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The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.91 in training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.

Background: There are currently no satisfactory biomarkers to predict prognosis and evaluate the benefit of immunotherapy in bladder urothelial carcinoma (BLCA) patients. This study aimed to develop a predictive signature that could accurately predict prognosis and evaluate the response to immunotherapy in BLCA. Methods: Differentially expressed genes (DEGs) were identified using the GEPIA and Oncomine databases, and the common genes between the two database were selected using a Venn diagram. In addition, gene ontology enrichment and protein–protein interaction (PPI) network analyses were performed. We further identified the prognosis-related hub genes using the survival R package and confirmed in three online databases (PROGgenesV2, PrognoScan, and OSblca). Moreover, the correlation between prognosis-related hub genes and clinical characteristics was analyzed. Finally, comprehensive bioinformatics analysis was carried out to investigate the association between the three genes and immunity. Results: A total of 750 and 1881 DEGs were identified from GEPIA and Oncomine, respectively, and 69 common DEGs were selected. The most significantly enriched term among the 69 common DEGs was “mitotic cell cycle”, and 11 hub genes were detected by PPI analysis. Moreover, three prognosis-related hub genes, AURKA, BIRC5, and CKS1B, were identified, and which were associated with clinical characteristics, in particular, histological subtypes and TP53 mutation status. Furthermore, our results showed that the expression levels of the three genes were positively correlated with CD8+ T cells and tumor mutation burden (TMB), and with PD-L1, which had higher expression in responders to immunotherapy and in the C2 (IFN-gamma dominant) subtype. Drug–gene interaction network analysis demonstrated that these genes and related drugs could be used to help develop new targets for BLCA immunotherapy. Conclusions: Our study suggested that three key genes in BLCA were correlated with poor prognosis and immune cell infiltration, especially that of CD8+ T cells. The responses of these prognosis-related genes to immunotherapy in BLCA may be associated with CD8+ T cells, TMB, and PD-L1 expression. These key genes and their related drugs may help to develop new targets for BLCA immunotherapy.

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