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Metabolic diseases are increasing at staggering rates globally. The peroxisome proliferator-activated receptors (PPARα/γ/δ) are fatty acid sensors that help mitigate imbalances between energy uptake and utilization. Herein, we report compounds derived from phenolic lipids present in cashew nut shell liquid (CNSL), an abundant waste byproduct, in an effort to create effective, accessible, and sustainable drugs. Derivatives of anacardic acid and cardanol were tested for PPAR activity in HEK293 cell co-transfection assays, primary hepatocytes, and 3T3-L1 adipocytes.

While coronavirus disease 2019 (COVID-19) begins as a respiratory infection, it progresses as a systemic disease involving multiorgan microthromboses that underly the pathology. SARS-CoV-2 enters host cells

The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARα) is emerging as an important target in the brain for the treatment or prevention of cognitive disorders. The identification of high-affinity ligands for brain PPARα may reveal the mechanisms underlying the synaptic effects of this receptor and facilitate drug development. Here, using an affinity purification-untargeted mass spectrometry (AP-UMS) approach, we identified an endogenous, selective PPARα ligand, 7(

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

RBBP4 is a nuclear WD40 motif-containing protein widely implicated in various cancers and a putative drug target. It interacts with multiple proteins within diverse complexes such as nucleosome remodeling and deacetylase (NuRD) complex and polycomb repressive complex 2 (PRC2), as well as histone H3 and H4 through two distinct binding sites. B-cell lymphoma/leukemia 11A (BCL11A), friend of GATA-1 (FOG-1), plant homeodomain finger protein 6 (PHF6) and histone H3 bind to the top of the donut-shaped seven-bladed β-propeller fold of RBBP4, while suppressor of zeste 12 (SUZ12), metastasis associated protein 1 (MTA1) and histone H4 bind to a pocket on the side of the WD40 repeats of this protein. Here, we report the discovery of the first small molecule antagonists of the RBBP4 top pocket, competing with interacting peptides from proteins such as BCL11A and histone H3. We also determined the first crystal structure of RBBP4 in complex with a small molecule (OICR17251), paving the path for structure-guided design and optimization towards more potent antagonists.

Both previous and additional genetic knockdown studies reported herein implicate G protein-coupled receptor kinase 6 (GRK6) as a critical kinase required for the survival of multiple myeloma (MM) cells. Therefore, we sought to develop a small molecule GRK6 inhibitor as an MM therapeutic. From a focused library of known kinase inhibitors, we identified two hits with moderate biochemical potencies against GRK6. From these hits, we developed potent (IC50 < 10 nM) analogues with selectivity against off-target kinases. Further optimization led to the discovery of an analogue (18) with an IC50 value of 6 nM against GRK6 and selectivity against a panel of 85 kinases. Compound 18 has potent cellular target engagement and antiproliferative activity against MM cells and is synergistic with bortezomib. In summary, we demonstrate that targeting GRK6 with small molecule inhibitors represents a promising approach for MM and identify 18 as a novel, potent, and selective GRK6 inhibitor.

Boronic acids have attracted the attention of synthetic and medicinal chemists due to boron's ability to modulate enzyme function. Recently, we demonstrated that boron-containing amphoteric building blocks facilitate the discovery of bioactive aminoboronic acids. Herein, we have augmented this capability with a de novo library design and a virtual screening platform modified for covalent ligands. This technique has allowed us to rapidly design and identify a series of α-aminoboronic acids as the first inhibitors of human ClpXP, which is responsible for the degradation of misfolded proteins.

There are currently no effective chemotherapeutic drugs approved for the treatment of diffuse intrinsic pontine glioma (DIPG), an aggressive pediatric cancer resident in the pons region of the brainstem. Radiation therapy is beneficial but not curative, with the condition being uniformly fatal. Analysis of the genomic landscape surrounding DIPG has revealed that activin receptor-like kinase-2 (ALK2) constitutes a potential target for therapeutic intervention given its dysregulation in the disease. We adopted an open science approach to develop a series of potent, selective, orally bioavailable, and brain-penetrant ALK2 inhibitors based on the lead compound LDN-214117. Modest structural changes to the C-3, C-4, and C-5 position substituents of the core pyridine ring afforded compounds M4K2009, M4K2117, and M4K2163, each with a superior potency, selectivity, and/or blood-brain barrier (BBB) penetration profile. Robust in vivo pharmacokinetic (PK) properties and tolerability mark these inhibitors as advanced preclinical compounds suitable for further development and evaluation in orthotopic models of DIPG.

