Preparation of von Hippel-Lindau (VHL) E3 ubiquitin ligase ligands exploiting constitutive hydroxyproline for benzylic amine protection.

The von Hippel-Lindau (VHL) protein serves as the substrate recognition subunit of the multi-subunit Cullin-2 RING E3 ubiquitin ligase (CRL2VHL), which regulates intracellular concentrations of hypoxia inducible factors (HIFs) through a ubiquitin proteasome system (UPS) cascade. Strategic recruitment of CRL2VHL by bi- or trifunctional targeted protein degraders (e.g., PROTACs®) offers the prospect of promoting aberrant polyubiquitination and ensuing proteasomal degradation of disease-related proteins. Non-peptidic, l-hydroxyproline-bearing VHL ligands such as VH032 (1) and its chiral benzylic amine analog Me-VH032 (2), are functional components of targeted protein degraders commonly employed for this purpose. Herein, we compare two approaches for the preparation of 1 and 2 primarily highlighting performance differences between Pd(OAc)2 and Pd-PEPPSI-IPr for the key C-H arylation of 4-methylthiazole. Results from this comparison prompted the development of a unified, five-step route for the preparation of either VH032 (1) or Me-VH032 (2) in multigram quantities, resulting in yields of 56% and 61% for 1 and 2, respectively. Application of N-Boc-l-4-hydroxyproline rather than N-tert-butoxycarbonyl to shield the benzylic amine during the coupling step enhances step economy. Additionally, we identified previously undisclosed minor byproducts generated during arylation steps along with observations from amine deprotection and amidation reaction steps that may prove helpful not only for the preparation of 1 and 2, but for other VHL recruiting ligands, as well.

Targeted degrader technologies as prospective SARS-CoV-2 therapies.

COVID-19 remains a severe public health threat despite the WHO declaring an end to the public health emergency in May 2023. Continual development of SARS-CoV-2 variants with resistance to vaccine-induced or natural immunity necessitates constant vigilance as well as new vaccines and therapeutics. Targeted protein degradation (TPD) remains relatively untapped in antiviral drug discovery and holds the promise of attenuating viral resistance development. From a unique structural design perspective, this review covers antiviral degrader merits and challenges by highlighting key coronavirus protein targets and their co-crystal structures, specifically illustrating how TPD strategies can refine existing SARS-CoV-2 3CL protease inhibitors to potentially produce superior protease-degrading agents.

Novel Non-Cyclooxygenase Inhibitory Derivative of Sulindac Inhibits Breast Cancer Cell Growth In Vitro and Reduces Mammary Tumorigenesis in Rats.

The nonsteroidal anti-inflammatory drug (NSAID) sulindac demonstrates attractive anticancer activity, but the toxicity resulting from cyclooxygenase (COX) inhibition and the suppression of physiologically important prostaglandins precludes its long-term, high dose use in the clinic for cancer prevention or treatment. While inflammation is a known tumorigenic driver, evidence suggests that sulindac's antineoplastic activity is partially or fully independent of its COX inhibitory activity. One COX-independent target proposed for sulindac is cyclic guanosine monophosphate phosphodiesterase (cGMP PDE) isozymes. Sulindac metabolites, i.e., sulfide and sulfone, inhibit cGMP PDE enzymatic activity at concentrations comparable with those associated with cancer cell growth inhibitory activity. Additionally, the cGMP PDE isozymes PDE5 and PDE10 are overexpressed during the early stages of carcinogenesis and appear essential for cancer cell proliferation and survival based on gene silencing experiments. Here, we describe a novel amide derivative of sulindac, sulindac sulfide amide (SSA), which was rationally designed to eliminate COX-inhibitory activity while enhancing cGMP PDE inhibitory activity. SSA was 68-fold and 10-fold less potent than sulindac sulfide (SS) in inhibiting COX-1 and COX-2, respectively, but 10-fold more potent in inhibiting growth and inducing apoptosis in breast cancer cells. The pro-apoptotic activity of SSA was associated with inhibition of cGMP PDE activity, elevation of intracellular cGMP levels, and activation of cGMP-dependent protein kinase (PKG) signaling, as well as the inhibition of ß-catenin/Tcf transcriptional activity. SSA displayed promising in vivo anticancer activity, resulting in a 57% reduction in the incidence and a 62% reduction in the multiplicity of tumors in the N-methyl-N-nitrosourea (MNU)-induced model of breast carcinogenesis. These findings provide strong evidence for cGMP/PKG signaling as a target for breast cancer prevention or treatment and the COX-independent anticancer properties of sulindac. Furthermore, this study validates the approach of optimizing off-target effects by reducing the COX-inhibitory activity of sulindac for future targeted drug discovery efforts to enhance both safety and efficacy.

