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The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Di Filippo, Bollini and Cavasotto.)

Bovine viral diarrhea virus (BVDV) belongs to the Pestivirus genus ( Flaviviridae ). In spite of the availability of vaccines, the virus is still causing substantial financial losses to the livestock industry. In this context, the use of antiviral agents could be an alternative strategy to control and reduce viral infections. The viral RNA-dependent RNA polymerase (RdRp) is essential for the replication of the viral genome and constitutes an attractive target for the identification of antiviral compounds. In a previous work, we have identified potential molecules that dock into an allosteric binding pocket of BVDV RdRp via a structure-based virtual screening approach. One of them, N -(2-morpholinoethyl)-2-phenylquinazolin-4-amine [ 1 , 50% effective concentration (EC 50 ) = 9.7 ± 0.5 μM], was selected to perform different chemical modifications. Among 24 derivatives synthesized, eight of them showed considerable antiviral activity. Molecular modeling of the most active compounds showed that they bind to a pocket located in the fingers and thumb domains in BVDV RdRp, which is different from that identified for other non-nucleoside inhibitors (NNIs) such as thiosemicarbazone (TSC). We selected compound 2-[4-(2-phenylquinazolin-4-yl)piperazin-1-yl]ethanol ( 1.9 ; EC 50 = 1.7 ± 0.4 μM) for further analysis. Compound 1.9 was found to inhibit the in vitro replication of TSC-resistant BVDV variants, which carry the N264D mutation in the RdRp. In addition, 1.9 presented adequate solubility in different media and a high-stability profile in murine and bovine plasma., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Fernández, Castro, Rosas, Fidalgo, Adler, Battini, España de Marco, Fabiani, Bruno, Bollini and Cavallaro.)

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus within the family Flaviviridae. BVDV causes both acute and persistent infections in cattle, leading to substantial financial losses to the livestock industry each year. The global prevalence of persistent BVDV infection and the lack of a highly effective antiviral therapy have spurred intensive efforts to discover and develop novel anti-BVDV therapies in the pharmaceutical industry. Antiviral targeting of virus envelope proteins is an effective strategy for therapeutic intervention of viral infections. We performed prospective small-molecule high-throughput docking to identify molecules that likely bind to the region delimited by domains I and II of the envelope protein E2 of BVDV. Several structurally different compounds were purchased or synthesized, and assayed for antiviral activity against BVDV. Five of the selected compounds were active displaying IC 50 values in the low- to mid-micromolar range. For these compounds, their possible binding determinants were characterized by molecular dynamics simulations. A common pattern of interactions between active molecules and aminoacid residues in the binding site in E2 was observed. These findings could offer a better understanding of the interaction of BVDV E2 with these inhibitors, as well as benefit the discovery of novel and more potent BVDV antivirals.