Pub Date : 2022-11-02DOI: 10.3389/fddsv.2022.1013285
Cyril T. Namba-Nzanguim, Gemma Turon, C. V. Simoben, I. Tietjen, L. Montaner, S. M. Efange, Miquel Duran-Frigola, F. Ntie‐Kang
Current antiviral drug discovery efforts face many challenges, including development of new drugs during an outbreak and coping with drug resistance due to rapidly accumulating viral mutations. Emerging artificial intelligence and machine learning (AI/ML) methods can accelerate anti-infective drug discovery and have the potential to reduce overall development costs in Low and Middle-Income Countries (LMIC), which in turn may help to develop new and/or accessible therapies against communicable diseases within these countries. While the marketplace currently offers a plethora of data-driven AI/ML tools, most to date have been developed within the context of non-communicable diseases like cancer, and several barriers have limited the translation of existing tools to the discovery of drugs against infectious diseases. Here, we provide a perspective on the benefits, limitations, and pitfalls of AI/ML tools in the discovery of novel therapeutics with a focus on antivirals. We also discuss available and emerging data sharing models including intellectual property-preserving AI/ML. In addition, we review available data sources and platforms and provide examples for low-cost and accessible screening methods and other virus-based bioassays suitable for implementation of AI/ML-based programs in LMICs. Finally, we introduce an emerging AI/ML-based Center in Cameroon (Central Africa) which is currently developing methods and tools to promote local, independent drug discovery and represents a model that could be replicated among LMIC globally.
{"title":"Artificial intelligence for antiviral drug discovery in low resourced settings: A perspective","authors":"Cyril T. Namba-Nzanguim, Gemma Turon, C. V. Simoben, I. Tietjen, L. Montaner, S. M. Efange, Miquel Duran-Frigola, F. Ntie‐Kang","doi":"10.3389/fddsv.2022.1013285","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1013285","url":null,"abstract":"Current antiviral drug discovery efforts face many challenges, including development of new drugs during an outbreak and coping with drug resistance due to rapidly accumulating viral mutations. Emerging artificial intelligence and machine learning (AI/ML) methods can accelerate anti-infective drug discovery and have the potential to reduce overall development costs in Low and Middle-Income Countries (LMIC), which in turn may help to develop new and/or accessible therapies against communicable diseases within these countries. While the marketplace currently offers a plethora of data-driven AI/ML tools, most to date have been developed within the context of non-communicable diseases like cancer, and several barriers have limited the translation of existing tools to the discovery of drugs against infectious diseases. Here, we provide a perspective on the benefits, limitations, and pitfalls of AI/ML tools in the discovery of novel therapeutics with a focus on antivirals. We also discuss available and emerging data sharing models including intellectual property-preserving AI/ML. In addition, we review available data sources and platforms and provide examples for low-cost and accessible screening methods and other virus-based bioassays suitable for implementation of AI/ML-based programs in LMICs. Finally, we introduce an emerging AI/ML-based Center in Cameroon (Central Africa) which is currently developing methods and tools to promote local, independent drug discovery and represents a model that could be replicated among LMIC globally.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47471427","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}
Pub Date : 2022-10-25DOI: 10.3389/fddsv.2022.992087
Mahdi Saghari, M. A. Jansen, H. Grievink, R. Rissmann, M. Moerland
The pharmacological activity assessment of novel immunomodulatory drugs in early-stage drug development is challenging as healthy volunteers do not express relevant immune biomarkers. Alternatively, the immune system can be challenged with keyhole limpet hemocyanin (KLH), a suitable antigen for studying adaptive immune responses. This report systemically reviews the KLH challenge in clinical studies focusing on the characterization of the KLH-driven systemic and local immune responses, identification of the KLH-induced biomarkers, and the evaluation of the effect of pharmacological interventions and diseases on the KLH response. A systematic literature review was carried out in PubMed spanning from 1967 to 2022. The systemic humoral KLH responses could be characterized by ELISA after 3 weeks following immunization. For the systemic cellular and molecular immune responses multiple KLH immunizations and the use of novel techniques such as flow cytometry and ELISpot yield optimal results. The objective evaluation of dermal KLH rechallenge allows for more accurate and sensitive quantification of the local response compared to subjective scoring. For the local cellular and molecular assays after KLH dermal rechallenge we also advocate the use of multiple KLH immunizations. Furthermore, oral KLH feeding, age, physical activity, alcohol consumption, stress, as well as certain auto-immune diseases also play a role in the KLH-induced immune response. Importantly, based on the KLH challenges, the effect of (novel) immunomodulatory drugs could be demonstrated in healthy volunteers, providing valuable information for the clinical development of these compounds. This review underlines the value of KLH challenges in clinical studies, but also the need for standardized and well-controlled methodology to induce and evaluate KLH responses. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022335419
