Abstract: Ischemic stroke is a primary cause of morbidity and mortality worldwide. Beyond the approved thrombolytic therapies, there is no effective treatment to mitigate its progression. Drug repositioning combinational therapies are becoming promising approaches to identify new uses of existing drugs to synergically target multiple disease-response mechanisms underlying complex pathologies. Here, we used a systems biology-based approach based on artificial intelligence and pattern recognition tools to generate in silico mathematical models mimicking the ischemic stroke pathology. Combinational treatments were acquired by screening these models with more than 5 million two-by-two combinations of drugs. A drug combination (CA) formed by ceruletide and alpha-1 antitrypsin showing a predicted value of neuroprotection of 92% was evaluated for their synergic neuroprotective effects in a mouse pre-clinical stroke model. The administration of both drugs in combination was safe and effective in reducing by 39.42% the infarct volume 24 h after cerebral ischemia. This neuroprotection was not observed when drugs were given individually. Importantly, potential incompatibilities of the drug combination with tPA thrombolysis were discarded in vitro and in vivo by using a mouse thromboembolic stroke model with t-PA-induced reperfusion, revealing an improvement in the forepaw strength 72 h after stroke in CA-treated mice. Finally, we identified the predicted mechanisms of action of ceruletide and alpha-1 antitrypsin and we demonstrated that CA modulates EGFR and ANGPT-1 levels in circulation within the acute phase after stroke. In conclusion, we have identified a promising combinational treatment with neuroprotective effects for the treatment of ischemic stroke.

Abstract: BACKGROUND: Databases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort. Biomedical Relation Extraction (BioRE) aims to shift this burden to machines. Among its different applications, the discovery of Gene-Disease Associations (GDAs) is one of BioRE most relevant tasks. Nevertheless, few resources have been developed to train models for GDA extraction. Besides, these resources are all limited in size-preventing models from scaling effectively to large amounts of data. RESULTS: To overcome this limitation, we have exploited the DisGeNET database to build a large-scale, semi-automatically annotated dataset for GDA extraction. DisGeNET stores one of the largest available collections of genes and variants involved in human diseases. Relying on DisGeNET, we developed TBGA: a GDA extraction dataset generated from more than 700K publications that consists of over 200K instances and 100K gene-disease pairs. Each instance consists of the sentence from which the GDA was extracted, the corresponding GDA, and the information about the gene-disease pair. CONCLUSIONS: TBGA is amongst the largest datasets for GDA extraction. We have evaluated state-of-the-art models for GDA extraction on TBGA, showing that it is a challenging and well-suited dataset for the task. We made the dataset publicly available to foster the development of state-of-the-art BioRE models for GDA extraction.

Abstract: Background: In the clinical use of third-line treatment of non-Hodgkin lymphoma (NHL), the combination treatment is increasingly used due to problems such as drug resistance, and while their efficacy has been proven, whether they are economical has become a new issue. A recent trial showed copanlisib plus rituximab combination therapy (CRCT) had better efficacy in the treatment of relapsed indolent NHL (iNHL) compared to rituximab monotherapy (RM). However, the long-term cost and effectiveness of this regimen is not known. We are the first to evaluate the cost effectiveness of CRCT in third-line treatment of relapsed iNHL from the perspective of US payers. Methods: We used a Markov model to evaluate cost and quality-adjusted life years (QALYs) which included a population from CHRONOS-3 with mean age of 62.5 years and total cycle length of 16.3 years. The cycle length was 1 month, adverse reaction rates were from CHRONOS-3, mean body surface area was referenced from published literature, cost values are referenced from published literature and Drugbank, utility values were referenced from the published literature, and the primary endpoint was the incremental cost-effectiveness ratio (ICER). The willingness to pay (WTP) threshold was set at $150,000 per QALYs, and one-way sensitivity analysis and probabilistic sensitivity analysis were used to verify the robustness of the model. All costs are expressed in 2021 dollars and costs and utilities have been calculated at a discount rate of 3% per year. Results: CRCT and RM obtained 6.53 QALYs and 5.15 QALYs, respectively, and the ICER of CRCT vs. RM was $358,895.2/QALYs. Parameters having the greatest impact on the robustness of the model were the drug cost of copanlisib and the utility value of the progression-free survival (PFS) state. When the WTP threshold was $150,000, the probability of CRCT and RM being the most cost effective was 0.4% and 99.6% respectively. Conclusions: From a US payer perspective, CRCT is not cost-effective in treating relapsed iNHL at current prices compared to RM. But given its positive clinical efficacy, appropriate price discounts or assistance programs should be considered to make CRCT more affordable to patients with relapsed iNHL.