The RAF family kinases function in the RAS-ERK pathway to transmit signals from activated RAS to the downstream kinases MEK and ERK. This pathway regulates cell proliferation, differentiation and survival, enabling mutations in RAS and RAF to act as potent drivers of human cancers. Drugs targeting the prevalent oncogenic mutant BRAF(V600E) have shown great efficacy in the clinic, but long-term effectiveness is limited by resistance mechanisms that often exploit the dimerization-dependent process by which RAF kinases are activated. Here, we investigated a proteolysis-targeting chimera (PROTAC) approach to BRAF inhibition. The most effective PROTAC, termed P4B, displayed superior specificity and inhibitory properties relative to non-PROTAC controls in BRAF(V600E) cell lines. In addition, P4B displayed utility in cell lines harboring alternative BRAF mutations that impart resistance to conventional BRAF inhibitors. This work provides a proof of concept for a substitute to conventional chemical inhibition to therapeutically constrain oncogenic BRAF.

The problem of designing new antitubercular drugs against multiple drug‐resistant tuberculosis (MDR‐TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR‐TB, we collected a large literature data set and developed models against the non‐resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2=.7–.8 (regression models) and balanced accuracies of about 80% (classification models) with cross‐validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR‐TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR‐TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti‐TB activity of new chemicals.

Activating mutations in the epidermal growth factor receptor (EGFR) are common driver mutations in non-small cell lung cancer (NSCLC). First, second and third generation EGFR tyrosine kinase inhibitors (TKIs) are effective at inhibiting mutant EGFR NSCLC, however, acquired resistance is a major issue, leading to disease relapse. Here, we characterize a small molecule, EMI66, an analog of a small molecule which we previously identified to inhibit mutant EGFR signalling via a novel mechanism of action. We show that EMI66 attenuates receptor tyrosine kinase (RTK) expression and signalling and alters the electrophoretic mobility of Coatomer Protein Complex Beta 2 (COPB2) protein in mutant EGFR NSCLC cells. Moreover, we demonstrate that EMI66 can alter the subcellular localization of EGFR and COPB2 within the early secretory pathway. Furthermore, we find that COPB2 knockdown reduces the growth of mutant EGFR lung cancer cells, alters the post-translational processing of RTKs, and alters the endoplasmic reticulum (ER) stress response pathway. Lastly, we show that EMI66 treatment also alters the ER stress response pathway and inhibits the growth of mutant EGFR lung cancer cells and organoids. Our results demonstrate that targeting of COPB2 with EMI66 presents a viable approach to attenuate mutant EGFR signalling and growth in NSCLC.

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human β-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Acid sphingomyelinase (ASM) is a lysosomal phosphodiesterase that catalyzes the hydrolysis of sphingomyelin to produce ceramide and phosphocholine. While other lysosomal sphingolipid hydrolases require a saposin activator protein for full activity, the ASM polypeptide incorporates a built-in N-terminal saposin domain and does not require an external activator protein. Here, we report the crystal structure of human ASM and describe the organization of the three main regions of the enzyme: the N-terminal saposin domain, the proline-rich connector, and the catalytic domain. The saposin domain is tightly associated along an edge of the large, bowl-shaped catalytic domain and adopts an open form that exposes a hydrophobic concave surface approximately 30Å from the catalytic center. The calculated electrostatic potential of the enzyme is electropositive at the acidic pH of the lysosome, consistent with the strict requirement for the presence of acidic lipids in target membranes. Docking studies indicate that sphingomyelin binds with the ceramide-phosphate group positioned at the binuclear zinc center and molecular dynamic simulations indicate that the intrinsic flexibility of the saposin domain is important for monomer-dimer exchange and for membrane interactions. Overall, ASM uses a combination of electrostatic and hydrophobic interactions to cause local disruptions of target bilayers in order to bring the lipid headgroup to the catalytic center in a membrane-bound reaction.

