Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter

Thiazide Diuretics and Risk of Colorectal Cancer: A Population-Based Cohort Study

Evidence from clinical trials and observational studies on the association between thiazide diuretics and colorectal cancer risk is conflicting. We aimed to determine whether thiazide diuretics are associated with an increased colorectal cancer risk compared with dihydropyridine calcium channel blockers (dCCBs). A population-based, new-user cohort was assembled using the UK Clinical Practice Research Datalink. Between 1990-2018, we compared thiazide diuretic initiators with dCCB initiators and estimated hazard ratios (HR) with 95% confidence intervals (CIs) of colorectal cancer using Cox proportional hazard models. Models were weighted using standardized morbidity ratio weights generated from calendar time-specific propensity scores. The cohort included 377,760 thiazide diuretic initiators and 364,300 dCCB initiators, generating 3,619,883 person-years of follow-up. Compared with dCCBs, thiazide diuretics were not associated with colorectal cancer (weighted HR = 0.97, 95% CI: 0.90, 1.04). Secondary analyses yielded similar results, although an increased risk was observed among patients with inflammatory bowel disease (weighted HR = 2.45, 95% CI: 1.13, 5.35) and potentially polyps (weighted HR = 1.46, 95% CI: 0.93, 2.30). Compared with dCCBs, thiazide diuretics were not associated with an overall increased colorectal cancer risk. While these findings provide some reassurance, research is needed to corroborate the elevated risks observed among patients with inflammatory bowel disease and history of polyps.

Repurposable Drugs for Immunotherapy and Strategies to Find Candidate Drugs

Conventional drug discovery involves significant steps, time, and expenses; therefore, novel methods for drug discovery remain unmet, particularly for patients with intractable diseases. For this purpose, the drug repurposing method has been recently used to search for new therapeutic agents. Repurposed drugs are mostly previously approved drugs, which were carefully tested for their efficacy for other diseases and had their safety for the human body confirmed following careful pre-clinical trials, clinical trials, and post-marketing surveillance. Therefore, using these approved drugs for other diseases that cannot be treated using conventional therapeutic methods could save time and economic costs for testing their clinical applicability. In this review, we have summarized the methods for identifying repurposable drugs focusing on immunotherapy.

N-(4-[18F]fluorobenzyl)cholylglycine, a potential tracer for positron emission tomography of enterohepatic circulation and drug-induced inhibition of ileal bile acid transport. A proof-of-concept PET/CT study in pigs

INTRODUCTION

Enterohepatic circulation (EHC) of conjugated bile acids is an important physiological process crucial for bile acids to function as detergents and signal carriers. Perturbation of the EHC by disease or drugs may lead to serious and life-threatening liver and gastrointestinal disorders. In this proof-of-concept study in pigs, we investigate the potential of N-(4-[¹⁸F]fluorobenzyl)cholylglycine ([¹⁸F]FBCGly) as tracer for quantitative positron emission tomography (PET) of the EHC of conjugated bile acids.

METHODS

The biodistribution of [¹⁸F]FBCGly was investigated by PET/CT in domestic pigs following intravenous and intraileal administration of the tracer. Hepatic kinetics were estimated from PET and blood data using a 2-tissue compartmental model and dual-input of [¹⁸F]FBCGly. The ileal uptake of [¹⁸F]FBCGly was investigated with co-injection of nifedipine and endogenous cholyltaurine. Dosimetry was estimated from the PET data using the Olinda 2.0 software. Blood, bile and urine samples were analyzed for possible fluorine-18 labelled metabolites of [¹⁸F]FBCGly.

RESULTS

[¹⁸F]FBCGly was rapidly taken up by the liver and excreted into bile, and underwent EHC without being metabolized. Both nifedipine and endogenous cholyltaurine inhibited the ileal uptake of [¹⁸F]FBCGly. The flow-dependent hepatic uptake clearance was estimated to median 1.2 mL blood/min/mL liver tissue. The mean residence time of [¹⁸F]FBCGly in hepatocytes was 4.0 ± 1.1 min. Critical organs for [¹⁸F]FBCGly were the gallbladder wall (0.94 mGy/MBq) and the small intestine (0.50 mGy/MBq). The effective dose for [¹⁸F]FBCGly was 36 μSv/MBq.

CONCLUSION

We have shown that [¹⁸F]FBCGly undergoes EHC in pigs without being metabolized and that its ileal uptake is inhibited by nifedipine and endogenous bile acids. Combined with our previous findings in rats, we believe that [¹⁸F]FBCGly has potential as PET tracer for assessment of EHC of conjugated bile acids under physiological conditions as well as conditions with perturbed hepatic and ileal bile acid transport.

Enhanced Oral Absorption and Liver Distribution of Polymeric Nanoparticles through Traveling the Enterohepatic Circulation Pathways of Bile Acid

The intestinal epithelium is known to be a main hindrance to oral delivery of nanoparticles. Even though surface ligand modification can enhance cellular uptake of nanoparticles, the "easy entry and hard across" was frequently observed for many active targeting nanoparticles. Here, we fabricated polymeric nanoparticles relayed by bile acid transporters with monomethoxy poly(ethylene glycol)-poly(D,l-lactide) and deoxycholic acid-conjugated poly(2-ethyl-2-oxazoline)-poly(D,l-lactide) based on structural characteristics of intestine epithelium and the absorption characteristics of endogenous substances. As anticipated, deoxycholic acid-modified polymeric nanoparticles featuring good stability in simulated gastrointestinal fluid could notably promote the internalization of their payload by Caco-2 cells through mediation of apical sodium-dependent bile acid transporter (ASBT) and transmembrane transport of the nanoparticles across Caco-2 cell monolayers via relay-guide of ASBT, ileal bile acid-binding protein, and the heteromeric organic solute transporter (OSTα-OSTβ) along with multidrug resistance-associated protein 3 (MRP3) evidenced by competitive inhibition and fluorescence immunoassay, which was further visually confirmed by the stronger fluorescence from C6-labeled nanoparticles inside enterocytes and the basal side of the intestinal epithelium of mice. The transcellular transport of deoxycholic acid-modified nanoparticles in an intact form was mediated by caveolin/lipid rafts and clathrin with intracellular trafficking trace of endosome-lysosome-ER-Golgi apparatus and bile acid transport route. Furthermore, the increased uptake by HepG2 cells compared with unmodified nanoparticles evidenced the target ability of deoxycholic acid-modified nanoparticles to the liver, which was further supported by ex vivo imaging of excised major organs of mice. Thus, this study provided a feasible and potential strategy to further enhance transepithelial transport efficiency and liver-targeted ability of nanoparticles by means of the specific enterohepatic circulation pathways of bile acid.

