Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

Defects in perforin lead to the failure of T and NK cell cytotoxicity, hypercytokinemia, and the immune dysregulatory condition known as familial hemophagocytic lymphohistiocytosis (FHL). The only curative treatment is allogeneic hematopoietic stem cell transplantation which carries substantial risks. We used lentiviral vectors (LV) expressing the human perforin gene, under the transcriptional control of the ubiquitous phosphoglycerate kinase promoter or a lineage-specific perforin promoter, to correct the defect in different murine models. Following LV-mediated gene transfer into progenitor cells from perforin-deficient mice, we observed perforin expression in mature T and NK cells, and there was no evidence of progenitor cell toxicity when transplanted into irradiated recipients. The resulting perforin-reconstituted NK cells showed partial recovery of cytotoxicity, and we observed full recovery of cytotoxicity in polyclonal CD8+ T cells. Furthermore, reconstituted T cells with defined antigen specificity displayed normal cytotoxic function against peptide-loaded targets. Reconstituted CD8+ lymphoblasts had reduced interferon-γ secretion following stimulation in vitro, suggesting restoration of normal immune regulation. Finally, upon viral challenge, mice with >30% engraftment of gene-modified cells exhibited reduction of cytokine hypersecretion and cytopenias. This study demonstrates the potential of hematopoietic stem cell gene therapy as a curative treatment for perforin-deficient FHL.

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Airway hyperresponsiveness (AHR) affects 55%-77% of children with sickle cell disease (SCD) and occurs even in the absence of asthma. While asthma increases SCD morbidity and mortality, the mechanisms underlying the high AHR prevalence in a hemoglobinopathy remain unknown. We hypothesized that placenta growth factor (PlGF), an erythroblast-secreted factor that is elevated in SCD, mediates AHR. In allergen-exposed mice, loss of Plgf dampened AHR, reduced inflammation and eosinophilia, and decreased expression of the Th2 cytokine IL-13 and the leukotriene-synthesizing enzymes 5-lipoxygenase and leukotriene-C4-synthase. Plgf-/- mice treated with leukotrienes phenocopied the WT response to allergen exposure; conversely, anti-PlGF Ab administration in WT animals blunted the AHR. Notably, Th2-mediated STAT6 activation further increased PlGF expression from lung epithelium, eosinophils, and macrophages, creating a PlGF/leukotriene/Th2-response positive feedback loop. Similarly, we found that the Th2 response in asthma patients is associated with increased expression of PlGF and its downstream genes in respiratory epithelial cells. In an SCD mouse model, we observed increased AHR and higher leukotriene levels that were abrogated by anti-PlGF Ab or the 5-lipoxygenase inhibitor zileuton. Overall, our findings indicate that PlGF exacerbates AHR and uniquely links the leukotriene and Th2 pathways in asthma. These data also suggest that zileuton and anti-PlGF Ab could be promising therapies to reduce pulmonary morbidity in SCD.