J Mol Model. 2022 Jul 6;28(8):212. doi: 10.1007/s00894-022-05213-9.

ABSTRACT

The recent outbreak "Coronavirus Disease 2019 (COVID-19)" is caused by fast-spreading and highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). This virus enters into the human respiratory system by binding of the viral surface spike glycoprotein (S-protein) to an angiotensin-converting enzyme2 (ACE2) receptor that is found in the nasal passage and oral cavity of a human. Both spike protein and the ACE2 receptor have been identified as promising therapeutic targets to develop anti-SARS-CoV2 drugs. No therapeutic drugs have been developed as of today except for some vaccines. Therefore, potent therapeutic agents are urgently needed to combat the COVID-19 infections. This goal would be achieved only by applying drug repurposing and computational approaches. Thus, based on drug repurposing approach, we have investigated 16 bioactive components (1-16) from different nasal spray solutions to check their efficacies against human ACE2 and SARS-CoV2 spike proteins by performing molecular docking and molecular dynamic (MD) simulation studies. In this study, three bioactive components namely ciclesonide (8), levocabastine (13), and triamcinolone acetonide (16) have been found as promising inhibitory agents against SARS-CoV2 spike and human ACE2 receptor proteins with excellent binding affinities, comparing to reference drugs such as nafamostat, arbidol, losartan, and benazepril. Furthermore, MD simulations were performed (triplicate) for 100 ns to confirm the stability of 8, 13, and 16 with said protein targets and to compute MM-PBSA-based binding-free energy calculations. Thus, bioactive components 8, 13, and 16 open the door for researchers and scientist globally to investigate them against SARS-CoV2 through in vitro and in vivo analysis.

PMID:35794497 | DOI:10.1007/s00894-022-05213-9

APMIS. 2022 Jul 5. doi: 10.1111/apm.13260. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Biofilms formed by Candida species present a significant clinical problem due to the ineffectiveness of many conventional antifungal agents, in particular the azole class. We urgently require new and clinically approved antifungal agents quickly for treatment of critically ill patients.

METHODS: To improve efficiency in antifungal drug development, we utilized a library of 1280 biologically active molecules within the Tocriscreen 2.0 Micro library. C. auris NCPF 8973 and C. albicans SC5314 were initially screened for biofilm inhibitory activity using metabolic and biomass quantitative assessment methods, followed up by targeted evaluation of five selected hits.

RESULTS: The initial screening (80% metabolic inhibition rate) revealed that there was 90 and 87 hits (approx. 7%) for C. albicans and C. auris, respectively. Additionally, all five compounds selected from the initial hits exhibited a biofilm inhibition effect against several key Candida species tested, including C. glabrata and C. krusei. Toyocamycin displayed the most potent activity at concentrations as low as 0.5 µg/mL, though was limited to inhibition. Darapladib demonstrated an efficacy for biofilm inhibition and treatment at a concentration range from 8 to 32 µg/mL and from 16 to 256 µg/mL, respectively. Combinational testing with conventional antifungals against C. albicans strains demonstrated a range of synergies for planktonic cells, and notably an anti-biofilm synergy for darapladib and caspofungin.

INTERPRETATION: Together, these data provide new insights into antifungal management possibilities for Candida biofilms.

PMID:35791082 | DOI:10.1111/apm.13260

Anticancer Res. 2022 Jul;42(7):3483-3494. doi: 10.21873/anticanres.15835.

ABSTRACT

BACKGROUND/AIM: Patients with metastatic tumors commonly have a poor prognosis. Frequently, patients suffering from progressive tumors have a high willingness for the compassionate use of non-approved medications. One of these medications is the antimalarial drug artesunate (ART) which also showed profound anticancer activity in vitro, in vivo, and in preliminary clinical pilot studies. Herein, we report on the compassionate use of ART in a patient with metastatic breast cancer.

PATIENTS AND METHODS: The clinical course of a Caucasian female who was diagnosed with ductal breast cancer at the age of 33 is described. Tumor markers in the blood have been measured, and tumor-associated protein expression has been determined by immunohistochemistry. Microscale thermophoresis and molecular docking in silico were used to study protein-drug interactions.

