Chemoenzymatic Synthesis of Complex Phenylpropanoid Derivatives by the Botrytis cinerea Secretome and Evaluation of Their Wnt Inhibition Activity

In this study, a series of complex phenylpropanoid derivatives were obtained by chemoenzymatic biotransformation of ferulic acid, caffeic acid, and a mixture of both acids using the enzymatic secretome of Botrytis cinerea. These substrates were incubated with fungal enzymes, and the reactions were monitored using state-of-the-art analytical methods. Under such conditions, a series of dimers, trimers, and tetramers were generated. The reactions were optimized and scaled up. The resulting mixtures were purified by high-resolution semi-preparative HPLC combined with dry load introduction. This approach generated a series of 23 phenylpropanoid derivatives, 11 of which are described here for the first time. These compounds are divided into 12 dimers, 9 trimers (including a completely new structural scaffold), and 2 tetramers. Elucidation of their structures was performed with classical spectroscopic methods such as NMR and HRESIMS analyses. The resulting compound series were analyzed for anti-Wnt activity in TNBC cells, with several derivatives demonstrating specific inhibition.

Identification of Novel Chemical Scaffolds that Inhibit the Growth of Mycobacterium tuberculosis in Macrophages

Mycobacterium tuberculosis is an important global pathogen for which new drugs are urgently required. The ability of the organism to survive and multiply within macrophages may contribute to the lengthy treatment regimen with multiple drugs that are required to cure the infection. We screened the MyriaScreen II diversity library of 10,000 compounds to identify novel inhibitors of M. tuberculosis growth within macrophage-like cells using high content analysis. Hits were selected which inhibited the intramacrophage growth of M. tuberculosis without significant cytotoxicity to infected macrophages. We selected and prioritized compound series based on their biological and physicochemical properties and the novelty of the chemotypes. We identified five chemical classes of interest and conducted limited catalog structure-activity relationship studies to determine their tractability. We tested activity against intracellular and extracellular M. tuberculosis, as well as cytoxicity against murine RAW264.7 and human HepG2 cells. Benzene amide ethers, thiophene carboxamides and thienopyridines were only active against intracellular bacteria, whereas the phenylthiourea series was also active against extracellular bacteria. One member of a phenyl pyrazole series was moderately active against extracellular bacteria. We identified the benzene amide ethers as an interesting series for further work. These new compound classes serve as starting points for the development of novel drugs to target intracellular M. tuberculosis.

Small Molecule CD38 Inhibitors: Synthesis of 8-Amino-N1-inosine 5′-monophosphate, Analogues and Early Structure-Activity Relationship

Although a monoclonal antibody targeting the multifunctional ectoenzyme CD38 is an FDA-approved drug, few small molecule inhibitors exist for this enzyme that catalyzes inter alia the formation and metabolism of the N1-ribosylated, Ca2+-mobilizing, second messenger cyclic adenosine 5′-diphosphoribose (cADPR). N1-Inosine 5′-monophosphate (N1-IMP) is a fragment directly related to cADPR. 8-Substituted-N1-IMP derivatives, prepared by degradation of cyclic parent compounds, inhibit CD38-mediated cADPR hydrolysis more efficiently than related cyclic analogues, making them attractive for inhibitor development. We report a total synthesis of the N1-IMP scaffold from adenine and a small initial compound series that facilitated early delineation of structure-activity parameters, with analogues evaluated for inhibition of CD38-mediated hydrolysis of cADPR. The 5′-phosphate group proved essential for useful activity, but substitution of this group by a sulfonamide bioisostere was not fruitful. 8-NH2-N1-IMP is the most potent inhibitor (IC50 = 7.6 μM) and importantly HPLC studies showed this ligand to be cleaved at high CD38 concentrations, confirming its access to the CD38 catalytic machinery and demonstrating the potential of our fragment approach.

Identification of novel chemical scaffolds that inhibit the growth of Mycobacterium tuberculosis in macrophages

Mycobacterium tuberculosis is an important global pathogen for which new drugs are urgently required. The ability of the organism to survive and multiply within macrophages may contribute to the lengthy treatment regimen with multiple drugs that are required to cure the infection. We screened the MyriaScreen II diversity library of 10,000 compounds to identify novel inhibitors of M. tuberculosis growth within macrophage-like cells using high content analysis. Hits were selected which inhibited the intramacrophage growth of M. tuberculosis without significant cytotoxicity to infected macrophages. We selected and prioritized compound series based on their biological and physicochemical properties and the novelty of the chemotypes. We identified five chemical classes of interest and conducted limited catalog structure-activity relationship studies to determine their tractability. We tested activity against intracellular and extracellular M.tuberculosis, as well as cytoxicity against murine RAW264.7 and human HepG2 cells. Benzene amide ethers, thiophene carboxamides and thienopyridines were only active against intracellular bacteria, whereas the phenylthiourea series was also active against extracellular bacteria. One member of a phenyl pyrazole series was moderately active against extracellular bacteria. We identified the benzene amide ethers as an interesting series for further work. These new compound classes serve as starting points for the development of novel drugs to target intracellular M. tuberculosis.