Molecule Property Analyses of Active Compounds for Mycobacterium tuberculosis.

Tuberculosis (TB) continues to claim the lives of around 1.7 million people per year. Most concerning are the reports of multidrug drug resistance. Paradoxically, this global health pandemic is demanding new therapies when resources and interest are waning. However, continued tuberculosis drug discovery is critical to address the global health need and burgeoning multidrug resistance. Many diverse classes of antitubercular compounds have been identified with activity in vitro and in vivo. Our analyses of over 100 active leads are representative of thousands of active compounds generated over the past decade, suggests that they come from few chemical classes or natural product sources. We are therefore repeatedly identifying compounds that are similar to those that preceded them. Our molecule-centered cheminformatics analyses point to the need to dramatically increase the diversity of chemical libraries tested and get outside of the historic Mtb property space if we are to generate novel improved antitubercular leads.

Identification of DOT1L inhibitors by structure-based virtual screening adapted from a nucleoside-focused library.

Disruptor of Telomeric Silencing 1-Like (DOT1L), the sole histone H3 lysine 79 (H3K79) methyltransferase, is required for leukemogenic transformation in a subset of leukemias bearing chromosomal translocations of the Mixed Lineage Leukemia (MLL) gene, as well as other cancers. Thus, DOT1L is an attractive therapeutic target and discovery of small molecule inhibitors remain of high interest. Herein, we are presenting screening results for a unique focused library of 1200 nucleoside analogs originally produced under the aegis of the NIH Pilot Scale Library Program. The complete nucleoside set was screened virtually against DOT1L, resulting in 210 putative hits. In vitro screening of the virtual hits resulted in validation of 11 compounds as DOT1L inhibitors clustered into two distinct chemical classes, adenosine-based inhibitors and a new chemotype that lacks adenosine. Based on the developed DOT1L ligand binding model, a structure-based design strategy was applied and a second-generation of non-nucleoside DOT1L inhibitors was developed. Newly synthesized compound 25 was the most potent DOT1L inhibitor in the new series with an IC50 of 1.0 µM, showing 40-fold improvement in comparison with hit 9 and exhibiting reasonable on target effects in a DOT1L dependent murine cell line. These compounds represent novel chemical probes with a unique non-nucleoside scaffold that bind and compete with the SAM binding site of DOT1L, thus providing foundation for further medicinal chemistry efforts to develop more potent compounds.

Parallel Solution Phase Synthesis and Preliminary Biological Activity of a 5'-Substituted Cytidine Analog Library.

A 109-membered library of 5'-substituted cytidine analogs was synthesized, via funding through the NIH Roadmap Initiative and the Pilot Scale Library (PSL) Program. Reaction core compounds contained -NH2 (2) and -COOH (44 and 93) groups that were coupled to a diversity of reactants in a parallel, solution phase format to produce the target library. The assorted reactants included -NH2, -CHO, -SO2Cl, and -COOH functional groups, and condensation with the intermediate core materials 2 and 44 followed by acidic hydrolysis produced 3-91 in good yields and high purity. Linkage of the amino terminus of d-phenylalanine methyl ester to the free 5'-COOH of 44 and NaOH treatment led to core library -COOH precursor 93. In a libraries from libraries approach, compound 93 served as the vital building block for our unique library of dipeptidyl cytidine analogs 94-114 through amide coupling of the -COOH group with numerous commercial amines followed by acidic deprotection. Initial screening of the complete final library through the MLPCN program revealed a modest number of hits over diverse biological processes. These hits might be considered as starting points for hit-to-lead optimization and development studies.

Synthesis of Aza-acyclic Nucleoside Libraries of Purine, Pyrimidine, and 1,2,4-Triazole.

