Enrique Jiménez-Varo1, Marisa Cañadas-Garre2, Víctor Garcés-Robles3, María José Gutiérrez-Pimentel4, Miguel Ángel Calleja-Hernández5. 1. Pharmacogenetics Unit, UGC Provincial de Farmacia de Granada, Instituto de Investigación Biosanitaria de Granada, Complejo Hospitalario Universitario de Granada, Avda. Fuerzas Armadas, 2, 18014 Granada, Spain. Electronic address: ejimenezvaro@gmail.com. 2. Pharmacogenetics Unit, UGC Provincial de Farmacia de Granada, Instituto de Investigación Biosanitaria de Granada, Complejo Hospitalario Universitario de Granada, Avda. Fuerzas Armadas, 2, 18014 Granada, Spain. Electronic address: marisacgarre@gmail.com. 3. Pharmacogenetics Unit, UGC Provincial de Farmacia de Granada, Instituto de Investigación Biosanitaria de Granada, Complejo Hospitalario Universitario de Granada, Avda. Fuerzas Armadas, 2, 18014 Granada, Spain. Electronic address: vigaro89@gmail.com. 4. Haematology Department, Complejo Hospitalario Universitario de Granada, Avda. Fuerzas Armadas, 2, 18014 Granada, Spain. Electronic address: mjgutierrezpimentel@yahoo.es. 5. Pharmacogenetics Unit, UGC Provincial de Farmacia de Granada, Instituto de Investigación Biosanitaria de Granada, Complejo Hospitalario Universitario de Granada, Avda. Fuerzas Armadas, 2, 18014 Granada, Spain. Electronic address: mangel.calleja.exts@juntadeandalucia.es.
Abstract
INTRODUCTION: Acenocoumarol (ACN) has a narrow therapeutic range that is especially difficult to control at the start of its administration. Various dosing pharmacogenetic-guided dosing algorithms have been developed, but further work on their external validation is required. The aim of this study was to evaluate the extrapolation of pharmacogenetic algorithms for ACN as an alternative to the development of a specific algorithm for a given population. MATERIAL AND METHODS: The predictive performance, deviation, accuracy, and clinical significance of five pharmacogenetic algorithms (EU-PACT, Borobia, Rathore, Markatos, Krishna Kumar) were compared in 189 stable ACN patients representing all indications for anticoagulant treatment. RESULTS: The correlation between the dose predictions of the five pharmacogenetic models ranged from 7.7 to 70.6% and the percentage of patients with a correct prediction (deviation ≤20% from actual ACN dose) ranged from 5.9 to 40.7%. EU-PACT and Borobia pharmacogenetic dosing algorithms were the most accurate in our setting and evidenced the best clinical performance. CONCLUSIONS: Among the five models studied, the EU-PACT and Borobia pharmacogenetic dosing algorithms demonstrated the best potential for extrapolation.
INTRODUCTION:Acenocoumarol (ACN) has a narrow therapeutic range that is especially difficult to control at the start of its administration. Various dosing pharmacogenetic-guided dosing algorithms have been developed, but further work on their external validation is required. The aim of this study was to evaluate the extrapolation of pharmacogenetic algorithms for ACN as an alternative to the development of a specific algorithm for a given population. MATERIAL AND METHODS: The predictive performance, deviation, accuracy, and clinical significance of five pharmacogenetic algorithms (EU-PACT, Borobia, Rathore, Markatos, Krishna Kumar) were compared in 189 stable ACNpatients representing all indications for anticoagulant treatment. RESULTS: The correlation between the dose predictions of the five pharmacogenetic models ranged from 7.7 to 70.6% and the percentage of patients with a correct prediction (deviation ≤20% from actual ACN dose) ranged from 5.9 to 40.7%. EU-PACT and Borobia pharmacogenetic dosing algorithms were the most accurate in our setting and evidenced the best clinical performance. CONCLUSIONS: Among the five models studied, the EU-PACT and Borobia pharmacogenetic dosing algorithms demonstrated the best potential for extrapolation.
Authors: Elisa Danese; Sara Raimondi; Martina Montagnana; Angela Tagetti; Taimour Langaee; Paola Borgiani; Cinzia Ciccacci; Antonio J Carcas; Alberto M Borobia; Hoi Y Tong; Cristina Dávila-Fajardo; Mariana Rodrigues Botton; Stephane Bourgeois; Panos Deloukas; Michael D Caldwell; Jim K Burmester; Richard L Berg; Larisa H Cavallari; Katarzyna Drozda; Min Huang; Li-Zi Zhao; Han-Jing Cen; Rocio Gonzalez-Conejero; Vanessa Roldan; Yusuke Nakamura; Taisei Mushiroda; Inna Y Gong; Richard B Kim; Keita Hirai; Kunihiko Itoh; Carlos Isaza; Leonardo Beltrán; Enrique Jiménez-Varo; Marisa Cañadas-Garre; Alice Giontella; Marianne K Kringen; Kari Bente Foss Haug; Hye Sun Gwak; Kyung Eun Lee; Pietro Minuz; Ming Ta Michael Lee; Steven A Lubitz; Stuart Scott; Cristina Mazzaccara; Lucia Sacchetti; Ece Genç; Mahmut Özer; Anil Pathare; Rajagopal Krishnamoorthy; Andras Paldi; Virginie Siguret; Marie-Anne Loriot; Vijay Kumar Kutala; Guilherme Suarez-Kurtz; Jamila Perini; Josh C Denny; Andrea H Ramirez; Balraj Mittal; Saurabh Singh Rathore; Hersh Sagreiya; Russ Altman; Mohamed Hossam A Shahin; Sherief I Khalifa; Nita A Limdi; Charles Rivers; Aditi Shendre; Chrisly Dillon; Ivet M Suriapranata; Hong-Hao Zhou; Sheng-Lan Tan; Vacis Tatarunas; Vaiva Lesauskaite; Yumao Zhang; Anke H Maitland-van der Zee; Talitha I Verhoef; Anthonius de Boer; Monica Taljaard; Carlo Federico Zambon; Vittorio Pengo; Jieying Eunice Zhang; Munir Pirmohamed; Julie A Johnson; Cristiano Fava Journal: Clin Pharmacol Ther Date: 2019-02-17 Impact factor: 6.875
Authors: Hoi Y Tong; Cristina Lucía Dávila-Fajardo; Alberto M Borobia; Luis Javier Martínez-González; Rubin Lubomirov; Laura María Perea León; María J Blanco Bañares; Xando Díaz-Villamarín; Carmen Fernández-Capitán; José Cabeza Barrera; Antonio J Carcas Journal: PLoS One Date: 2016-03-15 Impact factor: 3.240