RESULTS: The tumor responded to ART administered at doses of 150-300 mg daily, and the patient experienced a stabilization of her disease for 1.5 years. ART treatment caused no or minimal side-effects (headache, dizziness, slight tachycardia, slight stomach upset, slight fatigue). Tumor marker determination in the blood of the patient revealed a reduction of carcinoembryonic antigen (CEA), but not CA 27.29 or CA 15.3 levels. We hypothesized that the reduction of CEA levels might be due to binding of ART to this protein. Microscale thermophoresis with recombinant CEA indeed showed binding of ART to this protein in vitro. This result was verified by molecular docking in silico. Immunohistochemical biomarker profiling and computerbased quantification of biomarker expression in a tumor biopsy revealed strong expression of COX2, GRP78, CD71, GSTP1, and c-MYC but weak or minimal expression of VEGFR, P-glycoprotein, survivin, and LOX1.

CONCLUSION: Among a panel of tumor-related proteins tested, the interaction with CEA may have contributed to the anticancer activity of ART in this patient. It deserves further investigation whether CEA represents not only a valuable biomarker but also a treatment target. ART might be useful for the individualized treatment of metastatic breast tumors.

PMID:35790273 | DOI:10.21873/anticanres.15835

J Virus Erad. 2022 Jun 20;8(2):100074. doi: 10.1016/j.jve.2022.100074. eCollection 2022 Jun.

ABSTRACT

Coxsackievirus B3 (CVB3) infections may cause life-threatening diseases and have no approved specific treatment. Some promising approaches to treat viral diseases include drug repurposing and combination therapy. We have selected in this study dasabuvir, an approved antiviral drug, and PSI-6206, an experimental drug and determined their individual and combined antiviral activity against CVB3 replication in vitro. Our results show that the individual drugs inhibited CVB3 infection in a dose-dependent manner, at a selective index >10 with a strong synergetic antiviral effect of the two compounds. Given that dasabuvir has already been approved for the treatment of hepatitis C virus infection, treatment of CVB3-related disease with this drug may represent a promising treatment strategy.

PMID:35789934 | PMC:PMC9249823 | DOI:10.1016/j.jve.2022.100074

Struct Chem. 2022 Jun 27:1-15. doi: 10.1007/s11224-022-01999-9. Online ahead of print.

ABSTRACT

A virus called severe acute respiratory distress syndrome coronavirus type 2 (SARS-CoV-2) is the causing organism of coronavirus disease 2019 (COVID-19), which has severely affected human life and threatened public health. The pandemic took millions of lives worldwide and caused serious negative effects on human society and the economy. SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) are interesting targets due to their crucial role in viral replication and growth. Since there is only one approved therapy for COVID-19, drug repurposing is a promising approach to finding molecules with potential activity against COVID-19 in a short time and at minimal cost. In this study, virtual screening was performed on the ChEMBL library containing 9923 FDA-approved drugs, using various docking filters with different accuracy. The best drugs with the highest docking scores were further examined for molecular dynamics (MD) studies and MM-GBSA calculations. The results of this study suggest that nadide, cangrelor and denufosol are promising potential candidates against COVID-19. Further in vitro, preclinical and clinical studies of these candidates would help to discover safe and effective anti-COVID-19 drugs.

PMID:35789829 | PMC:PMC9243907 | DOI:10.1007/s11224-022-01999-9

Brief Bioinform. 2022 Jul 5:bbac269. doi: 10.1093/bib/bbac269. Online ahead of print.

ABSTRACT

Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, the machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the DTA datasets shows that our proposed method has advantages compared to other pre-training methods in the DTA task.

PMID:35788823 | DOI:10.1093/bib/bbac269

IEEE J Biomed Health Inform. 2022 Jul 5;PP. doi: 10.1109/JBHI.2022.3188503. Online ahead of print.

ABSTRACT

Identifying gene-drug interactions is vital to understanding biological mechanisms and achieving precise drug repurposing. High-throughput technologies produce a large amount of pharmacological and genomic data, providing an opportunity to explore the associations between oncogenic genes and therapeutic drugs. However, most studies only focus on "one-to-one" or "one-to-many" interactions, ignoring the multivariate patterns between genes and drugs. In this article, a high-order graph matching model with hypergraph constraints is proposed to discover the gene-drug common regulatory modules. Moreover, the prior knowledge is formulated into hypergraph constraints to reveal their multiple correspondences, penalizing the tensor matching process. The experimental results on the synthetic data demonstrate the proposed model is robust to noise contamination and outlier corruption, achieving a better performance than four state-of-the-art methods. We then evaluate the statistical power of our proposed method on the pharmacogenomics data. Our identified gene-drug common modules not only show significantly enriched pathways associated with cancer but also manifest the highly close gene-drug interactions.

