Mycobacterial Dihydrofolate Reductase Inhibitors Identified Using Chemogenomic Methods and In Vitro Validation
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{"title"=>"Mycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validation", "type"=>"journal", "authors"=>[{"first_name"=>"Grace", "last_name"=>"Mugumbate", "scopus_author_id"=>"57193972995"}, {"first_name"=>"Katherine A.", "last_name"=>"Abrahams", "scopus_author_id"=>"55542599000"}, {"first_name"=>"Jonathan A.G.", "last_name"=>"Cox", "scopus_author_id"=>"55542146100"}, {"first_name"=>"George", "last_name"=>"Papadatos", "scopus_author_id"=>"7801435004"}, {"first_name"=>"Gerard", "last_name"=>"Van Westen", "scopus_author_id"=>"35770054200"}, {"first_name"=>"Joël", "last_name"=>"Lelièvre", "scopus_author_id"=>"36671481100"}, {"first_name"=>"Szymon T.", "last_name"=>"Calus", "scopus_author_id"=>"56479093200"}, {"first_name"=>"Nicholas J.", "last_name"=>"Loman", "scopus_author_id"=>"6701414703"}, {"first_name"=>"Lluis", "last_name"=>"Ballell", "scopus_author_id"=>"6508364138"}, {"first_name"=>"David", "last_name"=>"Barros", "scopus_author_id"=>"56653725900"}, {"first_name"=>"John P.", "last_name"=>"Overington", "scopus_author_id"=>"7004362915"}, {"first_name"=>"Gurdyal S.", "last_name"=>"Besra", "scopus_author_id"=>"7004651537"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25799414", "doi"=>"10.1371/journal.pone.0121492", "sgr"=>"84925678948", "isbn"=>"1471210510", "scopus"=>"2-s2.0-84925678948", "issn"=>"19326203", "pui"=>"603282260"}, "id"=>"8088f678-5694-39d4-a102-ea20ced48476", "abstract"=>"The lack of success in target-based screening approaches to the discovery of antibacterial agents has led to reemergence of phenotypic screening as a successful approach of identifying bioactive, antibacterial compounds. A challenge though with this route is then to identify the molecular target(s) and mechanism of action of the hits. This target identification, or deorphanization step, is often essential in further optimization and validation studies. Direct experimental identification of the molecular target of a screening hit is often complex, precisely because the properties and specificity of the hit are not yet optimized against that target, and so many false positives are often obtained. An alternative is to use computational, predictive, approaches to hypothesize a mechanism of action, which can then be validated in a more directed and efficient manner. Specifically here we present experimental validation of an in silico prediction from a large-scale screen performed against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. The two potent anti-tubercular compounds studied in this case, belonging to the tetrahydro-1,3,5-triazin-2-amine (THT) family, were predicted and confirmed to be an inhibitor of dihydrofolate reductase (DHFR), a known essential Mtb gene, and already clinically validated as a drug target. Given the large number of similar screening data sets shared amongst the community, this in vitro validation of these target predictions gives weight to computational approaches to establish the mechanism of action (MoA) of novel screening hit.", "link"=>"http://www.mendeley.com/research/mycobacterial-dihydrofolate-reductase-inhibitors-identified-using-chemogenomic-methods-vitro-validat", "reader_count"=>33, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>9, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>9, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>5, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>12, "Arts and Humanities"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemistry"=>6, "Psychology"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>6}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Unspecified"=>{"Unspecified"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "United Kingdom"=>2, "Germany"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1983238"], "description"=>"<p>Restriction sites used in the cloning procedure are underlined (<i>Bam</i>HI, <i>Hind</i>III)</p><p>Primers used in the generation of constructs pMV261::<i>dfrA</i> and pMV261::<i>thyA</i>.</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351596, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.t001", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Primers_used_in_the_generation_of_constructs_pMV261_dfrA_and_pMV261_thyA_/1351596", "title"=>"Primers used in the generation of constructs pMV261::<i>dfrA</i> and pMV261::<i>thyA</i>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983235"], "description"=>"<p>Residues in close contact and interacting through hydrophobic interactions are shown in red and H-bonds (green dots) and their distances are in green. Black lines depict residues forming polar contacts with the ligand.</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351593, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interactions_of_A_THT1_and_B_THT2_with_binding_pocket_residues_in_DHFR_/1351593", "title"=>"Interactions of (A) THT1 and (B) THT2 with binding pocket residues in DHFR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983233"], "description"=>"<p>The MICs of THT1 and THT2 were determined in <i>M</i>. <i>bovis</i> BCG containing pMV261, pMV261::<i>thyA</i> and pMV261::<i>dfrA</i>. Plates are shown at 0.5, 1.0, 4.0 and 8.0 x MIC of each compound (with respect to the empty vector), the structures of which are given along with the IUPAC nomenclature for THT1 and THT2 and tabulated MIC data.</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351591, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_on_the_MIC_of_THT1_and_THT2_upon_the_over_expression_of_ThyA_and_DHFR_with_Isoniazid_INH_and_para_aminosalicylic_acid_PAS_as_negative_and_positive_controls_/1351591", "title"=>"Impact on the MIC of THT1 and THT2 upon the over-expression of ThyA and DHFR with Isoniazid (INH) and <i>para</i>-aminosalicylic acid (PAS) as negative and positive controls.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983234"], "description"=>"<p>(A) Grey defines the molecular surface and the cartoon represents the secondary structure of <i>Mtb</i> DHFR. Sticks represent bonds and atoms of THT1 (brown), THT2 (yellow) and the binding pocket residues within a radius of 5 Å from the ligand. (B) A comparison of binding mode of THT1 and cycloguanil (green).</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351592, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.g004", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Binding_mode_of_BRL_7980SA_THT1_and_BRL_10143SA_THT2_/1351592", "title"=>"Binding mode of BRL-7980SA (THT1) and BRL-10143SA (THT2).