Lack of an Effect of Polysorbate 80 on Intestinal Drug Permeability in Humans

PURPOSE

Despite no broad, direct evidence in humans, there is a potential concern that surfactants alter active or passive drug intestinal permeation to modulate oral drug absorption. The purpose of this study was to investigate the impact of the surfactant polysorbate 80 on active and passive intestinal drug absorption in humans.

METHODS

The human (n = 12) pharmacokinetics (PK) of three probe substrates of intestinal absorption, valacyclovir, chenodeoxycholic acid (CDCA), and enalaprilat, were assessed. Endogenous bile acid levels were assessed as a secondary measure of transporter and microbiota impact.

RESULTS

Polysorbate 80 did not inhibit peptide transporter 1 (PepT1)- or apical sodium bile acid transporter (ASBT)-mediated PK of valacyclovir and CDCA, respectively. Polysorbate 80 did not increase enalaprilat absorption. Modest increases in unconjugated secondary bile acid C_max ratios suggest a potential alteration of the in vivo intestinal microbiota by polysorbate 80.

CONCLUSIONS

Polysorbate 80 did not alter intestinal membrane fluidity or cause intestinal membrane disruption. This finding supports regulatory relief of excipient restrictions for Biopharmaceutics Classification System-based biowaivers.

Risk Factors of Patients With Diarrhea for Having Clostridioides (Clostridium) difficile Infection

Nosocomial infections with Clostridioides (Clostridium) difficile have become an emergent health threat. We sought to define risk factors for a C. difficile infection (CDI) beyond the widely known ones, such as antibiotic use and prior hospital stay. We therefore focused on a group of patients with diarrhea in order to identify risk factors for C. difficile infection among this symptomatic cohort. A total of 121 hospitalized patients from Seesen/Germany with diarrhea were included who submitted a stool sample and were interviewed about their socio-demographic background, lifestyle and state of health using a standardized questionnaire. Antibiotic potential of diuretics was examined by agar diffusion test. C. difficile was identified in 29 patients resulting in a prevalence of 24.0%. The infection was hospital-acquired in most cases (p < 0.001, 82.1%; n = 23/28, versus 29/91, 31.9%). The generally accepted risk factor previous antibiotic use was confirmed in this study (p = 0.002, n = 23/28 CDI patients, 82.1%, versus n = 44/91 non-CDI patients, 48.4%). The following additional risk factors were identified: regular consumption of proton pump inhibitors; PPI (p = 0.011, n = 24/29, 82.8% vs. n = 52/92, 56.5%), CDI patients ate less vegetables (p = 0.001, n = 12/29, 41.4% vs. 69/92, 75.0%). The intake of the diuretic agent torasemid in patients with CDI (p = 0.005, n = 18/29, 62.1%) was higher than in patients without (n = 30/92, 32.6%). More patients with CDI had to undergo a surgery in the previous year (p = 0.022, n = 13/29, 44.8% vs. n = 21/92, 22.8%) and held more birds (p = 0.056, n = 4/29, 13.8%) than individuals of the negative group (n = 3/92, 3.3%). In conclusion, although no antibiotic potential was detected in diuretics, especially torasemid seems to have significant influence for the occurrence of a CDI as well as a nutrition poor in vegetables. A diet rich in vegetables represented a fourfold lower risk for a CDI (OR 0.240, CI (0.0720 - 0.796]).

Machine Learning Models for Mycobacterium tuberculosisIn Vitro Activity: Prediction and Target Visualization

Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis (Mtb). There have been many large-scale phenotypic screens that have led to the identification of thousands of new compounds. Yet, there is very limited investment in TB drug discovery which points to the need for new methods to increase the efficiency of drug discovery against Mtb. We have used machine learning approaches to learn from the public Mtb data, resulting in many data sets and models with robust enrichment and hit rates leading to the discovery of new active compounds. Recently, we have curated predominantly small-molecule Mtb data and developed new machine learning classification models with 18 886 molecules at different activity cutoffs. We now describe the further validation of these Bayesian models using a library of over 1000 molecules synthesized as part of EU-funded New Medicines for TB and More Medicines for TB programs. We highlight molecular features which are enriched in these active compounds. In addition, we provide new regression and classification models that can be used for scoring compound libraries or used to design new molecules. We have also visualized these molecules in the context of known molecular targets and identified clusters in chemical property space, which may aid in future target identification efforts. Finally, we are also making these data sets publicly available, representing a significant increase to the available Mtb inhibition data in the public domain.

