Frontiers in drug discovery最新文献

{"title":"A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase","authors":"L. A. Machado, Eduardo Krempser, A. Guimarães","doi":"10.3389/fddsv.2022.954911","DOIUrl":"https://doi.org/10.3389/fddsv.2022.954911","url":null,"abstract":"HIV-1 integrase is an essential enzyme for the HIV-1 replication cycle, and currently, integrase inhibitors are in the first line of treatment in many guidelines. Despite the discovery of new inhibitors, including a new class of molecules with different mechanisms of action, resistance is still a relevant problem, and adding new options to the therapeutic arsenal to fight viral resistance is a Sisyphean task. Because of the difficulty and cost of in vitro screenings, machine learning-driven ligand-based virtual screenings are an alternative that can not only cut costs but also use valuable information about active compounds with yet unknown mechanisms of action. In this work, we describe a thorough model exploration and hyperparameter tuning procedure in a dataset with class imbalance and show several models capable of distinguishing between compounds that are active or inactive against the HIV-1 integrase. The best of the models was then used to screen the natural product atlas for active compounds, resulting in a myriad of molecules that share features with known integrase inhibitors. Here we also explore the strengths and shortcomings of our models and discuss the use of the applicability domain to guide in vitro screenings and differentiate between the “predictable” and “unknown” regions of the chemical space.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46629555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nine receptors and binding proteins, four drugs, and one woman: Historical and personal perspectives","authors":"D. Novick","doi":"10.3389/fddsv.2022.1001487","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1001487","url":null,"abstract":"In the era of bioinformatics and high-throughput techniques, it is tempting to forget the advantage of an old yet efficient and straightforward technique, ligand affinity chromatography, in the search for unknown proteins. This type of separation is based on an interaction between the target analyte potentially present in a crude mixture of proteins and a ligand coupled covalently to a resin. This process allows thousands-fold purification in a single step, which is crucial when using an extremely rich source of naturally occurring proteins such as human urine or plasma. Before the completion of The Genome Project, this method facilitated the rapid and reliable cloning of the corresponding gene based on the partial amino acid sequence of the isolated protein. Upon completion of this project, a partial protein sequence was enough to retrieve its complete mRNA and, hence, its complete protein sequence. Ligand affinity chromatography is indispensable for the isolation of both expected and unexpected binding proteins found by serendipity. My approach of combining a rich source of human proteins (1,000-fold concentrated human urine) together with this highly specific isolation method yielded proteins from both groups. The expected proteins included the two receptors for TNF (TBPI and TBPII), type I and type II interferon receptors (IFNα/βR, IFN-γR), and IL-6 and LDL receptors. The unexpected group of proteins included IL-18 binding protein (IL-18BP), IL-32 binding protein (Proteinase 3), and heparanase binding protein, the resistin. The discovery of the type I IFN receptor was a “eureka” moment in my life since it put an end to a 35-year worldwide search for this receptor. Using chemical purification methods, the TBPII might have never been discovered. Years later, TBPII was translated into the blockbuster drug Enbrel® to treat mainly rheumatoid arthritis. IFN-beta was translated into the blockbuster drug Rebif® to treat the autoimmune disease multiple sclerosis. IL-18BP translated into the drug Tadekinig alfa™ and is in a phase III clinical study for inflammatory and autoimmune pathologies. It has saved the lives of children born with mutations (NLRC4, XIAP) and is an example of personalized medicine. COVID-19 and CAR-T cytokine storms are the recent targets of IL-18BP.