Expert Rev Anti Infect Ther. 2021 Oct 6. doi: 10.1080/14787210.2022.1990042. Online ahead of print.

ABSTRACT

BACKGROUND: Treatment and control of schistosomiasis rely on a single drug, praziquantel. New orally active antischistosomals featuring novel molecular scaffolds are urgently needed to prevent the emergence of resistance.

METHODS: We screened 400 drug-like compounds contained in the open-access Pandemic Response Box (PRB) against newly transformed schistosomula (NTS) at a concentration of 10µM scoring death, changes in motility and morphological alterations. Compounds displaying an activity ≥66% at 72h underwent testing against adult Schistosoma mansoni in vitro. Fast-acting (≥66% at 24h), non-toxic clinical candidates were selected as hits and were investigated in the patent S. mansoni mouse model.

RESULTS: We identified 26 hits active against NTS, of which 17 elicited ≥66% activity against adult S. mansoni following 24h of drug exposure. The highest activity against adult S. mansoni was observed with MMV1581558 (EC50 value of 0.18±0.01µM) and nitazoxanide (0.47±0.07µM). Of five compounds tested in vivo, MMV1581558 and the approved drug ozanimod, reduced average worm burden versus controls by 42±25% and 36±19% respectively, after a single oral dose of 200mg/kg bodyweight in mice harboring a chronic S. mansoni infection.

CONCLUSION: MMV1581558 discovered from screening the PRB represents a novel antischistosomal scaffold with high in vitro antischistosomal activity amenable to chemical modification for drug development.

PMID:34612126 | DOI:10.1080/14787210.2022.1990042

Biocatal Agric Biotechnol. 2021 Oct 1:102178. doi: 10.1016/j.bcab.2021.102178. Online ahead of print.

ABSTRACT

The recent outbreak of COVID-19, caused by the novel pathogen SARS-coronavirus 2 (SARS-CoV-2) is a severe health emergency. In this pandemic, drug repurposing seems to be the most promising alternative to identify effective therapeutic agents for immediate treatment of infected patients. The present study aimed to evaluate all the drugs present in drug bank as potential novel SARS-CoV-2 inhibitors, using computational drug repurposing studies. Docking-based virtual screening and binding energy prediction were performed, followed by Absorption Distribution Metabolism Excretion calculation. Hydroxychloroquine and Nelfinavir have been identified as the best potential inhibitor against the SARS-CoV-2, therefore, they were used as reference compounds in computational DR studies. The docking study revealed 13 best compounds based on their highest binding affinity, binding energy, and dock score concerning the other screened compounds. Out of 13, only 4 compounds were further shortlisted based on their binding energy and best ADME properties. The hierarchical virtual screening yielded the best 04 drugs, DB07042 (compound 2), DB13035 (compound 3), DB13604 (compound 5) and DB08253 (compound 6), with commendable binding energies in kcal/mol, i.e. -65.45, -62.01, -52.09 and -51.70 respectively. Further, Molecular dynamics simulation with 04 best-retrieved hits has confirmed stable trajectories in protein in terms of root mean square deviation and root mean square fluctuation. During 30 nanosecond simulation, the interactions were also found similar to the docking-based studies. However, clinical studies are necessary to investigate their therapeutic use against this outbreak.

PMID:34611467 | PMC:PMC8483991 | DOI:10.1016/j.bcab.2021.102178

Mol Omics. 2021 Oct 5. doi: 10.1039/d1mo00237f. Online ahead of print.

ABSTRACT

Drug repositioning, a method that relies on the information from the original drug-disease association matrix, aims to identify new indications for existing drugs and is expected to greatly reduce the cost and time of drug development. However, most current drug repositioning methods make use of the original drug-disease association matrix directly without preconditioning. As relatively only a few associations between drugs and diseases have been determined from actual observations, the original drug-disease association matrix used in the prediction is sparse, which affects the performance of the prediction method. A method for mining similar features of drugs and diseases is still lacking. To solve these problems, we developed a bipartite graph diffusion algorithm with multiple similarity integration for drug-disease association prediction (BGMSDDA). First, the weight K nearest known neighbors (WKNKN) algorithm was used to reconstruct the drug-disease association matrix. Secondly, an effective method was designed to extract similar characteristics of drugs and diseases based on integrating linear neighborhood similarity and Gaussian kernel similarity. Finally, bipartite graph diffusion was used to infer undiscovered drug-disease associations. After carrying out 10-fold cross-validation experiments, BGMSDDA showed excellent performance on two datasets, specifically with AUC values of 0.939 (Fdataset) and 0.954 (Cdataset), and AUPR values of 0.466 (Fdataset) and 0.565 (Cdataset). Furthermore, to evaluate the accuracy of the results of BGMSDDA, we conducted case studies on three medically used drugs selected from Fdataset and Cdataset and validated the predictive associated diseases of each drug with some databases. Based on the results obtained, BGMSDDA was demonstrated to be useful for predicting drug-disease associations.