Under the aegis of the Pilot Scale Library Program of the NIH Roadmap Initiative, a new library of propan-1-amine containing aza acyclic nucleosides was designed and prepared, and we now report a diverse set of 157 purine, pyrimidine, and 1,2,4-triazole- N-acetamide analogues. These new nucleoside analogues were prepared in a parallel high throughput solution-phase format. A set of diverse amines was reacted with several nucleobase N-propaldehydes utilizing reductive amination with sodium triacetoxyborohydride coupling to produce a small and diverse aza acyclic nucleoside library. All reactions were performed using 24-well reaction blocks and an automatic reagent-dispensing platform under an inert atmosphere. Final targets were purified on an automated system using solid sample loading prepacked cartridges and prepacked silica gel columns. All compounds were characterized by NMR and HRMS and were analyzed for purity by HPLC prior to submission to the Molecular Libraries Small Molecule Repository (MLSMR). Initial screening through the Molecular Libraries Probe Production Centers Network (MLPCN) demonstrated diverse and interesting biological activities.

A small diversity library of α-methyl amide analogs of sulindac for probing anticancer structure-activity relationships.

Non-steroidal anti-inflammatory drugs (NSAIDs) have a variety of potential indications that include management of pain and inflammation as well as chemoprevention and/or treatment of cancer. Furthermore, a specific form of ibuprofen, dexibuprofen or the S-(+) form, shows interesting neurological activities and has been proposed for the treatment of Alzheimer's disease. In a continuation of our work probing the anticancer activity of small sulindac libraries, we have prepared and screened a small diversity library of α-methyl substituted sulindac amides in the profen class. Several compounds of this series displayed promising activity compared with a lead sulindac analog.

Oxazole and thiazole analogs of sulindac for cancer prevention.

AIM: Experimental and epidemiological studies and clinical trials suggest that nonsteroidal anti-inflammatory drugs possess antitumor potential. Sulindac, a widely used nonsteroidal anti-inflammatory drug, can prevent adenomatous colorectal polyps and colon cancer, especially in patients with familial adenomatous polyposis. Sulindac sulfide amide (SSA) is an amide-linked sulindac sulfide analog that showed in vivo antitumor activity in a human colon tumor xenograft model. Results/methodology: A new analog series with heterocyclic rings such as oxazole or thiazole at the C-2 position of sulindac was prepared and screened against prostate, colon and breast cancer cell lines to probe the effect of these novel substitutions on the activity of sulindac analogs. CONCLUSION: In general, replacement of the amide function of SSA analogs had a negative impact on the cell lines tested. A small number of hits incorporating rigid oxazole or thiazole groups in the sulindac scaffold in place of the amide linkage show comparable activity to our lead agent SSA.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

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

Amine Containing Analogs of Sulindac for Cancer Prevention.

BACKGROUND: Sulindac belongs to the chemically diverse family of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) that effectively prevent adenomatous colorectal polyps and colon cancer, especially in patients with familial adenomatous polyposis. Sulindac sulfide amide (SSA), an amide analog of sulindac sulfide, shows insignificant COX-related activity and toxicity while enhancing anticancer activity in vitro and demonstrating in vivo xenograft activity. OBJECTIVE: Develop structure-activity relationships in the sulindac amine series and identify analogs with promising anticancer activities. METHOD: A series of sulindac amine analogs were designed and synthesized and then further modified in a "libraries from libraries" approach to produce amide, sulfonamide and N,N-disubstituted sulindac amine sub-libraries. All analogs were screened against three cancer cell lines (prostate, colon and breast). RESULTS: Several active compounds were identified viain vitro cancer cell line screening with the most potent compound (26) in the nanomolar range. CONCLUSION: Compound 26 and analogs showing the most potent inhibitory activity may be considered for further design and optimization efforts as anticancer hit scaffolds.

Synthesis and preliminary assessment of the anticancer and Wnt/ß-catenin inhibitory activity of small amide libraries of fenamates and profens.

As part of an ongoing program to study the anticancer activity of non-steroidal anti-inflammatory drugs (NSAIDs) through generating diversity libraries of multiple NSAID scaffolds, we synthesized a series of NSAID amide derivatives and screened these sets against three cancer cell lines (prostate, colon and breast) and Wnt/ß-catenin signaling. The evaluated amide analog libraries show significant anticancer activity/cell proliferation inhibition, and specific members of the sets show inhibition of Wnt/ß-catenin signaling.

Diverse amide analogs of sulindac for cancer treatment and prevention.

