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PayloadGenX, a multi-stage hybrid virtual screening approach for payload design: A microtubule inhibitor case study
Faheem Ahmed1, Anupama Samantasinghar1, Naina Sunildutt2
1Biologics4U, 27, Dongil-ro 174-gil, Nowon-gu, Seoul, Republic of Korea.
Abstract:
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.
Insights
Researchers developed a virtual screening method to discover new anticancer drug payloads. This approach identified promising compounds targeting cancer cells, offering hope for overcoming treatment resistance.
Area of Science:
- Drug Discovery
- Computational Chemistry
- Oncology
Background:
- Treatment-resistant cancers necessitate novel therapeutic strategies.
- Antibody-drug conjugates (ADCs) offer targeted delivery of cytotoxic payloads to cancer cells.
- Identifying effective cytotoxic payloads is crucial for ADC efficacy and overcoming resistance.
Purpose of the Study:
- To develop and apply a multi-stage virtual screening approach for identifying novel cytotoxic payloads for Antibody-drug conjugates (ADCs).
- To discover potent anticancer agents capable of overcoming drug resistance.
Main Methods:
- A hybrid virtual screening approach was employed, integrating large molecular databases (ZINC12, ChEMBL, PubChem, QM9) and approved anticancer drugs.
- Initial filtering based on Lipinski's Rule of Five (RO5) reduced the dataset to drug-like molecules.
- Fragment-based screening, molecular docking against β-tubulin, ADMET analysis, synthetic validation, and molecular dynamics simulations were performed.
Main Results:
- Over 900 million molecules were screened, with 20 million meeting drug-like criteria.
- Fragment-based screening identified numerous anticancer-like drug candidates.
- Molecular docking and subsequent analyses led to the identification of 5 highly effective payloads, including microtubule inhibitors, with confirmed structural stability.
Conclusions:
- The multi-stage virtual screening strategy effectively identified potent cytotoxic payloads for ADCs.
- This approach demonstrates significant potential for discovering novel anticancer therapies to combat drug resistance.
- The identified payloads warrant further preclinical investigation for enhanced cancer treatment.