PMID:35788454 | DOI:10.1109/JBHI.2022.3188503

Transl Res. 2022 Jul 1:S1931-5244(22)00151-7. doi: 10.1016/j.trsl.2022.06.017. Online ahead of print.

ABSTRACT

We previously demonstrated that Annexin A2 (ANXA2) is a pivotal mediator of the pro-oncogenic features displayed by glioblastoma (GBM) tumors, the deadliest adult brain malignancies, being involved in cell stemness, proliferation and invasion, thus negatively impacting patient prognosis. Based on these results, we hypothesized that compounds able to revert ANXA2-dependent transcriptional features could be exploited as reliable treatments to inhibit GBM cell aggressiveness by hampering their proliferative and migratory potential. Transcriptional signatures obtained by the modulation of ANXA2 activity/levels were functionally mapped through the QUADrATiC bioinformatic tool for compound identification. Selected compounds were screened by cell proliferation and migration assays in primary GBM cells, and we identified Homoharringtonine (HHT) as a potent inhibitor of GBM cell motility and proliferation, without affecting their viability. A further molecular characterization of the effects displayed by HHT, confirmed its ability to inhibit a transcriptional program involved in cell migration and invasion. Moreover, we demonstrated that the multiple antitumoral effects displayed by HHT are correlated to the inhibition of a PDGFRα-dependent intracellular signaling through the impairment of STAT3 and RhoA axes. Our results demonstrate that HHT may act as a potent inhibitor of cancer cell proliferation and invasion in GBM, by hampering multiple PDGFRα-dependent oncogenic signals transduced through the STAT3 and RhoA intracellular components, finally suggesting its potential transferability for achieving an effective impairment of peculiar GBM hallmarks.

PMID:35788055 | DOI:10.1016/j.trsl.2022.06.017

Expert Opin Drug Discov. 2022 Jul 5. doi: 10.1080/17460441.2022.2097659. Online ahead of print.

ABSTRACT

INTRODUCTION: : Some of otherwise useful fungi are pathogenic to humans, and unfortunately, the number of these pathogens is increasing. In addition to common skin infections, these opportunistic pathogens are able to cause severe, often incurable, systemic mycoses.

AREAS COVERED: : The number of antifungal drugs is limited, especially drugs that can be used for systemic administration, and resistance to these drugs is very common. This review summarizes various approaches to the discovery and development of new antifungal drugs, provides an overview of the most important molecules in terms of basic (laboratory) research and compounds currently in clinical trials, and focuses on drug repurposing strategy, while providing an overview of drugs of other indications that have been tested in vitro for their antifungal activity for possible expansion of antifungal drugs and/or support of existing antimycotics.

EXPERT OPINION: : Despite the limitations of the research of new antifungal drugs by pharmaceutical manufacturers, in addition to innovated molecules based on clinically used drugs, several completely new small entities with unique mechanisms of actions have been identified. The identification of new molecular targets that offer alternatives for the development of new unique selective antifungal highly effective agents has been an important outcome of repurposing of non-antifungal drugs to antifungal drug. Also, given the advances in monoclonal antibodies and their application to immunosuppressed patients, it may seem possible to predict a more optimistic future for antifungal therapy than has been the case in recent decades.

PMID:35787715 | DOI:10.1080/17460441.2022.2097659

Front Public Health. 2022 Jun 16;10:902123. doi: 10.3389/fpubh.2022.902123. eCollection 2022.

ABSTRACT

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

PMID:35784208 | PMC:PMC9244710 | DOI:10.3389/fpubh.2022.902123

Microb Pathog. 2022 Jun 30:105665. doi: 10.1016/j.micpath.2022.105665. Online ahead of print.

ABSTRACT

Urinary tract infections (UTIs) are a serious health concern worldwide. Treatment of UTIs is becoming a challenge as uropathogenic Escherichia coli (UPEC), which is the most common etiological agent, has developed resistance to the main classes of antibiotics. Small molecules that interfere with metabolic processes rather than growth are attractive alternatives to conventional antibiotics. Repurposing of already known drugs for treating infectious diseases could be an attractive avenue for finding novel therapeutics against infections caused by UPEC. Virtual screenings enable the rapid and economical identification of target ligands from large libraries of compounds, reducing the cost and time of traditional drug discovery. Moreover, the drugs that have been approved by the FDA have low cytotoxicity and good pharmacological characteristics. In this work, we targeted the HisC enzyme of the histidine biosynthetic pathway as enzymes of this pathway are absent in humans. We screened the library of FDA-approved drugs against HisC via molecular docking, and four hits (Docetaxel, Suramin, Digitoxin, and Nystatin) showing the highest binding energy were selected. These were further tested for antibacterial activity, which was observed only for Docetaxel (MIC value of 640 μg/ml); therefore, Docetaxel was further tested for its efficacy in vivo in murine catheter UTI model and antibiofilm activity using crystal violet staining and scanning electron microscopy. Docetaxel inhibited biofilm formation and reduced the bacterial load in urine, kidney, and bladder. Docking studies revealed that Docetaxel acts by blocking the binding site of HisC to the native substrate by competitive inhibition. Docetaxel may be a potential new inhibitor for UPEC with antibacterial and antibiofilm capability.

