Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Area of Science:

• Computational biology and bioinformatics
• Drug discovery and development
• Cancer immunotherapy

Background:

• Immune-oncology therapies utilize data-driven methods to identify immune targets and drug candidates.
• The development of PD-1/PD-L1 immune checkpoint inhibitors (ICIs) is a key area, employing cheminformatics and bioinformatics.
• There is an ongoing need for improved ICIs and reliable predictive biomarkers in cancer treatment.

Purpose of the Study:

• To review computational methodologies for discovering and developing PD-1/PD-L1 ICIs.
• To focus on advancements in computational approaches within the last five years.
• To compile relevant databases and web tools for cancer immunotherapy research.

Main Methods:

• Utilizing computer-aided drug design (CADD) including structure- and ligand-based virtual screening.
• Employing molecular docking, homology modeling, and molecular dynamics simulations.
• Analyzing large datasets of molecules, gene expression, and protein-protein interactions.

Main Results:

• Computational approaches are valuable tools for discovering and developing ICIs.
• Specific methodologies like virtual screening, molecular docking, and simulations are crucial for drug discovery campaigns.
• A compilation of recent databases and web tools relevant to cancer and immunotherapy has been created.

Conclusions:

• Computational methods significantly aid in the discovery and development of immune checkpoint inhibitors.
• Despite progress, improved ICIs and predictive biomarkers remain critical unmet needs.
• The provided resources aim to support ongoing research in cancer immunotherapy.