Boronic acids have attracted the attention of synthetic and medicinal chemists due to boron’s ability to modulate enzyme function. Recently, we demonstrated that boron-containing amphoteric building blocks facilitate the discovery of bioactive aminoboronic acids. Herein, we have augmented this capability with a de novo library design and virtural screening platform modified for covalent ligands. This technique has allowed us to rapidly design and identify a series of α-aminoboronic acids as the first inhibitors of human ClpXP, which is responsible for the degradation of misfolded proteins.

Boronic acids have attracted the attention of synthetic and medicinal chemists due to boron’s ability to modulate enzyme function. Recently, we demonstrated that boron-containing amphoteric building blocks facilitate the discovery of bioactive aminoboronic acids. Herein, we have augmented this capability with a de novo library design and virtural screening platform modified for covalent ligands. This technique has allowed us to rapidly design and identify a series of α-aminoboronic acids as the first inhibitors of human ClpXP, which is responsible for the degradation of misfolded proteins.

Eleven-nineteen leukemia (ENL) contains an epigenetic reader domain (YEATS domain) that recognizes lysine acylation on histone 3 and facilitates transcription initiation and elongation through its interactions with the super elongation complex (SEC) and the histone methyl transferase DOT1L. Although it has been known for its role as a fusion protein in mixed lineage leukemia (MLL), overexpression of native ENL, and thus dysregulation of downstream genes in acute myeloid leukemia (AML), has recently been implicated as a driver of disease that is reliant on the epigenetic reader activity of the YEATS domain. We developed a peptide displacement assay (histone 3 tail with acylated lysine) and screened a small-molecule library totaling more than 24,000 compounds for their propensity to disrupt the YEATS domain-histone peptide binding. Among these, we identified a first-in-class dual inhibitor of ENL ( Kd = 745 ± 45 nM) and its paralog AF9 ( Kd = 523 ± 53 nM) and performed "SAR by catalog" with the aim of starting the development of a chemical probe for ENL.

YEATS domain (YD) containing proteins are an emerging class of epigenetic targets in drug discovery. Dysregulation of these modified lysine binding proteins has been linked to the onset and progression of cancers. We herein report the discovery and characterisation of the first small molecule chemical probe, SGC-iMLLT, for the YD of MLLT1 (ENL/YEATS1) and MLLT3 (AF9/YEATS3). SGC-iMLLT is a potent and selective inhibitor of MLLT1/3 -histone interactions. Excellent selectivity over other human YD proteins (YEATS2/4) and bromodomains was observed. Furthermore, our probe displays cellular target engagement of MLLT1 and MLLT3. The first small molecule X-ray co-crystal structures with the MLLT1 YD are also reported. This first in class probe molecule can be used to understand MLLT1/3 associated biology and the therapeutic potential of small molecule YD inhibitors.

Automated docking is one of the most important tools for structure-based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein-ligand complexes from pdbbind database (www.pdbbind.org.cn, version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein-ligand complexes, as measured by the non-parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root-mean-square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.

Here we report a novel, fast and efficient algorithm for variable selection, the batch pruning algorithm (BPA). The method combines the artificial neural networks (ANN) ensemble learning and self-organized map (SOM) of Kohonen for clustering of descriptors, followed up with a selection of an optimal smaller subset of descriptors from each cluster based on calculated sensitivity of input neurons. BPA was validated on two publicly available, structurally diverse datasets: 584 inhibitors of M. Tuberculosis (MTB) growth and 1015 phosphodiesterase type 4 (PDE4) inhibitors. BPA was able to identify a smaller subset of 5% of molecular descriptors (out of about 1200 calculated with Talete Dragon) 50–100 times faster compared to conventional stepwise pruning methods (SPM), and yielded QSAR models of similar or slightly better accuracy as measured by Q 2 (0.73–0.77), RMSE (0.50–0.72) and MAE (0.36–0.57). 97% of compounds were predicted within 1 log unit. It took only 1.47 h to find the best set of descriptors by BPA compared to 119 h by ANN SPM for the MTB dataset, and 3.0 h compared to 237 h for the PDE4 set. Due to its high predictive accuracy and speed, BPA may find wide applicability in building better machine learning models to predict activity, selectivity, physical and ADMET properties for large datasets, and a large number of descriptors within reasonable time.

