An electrophilic fragment screening for the development of small molecules targeting caspase-2.

Recent Alzheimer's research has shown increasing interest in the caspase-2 (Casp2) enzyme. However, the available Casp2 inhibitors, which have been pentapeptides or peptidomimetics, face challenges for use as CNS drugs. In this study, we successfully screened a 1920-compound chloroacetamide-based, electrophilic fragment library from Enamine. Our two-point dose screen identified 64 Casp2 hits, which were further evaluated in a ten-point dose-response study to assess selectivity over Casp3. We discovered compounds with inhibition values in the single-digit micromolar and sub-micromolar range, as well as up to 32-fold selectivity for Casp2 over Casp3. Target engagement analysis confirmed the covalent-irreversible binding of the selected fragments to Cys320 at the active site of Casp2. Overall, our findings lay a strong foundation for the future development of small-molecule Casp2 inhibitors.

Discovery of Novel Small-Molecule Scaffolds for the Inhibition and Activation of WIP1 Phosphatase from a RapidFire Mass Spectrometry High-Throughput Screen.

Wild-type P53-induced phosphatase 1 (WIP1), also known as PPM1D or PP2Cδ, is a serine/threonine protein phosphatase induced by P53 after genotoxic stress. WIP1 inhibition has been proposed as a therapeutic strategy for P53 wild-type cancers in which it is overexpressed, but this approach would be ineffective in P53-negative cancers. Furthermore, there are several cancers with mutated P53 where WIP1 acts as a tumor suppressor. Therefore, activating WIP1 phosphatase might also be a therapeutic strategy, depending on the P53 status. To date, no specific, potent WIP1 inhibitors with appropriate pharmacokinetic properties have been reported, nor have WIP1-specific activators. Here, we report the discovery of new WIP1 modulators from a high-throughput screen (HTS) using previously described orthogonal biochemical assays suitable for identifying both inhibitors and activators. The primary HTS was performed against a library of 102 277 compounds at a single concentration using a RapidFire mass spectrometry assay. Hits were further evaluated over a range of 11 concentrations with both the RapidFire MS assay and an orthogonal fluorescence-based assay. Further biophysical, biochemical, and cell-based studies of confirmed hits revealed a WIP1 activator and two inhibitors, one competitive and one uncompetitive. These new scaffolds are prime candidates for optimization which might enable inhibitors with improved pharmacokinetics and a first-in-class WIP1 activator.

A quantitative high-throughput screen identifies compounds that lower expression of the SCA2-and ALS-associated gene ATXN2.

CAG repeat expansions in the ATXN2 (ataxin-2) gene can cause the autosomal dominant disorder spinocerebellar ataxia type 2 (SCA2) as well as increase the risk of ALS. Abnormal molecular, motor, and neurophysiological phenotypes in SCA2 mouse models are normalized by lowering ATXN2 transcription, and reduction of nonmutant Atxn2 expression has been shown to increase the life span of mice overexpressing the TDP-43 (transactive response DNA-binding protein 43 kDa) ALS protein, demonstrating the potential benefits of targeting ATXN2 transcription in humans. Here, we describe a quantitative high-throughput screen to identify compounds that lower ATXN2 transcription. We screened 428,759 compounds in a multiplexed assay using an ATXN2-luciferase reporter in human embryonic kidney 293 (HEK-293) cells and identified a diverse set of compounds capable of lowering ATXN2 transcription. We observed dose-dependent reductions of endogenous ATXN2 in HEK-293 cells treated with procillaridin A, 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG), and heat shock protein 990 (HSP990), known inhibitors of HSP90 and Na+/K+-ATPases. Furthermore, HEK-293 cells expressing polyglutamine-expanded ATXN2-Q58 treated with 17-DMAG had minimally detectable ATXN2, as well as normalized markers of autophagy and endoplasmic reticulum stress, including STAU1 (Staufen 1), molecular target of rapamycin, p62, LC3-II (microtubule-associated protein 1A/1B-light chain 3II), CHOP (C/EBP homologous protein), and phospho-eIF2α (eukaryotic initiation factor 2α). Finally, bacterial artificial chromosome ATXN2-Q22 mice treated with 17-DMAG or HSP990 exhibited highly reduced ATXN2 protein abundance in the cerebellum. Taken together, our study demonstrates inhibition of HSP90 or Na+/K+-ATPases as potentially effective therapeutic strategies for treating SCA2 and ALS.

