Virtual Screening-Based Study of Novel Anti-Cancer Drugs Targeting G-Quadruplex

PayloadGenX, a multi-stage hybrid virtual screening approach for payload design: A microtubule inhibitor case study

Due to the rapid emergence of treatment-resistant cancers, there is a growing need to discover new anticancer therapies. Antibody-drug conjugates (ADCs) are aimed at solving this problem by specifically targeting and delivering cytotoxic payloads directly to cancer cells, thereby minimizing damage to healthy cells and enhancing treatment efficacy. Therefore, it is highly important to find an effective cytotoxic payload to ensure maximum therapeutic benefit and overcome cancer resistance. To address this challenge, we have developed a multi-stage hybrid virtual screening (VS) approach for payload design. We collected approximately 900 million molecules from databases such as ZINC12, ChEMBL, PubChem, and QM9. Additionally, 220 approved small molecule anticancer drugs were collected. Initially, these molecules were screened based on the Lipinski Rule of Five (RO5) criteria, resulting in 20 million molecules that met the drug-like properties criteria. Subsequently, fragments being key factor in this approach were generated from approved small molecule cancer drugs. This fragment-based screening approach resulted in identifying 6500, 36770, and 150,000 anticancer-like drugs with a similarity threshold greater than 0.6, 0.5, and 0.4. Similarity threshold when increased near to 1 bears better chance of discovering cancer like drugs. Further molecular docking of these anticancer-like drugs with β-tubulin resulted in identifying the top 1000 ranked drugs as microtubule inhibitors. ADMET analysis and synthetic validation followed by cell cytotoxicity further helps in shortlisting the 5 most effective payloads for further confirmation in preclinical setting. Additionally, molecular dynamics simulation was performed to confirm the structural stability and conformational dynamics of the Beta-tubulin-ligand complexes over a 100 ns simulation. In conclusion, this study effectively utilizes extensive compound databases and multi-stage screening methods to identify potent payloads, demonstrating promising advancements in discovering effective anticancer therapies.

The Discovery of Novel Agents against Staphylococcus aureus by Targeting Sortase A: A Combination of Virtual Screening and Experimental Validation

Staphylococcus aureus (S. aureus), commonly known as "superbugs", is a highly pathogenic bacterium that poses a serious threat to human health. There is an urgent need to replace traditional antibiotics with novel drugs to combat S. aureus. Sortase A (SrtA) is a crucial transpeptidase involved in the adhesion process of S. aureus. The reduction in virulence and prevention of S. aureus infections have made it a significant target for antimicrobial drugs. In this study, we combined virtual screening with experimental validation to identify potential drug candidates from a drug library. Three hits, referred to as Naldemedine, Telmisartan, and Azilsartan, were identified based on docking binding energy and the ratio of occupied functional sites of SrtA. The stability analysis manifests that Naldemedine and Telmisartan have a higher binding affinity to the hydrophobic pockets. Specifically, Telmisartan forms stable hydrogen bonds with SrtA, resulting in the highest binding energy. Our experiments prove that the efficiency of adhesion and invasion by S. aureus can be decreased without significantly affecting bacterial growth. Our work identifies Telmisartan as the most promising candidate for inhibiting SrtA, which can help combat S. aureus infection.