Current Models for Predicting Drug-induced Cholestasis: The Role of Hepatobiliary Transport System

Drug-induced cholestasis is the main type of liver disorder accompanied by high morbidity and mortality. Evidence for the role of hepatobiliary pumps in the cholestasis patho-mechanism is constantly increasing. Recognition of the interactions of chemical agents with these transporters at the initial phases of drug discovery can help develop new drug candidates with low cholestasis potential. This review delivers an outline of the role of these transport proteins in bile creation. It addresses the pathophysiological mechanism for drug-induced cholestasis. In-vitro models, including cell-based and membrane-based approaches and In-vivo models such as genetic knockout animals, are considered. The benefits and restrictions of each model are discussed in this review. Current understandings into the cellular and molecular process that control the activity of hepatobiliary pumps have directed to a better understanding of the pathophysiology of drug-induced cholestasis. A combination of in-vitro monitoring for transport interaction, in-silico predicting systems, and consideration of and metabolic and physicochemical properties must cause more effective monitoring of possible liver problems.

Evaluation for Potential Drug-Drug Interaction of MT921 Using In Vitro Studies and Physiologically-Based Pharmacokinetic Models

MT921 is a new injectable drug developed by Medytox Inc. to reduce submental fat. Cholic acid is the active pharmaceutical ingredient, a primary bile acid biosynthesized from cholesterol, endogenously produced by liver in humans and other mammals. Although individuals treated with MT921 could be administered with multiple medications, such as those for hypertension, diabetes, and hyperlipidemia, the pharmacokinetic drug–drug interaction (DDI) has not been investigated yet. Therefore, we studied in vitro against drug-metabolizing enzymes and transporters. Moreover, we predicted the potential DDI between MT921 and drugs for chronic diseases using physiologically-based pharmacokinetic (PBPK) modeling and simulation. The magnitude of DDI was found to be negligible in in vitro inhibition and induction of cytochrome P450s and UDP-glucuronosyltransferases. Organic anion transporting polypeptide (OATP)1B3, organic anion transporter (OAT)3, Na⁺-taurocholate cotransporting polypeptide (NTCP), and apical sodium-dependent bile acid transporter (ASBT) are mainly involved in MT921 transport. Based on the result of in vitro experiments, the PBPK model of MT921 was developed and evaluated by clinical data. Furthermore, the PBPK model of amlodipine was developed and evaluated. PBPK DDI simulation results indicated that the pharmacokinetics of MT921 was not affected by the perpetrator drugs. In conclusion, MT921 could be administered without a DDI risk based on in vitro study and related in silico simulation. Further clinical studies are needed to validate this finding.

Identification prognosis-associated immune genes in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most prevalent malignant tumors worldwide. Immune genes (IGs) have a considerable correlation with tumor initiation and prognosis. The present paper aims to identify the prognosis value of IGs in COAD and conduct a prognosis model for clinical utility. Gene expression data of COAD were downloaded from The Cancer Genome Atlas (TCGA), screening and analyzing differentially expressed IGs by bioinformatics. Core genes were screened by univariate and multivariate Cox regression analyses. Survival analysis was appraised by the Kaplan–Meier method and the log-rank test. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis (GSEA) were used to identify IGs’ relevant signal pathways. We predicted the overall survival (OS) by nomogram. Finally, a prognosis model was conducted based on 12 IGs (SLC10A2, CXCL3, NOX4, FABP4, ADIPOQ, IGKV1-33, IGLV6-57, INHBA, UCN, VIP, NGFR, and TRDC). The risk score was an independent prognostic factor, and a nomogram could accurately predict the OS of individual COAD patients. These results were validated in GSE39582, GSE12945, and GSE103479 cohorts. Functional enrichment analysis demonstrated that these IGs are mainly enriched in hormone secretion, hormone transport, lipid transport, cytokine–cytokine receptor interaction, and peroxisome proliferators-activated receptor signaling pathway. In summary, the risk score is an independent prognostic biomarker. We also excavated several IGs related to COAD’s survival and maybe potential biomarkers for COAD diagnosis and treatment.

The Discovery of Novel BCR-ABL Tyrosine Kinase Inhibitors Using a Pharmacophore Modeling and Virtual Screening Approach

Chronic myelogenous leukemia (CML) typically results from a reciprocal translocation between chromosomes 9 and 22 to produce the bcr-abl oncogene that when translated, yields the p210 BCR-ABL protein in more than 90% of all CML patients. This protein has constitutive tyrosine kinase activity that activates numerous downstream pathways that ultimately produces uncontrolled myeloid proliferation. Although the use of the BCR-ABL tyrosine kinase inhibitors (TKIs), such as imatinib, nilotinib, dasatinib, bosutinib, and ponatinib have increased the overall survival of CML patients, their use is limited by drug resistance and severe adverse effects. Therefore, there is the need to develop novel compounds that can overcome these problems that limit the use of these drugs. Therefore, in this study, we sought to find novel compounds using Hypogen and Hiphip pharmacophore models based on the structures of clinically approved BCR-ABL TKIs. We also used optimal pharmacophore models such as three-dimensional queries to screen the ZINC database to search for potential BCR-ABL inhibitors. The hit compounds were further screened using Lipinski’s rule of five, ADMET and molecular docking, and the efficacy of the hit compounds was evaluated. Our in vitro results indicated that compound ZINC21710815 significantly inhibited the proliferation of K562, BaF3/WT, and BaF3/T315I leukemia cells by inducing cell cycle arrest. The compound ZINC21710815 decreased the expression of p-BCR-ABL, STAT5, and Crkl and produced apoptosis and autophagy. Our results suggest that ZINC21710815 may be a potential BCR-ABL inhibitor that should undergo in vivo evaluation.

[The inhibitors of the apical sodium-dependent bile acid transporter (ASBT) as promising drugs]

Inhibition of the apical sodium-dependent bile acid transporter (ASBT, also known as IBAT - ileal bile acid transporter, SLC10A2) leads to disruption of the enterohepatic circulation of bile acids and their excretion with fecal masses. This is accompanied by cholesterol utilization for synthesis of new bile acids. ASBT inhibitors are promising drugs for the treatment of such diseases as non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, type 2 diabetes mellitus, necrotic enterocolitis, chronic constipation, atherosclerosis. To date the most known chemically synthesized inhibitors are: A3309, SHP626, A4250, 264W94, GSK2330672, SC-435. All of them are at different stages of clinical trials, which confirm the high efficacy and good tolerance of these inhibitors. Current trends in this field also include directed chemical synthesis of ASBT inhibitors, as well as their search among substances of plant origin.