PMID:35781005 | DOI:10.1016/j.micpath.2022.105665

Biosens Bioelectron. 2022 Jun 24;214:114503. doi: 10.1016/j.bios.2022.114503. Online ahead of print.

ABSTRACT

Human epidermal growth factor receptors HER1 and HER2, overexpressed in non-small cell lung cancer, colorectal cancer, liver cancer, are key regulators of tumor cells proliferation, invasion and survival. Many antibody- or peptide-based fluorescent probes targeted to HER1/HER2 are under active clinical evaluation. However, the effective small-molecule near-infrared (NIR) fluorescent probes are still lacking. Herein, with the strategy of drug repurposing, we developed a series of HER1/HER2-targeted probes YQ-H (01-07) composed of fluorophore (a cyanine dye, MPA), linker unit and targeted unit (lapatinib, LAP). The synthesized probes were evaluated in vitro and in vivo tumor specificity/affinity. Specially, the probe YQ-H-06 exhibited optimal pharmacokinetic property and tumor/normal tissue ratio (T/N) in tumor-bearing mice. Furthermore, we evaluated the targeting capability of YQ-H-06 in orthotopic colorectal cancer and orthotopic hepatic carcinoma mice. It was indicated that YQ-H-06 had the characteristic of great biosafety, favorable pH and chemical stability, as well as provided excellent tumor contrast in orthotopic murine tumor models. The NIR fluorescent probe YQ-H-06 will shed light on tumor detection and fluorescence-guided surgery.

PMID:35779413 | DOI:10.1016/j.bios.2022.114503

Sci Rep. 2022 Jul 1;12(1):11189. doi: 10.1038/s41598-022-14721-w.

ABSTRACT

The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of 'omics' data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network's topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar.

PMID:35778411 | DOI:10.1038/s41598-022-14721-w

Biomed Pharmacother. 2022 Jun 28;153:113350. doi: 10.1016/j.biopha.2022.113350. Online ahead of print.

ABSTRACT

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

PMID:35777222 | DOI:10.1016/j.biopha.2022.113350

PLoS One. 2022 Jun 30;17(6):e0269563. doi: 10.1371/journal.pone.0269563. eCollection 2022.

ABSTRACT

SARS-CoV-2 causes the current global pandemic coronavirus disease 2019. Widely-available effective drugs could be a critical factor in halting the pandemic. The main protease (3CLpro) plays a vital role in viral replication; therefore, it is of great interest to find inhibitors for this enzyme. We applied the combination of virtual screening based on molecular docking derived from the crystal structure of the peptidomimetic inhibitors (N3, 13b, and 11a), and experimental verification revealed FDA-approved drugs that could inhibit the 3CLpro of SARS-CoV-2. Three drugs were selected using the binding energy criteria and subsequently performed the 3CLpro inhibition by enzyme-based assay. In addition, six common drugs were also chosen to study the 3CLpro inhibition. Among these compounds, lapatinib showed high efficiency of 3CLpro inhibition (IC50 value of 35 ± 1 μM and Ki of 23 ± 1 μM). The binding behavior of lapatinib against 3CLpro was elucidated by molecular dynamics simulations. This drug could well bind with 3CLpro residues in the five subsites S1', S1, S2, S3, and S4. Moreover, lapatinib's key chemical pharmacophore features toward SAR-CoV-2 3CLpro shared important HBD and HBA with potent peptidomimetic inhibitors. The rational design of lapatinib was subsequently carried out using the obtained results. Our discovery provides an effective repurposed drug and its newly designed analogs to inhibit SARS-CoV-2 3CLpro.

PMID:35771802 | PMC:PMC9246117 | DOI:10.1371/journal.pone.0269563

Front Pharmacol. 2022 Jun 13;13:884548. doi: 10.3389/fphar.2022.884548. eCollection 2022.

