Diarylcyclopropane hydroxamic acid inhibitors of histone deacetylase 4 designed by combinatorial approach and QM/MM calculations

A modified bonded model approach for molecular dynamics simulations of New Delhi Metallo-β-lactamase

Modelling metalloproteins using the classical force fields is challenging. Several methods have been devised to model metalloproteins in force fields. Of these methods, the bonded model, combined with Restrained Electrostatic Potential (RESP) charge fitting, proved its superiority. The latter method was facilitated by the development of the python-based Metal Centre Parameter Builder (MCPB.py) AmberTool. However, the standard bonded model method offered by the MCPB.py tool may not be appropriate for validating and refining the binding modes predicted by docking when crystal structures are lacking. That is because the representation of coordination interactions between any bound ligand and metal ions by covalent bonds can hinder the flexibility of the ligand. Therefore, a new modification to the standard bonded model approach is proposed here. Molecular dynamics (MD) simulations based on the new modified bonded model (MBM) approach avoid the bias caused by coordination bonds and, unlike hybrid QM/MM MD, allow for sufficient sampling of the binding mode given the currently available computational power. The MBM MD approach reproduced the studied crystal structure conformations of New Delhi Metallo-β-lactamase 1 (NDM-1). Furthermore, the MBM approach described the binding interactions of intact β-lactams with NDM-1 reasonably, and predicted a non-productive binding mode for the poor NDM-1 substrate aztreonam whilst predicting productive binding modes for known good substrates. This study presents a useful MD method for metallo-β-lactamases and provides better understanding of β-lactam substrates recognition by NDM-1. The proposed MBM approach might also be useful in the investigation of other metal-containing protein targets.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

In silico Design of Novel Histone Deacetylase 4 Inhibitors: Design Guidelines for Improved Binding Affinity

Histone deacetylases (HDAC) are being targeted for a number of diseases such as cancer, inflammatory disease, and neurological disorders. Within this family of 18 isozymes, HDAC4 is a prime target for glioma, one of the most aggressive brain tumors reported. Thus, the development of HDAC4 inhibitors could present a novel therapeutic route for glioma. In this work, molecular docking studies on cyclopropane hydroxamic acid derivatives identified five novel molecular interactions to the HDAC4 receptor that could be harnessed to enhance inhibitor binding. Thus, design guidelines for the optimization of potent HDAC4 inhibitors were developed which can be utilized to further the development of HDAC4 inhibitors. Using the developed guidelines, eleven novel cyclopropane hydroxamic acid derivatives were designed that outcompeted all original cyclopropane hydroxamic acids HDAC4 inhibitors studied in silico. The results of this work will be an asset to paving the way for further design and optimization of novel potent HDAC4 inhibitors for gliomas.