Bile acid transporter-mediated oral drug delivery

Bile acids are synthesized in the liver, stored in the gallbladder, and secreted into the duodenum at meals. Apical sodium-dependent bile acid transporter (ASBT), an ileal Na⁺-dependent transporter, plays the leading role of bile acid absorption into enterocytes, where bile acids are delivered to basolateral side by ileal bile acid binding protein (IBABP) and then released by organic solute transporter OSTα/β. The absorbed bile acids are delivered to the liver via portal vein. In this process called "enterohepatic recycling", only 5% of the bile acid pool (~3 g in human) is excreted in feces, indicating the large recycling capacity and high transport efficacy of ASBT-mediated absorption. Therefore, bile acid transporter-mediated oral drug delivery has been regarded as a feasible and potential strategy to improve the oral bioavailability. This review introduces the key factors in enterohepatic recycling, especially the mechanism of bile acid uptake by ASBT, and the development of bile acid-based oral drug delivery for ASBT-targeting, including bile acid-based prodrugs, bile acid/drug electrostatic complexation and bile acid-containing nanocarriers. Furthermore, the specific transport pathways of bile acid in enterocytes are described and the recent finding of lymphatic delivery of bile acid-containing nanocarriers is discussed.

Apical Sodium-Dependent Bile Acid Cotransporter, A Novel Transporter of Indocyanine Green, and Its Application in Drug Screening

Bile acid plays critical roles in the elimination of inorganic compounds such as bilirubin, heavy metals, and drug metabolites. Apical sodium-dependent bile acid cotransporter (ASBT), a solute carrier membrane transport protein, transports bile acids. Several inhibitors of ASBT have been evaluated in clinical trials. Sodium taurocholate cotransporting polypeptide (NTCP), belonging to the same family as ASBT, has fluorescein 5(6)-isothiocyanate (FITC) and indocyanine green (ICG) transportability. ICG, a Food and Drug Administration-approved fluorophore at near-infrared range, has perfect optical characteristics, so it can be applied in cell tracking and drug screening. In this study, ASBT and NTCP were transduced into the HT-1080 cell line. Nude mice were subcutaneously xenografted with control and ASBT-expressing cells. ICG transportability was observed through flow cytometry, fluorescent microscopy, multi-mode plate readers, and an in vivo imaging system. Several molecules, including taurocholate, sodium deoxycholate, cyclosporine A, nifedipine, and Primovist, were used to evaluate an in vitro drug-screening platform by using the combination of ICG and ASBT through flow cytometry. ICG and FITC were validated and shown to be transported by ASBT. NTCP had a higher ICG intensity compared with ASBT. For cell tracking, the ASBT xenograft had similar ICG signals as the control. For a drug-screening platform, the ICG intensity decreased with 186 μM taurocholate (56.8%), deoxycholate (83.8%), and increased with nifedipine (133.2%). These findings are suggestive of opportunities for the high-throughput drug screening of cholestasis and other diseases that are related to the dynamics of bile acid reabsorption.

A multiparametric organ toxicity predictor for drug discovery

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

Repurposing Approved Drugs as Inhibitors of Kv7.1 and Nav1.8 to Treat Pitt Hopkins Syndrome

PURPOSE

Pitt Hopkins Syndrome (PTHS) is a rare genetic disorder caused by mutations of a specific gene, transcription factor 4 (TCF4), located on chromosome 18. PTHS results in individuals that have moderate to severe intellectual disability, with most exhibiting psychomotor delay. PTHS also exhibits features of autistic spectrum disorders, which are characterized by the impaired ability to communicate and socialize. PTHS is comorbid with a higher prevalence of epileptic seizures which can be present from birth or which commonly develop in childhood. Attenuated or absent TCF4 expression results in increased translation of peripheral ion channels K_v7.1 and Na_v1.8 which triggers an increase in after-hyperpolarization and altered firing properties.

METHODS

We now describe a high throughput screen (HTS) of 1280 approved drugs and machine learning models developed from this data. The ion channels were expressed in either CHO (K_V7.1) or HEK293 (Na_v1.8) cells and the HTS used either ⁸⁶Rb⁺ efflux (K_V7.1) or a FLIPR assay (Na_v1.8).

RESULTS

The HTS delivered 55 inhibitors of K_v7.1 (4.2% hit rate) and 93 inhibitors of Na_v1.8 (7.2% hit rate) at a screening concentration of 10 μM. These datasets also enabled us to generate and validate Bayesian machine learning models for these ion channels. We also describe a structure activity relationship for several dihydropyridine compounds as inhibitors of Na_v1.8.

CONCLUSIONS

This work could lead to the potential repurposing of nicardipine or other dihydropyridine calcium channel antagonists as potential treatments for PTHS acting via Na_v1.8, as there are currently no approved treatments for this rare disorder.

