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

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Automated summary

Computational approaches are being used to identify potential immune targets and develop novel drugs in immune-oncology therapies, with a particular focus on PD-1/PD-L1 immune checkpoint inhibitors. These approaches involve analyzing large datasets of molecules, gene expression, and protein-protein interactions using cheminformatics and bioinformatics tools. The computational methodologies used in the discovery and development of PD-1/PD-L1 inhibitors, such as computer-aided drug design and virtual screening, are discussed. Recent databases and web tools related to cancer and immunotherapy are also compiled to aid in this research. In summary, computational approaches have become valuable tools for discovering and developing immune checkpoint inhibitors, but there is still a need for improved drugs and biomarkers.