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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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology., Methods: Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >10 5 drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits., Results: Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae., Conclusions: This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.

Purpose: Individuals with the rare genetic disorder Pitt Hopkins Syndrome (PTHS) do not have sufficient expression of the transcription factor 4 (TCF4) which is located on chromosome 18. TCF4 is a basic helix-loop-helix E protein that is critical for the normal development of the nervous system and the brain in humans. PTHS patients lacking sufficient TCF4 frequently display gastrointestinal issues, intellectual disability and breathing problems. PTHS patients also commonly do not speak and display distinctive facial features and seizures. Recent research has proposed that decreased TCF4 expression can lead to the increased translation of the sodium channel Na v 1.8. This in turn results in increased after-hyperpolarization as well as altered firing properties. We have recently identified through a drug repurposing screen an FDA approved dihydropyridine calcium antagonist nicardipine used to treat angina, which inhibited Na v 1.8., Methods: We have now performed behavioral testing in groups of 10 male Tcf4(± ) PTHS mice dosing by oral gavage at 3 mg/kg once a day for 3 weeks using standard methods to assess sociability, nesting, fear conditioning, self-grooming, open field and test of force., Results: Nicardipine returned this spectrum of behavioral deficits in the Tcf4(± ) PTHS mouse model to WT levels and resulted in statistically significant results., Conclusions: These in vivo results in the well characterized Tcf4(± ) PTHS mice may suggest the potential to test this already approved drug further in a clinical study with PTHS patients or suggest the potential for use off label under compassionate use with their physician.

The COVID-19 pandemic has highlighted an important role for drug repurposing. Quaternary ammonium compounds such as ammonium chloride, cetylpyridinium and miramistin represent widely accessible antiseptic molecules with well-known broad-spectrum antiviral activities and represent a repurposing opportunity as therapeutics against SARS-CoV-2.

For the last 50 years we have known of a broad-spectrum agent tilorone dihydrochloride (Tilorone). This is a small-molecule orally bioavailable drug that was originally discovered in the USA and is currently used clinically as an antiviral in Russia and the Ukraine. Over the years there have been numerous clinical and non-clinical reports of its broad spectrum of antiviral activity. More recently we have identified additional promising antiviral activities against Middle East Respiratory Syndrome, Chikungunya, Ebola and Marburg which highlights that this old drug may have other uses against new viruses. This may in turn inform the types of drugs that we need for virus outbreaks such as for the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Tilorone has been long neglected by the west in many respects but it deserves further reassessment in light of current and future needs for broad-spectrum antivirals.

The discovery of drugs to treat tuberculosis (TB) was a major medical milestone in the twentieth century. However, from the outset, drug resistance was observed. Currently, of the 10 million people that exhibit TB symptoms each year, 450,000 have multidrug or extensively drug resistant (MDR or XDR) TB. While greater understanding of the host and pathogen (Mycobacterium tuberculosis, Mtb) coupled with scientific ingenuity will lead to new drugs and vaccines, in the meantime 4000 people die daily from TB. Thus, efforts to improve existing TB drugs should also be prioritized. Improved efficacy and decreased dose and associated toxicity of existing drugs would translate to greater compliance, life expectancy and quality of life of Mtb infected individuals. One potential strategy to improve existing drugs is to deliver them by inhalation as aerosols to the lung, the primary site of Mtb infection. Inhaled drugs are used for other pulmonary diseases, but they have yet to be utilized for TB. Inhaled therapies for TB represent an untapped opportunity that the pharmaceutical, clinical and regulatory communities should consider.

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 v 7.1 and Na v 1.8 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 V 7.1) or HEK293 (Na v 1.8) cells and the HTS used either 86 Rb + efflux (K V 7.1) or a FLIPR assay (Na v 1.8)., Results: The HTS delivered 55 inhibitors of K v 7.1 (4.2% hit rate) and 93 inhibitors of Na v 1.8 (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 v 1.8., 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 v 1.8, as there are currently no approved treatments for this rare disorder.

