Chemistry-oriented synthesis (ChOS) and target deconvolution on neuroprotective effect of a novel scaffold, oxaza spiroquinone

Metal-free synthesis of dihydrofuran derivatives as anti-vicinal amino alcohol isosteres

Dihydrofuran cores are commonly incorporated into synthetically and pharmacologically significant scaffolds in natural product and drug discovery chemistry. Herein, we report a concise and practical strategy to construct spiro-dihydrofuran and amino dihydrofuran scaffolds as anti-vicinal amino alcohol isosteres. Hypervalent iodine (PhI(OAc)(NTs₂))-mediated C-H activation of alkynes resulted in two-bond formations with one pi bond cleavage: (i) C(sp²)-N(sp³) and O(sp³)-C(sp²); (ii) C(sp²)-N(sp³) and C(sp³)-C(sp²). The metal-free 5-endo-dig oxidative cyclization provided versatile amino 2,3- and 2,5-dihydrofurans bearing the C₅ quaternary carbon. The non-toxicity of all synthesised dihydrofurans was verified via in vitro cell viability assay.

Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence

Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative extraction of drug features is important for predicting drug–target interaction (DTI), which is a large-scale virtual screening of known drugs. Herein, we report Kullbeck–Leibler divergence (KLD) as a DTI feature and the feature-driven classification model applicable to DTI prediction. For the purpose, E3FP three-dimensional (3D) molecular fingerprints of drugs as a molecular representation allow the computation of 3D similarities between ligands within each target (Q–Q matrix) to identify the uniqueness of pharmacological targets and those between a query and a ligand (Q–L vector) in DTIs. The 3D similarity matrices are transformed into probability density functions via kernel density estimation as a nonparametric estimation. Each density model can exploit the characteristics of each pharmacological target and measure the quasi-distance between the ligands. Furthermore, we developed a random forest model from the KLD feature vectors to successfully predict DTIs for representative 17 targets (mean accuracy: 0.882, out-of-bag score estimate: 0.876, ROC AUC: 0.990). The method is applicable for 2D chemical similarity.

Recent in vivo advances of spirocyclic scaffolds for drug discovery

INTRODUCTION

Spirocyclic scaffolds are an exceptional tool in drug design, allowing fine-tuning of a molecule's conformational and physicochemical properties. As it expands and diversifies, so does the number of therapeutics that contain this core. Several spirocyclic drugs are already marketed, and considerably more have shown promising results.

AREAS COVERED

This review addresses recent in vivo studies (2017-2021) on applying spirocyclic compounds to treat various diseases, mainly grouped within neurological, infectious, and metabolic diseases and cancer. An emphasis is given on the influence of the spiro-structure on activity and consequent structure-activity study. In vivo results and their significance in the future progression towards clinical trials are also presented.

EXPERT OPINION

Spirocyclic compounds present an exciting opportunity as an unexplored chemical space in medicinal chemistry. However, their development is hindered by their complexity and synthesis challenges. Furthermore, a clear preference is still seen for readily available spirocyclic compounds involving amine or amide bonds. Nevertheless, these are temporary as high-throughput synthesis, and computational techniques allow fast optimization studies. In our opinion, the field of spirocyclic chemistry will continue to thrive and contribute to drug development, improving activity and selectivity on emergent ailments, such as cancer, metabolic, infectious, and neurological diseases.

Positioning of an unprecedented 1,5-oxaza spiroquinone scaffold into SMYD2 inhibitors in epigenetic space

Lysine methyltransferases are important regulators of epigenetic signaling and are emerging as a novel drug target for drug discovery. This work demonstrates the positioning of novel 1,5-oxaza spiroquinone scaffold into selective SET and MYND domain-containing proteins 2 methyltransferases inhibitors. Selectivity of the scaffold was identified by epigenetic target screening followed by SAR study for the scaffold. The optimization was performed iteratively by two-step optimization consisting of iterative synthesis and computational studies (docking, metadynamics simulations). Computational binding studies guided the important interactions of the spiro[5.5]undeca scaffold in pocket 1 and Lysine channel and suggested extension of tail length for the improvement of potency (IC₅₀: up to 399 nM). The effective performance of cell proliferation assay for chosen compounds (IC₅₀: up to 11.9 nM) led to further evaluation in xenograft assay. The potent compound 24 demonstrated desirable in vivo efficacy with growth inhibition rate of 77.7% (4 fold decrease of tumor weight and 3 fold decrease of tumor volume). Moreover, mirosomal assay and pharmacokinetic profile suggested further developability of this scaffold through the identification of major metabolites (dealkylation at silyl group, reversible hydration product, the absence of toxic quinone fragments) and enough exposure of the testing compound 24 in plasma. Such spiro[5.5]undeca framework or ring system was neither been reported nor suggested as a modulator of methyltransferases. The chemo-centric target positioning and structural novelty can lead to potential pharmacological benefit.