Retro Drug Design: From Target Properties to Molecular Structures.

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

Virtual Screening for the Discovery of Microbiome ß-Glucuronidase Inhibitors to Alleviate Cancer Drug Toxicity.

Despite the potency of most first-line anti-cancer drugs, nonadherence to these drug regimens remains high and is attributable to the prevalence of "off-target" drug effects that result in serious adverse events (SAEs) like hair loss, nausea, vomiting, and diarrhea. Some anti-cancer drugs are converted by liver uridine 5'-diphospho-glucuronosyltransferases through homeostatic host metabolism to form drug-glucuronide conjugates. These sugar-conjugated metabolites are generally inactive and can be safely excreted via the biliary system into the gastrointestinal tract. However, ß-glucuronidase (ßGUS) enzymes expressed by commensal gut bacteria can remove the glucuronic acid moiety, producing the reactivated drug and triggering dose-limiting side effects. Small-molecule ßGUS inhibitors may reduce this drug-induced gut toxicity, allowing patients to complete their full course of treatment. Herein, we report the discovery of novel chemical series of ßGUS inhibitors by structure-based virtual high-throughput screening (vHTS). We developed homology models for ßGUS and applied them to large-scale vHTS against nearly 400,000 compounds within the chemical libraries of the National Center for Advancing Translational Sciences at the National Institutes of Health. From the vHTS results, we cherry-picked 291 compounds via a multifactor prioritization procedure, providing 69 diverse compounds that exhibited positive inhibitory activity in a follow-up ßGUS biochemical assay in vitro. Our findings correspond to a hit rate of 24% and could inform the successful downstream development of a therapeutic adjunct that targets the human microbiome to prevent SAEs associated with first-line, standard-of-care anti-cancer drugs.

A Comprehensive Phenotypic Screening Strategy to Identify Modulators of Cargo Translocation by the Bacterial Type IVB Secretion System.

Bacterial type IV secretion systems (T4SSs) are macromolecular machines that translocate effector proteins across multiple membranes into infected host cells. Loss of function mutations in genes encoding protein components of the T4SS render bacteria avirulent, highlighting the attractiveness of T4SSs as drug targets. Here, we designed an automated high-throughput screening approach for the identification of compounds that interfere with the delivery of a reporter-effector fusion protein from Legionella pneumophila into RAW264.7 mouse macrophages. Using a fluorescence resonance energy transfer (FRET)-based detection assay in a bacteria/macrophage coculture format, we screened a library of over 18,000 compounds and, upon vetting compound candidates in a variety of in vitro and cell-based secondary screens, isolated several hits that efficiently interfered with biological processes that depend on a functional T4SS, such as intracellular bacterial proliferation or lysosomal avoidance, but had no detectable effect on L. pneumophila growth in culture medium, conditions under which the T4SS is dispensable. Notably, the same hit compounds also attenuated, to varying degrees, effector delivery by the closely related T4SS from Coxiella burnetii, notably without impacting growth of this organism within synthetic media. Together, these results support the idea that interference with T4SS function is a possible therapeutic intervention strategy, and the emerging compounds provide tools to interrogate at a molecular level the regulation and dynamics of these virulence-critical translocation machines. IMPORTANCE Multi-drug-resistant pathogens are an emerging threat to human health. Because conventional antibiotics target not only the pathogen but also eradicate the beneficial microbiota, they often cause additional clinical complications. Thus, there is an urgent need for the development of "smarter" therapeutics that selectively target pathogens without affecting beneficial commensals. The bacterial type IV secretion system (T4SS) is essential for the virulence of a variety of pathogens but dispensable for bacterial viability in general and can, thus, be considered a pathogen's Achilles heel. By identifying small molecules that interfere with cargo delivery by the T4SS from two important human pathogens, Legionella pneumophila and Coxiella burnetii, our study represents the first step in our pursuit toward precision medicine by developing pathogen-selective therapeutics capable of treating the infections without causing harm to commensal bacteria.