Purpose: Since the 2014 Ebola virus (EBOV) outbreak in West Africa there has been considerable effort towards developing drugs to treat Ebola virus disease and yet to date there is no FDA approved treatment. This is important as at the time of writing this manuscript there is an ongoing outbreak in the Democratic Republic of the Congo which has killed over 1000., Methods: We have evaluated a small number of natural products, some of which had shown antiviral activity against other pathogens. This is exemplified with eugenol, which is found in high concentrations in multiple essential oils, and has shown antiviral activity against feline calicivirus, tomato yellow leaf curl virus, Influenza A virus, Herpes Simplex virus type 1 and 2, and four airborne phages., Results: Four compounds possessed EC 50 values less than or equal to 11 μM. Of these, eugenol, had an EC 50 of 1.3 μM against EBOV and is present in several plants including clove, cinnamon, basil and bay. Eugenol is much smaller and structurally unlike any compound that has been previously identified as an inhibitor of EBOV, therefore it may provide new mechanistic insights., Conclusion: This compound is readily accessible in bulk quantities, is inexpensive, and has a long history of human consumption, which endorses the idea for further assessment as an antiviral therapeutic. This work also suggests that a more exhaustive assessment of natural product libraries against EBOV and other viruses is warranted to improve our ability to identify compounds that are so distinct from FDA approved drugs.

Purpose: Neglected tropical diseases (NTDs) represent are a heterogeneous group of communicable diseases that are found within the poorest populations of the world. There are 23 NTDs that have been prioritized by the World Health Organization, which are endemic in 149 countries and affect more than 1.4 billion people, costing these developing economies billions of dollars annually. The NTDs result from four different causative pathogens: protozoa, bacteria, helminth and virus. The majority of the diseases lack effective treatments. Therefore, new therapeutics for NTDs are desperately needed., Methods: We describe various high throughput screening and computational approaches that have been performed in recent years. We have collated the molecules identified in these studies and calculated molecular properties., Results: Numerous global repurposing efforts have yielded some promising compounds for various neglected tropical diseases. These compounds when analyzed as one would expect appear drug-like. Several large datasets are also now in the public domain and this enables machine learning models to be constructed that then facilitate the discovery of new molecules for these pathogens., Conclusions: In the space of a few years many groups have either performed experimental or computational repurposing high throughput screens against neglected diseases. These have identified compounds which in many cases are already approved drugs. Such approaches perhaps offer a more efficient way to develop treatments which are generally not a focus for global pharmaceutical companies because of the economics or the lack of a viable market. Other diseases could perhaps benefit from these repurposing approaches.

The face of rare disease drug discovery and development is changing right before our eyes. The outliers of the past were the plucky parents who summoned up the courage to try to treat their children against all odds. Think of the rare disease focused movies 'Lorenzo's Oil' and 'Extraordinary Measures' but now accelerated to develop treatments even quicker. Parents, patient advocates and their collaborators are now capable of doing it all themselves. We think this will have profound implications for everyone from the incumbent rare disease foundations that have held sway for decades to the multibillion dollar rare disease market, BioPharma companies, VCs and angel investors that inhabit this space. The repercussions of this disruption will no doubt impact healthcare in general and ultimately influence how we develop treatments for major diseases as well. We present several lines of evidence for our viewpoint from our personal experiences interacting with many rare disease families and patient advocates in recent years.

Purpose: To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC 50 ) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds., Methods: Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem., Results: Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors., Conclusions: The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. Graphical Abstract Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.

