Halogen Substitution Influences Ketamine Metabolism by Cytochrome P450 2B6: In Vitro and Computational Approaches

2-Fluorodeschloroketamine consumption: About two deaths and a case of self-mutilation

2-Fluorodeschloroketamine (2-FDCK) is a new psychoactive substance (NPS), close to the ketamine structure. Few cases of 2-FDCK intake are described in the forensic literature, especially concerning death cases. We report here a case of self-mutilation (Case 1) and two forensic deaths linked to 2-FDCK consumption. The second case involved a man found dead in the street, having been stabbed. The third case was a man found dead following a suspected overdose and in an advanced state of putrefaction. For all three cases, biological fluids such as blood and urine were analyzed, as was hair for the two fatal cases. The aim of this study was to identify and quantify 2-FDCK and its main metabolites in different matrices. Biological fluids and hair were analyzed by liquid chromatography coupled with tandem mass spectrometry after decontamination and extraction. Seized products were analyzed by gas chromatography-mass spectrometry and assayed, when possible, by ultra-performance liquid chromatography with diode-array detection. 2-FDCK was detected and quantified in the peripheral blood of Cases 1, 2 and 3 (457, 758 and 5885 µg/L, respectively), as were its main metabolites nor-2-FDCK, dihydro-nor-2-FDCK and dihydro-2-FDCK. In the 1 cm long hair of Cases 2 and 3, 2-FDCK was also detected (approximately 4149 and 79824 pg/mg, respectively). Deschloroketamine (DCK) was found in the biological fluids of Cases 1, 2 and 3 (10, 8 and 350 µg/L, respectively), as well as in hair of Cases 2 and 3 (65 and around 8119 pg/mg, respectively). In Case 3, as a small bag containing DCK powder was seized from his home, we can assume that DCK was taken. On the contrary, to our knowledge, it has not been established that Case 2 took DCK alone, so we can assume that it may be the first case to report DCK from 2-FDCK metabolism in fluids as well as in hair.

In vitro and in vivo metabolic study of three new psychoactive β-keto-arylcyclohexylamines

Since the 2000s, an increasing number of new psychoactive substances have appeared on the illicit drug market. β-Keto-arylcyclohexylamine compounds play important pharmacological roles in anesthesia; however, because these new psychoactive substances have rapidly increasing illicit recreational use, the lack of detailed toxicity data are of particular concern. Therefore, analysis of their metabolites can help forensic personnel provide references and suggestions on whether a suspect has taken an illicit new psychoactive β-keto-arylcyclohexylamine. The present study investigated the in vitro and in vivo metabolism and metabolites of three β-keto-arylcyclohexylamines: deschloro-N-ethyl-ketamine, fluoro-N-ethyl-ketamine and bromoketamine. In vitro and in vivo models were established using zebrafish and human liver microsomes for analysis of Phase I and Phase II metabolites by liquid chromatography-high-resolution mass spectrometry. Altogether, 49 metabolites were identified. The results were applied for the subject urine samples of known fluoro-N-ethyl-ketamine consumer screen analysis in forensic cases. Hydroxy-deschloro-N-ethyl-ketamine, hydroxy-fluoro-N-ethyl-ketamine and hydroxy-bromoketamine were recommended as potential biomarkers for documenting intake in clinical and forensic cases.

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing

Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"1. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs.

Halogenated Antimicrobial Agents to Combat Drug-Resistant Pathogens

Antimicrobial resistance presents us with a potential global crisis as it undermines the abilities of conventional antibiotics to combat pathogenic microbes. The history of antimicrobial agents is replete with examples of scaffolds containing halogens. In this review, we discuss the impacts of halogen atoms in various antibiotic types and antimicrobial scaffolds and their modes of action, structure-activity relationships, and the contributions of halogen atoms in antimicrobial activity and drug resistance. Other halogenated molecules, including carbohydrates, peptides, lipids, and polymeric complexes, are also reviewed, and the effects of halogenated scaffolds on pharmacokinetics, pharmacodynamics, and factors affecting antimicrobial and antivirulence activities are presented. Furthermore, the potential of halogenation to circumvent antimicrobial resistance and rejuvenate impotent antibiotics is addressed. This review provides an overview of the significance of halogenation, the abilities of halogens to interact in biomolecular settings and enhance pharmacological properties, and their potential therapeutic usages in preventing a postantibiotic era. SIGNIFICANCE STATEMENT: Antimicrobial resistance and the increasing impotence of antibiotics are critical threats to global health. The roles and importance of halogen atoms in antimicrobial drug scaffolds have been established, but comparatively little is known of their pharmacological impacts on drug resistance and antivirulence activities. This review is the first to extensively evaluate the roles of halogen atoms in various antibiotic classes and pharmacological scaffolds and to provide an overview of their ability to overcome antimicrobial resistance.