PMID:34610633 | DOI:10.1039/d1mo00237f

MRS Bull. 2021 Sep 29:1-7. doi: 10.1557/s43577-021-00185-2. Online ahead of print.

ABSTRACT

ABSTRACT: From Operation Warp Speed to the lipid mRNA vaccine, the COVID-19 pandemic has been a watershed moment for technological development, production, and implementation. The scale and pace of innovation and global collaboration has likely not been experienced since World War II. This article highlights some of the engineering accomplishments that occurred during the pandemic. We provide a broad overview of the technological achievements in vaccine design, antibody engineering, drug repurposing, and rapid diagnostic testing. We also discuss what the future of these technologies and the future of large-scale collaborations might look like moving forward.

PMID:34608355 | PMC:PMC8480751 | DOI:10.1557/s43577-021-00185-2

Curr Med Imaging. 2021 Oct 4. doi: 10.2174/1573405617666211004115208. Online ahead of print.

ABSTRACT

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.

PMID:34607548 | DOI:10.2174/1573405617666211004115208

Curr Top Med Chem. 2021 Oct 3. doi: 10.2174/1568026621666211004095917. Online ahead of print.

ABSTRACT

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE: The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS: Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS: The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION: Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

PMID:34607546 | DOI:10.2174/1568026621666211004095917

Curr Top Med Chem. 2021 Oct 3. doi: 10.2174/1568026621666211004093023. Online ahead of print.

NO ABSTRACT

PMID:34607543 | DOI:10.2174/1568026621666211004093023

Heliyon. 2021 Oct;7(10):e08089. doi: 10.1016/j.heliyon.2021.e08089. Epub 2021 Sep 29.

ABSTRACT

Many studies have shown that the lysosomal cathepsins, especially cathepsins B/L (CTSB/L) are required for SARS-CoV-2 entry into host cells. Lysosomal proteases, cathepsins are indispensable for normal health and are involved in several brain disorders occurring at different development age periods. On the other hand, it has been well known that COVID-19 infection is largely associated with several neurological disorders. Taken together these findings and given the high levels of expression of CTSB/L in the brain, we here proposed a reasonable hypothesis about the involvement of CTSB/L in the neurological manifestations linked to COVID-19. Pharmacological inhibitions of the CTSB/L could be a potential therapeutic target to block the virus entry as well as to mitigate the brain disorders. To this end, we utilized the network-based drug repurposing analyses to identify the possible drugs that can target CTSB/L. This study identifies the molecules like cyclosporine, phenytoin, and paclitaxel as potential drugs with binding ability to the CTSB/L. Further, we have performed molecular docking and all-atom molecular dynamics (MD) simulations to investigate the stability of CTSL-drug complexes. The results showed strong and stable binding of drugs with CTSL.

PMID:34604555 | PMC:PMC8479516 | DOI:10.1016/j.heliyon.2021.e08089

Biomed Res Int. 2021 Sep 23;2021:9921195. doi: 10.1155/2021/9921195. eCollection 2021.

ABSTRACT

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

PMID:34604388 | PMC:PMC8483911 | DOI:10.1155/2021/9921195

Front Mol Biosci. 2021 Sep 17;8:741316. doi: 10.3389/fmolb.2021.741316. eCollection 2021.

ABSTRACT

Copper (Cu) plays a pivotal role in cancer progression by acting as a co-factor that regulates the activity of many enzymes and structural proteins in cancer cells. Therefore, Cu-based complexes have been investigated as novel anticancer metallodrugs and are considered as a complementary strategy for currently used platinum agents with undesirable general toxicity. Due to the high failure rate and increased cost of new drugs, there is a global drive towards the repositioning of known drugs for cancer treatment in recent years. Disulfiram (DSF) is a first-line antialcoholism drug used in clinics for more than 65 yr. In combination with Cu, it has shown great potential as an anticancer drug by targeting a wide range of cancers. The reaction between DSF and Cu ions forms a copper diethyldithiocarbamate complex (Cu(DDC)2 also known as CuET) which is the active, potent anticancer ingredient through inhibition of NF-κB and ubiquitin-proteasome system as well as alteration of the intracellular reactive oxygen species (ROS). Importantly, DSF/Cu inhibits several molecular targets related to drug resistance, stemness, angiogenesis and metastasis and is thus considered as a novel strategy for overcoming tumour recurrence and relapse in patients. Despite its excellent anticancer efficacy, DSF has proven unsuccessful in several cancer clinical trials. This is likely due to the poor stability, rapid metabolism and/or short plasma half-life of the currently used oral version of DSF and the inability to form Cu(DDC)2 at relevant concentrations in tumour tissues. Here, we summarize the scientific rationale, molecular targets, and mechanisms of action of DSF/Cu in cancer cells and the outcomes of oral DSF ± Cu in cancer clinical trials. We will focus on the novel insights on harnessing the immune system and hypoxic microenvironment using DSF/Cu complex and discuss the emerging delivery strategies that can overcome the shortcomings of DSF-based anticancer therapies and provide opportunities for translation of DSF/Cu or its Cu(DDC)2 complex into cancer therapeutics.