How do we inspire new ideas that could lead to potential treatments for rare or neglected diseases, and allow for serendipity that could help to catalyze them? How many potentially good ideas are lost because they are never tested? What if those ideas could have lead to new therapeutic approaches and major healthcare advances? If a clinician or anyone for that matter, has a new idea they want to test to develop a molecule or therapeutic that they could translate to the clinic, how would they do it without a laboratory or funding? These are not idle theoretical questions but addressing them could have potentially huge economic implications for nations. If we fail to capture the diversity of ideas and test them we may also lose out on the next blockbuster treatments. Many of those involved in the process of ideation may be discouraged and simply not know where to go. We try to address these questions and describe how there are options to raising funding, how even small scale investments can foster preclinical or clinical translation, and how there are several approaches to outsourcing the experiments, whether to collaborators or commercial enterprises. While these are not new or far from complete solutions, they are first steps that can be taken by virtually anyone while we work on other solutions to build a more concrete structure for the "idea-hypothesis testing-proof of concept-translation-breakthrough pathway".

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.

Starting biotech or pharmaceutical companies is traditionally thought to be based around a scientist, their technology platform or a clinical candidate spun out from another company. Between us we have taken a different approach and formed two small early stage companies after initially leveraging the perspective of a parent with a child with a life-threatening rare disease. Phoenix Nest ( http://www.phoenixnestbiotech.com/ ) was co-founded to work on treatments for Sanfilippo syndrome a devastating neurodegenerative lysosomal storage disorder. In the space of just over 3 years we have built up collaborations with leading scientists in academia and industry and been awarded multiple NIH small business grants. The second company, Collaborations Pharmaceuticals Inc. ( http://www.collaborationspharma.com/ ) was founded to address some of the other 7000 or so rare diseases as well as neglected infectious diseases. The Rare Pediatric Disease Priority Review Voucher is likely the most important incentive for companies working on rare diseases with very small populations. This may also be partially responsible for the recent acquisitions of rare disease companies with late stage candidates. Lessons learned in the process of starting our companies are that rare disease parents or patients can readily partner with a scientist and fund research through NIH grants rather than venture capital or angel investors initially. This process may be slow so patience and perseverance is key. We would encourage other pharmaceutical scientists to meet rare disease parents, patients or advocates and work with them to further the science on their diseases and create a source of future drugs.

Purpose: Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability., Methods: Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism)., Results: "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h., Conclusions: Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Purpose: We propose a framework with simple proxies to dissect the relative energy contributions responsible for standard drug discovery binding activity., Methods: We explore a rule of thumb using hydrogen-bond donors, hydrogen-bond acceptors and rotatable bonds as relative proxies for the thermodynamic terms. We apply this methodology to several datasets (e.g., multiple small molecules profiled against kinases, Mycobacterium tuberculosis (Mtb) high throughput screening (HTS) and structure based drug design (SBDD) derived compounds, and FDA approved drugs)., Results: We found that Mtb active compounds developed through SBDD methods had statistically significantly larger PEnthalpy values than HTS derived compounds, suggesting these compounds had relatively more hydrogen bond donor and hydrogen bond acceptors compared to rotatable bonds. In recent FDA approved medicines we found that compounds identified via target-based approaches had a more balanced enthalpic relationship between these descriptors compared to compounds identified via phenotypic screens, Conclusions: As it is common to experimentally optimize directly for total binding energy, these computational methods provide alternative calculations and approaches useful for compound optimization alongside other common metrics in available software and databases.

Purpose: Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested., Methods: A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate., Results: In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models., Conclusion: Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.

Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.

Purpose: New strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators., Methods: We now describe an approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as substrate mimics and exhibit activity against TB., Results: In this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N'-[(5-nitro-3-thienyl)carbonyl]thiourea and N-[(5-nitro-3-thienyl)carbonyl]-N'-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 μg/ml, respectively)., Conclusion: This is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.