Herein, we describe the bromomethyl acyl boronate linchpin--an enabling reagent for the condensation-driven assembly of novel bis(heteroaryl) motifs. This building block is readily accessible from commercially available starting materials. A variety of 2-amino- and 2-methylpyridines were reacted with MIDA-protected bromomethyl acylboronate to afford 2-boryl imidazo[1,2-a]pyridine and 2-boryl indolizine derivatives, respectively, in excellent yields. Subsequent condensation with hydroxyamidines and hydrazonamides converted the intermediate heterocycles into novel boron-containing bis(heteroaryl) units characterized by high thermal stability.

Receptor tyrosine kinases (RTKs) are transmembrane receptors of great clinical interest due to their role in disease. Historically, therapeutics targeting RTKs have been identified using in vitro kinase assays. Due to frequent development of drug resistance, however, there is a need to identify more diverse compounds that inhibit mutated but not wild-type RTKs. Here, we describe MaMTH-DS (mammalian membrane two-hybrid drug screening), a live-cell platform for high-throughput identification of small molecules targeting functional protein-protein interactions of RTKs. We applied MaMTH-DS to an oncogenic epidermal growth factor receptor (EGFR) mutant resistant to the latest generation of clinically approved tyrosine kinase inhibitors (TKIs). We identified four mutant-specific compounds, including two that would not have been detected by conventional in vitro kinase assays. One of these targets mutant EGFR via a new mechanism of action, distinct from classical TKI inhibition. Our results demonstrate how MaMTH-DS is a powerful complement to traditional drug screening approaches.

The transcription factor B cell lymphoma 6 (BCL6) is required for the generation of an effective humoral immune response through the development and maintenance of germinal centers (GCs). The inhibition of the protein−protein interaction between BCL6 and its corepressors has been implicated as a therapeutic target for diffuse large B-cell lymphoma (DLBCL), a type of non-Hodgkin’s lymphoma (NHL). Using structure-based drug design, we initiated a program to identify novel BCL6 inhibitors. We identified a high micromolar virtual screening hit which was then optimized for potent biophysical binding and anti-proliferative cellular activity resulting in the identification of OICR-10268, a potent and selective Bcl6 inhibitor. Citation Format: Iain D. Watson, Methvin Isaac, Brian Wilson, Anh Chau, Justin Morin, Pandiaraju Subramanian, Ahmed Mamai, Babu Joseph, Michael Prakesch, David Uehling, Ayome Abibi, Richard Marcellus, Craig Strathdee, Ratheesh Subramaniam, Brigitte Theriault, Jeffrey Winston, Manuel Chan, Carly Griffin, Herman Cheung, Taira Kiyota, Elijus Undzys, Ahmed Aman, Gennady Poda, Doug Kuntz, Neil C. Pomroy, Gil G. Privé, Rima Al-awar. Discovery of OICR-10268: A potent and selective BCL6 inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 7.

Herein, we describe the bromomethyl acyl boronate linchpin–an enabling reagent for the condensation-driven assembly of novel bis(heteroaryl) motifs. This building block is readily accessible from commercially available starting materials. A variety of 2-amino- and 2-methylpyridines were reacted with MIDA-protected bromomethyl acylboronate to afford 2-boryl imidazo[1,2-a]pyridine and 2-boryl indolizine derivatives, respectively, in excellent yields. Subsequent condensation with hydroxyamidines and hydrazonamides converted the intermediate heterocycles into novel boron-containing bis(heteroaryl) units characterized by high thermal stability.

