Sayar, O. (Onintza)
- Publications
- item.page.relationships.isContributorAdvisorOfPublication
- item.page.relationships.isContributorOfPublication
6 results
Search Results
Now showing 1 - 6 of 6
- Omics approaches in pancreatic adenocarcinoma(MDPI AG, 2019) Sayar, O. (Onintza); García, E. (Estefania); Arévalo, S. (Sara); Viudez, A. (Antonio); Zarate, R. (Ruth); Gonzalez, I. (Iranzu); Hernández-García, I. (Irene); Fernandez-Irigoyen, J. (Joaquín); Goñi, S. (Saioa); Arrazubi, V. (Virginia); Sala-Elarre, P. (Pablo); Pérez-Sanz, J. (Jairo); Oyaga-Iriarte, E. (Esther); Santamaria, E. (Enrique); Vera, R. (Ruth)Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients.
- Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters(Elsevier BV, 2019) Sayar, O. (Onintza); Aldaz, A. (Azucena); Insausti, A. (Asier); Oyaga-Iriarte, E. (Esther)Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patient
- Pharmacokinetic/pharmacodynamic modeling of the antinociceptive effects of (+)-tramadol in the rat: role of cytochrome P450 2D activity(American Society for Pharmacology and Experimental Therapeutics, 2003) Sayar, O. (Onintza); Troconiz, I.F. (Iñaki F.); Rapado, J. (Javier); Renedo, M.J. (María Jesús); Dios-Vieitez, M.C. (M. Carmen); Segura, C. (Cristina); Garrido, M.J. (María Jesús)In this study the role of cytochrome P450 2D (CYP2D) in the pharmacokinetic/pharmacodynamic relationship of (+)-tramadol [(+)-T] has been explored in rats. Male Wistar rats were infused with (+)-T in the absence of and during pretreatment with a reversible CYP2D inhibitor quinine (Q), determining plasma concentrations of Q, (+)-T, and (+)-O-demethyltramadol [(+)-M1], and measuring antinociception. Pharmacokinetics of (+)-M1, but not (+)-T, was affected by Q pretreatment: early after the start of (+)-T infusion, levels of (+)-M1 were significantly lower (P < 0.05). However, at later times during Q infusion those levels increased continuously, exceeding the values found in animals that did not receive the inhibitor. These results suggest that CYP2D is involved in the formation and elimination of (+)-M1. In fact, results from another experiment where (+)-M1 was given in the presence and in absence of Q showed that (+)-M1 elimination clearance (CL(ME0)) was significantly lower (P < 0.05) in animals receiving Q. Inhibition of both (+)-M1 formation clearance (CL(M10)) and CL(ME0) were modeled by an inhibitory E(MAX) model, and the estimates (relative standard error) of the maximum degree of inhibition (E(MAX)) and IC(50), plasma concentration of Q eliciting half of E(MAX) for CL(M10) and CL(ME0), were 0.94 (0.04), 97 (0.51) ng/ml, and 48 (0.42) ng/ml, respectively. The modeling of the time course of antinociception showed that the contribution of (+)-T was negligible and (+)-M1 was responsible for the observed effects, which depend linearly on (+)-M1 effect site concentrations. Therefore, the CYP2D activity is a major determinant of the antinociception elicited after (+)-T administration.
- Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites(University of Alberta Libraries, 2019) Sayar, O. (Onintza); Bueno, L. (Lorea); Aldaz, A. (Azucena); Insausti, A. (Asier); Oyaga-Iriarte, E. (Esther)Purpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases.
- Predicting severe haematological toxicity in gastrointestinal cancer patients undergoing 5-FU-based chemotherapy: A bayesian network approach(MDPI AG, 2023) Sayar, O. (Onintza); Aldaz, A. (Azucena); Ruiz-Sarrias, O. (Oskitz); Zumárraga-Lizundia, T. (Teresa); Gónzalez-Deza, C. (Cristina); Arrizibita-Iriarte, O. (Olast); Vizcay-Atienza, A. (Angel); Rodríguez-Rodríguez, J. (Javier)Purpose: Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions. Methods: We utilized a dataset of 267 gastrointestinal cancer patients, conducting preprocessing, and splitting it into TRAIN and TEST sets (80%:20% ratio). The RandomForest algorithm assessed variable importance based on MeanDecreaseGini coefficient. The bnlearn R library helped design a Bayesian network model using a 10-fold cross-validation on the TRAIN set and the aic-cg method for network structure optimization. The model’s performance was gauged based on accuracy, sensitivity, and specificity, using cross-validation on the TRAIN set and independent validation on the TEST set. Results: The model demonstrated satisfactory performance with an average accuracy of 0.85 (±0.05) and 0.80 on TRAIN and TEST datasets, respectively. The sensitivity and specificity were 0.82 (±0.14) and 0.87 (±0.07) for the TRAIN dataset, and 0.71 and 0.83 for the TEST dataset, respectively. A user-friendly tool was developed for clinical implementation. Conclusions: Despite several limitations, our Bayesian network model demonstrated a high level of accuracy in predicting the risk of developing severe haematological toxicity in gastrointestinal cancer patients receiving 5-FU-based chemotherapy. Future research should aim at model validation in larger cohorts of patients and different clinical settings.
- Phase I and pharmacokinetic study of gemcitabine administered at fixed-dose rate, combined with docetaxel/melphalan/carboplatin, with autologous hematopoietic progenitor-cell support, in patients with advanced refractory tumors(Elservier, 2007) Nieto, Y. (Yago); Aldaz, A. (Azucena); Rifon, J. J. (Jose J.); Perez-Calvo, J. (Javier); Zafra, A. (Ana); Zufía, L. (Laura); Viudez, A. (Antonio); Viteri, S. (S.); Aramendia, J.M. (José Manuel); Aristu, J. (Javier); Centeno, C. (Carlos); Moreno, M. (Marta); Sayar, O. (Onintza); Hernandez, M. (Milagros)The purpose of this trial was to define the maximum tolerated duration (MTD), dose-limiting toxicity (DLT), regimen-related toxicities (RRT), and pharmacokinetics of gemcitabine infused at a fixed dose rate (FDR) of 10 mg/m2/min, combined with docetaxel/melphalan/carboplatin, using autologous stem cell transplantation (ASCT). The duration of gemcitabine infusion was incrementally escalated as a single treatment on day −6 or as 4 daily infusions on days −5 to −2. Gemcitabine was followed by docetaxel (300 or 350 mg/m2) on day −5, and then melphalan (50 mg/m2/day) and carboplatin (333 mg/m2/day) on days −4 to −2. Fifty-two patients with refractory tumors were accrued with a median age of 40 (range: 6-66), a median of 3 (1-6) prior chemotherapy regimens, and 3 (1-7) organs involved. The gemcitabine MTD was defined at 20 hours (total dose 12,000 mg/m2) on both schedules. The DLT was enteritis. Three patients died from aspiration, catheter-related sepsis, and enteritis, respectively. The tumor response rate was 91%, with 50% complete responses. At current 2-year median follow-up, the event-free and overall survival (EFS, OS) rates are 54% (median 26 months) and 79% (median not reached), respectively. Gemcitabine area under the curve (AUC), but not clearance, increased linearly with infusion duration, and correlated with grade 3 RRT. Docetaxel showed a linear increase of its AUC and similar clearance compared with prior reports at lower doses. In conclusion, ASCT-supported infusions of gemcitabine at FDR could be prolonged up to 20 hours. The resulting gemcitabine/docetaxel/melphalan/carboplatin combination was highly active in refractory cancers and should be further tested in disease-specific trials.