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42798791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From drugs to targets: Reverse engineering the virtual screening process on a proteomic scale","authors":"Gustavo Schottlender, Juan Manuel Prieto, Miranda C. Palumbo, Florencia A Castello, F. Serral, E. Sosa, A. Turjanski, M. Marti, D. A. Fernández Do Porto","doi":"10.3389/fddsv.2022.969983","DOIUrl":"https://doi.org/10.3389/fddsv.2022.969983","url":null,"abstract":"Phenotypic screening is a powerful technique that allowed the discovery of antimicrobials to fight infectious diseases considered deadly less than a century ago. In high throughput phenotypic screening assays, thousands of compounds are tested for their capacity to inhibit microbial growth in-vitro. After an active compound is found, identifying the molecular target is the next step. Knowing the specific target is key for understanding its mechanism of action, and essential for future drug development. Moreover, this knowledge allows drug developers to design new generations of drugs with increased efficacy and reduced side effects. However, target identification for a known active compound is usually a very difficult task. In the present work, we present a powerful reverse virtual screening strategy, that can help researchers working in the drug discovery field, to predict a set of putative targets for a compound known to exhibit antimicrobial effects. The strategy combines chemical similarity methods, with target prioritization based on essentiality data, and molecular-docking. These steps can be tailored according to the researchers’ needs and pathogen’s available information. Our results show that using only the chemical similarity approach, this method is capable of retrieving potential targets for half of tested compounds. The results show that even for a low chemical similarity threshold whenever domains are retrieved, the correct domain is among those retrieved in more than 80% of the queries. Prioritizing targets by an essentiality criteria allows us to further reduce, up to 3–4 times, the number of putative targets. Lastly, docking is able to identify the correct domain ranked in the top two in about two thirds of cases. Bias docking improves predictive capacity only slightly in this scenario. We expect to integrate the presented strategy in the context of Target Pathogen database to make it available for the wide community of researchers working in antimicrobials discovery.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45564412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma brain injury markers are associated with volume status but not muscle health in heart failure patients","authors":"S. Lahiri, Mitra Mastali, J. V. Van Eyk, T. Hitzeman, C. Bresee, K. Raedschelders, P. Lyden, R. Gottlieb, J. Fang, R. Shaw, T. Hong","doi":"10.3389/fddsv.2022.1042737","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1042737","url":null,"abstract":"Background: Neurofilament light chain protein (NfL) and tau are plasma biomarkers of neuronal injury which can be elevated in patients with neurodegenerative diseases. N-terminal pro-brain natriuretic peptide (NT-proBNP) is an established marker of volume status in patients with heart failure (HF) and plasma cBIN1 score (CS) is an emerging biomarker of cardiac muscle health. It is not known if, in HF patients, there is a correlation between cardiac markers and brain injury markers. Methods: We studied ambulatory HF patients with either preserved and reduced ejection fraction (N = 50 with 25 HFrEF and 25 HFpEF) and age and sex matched healthy controls (N = 50). Plasma NT-proBNP and CS were determined using commercial kits. A bead-based ELISA assay was used to quantify femtomolar concentrations of plasma neuronal markers NfL and total tau. Results: Plasma levels of NT-proBNP and CS in heart failure patients were significantly higher than those from healthy controls. In both patients with HFrEF and HFpEF, we found independent and direct correlations between the volume status marker NT-proBNP, but not the cardiomyocyte origin muscle health marker CS, with NfL (r = 0.461, p = 0.0007) and tau (r = 0.333, p = 0.0183). Conclusion: In patients with HF with or without preserved ejection fraction, plasma levels of NfL and tau correlate with volume status rather than muscle health, indicating volume overload-associated neuronal injury.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45491348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current and potential therapeutic strategies for transthyretin cardiac amyloidosis","authors":"M. A. C. Williams, B. Shankar, J. Vaishnav, M. Ranek","doi":"10.3389/fddsv.2022.1015545","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1015545","url":null,"abstract":"Cardiac amyloidosis is a progressive disorder caused by the deposition of amyloid, abnormal proteins that aggregate to form insoluble plaques in the myocardium resulting in restrictive cardiomyopathy. The two most common subtypes of cardiac amyloidosis are immunoglobulin light chain (AL) and transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM). ATTR-CM can further be subdivided into two main categories, wild-type or hereditary TTR. TTR is a homotetrameric protein complex that is synthesized in the liver and is secreted into the circulation for retinol and vitamin A transfer. Genetic mutations in the TTR gene can disrupt the thermodynamic stability