High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents

Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.

Multidisciplinary Insights into the Structure-Function Relationship of the CYP2B6 Active Site

Cytochrome P450 2B6 (CYP2B6) is a highly polymorphic human enzyme involved in the metabolism of many clinically relevant drugs, environmental toxins, and endogenous molecules with disparate structures. Over the last 20-plus years, in silico and in vitro studies of CYP2B6 using various ligands have provided foundational information regarding the substrate specificity and structure-function relationship of this enzyme. Approaches such as homology modeling, X-ray crystallography, molecular docking, and kinetic activity assays coupled with CYP2B6 mutagenesis have done much to characterize this originally neglected monooxygenase. However, a complete understanding of the structural details that make new chemical entities substrates of CYP2B6 is still lacking. Surprisingly little in vitro data has been obtained about the structure-function relationship of amino acids identified to be in the CYP2B6 active site. Since much attention has already been devoted to elucidating the function of CYP2B6 allelic variants, here we review the salient findings of in silico and in vitro studies of the CYP2B6 structure-function relationship with a deliberate focus on the active site. In addition to summarizing these complementary approaches to studying structure-function relationships, we note gaps/challenges in existing data such as the need for more CYP2B6 crystal structures, molecular docking results with various ligands, and data coupling CYP2B6 active site mutagenesis with kinetic parameter measurement under standard expression conditions. Harnessing in silico and in vitro techniques in tandem to understand the CYP2B6 structure-function relationship will likely offer further insights into CYP2B6-mediated metabolism. SIGNIFICANCE STATEMENT: The apparent importance of cytochrome P450 2B6 (CYP2B6) in the metabolism of various xenobiotics and endogenous molecules has grown since its discovery with many in silico and in vitro studies offering a partial description of its structure-function relationship. Determining the structure-function relationship of CYP2B6 is difficult but may be aided by thorough biochemical investigations of the CYP2B6 active site that provide a more complete pharmacological understanding of this important enzyme.

Machine Learning for Discovery of New ADORA Modulators

Adenosine (ADO) is an extracellular signaling molecule generated locally under conditions that produce ischemia, hypoxia, or inflammation. It is involved in modulating a range of physiological functions throughout the brain and periphery through the membrane-bound G protein-coupled receptors, called adenosine receptors (ARs) A₁AR, A_2AAR, A_2BAR, and A₃AR. These are therefore important targets for neurological, cardiovascular, inflammatory, and autoimmune diseases and are the subject of drug development directed toward the cyclic adenosine monophosphate and other signaling pathways. Initially using public data for A₁AR agonists we generated and validated a Bayesian machine learning model (Receiver Operator Characteristic of 0.87) that we used to identify molecules for testing. Three selected molecules, crisaborole, febuxostat and paroxetine, showed initial activity in vitro using the HEK293 A₁AR Nomad cell line. However, radioligand binding, β-arrestin assay and calcium influx assay did not confirm this A₁AR activity. Nevertheless, several other AR activities were identified. Febuxostat and paroxetine both inhibited orthosteric radioligand binding in the µM range for A_2AAR and A₃AR. In HEK293 cells expressing the human A_2AAR, stimulation of cAMP was observed for crisaborole (EC₅₀ 2.8 µM) and paroxetine (EC₅₀ 14 µM), but not for febuxostat. Crisaborole also increased cAMP accumulation in A_2BAR-expressing HEK293 cells, but it was weaker than at the A_2AAR. At the human A₃AR, paroxetine did not show any agonist activity at 100 µM, although it displayed binding with a K_i value of 14.5 µM, suggesting antagonist activity. We have now identified novel modulators of A_2AAR, A_2BAR and A₃AR subtypes that are clinically used for other therapeutic indications, and which are structurally distinct from previously reported tool compounds or drugs.

