CSD in Action: Fighting Antibiotic Resistance at Merck – Fragment-based Drug Design using CSD-CrossMiner
Discovering a New Metallo-Beta-Lactamase-Inhibitor
Here we highlight work from Merck, where the team of scientists used a combination of virtual screening and structure-based drug design to identify new inhibitors of key enzymes in the fight against antibiotic resistance.
The full article can be accessed here – J. Med. Chem. 2022, 65, 16234−16251.
Image on the left – Klebsiella pneumoniae, the bacterium in which the enzyme New Delhi metallo-beta-lactamase 1 (NDM-1) was first identified. By CDC – From the Centers for Disease Control and Prevention’s Public Health Image Library (PHIL), identification number #6689.
How Bacteria Survive
With an estimated 5 million deaths from bacterial infection in 2019, antibiotic resistance is of increasing concern.
As a strategy to survive, bacteria can produce enzymes that reduce their affinity towards antibiotics, or that hydrolyze and inactivate them, like in the case of the beta-lactamase enzymes.
Antibiotic Resistance
Since its discovery in 2008, the enzyme New Delhi metallo-beta-lactamase 1 (NDM-1) spread across the world at an alarming rate.
NDM-1 can destroy most penicillin-like antibiotics, including powerful carbapenems. The search for new beta-lactamase inhibitors (BLIs) effective against NDM-1 and similar enzymes is hence critical for the fight against antibiotic resistance.
New Clinical Candidates
The team at Merck identified a novel pan-metallo-beta-lactamase inhibitor (pan-MBLI) and demonstrated its in vivo efficacy.
In silico virtual screening of known MBLIs identified targets with activity against NDM-1. Further investigations and metal binding design led to the identification of the pan-MBLI clinical candidate.
Identification of a Novel Pan-Metallo-Beta-Lactamase Inhibitor
The study started with a comprehensive literature search of known MBLIs, performed beside fragment and high-throughput screenings. Tetrazole compounds were identified as promising structures for their good percentage of enzymatic activity inhibition.
Virtual screening of a class of tetrazole compounds led to the discovery of novel NDM-1 inhibitors that were used as a starting point for further hypothesis-driven structure-activity relationship (SAR) exploration and structure-based drug design.
This combined approach enabled the scientists to perform several structural modifications and optimizations, and ultimately to obtain a series of compounds with a pan-metallo-beta-lactamase (pan-MBL) inhibition profile. One of these was chosen for exhibiting the best overall activity profiles in enzyme and cell assays. Three clinically relevant MBL enzymes were tested in this work: NDM-1; Verona integron-encoded metallo-beta-lactamase 1, VIM-1; and imipenemase 1, IMP-1.
Investigation of the Metal Binding Conformation
Fundamental to the identification of the new pan-MBLI was the investigation of the metal binding conformation.
The team at Merck searched the Protein Data Bank (PDB) using CSD-CrossMiner and built a pharmacophore query to study the binding mode of the sulfonamide moiety and analyse how this functional group interacts with the Zn ion.
CSD-CrossMiner is a valuable tool in our drug discovery research.
Using CSD-CrossMiner we were able to quickly identify all the structures in the PDB that contain interactions between two Zn ions and a SO2NH group. This was important in our literature survey for our MBLI X-ray crystal structure investigations to review the published landscape.
CSD-CrossMiner was used to validate the proposed binding conformation of our pan-MBL inhibitor where the N atom of the SO2NH group interacts with one of Zn ions.
Dr Li Xiao, Computational and Structural Chemistry, Merck & Co., Inc.
Fighting Antibiotic Resistance
Finally, the team demonstrated the in vivo efficacy of the novel pan-MBLI. The latter showed effectiveness in lowering antibiotic resistance when used in combination with the intravenous beta-lactam antibiotic Imipenem.
This study shows how a combination of virtual screening, chemistry design, and structural analysis can accelerate drug discovery and lead to the identification of optimal clinical candidates.