Abstract: Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.

Abstract: Raffino-oligosaccharide (ROS), the smallest oligosaccharide of the raffinose family, is a novel food ingredient. However, the anti-constipation effects of ROS remain obscure. This study investigates the anti-constipation effects of ROS based on the loperamide-induced mice model and reveals the underlying mechanism using constipation parameters, neurotransmitter level, 16S rRNA sequencing, and the targeted screening strategy. The prevention effects were firstly investigated by the gastro-intestinal transit rate experiment (50 mice) and defecation status experiment (50 mice), which were divided into five groups (n = 10/group): blank, model, and low-, medium- and high-dose ROS. Furthermore, the slow-transit constipation experiment (blank, model, and high-dose ROS, n = 10/group) was conducted to illustrate the underlying mechanism. The results showed that ROS aided in preventing the occurrence of constipation by improving the gastro-intestinal transit rate and the defecation frequency in mice, and ROS significantly reduced the serum levels of vasoactive intestinal peptide (VIP). In addition, ROS regulated the diversity and structure of intestinal flora. Among them, one specific family and six specific genera were significantly regulated in constipated mice. The targeted screening revealed that 29 targets related to the anti-constipation effects of ROS, indicating ROS may play a role by regulating multiple targets. Furthermore, the network pharmacology analysis showed that Akt1, Stat3, Mapk8, Hsp90aa1, Cat, Alb, Icam1, Sod2, and Gsk3b can be regarded as the core anti-constipation targets. In conclusion, ROS could effectively relieve constipation, possibly by inhibiting the level of neurotransmitters and regulating the gut flora in mice. This study also provides a novel network pharmacology-based targeted screening strategy to reveal the anti-constipation effects of ROS.

Abstract: Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

Abstract: Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets. Instead, the strongest adverse biomarkers represent widely expressed cell-cycle and housekeeping genes, and, correspondingly, nearly all therapies directed against these features have failed in clinical trials. In total, our analysis establishes a rich resource for prognostic biomarker analysis and clarifies the use of patient survival data in preclinical cancer research and therapeutic development.

Abstract: The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires treatments with rapid clinical translatability. Here we develop a multi-target and multi-ligand virtual screening method to identify FDA-approved drugs with potential activity against SARS-CoV-2 at traditional and understudied viral targets. 1,268 FDA-approved small molecule drugs were docked to 47 putative binding sites across 23 SARS-CoV-2 proteins. We compared drugs between binding sites and filtered out compounds that had no reported activity in an in vitro screen against SARS-CoV-2 infection of human liver (Huh-7) cells. This identified 17 "high-confidence", and 97 "medium-confidence" drug-site pairs. The "high-confidence" group was subjected to molecular dynamics simulations to yield six compounds with stable binding poses at their optimal target proteins. Three drugs-amprenavir, levomefolic acid, and calcipotriol-were predicted to bind to 3 different sites on the spike protein, domperidone to the Mac1 domain of the non-structural protein (Nsp) 3, avanafil to Nsp15, and nintedanib to the nucleocapsid protein involved in packaging the viral RNA. Our "two-way" virtual docking screen also provides a framework to prioritize drugs for testing in future emergencies requiring rapidly available clinical drugs and/or treating diseases where a moderate number of targets are known.

Abstract: Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit.

Abstract: Increased expression and activation of tumor necrosis factor-alpha (TNF-alpha) could lead to recurrent implantation failure (RIF). Therefore, TNF-alpha inhibition may be a strategic way to enhance the implantation rate in women with RIF. Nowadays, monoclonal antibodies are considered an effective therapeutic method for TNF-alpha inhibition. Unfortunately, monoclonal antibody treatments have several disadvantages. Thus, the design of small molecules capable of inhibiting TNF-alpha has become critical in recent years. In silico drug repurposing of FDA-approved drugs for TNF-alpha inhibition was used in this study. PyRx tools were employed for virtual screening. Additionally, the free energy of binding, the number of hydrogen bonds, and the number of drug contacts with the protein were calculated using the molecular dynamics (MD) simulation method. Virtual screening results reveal that 17 of 2471 FDA-approved drugs benefited from favorable binding energy with TNF-alpha (delta G < - 10 kcal/mol). Two of the 17 drugs, progesterone and prednisone, were the most frequently used without adverse effects during pregnancy. As a result, MD simulation was used to investigate these two drugs further. According to the MD simulation results, prednisone appears to have a higher affinity for TNF-alpha than progesterone, and consequently, the prednisone complex stability is higher. For the first time, this study examined the possible role of prednisone and progesterone in inhibiting TNF-alpha using in silico methods.