The effect of halogenation of salicylaldehyde on the antiproliferative activities of {Δ/Λ-[Ru(bpy)2(X,Y-sal)]BF4} complexes

Ru(II) polypyridyl complexes are widely used in biological fields, due to their physico-chemical and photophysical properties. In this paper, a series of new chiral Ru(II) polypyridyl complexes (1-5) with the general formula {Δ/Λ-[Ru(bpy)₂(X,Y-sal)]BF₄} (bpy = 2,2'-bipyridyl; X,Y-sal = 5-bromosalicylaldehyde (1), 3,5-dibromosalicylaldehyde (2), 5-chlorosalicylaldehyde (3), 3,5-dichlorosalicylaldehyde (4) and 3-bromo-5-chlorosalicylaldehy (5)) were synthesized and characterized by elemental analysis, FT-IR, and ¹H/¹³C NMR spectroscopy. Also, the structures of complexes 1 and 5 were determined by X-ray crystallography; these results showed that the central Ru atom adopts a distorted octahedral coordination sphere with two bpy and one halogen-substituted salicylaldehyde. DFT and TD-DFT calculations have been performed to explain the excited states of these complexes. The singlet states with higher oscillator strength are correlated with the absorption signals and are mainly described as ¹MLCT from the ruthenium centre to the bpy ligands. The lowest triplet states (T₁) are described as ³MLCT from the ruthenium center to the salicylaldehyde ligand. The theoretical results are in good agreement with the observed unstructured band at around 520 nm for complexes 2, 4 and 5. Biological studies on human cancer cells revealed that dihalogenated ligands endow the Ru(II) complexes with enhanced cytotoxicity compared to monohalogenated ligands. In addition, as far as the type of halogen is concerned, bromine is the halogen that provides the highest cytotoxicity to the synthesized complexes. All complexes induce cell cycle arrest in G0/G1 and apoptosis, but only complexes bearing Br are able to provoke an increase in intracellular ROS levels and mitochondrial dysfunction.

Mycobacterium abscessus drug discovery using machine learning

The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant problem for patients with underlying lung diseases such as bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis. Current treatment regimens are lengthy, complicated, toxic and they are often unsuccessful as seen by disease recurrence. Mycobacterium abscessus is one of the most commonly encountered organisms in nontuberculous mycobacteria disease and it is the most difficult to eradicate. There is currently no systematically proven regimen that is effective for treating M. abscessus infections. Our approach to drug discovery integrates machine learning, medicinal chemistry and in vitro testing and has been previously applied to Mycobacterium tuberculosis. We have now identified several novel 1-(phenylsulfonyl)-1H-benzimidazol-2-amines that have weak activity on M. abscessus in vitro but may represent a starting point for future further medicinal chemistry optimization. We also address limitations still to be overcome with the machine learning approach for M. abscessus.

CYP 450 enzymes influence (R,S)-ketamine brain delivery and its antidepressant activity

Esketamine, the S-stereoisomer of (R,S)-ketamine was recently approved by drug agencies (FDA, EMA), as an antidepressant drug with a new mechanism of action. (R,S)-ketamine is a N-methyl-d-aspartate receptor (NMDA-R) antagonist putatively acting on GABAergic inhibitory synapses to increase excitatory synaptic glutamatergic neurotransmission. Unlike monoamine-based antidepressants, (R,S)-ketamine exhibits rapid and persistent antidepressant activity at subanesthetic doses in preclinical rodent models and in treatment-resistant depressed patients. Its major brain metabolite, (2R,6R)-hydroxynorketamine (HNK) is formed following (R,S)-ketamine metabolism by various cytochrome P450 enzymes (CYP) mainly activated in the liver depending on routes of administration [e.g., intravenous (largely used for a better bioavailability), intranasal spray, intracerebral, subcutaneous, intramuscular or oral]. Experimental or clinical studies suggest that (2R,6R)-HNK could be an antidepressant drug candidate. However, questions still remain regarding its molecular and cellular targets in the brain and its role in (R,S)-ketamine's fast-acting antidepressant effects. The purpose of the present review is: 1) to review (R,S)-ketamine pharmacokinetic properties in humans and rodents and its metabolism by CYP enzymes to form norketamine and HNK metabolites; 2) to provide a summary of preclinical strategies challenging the role of these metabolites by modifying (R,S)-ketamine metabolism, e.g., by administering a pre-treatment CYP inducers or inhibitors; 3) to analyze the influence of sex and age on CYP expression and (R,S)-ketamine metabolism. Importantly, this review describes (R,S)-ketamine pharmacodynamics and pharmacokinetics to alert clinicians about possible drug-drug interactions during a concomitant administration of (R,S)-ketamine and CYP inducers/inhibitors that could enhance or blunt, respectively, (R,S)-ketamine's therapeutic antidepressant efficacy in patients.