Understanding the regulation of gene expression is critical to many areas of biology while control via RNAs has found considerable interest as a tool for scientific discovery and potential therapeutic applications. For example whole genome RNA interference (RNAi) screens and whole proteome scans provide views of how the entire transcriptome or proteome responds to biological, chemical or environmental perturbations of a gene's activity. Small RNA (sRNA) or MicroRNA (miRNA) are known to regulate pathways and bind mRNA, while the function of miRNAs discovered in experimental studies is often unknown. In both cases, RNAi and miRNA require labor intensive studies to tease out their functions within gene networks. Available software to analyze relationships is currently an ad hoc and often a manual process that can take up to several hours to analyze a single candidate RNAi or miRNA. With experiments frequently highlighting tens to hundreds of candidates this represents a considerable bottleneck. We suggest there is a gap in miRNA and RNAi research caused by inadequate current software that could be improved. For example a new software application could be created that provides interactive, comprehensive target analysis that leverages past datasets to lead to statistically stronger analyses.

Purpose: The search for small molecules with activity against Mycobacterium tuberculosis (Mtb) increasingly uses high throughput screening and computational methods. Several public datasets from the Collaborative Drug Discovery Tuberculosis (CDD TB) database have been evaluated with cheminformatics approaches to validate their utility and suggest compounds for testing., Methods: Previously reported Bayesian classification models were used to predict a set of 283 Novartis compounds tested against Mtb (containing aerobic and anaerobic hits) and to search FDA approved drugs. The Novartis compounds were also filtered with computational SMARTS alerts to identify potentially undesirable substructures., Results: Using the Novartis compounds as a test set for the Bayesian models demonstrated a >4.0-fold enrichment over random screening for finding aerobic hits not in the computational models (N = 34). A 10-fold enrichment was observed for finding Mtb active compounds in the FDA drugs database. 85.9% of the Novartis compounds failed the Abbott SMARTS alerts, a value substantially higher than for known TB drugs. Higher levels of failures of SMARTS filters from different groups also correlate with the number of Lipinski violations., Conclusions: These computational approaches may assist in finding desirable leads for Tuberculosis drug discovery.

From research published in the last six years we have identified 34 studies that have screened libraries of FDA-approved drugs against various whole cell or target assays. These studies have each identified one or more compounds with a suggested new bioactivity that had not been described previously. We now show that 13 of these drugs were active against more than one additional disease, thereby suggesting a degree of promiscuity. We also show that following compilation of all the studies, 109 molecules were identified by screening in vitro. These molecules appear to be statistically more hydrophobic with a higher molecular weight and AlogP than orphan-designated products with at least one marketing approval for a common disease indication or one marketing approval for a rare disease from the FDA's rare disease research database. Capturing these in vitro data on old drugs for new uses will be important for potential reuse and analysis by others to repurpose or reposition these or other existing drugs. We have created databases which can be searched by the public and envisage that these can be updated as more studies are published.

Cheminformatics is at a turning point, the pharmaceutical industry benefits from using the various methods developed over the last twenty years, but in our opinion we need to see greater development of novel approaches that non-experts can use. This will be achieved by more collaborations between software companies, academics and the evolving pharmaceutical industry. We suggest that cheminformatics should also be looking to other industries that use high performance computing technologies for inspiration. We describe the needs and opportunities which may benefit from the development of open cheminformatics technologies, mobile computing, the movement of software to the cloud and precompetitive initiatives.

Purpose: Acetylcholinesterase (AChE) is both a therapeutic target for Alzheimer's disease and a target for organophosphorus, carbamates and chemical warfare agents. Prediction of the likelihood of compounds interacting with this enzyme is therefore important from both therapeutic and toxicological perspectives., Materials and Methods: Support vector machine classification and regression models with molecular descriptors derived from Shape Signatures and the Molecular Operating Environment (MOE) application software were built and tested using a set of piperidine AChE inhibitors (N = 110)., Results: The combination of the alignment free Shape Signatures and 2D MOE descriptors with the Support Vector Regression method outperforms the models based solely on 2D and internal 3D (i3D) MOE descriptors, and is comparable with the best previously reported PLS model based on CoMFA molecular descriptors (r(2)(test,SVR) = 0.48 vs. r(2)(test,PLS) = 0.47 from Sutherland et al. J Med Chem 47:5541-5554, 2004). Support Vector Classification algorithms proved superior to a classifier based on scores from the molecular docking program GOLD, with the overall prediction accuracies being Q(SVC(10CV)) = 74% and Q(SVC(LNO)) = 67% vs. Q(GOLD) = 56%., Conclusions: These new machine learning models with combined descriptor schemes may find utility for predicting novel AChE inhibitors.