Discovery of Small-Molecule VapC1 Nuclease Inhibitors by Virtual Screening and Scaffold Hopping from an Atomic Structure Revealing Protein-Protein Interactions with a Native VapB1 Inhibitor.

Nontypeable Haemophilus influenzae (NTHi) are clinically important Gram-negative bacteria that are responsible for various human mucosal diseases, including otitis media (OM). Recurrent OM caused by NTHi is common, and infections that recur less than 2 weeks following antimicrobial therapy are largely attributable to the recurrence of the same strain of bacteria. Toxin-antitoxin (TA) modules encoded by bacteria enable rapid responses to environmental stresses and are thought to facilitate growth arrest, persistence, and tolerance to antibiotics. The vapBC-1 locus of NTHi encodes a type II TA system, comprising the ribonuclease toxin VapC1 and its cognate antitoxin VapB1. The activity of VapC1 has been linked to the survival of NTHi during antibiotic treatment both in vivo and ex vivo. Therefore, inhibitors of VapC1 might serve as adjuvants to antibiotics, preventing NTHi from entering growth arrest and surviving; however, none have been reported to date. A truncated VapB1 peptide from a crystal structure of the VapBC-1 complex was used to generate pharmacophore queries to facilitate a scaffold hopping approach for the identification of small-molecule VapC1 inhibitors. The National Center for Advancing Translational Sciences small-molecule library was virtually screened using the shape-based method rapid overlay of chemical structures (ROCS), and the top-ranking hits were docked into the VapB1 binding pocket of VapC1. Two hundred virtual screening hits with the best docking scores were selected and tested in a biochemical VapC1 activity assay, which confirmed eight compounds as VapC1 inhibitors. An additional 60 compounds were selected with structural similarities to the confirmed VapC1 inhibitors, of which 20 inhibited VapC1 activity. Intracellular target engagement of five inhibitors was indicated by the destabilization of VapC1 within bacterial cells from a cellular thermal shift assay; however, no impact on bacterial growth was observed. Thus, this virtual screening and scaffold hopping approach enabled the discovery of VapC1 ribonuclease inhibitors that might serve as starting points for preclinical development.

The AKT modulator A-443654 reduces α-synuclein expression and normalizes ER stress and autophagy.

Accumulation of α-synuclein is a main underlying pathological feature of Parkinson's disease and α-synucleinopathies, for which lowering expression of the α-synuclein gene (SNCA) is a potential therapeutic avenue. Using a cell-based luciferase reporter of SNCA expression we performed a quantitative high-throughput screen of 155,885 compounds and identified A-443654, an inhibitor of the multiple functional kinase AKT, as a potent inhibitor of SNCA. HEK-293 cells with CAG repeat expanded ATXN2 (ATXN2-Q58 cells) have increased levels of α-synuclein. We found that A-443654 normalized levels of both SNCA mRNA and α-synuclein monomers and oligomers in ATXN2-Q58 cells. A-443654 also normalized levels of α-synuclein in fibroblasts and iPSC-derived dopaminergic neurons from a patient carrying a triplication of the SNCA gene. Analysis of autophagy and endoplasmic reticulum stress markers showed that A-443654 successfully prevented α-synuclein toxicity and restored cell function in ATXN2-Q58 cells, normalizing the levels of mTOR, LC3-II, p62, STAU1, BiP, and CHOP. A-443654 also decreased the expression of DCLK1, an inhibitor of α-synuclein lysosomal degradation. Our study identifies A-443654 and AKT inhibition as a potential strategy for reducing SNCA expression and treating Parkinson's disease pathology.