Sulindac is a non-steroidal anti-inflammatory drug (NSAID) that has shown significant anticancer activity. Sulindac sulfide amide (1) possessing greatly reduced COX-related inhibition relative to sulindac displayed in vivo antitumor activity that was comparable to sulindac in a human colon tumor xenograft model. Inspired by these observations, a panel of diverse sulindac amide derivatives have been synthesized and their activity probed against three cancer cell lines (prostate, colon and breast). A neutral analog, compound 79 was identified with comparable potency relative to lead 1 and activity against a panel of lymphoblastic leukemia cell lines. Several new series also show good activity relative to the parent (1), including five analogs that also possess nanomolar inhibitory potencies against acute lymphoblastic leukemia cells. Several new analogs identified may serve as anticancer lead candidates for further development.

Screening and Development of New Inhibitors of FtsZ from M. Tuberculosis.

A variety of commercial analogs and a newer series of Sulindac derivatives were screened for inhibition of M. tuberculosis (Mtb) in vitro and specifically as inhibitors of the essential mycobacterial tubulin homolog, FtsZ. Due to the ease of preparing diverse analogs and a favorable in vivo pharmacokinetic and toxicity profile of a representative analog, the Sulindac scaffold may be useful for further development against Mtb with respect to in vitro bacterial growth inhibition and selective activity for Mtb FtsZ versus mammalian tubulin. Further discovery efforts will require separating reported mammalian cell activity from both antibacterial activity and inhibition of Mtb FtsZ. Modeling studies suggest that these analogs bind in a specific region of the Mtb FtsZ polymer that differs from human tubulin and, in combination with a pharmacophore model presented herein, future hybrid analogs of the reported active molecules that more efficiently bind in this pocket may improve antibacterial activity while improving other drug characteristics.

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015).

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtb in vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitro Mtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivo Mtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user's own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Biochemical and structural characterization of mycobacterial aspartyl-tRNA synthetase AspS, a promising TB drug target.

The human pathogen Mycobacterium tuberculosis is the causative agent of pulmonary tuberculosis (TB), a disease with high worldwide mortality rates. Current treatment programs are under significant threat from multi-drug and extensively-drug resistant strains of M. tuberculosis, and it is essential to identify new inhibitors and their targets. We generated spontaneous resistant mutants in Mycobacterium bovis BCG in the presence of 10× the minimum inhibitory concentration (MIC) of compound 1, a previously identified potent inhibitor of mycobacterial growth in culture. Whole genome sequencing of two resistant mutants revealed in one case a single nucleotide polymorphism in the gene aspS at (535)GAC>(535)AAC (D179N), while in the second mutant a single nucleotide polymorphism was identified upstream of the aspS promoter region. We probed whole cell target engagement by overexpressing either M. bovis BCG aspS or Mycobacterium smegmatis aspS, which resulted in a ten-fold and greater than ten-fold increase, respectively, of the MIC against compound 1. To analyse the impact of inhibitor 1 on M. tuberculosis AspS (Mt-AspS) activity we over-expressed, purified and characterised the kinetics of this enzyme using a robust tRNA-independent assay adapted to a high-throughput screening format. Finally, to aid hit-to-lead optimization, the crystal structure of apo M. smegmatis AspS was determined to a resolution of 2.4 Å.

Synthesis and general biological activity of a small adenosine-5'-(carboxamide and sulfanilamide) library.

A small library of fifty-five adenosine peptide analogs was synthesized, under the Pilot Scale Library (PSL) Program of the NIH Roadmap initiative, from 2',3'-O-isopropylideneadenosine-5'-carboxylic acid 2. The coupling of amine or sulfanilamide reactants to the free 5'-carboxylic acid moiety of 2, in automated solution-phase fashion, led after acid-mediated hydrolysis to target compounds 3-57 in good yields and high purity. No marked anticancer or antimalarial activity was noted on preliminary cellular testing. Initial screening through the MLPCN program, however, indicates that these analogs may show diverse and interesting biological activities.

Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis.

Tuberculosis is a major, neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event data sets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose-response, single-point and dual-event dose-response as well as combined data sets for three distinct data sets from the same laboratory. We used a fourth data set of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose-response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual-event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25 000-350 000 molecules. Our results have implications for justifying further high-throughput screening versus focused testing based on model predictions.