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{"title"=>"An acenocoumarol dosing algorithm using clinical and pharmacogenetic data in Spanish patients with thromboembolic disease", "type"=>"journal", "authors"=>[{"first_name"=>"Alberto M.", "last_name"=>"Borobia", "scopus_author_id"=>"36899946400"}, {"first_name"=>"Rubin", "last_name"=>"Lubomirov", "scopus_author_id"=>"21734812400"}, {"first_name"=>"Elena", "last_name"=>"Ramírez", "scopus_author_id"=>"23467680600"}, {"first_name"=>"Alicia", "last_name"=>"Lorenzo", "scopus_author_id"=>"17341048500"}, {"first_name"=>"Armando", "last_name"=>"Campos", "scopus_author_id"=>"55228629200"}, {"first_name"=>"Raul", "last_name"=>"Muñoz-Romo", "scopus_author_id"=>"55318672300"}, {"first_name"=>"Carmen", "last_name"=>"Fernández-Capitán", "scopus_author_id"=>"6602688210"}, {"first_name"=>"Jesús", "last_name"=>"Frías", "scopus_author_id"=>"8966918600"}, {"first_name"=>"Antonio J.", "last_name"=>"Carcas", "scopus_author_id"=>"6701664907"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"22911785", "sgr"=>"84864103206", "doi"=>"10.1371/journal.pone.0041360", "scopus"=>"2-s2.0-84864103206", "pui"=>"365290634", "isbn"=>"1932-6203", "issn"=>"19326203"}, "id"=>"9bbfb0c0-18c5-3bc8-9f6c-a7d4f641b2b9", "abstract"=>"Appropriate dosing of coumarins is difficult to establish, due to significant inter-individual variability in the dose required to obtain stable anticoagulation. Several genetic and other clinical factors have been associated with the coumarins dose, and some pharmacogenetic-guided dosing algorithms for warfarin and acenocoumarol have been developed for mixed populations. We recruited 147 patients with thromboembolic disease who were on stable doses and with an international normalized ratio (INR) between 2 and 3. We ascertained the influence of clinical and genetic variables on the stable acenocoumarol dose by multiple linear regression analysis in a derivation cohort (DC; n = 117) and developed an algorithm for dosing that included clinical factors (age, body mass index and concomitant drugs) and genetic variations of VKORC1, CYP2C9, CYP4F2 and APOE. For purposes of comparison, a model including only clinical data was created. The clinical factors explained 22% of the dose variability, which increased to 60.6% when pharmacogenetic information was included (p<0.001); CYP4F2 and APOE variants explained 4.9% of this variability. The mean absolute error of the predicted acenocoumarol dose (mg/week) obtained with the pharmacogenetic algorithm was 3.63 vs. 5.08 mg/week with the clinical algorithm (95% CI: 0.88 to 2.04). In the testing cohort (n = 30), clinical factors explained a mere 7% of the dose variability, compared to 39% explained by the pharmacogenetic algorithm. Considering a more clinically relevant parameter, the pharmacogenetic algorithm correctly predicted the real stable dose in 59.8% of the cases (DC) vs. only 37.6% predicted by the clinical algorithm (95% CI: 10 to 35). Therefore the number of patients needed to genotype to avoid one over- or under-dosing was estimated to be 5.", "link"=>"http://www.mendeley.com/research/acenocoumarol-dosing-algorithm-using-clinical-pharmacogenetic-data-spanish-patients-thromboembolic-d", "reader_count"=>30, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>5, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>4, "Student > Master"=>1, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>5, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>4, "Student > Master"=>1, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>14, "Pharmacology, Toxicology and Pharmaceutical Science"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>5}}, "reader_count_by_country"=>{"Netherlands"=>1, "Chile"=>1, "Portugal"=>1}, "group_count"=>5}

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• http://doi.org/10.5582/ddt.2017.01031
• http://doi.org/10.18632/oncotarget.25579
• http://doi.org/10.1186/1745-6215-13-239
