Bacterial metabolism rescues the inhibition of intestinal drug absorption by food and drug additives.

Food and drug products contain diverse and abundant small-molecule additives (excipients) with unclear impacts on human physiology, drug safety, and response. Here, we evaluate their potential impact on intestinal drug absorption. By screening 136 unique compounds for inhibition of the key intestinal transporter OATP2B1 we identified and validated 24 potent OATP2B1 inhibitors, characterized by higher molecular weight and hydrophobicity compared to poor or noninhibitors. OATP2B1 inhibitors were also enriched for dyes, including 8 azo (R-N=N-R') dyes. Pharmacokinetic studies in mice confirmed that FD&C Red No. 40, a common azo dye excipient and a potent inhibitor of OATP2B1, decreased the plasma level of the OATP2B1 substrate fexofenadine, suggesting that FD&C Red No. 40 has the potential to block drug absorption through OATP2B1 inhibition in vivo. However, the gut microbiomes of multiple unrelated healthy individuals as well as diverse human gut bacterial isolates were capable of inactivating the identified azo dye excipients, producing metabolites that no longer inhibit OATP2B1 transport. These results support a beneficial role for the microbiome in limiting the unintended effects of food and drug additives in the intestine and provide a framework for the data-driven selection of excipients. Furthermore, the ubiquity and genetic diversity of gut bacterial azoreductases coupled to experiments in conventionally raised and gnotobiotic mice suggest that variations in gut microbial community structure may be less important to consider relative to the high concentrations of azo dyes in food products, which have the potential to saturate gut bacterial enzymatic activity.

Interactions of Oral Molecular Excipients with Breast Cancer Resistance Protein, BCRP.

Mechanistic-understanding-based selection of excipients may improve formulation development strategies for generic drug products and potentially accelerate their approval. Our study aimed at investigating the effects of molecular excipients present in orally administered FDA-approved drug products on the intestinal efflux transporter, BCRP (ABCG2), which plays a critical role in drug absorption with potential implications on drug safety and efficacy. We determined the interactions of 136 oral molecular excipients with BCRP in isolated membrane vesicles and identified 26 excipients as BCRP inhibitors with IC50 values less than 5 µM using 3H-cholecystokinin octapeptide (3H-CCK8). These BCRP inhibitors belonged to three functional categories of excipients: dyes, surfactants, and flavoring agents. Compared with noninhibitors, BCRP inhibitors had significantly higher molecular weights and SLogP values. The inhibitory effects of excipients identified in membrane vesicles were also evaluated in BCRP-overexpressing HEK293 cells at similar concentrations. Only 1 of the 26 inhibitors of BCRP identified in vesicles inhibited BCRP-mediated 3H-oxypurinol uptake by more than 50%, consistent with the notion that BCRP inhibition depends on transmembrane or intracellular availability of the inhibitors. Collectively, the results of this study provide new information on excipient selection during the development of drug products with active pharmaceutical ingredients that are BCRP substrates.

Computational Discovery and Experimental Validation of Inhibitors of the Human Intestinal Transporter OATP2B1.

Human organic anion transporters (OATPs) are vital for the uptake and efflux of drugs and endogenous compounds. Current identification of inhibitors of these transporters is based on experimental screening. Virtual screening remains a challenge due to a lack of experimental three-dimensional protein structures. Here, we describe a workflow to identify inhibitors of the OATP2B1 transporter in the DrugBank library of over 5,000 drugs and druglike molecules. OATP member 2B1 transporter is highly expressed in the intestine, where it participates in oral absorption of drugs. Predictions from a Random forest classifier, prioritized by docking against multiple comparative protein structure models of OATP2B1, indicated that 33 of the 5,000 compounds were putative inhibitors of OATP2B1. Ten predicted inhibitors that are prescription drugs were tested experimentally in cells overexpressing the OATP2B1 transporter. Three of these ten were validated as potent inhibitors of estrone-3-sulfate uptake (defined as more than 50% inhibition at 20 µM) and tested in multiple concentrations to determine exact IC50. The IC50 values of bicalutamide, ticagrelor, and meloxicam suggest that they might inhibit intestinal OATP2B1 at clinically relevant concentrations and therefore modulate the absorption of other concomitantly administered drugs.

ModBase, a database of annotated comparative protein structure models and associated resources.