{"title":"Characterization of KLH-driven immune responses in clinical studies: A systematic review","authors":"Mahdi Saghari, M. A. Jansen, H. Grievink, R. Rissmann, M. Moerland","doi":"10.3389/fddsv.2022.992087","DOIUrl":"https://doi.org/10.3389/fddsv.2022.992087","url":null,"abstract":"The pharmacological activity assessment of novel immunomodulatory drugs in early-stage drug development is challenging as healthy volunteers do not express relevant immune biomarkers. Alternatively, the immune system can be challenged with keyhole limpet hemocyanin (KLH), a suitable antigen for studying adaptive immune responses. This report systemically reviews the KLH challenge in clinical studies focusing on the characterization of the KLH-driven systemic and local immune responses, identification of the KLH-induced biomarkers, and the evaluation of the effect of pharmacological interventions and diseases on the KLH response. A systematic literature review was carried out in PubMed spanning from 1967 to 2022. The systemic humoral KLH responses could be characterized by ELISA after 3 weeks following immunization. For the systemic cellular and molecular immune responses multiple KLH immunizations and the use of novel techniques such as flow cytometry and ELISpot yield optimal results. The objective evaluation of dermal KLH rechallenge allows for more accurate and sensitive quantification of the local response compared to subjective scoring. For the local cellular and molecular assays after KLH dermal rechallenge we also advocate the use of multiple KLH immunizations. Furthermore, oral KLH feeding, age, physical activity, alcohol consumption, stress, as well as certain auto-immune diseases also play a role in the KLH-induced immune response. Importantly, based on the KLH challenges, the effect of (novel) immunomodulatory drugs could be demonstrated in healthy volunteers, providing valuable information for the clinical development of these compounds. This review underlines the value of KLH challenges in clinical studies, but also the need for standardized and well-controlled methodology to induce and evaluate KLH responses. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022335419","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41554473","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}
Pub Date : 2022-10-25DOI: 10.3389/fddsv.2022.1038224
Yehong Zhou, Fuxing Shu, S. Sarsaiya, Hu Jiang, Chengyan Jiang, Ting Qu, Ruixia Wang
Although Siraitia grosvenorii (abbreviated as S.g.) is frequently used to prevent and cure diabetes problems, the precise mechanism underlying its ability to do so remains unknown. Through network pharmacology and molecular docking techniques, we studied the early molecular mechanisms of S.g in the treating of proliferative diabetic retinopathy (PDR) in this study. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen the active compounds and related targets of S.g. Oral bioavailability (OB) 30% and drug likeness (DL) 0.18 were used as screening criteria. The active compounds without knowledge of a probable target were excluded. The Uniprot database included converted symbols for the associated targets. GEO2R was used to explore several genes related to PDR. Using jvenn web service to intersect targets of S.g and PDR. The Xiantao Academic Online website was used to examine the expression patterns of intersect targets in PDR samples. The STRING database was used to create a protein-protein interaction (PPI) network of intersecting targets. Cytoscape software was used to show the PPI network, MCODE software was used to evaluate the network’s core proteins, and CytoHubba software was used to extract the important networks of the top three targets. Omicshare platform carried a functional analysis using the Gene Ontology (GO) and pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Pymol, AutoDock Vina software, Schrödinger Software were used to conduct molecular docking experiments or pockets search on the top three targets. The results showed that 85 targets were matched to six active compounds of S.g. 18 intersect targets were found. Seven DEGs were up-regulated and eleven genes were down-regulated when these targets were divided into two groups. TNF, PTGS2, and CASP3 were the main targets, according to the PPI network. The intersect targets were mostly related to angiogenesis, cell proliferation, oxidative stress, inflammatory response, and metabolism. It was discovered that the core targets TNF, PTGS2, and CASP3 had various levels of affinity for their respective compounds. Interestingly, multiple good drug-forming pockets for CASP3 and PTGS2 targets were identified through Schrödinger software. In particular, six compounds bind to the top three core targets to inhibit IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Pathways in cancer and 14 other signaling pathways to inhibit inflammation, apoptosis, oxidative stress, arachidonic acid metabolism, and angiogenesis to prevent and treat PDR. The study’s findings, which served as a guide for the widespread use of S.g in PDR clinical practise, included multi-substances and targets of S.g to prevent and cure PDR.