Molecular docking of small molecules in the protein binding sites is the most widely used computational technique in modern structure-based drug discovery. Although accurate prediction of binding modes of small molecules can be achieved in most cases, estimation of their binding affinities remains mediocre at best. As an attempt to improve the correlation between the inhibitory constants, pKi, and scoring, we created a new, hybrid scoring function. The new function is a linear combination of the terms of the scoring functions of AutoDock and AutoDock Vina. It was trained on 2,412 protein-ligand complexes from the PDBbind database (www.pdbbind.org.cn, version 2012) and validated on a set of 313 complexes released in the 2013 version as a test set. The new function was included in a modified version of AutoDock. The hybrid scoring function showed a statistically significant improvement in both training and test sets in terms of correlation with and root mean square and mean absolute errors in prediction of pKi values. It was also tested on the CSAR 2014 Benchmark Exercise dataset (team T) and produced reasonably good results.

Human σ opioid receptor (σOR), a G-protein-coupled receptor, has been modeled using the helix axes as revealed by the crystallographic structure of bacteriorhodopsin and ligand binding profiles of single-point mutants of σOR. The model revealed feasibility of existence of a second disulfide bridge between the transmembrane helices (TMHs) 6 and 7, Cys273-Cys303. A common binding site has been suggested for high-affinity selective agonists DPDPE, DPLPE, DTLET, BW373U86 and antagonist Naltrindole. Docking calculations have shown that the amino group of the ligands forms a hydrogen bond with the imidazole ring of His301 (TMH7) rather than with Asp128 (TMH3) and is not a cation counterpart of this highly conserved aspartyl residue. All the findings and the model shed light on the putative structure and functioning of opioid receptors and can be used for designing further mutagenesis experiments.

Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.

Publisher Summary This chapter describes methods to develop opioid G protein-coupled receptors (GPCR) structural models for use in molecular docking and drug design. They are characterized by seven alpha helical transmembrane (TM) domains connected by three intracellular and three extracellular loops. The extracellular (EL) N-terminus and the intracellular (IL) C-terminus are both targets for posttranslational glycosylation. Although, the widespread cloning of GPCRs and the determination of high-resolution X-ray data on rhodopsin has greatly advanced this field over the last decade, significant challenges still remain in the development of molecular models for predicting ligand binding and selectivity, function, and the structural basis to signal transduction. The models reported are developed with a primary focus on ligand binding and recognition. To some extent, current models could be viewed as “averaged” structures as a wide range of data are accounted for in the model building. As more receptor-type specific data is available, however, these models will continue to evolve to capture individual conformational preferences for the receptors leading to more reliable automated docking and virtual screens. It also reviews that one of the main applications of GPCR model building is the rationalization and development of structure-based concepts of ligand recognition. It focuses on the automated docking of naloxone and naltrexone-derived ligands. The chapter concludes that virtual screening across diverse ligand databases may be out of the reach of current model resolutions; there is little doubt that the application will be expanded as more experimental and structural data on GPCRs becomes available and integrated into structural models of ligand-receptor recognition.

We first review the state-of-the-art in development of log P prediction approaches falling in two major categories: substructure-based and property-based methods. Then, we compare the predictive power of representative methods for one public (N ¼ 266) and two in house datasets from Nycomed (N ¼ 882) and Pfizer (N ¼ 95809). A total of 30 and 18 methods were tested for public and industrial datasets, respectively. Accuracy of models declined with the number of nonhydrogen atoms. The Arithmetic Average Model (AAM), which predicts the same value (the arithmetic mean) for all compounds, was used as a baseline model for comparison. Methods with Root Mean Squared Error (RMSE) greater than RMSE produced by the AAM were considered as unacceptable. The majority of analyzed methods produced reasonable results for the public dataset but only seven methods were successful on the both in house datasets. We proposed a simple equation based on the number of carbon atoms, NC, and the number of hetero atoms, NHET: log P ¼ 1.46(� 0.02) þ 0.11(� 0.001) NC� 0.11(� 0.001) NHET. This equation outperformed a large number of programs benchmarked in this study. Factors influencing the accuracy of log P predictions were elucidated and discussed. 2008 Wiley