The pregnane X receptor belongs to the nuclear hormone receptor superfamily and is involved in the transcriptional control of numerous genes. It was originally thought that it was a xenobiotic sensor controlling detoxification pathways. Recent studies have shown an increasingly important role in inflammation and cancer, supporting its function in abrogating tissue damage. PXR orthologs and PXR-like pathways have been identified in several non-mammalian species which corroborate a conserved role for PXR in cellular detoxification. In summary, PXR has a multiplicity of roles in vivo and is being revealed as behaving like a "Jekyll and Hyde" nuclear hormone receptor. The importance of this review is to elucidate the need for discovery of antagonists of PXR to further probe its biology and therapeutic applications. Although several PXR agonists are already reported, virtually nothing is known about PXR antagonists. Here, we propose the development of PXR antagonists through chemical, genetic and molecular modeling approaches. Based on this review it will be clear that antagonists of PXR and PXR-like pathways will have widespread utility in PXR biology and therapeutics.

Purpose: The objective was to elucidate the inhibition requirements of the human organic cation/carnitine transporter (hOCTN2)., Methods: Twenty-seven drugs were screened initially for their potential to inhibit uptake of L-carnitine into a stably transfected hOCTN2-MDCK cell monolayer. A HipHop common features pharmacophore was developed and used to search a drug database. Fifty-three drugs, including some not predicted to be inhibitors, were selected and screened in vitro., Results: A common features pharmacophore was derived from initial screening data and consisted of three hydrophobic features and a positive ionizable feature. Among the 33 tested drugs that were predicted to map to the pharmacophore, 27 inhibited hOCTN2 in vitro (40% or less L-carnitine uptake from 2.5 microM L-carnitine solution in presence of 500 microM drug, compared to L-carnitine uptake without drug present). Hence, the pharmacophore accurately prioritized compounds for testing. K(i) measurements showed low micromolar inhibitors belonged to diverse therapeutic classes of drugs, including many not previously known to inhibit hOCTN2. Compounds were more likely to cause rhabdomyolysis if the C(max)/K(i) ratio was higher than 0.0025., Conclusion: A combined pharmacophore and in vitro approach found new, structurally diverse inhibitors for hOCTN2 that may possibly cause clinical significant toxicity such as rhabdomyolysis.

Purpose: The human pregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses, Materials and Methods: Computational models were developed and validated for a dataset consisting of human PXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results., Results: The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available human PXR data sets we revealed those molecular features that are associated with human PXR activators., Conclusions: These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.

Purpose: The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling., Materials and Methods: The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models., Results: The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80-83% and leave-20%-out testing prediction accuracy between 80-82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs., Conclusions: Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood-brain barrier permeation in drug discovery.

Purpose: We applied in silico methods to automatically classify drugs according to the Biopharmaceutics Drug Disposition Classification System (BDDCS)., Materials and Methods: Models were developed using machine learning methods including recursive partitioning (RP), random forest (RF) and support vector machine (SVM) algorithms with ChemDraw, clogP, polar surface area, VolSurf and MolConnZ descriptors. The dataset consisted of 165 training and 56 test set molecules., Results: RF model 3, RP model 1, and SVM model 1 can correctly predict 73.1, 63.6 and 78.6% test compounds in classes 1, 2 and 3, respectively. Both RP and SVM models can be used for class 4 prediction. The inclusion of consensus analysis resulted in improved test set predictions for class 2 and 4 drugs., Conclusions: The models can be used to predict BDDCS class for new compounds from molecular structure using readily available molecular descriptors and software, representing an area where in silico approaches could aid the pharmaceutical industry in speeding drugs to the patient and reducing costs. This could have significant applications in drug discovery to identify molecules that may have future developability issues.