Early Warning System for Illicit Drug Use at Large Public Events: Trace Residue Analysis of Discarded Drug Packaging Samples

Inspired by Locard's exchange principle, which states "every contact leaves a trace", a trace residue sampling strategy has been developed for the analysis of discarded drug packaging samples (DPS), as part of an early warning system for illicit drug use at large public events including music/dance festivals. Using direct analysis in real time/mass spectrometry and tandem mass spectrometry, rapid and high-throughput identification and characterization of a wide range of illicit drugs and adulterant substances was achieved, including in complex polydrug mixtures and at low relative ion abundances. A total of 1362 DPS were analyzed either off-site using laboratory-based instrumentation or on-site and in close to real time using a transportable mass spectrometer housed within a mobile analytical laboratory, with each analysis requiring less than 1 min per sample. Of the DPS analyzed, 92.2% yielded positive results for at least one of 15 different drugs and/or adulterants, including cocaine, MDMA, and ketamine, as well as numerous novel psychoactive substances (NPS). Also, 52.6% of positive DPS were found to contain polydrug mixtures, and a total of 42 different drug and polydrug combinations were observed throughout the study. For analyses performed on-site, reports to key stakeholders including event organizers, first aid and medical personnel, and peer-based harm reduction workers could be provided in as little as 5 min after sample collection. Following risk assessment of the potential harms associated with their use, drug advisories or alerts were then disseminated to event staff and patrons and subsequently to the general public when substances with particularly toxic properties were identified.

Machine Learning Models Identify Inhibitors of SARS-CoV-2

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (K_d 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model

The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable. In both cases, these methods predict the ability of small molecules to cross the BBB using the molecular structure information on its own without in vitro or in vivo data. We describe here the implementation of two versions of Pfizer's algorithm (Pf-MPO.v1 and Pf-MPO.v2) and compare it with a Bayesian machine learning model of BBB penetration trained on a data set of 2296 active and inactive compounds using extended connectivity fingerprint descriptors. The predictive ability of these approaches was compared with 40 known CNS active drugs initially used by Pfizer as their positive set for validation of the Pf-MPO.v1 score. 37/40 (92.5%) compounds were predicted as active by the Bayesian model, while only 30/40 (75%) received a desirable Pf-MPO.v1 score ≥4 and 33/40 (82.5%) received a desirable Pf-MPO.v2 score ≥4, suggesting the Bayesian model is more accurate than MPO algorithms. This also indicates machine learning models are more flexible and have better predictive power for BBB penetration than simple rule sets that require multiple, accurate descriptor calculations. Our machine learning model statistics are comparable to recent published studies. We describe the implications of these findings and how machine learning may have a role alongside more interpretable methods.

Flavonoids from Pterogyne nitens as Zika virus NS2B-NS3 protease inhibitors

Although the widespread epidemic of Zika virus (ZIKV) and its neurological complications are well-known there are still no approved drugs available to treat this arboviral disease or vaccine to prevent the infection. Flavonoids from Pterogyne nitens have already demonstrated anti-flavivirus activity, although their target is unknown. In this study, we virtually screened an in-house database of 150 natural and semi-synthetic compounds against ZIKV NS2B-NS3 protease (NS2B-NS3p) using docking-based virtual screening, as part of the OpenZika project. As a result, we prioritized three flavonoids from P. nitens, quercetin, rutin and pedalitin, for experimental evaluation. We also used machine learning models, built with Assay Central® software, for predicting the activity and toxicity of these flavonoids. Biophysical and enzymatic assays generally agreed with the in silico predictions, confirming that the flavonoids inhibited ZIKV protease. The most promising hit, pedalitin, inhibited ZIKV NS2B-NS3p with an IC50 of 5 μM. In cell-based assays, pedalitin displayed significant activity at 250 and 500 µM, with slight toxicity in Vero cells. The results presented here demonstrate the potential of pedalitin as a candidate for hit-to-lead (H2L) optimization studies towards the discovery of antiviral drug candidates to treat ZIKV infections.

