R. Lubomirov
Autonomous University of Madrid
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by R. Lubomirov.
PLOS ONE | 2012
Alberto M. Borobia; R. Lubomirov; Elena Ramírez; Alicia Lorenzo; Armando Campos; Raul Muñoz-Romo; Carmen Fernández-Capitán; Jesús Frías; Antonio J. Carcas
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.
PLOS ONE | 2016
Hoi Y. Tong; Cristina Lucía Dávila-Fajardo; Alberto M. Borobia; Luis Javier Martinez-Gonzalez; R. Lubomirov; Laura María Perea León; María J. Blanco Bañares; Xando Díaz-Villamarín; Carmen Fernández-Capitán; José Cabeza Barrera; Antonio J. Carcas
There is a strong association between genetic polymorphisms and the acenocoumarol dosage requirements. Genotyping the polymorphisms involved in the pharmacokinetics and pharmacodynamics of acenocoumarol before starting anticoagulant therapy would result in a better quality of life and a more efficient use of healthcare resources. The objective of this study is to develop a new algorithm that includes clinical and genetic variables to predict the most appropriate acenocoumarol dosage for stable anticoagulation in a wide range of patients. We recruited 685 patients from 2 Spanish hospitals and 1 primary healthcare center. We randomly chose 80% of the patients (n = 556), considering an equitable distribution of genotypes to form the generation cohort. The remaining 20% (n = 129) formed the validation cohort. Multiple linear regression was used to generate the algorithm using the acenocoumarol stable dosage as the dependent variable and the clinical and genotypic variables as the independent variables. The variables included in the algorithm were age, weight, amiodarone use, enzyme inducer status, international normalized ratio target range and the presence of CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), VKORC1 (rs9923231) and CYP4F2 (rs2108622). The coefficient of determination (R2) explained by the algorithm was 52.8% in the generation cohort and 64% in the validation cohort. The following R2 values were evaluated by pathology: atrial fibrillation, 57.4%; valve replacement, 56.3%; and venous thromboembolic disease, 51.5%. When the patients were classified into 3 dosage groups according to the stable dosage (<11 mg/week, 11–21 mg/week, >21 mg/week), the percentage of correctly classified patients was higher in the intermediate group, whereas differences between pharmacogenetic and clinical algorithms increased in the extreme dosage groups. Our algorithm could improve acenocoumarol dosage selection for patients who will begin treatment with this drug, especially in extreme-dosage patients. The predictability of the pharmacogenetic algorithm did not vary significantly between diseases.
Transplant International | 2014
Gonzalo N. Almeida-Paulo; R. Lubomirov; Nazareth Laura Alonso-Sanchez; Laura Espinosa-Román; Carlota Fernández Camblor; Carmen Díaz; Gema Muñoz Bartola; Antonio J. Carcas-Sansuán
To develop limited sampling strategies (LSSs) to predict total tacrolimus exposure (AUC0‐24) after the administration of Advagraf® and Prograf® (Astellas Pharma S.A, Madrid, Spain) to pediatric patients with stable liver or kidney transplants. Forty‐one pharmacokinetic profiles were obtained after Prograf® and Advagraf® administration. LSSs predicting AUC0‐24 were developed by linear regression using three extraction time points. Selection of the most accurate LSS was made based on the r2, mean error, and mean absolute error. All selected LSSs had higher correlation with AUC0‐24 than the correlation found between C0 and AUC0‐24. Best LSS for Prograf® in liver transplants was C0_1.5_4 (r2 = 0.939) and for kidney transplants C0_1_3 (r2 = 0.925). For Advagraf®, the best LSS in liver transplants was C0_1_2.5 (r2 = 0.938) and for kidney transplants was C0_0.5_4 (r2 = 0.931). Excluding transplant type variable, the best LSS for Prograf® is C0‐1‐3 (r2 = 0.920) and the best LSS for Advagraf® was C0_0.5_4 (r2 = 0.926). Considering transplant type irrespective of the formulation used, the best LSS for liver transplants was C0_2_3 (r2 = 0.913) and for kidney transplants was C0_0.5_4 (r2 = 0.898). Best LSS, considering all data together, was C0_1_4 (r2 = 0.898). We developed several LSSs to predict AUC0‐24 for tacrolimus in children and adolescents with kidney or liver transplants after Prograf® and/or Advagraf® treatment.