PMID:34604310 | PMC:PMC8484884 | DOI:10.3389/fmolb.2021.741316

Front Cell Dev Biol. 2021 Sep 16;9:732370. doi: 10.3389/fcell.2021.732370. eCollection 2021.

ABSTRACT

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein-protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene-disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene-disease and disease-drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene-disease, and disease-drug networks.

PMID:34604234 | PMC:PMC8481783 | DOI:10.3389/fcell.2021.732370

Front Pharmacol. 2021 Sep 17;12:718902. doi: 10.3389/fphar.2021.718902. eCollection 2021.

ABSTRACT

This study aimed to investigate the interactions between fingolimod, a sphingosine 1-phosphate receptor (S1PR) agonist, and melanocortin receptors 1 and 5 (MCR1, MCR5). In particular, we investigated the effects of fingolimod, a drug approved to treat relapsing-remitting multiple sclerosis, on retinal angiogenesis in a mouse model of diabetic retinopathy (DR). We showed, by a molecular modeling approach, that fingolimod can bind with good-predicted affinity to MC1R and MC5R. Thereafter, we investigated the fingolimod actions on retinal MC1Rs/MC5Rs in C57BL/6J mice. Diabetes was induced in C57BL/6J mice through streptozotocin injection. Diabetic and control C57BL/6J mice received fingolimod, by oral route, for 12 weeks and a monthly intravitreally injection of MC1R antagonist (AGRP), MC5R antagonist (PG20N), and the selective S1PR1 antagonist (Ex 26). Diabetic animals treated with fingolimod showed a decrease of retinal vascular endothelial growth factor A (VEGFA) and vascular endothelial growth factor receptors 1 and 2 (VEGFR1 and VEGFR2), compared to diabetic control group. Fingolimod co-treatment with MC1R and MC5R selective antagonists significantly (p < 0.05) increased retinal VEGFR1, VEGFR2, and VEGFA levels compared to mice treated with fingolimod alone. Diabetic animals treated with fingolimod plus Ex 26 (S1PR1 selective blocker) had VEGFR1, VEGFR2, and VEGFA levels between diabetic mice group and the group of diabetic mice treated with fingolimod alone. This vascular protective effect of fingolimod, through activation of MC1R and MC5R, was evidenced also by fluorescein angiography in mice. Finally, molecular dynamic simulations showed a strong similarity between fingolimod and the MC1R agonist BMS-470539. In conclusion, the anti-angiogenic activity exerted by fingolimod in DR seems to be mediated not only through S1P1R, but also by melanocortin receptors.

PMID:34603029 | PMC:PMC8484636 | DOI:10.3389/fphar.2021.718902

Front Aging Neurosci. 2021 Sep 16;13:718426. doi: 10.3389/fnagi.2021.718426. eCollection 2021.

ABSTRACT

Innate immune system activation and inflammation are associated with and may contribute to clinical outcomes in people with Down syndrome (DS), neurodegenerative diseases such as Alzheimer's disease (AD), and normal aging. In addition to serving as potential diagnostic biomarkers, innate immune system activation and inflammation may play a contributing or causal role in these conditions, leading to the hypothesis that effective therapies should seek to dampen their effects. However, recent intervention studies with the innate immune system activator granulocyte-macrophage colony-stimulating factor (GM-CSF) in animal models of DS, AD, and normal aging, and in an AD clinical trial suggest that activating the innate immune system and inflammation may instead be therapeutic. We consider evidence that DS, AD, and normal aging are accompanied by innate immune system activation and inflammation and discuss whether and when during the disease process it may be therapeutically beneficial to suppress or promote such activation.