Human GPR17 missense variants identified in metabolic disease patients have distinct downstream signaling profiles.

GPR17 is a G-protein-coupled receptor (GPCR) implicated in the regulation of glucose metabolism and energy homeostasis. Such evidence is primarily drawn from mouse knockout studies and suggests GPR17 as a potential novel therapeutic target for the treatment of metabolic diseases. However, links between human GPR17 genetic variants, downstream cellular signaling, and metabolic diseases have yet to be reported. Here, we analyzed GPR17 coding sequences from control and disease cohorts consisting of individuals with adverse clinical metabolic deficits including severe insulin resistance, hypercholesterolemia, and obesity. We identified 18 nonsynonymous GPR17 variants, including eight variants that were exclusive to the disease cohort. We characterized the protein expression levels, membrane localization, and downstream signaling profiles of nine GPR17 variants (F43L, V96M, V103M, D105N, A131T, G136S, R248Q, R301H, and G354V). These nine GPR17 variants had similar protein expression and subcellular localization as wild-type GPR17; however, they showed diverse downstream signaling profiles. GPR17-G136S lost the capacity for agonist-mediated cAMP, Ca2+, and ß-arrestin signaling. GPR17-V96M retained cAMP inhibition similar to GPR17-WT, but showed impaired Ca2+ and ß-arrestin signaling. GPR17-D105N displayed impaired cAMP and Ca2+ signaling, but unaffected agonist-stimulated ß-arrestin recruitment. The identification and functional profiling of naturally occurring human GPR17 variants from individuals with metabolic diseases revealed receptor variants with diverse signaling profiles, including differential signaling perturbations that resulted in GPCR signaling bias. Our findings provide a framework for structure-function relationship studies of GPR17 signaling and metabolic disease.

Retro Drug Design: From Target Properties to Molecular Structures.

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate µ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

Identification of SARS-CoV-2 viral entry inhibitors using machine learning and cell-based pseudotyped particle assay.

In response to the pandemic caused by SARS-CoV-2, we constructed a hybrid support vector machine (SVM) classification model using a set of publicly posted SARS-CoV-2 pseudotyped particle (PP) entry assay repurposing screen data to identify novel potent compounds as a starting point for drug development to treat COVID-19 patients. Two different molecular descriptor systems, atom typing descriptors and 3D fingerprints (FPs), were employed to construct the SVM classification models. Both models achieved reasonable performance, with the area under the curve of receiver operating characteristic (AUC-ROC) of 0.84 and 0.82, respectively. The consensus prediction outperformed the two individual models with significantly improved AUC-ROC of 0.91, where the compounds with inconsistent classifications were excluded. The consensus model was then used to screen the 173,898 compounds in the NCATS annotated and diverse chemical libraries. Of the 255 compounds selected for experimental confirmation, 116 compounds exhibited inhibitory activities in the SARS-CoV-2 PP entry assay with IC50 values ranged between 0.17 µM and 62.2 µM, representing an enrichment factor of 3.2. These 116 active compounds with diverse and novel structures could potentially serve as starting points for chemistry optimization for COVID-19 drug discovery.

Chemoprotective antimalarials identified through quantitative high-throughput screening of Plasmodium blood and liver stage parasites.

The spread of Plasmodium falciparum parasites resistant to most first-line antimalarials creates an imperative to enrich the drug discovery pipeline, preferably with curative compounds that can also act prophylactically. We report a phenotypic quantitative high-throughput screen (qHTS), based on concentration-response curves, which was designed to identify compounds active against Plasmodium liver and asexual blood stage parasites. Our qHTS screened over 450,000 compounds, tested across a range of 5 to 11 concentrations, for activity against Plasmodium falciparum asexual blood stages. Active compounds were then filtered for unique structures and drug-like properties and subsequently screened in a P. berghei liver stage assay to identify novel dual-active antiplasmodial chemotypes. Hits from thiadiazine and pyrimidine azepine chemotypes were subsequently prioritized for resistance selection studies, yielding distinct mutations in P. falciparum cytochrome b, a validated antimalarial drug target. The thiadiazine chemotype was subjected to an initial medicinal chemistry campaign, yielding a metabolically stable analog with sub-micromolar potency. Our qHTS methodology and resulting dataset provides a large-scale resource to investigate Plasmodium liver and asexual blood stage parasite biology and inform further research to develop novel chemotypes as causal prophylactic antimalarials.

Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.

Predictive models for estimating cytotoxicity on the basis of chemical structures.

Cytotoxicity is a critical property in determining the fate of a small molecule in the drug discovery pipeline. Cytotoxic compounds are identified and triaged in both target-based and cell-based phenotypic approaches due to their off-target toxicity or on-target and on-mechanism toxicity for oncology and neurodegenerative targets. It is critical that chemical-induced cytotoxicity be reliably predicted before drug candidates advance to the late stage of development, or more ideally, before compounds are synthesized. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in NCATS annotated libraries against four 'normal' cell lines (HEK 293, NIH 3T3, CRL-7250 and HaCat) using CellTiter-Glo (CTG) technology and constructed highly predictive models to estimate cytotoxicity from chemical structures. There are 5,241 non-redundant compounds having unambiguous activities in the four different cell lines, among which 11.8% compounds exhibited cytotoxicity in two or more cell lines and are thus labelled cytotoxic. The support vector classification (SVC) models trained with 80% randomly selected molecules achieved the area under the receiver operating characteristic curve (AUC-ROC) of 0.88 on average for the remaining 20% compounds in the test sets in 10 repeating experiments. Application of under-sampling rebalancing method further improved the averaged AUC-ROC to 0.90. Analysis of structural features shared by cytotoxic compounds may offer medicinal chemists heuristic design ideas to eliminate undesirable cytotoxicity. The profiling of cytotoxicity of drug-like molecules with annotated primary mechanism of action (MOA) will inform on the roles played by different targets or pathways in cellular viability. The predictive models for cytotoxicity (accessible at https://tripod.nih.gov/web_adme/cytotox.html) provide the scientific community a fast yet reliable way to prioritize molecules with little or no cytotoxicity for downstream development.

Physiologically relevant orthogonal assays for the discovery of small-molecule modulators of WIP1 phosphatase in high-throughput screens.

WT P53-Induced Phosphatase 1 (WIP1) is a member of the magnesium-dependent serine/threonine protein phosphatase (PPM) family and is induced by P53 in response to DNA damage. In several human cancers, the WIP1 protein is overexpressed, which is generally associated with a worse prognosis. Although WIP1 is an attractive therapeutic target, no potent, selective, and bioactive small-molecule modulator with favorable pharmacokinetics has been reported. Phosphatase enzymes are among the most challenging targets for small molecules because of the difficulty of achieving both modulator selectivity and bioavailability. Another major obstacle has been the availability of robust and physiologically relevant phosphatase assays that are suitable for high-throughput screening. Here, we describe orthogonal biochemical WIP1 activity assays that utilize phosphopeptides from native WIP1 substrates. We optimized an MS assay to quantify the enzymatically dephosphorylated peptide reaction product in a 384-well format. Additionally, a red-shifted fluorescence assay was optimized in a 1,536-well format to enable real-time WIP1 activity measurements through the detection of the orthogonal reaction product, Pi We validated these two optimized assays by quantitative high-throughput screening against the National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection and used secondary assays to confirm and evaluate inhibitors identified in the primary screen. Five inhibitors were further tested with an orthogonal WIP1 activity assay and surface plasmon resonance binding studies. Our results validate the application of miniaturized physiologically relevant and orthogonal WIP1 activity assays to discover small-molecule modulators from high-throughput screens.

Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity.