of the homotetrameric complex causing dissociation into monomers that, when taken up by the myocardium, will aggregate to form insoluble fibers. Though the mechanism of wild-type TTR is not fully elucidated, it is thought to be an age-related process. Myocardial uptake and aggregation of TTR monomeric subunits result in cytotoxicity, impaired cardiac function, and eventually heart failure. Historically, ATTR-CM had a poor prognosis, with no therapeutics available to specifically target ATTR-CM and treatment focused on managing symptoms and disease-related complications. In 2019, the FDA approved the first-in-class TTR stabilizer for ATTR-CM, which has led to improved outcomes. In recent years, several promising novel therapies have emerged which aim to target various points of the ATTR-CM amyloidogenic cascade. In this review, we discuss the mechanistic underpinnings of ATTR-CM, review current FDA-approved strategies for treatment, and highlight ongoing research efforts as potential therapeutic options in the future.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43326129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemically induced phenotypes during the blood stage development of Plasmodium falciparum as indicators of the drug mode of action","authors":"K. Reghunandanan, Rajesh Chandramohanadas","doi":"10.3389/fddsv.2022.920850","DOIUrl":"https://doi.org/10.3389/fddsv.2022.920850","url":null,"abstract":"Malaria remains a health and economic burden, particularly in marginalized populations worldwide. The current strategies for combating malaria rely on eliminating the mosquito vector, using insecticide-treated nets, and other management policies or through the administration of small molecule drugs to perturb the intra-erythrocytic development of the parasite. However, resistance against commonly used drugs such as artemisinin has recently become a concern necessitating the identification of novel pharmacophores with unique mechanisms of action. This review summarizes the various life-stage events of the malaria parasite, Plasmodium falciparum, during the in vitro development, which can be targeted by different classes of small molecules. We also describe various chemically induced phenotypes and methods to ascertain and validate drug-induced changes to derive early insights into which cellular mechanisms are affected.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41955676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence platform, RADR®, aids in the discovery of DNA damaging agent for the ultra-rare cancer Atypical Teratoid Rhabdoid Tumors","authors":"J. McDermott, D. Sturtevant, Umesh Kathad, S. Varma, Jianli Zhou, A. Kulkarni, Neha Biyani, Caleb Schimke, W. Reinhold, Fathi Elloumi, Peter Carr, Y. Pommier, K. Bhatia","doi":"10.3389/fddsv.2022.1033395","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1033395","url":null,"abstract":"Over the last decade the next-generation sequencing and ‘omics techniques have become indispensable tools for medicine and drug discovery. These techniques have led to an explosion of publicly available data that often goes under-utilized due to the lack of bioinformatic expertise and tools to analyze that volume of data. Here, we demonstrate the power of applying two novel computational platforms, the NCI’s CellMiner Cross Database and Lantern Pharma’s proprietary artificial intelligence (AI) and machine learning (ML) RADR® platform, to identify biological insights and potentially new target indications for the acylfulvene derivative drugs LP-100 (Irofulven) and LP-184. Analysis of multi-omics data of both drugs within CellMinerCDB generated discoveries into their mechanism of action, gene sets uniquely enriched to each drug, and how these drugs differed from existing DNA alkylating agents. Data from CellMinerCDB suggested that LP-184 and LP-100 were predicted to be effective in cancers with chromatin remodeling deficiencies, like the ultra-rare and fatal childhood cancer Atypical Teratoid Rhabdoid Tumors (ATRT). Lantern’s AI and ML RADR® platform was then utilized to build a model to test, in silico, if LP-184 would be efficacious in ATRT patients. In silico, RADR® aided in predicting that, indeed, ATRT would be sensitive to LP-184, which was then validated in vitro and in vivo. Applying computational tools and AI, like CellMinerCDB and RADR®, are novel and efficient translational approaches to drug discovery for rare cancers like ATRT.