Purpose: To evaluate in vitro and computationally model the effects of selected antipsychotic drugs on several ionic currents that contribute to changes in the action potential in cardiac tissue., Methods: Fourteen antipsychotic drugs or metabolites were examined to determine whether QT interval prolongation could be accounted for by an effect on one or more myocardial ion channels [I(to), I(Na), I(sus), I(K1), and human ether-a-go-go related gene (hERG)]. Using the patch clamp technique, drug effects on these human cardiac currents were tested., Results: All molecules had little inhibitory effect on ion channels (blocking at concentrations >5 microM) other than hERG. A significant correlation was observed between the estimated hERG blockade and the increase in corrected QT for five of the antipsychotics. Molecular modeling identified hydrophobic features related to the interaction with hERG and correctly rank-ordered the test set molecules olanzapine and its metabolites. A network analysis of ligand and protein interactions around hERG using MetaCore (GeneGo Inc., St. Joseph, MI, USA) was used to visualize antipsychotics with affinity for this channel and their interactions with other proteins in this database., Conclusion: The antipsychotics do not inhibit the ion channels I(to), I(Na), I(sus), I(K1) to any appreciable extent; however, blockade of hERG is a likely mechanism for the prolongation of the QT interval.

Purpose: The human proton-coupled small peptide carrier (hPEPT1) is a low-affinity, high-capacity transporter with broad substrate specificity. We have taken an iterative in vitro and in silico approach to the discovery of molecules with hPEPT1 affinity., Methods: A pharmacophore-based approach was taken to identifying hPEPT1 inhibitors. The well-characterized and relatively high affinity ligands Gly-Sar, bestatin, and enalapril were used to generate a common features (HIPHOP) pharmacophore. This consisted of two hydrophobic features, a hydrogen bond donor, acceptor, and a negative ionizable feature., Results: The pharmacophore was used to search the Comprehensive Medicinal Chemistry (CMC) database of more than 8000 drug-like molecules and retrieved 145 virtual hits mapping to the pharmacophore features. The highest scoring compounds within this set were selected and tested in a stably transfected CHO-hPepT1 cell model. The antidiabetic repaglinide and HMG CoA reductase inhibitor fluvastatin were found to inhibit hPEPT1 with sub-millimolar potency (IC(50) 178 +/- 1.0 and 337 +/- 4 microM, respectively). The pharmacophore was also able to identify known hPEPT1 substrates and inhibitors in further database mining of more than 500 commonly prescribed drugs., Conclusions: This study demonstrates the potential of combining computational and in vitro approaches to determine the affinity of compounds for hPEPT1 and, in turn, provides insights into key molecular interactions with this transporter.

In recent years discussion of nuclear hormone receptors, transporters, and drug-metabolizing enzymes has begun to take place as our knowledge of the overlapping ligand specificity of each of these proteins has deepened. This ligand specificity is potentially valuable information for influencing future drug design, as it is important to avoid certain enzymes or transporters in order to circumvent potential drug-drug interactions. Similarly, it is critical that the induction of these same proteins via nuclear hormone receptors is avoided, as this can result in further toxicities. Using a ligand-based approach in this review we describe new and previously published computational models for PXR, CAR, FXR, LXRalpha, and LXRbeta that may help in understanding the complexity of interactions between transporters and enzymes. The value of these types of models is that they may enable us to design molecules to selectively modulate pathways for therapeutic effect and in addition predict the potential for drug interactions more reliably. Simultaneously, we might learn which came first: the transporter, the enzyme, or the nuclear hormone receptor?