Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Abstract

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R 2 = 0.958) and a satisfactory predictive power (Q 2 = 0.822; Q 2 F3 = 0.894). The model was validated (r 2 ext_ts = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner-Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity.Molecules 2020, 25, 1952 2 of 20 chromatin structure which concomitantly restricts the accessibility of related transcriptional factors to their target genes, thereby suppressing gene expression including tumor suppressor genes [6][7][8]. The abnormal regulation of this process culminates with the high expression level of HDACs. This event has been observed in the development of several human cancers. Consequently, effective inhibition of HDACs has recently gained importance as a valid therapeutic strategy to reverse aberrant epigenetic changes associated with cancer [9][10][11]. HDAC inhibitors (HDACIs) induce histone hyperacetylation and subsequent transcriptional re-activation of suppressed genes which are correlated with a variety of effects on tumor cells including apoptosis, differentiation, cell cycle arrest, inhibition of proliferation and cytostasis [12,13].The HDAC family comprises 18 isoforms in mammalian cells which are categorized into four main classes (class I-IV) based on their structural and functional characteristics. HDACs belonging to class I (HDAC1-3 and 8), II (HDACs 4-7, 9 and 10) and IV (HDAC11) are all zinc-dependent metalloenzymes, while class III HDACs, also known as the sirtuins (SIRT1-7), requires NAD + as a cofactor for their catalytic function [6,14].Extensive efforts over recent decades have led to the identification of four chemically diverse classes of HDACIs as potent antineoplastic agents including, hydroxamates, benzamides, cyclic peptides, and short-chain fatty acids [3]. The main...

Artificial Intelligence in Translational Medicine

“…Beyond the classical computational approaches in drug discovery, such as ligand-(mainly QSAR methods and pharmacophore modelling) [28][29][30][31] and structure-based strategies (mainly based on molecular docking and molecular dynamics) [32][33][34][35] or a combination of them [36][37][38][39], currently these computational methods are integrated with ML technologies for improving the reliability of the calculation, avoiding false positive outcomes and enhancing the success ratio in identifying safer hit compounds. Some examples are represented by QSAR-ML models [40][41][42][43], multi-and combi-QSAR approaches [44][45][46][47][48][49][50].…”

Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing

“…This computational evaluation is crucial for reducing off-target effects, and therefore the total number of animals required for the in vivo test. Computational pharmacology and toxicology take advantage from numerous scientific disciplines and usually include the application of in silico and statistical approaches for evaluating the bioactive profile of molecules for which a specific pharmacological or toxicological effect is not known, starting from a group of molecules for which the mentioned effect has been proven (training set) [ 47 , 48 , 49 , 50 ].…”

Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing

“…Computational pharmacology and toxicology take advantage from numerous scientific disciplines and usually includes the application of in silico and statistical approaches for evaluating the bioactive profile of molecules for which a specific pharmacological or toxicological effect is not known, starting from a group of molecules for which the mentioned effect have been proven (training set). [44][45][46][47] Accordingly, in silico strategies used for assessing the profile of compounds are mainly based on structure-activity relationship (SAR) and quantitative SAR (QSAR). In fact, most categories of computational methods in pharmacology and toxicology are based on the similarity principle: the hypothesis that compounds possessing a structural similarity could show comparable pharmacological or toxicological profiles.…”