Discovery of Potent Inhibitors of 11β-Hydroxysteroid Dehydrogenase Type 1 Using a Novel Growth-Based Protocol of in Silico Screening and Optimization in CONTOUR

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.

Computational prediction of 11β-hydroxysteroid dehydrogenase inhibitors from n-butanol fraction of Blighia welwetschii (Hiern) leaf for the management of type-2 diabetes

Human 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD-1) is an enzyme that catalyzes the generation of active cortisol from cortisone, thus regulating the availability of glucocorticoids for the steroid receptor. The involvement of this process in insulin insensitivity has established the catalyst as therapeutic target in type-2 diabetes management. Herein, potent antagonists of 11β-HSD-1 were predicted from bioactive compounds identified from n-butanol fraction of B. welwitschi leaf using chromatography method (HPLC). Molecular docking, MM/GBSA evaluation, autoQSAR modeling, e-pharmacophore modeling, and molecular dynamics simulation of the bioactive compounds were carried out against 11β-HSD-1 employing Schrodinger suite (2017-1). Seven out of the ten bioactive compounds from the fraction showed a higher degree of binding affinity against 11β-HSD-1 compared with the co-crystalized ligand. The post-docking analysis revealed strong interaction due to the hydrogen bond formation between the molecules and amino acid present at the catalytic site of 11β-HSD-1. Rutin showed the highest binding affinity (-13.980 kcal/mol) among the hits comparable to the co-crystalized ligand (-7.576 kcal/mol). The binding free energy (ΔGbind) evaluation validates the inhibitory potential of the docked complexes, which exclusively confirmed cyaniding-3-o-glucoside (-62.022 kcal/mol) with the highest binding energy followed by rutin (-59.629 kcal/mol). The molecular dynamics simulations predicted the stability of rutin and quercetin-3-o-glycoside complex with 11β-HSD-1 through 100 ns with minimum fluctuation and more H-bond observed between the two top scored 11β-HSD-1-compound complexes compared to the 11β-HSD-1-co-crystalized ligand complex. The pharmacokinetic profile revealed that the hit compounds are promising drug candidates except for rutin which violated more than one Lipinski's rule of five. This study revealed that bioactive compounds identified from B. welwitschi leaves demonstrated good inhibitory potential against 11β-HSD-1. Therefore, these bioactive molecules require experimental validation as 11β-HSD-1 antagonists for type 2 diabetes management.Communicated by Ramaswamy H. Sarma.

Discovery of pyrazolopyrrolidinones as potent, broad-spectrum inhibitors of Leishmania infection

Introduction

Leishmaniasis is a parasitic disease that affects more than 1 million people worldwide annually, predominantly in resource-limited settings. The challenge in compound development is to exhibit potent activity against the intracellular stage of the parasite (the stage present in the mammalian host) without harming the infected host cells. We have identified a compound series (pyrazolopyrrolidinones) active against the intracellular parasites of Leishmania donovani and L. major; the causative agents of visceral and cutaneous leishmaniasis in the Old World, respectively.

Methods

In this study, we performed medicinal chemistry on a newly discovered antileishmanial chemotype, with over 100 analogs tested. Studies included assessments of antileishmanial potency, toxicity towards host cells, and in vitro ADME screening of key drug properties.

Results and discussion

Members of the series showed high potency against the deadliest form, visceral leishmaniasis (approximate EC₅₀ ≥ 0.01 μM without harming the host macrophage up to 10.0 μM). In comparison, the most efficient monotherapy treatment for visceral leishmaniasis is amphotericin B, which presents similar activity in the same assay (EC₅₀ = 0.2 μM) while being cytotoxic to the host cell at 5.0 μM. Continued development of this compound series with the Discovery Partnership with Academia (DPAc) program at the GlaxoSmithKline Diseases of the Developing World (GSK DDW) laboratories found that the compounds passed all of GSK's criteria to be defined as a potential lead drug series for leishmaniasis.

Conclusion

Here, we describe preliminary structure-activity relationships for antileishmanial pyrazolopyrrolidinones, and our progress towards the identification of candidates for future in vivo assays in models of visceral and cutaneous leishmaniasis.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

Shape-Based Descriptors for Efficient Structure-Based Fragment Growing

Structure-based fragment growing is one of the key techniques in fragment-based drug design. Fragment growing is commonly practiced based on structural and biophysical data. Computational workflows are employed to predict which fragment elaborations could lead to high-affinity binders. Several such workflows exist but many are designed to be long running noninteractive systems. Shape-based descriptors have been proven to be fast and perform well at virtual-screening tasks. They could, therefore, be applied to the fragment-growing problem to enable an interactive fragment-growing workflow. In this work, we describe and analyze the use of specific shape-based directional descriptors for the task of fragment growing. The performance of these descriptors that we call ray volume matrices (RVMs) is evaluated on two data sets containing protein-ligand complexes. While the first set focuses on self-growing, the second measures practical performance in a cross-growing scenario. The runtime of screenings using RVMs as well as their robustness to three dimensional perturbations is also investigated. Overall, it can be shown that RVMs are useful to prefilter fragment candidates. For up to 84% of the 3299 generated self-growing cases and for up to 66% of the 326 generated cross-growing cases, RVMs could create poses with less than 2 Å root-mean-square deviation to the crystal structure with average query speeds of around 30,000 conformations per second. This opens the door for fast explorative screenings of fragment libraries.