PMID:34603007 | PMC:PMC8481947 | DOI:10.3389/fnagi.2021.718426

J Mol Model. 2021 Oct 2;27(11):312. doi: 10.1007/s00894-021-04923-w.

ABSTRACT

A novel coronavirus known as severe acute respiratory syndrome is rapidly spreading worldwide. The international health authorities are putting all their efforts on quick diagnosis and placing the patients in quarantine. Although different vaccines have come for quick use as prophylactics, drug repurposing seems to be of paramount importance because of inefficient therapeutic options and clinical trial limitations. Here, we used structure-based drug designing approach to find and check the efficacy of the possible drug that can inhibit coronavirus main protease which is involved in polypeptide processing to functional protein. We performed virtual screening, molecular docking and molecular dynamics simulations of the FDA-approved drugs against the main protease of SARS-CoV-2. Using well-defined computational methods, we identified amprenavir, cefoperazone, riboflavin, diosmin, nadide and troxerutin approved for human therapeutic uses, as COVID-19 main protease inhibitors. These drugs bind to the SARS-CoV-2 main protease conserved residues of substrate-binding pocket and formed a remarkable number of non-covalent interactions. We have found diosmin as an inhibitor which binds covalently to the COVID-19 main protease. This study provides enough evidences for therapeutic use of these drugs in controlling COVID-19 after experimental validation and clinical demonstration.

PMID:34601658 | DOI:10.1007/s00894-021-04923-w

Drug Discov Today. 2021 Sep 29:S1359-6446(21)00423-2. doi: 10.1016/j.drudis.2021.09.016. Online ahead of print.

ABSTRACT

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called 'Manifold Medicine', in which disease states are described by vectorial positions on several body-wide axes. Thus, pathological states are represented by multidimensional 'vectors' that traverse the body-wide axes. We then delineate the manifold nature of drug action to provide a strategy for designing manifold drug cocktails by design using state-of-the-art biomedical and technological innovations. Manifold Medicine offers a roadmap for translating knowledge gained from next-generation technologies into individualized clinical practice.

PMID:34600126 | DOI:10.1016/j.drudis.2021.09.016

Eur J Cancer. 2021 Sep 28;157:428-440. doi: 10.1016/j.ejca.2021.08.033. Online ahead of print.

ABSTRACT

Although therapeutic drug monitoring (TDM) is an important tool in guiding drug dosing for other areas of medicine including infectious diseases, cardiology, psychiatry and transplant medicine, it has not gained wide acceptance in oncology. For imatinib and other tyrosine kinase inhibitors, a flat dosing approach is utilised for management of oral chemotherapy. There are many published studies examining the correlation of blood concentrations with clinical effects of imatinib. The International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) determined that there was a need to examine the published literature regarding utility of TDM in imatinib therapy and to develop consensus guidelines for TDM based on the available data. This article summarises the scientific evidence regarding TDM of imatinib, as well as the consensus guidelines developed by the IATDMCT.

PMID:34597977 | DOI:10.1016/j.ejca.2021.08.033

Cytokine. 2021 Sep 25;148:155719. doi: 10.1016/j.cyto.2021.155719. Online ahead of print.

ABSTRACT

Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed. Maladaptive hyperinflammation and excessive cytokine release underlie the disease severity, with antiinflammatory and cytokine inhibiting agents expected to exert therapeutic effects. A major present challenge is identification of appropriate phase of the illness for a given intervention to yield optimum outcomes. Considering its established disease biomarker and drug discovery potential, a compendious analysis of existing transcriptomic data is presented here toward addressing this gap. The analysis is based on COVID-19 data related to intensive care unit (ICU) and non-ICU admissions, discharged and deceased patients, ventilation and non-ventilation phases, and high oxygen supplementation. It integrates transcriptomic data related to the effects of, in various cellular treatment models, the COVID-19 randomized clinical trial (RCT) successful drug dexamethasone, and the failed drug, with a potential to harm, hydroxychloroquine/chloroquine. Similarly, effects of various COVID-19 candidate drugs/anticytokines as well as proinflammatory cytokines implicated in the illness are also examined. The underlying assumption was that compared to COVID-19, an effective drug/anticytokine and a disease aggravating agent would affect gene regulation in opposite and same direction, in that order. Remarkably, the assumption was supported with respect to both the RCT drugs. With this control validation, etanercept, followed by tofacitinib and adalimumab, showed transcriptomic effects predictive of benefits in both ventilation and non-ventilation ICU stages as well as in non-ICU phase. On the other hand, canakinumab showed potential for effectiveness in high oxygen supplementation phase. These findings may inform experimental and clinical studies toward drug repurposing in COVID-19.

