Front Pharmacol. 2021 Apr 13;12:636265. doi: 10.3389/fphar.2021.636265. eCollection 2021.

ABSTRACT

Translational studies involving the reuse and association of drugs are approaches that can result in higher success rates in the discovery and development of drugs for serious public health problems, including leishmaniasis. If we consider the number of pathogenic species in relation to therapeutic options, this arsenal is still small, and each drug possesses a disadvantage in terms of toxicity, efficacy, price, or treatment regimen. In the search for new drugs, we performed a drug screening of L. amazonensis promastigotes and intracellular amastigotes of fifty available drugs belonging to several classes according to their pharmacophoric group. Spironolactone, a potassium-sparing diuretic, proved to be the most promising drug candidate. After demonstrating the in vitro antileishmanial activity, we evaluated the efficacy on a murine experimental model with L. amazonensis and L. infantum. The treatment controlled the cutaneous lesion and reduced the parasite burden of L. amazonensis significantly, as effectively as meglumine antimoniate. The treatment of experimental visceral leishmaniasis was effective in reducing the parasite load on the main affected organs (spleen and liver) via high doses of spironolactone. The association between spironolactone and meglumine antimoniate promoted better control of the parasite load in the spleen and liver compared to the group treated with meglumine antimoniate alone. These results reveal a possible benefit of the concomitant use of spironolactone and meglumine antimoniate that should be studied more in depth for the future possibility of repositioning for leishmaniasis co-therapy.

PMID:33927619 | PMC:PMC8077169 | DOI:10.3389/fphar.2021.636265

Biomolecules. 2021 Apr 27;11(5):643. doi: 10.3390/biom11050643.

ABSTRACT

The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions' prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23 datasets show that CSConv2d performs better than the original DEEPScreen model in predicting protein-ligand binding affinity, as well as some state-of-the-art DTIs (drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. In practice, the docking results of protein (PDB ID: 5ceo) and ligand (Chemical ID: 50D) and a series of kinase inhibitors are operated to verify the robustness.

PMID:33925310 | DOI:10.3390/biom11050643

Molecules. 2021 Apr 28;26(9):2581. doi: 10.3390/molecules26092581.

ABSTRACT

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

PMID:33925237 | DOI:10.3390/molecules26092581

Int J Mol Sci. 2021 Apr 28;22(9):4632. doi: 10.3390/ijms22094632.

ABSTRACT

A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model's outputs of natural products are reliable. Case studies have proved our model's performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.

PMID:33924898 | DOI:10.3390/ijms22094632

Biomedicines. 2021 Apr 20;9(4):440. doi: 10.3390/biomedicines9040440.

ABSTRACT

Metachromatic leukodystrophy (MLD) is a rare neurodegenerative disease that results from a deficiency of the lysosomal enzyme arylsulfatase A (ARSA). Worldwide, there are between one in 40,000 and one in 160,000 people living with the disease. While there are currently no effective treatments for MLD, induced pluripotent stem cell-derived brain organoids have the potential to provide a better understanding of MLD pathogenesis. However, developing brain organoid models is expensive, time consuming and may not accurately reflect disease progression. Using accurate and inexpensive computer simulations of human brain organoids could overcome the current limitations. Artificially induced whole-brain organoids (aiWBO) have the potential to greatly expand our ability to model MLD and guide future wet lab research. In this study, we have upgraded and validated our artificially induced whole-brain organoid platform (NEUBOrg) using our previously validated machine learning platform, DeepNEU (v6.2). Using this upgraded NEUBorg, we have generated aiWBO simulations of MLD and provided a novel approach to evaluate factors associated with MLD pathogenesis, disease progression and new potential therapeutic options.

PMID:33923989 | DOI:10.3390/biomedicines9040440

Viruses. 2021 Apr 16;13(4):685. doi: 10.3390/v13040685.