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein-protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein-ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.

High selectivity of the γ-aminobutyric acid transporter 2 (GAT-2, SLC6A13) revealed by structure-based approach.

The solute carrier 6 (SLC6) is a family of ion-dependent transporters that mediate uptake into the cell of osmolytes such as neurotransmitters and amino acids. Four SLC6 members transport GABA, a key neurotransmitter that triggers inhibitory signaling pathways via various receptors (e.g., GABA(A)). The GABA transporters (GATs) regulate the concentration of GABA available for signaling and are thus targeted by a variety of anticonvulsant and relaxant drugs. Here, we characterize GAT-2, a transporter that plays a role in peripheral GABAergic mechanisms, by constructing comparative structural models based on crystallographic structures of the leucine transporter LeuT. Models of GAT-2 in two different conformations were constructed and experimentally validated, using site-directed mutagenesis. Computational screening of 594,166 compounds including drugs, metabolites, and fragment-like molecules from the ZINC database revealed distinct ligands for the two GAT-2 models. 31 small molecules, including high scoring compounds and molecules chemically related to known and predicted GAT-2 ligands, were experimentally tested in inhibition assays. Twelve ligands were found, six of which were chemically novel (e.g., homotaurine). Our results suggest that GAT-2 is a high selectivity/low affinity transporter that is resistant to inhibition by typical GABAergic inhibitors. Finally, we compared the binding site of GAT-2 with those of other SLC6 members, including the norepinephrine transporter and other GATs, to identify ligand specificity determinants for this family. Our combined approach may be useful for characterizing interactions between small molecules and other membrane proteins, as well as for describing substrate specificities in other protein families.

Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes.

Cells are the basic building blocks of human organisms, and the identification of their types and states in transcriptomic data is an important and challenging task. Many of the existing approaches to cell-type prediction are based on clustering methods that optimize only one criterion. In this paper, a multi-objective Genetic Algorithm for cluster analysis is proposed, implemented, and systematically validated on 48 experimental and 60 synthetic datasets. The results demonstrate that the performance and the accuracy of the proposed algorithm are reproducible, stable, and better than those of single-objective clustering methods. Computational run times of multi-objective clustering of large datasets were studied and used in supervised machine learning to accurately predict the execution times of clustering of new single-cell transcriptomes.

Global Protease Activity Profiling Identifies HER2-Driven Proteolysis in Breast Cancer.

Differential expression of extracellular proteases and endogenous protease inhibitors has been associated with distinct molecular subtypes of breast cancer. However, due to the tight post-translational regulation of protease activity, protease expression-level data alone are not sufficient to understand the role of proteases in malignant transformation. Therefore, we hypothesized that global profiles of extracellular protease activity could more completely reflect differences observed at the transcriptional level in breast cancer and that subtype-associated protease activity may be leveraged to identify specific proteases that play a functional role in cancer signaling. Here, we used a global peptide library-based approach to profile the activities of proteases within distinct breast cancer subtypes. Analysis of 3651 total peptide cleavages from a panel of well-characterized breast cancer cell lines demonstrated differences in proteolytic signatures between cell lines. Cell line clustering based on protease cleavages within the peptide library expanded upon the expected classification derived from transcriptional profiling. An isogenic cell line model developed to further interrogate proteolysis in the HER2 subtype revealed a proteolytic signature consistent with activation of TGF-ß signaling. Specifically, we determined that a metalloprotease involved in TGF-ß signaling, BMP1, was upregulated at both the protein (2-fold, P = 0.001) and activity (P = 0.0599) levels. Inhibition of BMP1 and HER2 suppressed invasion of HER2-expressing cells by 35% (P < 0.0001), compared to 15% (P = 0.0086) observed in cells where only HER2 was inhibited. In summary, through global identification of extracellular proteolysis in breast cancer cell lines, we demonstrate subtype-specific differences in protease activity and elucidate proteolysis associated with HER2-mediated signaling.

Drug-nutrient interactions: discovering prescription drug inhibitors of the thiamine transporter ThTR-2 (SLC19A3).