The Antiviral Drug Tilorone Is a Potent and Selective Inhibitor of Acetylcholinesterase

Acetylcholinesterase (AChE) is an important drug target in neurological disorders like Alzheimer's disease, Lewy body dementia, and Parkinson's disease dementia as well as for other conditions like myasthenia gravis and anticholinergic poisoning. In this study, we have used a combination of high-throughput screening, machine learning, and docking to identify new inhibitors of this enzyme. Bayesian machine learning models were generated with literature data from ChEMBL for eel and human AChE inhibitors as well as butyrylcholinesterase inhibitors (BuChE) and compared with other machine learning methods. High-throughput screens for the eel AChE inhibitor model identified several molecules including tilorone, an antiviral drug that is well-established outside of the United States, as a newly identified nanomolar AChE inhibitor. We have described how tilorone inhibits both eel and human AChE with IC₅₀'s of 14.4 nM and 64.4 nM, respectively, but does not inhibit the closely related BuChE IC₅₀ > 50 μM. We have docked tilorone into the human AChE crystal structure and shown that this selectivity is likely due to the reliance on a specific interaction with a hydrophobic residue in the peripheral anionic site of AChE that is absent in BuChE. We also conducted a pharmacological safety profile (SafetyScreen44) and kinase selectivity screen (SelectScreen) that showed tilorone (1 μM) only inhibited AChE out of 44 toxicology target proteins evaluated and did not appreciably inhibit any of the 485 kinases tested. This study suggests there may be a potential role for repurposing tilorone or its derivatives in conditions that benefit from AChE inhibition.

Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies, we and others have applied multiple machine learning algorithms and modeling metrics and, in some cases, compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and in comparison of our proprietary software Assay Central with random forest, k-nearest neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (three layers). Model performance was assessed using an array of fivefold cross-validation metrics including area-under-the-curve, F1 score, Cohen's kappa, and Matthews correlation coefficient. Based on ranked normalized scores for the metrics or datasets, all methods appeared comparable, while the distance from the top indicated that Assay Central and support vector classification were comparable. Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case. If anything, Assay Central may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay Central performance, although support vector classification seems to be a strong competitor. We also applied Assay Central to perform prospective predictions for the toxicity targets PXR and hERG to further validate these models. This work appears to be the largest scale comparison of these machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors, and machine learning algorithms and further refine the methods for evaluating and comparing such models.

Comparing Machine Learning Models for Aromatase (P450 19A1)

Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals. Therefore, predicting potential endocrine disruption via exogenous chemicals requires that aromatase inhibition be considered in addition to androgen and estrogen pathway interference. Bayesian machine learning methods can be used for prospective prediction from the molecular structure without the need for experimental data. Herein, the generation and evaluation of multiple machine learning models utilizing different sources of aromatase inhibition data are described. These models are applied to two test sets for external validation with molecules relevant to drug discovery from the public domain. In addition, the performance of multiple machine learning algorithms was evaluated by comparing internal five-fold cross-validation statistics of the training data. These methods to predict aromatase inhibition from molecular structure, when used in concert with estrogen and androgen machine learning models, allow for a more holistic assessment of endocrine-disrupting potential of chemicals with limited empirical data and enable the reduction of the use of hazardous substances.

Machine Learning for Discovery of GSK3β Inhibitors

Alzheimer's disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine-threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine-threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC50 = 97.3 nM) and GSK3α (IC50 = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction

The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.