ABSTRACT

Hemorrhagic fever viruses, among them orthohantaviruses, arenaviruses and filoviruses, are responsible for some of the most severe human diseases and represent a serious challenge for public health. The current limited therapeutic options and available vaccines make the development of novel efficacious antiviral agents an urgent need. Inhibiting viral attachment and entry is a promising strategy for the development of new treatments and to prevent all subsequent steps in virus infection. Here, we developed a fluorescence-based screening assay for the identification of new antivirals against hemorrhagic fever virus entry. We screened a phytochemical library containing 320 natural compounds using a validated VSV pseudotype platform bearing the glycoprotein of the virus of interest and encoding enhanced green fluorescent protein (EGFP). EGFP expression allows the quantitative detection of infection and the identification of compounds affecting viral entry. We identified several hits against four pseudoviruses for the orthohantaviruses Hantaan (HTNV) and Andes (ANDV), the filovirus Ebola (EBOV) and the arenavirus Lassa (LASV). Two selected inhibitors, emetine dihydrochloride and tetrandrine, were validated with infectious pathogenic HTNV in a BSL-3 laboratory. This study provides potential therapeutics against emerging virus infection, and highlights the importance of drug repurposing.

PMID:33923413 | DOI:10.3390/v13040685

Int J Mol Sci. 2021 Apr 22;22(9):4394. doi: 10.3390/ijms22094394.

ABSTRACT

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.

PMID:33922356 | DOI:10.3390/ijms22094394

Int J Mol Sci. 2021 Apr 22;22(9):4371. doi: 10.3390/ijms22094371.

ABSTRACT

In the connectivity map (CMap) approach to drug repositioning and development, transcriptional signature of disease is constructed by differential gene expression analysis between the diseased tissue or cells and the control. The negative correlation between the transcriptional disease signature and the transcriptional signature of the drug, or a bioactive compound, is assumed to indicate its ability to "reverse" the disease process. A major limitation of traditional CMaP analysis is the use of signatures derived from bulk disease tissues. Since the key driver pathways are most likely dysregulated in only a subset of cells, the "averaged" transcriptional signatures resulting from bulk analysis lack the resolution to effectively identify effective therapeutic agents. The use of single-cell RNA-seq (scRNA-seq) transcriptomic assay facilitates construction of disease signatures that are specific to individual cell types, but methods for using scRNA-seq data in the context of CMaP analysis are lacking. Lymphangioleiomyomatosis (LAM) mutations in TSC1 or TSC2 genes result in the activation of the mTOR complex 1 (mTORC1). The mTORC1 inhibitor Sirolimus is the only FDA-approved drug to treat LAM. Novel therapies for LAM are urgently needed as the disease recurs with discontinuation of the treatment and some patients are insensitive to the drug. We developed methods for constructing disease transcriptional signatures and CMaP analysis using scRNA-seq profiling and applied them in the analysis of scRNA-seq data of lung tissue from naïve and sirolimus-treated LAM patients. New methods successfully implicated mTORC1 inhibitors, including Sirolimus, as capable of reverting the LAM transcriptional signatures. The CMaP analysis mimicking standard bulk-tissue approach failed to detect any connection between the LAM signature and mTORC1 signaling. This indicates that the precise signature derived from scRNA-seq data using our methods is the crucial difference between the success and the failure to identify effective therapeutic treatments in CMaP analysis.

PMID:33922083 | DOI:10.3390/ijms22094371

Molecules. 2021 Apr 15;26(8):2304. doi: 10.3390/molecules26082304.

ABSTRACT

Malaria is one of the most life-threatening infectious diseases and constitutes a major health problem, especially in Africa. Although artemisinin combination therapies remain efficacious to treat malaria, the emergence of resistant parasites emphasizes the urgent need of new alternative chemotherapies. One strategy is the repurposing of existing drugs. Herein, we reviewed the antimalarial effects of marketed antibiotics, and described in detail the fast-acting antibiotics that showed activity in nanomolar concentrations. Antibiotics have been used for prophylaxis and treatment of malaria for many years and are of particular interest because they might exert a different mode of action than current antimalarials, and can be used simultaneously to treat concomitant bacterial infections.

PMID:33921170 | DOI:10.3390/molecules26082304

Behav Sci (Basel). 2021 Apr 17;11(4):54. doi: 10.3390/bs11040054.

