The transporter-mediated cellular uptake of pharmaceutical drugs is based on their metabolite-likeness and not on their bulk biophysical properties: Towards a systems pharmacology

A protet-based model that can account for energy coupling in oxidative and photosynthetic phosphorylation

Two-stage (e.g. light-dark) phosphorylation experiments showed that there is a stored 'high-energy' intermediate linking electron transport and phosphorylation. Large, artificial electrochemical proton gradients (protonmotive forces or pmfs) can also drive phosphorylation, a fact seen as strongly supportive of the chemiosmotic coupling hypothesis that a pmf is the 'high-energy' intermediate. However, in such experiments there is an experimental threshold (pmf >170 mV, equivalent to ΔpH ∼2.8) below which no phosphorylation is in fact observed, and 220 mV are required to recreate in vivo rates. This leads to the correct question, which is then whether those values of the pmf generated by electron transport are large enough. Even the lower ones as required for any phosphorylation (leave alone those required to explain in vivo rates) are below the threshold [1, 2], whether measured directly with microelectrodes or via the use of membrane-permeant ions and/or acids/bases (which are always transporter substrates [3], so all such measurements are in fact artefactual). The single case that seemed large enough (220 mV) is now admitted to be a diffusion potential artefact [4]. Many other observables (inadequate bulk H+ in 'O2-pulse'-type experiments, alkaliphilic bacteria, dual-inhibitor titrations, uncoupler-binding proteins, etc.) are consistent with the view that values of the pmf, and especially of Δψ, are actually very low. A protet-based charge separation model [2], a protonic version analogous to how energy may be stored in devices called electrets, provides a high-energy intermediate that can explain the entire literature, including the very striking demonstration [5] that close proximity is required between electron transport and ATP synthase complexes for energy coupling between them to allow phosphorylation to occur. A chief purpose of this article is thus to summarise the extensive and self-consistent literature, much of which is of some antiquity and rarely considered by modern researchers, despite its clear message of the inadequacy of chemiosmotic coupling to explain these phenomena.

Current State of SLC and ABC Transporters in the Skin and Their Relation to Sweat Metabolites and Skin Diseases

With a relatively large surface area (2 m²) and 15% of total body mass, the skin forms the largest organ of the human body. The main functions of the skin include regulation of body temperature by insulation or sweating, regulation of the nervous system, regulation of water content, and protection against external injury. To perform these critical functions, the skin encodes genes for transporters responsible for the cellular trafficking of essential nutrients and metabolites to maintain cellular hemostasis. However, the knowledge on the expression, regulation, and function of these transporters is very limited and needs more work to elucidate how these transporters play a role both in disease progression and in healing. Furthermore, SLC and ABC transporters are understudied, and even less studied in skin. There are sparse reports on relation between transporters in skin and sweat metabolites. This mini review focuses on the current state of SLC and ABC transporters in the skin and their relation to sweat metabolites and skin diseases.

A protet-based, protonic charge transfer model of energy coupling in oxidative and photosynthetic phosphorylation

Textbooks of biochemistry will explain that the otherwise endergonic reactions of ATP synthesis can be driven by the exergonic reactions of respiratory electron transport, and that these two half-reactions are catalyzed by protein complexes embedded in the same, closed membrane. These views are correct. The textbooks also state that, according to the chemiosmotic coupling hypothesis, a (or the) kinetically and thermodynamically competent intermediate linking the two half-reactions is the electrochemical difference of protons that is in equilibrium with that between the two bulk phases that the coupling membrane serves to separate. This gradient consists of a membrane potential term Δψ and a pH gradient term ΔpH, and is known colloquially as the protonmotive force or pmf. Artificial imposition of a pmf can drive phosphorylation, but only if the pmf exceeds some 150-170mV; to achieve in vivo rates the imposed pmf must reach 200mV. The key question then is 'does the pmf generated by electron transport exceed 200mV, or even 170mV?' The possibly surprising answer, from a great many kinds of experiment and sources of evidence, including direct measurements with microelectrodes, indicates it that it does not. Observable pH changes driven by electron transport are real, and they control various processes; however, compensating ion movements restrict the Δψ component to low values. A protet-based model, that I outline here, can account for all the necessary observations, including all of those inconsistent with chemiosmotic coupling, and provides for a variety of testable hypotheses by which it might be refined.

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.