Aqueous solubility is one of the most important properties in drug discovery, as it has profound impact on various drug properties, including biological activity, pharmacokinetics (PK), toxicity, and in vivo efficacy. Both kinetic and thermodynamic solubilities are determined during different stages of drug discovery and development. Since kinetic solubility is more relevant in preclinical drug discovery research, especially during the structure optimization process, we have developed predictive models for kinetic solubility with in-house data generated from 11,780 compounds collected from over 200 NCATS intramural research projects. This represents one of the largest kinetic solubility datasets of high quality and integrity. Based on the customized atom type descriptors, the support vector classification (SVC) models were trained on 80% of the whole dataset, and exhibited high predictive performance for estimating the solubility of the remaining 20% compounds within the test set. The values of the area under the receiver operating characteristic curve (AUC-ROC) for the compounds in the test sets reached 0.93 and 0.91, when the threshold for insoluble compounds was set to 10 and 50 µg/mL respectively. The predictive models of aqueous solubility can be used to identify insoluble compounds in drug discovery pipeline, provide design ideas for improving solubility by analyzing the atom types associated with poor solubility and prioritize compound libraries to be purchased or synthesized.

Thymine DNA glycosylase as a novel target for melanoma.

Melanoma is an aggressive neoplasm with increasing incidence that is classified by the NCI as a recalcitrant cancer, i.e., a cancer with poor prognosis, lacking progress in diagnosis and treatment. In addition to conventional therapy, melanoma treatment is currently based on targeting the BRAF/MEK/ERK signaling pathway and immune checkpoints. As drug resistance remains a major obstacle to treatment success, advanced therapeutic approaches based on novel targets are still urgently needed. We reasoned that the base excision repair enzyme thymine DNA glycosylase (TDG) could be such a target for its dual role in safeguarding the genome and the epigenome, by performing the last of the multiple steps in DNA demethylation. Here we show that TDG knockdown in melanoma cell lines causes cell cycle arrest, senescence, and death by mitotic alterations; alters the transcriptome and methylome; and impairs xenograft tumor formation. Importantly, untransformed melanocytes are minimally affected by TDG knockdown, and adult mice with conditional knockout of Tdg are viable. Candidate TDG inhibitors, identified through a high-throughput fluorescence-based screen, reduced viability and clonogenic capacity of melanoma cell lines and increased cellular levels of 5-carboxylcytosine, the last intermediate in DNA demethylation, indicating successful on-target activity. These findings suggest that TDG may provide critical functions specific to cancer cells that make it a highly suitable anti-melanoma drug target. By potentially disrupting both DNA repair and the epigenetic state, targeting TDG may represent a completely new approach to melanoma therapy.

Quantitative high-throughput phenotypic screening of pediatric cancer cell lines identifies multiple opportunities for drug repurposing.

Drug repurposing approaches have the potential advantage of facilitating rapid and cost-effective development of new therapies. Particularly, the repurposing of drugs with known safety profiles in children could bypass or streamline toxicity studies. We employed a phenotypic screening paradigm on a panel of well-characterized cell lines derived from pediatric solid tumors against a collection of ∼3,800 compounds spanning approved drugs and investigational agents. Specifically, we employed titration-based screening where compounds were tested at multiple concentrations for their effect on cell viability. Molecular and cellular target enrichment analysis indicated that numerous agents across different therapeutic categories and modes of action had an antiproliferative effect, notably antiparasitic/protozoal drugs with non-classic antineoplastic activity. Focusing on active compounds with dosing and safety information in children according to the Children's Pharmacy Collaborative database, we identified compounds with therapeutic potential through further validation using 3D tumor spheroid models. Moreover, we show that antiparasitic agents induce cell death via apoptosis induction. This study demonstrates that our screening platform enables the identification of chemical agents with cytotoxic activity in pediatric cancer cell lines of which many have known safety/toxicity profiles in children. These agents constitute attractive candidates for efficacy studies in pre-clinical models of pediatric solid tumors.

Highly predictive and interpretable models for PAMPA permeability.

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.