Chloroformate-mediated ring cleavage of indole alkaloids leads to re-engineered antiplasmodial agents.

Natural product ring distortion strategies have enabled rapid access to unique libraries of stereochemically complex compounds to explore new chemical space and increase our understanding of biological processes related to human disease. Herein is described the development of a ring-cleavage strategy using the indole alkaloids yohimbine, apovincamine, vinburnine, and reserpine that were reacted with a diversity of chloroformates paired with various alcohol/thiol nucleophiles to enable the rapid synthesis of 47 novel small molecules. Ring cleavage reactions of yohimbine and reserpine produced two diastereomeric products in moderate to excellent yields, whereas apovincamine and vinburnine produced a single diastereomeric product in significantly lower yields. Free energy calculations indicated that diastereoselectivity regarding select ring cleavage reactions from yohimbine and apovincamine is dictated by the geometry and three-dimensional structure of reactive cationic intermediates. These compounds were screened for antiplasmodial activity due to the need for novel antimalarial agents. Reserpine derivative 41 was found to exhibit interesting antiplasmodial activities against Plasmodium falciparum parasites (EC50 = 0.50 µM against Dd2 cultures), while its diastereomer 40 was found to be three-fold less active (EC50 = 1.78 µM). Overall, these studies demonstrate that the ring distortion of available indole alkaloids can lead to unique compound collections with re-engineered biological activities for exploring and potentially treating human disease.

Design Principle of Heparanase Inhibitors: A Combined In Vitro and In Silico Study.

Heparanase (HPSE) is an enzyme that cleaves heparan sulfate (HS) side chains from heparan sulfate proteoglycans (HSPGs). Overexpression of HPSE is associated with various types of cancer, inflammation, and immune disorders, making it a highly promising therapeutic target. Previously developed HPSE inhibitors that have advanced to clinical trials are polysaccharide-derived compounds or their mimetics; however, these molecules tend to suffer from poor bioavailability, side effects via targeting other saccharide binding proteins, and heterogeneity. Few small-molecule inhibitors have progressed to the preclinical or clinical stages, leaving a gap in HPSE drug discovery. In this study, a novel small molecule that can inhibit HPSE activity was discovered through high-throughput screening (HTS) using an ultrasensitive HPSE probe. Computational tools were employed to elucidate the mechanisms of inhibition. The essential structural features of the hit compound were summarized into a structure-activity relationship (SAR) theory, providing insights into the future design of HPSE small-molecule inhibitors.

Isolation, Structure Elucidation, and Biological Activity of the Selective TACR2 Antagonist Tumonolide and its Aldehyde from a Marine Cyanobacterium.

The macrocyclic tumonolide (1) with enamide functionality and the linear tumonolide aldehyde (2) are new interconverting natural products from a marine cyanobacterium with a peptide-polyketide skeleton, representing a hybrid of apratoxins and palmyrolides or laingolides. The planar structures were established by NMR and mass spectrometry. The relative configuration of the stereogenically-rich apratoxin-like polyketide portion was determined using J-based configuration analysis. The absolute configuration of tumonolide (1) was determined by chiral analysis of the amino acid units and computational methods, followed by NMR chemical shift and ECD spectrum prediction, indicating all-R configuration for the polyketide portion, as in palmyrolide A and contrary to the all-S configuration in apratoxins. Functional screening against a panel of 168 GPCR targets revealed tumonolide (1) as a selective antagonist of TACR2 with an IC50 of 7.0 µM, closely correlating with binding affinity. Molecular docking studies established the binding mode and rationalized the selectivity for TACR2 over TACR1 and TACR3. RNA sequencing upon treatment of HCT116 colorectal cancer cells demonstrated activation of the pulmonary fibrosis idiopathic signaling pathway and the insulin secretion signaling pathway at 20 µM, indicating its potential to modulate these pathways.

Assessing the Performance of In silico Tools and Molecular Dynamics Simulations for Predicting Pharmacogenetic Variant Impact.

The ability of freely available in silico tools to predict the effect of non-synonymous single nucleotide polymorphisms (nsSNPs) in pharmacogenes on protein function is not well defined. We assessed the performance of seven sequence-based (SIFT, PolyPhen2, mutation accessor, FATHMM, PhD-SNP, MutPred2, and SNPs & Go) and five structure-based (mCSM, SDM, DDGun, CupSat, and MAESTROweb) tools in predicting the impact of 118 nsSNPs in the CYP2C19, CYP2C9, CYP2B6, CYP2D6, and DPYD genes with known function (24 normal, one increased, 42 decreased, and 51 no-function). Sequence-based tools had a higher median (IQR) positive predictive value (89% [89-94%] vs. 12% [10-15%], P < 0.001) and lower negative predictive value (30% [24-34%] vs. 90% [80-93%], P < 0.001) than structure-based tools. Accuracy did not significantly differ between sequence-based (59% [37-67%]) and structure-based (34% [23-44%]) tools (P = 0.070). Notably, the no-function CYP2C9*3 allele and decreased function CYP2C9*8 allele were predicted incorrectly as tolerated by 100% of sequenced-based tools and as stabilizing by 60% and 20% of structure-based tools, respectively. As a case study, we performed mutational analysis for the CYP2C9*1, *3 (I359L), and *8 (R150H) proteins through molecular dynamic (MD) simulations using S-warfarin as the substrate. The I359L variant increased the distance of the major metabolic site of S-warfarin to the oxy-ferryl center of CYP2C9, and I359L and R150H caused shifts in the conformation of S-warfarin to a position less favorable for metabolism. These data suggest that MD simulations may better capture the impact of nsSNPs in pharmacogenes than other tools.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Morphological Profiling for Drug Discovery in the Era of Deep Learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Chlorinated Enyne Fatty Acid Amides from a Marine Cyanobacterium: Discovery of Taveuniamides L-M and Pharmacological Characterization of Taveuniamide F as a GPCR Antagonist with CNR1 Selectivity.

NMR and MS/MS-based metabolomics of a cyanobacterial extract from Piti Bomb Holes, Guam, indicated the presence of unique enyne-containing halogenated fatty acid amides. We isolated three new compounds of this class, taveuniamides L-N (1-3), along with the previously reported taveuniamide F (4), which was the most abundant analog. The planar structures of the new compounds were established using 1D and 2D NMR as well as mass spectrometry. We established the configuration of this chemical class to be R at C-8 via Mosher's analysis of 4 after reduction of the carboxamide group. Our biological investigations with 4 revealed that the compound binds to the cannabinoid receptor CNR1, acting as an antagonist/inverse agonist in the canonical G-protein signaling pathways. In selectivity profiling against 168 GPCR targets using the ß-arrestin functional assay, we found that 4 antagonizes GPR119, NPSR1b, CCR9, CHRM4, GPR120, HTR2A, and GPR103, in addition to CNR1. Interestingly, 4 showed a 6.8-fold selectivity for CNR1 over CNR2. The binding mode of 4 to CNR1 was investigated using docking and molecular dynamics simulations with both natural and unnatural stereoisomers, revealing important CNR1 residues for the interaction and also providing a possible reasoning for the observed CNR1/CNR2 selectivity.