this ignored two other criteria, namely the commercial availability and the financial cost of the individual elements of these clusters. We here develop a small representative library in which we to seek to satisfy the somewhat competing criteria of coverage (‘representativeness’), availability and cost. It is intended that the library chosen might serve as a testbed of molecules that may or may not be substrates for known or orphan drug transporters. A supplementary spreadsheet provides details, and their availability via a particular supplier.

The human endogenous metabolome as a pharmacology baseline for drug discovery

We have limited understanding of the variation in in vitro affinities of drugs for their targets. An analysis of a highly curated set of 815 interactions between 566 drugs and 129 primary targets reveals that 71% of drug-target affinities have values above that of the corresponding endogenous ligand, 96% of them fitting within a range of two orders of magnitude. Our findings suggest that the evolutionary optimised affinity of endogenous ligands for their native proteins can serve as a baseline for the primary pharmacology of drugs. We show that the degree of off-target selectivity and safety risks of drugs derived from their secondary pharmacology depend very much on that baseline. Thus, we propose a new approach for estimating safety margins.

GeneGini: Assessment via the Gini Coefficient of Reference "Housekeeping" Genes and Diverse Human Transporter Expression Profiles

The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping" genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

INTRODUCTION

Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis.

OBJECTIVES

The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles.

METHODS

Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates.

RESULTS

Baroni-Urbani-Buser (BUB) and Hawkins-Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis.

CONCLUSION

Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.

Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries

Armed with the digital availability of two natural products libraries, amounting to some 195 885 molecular entities, we ask the question of how we can best sample from them to maximize their "representativeness" in smaller and more usable libraries of 96, 384, 1152, and 1920 molecules. The term "representativeness" is intended to include diversity, but for numerical reasons (and the likelihood of being able to perform a QSAR) it is necessary to focus on areas of chemical space that are more highly populated. Encoding chemical structures as fingerprints using the RDKit "patterned" algorithm, we first assess the granularity of the natural products space using a simple clustering algorithm, showing that there are major regions of "denseness" but also a great many very sparsely populated areas. We then apply a "hybrid" hierarchical K-means clustering algorithm to the data to produce more statistically robust clusters from which representative and appropriate numbers of samples may be chosen. There is necessarily again a trade-off between cluster size and cluster number, but within these constraints, libraries containing 384 or 1152 molecules can be found that come from clusters that represent some 18 and 30% of the whole chemical space, with cluster sizes of, respectively, 50 and 27 or above, just about sufficient to perform a QSAR. By using the online availability of molecules via the Molport system (www.molport.com), we are also able to construct (and, for the first time, provide the contents of) a small virtual library of available molecules that provided effective coverage of the chemical space described. Consistent with this, the average molecular similarities of the contents of the libraries developed is considerably smaller than is that of the original libraries. The suggested libraries may have use in molecular or phenotypic screening, including for determining possible transporter substrates.