A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors

HDAC3 inhibitors: a patent review of their broad-spectrum applications as therapeutic agents

INTRODUCTION

Histone deacetylases (HDACs) are a class of zinc-dependent enzymes. They maintain acetylation homeostasis, with numerous biological functions and are associated with many diseases. HDAC3 strictly requires multi-subunit complex formation for activity. It is associated with the progression of numerous non-communicable diseases. Its widespread involvement in diseases makes it an epigenetic drug target. Preexisting HDAC3 inhibitors have many uses, highlighting the need for continued research in the discovery of HDAC3-selective inhibitors.

AREA COVERED

This review provides an overview of 24 patents published from 2010 to 2023, focusing on compounds that inhibit the HDAC3 isoenzyme.

EXPERT OPINION

HDAC3-selective inhibitors - pivotal for pharmacological applications, as single or combination therapies - are gaining traction as a strategy to move away from complications laden pan-HDAC inhibitors. Moreover, there is an unmet need for HDAC3 inhibitors with alternative zinc-binding groups (ZBGs) because some preexisting ZBGs have limitations related to toxicity and side effects. Difficulties in achieving HDAC3 selectivity may be due to isoform selectivity. However, advancements in computer-aided drug design and experimental data of HDAC3 3D co-crystallized models could lead to the discovery of novel HDAC3-selective inhibitors, which bear alternative ZBGs with balanced selectivity for HDAC3 and potency.

Natural bioactive compounds as a new source of promising G protein-coupled estrogen receptor (GPER) modulators: comprehensive in silico approach

Cancer ranks in second place among the cause of death worldwide. Cancer progress in multiple stages of carcinogenesis and metastasis programs through complex pathways. Sex hormones and their receptors are the major factors in promoting cancer progression. Among them, G protein-coupled estrogen receptor-1 (GPER) has shown to mediate cellular signaling pathways and cancer cell proliferation. However, the lack of GPER protein structure limited the search for new modulators. In this study, we curated an extensive database of natural products to discover new potential GPER modulators. We used a combination of virtual screening techniques to generate a homology model of GPER and subsequently used that for the screening of 30,926 natural products from a public database to identify potential active modulators of GPER. The best hits were further screened through the ADMET filter and confirmed by docking analysis. Moreover, molecular dynamics simulations of best hits were also carried out to assess the stability of the ligand-GPER complex. This study predicted several potential GPER modulators with novel scaffolds that could be further investigated and used as the core for the development of novel GPER modulators.Communicated by Ramaswamy H. Sarma.

Sequential ligand- and structure-based virtual screening approach for the identification of potential G protein-coupled estrogen receptor-1 (GPER-1) modulators

G protein-coupled estrogen receptor-1 (GPER-1) is a seven transmembrane receptor, responsible for mediating rapid estrogen signaling in many physiological responses in reproductive, nervous, endocrine, immune and cardiovascular systems. Due to unavailability of the crystal structure of GPER-1, we have performed sequential ligand-based virtual screening (LBVS) and structure-based screening (SBVS) to identify potential GPER-1 modulators. LBVS and SBVS approaches were validated retrospectively using the Receiver Operating Curve (ROC) plot and the early Enrichment Factor (EF). LBVS was performed based on a GPER-1 agonist, G1, as a query model for screening of the eMolecules library using the Rapid Overlay of Chemical Structure (ROCS) and the electrostatic potential screening (EON) approaches. Top-scored hits from LBVS were further screened by SBVS. SBVS was based on generating homology models of GPER-1 and subsequent molecular docking studies. Using Chemguass4 score, we filtered the final hits with the higher score in comparison to G1 (Chemguass4 score = −11.575). The top-ranked hits were clustered based on similarity in their scaffolds. Prospective validation was performed by evaluating the antiproliferative activity of synthesized compounds (SK0 and SK0P) which were representative of top hits obtained from our virtual screening approach.

This paper presents the application of sequential ligand- and structure-based virtual screening approach for the identification of G protein-coupled estrogen receptor-1 (GPER-1/GPR30) modulators.

Computer-Aided Drug Design in Epigenetics

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay

Histone deacetylase 3 (HDAC3) is a potential target for the treatment of human diseases such as cancers, diabetes, chronic inflammation and neurodegenerative diseases. Previously, we proposed a virtual screening (VS) pipeline named “Hypo1_FRED_SAHA-3” for the discovery of HDAC3 inhibitors (HDAC3Is) and had thoroughly validated it by theoretical calculations. In this study, we attempted to explore its practical utility in a large-scale VS campaign. To this end, we used the VS pipeline to hierarchically screen the Specs chemical library. In order to facilitate compound cherry-picking, we then developed a knowledge-based pose filter (PF) by using our in-house quantitative structure activity relationship- (QSAR-) modelling approach and coupled it with FRED and Autodock Vina. Afterward, we purchased and tested 11 diverse compounds for their HDAC3 inhibitory activity in vitro. The bioassay has identified compound 2 (Specs ID: AN-979/41971160) as a HDAC3I (IC₅₀ = 6.1 μM), which proved the efficacy of our workflow. As a medicinal chemistry study, we performed a follow-up substructure search and identified two more hit compounds of the same chemical type, i.e. 2–1 (AQ-390/42122119, IC₅₀ = 1.3 μM) and 2–2 (AN-329/43450111, IC₅₀ = 12.5 μM). Based on the chemical structures and activities, we have demonstrated the essential role of the capping group in maintaining the activity for this class of HDAC3Is. In addition, we tested the hit compounds for their in vitro activities on other HDACs, including HDAC1, HDAC2, HDAC8, HDAC4 and HDAC6. We have identified these compounds are HDAC1/2/3 selective inhibitors, of which compound 2 show the best selectivity profile. Taken together, the present study is an experimental validation and an update to our earlier VS strategy. The identified hits could be used as starting structures for the development of highly potent and selective HDAC3Is.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

Artificial Mitochondria Transfer: Current Challenges, Advances, and Future Applications

The objective of this review is to outline existing artificial mitochondria transfer techniques and to describe the future steps necessary to develop new therapeutic applications in medicine. Inspired by the symbiotic origin of mitochondria and by the cell's capacity to transfer these organelles to damaged neighbors, many researchers have developed procedures to artificially transfer mitochondria from one cell to another. The techniques currently in use today range from simple coincubations of isolated mitochondria and recipient cells to the use of physical approaches to induce integration. These methods mimic natural mitochondria transfer. In order to use mitochondrial transfer in medicine, we must answer key questions about how to replicate aspects of natural transport processes to improve current artificial transfer methods. Another priority is to determine the optimum quantity and cell/tissue source of the mitochondria in order to induce cell reprogramming or tissue repair, in both in vitro and in vivo applications. Additionally, it is important that the field explores how artificial mitochondria transfer techniques can be used to treat different diseases and how to navigate the ethical issues in such procedures. Without a doubt, mitochondria are more than mere cell power plants, as we continue to discover their potential to be used in medicine.