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983232"], "description"=>"<p>Number of potential inhibitors of <i>Mtb</i> DHFR identified by the multiple category naïve Bayesian classifier (MCNBC), Similarity Ensemble Approach (SEA) and docking calculations.</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351590, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_potential_inhibitors_of_Mtb_DHFR_identified_by_the_multiple_category_na_ve_Bayesian_classifier_MCNBC_Similarity_Ensemble_Approach_SEA_and_docking_calculations_/1351590", "title"=>"Number of potential inhibitors of <i>Mtb</i> DHFR identified by the multiple category naïve Bayesian classifier (MCNBC), Similarity Ensemble Approach (SEA) and docking calculations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983240", "https://ndownloader.figshare.com/files/1983241", "https://ndownloader.figshare.com/files/1983242", "https://ndownloader.figshare.com/files/1983243", "https://ndownloader.figshare.com/files/1983244"], "description"=>"<div><p>The lack of success in target-based screening approaches to the discovery of antibacterial agents has led to reemergence of phenotypic screening as a successful approach of identifying bioactive, antibacterial compounds. A challenge though with this route is then to identify the molecular target(s) and mechanism of action of the hits. This target identification, or deorphanization step, is often essential in further optimization and validation studies. Direct experimental identification of the molecular target of a screening hit is often complex, precisely because the properties and specificity of the hit are not yet optimized against that target, and so many false positives are often obtained. An alternative is to use computational, predictive, approaches to hypothesize a mechanism of action, which can then be validated in a more directed and efficient manner. Specifically here we present experimental validation of an <i>in silico</i> prediction from a large-scale screen performed against <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>), the causative agent of tuberculosis. The <b>two</b> potent anti-tubercular compounds studied in this case, belonging to the tetrahydro-1,3,5-triazin-2-amine (THT) family, were predicted and confirmed to be an inhibitor of dihydrofolate reductase (DHFR), a known essential <i>Mtb</i> gene, and already clinically validated as a drug target. Given the large number of similar screening data sets shared amongst the community, this <i>in vitro</i> validation of these target predictions gives weight to computational approaches to establish the mechanism of action (MoA) of novel screening hit.</p></div>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351598, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0121492.s001", "https://dx.doi.org/10.1371/journal.pone.0121492.s002", "https://dx.doi.org/10.1371/journal.pone.0121492.s003", "https://dx.doi.org/10.1371/journal.pone.0121492.s004", "https://dx.doi.org/10.1371/journal.pone.0121492.s005"], "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Mycobacterial_Dihydrofolate_Reductase_Inhibitors_Identified_Using_Chemogenomic_Methods_and_In_Vitro_Validation/1351598", "title"=>"Mycobacterial Dihydrofolate Reductase Inhibitors Identified Using Chemogenomic Methods and <i>In Vitro</i> Validation", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983237"], "description"=>"<p>Enzymes are highlighted in blue. ThyA, thymidylate synthase; DHFR, dihydrofolate reductase; SHMT, serine hydroxymethyltransferase; DHF, dihydrofolate; THF, tetrahydrofolate; mTHF, methyl tetrahydrofolate.</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351595, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.g006", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_biochemical_relationship_between_ThyA_and_DHFR_/1351595", "title"=>"The biochemical relationship between ThyA and DHFR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 05:56:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1983231"], "description"=>"<p>(A) Validation results of the model generated from randomly selected 80% target-ligand pairs and the remaining 20% was used as a test set. (B) Validation results of the model built using target-ligand pairs from 80% of the published articles and the test set consisted of target-ligand pairs from the remaining 20%.</p>", "links"=>[], "tags"=>["screening data sets", "target predictions", "tht", "target identification", "approach", "validation studies", "Mycobacterial Dihydrofolate Reductase Inhibitors Identified", "Mycobacterium tuberculosis", "silico prediction", "dihydrofolate reductase", "drug target", "DHFR", "phenotypic screening", "Chemogenomic Methods", "Mtb gene", "novel screening", "mechanism", "deorphanization step", "Vitro Validation"], "article_id"=>1351589, "categories"=>["Biological Sciences"], "users"=>["Grace Mugumbate", "Katherine A. Abrahams", "Jonathan A. G. Cox", "George Papadatos", "Gerard van Westen", "Joel Lelièvre", "Szymon T. Calus", "Nicholas J. Loman", "Lluis Ballell", "David Barros", "John P. Overington", "Gurdyal S. Besra"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0121492.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_multiple_category_na_239_ve_Bayesian_classifier_validation_results_/1351589", "title"=>"The multiple category naïve Bayesian classifier validation results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-23 05:56:58"}

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  • {"unique-ip"=>"17", "full-text"=>"18", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"16", "full-text"=>"19", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"11", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"5", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"18", "full-text"=>"26", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"22", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"17", "full-text"=>"15", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"14", "full-text"=>"18", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"9", "supp-data"=>"3", "cited-by"=>"1", "year"=>"2020", "month"=>"9"}
  • {"unique-ip"=>"14", "full-text"=>"12", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
  • {"unique-ip"=>"19", "full-text"=>"17", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
  • {"unique-ip"=>"14", "full-text"=>"15", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
  • {"unique-ip"=>"14", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2021", "month"=>"1"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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