BACKGROUND: Transporter-mediated drug-nutrient interactions have the potential to cause serious adverse events. However, unlike drug-drug interactions, these drug-nutrient interactions receive little attention during drug development. The clinical importance of drug-nutrient interactions was highlighted when a phase III clinical trial was terminated due to severe adverse events resulting from potent inhibition of thiamine transporter 2 (ThTR-2; SLC19A3). OBJECTIVE: In this study, we tested the hypothesis that therapeutic drugs inhibit the intestinal thiamine transporter ThTR-2, which may lead to thiamine deficiency. METHODS: For this exploration, we took a multifaceted approach, starting with a high-throughput in vitro primary screen to identify inhibitors, building in silico models to characterize inhibitors, and leveraging real-world data from electronic health records to begin to understand the clinical relevance of these inhibitors. RESULTS: Our high-throughput screen of 1360 compounds, including many clinically used drugs, identified 146 potential inhibitors at 200 µM. Inhibition kinetics were determined for 28 drugs with half-maximal inhibitory concentration (IC50) values ranging from 1.03 µM to >1 mM. Several oral drugs, including metformin, were predicted to have intestinal concentrations that may result in ThTR-2-mediated drug-nutrient interactions. Complementary analysis using electronic health records suggested that thiamine laboratory values are reduced in individuals receiving prescription drugs found to significantly inhibit ThTR-2, particularly in vulnerable populations (e.g., individuals with alcoholism). CONCLUSIONS: Our comprehensive analysis of prescription drugs suggests that several marketed drugs inhibit ThTR-2, which may contribute to thiamine deficiency, especially in at-risk populations.

Challenges in the Regulation of High-Cost Treatments: An Overview From Brazil.

Regulation of drug prices that ensures adequate access to effective treatments and promotes innovation is a global challenge. In the United States, the government does not regulate drug prices when they come onto market. On the other hand, in countries such as France and Brazil, government agencies are responsible for setting up price limits by leveraging the interests of the companies and the countries' population. In Brazil, safety and efficacy of drugs are regulated by the Brazilian Health Regulatory Agency, and drug prices are regulated by the Pharmaceutical Market Regulation Chamber with a participation of Brazilian Health Regulatory Agency. Here, we introduce the current challenges faced by the Brazilian government in the drug price regulation and present proposed initiatives aiming to streamline access to innovative treatments for its citizens.

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition.

Accurate predictions of T-cell epitopes would be useful for designing vaccines, immunotherapies for cancer and autoimmune diseases, and improved protein therapies. The humoral immune response involves uptake of antigens by antigen presenting cells (APCs), APC processing and presentation of peptides on MHC class II (pMHCII), and T-cell receptor (TCR) recognition of pMHCII complexes. Most in silico methods predict only peptide-MHCII binding, resulting in significant over-prediction of CD4 T-cell epitopes. We present a method, ITCell, for prediction of T-cell epitopes within an input protein antigen sequence for given MHCII and TCR sequences. The method integrates information about three stages of the immune response pathway: antigen cleavage, MHCII presentation, and TCR recognition. First, antigen cleavage sites are predicted based on the cleavage profiles of cathepsins S, B, and H. Second, for each 12-mer peptide in the antigen sequence we predict whether it will bind to a given MHCII, based on the scores of modeled peptide-MHCII complexes. Third, we predict whether or not any of the top scoring peptide-MHCII complexes can bind to a given TCR, based on the scores of modeled ternary peptide-MHCII-TCR complexes and the distribution of predicted cleavage sites. Our benchmarks consist of epitope predictions generated by this algorithm, checked against 20 peptide-MHCII-TCR crystal structures, as well as epitope predictions for four peptide-MHCII-TCR complexes with known epitopes and TCR sequences but without crystal structures. ITCell successfully identified the correct epitopes as one of the 20 top scoring peptides for 22 of 24 benchmark cases. To validate the method using a clinically relevant application, we utilized five factor VIII-specific TCR sequences from hemophilia A subjects who developed an immune response to factor VIII replacement therapy. The known HLA-DR1-restricted factor VIII epitope was among the six top-scoring factor VIII peptides predicted by ITCall to bind HLA-DR1 and all five TCRs. Our integrative approach is more accurate than current single-stage epitope prediction algorithms applied to the same benchmarks. It is freely available as a web server (http://salilab.org/itcell).

Discovery of Competitive and Noncompetitive Ligands of the Organic Cation Transporter 1 (OCT1; SLC22A1).