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• {"files"=>["https://ndownloader.figshare.com/files/316636", "https://ndownloader.figshare.com/files/316656", "https://ndownloader.figshare.com/files/316681", "https://ndownloader.figshare.com/files/316698"], "description"=>"<div><p>Appropriate dosing of coumarins is difficult to establish, due to significant inter-individual variability in the dose required to obtain stable anticoagulation. Several genetic and other clinical factors have been associated with the coumarins dose, and some pharmacogenetic-guided dosing algorithms for warfarin and acenocoumarol have been developed for mixed populations. We recruited 147 patients with thromboembolic disease who were on stable doses and with an international normalized ratio (INR) between 2 and 3. We ascertained the influence of clinical and genetic variables on the stable acenocoumarol dose by multiple linear regression analysis in a derivation cohort (DC; n = 117) and developed an algorithm for dosing that included clinical factors (age, body mass index and concomitant drugs) and genetic variations of <em>VKORC1</em>, <em>CYP2C9</em>, <em>CYP4F2</em> and <em>APOE</em>. For purposes of comparison, a model including only clinical data was created. The clinical factors explained 22% of the dose variability, which increased to 60.6% when pharmacogenetic information was included (p<0.001); <em>CYP4F2</em> and <em>APOE</em> variants explained 4.9% of this variability. The mean absolute error of the predicted acenocoumarol dose (mg/week) obtained with the pharmacogenetic algorithm was 3.63 vs. 5.08 mg/week with the clinical algorithm (95% CI: 0.88 to 2.04). In the testing cohort (n = 30), clinical factors explained a mere 7% of the dose variability, compared to 39% explained by the pharmacogenetic algorithm. Considering a more clinically relevant parameter, the pharmacogenetic algorithm correctly predicted the real stable dose in 59.8% of the cases (DC) vs. only 37.6% predicted by the clinical algorithm (95% CI: 10 to 35). Therefore the number of patients needed to genotype to avoid one over- or under-dosing was estimated to be 5.</p> </div>", "links"=>[], "tags"=>["acenocoumarol", "dosing", "algorithm", "pharmacogenetic", "spanish", "patients", "thromboembolic"], "article_id"=>122467, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.s001", "https://dx.doi.org/10.1371/journal.pone.0041360.s002", "https://dx.doi.org/10.1371/journal.pone.0041360.s003", "https://dx.doi.org/10.1371/journal.pone.0041360.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/An_Acenocoumarol_Dosing_Algorithm_Using_Clinical_and_Pharmacogenetic_Data_in_Spanish_Patients_with_Thromboembolic_Disease/122467", "title"=>"An Acenocoumarol Dosing Algorithm Using Clinical and Pharmacogenetic Data in Spanish Patients with Thromboembolic Disease", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-07-20 00:41:07"}
• {"files"=>["https://ndownloader.figshare.com/files/606066"], "description"=>"*<p>Between-group comparisons calculated by paired “t” test.</p>", "links"=>[], "tags"=>["mae", "pharmacogenetic", "algorithms"], "article_id"=>276552, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Precision_expressed_as_MAE_SD_of_pharmacogenetic_and_clinical_algorithms_by_dose_group_in_the_entire_cohort_/276552", "title"=>"Precision expressed as MAE (SD) of pharmacogenetic and clinical algorithms by dose group in the entire cohort.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:49:12"}
• {"files"=>["https://ndownloader.figshare.com/files/606110"], "description"=>"<p>DC: Derivation cohort; EC: entire cohort; VC: Validation/Testing cohort; MAE: mean absolute error (mg/week). MAE for warfarin dose has been corrected considering a dose equivalence ratio of 0.57 between both drugs.</p>", "links"=>[], "tags"=>["mae", "studies"], "article_id"=>276596, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_R_2_and_MAE_in_our_study_and_two_other_studies_IWPC_and_EU_PACT_/276596", "title"=>"Comparison of R² and MAE in our study and two other studies (IWPC and EU-PACT).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:49:56"}
