. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

L-Dopa decarboxylase (DDC) is the most significantly co-expressed gene with ACE2, which encodes for the SARS-CoV-2 receptor angiotensin-converting enzyme 2 and the interferon-inducible truncated isoform dACE2. Our group previously showed the importance of DDC in viral infections. We hereby aimed to investigate DDC expression in COVID-19 patients and cultured SARS-CoV-2-infected cells, also in association with ACE2 and dACE2. We concurrently evaluated the expression of the viral infection- and interferon-stimulated gene ISG56 and the immune-modulatory, hypoxia-regulated gene EPO. Viral load and mRNA levels of DDC, ACE2, dACE2, ISG56 and EPO were quantified by RT-qPCR in nasopharyngeal swab samples from COVID-19 patients, showing no or mild symptoms, and from non-infected individuals. Samples from influenza-infected patients were analyzed in comparison. SARS-CoV-2-mediated effects in host gene expression were validated in cultured virus-permissive epithelial cells. We found substantially higher gene expression of DDC in COVID-19 patients (7.6-fold; p = 1.2e-13) but not in influenza-infected ones, compared to non-infected subjects. dACE2 was more elevated (2.9-fold; p = 1.02e-16) than ACE2 (1.7-fold; p = 0.0005) in SARS-CoV-2-infected individuals. ISG56 (2.5-fold; p = 3.01e-6) and EPO (2.6-fold; p = 2.1e-13) were also increased. Detected differences were not attributed to enrichment of specific cell populations in nasopharyngeal tissue. While SARS-CoV-2 virus load was positively associated with ACE2 expression (r≥0.8, p<0.001), it negatively correlated with DDC, dACE2 (r≤−0.7, p<0.001) and EPO (r≤−0.5, p<0.05). Moreover, a statistically significant correlation between DDC and dACE2 expression was observed in nasopharyngeal swab and whole blood samples of both COVID-19 and non-infected individuals (r≥0.7). In VeroE6 cells, SARS-CoV-2 negatively affected DDC, ACE2, dACE2 and EPO mRNA levels, and induced cell death, while ISG56 was enhanced at early hours post-infection. Thus, the regulation of DDC, dACE2 and EPO expression in the SARS-CoV-2-infected nasopharyngeal tissue is possibly related with an orchestrated antiviral response of the infected host as the virus suppresses these genes to favor its propagation.

Severe COVID-19 is characterized by acute respiratory distress syndrome (ARDS)-like hyperinflammation and endothelial dysfunction, that can lead to respiratory and multi organ failure and death. Interstitial lung diseases (ILD) and pulmonary fibrosis confer an increased risk for severe disease, while a subset of COVID-19-related ARDS surviving patients will develop a fibroproliferative response that can persist post hospitalization. Autotaxin (ATX) is a secreted lysophospholipase D, largely responsible for the extracellular production of lysophosphatidic acid (LPA), a pleiotropic signaling lysophospholipid with multiple effects in pulmonary and immune cells. In this review, we discuss the similarities of COVID-19, ARDS and ILDs, and suggest ATX as a possible pathologic link and a potential common therapeutic target.

We evaluated the therapeutic effect of secretory phospholipase A 2 (sPLA 2 )-inhibitory peptide at a cellular level on joint erosion, cartilage destruction, and synovitis in the human tumor necrosis factor (TNF) transgenic mouse model of arthritis. Tg197 mice (N = 18) or wild-type (N = 10) mice at 4 weeks of age were given intraperitoneal doses (7.5 mg/kg) of a selective sPLA 2 inhibitory peptide, P-NT.II, or a scrambled P-NT.II (negative control), three times a week for 4 weeks. Untreated Tg197 mice (N = 10) were included as controls. Pathogenesis was monitored weekly for 4 weeks by use of an arthritis score and histologic examinations. Histopathologic analysis revealed a significant reduction after P-NT.II treatment in synovitis, bone erosion, and cartilage destruction in particular. Conspicuous ultrastructural alterations seen in articular chondrocytes (vacuolated cytoplasm and loss of nuclei) and synoviocytes (disintegrating nuclei and vacuoles, synovial adhesions) of untreated or scrambled-P-NT.II-treated Tg197 mice were absent in the P-NT.II-treated Tg197 group. Histologic scoring and ultrastructural evidence suggest that the chondrocyte appears to be the target cell mainly protected by the peptide during arthritis progression in the TNF transgenic mouse model. This is the first time ultrastructural evaluation of this model has been presented. High levels of circulating sPLA 2 detected in untreated Tg197 mice at age 8 weeks of age were reduced to basal levels by the peptide treatment. Attenuation of lipopolysaccharide-and TNF-induced release of prostaglandin E 2 from cultured macrophage cells by P-NT.II suggests that the peptide may influence the prostaglandin-mediated inflammatory response in rheumatoid arthritis by limiting the bioavailability of arachidonic acid through sPLA 2 inhibition.

Highlights d Autotaxin (Atx) is a key regulator of satellite cell function d Conditional deletion of Atx in satellite cells impairs skeletal muscle regeneration d Atx-LPA induces S6K-mTOR signaling via LPAR1 d Atx-LPA promotes muscle hypertrophy