Major advances have recently been made in the development and application of CFTR (cystic fibrosis transmembrane conductance regulator) mutation class-specific modulator therapies, but to date, there are no approved modulators for Class I mutations, i.e., those introducing a premature termination codon (PTC) into the CFTR mRNA. Such mutations induce nonsense-mediated decay (NMD), a cellular quality control mechanism that reduces the quantity of PTC bearing mRNAs, presumably to avoid translation of potentially deleterious truncated CFTR proteins. The NMD-mediated reduction of PTC-CFTR mRNA molecules reduces the efficacy of one of the most promising approaches to treatment of such mutations, namely, PTC readthrough therapy, using molecules that induce the incorporation of near-cognate amino acids at the PTC codon, thereby enabling translation of a full-length protein. In this study, we measure the effect of three different PTC mutations on the abundance, integrity, and stability of respective CFTR mRNAs, using CFTR specific RT-qPCR-based assays. Altogether, our data suggest that optimized rescue of PTC mutations has to take into account (1) the different steady-state levels of the CFTR mRNA associated with each specific PTC mutation; (2) differences in abundance between the 3′ and 5′ regions of CFTR mRNA, even following PTC readthrough or NMD inhibition; and (3) variable effects on CFTR mRNA stability for each specific PTC mutation.

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.