Adapting the DeepSARM approach for dual-target ligand design

The structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

Discovery of a first-in-class inhibitor of the PRMT5-substrate adaptor interaction

PRMT5 and its substrate adaptor proteins (SAPs), pICln and Riok1, are synthetic lethal dependencies in MTAP-deleted cancer cells. SAPs share a conserved PRMT5 binding motif (PBM) which mediates binding to a surface of PRMT5 distal to the catalytic site. This interaction is required for methylation of several PRMT5 substrates, including histone and spliceosome complexes. We screened for small molecule inhibitors of the PRMT5-PBM interaction and validated a compound series which binds to the PRMT5-PBM interface and directly inhibits binding of SAPs. Mode of action and structure determination studies revealed that these compounds form a covalent bond between a halogenated pyridazinone group and cysteine 278 of PRMT5. Optimization of the starting hit produced a lead compound, BRD0639, which engages the target in cells, disrupts the PRMT5-RIOK1 complex, and reduces substrate methylation. BRD0639 is a first-in-class PBM-competitive small molecule that can support studies of PBM-dependent PRMT5 activities and the development of novel PRMT5 inhibitors that selectively target these functions.

Balancing Data on Proteochemometrics Activity Classification

<div>In silico analysis of biological activity data has become an essential technique in pharmaceutical development. </div><div>Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity prediction models. </div><div>However, bioactivity datasets used in proteochemometrics modeling are usually imbalanced, which could potentially affect the performance of the models. In this work, we explored the effect of different balancing strategies in deep learning proteochemometric target-compound activity classification models while controlling for the compound series bias through clustering. These strategies were: (1) no_resampling, (2) resampling_after_clustering, (3) resampling_before_clustering and (4) semi_resampling. </div><div>These schemas were evaluated in kinases and GPCRs from BindingDB. </div><div>We observed that the predicted proportion of positives was driven by the actual data balance in the test set. </div><div>Additionally, it was confirmed that data balance had an impact on the performance estimates of the proteochemometrics model. </div><div>We recommend a combination of data augmentation and clustering in the training set (semi_resampling) in order to mitigate the data imbalance effect in a realistic scenario. </div><div>The code of this analysis is publicly available at https://github.com/b2slab/imbalance_pcm_benchmark.</div>

Balancing Data on Proteochemometrics Activity Classification

<div>In silico analysis of biological activity data has become an essential technique in pharmaceutical development. </div><div>Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity prediction models. </div><div>However, bioactivity datasets used in proteochemometrics modeling are usually imbalanced, which could potentially affect the performance of the models. In this work, we explored the effect of different balancing strategies in deep learning proteochemometric target-compound activity classification models while controlling for the compound series bias through clustering. These strategies were: (1) no_resampling, (2) resampling_after_clustering, (3) resampling_before_clustering and (4) semi_resampling. </div><div>These schemas were evaluated in kinases and GPCRs from BindingDB. </div><div>We observed that the predicted proportion of positives was driven by the actual data balance in the test set. </div><div>Additionally, it was confirmed that data balance had an impact on the performance estimates of the proteochemometrics model. </div><div>We recommend a combination of data augmentation and clustering in the training set (semi_resampling) in order to mitigate the data imbalance effect in a realistic scenario. </div><div>The code of this analysis is publicly available at https://github.com/b2slab/imbalance_pcm_benchmark.</div>

Hypoxia-Activated Prodrug Derivatives of Carbonic Anhydrase Inhibitors in Benzenesulfonamide Series: Synthesis and Biological Evaluation

Hypoxia, a common feature of solid tumours’ microenvironment, is associated with an aggressive phenotype and is known to cause resistance to anticancer chemo- and radiotherapies. Tumour-associated carbonic anhydrases isoform IX (hCA IX), which is upregulated under hypoxia in many malignancies participating to the microenvironment acidosis, represents a valuable target for drug strategy against advanced solid tumours. To overcome cancer cell resistance and improve the efficacy of therapeutics, the use of bio-reducible prodrugs also known as Hypoxia-activated prodrugs (HAPs), represents an interesting strategy to be applied to target hCA IX isozyme through the design of selective carbonic anhydrase IX inhibitors (CAIs). Here, we report the design, synthesis and biological evaluations including CA inhibition assays, toxicity assays on zebrafish and viability assays on human cell lines (HT29 and HCT116) of new HAP-CAIs, harboring different bio-reducible moieties in nitroaromatic series and a benzenesulfonamide warhead to target hCA IX. The CA inhibition assays of this compound series showed a slight selectivity against hCA IX versus the cytosolic off-target hCA II and hCA I isozymes. Toxicity and viability assays have highlighted that the compound bearing the 2-nitroimidazole moiety possesses the lowest toxicity (LC50 of 1400 µM) and shows interesting results on viability assays.