Membrane interactions in drug delivery: Model cell membranes and orthogonal techniques

To generate the desired effect in the human body, the active pharmaceutical ingredient usually needs to interact with a receptor located on the cell membrane or inside the cell. Thus, understanding membrane interactions is of great importance when it comes to the development and testing of new drug molecules or new drug delivery systems. Nowadays, there is a tremendous selection of both model cell membranes and of techniques that can be used to characterize interactions between selected model cell membranes and a drug molecule, an excipient, or a drug delivery system. Having such a wide selection of model cell membranes and techniques available makes it sometimes challenging to select the optimal combination for a specific study. Furthermore, it is difficult to compare results obtained using different model cell membranes and techniques, and not all in vitro studies translate as well to an estimation of the in vivo biological activity or understanding of mode of action. This review provides an overview of the available lipid bilayer-based model cell membranes and of the most widely employed techniques for studying membrane interactions. Finally, the need for employing complimentary characterization techniques in order to acquire more reliable and in-depth information is highlighted.

The Biology of Lactoferrin, an Iron-Binding Protein That Can Help Defend Against Viruses and Bacteria

Lactoferrin is a nutrient classically found in mammalian milk. It binds iron and is transferred via a variety of receptors into and between cells, serum, bile, and cerebrospinal fluid. It has important immunological properties, and is both antibacterial and antiviral. In particular, there is evidence that it can bind to at least some of the receptors used by coronaviruses and thereby block their entry. Of importance are Heparan Sulfate Proteoglycans (HSPGs) and the host receptor angiotensin-converting enzyme 2 (ACE2), as based on other activities lactoferrin might prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from attaching to the host cells. Lactoferrin (and more specifically enteric-coated LF because of increased bioavailability) may consequently be of preventive and therapeutic value during the present COVID-19 pandemic.

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.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Neonicotinoid insecticides are potential substrates of the multixenobiotic resistance (MXR) mechanism in the non-target invertebrate, Dreissena sp

Mussels are among the most frequently used invertebrate animals in aquatic toxicology to detect toxic exposure in the environment. The presence and activity of a cellular defence system, the multixenobiotic resistance (MXR) mechanism, was also established in these organisms. In isolated gill tissues of dreissenid mussels (D. bugensis) the MXR activity was assayed after treatment by commercially available insecticides (formulated products) which contain neonicotinoids as their active ingredients: Actara (thiamethoxam), Apacs (clothianidin), Calypso (thiacloprid) and Kohinor (imidacloprid), respectively. While applying the accumulation assay method, 0.5 μM rhodamine B was used as model substrate and 20 μM verapamil as model inhibitor of the MXR mechanism. In acute (in vitro) experiments when isolated gills were co-incubated in graded concentrations of insecticides and rhodamine B simultaneously, Calypso and Kohinor treatment resulted increasing rhodamine accumulation. Chemical analysis of gills in vitro incubated in insecticides demonstrated higher tissue concentrations of thiamethoxam, clothianidin and thiacloprid in the presence of verapamil suggesting that the active ingredients of Actara, Apacs and Calypso are potential substrates of the MXR mediated cellular efflux. In contrast, verapamil did significantly alter the accumulated imidacloprid concentrations in gills, suggesting that the active component of Kohinor is not transported by the MXR mechanism. Chronic (in vivo) exposures of the intact animals in lower, 1, 10 mg/L concentration of neonicotinoid products, resulted in a decreased level of both rhodamine accumulation and verapamil inhibition by the 12th-14th days of treatment. These results suggest an enhancement of MXR activity (chemostimulation), building up gradually in the animals exposed to Actara, Apacs and Kohinor, respectively. Neonicotinoid-type insecticides are generally considered as selective neurotoxins for insects, targeting the nicotinic type acetylcholine receptors (nAChRs) in their central nervous system. Our present results provide the first evidences that neonicotinoid insecticides are also able to alter the transmembrane transport mechanisms related to the MXR system.

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.

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.