A novel ASBT inhibitor, IMB17-15, repressed nonalcoholic fatty liver disease development in high-fat diet-fed Syrian golden hamsters

The manipulation of bile acid (BA) homeostasis by blocking the ileal apical Na⁺-dependent bile salt transporter (ASBT/SLC10A2) may have therapeutic effects in nonalcoholic fatty liver disease. We developed a novel ASBT inhibitor, an N-(3,4-o-dichlorophenyl)-2-(3-trifluoromethoxy) benzamide derivative referred to as IMB17-15, and investigated its therapeutic effects and the molecular mechanisms underlying the effects. Syrian golden hamsters were challenged with high-fat diet (HFD) to induce NAFLD and were subsequently administered 400 mg/kg IMB17-15 by gavage daily for 21 days. Serum, liver, and fecal samples were collected for further analysis. Plasma concentration-time profiles of IMB17-15 were also constructed. The human hepatocyte cell line HL-7702 was treated with Oleic acid (OA) with or without IMB17-15. Western blotting and real-time PCR were used to study the molecular mechanisms of IMB17-15. We found that IMB17-15 inhibited ASBT and subsequently suppressed ileal farnesoid X receptor (FXR) and FXR-activated fibroblast growth factor15/19 (FGF15/19) expression, which reduced the hepatic phosphorylated extracellular regulated protein kinase (ERK) and c-Jun N-terminal kinase (JNK) levels and upregulated the cholesterol 7α-hydroxylase (CYP7A1) activity. Additionally, IMB17-15 stimulated adenosine monophosphate (AMP)-activated protein kinase (AMPKα) phosphorylation and enhanced peroxisome proliferator activated receptor α (PPARα) expression and thus promoted triglyceride (TG) oxidation and high-density lipoprotein cholesterol (HDL-c) metabolism through an ASBT-independent mechanism. In conclusion, a novel ASBT inhibitor known as IMB17-15 protected hamsters against HFD-induced NFALD by manipulating BA and lipid homeostasis. IMB17-15 also reduced lipid deposition in human hepatic cell lines, indicating that it may be useful as a therapy for NAFLD patients.

A 19F magnetic resonance imaging-based diagnostic test for bile acid diarrhea

In up to 50% of people diagnosed with a common ailment, diarrhea-predominant irritable bowel syndrome, diarrhea results from excess spillage of bile acids into the colon-data emerging over the past decade identified deficient release of a gut hormone, fibroblast growth factor 19 (FGF19), and a consequent lack of feedback suppression of bile acid synthesis as the most common cause. ⁷⁵Selenium homotaurocholic acid (SeHCAT) testing, considered the most sensitive and specific means of identifying individuals with bile acid diarrhea, is unavailable in many countries, including the United States. Other than SeHCAT, tests to diagnose bile acid diarrhea are cumbersome, non-specific, or insufficiently validated; clinicians commonly rely on a therapeutic trial of bile acid binders. Here, we review bile acid synthesis and transport, the pathogenesis of bile acid diarrhea, the reasons clinicians frequently overlook this disorder, including the limitations of currently available tests, and our efforts to develop a novel ¹⁹F magnetic resonance imaging (MRI)-based diagnostic approach. We created ¹⁹F-labeled bile acid analogues whose in vitro and in vivo transport mimics that of naturally occurring bile acids. Using dual ¹H/¹⁹F MRI of the gallbladders of live mice fed ¹⁹F-labeled bile acid analogues, we were able to differentiate wild-type mice from strains deficient in intestinal expression of a key bile acid transporter, the apical sodium-dependent bile acid transporter (ASBT), or FGF15, the mouse homologue of FGF19. In addition to reviewing our development of ¹⁹F-labeled bile acid analogue-MRI to diagnose bile acid diarrhea, we discuss challenges to its clinical implementation. A major limitation is the paucity of clinical MRI facilities equipped with the appropriate coil and software needed to detect ¹⁹F signals.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

Predicting drug-induced cholestasis: preclinical models

INTRODUCTION

In almost 50% of patients with drug-induced liver injury (DILI), the bile flow from the liver to the duodenum is impaired, a condition known as cholestasis. However, this toxic response only appears in a small percentage of the treated patients (idiosyncrasy). Prediction of drug-induced cholestasis (DIC) is challenging and emerges as a safety issue that requires attention by professionals in clinical practice, regulatory authorities, pharmaceutical companies, and research institutions. Area covered: The current synopsis focuses on the state-of-the-art in preclinical models for cholestatic DILI prediction. These models differ in their goal, complexity, availability, and applicability, and can widely be classified in experimental animals and in vitro models. Expert opinion: Drugs are a growing cause of cholestasis, but the progress made in explaining mechanisms and differences in susceptibility is not growing at the same rate. We need reliable models able to recapitulate the features of DIC, particularly its idiosyncrasy. The homogeneity and the species-specific differences move animal models away from a fair predictability. However, in vitro human models are improving and getting closer to the real hepatocyte phenotype, and they will likely be the choice in the near future. Progress in this area will not only need reliable predictive models but also mechanistic insights.

Diminished gallbladder filling, increased fecal bile acids, and promotion of colon epithelial cell proliferation and neoplasia in fibroblast growth factor 15-deficient mice

Fibroblast growth factor-19 (human FGF19; murine FGF15) suppresses bile acid synthesis. In FGF19 deficiency, diarrhea resulting from bile acid spillage into the colon mimics irritable bowel syndrome. To seek other consequences of FGF19/15 deficiency, we used Fgf15-/- and wild-type (WT) mice to assess gallbladder filling, the bile acid pool, fecal bile acid levels, and colon neoplasia. We fasted mice for six hours before assessing gallbladder size by magnetic resonance imaging (MRI). We measured bile acid levels in different compartments by enzymatic assay, and induced colon neoplasia with azoxymethane (AOM)/dextran sodium sulfate (DSS) and quantified epithelial Ki67 immunostaining and colon tumors 20 weeks later. In vivo MRI confirmed the gross finding of tubular gallbladders in FGF15-deficient compared to WT mice, but fasting gallbladder volumes overlapped. After gavage with a bile acid analogue, ex vivo MRI revealed diminished gallbladder filling in FGF15-deficient mice (P = 0.0399). In FGF15-deficient mice, the total bile acid pool was expanded 45% (P <0.05) and fecal bile acid levels were increased 2.26-fold (P <0.001). After AOM/DSS treatment, colons from FGF15-deficient mice had more epithelial cell Ki67 staining and tumors (7.33 ± 1.32 vs. 4.57 ± 0.72 tumors/mouse; P = 0.003 compared to WT mice); carcinomas were more common in FGF15-deficient mice (P = 0.01). These findings confirm FGF15, the murine homolog of FGF19, plays a key role in modulating gallbladder filling and bile acid homeostasis. In a well-characterized animal model of colon cancer, increased fecal bile acid levels in FGF15-deficient mice promoted epithelial proliferation and advanced neoplasia.