Repurposing Pyramax®, quinacrine and tilorone as treatments for Ebola virus disease

We have recently identified three molecules (tilorone, quinacrine and pyronaridine tetraphosphate) which all demonstrated efficacy in the mouse model of infection with mouse-adapted Ebola virus (EBOV) model of disease and had similar in vitro inhibition of an Ebola pseudovirus (VSV-EBOV-GP), suggesting they interfere with viral entry. Using a machine learning model to predict lysosomotropism these compounds were evaluated for their ability to possess a lysosomotropic mechanism in vitro. We now demonstrate in vitro that pyronaridine tetraphosphate is an inhibitor of Lysotracker accumulation in lysosomes (IC50 = 0.56 μM). Further, we evaluated antiviral synergy between pyronaridine and artesunate (Pyramax®), which are used in combination to treat malaria. Artesunate was not found to have lysosomotropic activity in vitro and the combination effect on EBOV inhibition was shown to be additive. Pyramax® may represent a unique example of the repurposing of a combination product for another disease.

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug repurposing strategy represents an attractive approach. Drugs that have already advanced through human clinical safety trials have the potential to be approved more quickly than de novo discovered medicines on new targets. We have taken two strategies to enable this: (1) generated and validated machine learning models of chordoma inhibition and screened compounds of interest in vitro. (2) Tested combinations of approved kinase inhibitors already being individually evaluated for chordoma. Several published studies of compounds screened against chordoma cell lines were used to generate Bayesian Machine learning models which were then used to score compounds selected from the NIH NCATS industry-provided assets. Out of these compounds, the mTOR inhibitor AZD2014, was the most potent against chordoma cell lines (IC50 0.35 µM U-CH1 and 0.61 µM U-CH2). Several studies have shown the importance of the mTOR signaling pathway in chordoma and suggest it as a promising avenue for targeted therapy. Additionally, two currently FDA approved drugs, afatinib and palbociclib (EGFR and CDK4/6 inhibitors, respectively) demonstrated synergy in vitro (CI50 = 0.43) while AZD2014 and afatanib also showed synergy (CI50 = 0.41) against a chordoma cell in vitro. These findings may be of interest clinically, and this in vitro- and in silico approach could also be applied to other rare cancers.

Wogonoside alleviates colitis by improving intestinal epithelial barrier function via the MLCK/pMLC2 pathway

BACKGROUND

Intestinal epithelial barrier dysfunction, which involves myosin light chain kinase (MLCK) activation, contributes to the occurrence and progression of inflammation in inflammatory bowel disease (IBD). Wogonoside helps maintain intestinal homeostasis in mice with dextran sulfate sodium (DSS)-induced colitis, but it is unclear whether it modulates intestinal barrier function.

PURPOSE

Here, we demonstrate that wogonoside protects against intestinal barrier dysfunction in colitis via the MLCK/pMLC2 pathway both in vivo and in vitro.

METHODS

Caco-2 cell monolayers treated with the proinflammatory cytokine TNF-α showed barrier dysfunction and were assessed in the absence and presence of wogonoside for various physiological, morphological, and biochemical parameters. Colitis was induced by 3% DSS in mice, which were used as an animal model to explore the pharmacodynamics of wogonoside. We detected MLCK/pMLC2 pathway proteins via western blot analysis, assessed the cytokines IL-13 and IFN-γ via ELISA, tested bacterial translocation via fluorescence in situ hybridization (FISH) and a proper sampling of secondary lymphoid organs for bacterial culture. In addition, the docking affinity of wogonoside and MLCK was observed with DS2.5 software.

RESULTS

Wogonoside alleviated the disruption of transepithelial electrical resistance (TER) in TNF-α exposured Caco-2 cell; FITC-dextran hyperpermeability; loss of the tight junction (TJ) proteins occludin, ZO-1 and claudin-1 in Caco-2 cell monolayers; and bacterial translocation in colitic mice. Moreover, wogonoside reduced the levels of the proinflammatory cytokines IL-13 and IFN-γ to maintain intestinal immune homeostasis. Transmission electron microscopy (TEM) confirmed that wogonoside ameliorated the destruction of intestinal epithelial TJs. Wogonoside not only inhibited the cytoskeletal F-actin rearrangement induced by TNF-α, stabilized the cytoskeletal structure, suppressed MLCK protein expression, and reduced MLC2 phosphorylation. In addition, the results of molecular docking analysis showed that wogonoside had a high affinity for MLCK and formed hydrogen bonds with the amino acid residue LYS261 and π bonds with LYS229.