ABSTRACT

Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.

PMID:35770086 | PMC:PMC9234276 | DOI:10.3389/fphar.2022.884548

Nat Commun. 2022 Jun 29;13(1):3756. doi: 10.1038/s41467-022-31473-3.

ABSTRACT

Pneumonia remains one of the leading causes of death worldwide. In this study, we use genome-wide meta-analysis of lifetime pneumonia diagnosis (N = 391,044) to identify four association signals outside of the previously implicated major histocompatibility complex region. Integrative analyses and finemapping of these signals support clinically tractable targets, including the mucin MUC5AC and tumour necrosis factor receptor superfamily member TNFRSF1A. Moreover, we demonstrate widespread evidence of genetic overlap with pneumonia susceptibility across the human phenome, including particularly significant correlations with psychiatric phenotypes that remain significant after testing differing phenotype definitions for pneumonia or genetically conditioning on smoking behaviour. Finally, we show how polygenic risk could be utilised for precision treatment formulation or drug repurposing through pneumonia risk scores constructed using variants mapped to pathways with known drug targets. In summary, we provide insights into the genetic architecture of pneumonia susceptibility and genetics informed targets for drug development or repositioning.

PMID:35768473 | DOI:10.1038/s41467-022-31473-3

Epilepsia. 2022 Jun 29. doi: 10.1111/epi.17352. Online ahead of print.

ABSTRACT

OBJECTIVE: Decades of genetic studies on people with many different epilepsies, and on many non-human species, using many different technologies, have generated a huge body of literature about the genes associated with seizures/epilepsy. Collating this data can help uncover epilepsy genes, pathways and treatments that would otherwise be overlooked. We aimed to collate and structure these data into a database, and use the database to identify novel epilepsy genes and pathways, and to prioritise promising treatments.

METHODS: We collated all the genes associated with all types of seizures/epilepsy in all species, and quantified the supporting evidence for each gene, by manually screening ~10,000 publications, and by extracting data from existing databases.

RESULTS: The largest published dataset of epilepsy genes includes only 977 genes, whereas our database (www.sagas.ac) includes 2876 genes, which demonstrates that the number of genes that can potentially contribute to seizures/epilepsy is much higher than previously envisaged. We use our database to identify 12 hitherto unreported polygenic epilepsy genes, and 479 high-confidence monogenic epilepsy genes, and 394 more biological pathways than identified using the previous largest epilepsy genes dataset. We use a unique feature of SAGAS-the number of citations for each gene-to demonstrate that a drug is more likely to affect seizures if there is more evidence that the genes it affects are associated with seizures, and we use these data to identify promising candidate antiseizure drugs.

SIGNIFICANCE: This database offers insights into the causes of epilepsy and its treatments, and can accelerate future epilepsy research.

PMID:35767389 | DOI:10.1111/epi.17352

Biotechnol Bioeng. 2022 Jun 28. doi: 10.1002/bit.28166. Online ahead of print.

ABSTRACT

In the current pandemic, scenario the world is facing a huge shortage of effective drugs and other prophylactic medicine to treat patients which created havoc in several countries with poor resources. With limited demand and supply of effective drugs, researchers rushed to repurpose the existing approved drugs for the treatment of COVID-19. The process of drug screening and testing is very costly and requires several steps for validation and treatment efficacy evaluation ranging from in-vitro to in-vivo setups. After these steps, a clinical trial is mandatory for the evaluation of treatment efficacy and side effects in humans. These processes enhance the overall cost and sometimes the lead molecule show adverse effects in humans and the trial ends up in the final stages. Recently with the advent of 3D organoid culture which mimics the human tissue exactly the process of drug screening and testing can be done in a faster and cost-effective manner. Further 3D organoids prepared from stems cells taken from individuals can be beneficial for personalized drug therapy which could save millions of lives. This review discussed approaches and techniques for the synthesis of 3D-printed human organoids for drug screening. The key findings of the usage of organoids for personalized medicine for the treatment of COVID-19 have been discussed. In the end, the key challenges for the wide applicability of human organoids for drug screening with prospects of future orientation have been included. This article is protected by copyright. All rights reserved.

PMID:35765706 | DOI:10.1002/bit.28166

Comput Struct Biotechnol J. 2022 Jun 1;20:2839-2847. doi: 10.1016/j.csbj.2022.05.057. eCollection 2022.

ABSTRACT

Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.

PMID:35765655 | PMC:PMC9189996 | DOI:10.1016/j.csbj.2022.05.057