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

Abstract

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.

Keywords: computational methodologies; computer-aided drug design (CADD); databases; immune checkpoint inhibitor (ICI); immune oncology therapies; programmed cell death ligand 1 (PD-L1); programmed cell death protein 1 (PD-1); web tools.

Figure 1

Number of publications and percentages per year covering ICIs in cancer topics, period 2013–2023. Data source from Web of Science™ Core Collection.

Figure 2

Analysis of the 25 selected journals reporting ICIs in cancer topics, by number of records and average citation per record, since 2013. Data source from Web of Science™ Core Collection.

Figure 3

Small molecule (9–18) ICIs targeting PD-1/PD-L1 in combination clinical trials with mAbs.

Figure 4

Visualization of the co-crystal dimer structure of PD-L1 in complex with BMS-200 (Protein Data Bank, PDB ID: 5N2F), highlighted with the critical residues for ligand binding, using UCSF Chimera [57,58].

Figure 5

Small molecule inhibitors of PD-1/PD-L1 based on the BMS pharmacophoric model [59,61,62,74,75,76].

Figure 6

Small molecule inhibitors of PD-1/PD-L1 immune checkpoint [51,65,77,78,80,81,82].

Figure 7

(A) 3D interactions of sunitinib with CDK2 (PDB code: 3Ti1) using UCSF Chimera [58,84]. (B) 1. Structure of the designed hybridized molecules. 2. 2-indolinone pharmacophore motifs in CDK2 inhibitor commercially marketed drugs [58,84].

Figure 8

Sequence and structure of the most potent peptide designed, Ar5Y_4 [86].

Figure 9

Sequence and structure of RRQWFW-NH₂ and RRWWRR-NH₂ [90].

Figure 10

Sequence and structure of PD-1(122–138)^C123-S137C [91].

Figure 11

Structural representation of the antigen-binding fragment (Fab) of pembrolizumab (PDB ID: 5GGS) in complex with the extracellular domain of human PD-1 receptor using UCSF Chimera [58,97,98].

Figure 12

Structure of the KN035/ PD-L1 complex (PDB ID: 5JDS) represented using UCSF Chimera [58,99]. PD-L1 is shown as a slate, semi-transparent surface.

Figure 13

Small molecule ICIs predict targets that regulate PD-L1 expression [101,102,103,106,107].

Figure 14

Visualization of EGFR protein (PDB, ID 3W2S) with UCSF Chimera [58]. (A) Full view; (B) binding active pocket region [101,102].

Figure 15

Structures and sequences of the designed peptide inhibitors of PD-1 [110].