Organic cation transporter 1 (OCT1) plays a critical role in the hepatocellular uptake of structurally diverse endogenous compounds and xenobiotics. Here we identified competitive and noncompetitive OCT1-interacting ligands in a library of 1780 prescription drugs by combining in silico and in vitro methods. Ligands were predicted by docking against a comparative model based on a eukaryotic homologue. In parallel, high-throughput screening (HTS) was conducted using the fluorescent probe substrate ASP+ in cells overexpressing human OCT1. Thirty competitive OCT1 ligands, defined as ligands predicted in silico as well as found by HTS, were identified. Of the 167 ligands identified by HTS, five were predicted to potentially cause clinical drug interactions. Finally, virtual screening of 29 332 metabolites predicted 146 competitive OCT1 ligands, of which an endogenous neurotoxin, 1-benzyl-1,2,3,4-tetrahydroisoquinoline, was experimentally validated. In conclusion, by combining docking and in vitro HTS, competitive and noncompetitive ligands of OCT1 can be predicted.

A research roadmap for next-generation sequencing informatics.

Next-generation sequencing technologies are fueling a wave of new diagnostic tests. Progress on a key set of nine research challenge areas will help generate the knowledge required to advance effectively these diagnostics to the clinic.

Unmet needs: Research helps regulators do their jobs.

A plethora of innovative new medical products along with the need to apply modern technologies to medical-product evaluation has spurred seminal opportunities in regulatory sciences. Here, we provide eight examples of regulatory science research for diverse products. Opportunities abound, particularly in data science and precision health.

Molecular modeling and ligand docking for solute carrier (SLC) transporters.

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.

Discovery of potent, selective multidrug and toxin extrusion transporter 1 (MATE1, SLC47A1) inhibitors through prescription drug profiling and computational modeling.

The human multidrug and toxin extrusion (MATE) transporter 1 contributes to the tissue distribution and excretion of many drugs. Inhibition of MATE1 may result in potential drug-drug interactions (DDIs) and alterations in drug exposure and accumulation in various tissues. The primary goals of this project were to identify MATE1 inhibitors with clinical importance or in vitro utility and to elucidate the physicochemical properties that differ between MATE1 and OCT2 inhibitors. Using a fluorescence assay of ASP(+) uptake in cells stably expressing MATE1, over 900 prescription drugs were screened and 84 potential MATE1 inhibitors were found. We identified several MATE1 selective inhibitors including four FDA-approved medications that may be clinically relevant MATE1 inhibitors and could cause a clinical DDI. In parallel, a QSAR model identified distinct molecular properties of MATE1 versus OCT2 inhibitors and was used to screen the DrugBank in silico library for new hits in a larger chemical space.

Population level functional diversity in a microbial community revealed by comparative genomic and metagenomic analyses.

In microbial mat communities of Yellowstone hot springs, ribosomal RNA (rRNA) sequence diversity patterns indicate the presence of closely related bacterial populations along environmental gradients of temperature and light. To identify the functional bases for adaptation, we sequenced the genomes of two cyanobacterial (Synechococcus OS-A and OS-B') isolates representing ecologically distinct populations that dominate at different temperatures and are major primary producers in the mat. There was a marked lack of conserved large-scale gene order between the two Synechococcus genomes, indicative of extensive genomic rearrangements. Comparative genomic analyses showed that the isolates shared a large fraction of their gene content at high identity, yet, differences in phosphate and nitrogen utilization pathways indicated that they have adapted differentially to nutrient fluxes, possibly by the acquisition of genes by lateral gene transfer or their loss in certain populations. Comparisons of the Synechococcus genomes to metagenomic sequences derived from mats where these Synechococcus stains were originally isolated, revealed new facets of microbial diversity. First, Synechococcus populations at the lower temperature regions of the mat showed greater sequence diversity than those at high temperatures, consistent with a greater number of ecologically distinct populations at the lower temperature. Second, we found evidence of a specialized population that is apparently very closely related to Synechococcus OS-B', but contains genes that function in the uptake of reduced ferrous iron. In situ expression studies demonstrated that these genes are differentially expressed over the diel cycle, with highest expression when the mats are anoxic and iron may be in the reduced state. Genomic information from these mat-specific isolates and metagenomic information can be coupled to detect naturally occurring populations that are associated with different functionalities, not always represented by isolates, but which may nevertheless be important for niche partitioning and the establishment of microbial community structure.