PMID:34597919 | DOI:10.1016/j.cyto.2021.155719

Arch Biochem Biophys. 2021 Sep 28:109043. doi: 10.1016/j.abb.2021.109043. Online ahead of print.

ABSTRACT

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer represents approximately 15-30% of all invasive breast cancers. Despite the recent advances in therapeutic practices of HER2 subtype, drug resistance and tumor recurrence still have remained as major problems. Drug discovery is a long and difficult process, so the aim of this study is to find potential new application for existing therapeutic agents. Gene expression data for breast invasive carcinoma were retrieved from The Cancer Genome Atlas (TCGA) database. The normal and tumor samples were analyzed using Linear Models for Microarray Data (LIMMA) R package in order to find the differentially expressed genes (DEGs). These genes were used as entry for the library of integrated network-based cellular signatures (LINCS) L1000CDS2 software and suggested 24 repurposed drugs. According to the obtained results, some of these drugs including vorinostat, mocetinostat, alvocidib, CGP-60474, BMS-387032, AT-7519, and curcumin have significant functional similarity and structural correlation with FDA-approved breast cancer drugs. Based on the drug-target network, which consisted of the repurposed drugs and their target genes, the aforementioned drugs had the highest degrees. Moreover, the experimental approach verified curcumin as an effective therapeutic agent for HER2 positive breast cancer. Hence, our work suggested that some repurposed drugs based on gene expression data can be noticed as potential drugs for the treatment of HER2-positive breast cancer.

PMID:34597657 | DOI:10.1016/j.abb.2021.109043

Neurooncol Adv. 2021 Aug 2;3(1):vdab103. doi: 10.1093/noajnl/vdab103. eCollection 2021 Jan-Dec.

ABSTRACT

BACKGROUND: Mutations of the isocitrate dehydrogenase (IDH) gene occur in over 80% of low-grade gliomas and secondary glioblastomas. Despite considerable efforts, endogenous in vitro IDH-mutated glioma models remain scarce. Availability of these models is key for the development of new therapeutic interventions.

METHODS: Cell cultures were established from fresh tumor material and expanded in serum-free culture media. D-2-Hydroxyglutarate levels were determined by mass spectrometry. Genomic and transcriptomic profiling were carried out on the Illumina Novaseq platform, methylation profiling was performed with the Infinium MethylationEpic BeadChip array. Mitochondrial respiration was measured with the Seahorse XF24 Analyzer. Drug screens were performed with an NIH FDA-approved anti-cancer drug set and two IDH-mutant specific inhibitors.

RESULTS: A set of twelve patient-derived IDHmt cell cultures was established. We confirmed high concordance in driver mutations, copy numbers and methylation profiles between the tumors and derived cultures. Homozygous deletion of CDKN2A/B was observed in all cultures. IDH-mutant cultures had lower mitochondrial reserve capacity. IDH-mutant specific inhibitors did not affect cell viability or global gene expression. Screening of 107 FDA-approved anti-cancer drugs identified nine compounds with potent activity against IDHmt gliomas, including three compounds with favorable pharmacokinetic characteristics for CNS penetration: teniposide, omacetaxine mepesuccinate, and marizomib.

CONCLUSIONS: Our twelve IDH-mutant cell cultures show high similarity to the parental tissues and offer a unique tool to study the biology and drug sensitivities of high-grade IDHmt gliomas in vitro. Our drug screening studies reveal lack of sensitivity to IDHmt inhibitors, but sensitivity to a set of nine available anti-cancer agents.

PMID:34595478 | PMC:PMC8478778 | DOI:10.1093/noajnl/vdab103

Front Pharmacol. 2021 Sep 14;12:741413. doi: 10.3389/fphar.2021.741413. eCollection 2021.

ABSTRACT

The treatment failure rates of acute leukemia with rearrangements of the Mixed Lineage Leukemia (MLL) gene highlight the need for novel therapeutic approaches. Taking into consideration the limitations of the current therapies and the advantages of novel strategies for drug discovery, drug repurposing offers valuable opportunities to identify treatments and develop therapeutic approaches quickly and effectively for acute leukemia with MLL-rearrangements. These approaches are complimentary to de novo drug discovery and have taken advantage of increased knowledge of the mechanistic basis of MLL-fusion protein complex function as well as refined drug repurposing screens. Despite the vast number of different leukemia associated MLL-rearrangements, the existence of common core oncogenic pathways holds the promise that many such therapies will be broadly applicable to MLL-rearranged leukemia as a whole.

PMID:34594227 | PMC:PMC8478155 | DOI:10.3389/fphar.2021.741413