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

Therapeutic drug monitoring of neoadjuvant mFOLFIRINOX in resected pancreatic ductal adenocarcinoma

BACKGROUND

Despite a potentially curative treatment, the prognosis after upfront surgery and adjuvant chemotherapy for patients with resectable pancreatic ductal adenocarcinoma (PDAC) is poor. Modified FOLFIRINOX (mFOLFIRINOX) is a cornerstone in the systemic treatment of PDAC, including the neoadjuvant setting. Pharmacokinetic-guided (PKG) dosing has demonstrated beneficial effects in other tumors, but scarce data is available in pancreatic cancer.

METHODS

Forty-six patients with resected PDAC after mFOLFIRINOX neoadjuvant approach and included in an institutional protocol for anticancer drug monitoring were retrospectively analyzed. 5-Fluorouracil (5-FU) dosage was adjusted throughout neoadjuvant treatment according to pharmacokinetic parameters and Irinotecan (CPT-11) pharmacokinetic variables were retrospectively estimated.

RESULTS

By exploratory univariate analyses, a significantly longer progression-free survival was observed for patients with either 5-FU area under the curve (AUC) above 28 mcg·h/mL or CPT-11 AUC values below 10 mcg·h/mL. In the multivariate analyses adjusted by age, gender, performance status and resectability after stratification according to both pharmacokinetic parameters, the risk of progression was significantly reduced in patients with 5-FU AUC ≥28 mcg·h/mL [HR = 0.251, 95% CI 0.096-0.656; p = 0.005] and CPT-11 AUC <10 mcg·h/mL [HR = 0.189, 95% CI 0.073-0.486, p = 0.001].

CONCLUSIONS

Pharmacokinetically-guided dose adjustment of standard chemotherapy treatments might improve survival outcomes in patients with pancreatic ductal adenocarcinoma.

Population pharmacokinetic analysis of TQ-B3203 following intravenous administration of TQ-B3203 liposome injection in Chinese patients with advanced solid tumors

Background: TQ-B3203 is a novel topoisomerase I inhibitor currently in development for the treatment of advanced solid tumors. Great differences in pharmacokinetic characteristics were found among individuals according to the phase I clinical trial following intravenous administration of TQ-B3203 liposome injection (TLI) in Chinese patients with advanced solid tumors. Thus, it is significant to establish a population pharmacokinetic model to find the key factors and recognize their effect on pharmacokinetic parameters in order to guide individualized administration. Methods: Non-linear mixed effect models were developed using the plasma concentrations obtained from the phase I clinical trial by implementing the Phoenix NLME program. Covariates that may be related to pharmacokinetics were screened using stepwise methods. The final model was validated by goodness-of-fit plots, visual predictive check, non-parametric bootstrap and a test of normalized prediction distribution errors. Results: A three-compartment model with first-order elimination was selected as the best structural model to describe TQ-B3203 disposition adequately. Direct bilirubin (DBIL) and body mass index (BMI) were the two most influential factors on clearance, while lean body weight (LBW) was considered to affect the apparent distribution volume of the central compartment. The population estimations of clearance and central volume were typical at 3.97 L/h and 4.81 L, respectively. Model-based simulations indicated that LBW had a great impact on C_max, BMI exerted a considerable influence on AUC_0-t, and the significance of DBIL on both AUC_0-t and C_max was similarly excellent. Conclusion: The first robust population pharmacokinetic model of TQ-B3203 was successfully generated following intravenous administration of TLI in Chinese patients with advanced solid tumors. BMI, LBW and DBIL were significant covariates that affected the pharmacokinetics of TQ-B3203. This model could provide references for the dose regimen in the future study of TLI.

On the population pharmacokinetics and the enterohepatic recirculation of hyzetimibe and its main metabolite in Chinese healthy subjects

BACKGROUNDS

Hyzetimibe (HS-25), a new drug approved for hypercholesterolemia, exhibits obvious enterohepatic recirculation (EHC) after oral administration. Up to now, little is known about the kinetics of HS-25. Therefore, we performed this population pharmacokinetic (PopPK) analysis aiming to describe the PK behavior of HS-25 and its main metabolite (M1), and to identify significant covariates contributing to the variability.

METHODS

The plasma concentration data used for modeling were obtained from an open-label, single-dose, randomized, two-period crossover bioequivalence study. PopPK modeling was performed with NONMEM 7.4.1 using nonlinear mixed effect modeling approach. Goodness of fit plots, bootstrap and visual predictive check were used for model internal validation. Data from another study was used for external validation.

RESULTS

Data from 16 male and 8 female subjects were used in the PopPK analysis. HS-25 and M1 concentrations in the modeling cohort were well described by a one-compartment model incorporating first-pass metabolism and a gallbladder compartment, accounting for the EHC process. The release kinetic of gall was mimicked by a first-order constant plus a switch on/off effect. Body weight was identified as a significant covariate effecting on the clearance and apparent distribution volume of HS-25, as well as k_mg , the transfer rate from metabolite compartment to gallbladder compartment. Internal and external validation demonstrated an acceptable predictive ability of the final model.

CONCLUSIONS

We present the first PopPK model describing HS-25 and M1 concentrations simultaneously, with the EHC process considered. The modeling and simulation results could provide reference for the clinical use of HS-25.

Integration of DNA sequencing with population pharmacokinetics to improve the prediction of irinotecan exposure in cancer patients

BACKGROUND

Irinotecan (CPT-11) is an anticancer agent widely used to treat adult solid tumours. Large interindividual variability in the clearance of irinotecan and SN-38, its active and toxic metabolite, results in highly unpredictable toxicity.

METHODS

In 217 cancer patients treated with intravenous irinotecan single agent or in combination, germline DNA was used to interrogate the variation in 84 genes by next-generation sequencing. A stepwise analytical framework including a population pharmacokinetic model with SNP- and gene-based testing was used to identify demographic/clinical/genetic factors that influence the clearance of irinotecan and SN-38.

RESULTS

Irinotecan clearance was influenced by rs4149057 in SLCO1B1, body surface area, and co-administration of 5-fluorouracil/leucovorin/bevacizumab. SN-38 clearance was influenced by rs887829 in UGT1A1, pre-treatment total bilirubin, and EGFR rare variant burden. Within each UGT1A1 genotype group, elevated pre-treatment total bilirubin and/or presence of at least one rare variant in EGFR resulted in significantly lower SN-38 clearance. The model reduced the interindividual variability in irinotecan clearance from 38 to 34% and SN-38 clearance from 49 to 32%.

CONCLUSIONS

This new model significantly reduced the interindividual variability in the clearance of irinotecan and SN-38. New genetic factors of variability in clearance have been identified.

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.