Soluciones solitón y aplicación a las proteínas

Podemos encontrar en la naturaleza dos tipos de ondas. Por una lado est an las ondas lineales y por otro lado las ondas no lineales. Tradicionalmente, hablamos de las ondas lineales, que son las m as familia- res, las que estamos m as acostumbrados a encontrarnos en el d a a d a, y las que llevamos estudiando desde hace mucho tiempo. Entre ellas encontramos las ondas de la luz y las del sonido, por ejemplo. Estas ondas tienen, sea cual sea su forma, velocidad, amplitud y longitud de onda constantes. Asimismo, obedecen al principio de superposici on. Por otro lado, en este trabajo, destacaremos las ondas no lineales, que son menos familiares que las anteriores comentadas, pero no por ello menos im- portantes. Este tipo de ondas son muy diferentes a las lineales, ya que en ellas la amplitud, la longitud de onda y la velocidad no son constantes. Entre los ejemplos donde las encontramos, destacamos una ola en el mar aproxi- mandose a la orilla. Vemos que la distancia entre las crestas va decreciendo, la velocidad cambia y la altura de la ola va creciendo conforme va percibien- do el fondo; llegando a un punto en el que la ola se rompe ya que la parte superior se ha adelantado demasiado a la inferior. Con respecto a esta parte de la ciencia, la Matem atica y F sica No Lineal, cabe destacar sus grandes avances en la segunda mitad del siglo XX con la Teor a de Solitones, punto en el que centraremos el tema de este trabajo. En primer lugar daremos una de nici on sencilla de solit on: los solitones son ondas no lineales que exhiben un comportamiento extremadamente inespe- rado e interesante, son ondas solitarias que se propagan sin deformarse. De ah que su nombre derive de onda solitari

Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

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

Omics approaches in pancreatic adenocarcinoma

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

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations

Soluciones solitón y aplicación a las proteínas

Podemos encontrar en la naturaleza dos tipos de ondas. Por una lado est an las ondas lineales y por otro lado las ondas no lineales. Tradicionalmente, hablamos de las ondas lineales, que son las m as familia- res, las que estamos m as acostumbrados a encontrarnos en el d a a d a, y las que llevamos estudiando desde hace mucho tiempo. Entre ellas encontramos las ondas de la luz y las del sonido, por ejemplo. Estas ondas tienen, sea cual sea su forma, velocidad, amplitud y longitud de onda constantes. Asimismo, obedecen al principio de superposici on. Por otro lado, en este trabajo, destacaremos las ondas no lineales, que son menos familiares que las anteriores comentadas, pero no por ello menos im- portantes. Este tipo de ondas son muy diferentes a las lineales, ya que en ellas la amplitud, la longitud de onda y la velocidad no son constantes. Entre los ejemplos donde las encontramos, destacamos una ola en el mar aproxi- mandose a la orilla. Vemos que la distancia entre las crestas va decreciendo, la velocidad cambia y la altura de la ola va creciendo conforme va percibien- do el fondo; llegando a un punto en el que la ola se rompe ya que la parte superior se ha adelantado demasiado a la inferior. Con respecto a esta parte de la ciencia, la Matem atica y F sica No Lineal, cabe destacar sus grandes avances en la segunda mitad del siglo XX con la Teor a de Solitones, punto en el que centraremos el tema de este trabajo. En primer lugar daremos una de nici on sencilla de solit on: los solitones son ondas no lineales que exhiben un comportamiento extremadamente inespe- rado e interesante, son ondas solitarias que se propagan sin deformarse. De ah que su nombre derive de onda solitari

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

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 patien

Optimización de tratamientos con irinotecan y capecitabina en pacientes con cáncer colorrectar basada en técnicas de farmacocinética e inteligencia artificial