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41394622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ligand-based discovery of a novel GATA2 inhibitor targeting acute myeloid leukemia cells","authors":"J. Menendez-Gonzalez, K. Strange, Marcella Bassetto, A. Brancale, N. Rodrigues, S. Ferla","doi":"10.3389/fddsv.2022.1013229","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1013229","url":null,"abstract":"Despite major therapeutic advances leading to improved patient outcomes for other haematological malignancies, development of new therapeutics to improve prognosis for acute myeloid leukemia (AML) patients remains an area of unmet clinical need. Overexpression of GATA2, a member of the GATA family of zinc finger transcription factors, has been implicated in AML. In settings where GATA2 is overexpressed in human AML cells, K7174, a proteasome inhibitor that inhibits GATA2, induces apoptosis and enhances the killing activity of AML chemotherapeutics in vitro yet targeting the proteasome has been associated with high toxicity in the clinic. Using an in silico approach, we embarked on a screen to identify specific GATA2 inhibitors that will target AML cells independently of the proteasome. A shape-based virtual screening of an in-house library of small molecules was performed using a low-energy conformation of K7174. The virtual hit compounds were subsequently filtered according to their potential selectivity for GATA2 over the proteasome. From 15 selected compounds evaluated for their ability to kill AML cells in vitro, one compound, an asymmetrical substituted piperazine with Hepatitis C antiviral activity, exhibited superior ability to induce apoptosis and reduce cell cycling in AML cells without proteasome inhibition. This compound was also able to promote cell death of the relapse propagating leukemic stem cell (LSC) compartment while sparing Gata2 knockout LSCs, crucially demonstrating specificity to inhibit GATA2. We have identified a GATA2 specific inhibitor with promising capability to target AML cells in vitro, including LSCs that underpin poor prognosis in AML.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45531372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery","authors":"M. DiNuzzo","doi":"10.3389/fddsv.2022.1019706","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1019706","url":null,"abstract":"The pharmaceutical industry suffered a significant decline of innovation in the last few decades, whose simple reason is complex biology. Artificial intelligence (AI) promises to make the entire drug discovery and development process more efficient. Here I consider the potential benefits of using AI to deepen our mechanistic understanding of disease by leveraging data and knowledge for modeling and simulation of genome-scale biological networks. I outline recent developments that are moving the field forward and I identify several overarching challenges for advancing the state of the art towards the successful integration of AI with modeling and simulation in drug discovery.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42996186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating diverse layers of omic data to identify novel drug targets in Listeria monocytogenes","authors":"Miranda C. Palumbo, E. Sosa, Florencia A Castello, Gustavo Schottlender, F. Serral, A. Turjanski, M. M. Palomino, D. F. Do Porto","doi":"10.3389/fddsv.2022.969415","DOIUrl":"https://doi.org/10.3389/fddsv.2022.969415","url":null,"abstract":"Listeria monocytogenes (Lm) is a Gram-positive bacillus responsible for listeriosis in humans. Listeriosis has become a major foodborne illness in recent years. This illness is mainly associated with the consumption of contaminated food and ready-to-eat products. Recently, Lm has developed resistances to a broad range of antimicrobials, including those used as the first choice of therapy. Moreover, multidrug-resistant strains have been detected in clinical isolates and settings associated with food processing. This scenario punctuates the need for novel antimicrobials against Lm. On the other hand, increasingly available omics data for diverse pathogens has created new opportunities for rational drug discovery. Identification of an appropriate molecular target is currently accepted as a critical step of this process. In this work, we generated multiple layers of omics data related to Lm, aiming to prioritize proteins that could serve as attractive targets for antimicrobials against L. monocytogenes. We generated genomic, transcriptomic, metabolic, and protein structural information, and this data compendium was integrated onto a freely available web server (Target Pathogen). Thirty targets with desirable features from a drug development point of view were shortlisted. This set of target proteins participates in key metabolic processes such as fatty acid, pentose, rhamnose, and amino acids metabolism. Collectively, our results point towards novel targets for the control of Lm and related bacteria. We invite researchers working in the field of drug discovery to follow up experimentally on our revealed targets.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46383150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}