ABSTRACT

Cognitive impairment risk in Parkinson's disease increases with disease progression and poses a significant burden to the patients, their families and society. There are no disease-modifying therapies or preventative measures for Parkinson's disease mild cognitive impairment (PD-MCI), or Parkinson's disease dementia (PDD). This article reviews current and previously investigated treatments and those under investigation, including pharmacologic, non-pharmacologic and surgical procedures. There are currently no effective pharmacologic or non-pharmacologic treatments for PD-MCI. The only recommended treatment for PDD currently is rivastigmine, a cholinesterase inhibitor. Donepezil and galantamine-other cholinesterase inhibitors-are possibly useful. Memantine, a N-methyl-D-aspartate (NMDA) receptor antagonist, is considered investigational in PDD. Drug repurposing (atomoxetine, levodopa, insulin, atomoxetine for PD-MCI; ambroxol and ceftriaxone for PDD) and novel medications (SYN120, GRF6021, NYX-458 for PD-MCI; ANAVEX2-73, LY3154207, ENT-01, DAAOI-P for PDD) currently have insufficient evidence. There is growing research supporting exercise in the treatment of PD-MCI, but most non-pharmacological approaches have insufficient evidence for use in PD-MCI (cognitive rehabilitation, deep brain stimulation, transcranial direct current stimulation, transcranial ultrasound, vestibular nerve stimulation) and PDD (cognitive intervention, deep brain stimulation, transcranial alternating current stimulation, transcranial ultrasound, temporal blood brain barrier disruption). Research is needed for both disease-modifying and symptomatic treatments in PD cognitive impairment.

PMID:33920698 | DOI:10.3390/bs11040054

Pharmaceuticals (Basel). 2021 Apr 17;14(4):372. doi: 10.3390/ph14040372.

ABSTRACT

Repurposing of approved non-antitumor drugs represents a promising and affordable strategy that may help to increase the repertoire of effective anticancer drugs. Benzimidazole-based anthelmintics are antiparasitic drugs commonly employed both in human and veterinary medicine. Benzimidazole compounds are being considered for drug repurposing due to antitumor activities displayed by some members of the family. In this study, we explored the effects of a large series of benzimidazole-based anthelmintics (and some enantiomerically pure forms of those containing a stereogenic center) on the viability of different tumor cell lines derived from paraganglioma, pancreatic and colorectal cancer. Flubendazole, parbendazole, oxibendazole, mebendazole, albendazole and fenbendazole showed the most consistent antiproliferative effects, displaying IC50 values in the low micromolar range, or even in the nanomolar range. In silico evaluation of their physicochemical, pharmacokinetics and medicinal chemistry properties also provided useful information related to the chemical structures and potential of these compounds. Furthermore, in view of the potential repurposing of these drugs in cancer therapy and considering that pharmaceutically active compounds may have different mechanisms of action, we performed an in silico target prediction to assess the polypharmacology of these benzimidazoles, which highlighted previously unknown cancer-relevant molecular targets.

PMID:33920661 | DOI:10.3390/ph14040372

J Biomed Inform. 2021 Apr 26:103792. doi: 10.1016/j.jbi.2021.103792. Online ahead of print.

ABSTRACT

Enabling precision medicine requires developing robust patient stratification methods and developing drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that were most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used in this study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

PMID:33915273 | DOI:10.1016/j.jbi.2021.103792

Int J Biol Macromol. 2021 Apr 26:S0141-8130(21)00920-X. doi: 10.1016/j.ijbiomac.2021.04.148. Online ahead of print.

ABSTRACT

The world is currently facing a novel coronavirus (SARS-CoV-2) pandemic. The greatest threat that is disrupting the normal functioning of society is the exceptionally high species independent transmission. Drug repurposing is understood to be the best strategy to immediately deploy well-characterized agents against new pathogens. Several repurposable drugs are already in evaluation for determining suitability to treat COVID-19. One such promising compound includes heparin, which is widely used in reducing thrombotic events associated with COVID-19 induced pathology. As part of identifying target-specific antiviral compounds among FDA and world-approved libraries using high-throughput virtual screening (HTVS), we previously evaluated top hits for anti-SARS-CoV-2 activity. Here, we report results of highly efficacious viral entry blocking properties of heparin (IC50 = 12.3 nM) in the complete virus assay, and further, propose ways to use it as a potential transmission blocker. Exploring further, our in-silico analysis indicated that the heparin interacts with post-translational glycoconjugates present on spike proteins. The patterns of accessible spike-glycoconjugates in open and closed states are completely contrasted by one another. Heparin-binding to the open conformation of spike structurally supports the state and may aid ACE2 binding as reported with cell surface-bound heparan sulfate. We also studied spike protein mutant variants' heparin interactions for possible resistance. Based on available data and optimal absorption properties by the skin, heparin could potentially be used to block SARS-CoV-2 transmission. Studies should be designed to exploit its nanomolar antiviral activity to formulate heparin as topical or inhalation-based formulations, particularly on exposed areas and sites of primary viremia e.g. ACE2 rich epithelia of the eye (conjunctiva/lids), nasal cavity, and mouth.

