Steve O' Hagan

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) exhibits poor clinical response to gemcitabine, largely due to intrinsic and acquired mechanisms of chemoresistance. Identifying agents capable of enhancing gemcitabine efficacy without increasing cytotoxicity remains an unmet therapeutic need. Here, we characterise a small drug sensitiser molecule, B12, and evaluate its potential to sensitise PDAC cells to gemcitabine. Methods: Gemcitabine’s dose–response was assessed by MTT assay to determine IC₅₀ values and dose-modifying factor (DMF). Phenotypic consequences of co-treatment were examined using colony formation and wound scratch assays. Mitochondrial membrane potential (JC-1) and apoptosis (Annexin V/PI) were measured using flow cytometry. Transcriptomic profiling was performed using mRNA-seq with differential expression analysis and pathway enrichment (KEGG/GSEA). NF-κB activity was assessed by nuclear and cytoplasmic fractionation of p65, and RT-qPCR validation of NF-κB associated target genes. Results: B12 alone displayed minimal cytotoxicity in the PANC-1 cell line and normal pancreatic ductal HPDE cells, yet shifted the gemcitabine dose–response curve in PANC-1 cells, reducing the IC₅₀ and yielding a dose-modifying factor of 1.39. Functionally, B12 enhanced gemcitabine-induced suppression of colony formation and reduced wound closure relative to gemcitabine alone. The co-treatment also increased both mitochondrial depolarisation and apoptotic cell populations, with increased cell proliferation inhibition over time. Transcriptomic profiling identified a set of B12-associated genes downregulated both in B12-treated and B12 + gemcitabine conditions, including factors linked to growth, survival, inflammation, metabolism, and drug inactivation. Gene set enrichment analysis revealed negative enrichment of NF-κB associated pathways during B12 co-treatment. Consistently, nuclear-cytoplasmic fractionation showed that B12 reduced gemcitabine-induced nuclear accumulation of p65, accompanied by decreased expression of NF-κB associated targets such as BCL2L1, CCL20, SLC2A1, and MAP3K14. Conclusions: In PDAC cell models, B12 enhances gemcitabine cytotoxic response while displaying minimal intrinsic toxicity under the conditions tested. The sensitising phenotype is accompanied by increased apoptotic susceptibility and is associated with reduced NF-κB signalling at the pathway, transcript, and p65 nuclear localisation levels. However, to establish causality, the lack of sensitisation in HPDE cells will require further validation.

Our previous work demonstrated that two commonly used fluorescent dyes that were accumulated by wild-type Escherichia coli MG1655 were differentially transported in single-gene knockout strains, and also that they might be used as surrogates in flow cytometric transporter assays. We summarize the desirable properties of such stains, and here survey 143 candidate dyes. We eventually triage them (on the basis of signal, accumulation levels and cost) to a palette of 39 commercially available and affordable fluorophores that are accumulated significantly by wild-type cells of the ‘Keio’ strain BW25113, as measured flow cytometrically. Cheminformatic analyses indicate both their similarities and their (much more considerable) structural differences. We describe the effects of pH and of the efflux pump inhibitor chlorpromazine on the accumulation of the dyes. Even the ‘wild-type’ MG1655 and BW25113 strains can differ significantly in their ability to take up such dyes. We illustrate the highly differential uptake of our dyes into strains with particular lesions in, or overexpressed levels of, three particular transporters or transporter components (yhjV, yihN and tolC). The relatively small collection of dyes described offers a rapid, inexpensive, convenient and informative approach to the assessment of microbial physiology and phenotyping of membrane transporter function.

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.

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

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.

Introduction: It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. Objectives: Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to measure them. Methods: We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the ‘exometabolome’ or metabolic footprint). Results: Our method measures some 4,000-5,000 metabolic features in both ES ⁺ and ES ⁻ modes. We show that the metabolic footprints of different cell lines differ greatly from each other. Conclusion: Our new, 15-minute untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum, and its relation to differences in transporter expression.

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

Background: 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.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 a significant part of both 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.

We analyse two comprehensive transcriptome datasets from human tissues and human-derived cell lines in terms of the expression profiles of the SLC and ABC families of membrane transporters. The Gini index (coefficient) characterises inequalities of distributions, and is used in a novel way to describe the distribution of the expression of each transporter among the different tissues and cell lines. In many cases, transporters exhibit extremely high Gini coefficients, even when their supposed substrates might be expected to be available to all tissues, indicating a much higher degree of specialisation than is usually assumed. This is consistent with divergent evolution from a more restricted set of ancestors. Similar trends hold true for the expression profiles of transporters in different cell lines, suggesting that cell lines exhibit largely similar transport behaviour to that of tissues. By contrast, the Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying that some kind of a selection process has taken place. In particular, with some exceptions such as olfactory receptors and genes involved in keratin production, transporter genes are significantly more heterogeneously expressed than are most non-transporter genes. The Gini index also allows us to determine those transcripts with the most stable expression; these often differ significantly from the ‘housekeeping’ genes commonly used for normalisation in transcriptomics and qPCR studies. The lowest four in tissues are FAM32A, ABCB7, MRPL21 and PCBP1, while the lowest three in cell lines are SF3B2, NXF1 and RBM45. PCBP1 is both reasonably highly expressed and has a low Gini coefficient in both tissues and cell lines, and is an excellent novel housekeeping gene. Overall, our analyses provide novel opportunities for the normalisation of genome-wide expression profiling data.

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 analogue 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 2000 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 the more significant player. It seems, therefore, that while the effects we measured