CONCLUSION

Collectively, our study indicates that wogonoside alleviates colitis by protecting against intestinal barrier dysfunction, and the potential mechanism may involve regulation of TJs via the MLCK/pMLC2 signaling pathway. Meanwhile, our study also explains the success of S. baicalensis in the treatment of ulcerative colitis (UC).

Synthesis and identification of deschloroketamine metabolites in rats' urine and a quantification method for deschloroketamine and metabolites in rats' serum and brain tissue using liquid chromatography tandem mass spectrometry

Deschloroketamine (2-(methylamino)-2-phenyl-cyclohexanone) is a ketamine analog belonging to a group of dissociative anesthetics, which have been distributed within the illicit market since 2015. However, it was also being sold as 'ketamine' misleading people to believe that they were getting genuine ketamine. Dissociative anesthetics have also come to the attention of the psychiatric field due to their potential properties in the treatment of depression. At present, there is a dearth of information on deschloroketamine related to its metabolism, biodistribution, and its mechanism of action. We have therefore carried out a metabolomics study for deschloroketamine via non-targeted screening of urine samples employing liquid chromatography combined with high-resolution mass spectrometry. We developed and validated a multiple reaction monitoring method using a triple quadrupole instrument to track metabolites of deschloroketamine. Furthermore, significant metabolites of deschloroketamine, (trans-dihydrodeschloroketamine, cis- and trans-dihydronordeschloroketamine, and nordeschloroketamine), were synthesized in-house. The prepared standards were utilized in the developed multiple reaction monitoring method. The quantification method for serum samples provided intra-day accuracy ranging from 86% to 112% with precision of 3% on average. The concentrations of cis/trans-dihydronordeschloroketamines and trans-dihydrodeschloroketamine were lower than 10 ng/mL, nordeschloroketamine and deschloroketamine ranged from 0.5 to 860 ng/mL in real samples. The quantification method for brain tissue provided intra-day accuracy ranging from 80% to 125% with precision of 7% on average. The concentrations of cis/trans-dihydronordeschloroketamines and trans-dihydrodeschloroketamine ranged from 0.5 to 70 ng/g, nordeschloroketamine and deschloroketamine varied from 0.5 to 4700 ng/g in real samples.

Ketamine analogues: Comparative toxicokinetic in vitro-in vivo extrapolation and quantification of 2-fluorodeschloroketamine in forensic blood and hair samples

Recently, the new psychoactive substance (NPS) ketamine analogue 2-fluoro-deschloroketamine (2FDCK) was observed in driving-under-the-influence-of-drugs whole blood samples and in a forensic hair investigation case in Denmark. The molecular structure variations among the NPS subgroups may alter the metabolic fate and drug potency, thereby posing a threat for drug users. This study reports quantification of 2FDCK in whole blood samples and forensic hair and compares the following toxicokinetic parameters: unbound fraction (f_u) and in vitro-in vivo-extrapolation (IVIVE) of hepatic clearance for ketamine, norketamine, 2FDCK, methoxetamine and deschloroketamine. The f_u was investigated with ultrafiltration, while clearance studies were conducted at 1 μM with pooled human liver microsomes. Samples were analysed by liquid chromatography and mass spectrometry. For the first time, 2FDCK was determined in a concentration range between 0.005 and 0.48 mg/kg in blood samples. Segmental hair analysis demonstrated 2FDCK at concentrations from 0.007 to 0.034 ng/mg throughout the three investigated segments. Toxicokinetic comparison of the five compounds gave a f_u between 0.54 and 0.84, with ketamine being the most bound and deschloroketamine being the least bound, in accordance with the logP of the compounds. Conversely, a negative correlation was observed between the molecular weight of the halogen in the ortho-position and IVIVE hepatic clearance. The IVIVE of hepatic clearance, CL_{parallel-tube}, gave values from 18.1 to 5.44 mL/min/kg for ketamine and methoxetamine, respectively. The deschloroketamine IVIVE was disregarded due to low drug elimination under the experimental conditions used. This study provides a basis for toxicokinetic understanding of ketamine analogues.

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.