PMID:33915212 | DOI:10.1016/j.ijbiomac.2021.04.148

IEEE/ACM Trans Comput Biol Bioinform. 2021 Apr 29;PP. doi: 10.1109/TCBB.2021.3076259. Online ahead of print.

ABSTRACT

The novel SARS-CoV-2 uses the ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in the S protein and ACE2 interface is crucial in the search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to a small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on the RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate the spreading of the disease.

PMID:33914685 | DOI:10.1109/TCBB.2021.3076259

Circ Res. 2021 Apr 30;128(9):1317-1319. doi: 10.1161/CIRCRESAHA.121.319149. Epub 2021 Apr 29.

NO ABSTRACT

PMID:33914606 | DOI:10.1161/CIRCRESAHA.121.319149

FASEB J. 2021 May;35(5):e21573. doi: 10.1096/fj.202100280RR.

ABSTRACT

Coronavirus (CoV) 3-chymotrypsin (C)-like cysteine protease (3CLpro ) is a target for anti-CoV drug development and drug repurposing because along with papain-like protease, it cleaves CoV-encoded polyproteins (pp1a and pp1ab) into nonstructural proteins (nsps) for viral replication. However, the cleavage sites of 3CLpro and their relevant nsps remain unclear, which is the subject of this perspective. Here, we address the subject from three standpoints. First, we explore the inconsistency in the cleavage sites and relevant nsps across CoVs, and investigate the function of nsp11. Second, we consider the nsp16 mRNA overlapping of the spike protein mRNA, and analyze the effect of this overlapping on mRNA vaccines. Finally, we study nsp12, whose existence depends on ribosomal frameshifting, and investigate whether 3CLpro requires a large number of inhibitors to achieve full inhibition. This perspective helps us to clarify viral replication and is useful for developing anti-CoV drugs with 3CLpro as a target in the current coronavirus disease 2019 (COVID-19) pandemic.

PMID:33913206 | DOI:10.1096/fj.202100280RR

Front Mol Biosci. 2021 Apr 12;8:643004. doi: 10.3389/fmolb.2021.643004. eCollection 2021.

ABSTRACT

As of September 19, 2020, about 30 million people have been infected with the novel corona virus disease 2019 (COVID-19) globally, and the numbers are increasing at an alarming rate. The disease has a tremendous impact on every aspect of life, but one of the biggest, related to human health and medical sciences, is its effect on cancer. Nearly 2% of the total COVID-19 patients prior to May 2020 had cancer, and the statistics are quite frightening as the patient can be referred to as "doubly unfortunate" to suffer from cancer with the added misery of infection with COVID-19. Data regarding the present situation are scarce, so this review will focus on the deadly duo of COVID-19 and cancer. The focus is on molecular links between COVID-19 and cancer as inflammation, immunity, and the role of angiotensin converting enzyme 2 (ACE2). Complications may arise or severity may increase in cancer patients due to restrictions imposed by respective authorities as an effort to control COVID-19. The impact may vary from patient to patient and factors may include a delay in diagnosis, difficulty managing both cancer therapy and COVID-19 at same time, troubles in routine monitoring of cancer patients, and delays in urgent surgical procedures and patient care. The effect of anti-cancer agents on the condition of cancer patients suffering from COVID-19 and whether these anti-cancer agents can be repurposed for effective COVID-19 treatment are discussed. The review will be helpful in the management of deadly duo of COVID-19 and cancer.

PMID:33912588 | PMC:PMC8072279 | DOI:10.3389/fmolb.2021.643004

Eur J Pharmacol. 2021 Apr 25:174099. doi: 10.1016/j.ejphar.2021.174099. Online ahead of print.