Organic solute transporter OSTα/β is overexpressed in nonalcoholic steatohepatitis and modulated by drugs associated with liver injury

The heteromeric steroid transporter organic solute transporter α/β (OSTα/β, SLC51A/B) was discovered over a decade ago, but its physiological significance in the liver remains uncertain. A major challenge has been the lack of suitable models expressing OSTα/β. Based on observations first reported here that hepatic OSTα/β is upregulated in nonalcoholic steatohepatitis, the aim of this research was to develop an in vitro model to evaluate OSTα/β function and interaction with drugs and bile acids. OSTα/β expression in human liver tissue was analyzed by quantitative RT-PCR, Western blotting, and immunofluorescence. Radiolabeled compounds were used to determine OSTα/β-mediated transport in the established in vitro model. The effect of bile acids and drugs, including those associated with cholestatic drug-induced liver injury, on OSTα/β-mediated transport was evaluated. Expression of OSTα/β was elevated in the liver of patients with nonalcoholic steatohepatitis and primary biliary cholangitis, whereas hepatocyte expression of OSTα/β was low in control liver tissue. Studies in the novel cell-based system showed rapid and linear OSTα/β-mediated transport for all tested compounds: dehydroepiandrosterone sulfate, digoxin, estrone sulfate, and taurocholate. The interaction study with 26 compounds revealed novel OSTα/β inhibitors: a biomarker for cholestasis, glycochenodeoxycholic acid; the major metabolite of troglitazone, troglitazone sulfate; and a macrocyclic antibiotic, fidaxomicin. Additionally, some drugs (e.g., digoxin) consistently stimulated taurocholate uptake in OSTα/β-overexpressing cells. Our findings demonstrate that OSTα/β is an important transporter in liver disease and imply a role for this transporter in bile acid-bile acid and drug-bile acid interactions, as well as cholestatic drug-induced liver injury. NEW & NOTEWORTHY The organic solute transporter OSTα/β is highly expressed in hepatocytes of liver tissue obtained from patients with nonalcoholic steatohepatitis and primary biliary cholangitis. OSTα/β substrates exhibit rapid, linear, and concentration-driven transport in an OSTα/β-overexpressing cell line. Drugs associated with hepatotoxicity modulate OSTα/β-mediated taurocholate transport. These data suggest that hepatic OSTα/β plays an essential role in patients with cholestasis and may have important clinical implications for bile acid and drug disposition.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Comparison of the Micellar Incorporation and the Intestinal Cell Uptake of Cholecalciferol, 25-Hydroxycholecalciferol and 1-α-Hydroxycholecalciferol

In the context of the global prevalence of vitamin D insufficiency, we compared two key determinants of the bioavailability of 3 vitamin D forms with significant biopotencies: cholecalciferol, 25-hydroxycholecalciferol and 1-α-hydroxycholecalciferol. To this aim, we studied their incorporation into synthetic mixed micelles and their uptake by intestinal cells in culture. Our results show that 1-α-hydroxycholecalciferol was significantly more solubilized into mixed micelles compared to the other forms (1.6-fold and 2.9-fold improvement compared to cholecalciferol and 25-hydroxycholecalciferol, respectively). In Caco-2 TC7 cells, the hydroxylated forms were taken up more efficiently than cholecalciferol (p < 0.05), and conversely to cholecalciferol, their uptake was neither SR-BI(Scavenger-Receptor class B type I)- nor NPC1L1 (NPC1 like intracellular cholesterol transporter 1)-dependent. Besides, the apical membrane sodium–bile acid transporter ASBT (Apical Sodium-dependent Bile acid Transporter) was not involved, at least in vitro, in the uptake of any of the three vitamin D forms. Further investigations are needed to identify the uptake pathways of both 1-α-hydroxycholecalciferol and 25-hydroxycholecalciferol. However, considering its high bioavailability, our results suggest the potential interest of using 1-α-hydroxycholecalciferol in the treatment of severe vitamin D deficiency.

An important intestinal transporter that regulates the enterohepatic circulation of bile acids and cholesterol homeostasis: The apical sodium-dependent bile acid transporter (SLC10A2/ASBT)

The enterohepatic circulation of bile acids (BAs) is governed by specific transporters expressed in the liver and the intestine and plays a critical role in the digestion of fats and oils. During this process, the majority of the BAs secreted from the liver is reabsorbed in intestinal epithelial cells via the apical sodium-dependent bile acid transporter (ASBT/SLC10A2) and then transported into the portal vein. Previous studies revealed that regulation of the ASBT involves BAs and cholesterol. In addition, abnormal ASBT expression and function might lead to some diseases associated with disorders in the enterohepatic circulation of BAs and cholesterol homeostasis, such as diarrhoea and gallstones. However, decreasing cholesterol or BAs by partly inhibiting ASBT-mediated transport might be used for treatments of hypercholesterolemia, cholestasis and diabetes. This review mainly discusses the regulation of the ASBT by BAs and cholesterol and its relevance to diseases and treatment.

