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.

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 patient.

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.

Population pharmacokinetic model of irinotecan and its metabolites in patients with metastatic colorectal cancer.

PURPOSE: Irinotecan (CPT-11) is a drug used against a wide range of tumor types. The individualized dosing of CPT-11 is essential to ensure optimal pharmacotherapy in cancer patients, given the wide interindividual pharmacokinetic variability of this drug and its active metabolite SN-38. Moreover, the reabsorption from SN-38-G to SN-38, by enterohepatic recirculation, is critical due to its influence in the treatment tolerance. The aim of this research was to build a joint population pharmacokinetic model for CPT-11 and its metabolites (SN-38, and its glucuronide, SN-38-G) that enabled an individualized posology adjustment. METHODS: We used data of 53 treatment cycles of FOLFIRINOX scheme corresponding to 20 patients with metastatic colorectal cancer. In order to build the population pharmacokinetic model, we implemented parametric and non-parametric methods using the Pmetrics library package for R. We also built multivariate regression models to predict the area under the curve and the maximum concentration using basal covariates. RESULTS: The final model was a multicompartmental model which represented the transformations from CPT-11 to its active metabolite SN-38 and from SN-38 to inactive SN-38-G. Besides, the model also represented the extensive elimination of SN-38-G and the reconversion of the remaining SN-38-G to SN-38 by enterohepatic recirculation. We carried out internal validation with 1000 simulations. The regression models predicted the PK parameters with R squared adjusted up to 0.9499. CONCLUSION: CPT-11, SN-38, and SN-38-G can be correctly described by the multicompartmental model presented in this work. As far as we know, it is the first time that a joint model for CPT-11, SN-38, and SN-38-G that includes the process of reconversion from SN-38-G to SN-38 is characterized.

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.

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.

Hematological response of topotecan in tumor-bearing rats: modeling of the time course of different cellular populations.

PURPOSE: To evaluate the hematotoxicity of topotecan (TPT) in tumor-bearing rats by a pharmacokinetic/pharmacodynamic approach. METHODS: DHD/K12-PROb cells were subcutaneously injected in syngenic BD-IX rats. Three weeks after implantation of cells, animals received saline or 6 mg/kg i.p. dose of TPT (group II). Thirty days later, group II was divided into groups IIA receiving a single administration of 6 mg/kg and IIB treated with 3 mg/kg for 2 consecutive days. Leukocytes, neutrophils, and mature lymphocytes were measured in peripheral blood every 48 h for 45 days after first drug administration. Pharmacokinetic characteristics of TPT were also explored. RESULTS: Disposition of TPT in plasma was best described with a two-compartment model. A semiphysiological model discriminating between system-related and drug-effects parameters, such as the mean cell maturation or transition time (MTT) and the linear concentration-dependent inhibitory effects on cell proliferation (Slp), described adequately the time course of hematotoxicity. The estimates of MTT and Slp for the three cell populations ranged from 1.89 to 2.18 days and from 0.01 to 0.039 ml/ng, respectively. CONCLUSION: The time course of the hematotoxicity induced after two cycles of chemotherapy with TPT in tumor-bearing rats could be described by a semiphysiological model.

Pharmacokinetic/pharmacodynamic modeling of the antinociceptive effects of (+)-tramadol in the rat: role of cytochrome P450 2D activity.

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.