ABSTRACT

Despite the availability of more than 20 clinical antiepileptic drugs, approximately 30% of patients with epilepsy do not respond to antiepileptic drug treatment. Therefore, it is important to develop antiepileptic products that function via novel mechanisms. In the present study, we evaluated data from one of the largest global databases to identify drugs with antiepileptic effects, and subsequently attempted to understand the effect of the combination of antiepileptic drugs and valacyclovir in epileptic seizures using a kindling model. To induce kindling in mice, pentylenetetrazol at a dose of 40 mg/kg was administered once every 48 hours. Valacyclovir was orally administered 30 min before antiepileptic drug injection in kindled mice, and behavioral seizures were monitored for 20 min following pentylenetetrazol administration. Additionally, c-Fos expression in the hippocampal dentate gyrus was measured in kindled mice. Valacyclovir showed inhibitory effects on pentylenetetrazol-induced kindled seizures. In addition, simultaneous use of levetiracetam and valacyclovir caused more potent inhibition of seizure activity, and neither valproic acid nor diazepam augmented the anti-seizure effect in kindled mice. Furthermore, kindled mice showed increased c-Fos levels in the dentate gyrus. The increase in c-Fos expression was significantly inhibited by the simultaneous use of levetiracetam and valacyclovir. The findings of the present study indicate that a combination of levetiracetam and valacyclovir had possible anticonvulsive effects on pentylenetetrazol-induced kindled epileptic seizures. These results suggest that valacyclovir may have an antiseizure effect in patients with epilepsy.

PMID:33910036 | DOI:10.1016/j.ejphar.2021.174099

Behav Brain Res. 2021 Apr 25:113323. doi: 10.1016/j.bbr.2021.113323. Online ahead of print.

ABSTRACT

There is high clinical interest in improving the pharmacological treatment of individuals with Major Depressive Disorder (MDD). This neuropsychiatric disorder continues to cause significant morbidity and mortality worldwide, where existing pharmaceutical treatments such as selective serotonin reuptake inhibitors often have limited efficacy. In a recent publication, we demonstrated an antidepressant-like role for the acetylcholinesterase inhibitor (AChEI) donepezil in the C57BL/6 J mouse forced swim test (FST). Those data added to a limited literature in rodents and human subjects which suggests AChEIs have antidepressant properties, but added the novel finding that donepezil only showed antidepressant-like properties at lower doses (0.02, 0.2 mg/kg). At a high dose (2.0 mg/kg), donepezil tended to promote depression-like behavior, suggesting a u-shaped dose-response curve for FST immobility. Here we investigate the effects of three other AChEIs with varying molecular structures: galantamine, physostigmine, and rivastigmine, to test whether they also exhibit antidepressant-like effects in the FST. We find that these drugs do exhibit therapeutic-like effects at low but not high doses, albeit at lower doses for physostigmine. Further, we find that their antidepressant-like effects are not mediated by generalized hyperactivity in the novel open field test, and are also not accompanied by anxiolytic-like properties. These data further support the hypothesis that acetylcholine has a u-shaped dose-response relationship with immobility in the C57BL/6 J mouse FST, and provide a rationale for more thoroughly investigating whether reversible AChEIs as a class can be repurposed for the treatment of MDD in human subjects.

PMID:33910028 | DOI:10.1016/j.bbr.2021.113323

Cell Rep. 2021 Apr 27;35(4):109024. doi: 10.1016/j.celrep.2021.109024.

ABSTRACT

Glioblastoma stem cells (GSCs) resist current glioblastoma (GBM) therapies. GSCs rely highly on oxidative phosphorylation (OXPHOS), whose function requires mitochondrial translation. Here we explore the therapeutic potential of targeting mitochondrial translation and report the results of high-content screening with putative blockers of mitochondrial ribosomes. We identify the bacterial antibiotic quinupristin/dalfopristin (Q/D) as an effective suppressor of GSC growth. Q/D also decreases the clonogenicity of GSCs in vitro, consequently dysregulating the cell cycle and inducing apoptosis. Cryoelectron microscopy (cryo-EM) reveals that Q/D binds to the large mitoribosomal subunit, inhibiting mitochondrial protein synthesis and functionally dysregulating OXPHOS complexes. These data suggest that targeting mitochondrial translation could be explored to therapeutically suppress GSC growth in GBM and that Q/D could potentially be repurposed for cancer treatment.

PMID:33910005 | DOI:10.1016/j.celrep.2021.109024