Pharmacogenomics | 2017
Hoi Y. Tong; Alberto M. Borobia; José Carlos Martínez Ávila; R. Lubomirov; Mario Muñoz; María J. Blanco Bañares; Rafael Mas Hernández; Carmen Fernández Capitán; Elena Ramírez; Jesús Frías; Antonio J. Carcas
AIM To evaluate the influence of two variants of P450 oxidoreductase (POR), rs2868177 and POR*28, on the stable dosage of acenocoumarol. PATIENTS & METHODS For this observational, cross-sectional study, patients were undergone stable anticoagulant treatment with acenocoumarol. Univariate and multiple regression analyses were performed to assess the influence of POR polymorphisms. RESULTS About 340 patients were enrolled. Multiple regression had a coefficient of determination (R2) of 51.5% and an Akaike information criterion of 234.22. The inclusion of POR*28 polymorphisms increased the R2 to 52.0% and reduced the Akaike information criteria to 230.58. The POR*28 heterozygote showed statistical significance in the algorithm. CONCLUSION The POR*28 heterozygote appears to be associated with the stable dose of acenocoumarol, but its clinical contribution to the prediction of the dosing of this drug is minimal.
Clinical Therapeutics | 2013
G.N. Almeida-Paulo; R. Lubomirov; N.L. Alonso-Sanchez; L. Espinosa; A.J. Carcas
e56 Volume 35 Number 8S therapeutic range (2.0–3.0 + 0.2), encompassing a period of at least 2 weeks and with a maximum difference between the mean daily dosages of 25%. The association between genotypes and time-toachieve stability was evaluated using survival analysis techniques. A Cox proportional hazard model was used to assess the relative risk of achieving a first period of stability in the follow-up period. Results: Results showed that time-to-achieve treatment stability with acenocoumarol is decreased significantly for carriers of ABCB1 c.3435TT genotype and extended in wild-type and heterozygous subjects (HR, 2.94 [IC 1.22–7.09]). Similarly, carriers of ABCB1 c.2677GT or TT genotypes reached more rapidly stability than wild-type subjects (HR, 2.15 [IC, 1.07–4.98] and HR, 3.00 [IC, 1.08–8.36], respectively). The other tested polymorphisms (CYP2C9, CYP2C19 and VKORC1) had no influence on the time-to-achievestability. Conclusion: Our results suggest for the first time that time-to-achieve stability with acenocoumarol is shorter to reach in carriers of ABCB1 c.3435TT and carriers of ABCB1 c.2677GT/TT combined. Further studies are required to assess whether the identification of ABCB1 genotypes before treatment with acenocoumarol may be useful for a safer and rapid anticoagulation stabilization. Disclosure of Interest: None declared.
Clinical Therapeutics | 2015
H.Y. Tong; Alberto M. Borobia; R. Lubomirov; R. hernández; M. Muñoz; Elena Ramírez; Jesús Frías; Antonio J. Carcas
Clinical Therapeutics | 2015
B. Duque; Alberto M. Borobia; R. Lubomirov; Elena Ramírez; Pedro Guerra; Nicolás Medrano; H.Y. Tong; Antonio J. Carcas; Jesús Frías
Clinical Therapeutics | 2015
B. Duque; Alberto M. Borobia; R. Lubomirov; Elena Ramírez; Pedro Guerra; Nicolás Medrano; H.Y. Tong; Antonio J. Carcas; Jesús Frías
Clinical Therapeutics | 2015
Alberto M. Borobia; R. Lubomirov; H.Y. Tong; P. Arias; J. Tenorio; Jesús Frías; P. Lapunzina; Antonio J. Carcas
Clinical Therapeutics | 2015
Alberto M. Borobia; R. Lubomirov; F. Abad; H.Y. Tong; Elena Ramírez; Jesús Frías; Antonio J. Carcas