{"title":"Network pharmacology and molecular docking to explore Siraitia grosvenorii’s potential mechanism in preventing and treating proliferative diabetic retinopathy","authors":"Yehong Zhou, Fuxing Shu, S. Sarsaiya, Hu Jiang, Chengyan Jiang, Ting Qu, Ruixia Wang","doi":"10.3389/fddsv.2022.1038224","DOIUrl":"https://doi.org/10.3389/fddsv.2022.1038224","url":null,"abstract":"Although Siraitia grosvenorii (abbreviated as S.g.) is frequently used to prevent and cure diabetes problems, the precise mechanism underlying its ability to do so remains unknown. Through network pharmacology and molecular docking techniques, we studied the early molecular mechanisms of S.g in the treating of proliferative diabetic retinopathy (PDR) in this study. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen the active compounds and related targets of S.g. Oral bioavailability (OB) 30% and drug likeness (DL) 0.18 were used as screening criteria. The active compounds without knowledge of a probable target were excluded. The Uniprot database included converted symbols for the associated targets. GEO2R was used to explore several genes related to PDR. Using jvenn web service to intersect targets of S.g and PDR. The Xiantao Academic Online website was used to examine the expression patterns of intersect targets in PDR samples. The STRING database was used to create a protein-protein interaction (PPI) network of intersecting targets. Cytoscape software was used to show the PPI network, MCODE software was used to evaluate the network’s core proteins, and CytoHubba software was used to extract the important networks of the top three targets. Omicshare platform carried a functional analysis using the Gene Ontology (GO) and pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Pymol, AutoDock Vina software, Schrödinger Software were used to conduct molecular docking experiments or pockets search on the top three targets. The results showed that 85 targets were matched to six active compounds of S.g. 18 intersect targets were found. Seven DEGs were up-regulated and eleven genes were down-regulated when these targets were divided into two groups. TNF, PTGS2, and CASP3 were the main targets, according to the PPI network. The intersect targets were mostly related to angiogenesis, cell proliferation, oxidative stress, inflammatory response, and metabolism. It was discovered that the core targets TNF, PTGS2, and CASP3 had various levels of affinity for their respective compounds. Interestingly, multiple good drug-forming pockets for CASP3 and PTGS2 targets were identified through Schrödinger software. In particular, six compounds bind to the top three core targets to inhibit IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Pathways in cancer and 14 other signaling pathways to inhibit inflammation, apoptosis, oxidative stress, arachidonic acid metabolism, and angiogenesis to prevent and treat PDR. The study’s findings, which served as a guide for the widespread use of S.g in PDR clinical practise, included multi-substances and targets of S.g to prevent and cure PDR.","PeriodicalId":73080,"journal":{"name":"Frontiers in drug discovery","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49473467","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}
Pub Date : 2022-10-21DOI: 10.3389/fddsv.2022.954911
L. A. Machado, Eduardo Krempser, A. Guimarães
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.
{"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}
Pub Date : 2022-10-21DOI: 10.3389/fddsv.2022.1001487
D. Novick
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.
{"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}
Pub Date : 2022-10-20DOI: 10.3389/fddsv.2022.969983
Gustavo Schottlender, Juan Manuel Prieto, Miranda C. Palumbo, Florencia A Castello, F. Serral, E. Sosa, A. Turjanski, M. Marti, D. A. Fernández Do Porto
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.
{"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}
Pub Date : 2022-10-18DOI: 10.3389/fddsv.2022.1042737
S. Lahiri, Mitra Mastali, J. V. Van Eyk, T. Hitzeman, C. Bresee, K. Raedschelders, P. Lyden, R. Gottlieb, J. Fang, R. Shaw, T. Hong
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.
{"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}
Pub Date : 2022-10-12DOI: 10.3389/fddsv.2022.1015545
M. A. C. Williams, B. Shankar, J. Vaishnav, M. Ranek
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.
{"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}
Pub Date : 2022-10-12DOI: 10.3389/fddsv.2022.920850
K. Reghunandanan, Rajesh Chandramohanadas
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.
{"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}
Pub Date : 2022-10-11DOI: 10.3389/fddsv.2022.1033395
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
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.
{"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}
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