Introduction: Colorectal cancer is the fourth cancer with the highest incidence and the second with the highest mortality rate, according to 2018 data. Adenocarcinoma is the fundamental form in which it occurs, occupying 90% of the cases. The drugs used to combat this disease are numerous and have narrow therapeutic ranges and strong adverse effects. Controlling plasma levels is essential to achieve optimal pharmacotherapy. Irinotecan and capecitabine have been the drugs analyzed in this work. These drugs are administered both in monotherapy and in combination, in schemes such as FOLFIRINOX or FOLFIRI for irinotecan and XELOX for capecitabine. Both drugs have an active metabolite responsible for the therapeutic action of the drug. These metabolites, in addition to the treatment effect, are responsible for a large part of the toxicities derived from the treatment, hence, their correct characterization is pharmacologically relevant. Hypothesis: The prevalence of colorectal cancer and the necessity to increase positive results in health generates the need to incorporate new tools, such as those included in the framework of artificial intelligence, which, together with other more classic ones such as pharmacokinetic and pharmacodynamic modeling, make it possible to facilitate optimal use of chemotherapy in routine care practice. Results: A compartmental pharmacokinetic model has been obtained for each of the drugs and their corresponding metabolites, making use of parametric and non-parametric methodologies. The precision indices of these models reach values of R2=0.964 for the case of irinotecan and R2=0.886 for capecitabine. Moreover, the capecitabine model has permitted to obtain optimal sampling times for this drug. On the other hand, models based on machine learning have been developed to predict irinotecan derived toxicities such as late diarrhea, leukopenia and neutropenia with success rates of 91, 76 and 75%, respectively. These models are based both on particular characteristics of each patient and on values of their pharmacokinetic parameters. Finally, the proposed models for irinotecan have been integrated in a software enabling their correct use in clinical practice. Conclusions: The kinetics of irinotecan and capecitabine and their corresponding metabolites have been correctly characterized, allowing an individualized adjustment of the concentrations over time of each of the patients. Moreover, in the case of irinotecan, the reconversion from the glucuronide metabolite to the active metabolite, due to enterohepatic reabsorption, has been characterized for the first time in the literature, which is a fundamental feature of this drug. The models based on artificial intelligence allow to correctly predict the possibility for a new patient to develop late diarrhea, leukopenia and neutropenia, by means of particular characteristics of that patient. A strong relationship between pharmacokinetic parameters and the studied toxicities for irinotecan has been demonstrated, for the areas under the plasma curves and the maximum concentrations of this drug are linked with the degree of toxicity. The developed software permits to apply the results obtained in this thesis in clinical practice

Optimización de tratamientos con irinotecan y capecitabina en pacientes con cáncer colorrectar basada en técnicas de farmacocinética e inteligencia artificial

Introduction: Colorectal cancer is the fourth cancer with the highest incidence and the second with the highest mortality rate, according to 2018 data. Adenocarcinoma is the fundamental form in which it occurs, occupying 90% of the cases. The drugs used to combat this disease are numerous and have narrow therapeutic ranges and strong adverse effects. Controlling plasma levels is essential to achieve optimal pharmacotherapy. Irinotecan and capecitabine have been the drugs analyzed in this work. These drugs are administered both in monotherapy and in combination, in schemes such as FOLFIRINOX or FOLFIRI for irinotecan and XELOX for capecitabine. Both drugs have an active metabolite responsible for the therapeutic action of the drug. These metabolites, in addition to the treatment effect, are responsible for a large part of the toxicities derived from the treatment, hence, their correct characterization is pharmacologically relevant. Hypothesis: The prevalence of colorectal cancer and the necessity to increase positive results in health generates the need to incorporate new tools, such as those included in the framework of artificial intelligence, which, together with other more classic ones such as pharmacokinetic and pharmacodynamic modeling, make it possible to facilitate optimal use of chemotherapy in routine care practice. Results: A compartmental pharmacokinetic model has been obtained for each of the drugs and their corresponding metabolites, making use of parametric and non-parametric methodologies. The precision indices of these models reach values of R2=0.964 for the case of irinotecan and R2=0.886 for capecitabine. Moreover, the capecitabine model has permitted to obtain optimal sampling times for this drug. On the other hand, models based on machine learning have been developed to predict irinotecan derived toxicities such as late diarrhea, leukopenia and neutropenia with success rates of 91, 76 and 75%, respectively. These models are based both on particular characteristics of each patient and on values of their pharmacokinetic parameters. Finally, the proposed models for irinotecan have been integrated in a software enabling their correct use in clinical practice. Conclusions: The kinetics of irinotecan and capecitabine and their corresponding metabolites have been correctly characterized, allowing an individualized adjustment of the concentrations over time of each of the patients. Moreover, in the case of irinotecan, the reconversion from the glucuronide metabolite to the active metabolite, due to enterohepatic reabsorption, has been characterized for the first time in the literature, which is a fundamental feature of this drug. The models based on artificial intelligence allow to correctly predict the possibility for a new patient to develop late diarrhea, leukopenia and neutropenia, by means of particular characteristics of that patient. A strong relationship between pharmacokinetic parameters and the studied toxicities for irinotecan has been demonstrated, for the areas under the plasma curves and the maximum concentrations of this drug are linked with the degree of toxicity. The developed software permits to apply the results obtained in this thesis in clinical practice