Abiotic scaffolds in medicinal chemistry: not a waste of chemical space

It is well established that medicinal chemists should depart from the flat, sp²-dominated nature of traditional drugs and incorporate complexities of bioactive natural products, such as sp³-richness, 3D topology and chirality. There is a gray area, however, in the relevance of newly developed chemical scaffolds that exhibit these complexities but do not correlate to anything observed in nature. This can leave synthetic methodologists searching for structural similarities between their newly developed products and known natural products in search of justification. This article offers a perspective on how these types of complex 'abiotic' scaffolds can be appreciated purely on the basis of their structural novelty, and identifies the unique advantages arising when a complex chemical entity unrecognized by nature is introduced to biological systems.

Positioning of an unprecedented spiro[5.5]undeca ring system into kinase inhibitor space

In-house 1,5-oxaza spiroquinone 1, with spiro[5.5]undeca ring system, was announced as an unprecedented anti-inflammatory scaffold through chemistry-oriented synthesis (ChOS), a chemocentric approach. Herein, we studied how to best position the spiro[5.5]undeca ring system in kinase inhibitor space. Notably, late-stage modification of the scaffold 1 into compounds 2a-r enhanced kinase-likeness of the scaffold 1. The improvement could be depicted with (1) selectivity with target shift (from JNK-1 into GSK-3) and (2) potency (> 20-fold). In addition, ATP independent IC₅₀ of compound 2j suggested a unique binding mode of this scaffold between ATP site and substrate site, which was explained by docking based optimal site selection and molecular dynamic simulations of the optimal binding site. Despite the shift of kinase profiling, the anti-inflammatory activity of compounds 2a-r could be retained in hyperactivated microglial cells.

Repurposing of FDA approved ring systems through bi-directional target-ring system dual screening

In drug repurposing approaches, the chemically diverse and potentially safe molecules can be explored as therapeutic potential beyond those originally targeted indications. However, accessible information on a limited number of drug pipelines can lead to competitive over-heating issues, and intellectual property rights also restrict the free investigation in chemical space. As a complementary approach to the drawbacks, ring systems of approved drugs (instead of clinical drugs) can be optimized and used for repurposing purposes. In this study, bi-directional target (T) and ring system (R) dual screening (TR screening) was developed for the repurposing of their rarely used ring systems from FDA approved drugs. The TR screening suggested RAR β and cyproheptadine as the best pair of target and ring system to escape a saddle point. The selected ring system was virtually grown and elaborated with the defined criteria: synthesizability, drug-likeness, and docking pose showing the top scores. The achieved compounds were synthesized and biologically tested with an acceptable ADME/T profile.

Orthogonal Strapping of o-Haloaryl Ynones with Pyrazolones: A One-Pot, Domino Process toward Spiropyrazolones

A new class of spiroannulated pyrazolone scaffolds have been assembled from diverse o-haloaryl ynones and β-bromoalkenyl ynones via base mediated, one-pot, metal free, orthogonal strapping (tethering) mediated by the recursive anion(s) derived from pyrazolones. These convenient, preparatively useful transformations proceed through either a tandem Michael addition-intramolecular S_NAr reaction or a tandem Michael addition-intramolecular Ad_NE process to furnish a range of pharmacophoric, diverse, spiroannulated pyrazolones from readily accessible precursors.

Comparing a Query Compound with Drug Target Classes Using 3D-Chemical Similarity

3D similarity is useful in predicting the profiles of unprecedented molecular frameworks that are 2D dissimilar to known compounds. When comparing pairs of compounds, 3D similarity of the pairs depends on conformational sampling, the alignment method, the chosen descriptors, and the similarity coefficients. In addition to these four factors, 3D chemocentric target prediction of an unknown compound requires compound-target associations, which replace compound-to-compound comparisons with compound-to-target comparisons. In this study, quantitative comparison of query compounds to target classes (one-to-group) was achieved via two types of 3D similarity distributions for the respective target class with parameter optimization for the fitting models: (1) maximum likelihood (ML) estimation of queries, and (2) the Gaussian mixture model (GMM) of target classes. While Jaccard-Tanimoto similarity of query-to-ligand pairs with 3D structures (sampled multi-conformers) can be transformed into query distribution using ML estimation, the ligand pair similarity within each target class can be transformed into a representative distribution of a target class through GMM, which is hyperparameterized via the expectation-maximization (EM) algorithm. To quantify the discriminativeness of a query ligand against target classes, the Kullback-Leibler (K-L) divergence of each query was calculated and compared between targets. 3D similarity-based K-L divergence together with the probability and the feasibility index, (F_m), showed discriminative power with regard to some query-class associations. The K-L divergence of 3D similarity distributions can be an additional method for (1) the rank of the 3D similarity score or (2) the p-value of one 3D similarity distribution to predict the target of unprecedented drug scaffolds.