Enhancing Drug Efficacy and Therapeutic Index through Cheminformatics-Based Selection of Small Molecule Binary Weapons That Improve Transporter-Mediated Targeting: A Cytotoxicity System Based on Gemcitabine

The transport of drug molecules is mainly determined by the distribution of influx and efflux transporters for which they are substrates. To enable tissue targeting, we sought to develop the idea that we might affect the transporter-mediated disposition of small-molecule drugs via the addition of a second small molecule that of itself had no inhibitory pharmacological effect but that influenced the expression of transporters for the primary drug. We refer to this as a “binary weapon” strategy. The experimental system tested the ability of a molecule that on its own had no cytotoxic effect to increase the toxicity of the nucleoside analog gemcitabine to Panc1 pancreatic cancer cells. An initial phenotypic screen of a 500-member polar drug (fragment) library yielded three “hits.” The structures of 20 of the other 2,000 members of this library suite had a Tanimoto similarity greater than 0.7 to those of the initial hits, and each was itself a hit (the cheminformatics thus providing for a massive enrichment). We chose the top six representatives for further study. They fell into three clusters whose members bore reasonable structural similarities to each other (two were in fact isomers), lending strength to the self-consistency of both our conceptual and experimental strategies. Existing literature had suggested that indole-3-carbinol might play a similar role to that of our fragments, but in our hands it was without effect; nor was it structurally similar to any of our hits. As there was no evidence that the fragments could affect toxicity directly, we looked for effects on transporter transcript levels. In our hands, only the ENT1-3 uptake and ABCC2,3,4,5, and 10 efflux transporters displayed measurable transcripts in Panc1 cultures, along with a ribonucleoside reductase RRM1 known to affect gemcitabine toxicity. Very strikingly, the addition of gemcitabine alone increased the expression of the transcript for ABCC2 (MRP2) by more than 12-fold, and that of RRM1 by more than fourfold, and each of the fragment “hits” served to reverse this. However, an inhibitor of ABCC2 was without significant effect, implying that RRM1 was possibly the more significant player. These effects were somewhat selective for Panc cells. It seems, therefore, that while the effects we measured were here mediated more by efflux than influx transporters, and potentially by other means, the binary weapon idea is hereby fully confirmed: it is indeed possible to find molecules that manipulate the expression of transporters that are involved in the bioactivity of a pharmaceutical drug. This opens up an entirely new area, that of chemical genomics-based drug targeting.

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

In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.

The metabolome 18 years on: a concept comes of age

BACKGROUND

The term 'metabolome' was introduced to the scientific literature in September 1998.

AIM AND KEY SCIENTIFIC CONCEPTS OF THE REVIEW

To mark its 18-year-old 'coming of age', two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites

BACKGROUND

Previous studies compared the molecular similarity of marketed drugs and endogenous human metabolites (endogenites), using a series of fingerprint-type encodings, variously ranked and clustered using the Tanimoto (Jaccard) similarity coefficient (TS). Because this gives equal weight to all parts of the encoding (thence to different substructures in the molecule) it may not be optimal, since in many cases not all parts of the molecule will bind to their macromolecular targets. Unsupervised methods cannot alone uncover this. We here explore the kinds of differences that may be observed when the TS is replaced-in a manner more equivalent to semi-supervised learning-by variants of the asymmetric Tversky (TV) similarity, that includes α and β parameters.

RESULTS

Dramatic differences are observed in (i) the drug-endogenite similarity heatmaps, (ii) the cumulative "greatest similarity" curves, and (iii) the fraction of drugs with a Tversky similarity to a metabolite exceeding a given value when the Tversky α and β parameters are varied from their Tanimoto values. The same is true when the sum of the α and β parameters is varied. A clear trend toward increased endogenite-likeness of marketed drugs is observed when α or β adopt values nearer the extremes of their range, and when their sum is smaller. The kinds of molecules exhibiting the greatest similarity to two interrogating drug molecules (chlorpromazine and clozapine) also vary in both nature and the values of their similarity as α and β are varied. The same is true for the converse, when drugs are interrogated with an endogenite. The fraction of drugs with a Tversky similarity to a molecule in a library exceeding a given value depends on the contents of that library, and α and β may be "tuned" accordingly, in a semi-supervised manner. At some values of α and β drug discovery library candidates or natural products can "look" much more like (i.e., have a numerical similarity much closer to) drugs than do even endogenites.

CONCLUSIONS

Overall, the Tversky similarity metrics provide a more useful range of examples of molecular similarity than does the simpler Tanimoto similarity, and help to draw attention to molecular similarities that would not be recognized if Tanimoto alone were used. Hence, the Tversky similarity metrics are likely to be of significant value in many general problems in cheminformatics.