• {"files"=>["https://ndownloader.figshare.com/files/606141"], "description"=>"<p>ME: mean error (predicted – observed); %ME: mean error expressed as a percentage (%ME = ME/Observed*100); MAE: mean absolute error ( = SQR[(Pred-Obs)²]); %MAE: mean absolute error expressed as a percentage (%MAE = MAE/Obs*100).</p>‡<p>McNemar's test of paired proportions.</p>", "links"=>[], "tags"=>["pharmacogenetic"], "article_id"=>276631, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictive_performance_of_pharmacogenetic_and_clinical_algorithms_/276631", "title"=>"Predictive performance of pharmacogenetic and clinical algorithms.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:50:31"}
• {"files"=>["https://ndownloader.figshare.com/files/606175"], "description"=>"*<p>p<0.001</p>", "links"=>[], "tags"=>["classification", "algorithms"], "article_id"=>276665, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percentage_of_global_correct_classification_Predicted_Dose_within_177_20_of_Real_Dose_by_genetic_and_clinical_algorithms_in_the_derivation_test_and_entire_cohorts_/276665", "title"=>"Percentage of global correct classification (Predicted Dose within ±20% of Real Dose) by genetic and clinical algorithms in the derivation, test and entire cohorts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:51:05"}
• {"files"=>["https://ndownloader.figshare.com/files/606220"], "description"=>"<p>Beta: standardized regression coefficient, which reflects the relative weight of each variable included in the model.</p>", "links"=>[], "tags"=>["included", "acenocoumarol", "pharmacogenetic", "dosing", "beta", "reflecting"], "article_id"=>276700, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variables_ultimately_included_in_the_acenocoumarol_pharmacogenetic_and_clinical_dosing_algorithms_and_values_of_Beta_reflecting_their_relative_weight_in_the_final_model_/276700", "title"=>"Variables ultimately included in the acenocoumarol pharmacogenetic and clinical dosing algorithms, and values of Beta reflecting their relative weight in the final model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:51:40"}
• {"files"=>["https://ndownloader.figshare.com/files/606265"], "description"=>"<p>Unadjusted R² for each group of variables and resultant cumulative R² of the final model.</p>", "links"=>[], "tags"=>["variables", "resultant", "cumulative"], "article_id"=>276744, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unadjusted_R_2_for_each_group_of_variables_and_resultant_cumulative_R_2_of_the_final_model_/276744", "title"=>"Unadjusted R² for each group of variables and resultant cumulative R² of the final model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:52:24"}
• {"files"=>["https://ndownloader.figshare.com/files/606311"], "description"=>"(1)<p><i>CYP</i> inducers that were considered in this analysis included phenytoin, carbamazepine and rifampin.</p>(2)<p><i>CYP</i> inhibitors that were considered in this analysis included azoles, proton pump inhibitors and statins.</p>(3)<p>For <i>CYP2C9</i> genotype, the usual * designation is used (*2 = rs1799853 and *3 = rs1057910).</p>(4)<p>VV indicates homozygous V433 carriers; VM, heterozygous V433M carriers; MM, homozygous M433 carriers.</p>", "links"=>[], "tags"=>["genetics and genomics", "hematology", "computer science", "pharmacology", "pathology", "mathematics"], "article_id"=>276792, "categories"=>["Cell Biology", "Hematology", "Information And Computing Sciences", "Mathematics", "Pharmacology", "Genetics"], "users"=>["Alberto M. Borobia", "Rubin Lubomirov", "Elena Ramírez", "Alicia Lorenzo", "Armando Campos", "Raul Muñoz-Romo", "Carmen Fernández-Capitán", "Jesús Frías", "Antonio J. Carcas"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041360.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_study_cohorts_/276792", "title"=>"Characteristics of study cohorts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-20 01:53:12"}

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Relative Metric

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F1000Prime