Using Multi-fluorinated Bile Acids and In Vivo Magnetic Resonance Imaging to Measure Bile Acid Transport

Along with their traditional role as detergents that facilitate fat absorption, emerging literature indicates that bile acids are potent signaling molecules that affect multiple organs; they modulate gut motility and hormone production, and alter vascular tone, glucose metabolism, lipid metabolism, and energy utilization. Changes in fecal bile acids may alter the gut microbiome and promote colon pathology including cholerrheic diarrhea and colon cancer. Key regulators of fecal bile acid composition are the small intestinal Apical Sodium-dependent Bile Acid Transporter (ASBT) and fibroblast growth factor-19 (FGF19). Reduced expression and function of ASBT decreases intestinal bile acid up-take. Moreover, in vitro data suggest that some FDA-approved drugs inhibit ASBT function. Deficient FGF19 release increases hepatic bile acid synthesis and release into the intestines to levels that overwhelm ASBT. Either ASBT dysfunction or FGF19 deficiency increases fecal bile acids and may cause chronic diarrhea and promote colon neoplasia. Regrettably, tools to measure bile acid malabsorption and the actions of drugs on bile acid transport in vivo are limited. To understand the complex actions of bile acids, techniques are required that permit simultaneous monitoring of bile acids in the gut and metabolic tissues. This led us to conceive an innovative method to measure bile acid transport in live animals using a combination of proton (¹H) and fluorine (¹⁹F) magnetic resonance imaging (MRI). Novel tracers for fluorine (¹⁹F)-based live animal MRI were created and tested, both in vitro and in vivo. Strengths of this approach include the lack of exposure to ionizing radiation and translational potential for clinical research and practice.

The Next Era: Deep Learning in Pharmaceutical Research

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses

Bayesian models constructed from structure-derived fingerprints have been a popular and useful method for drug discovery research when applied to bioactivity measurements that can be effectively classified as active or inactive. The results can be used to rank candidate structures according to their probability of activity, and this ranking benefits from the high degree of interpretability when structure-based fingerprints are used, making the results chemically intuitive. Besides selecting an activity threshold, building a Bayesian model is fast and requires few or no parameters or user intervention. The method also does not suffer from such acute overtraining problems as quantitative structure–activity relationships or quantitative structure–property relationships (QSAR/QSPR). This makes it an approach highly suitable for automated workflows that are independent of user expertise or prior knowledge of the training data. We now describe a new method for creating a composite group of Bayesian models to extend the method to work with multiple states, rather than just binary. Incoming activities are divided into bins, each covering a mutually exclusive range of activities. For each of these bins, a Bayesian model is created to model whether or not the compound belongs in the bin. Analyzing putative molecules using the composite model involves making a prediction for each bin and examining the relative likelihood for each assignment, for example, highest value wins. The method has been evaluated on a collection of hundreds of data sets extracted from ChEMBL v20 and validated data sets for ADME/Tox and bioactivity.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Machine learning models identify molecules active against the Ebola virus in vitro

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC ₅₀ values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Slc10a2-null mice uncover colon cancer-promoting actions of endogenous fecal bile acids

Although epidemiological evidence in humans and bile acid feeding studies in rodents implicate bile acids as tumor promoters, the role of endogenous bile acids in colon carcinogenesis remains unclear. In this study, we exploited mice deficient in the ileal apical sodium-dependent bile acid transporter (ASBT, encoded by SLC10A2) in whom fecal bile acid excretion is augmented more than 10-fold. Wild-type and Asbt-deficient (Slc10a2 (-/-) ) male mice were treated with azoxymethane (AOM) alone to examine the development of aberrant crypt foci, the earliest histological marker of colon neoplasia and a combination of AOM and dextran sulfate sodium to induce colon tumor formation. Asbt-deficient mice exhibited a 54% increase in aberrant crypt foci, and 70 and 59% increases in colon tumor number and size, respectively. Compared to littermate controls, Asbt-deficient mice had a striking, 2-fold increase in the number of colon adenocarcinomas. Consistent with previous studies demonstrating a role for muscarinic and epidermal growth factor receptor signaling in bile acid-induced colon neoplasia, increasing bile acid malabsorption was associated with M3 muscarinic and epidermal growth factor receptor expression, and activation of extracellular signal-related kinase, a key post-receptor signaling molecule.

Making Transporter Models for Drug-Drug Interaction Prediction Mobile

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models.

SLC transporters as therapeutic targets: emerging opportunities

Solute carrier (SLC) transporters - a family of more than 300 membrane-bound proteins that facilitate the transport of a wide array of substrates across biological membranes - have important roles in physiological processes ranging from the cellular uptake of nutrients to the absorption of drugs and other xenobiotics. Several classes of marketed drugs target well-known SLC transporters, such as neurotransmitter transporters, and human genetic studies have provided powerful insight into the roles of more-recently characterized SLC transporters in both rare and common diseases, indicating a wealth of new therapeutic opportunities. This Review summarizes knowledge on the roles of SLC transporters in human disease, describes strategies to target such transporters, and highlights current and investigational drugs that modulate SLC transporters, as well as promising drug targets.

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Background

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.

Methodology/Principal Findings

In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC₅₀ < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi.

Conclusions/ Significance

We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.

Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The disease is endemic to Latin America but is increasingly found in North America and Europe, primarily through immigration, and the spread of this disease is bringing new attention to the need for novel, safe, and effective therapeutics to treat T. cruzi infection. We have used data from a phenotypic screen to build Bayesian models to predict anti-parasitic activity against T. cruzi in vitro. These models were used to score various small libraries of molecules. We selected less than 100 compounds for testing and found in vitro actives, some of which were tested in an in vivo efficacy model. We identified the antimalarial pyronaridine as having in vivo efficacy and provides us with a new starting point for further investigation and optimization.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL

In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

Sodium-dependent bile salt transporters of the SLC10A transporter family: more than solute transporters

The SLC10A transporter gene family consists of seven members and substrates transported by three members (SLC10A1, SLC10A2 and SLC10A6) are Na(+)-dependent. SLC10A1 (sodium taurocholate cotransporting polypeptide [NTCP]) and SLC10A2 (apical sodium-dependent bile salt transporter [ASBT]) transport bile salts and play an important role in maintaining enterohepatic circulation of bile salts. Solutes other than bile salts are also transported by NTCP. However, ASBT has not been shown to be a transporter for non-bile salt substrates. While the transport function of NTCP can potentially be used as liver function test, interpretation of such a test may be complicated by altered expression of NTCP in diseases and presence of drugs that may inhibit NTCP function. Transport of bile salts by NTCP and ASBT is inhibited by a number of drugs and it appears that ASBT is more permissive to drug inhibition than NTCP. The clinical significance of this inhibition in drug disposition and drug-drug interaction remains to be determined. Both NCTP and ASBT undergo post-translational regulations that involve phosphorylation/dephosphorylation, translocation to and retrieval from the plasma membrane and degradation by the ubiquitin-proteasome system. These posttranslational regulations are mediated via signaling pathways involving cAMP, calcium, nitric oxide, phosphoinositide-3-kinase (PI3K), protein kinase C (PKC) and protein phosphatases. There appears to be species difference in the substrate specificity and the regulation of plasma membrane localization of human and rodent NTCP. These differences should be taken into account when extrapolating rodent data for human clinical relevance and developing novel therapies. NTCP has recently been shown to play an important role in HBV and HDV infection by serving as a receptor for entry of these viruses into hepatocytes.

Sodium-dependent bile salt transporters of the SLC10A transporter family: more than solute transporters

The SLC10A transporter gene family consists of seven members and substrates transported by three members (SLC10A1, SLC10A2 and SLC10A6) are Na(+)-dependent. SLC10A1 (sodium taurocholate cotransporting polypeptide [NTCP]) and SLC10A2 (apical sodium-dependent bile salt transporter [ASBT]) transport bile salts and play an important role in maintaining enterohepatic circulation of bile salts. Solutes other than bile salts are also transported by NTCP. However, ASBT has not been shown to be a transporter for non-bile salt substrates. While the transport function of NTCP can potentially be used as liver function test, interpretation of such a test may be complicated by altered expression of NTCP in diseases and presence of drugs that may inhibit NTCP function. Transport of bile salts by NTCP and ASBT is inhibited by a number of drugs and it appears that ASBT is more permissive to drug inhibition than NTCP. The clinical significance of this inhibition in drug disposition and drug-drug interaction remains to be determined. Both NCTP and ASBT undergo post-translational regulations that involve phosphorylation/dephosphorylation, translocation to and retrieval from the plasma membrane and degradation by the ubiquitin-proteasome system. These posttranslational regulations are mediated via signaling pathways involving cAMP, calcium, nitric oxide, phosphoinositide-3-kinase (PI3K), protein kinase C (PKC) and protein phosphatases. There appears to be species difference in the substrate specificity and the regulation of plasma membrane localization of human and rodent NTCP. These differences should be taken into account when extrapolating rodent data for human clinical relevance and developing novel therapies. NTCP has recently been shown to play an important role in HBV and HDV infection by serving as a receptor for entry of these viruses into hepatocytes.

The solute carrier family 10 (SLC10): beyond bile acid transport

The solute carrier (SLC) family 10 (SLC10) comprises influx transporters of bile acids, steroidal hormones, various drugs, and several other substrates. Because the seminal transporters of this family, namely, sodium/taurocholate cotransporting polypeptide (NTCP; SLC10A1) and the apical sodium-dependent bile acid transporter (ASBT; SLC10A2), were primarily bile acid transporters, the term "sodium bile salt cotransporting family" was used for the SLC10 family. However, this notion became obsolete with the finding of other SLC10 members that do not transport bile acids. For example, the sodium-dependent organic anion transporter (SOAT; SLC10A6) transports primarily sulfated steroids. Moreover, NTCP was shown to also transport steroids and xenobiotics, including HMG-CoA inhibitors (statins). The SLC10 family contains four additional members, namely, P3 (SLC10A3; SLC10A3), P4 (SLC10A4; SLC10A4), P5 (SLC10A5; SLC10A5) and SLC10A7 (SLC10A7), several of which were unknown or considered hypothetical until approximately a decade ago. While their substrate specificity remains undetermined, great progress has been made towards their characterization in recent years. Explicitly, SLC10A4 may participate in vesicular storage or exocytosis of neurotransmitters or mastocyte mediators, whereas SLC10A5 and SLC10A7 may be involved in solute transport and SLC10A3 may have a role as a housekeeping protein. Finally, the newly found role of bile acids in glucose and energy homeostasis, via the TGR5 receptor, sheds new light on the clinical relevance of ASBT and NTCP. The present mini-review provides a brief summary of recent progress on members of the SLC10 family.

An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters

Drug-induced cholestasis is an important form of acquired liver disease and is associated with significant morbidity and mortality. Bile acids are key signaling molecules, but they can exert toxic responses when they accumulate in hepatocytes. This review focuses on the physiological mechanisms of drug-induced cholestasis associated with altered bile acid homeostasis due to direct (e.g., bile acid transporter inhibition) or indirect (e.g., activation of nuclear receptors, altered function/expression of bile acid transporters) processes. Mechanistic information about the effects of a drug on bile acid homeostasis is important when evaluating the cholestatic potential of a compound, but experimental data often are not available. The relationship between physicochemical properties, pharmacokinetic parameters, and inhibition of the bile salt export pump among 77 cholestatic drugs with different pathophysiological mechanisms of cholestasis (i.e., impaired formation of bile vs. physical obstruction of bile flow) was investigated. The utility of in silico models to obtain mechanistic information about the impact of compounds on bile acid homeostasis to aid in predicting the cholestatic potential of drugs is highlighted.