Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Subscribers
    • Advertisers
    • Editorial Board
    • Special Issues 2025
  • Journal Metrics
  • Other Publications
    • In Vivo
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
    • 2008 Nobel Laureates
  • About Us
    • General Policy
    • Contact
  • Other Publications
    • Anticancer Research
    • In Vivo
    • Cancer Genomics & Proteomics

User menu

  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Anticancer Research
  • Other Publications
    • Anticancer Research
    • In Vivo
    • Cancer Genomics & Proteomics
  • Register
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Anticancer Research

Advanced Search

  • Home
  • Current Issue
  • Archive
  • Info for
    • Authors
    • Editorial Policies
    • Subscribers
    • Advertisers
    • Editorial Board
    • Special Issues 2025
  • Journal Metrics
  • Other Publications
    • In Vivo
    • Cancer Genomics & Proteomics
    • Cancer Diagnosis & Prognosis
  • More
    • IIAR
    • Conferences
    • 2008 Nobel Laureates
  • About Us
    • General Policy
    • Contact
  • Visit us on Facebook
  • Follow us on Linkedin
Review ArticleReviews

Application of Pharmacometrics of 5-Fluorouracil to Personalized Medicine: A Tool for Predicting Pharmacokinetic–Pharmacodynamic/Toxicodynamic Responses

SHINJI KOBUCHI and YUKAKO ITO
Anticancer Research December 2020, 40 (12) 6585-6597; DOI: https://doi.org/10.21873/anticanres.14683
SHINJI KOBUCHI
Department of Pharmacokinetics, Kyoto Pharmaceutical University, Kyoto, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: kobuchi@mb.kyoto-phu.ac.jp
YUKAKO ITO
Department of Pharmacokinetics, Kyoto Pharmaceutical University, Kyoto, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Recently, therapeutic drug monitoring of 5-fluorouracil (5-FU), the key chemotherapeutic drug for colorectal cancer, has been applied in daily clinical practice and has contributed towards improving clinical outcomes. However, current dose modifications are based only on values of the area under the plasma concentration–time profile, which are simply calculated from plasma 5-FU concentrations and infusion periods. When dose-limiting toxicities occur, the dosing is empirically reduced or discontinued, leading to treatment failure. To prevent this predictable failure and obtain better clinical outcomes, rational dosage-based strategies are required for 5-FU. Combining therapeutic drug monitoring with a mathematical approach using a pharmacokinetic– pharmacodynamic/toxicodynamic model is expected to help simulate time-course profiles of the efficacy of drugs and the degree of toxicity, thereby contributing towards dose setting for individual patients. Therefore, to facilitate pharmacometric modelling and simulation techniques for optimising current oncology therapies, this review focuses on pharmacometrics approaches for personalizing 5-FU-based chemotherapy.

Key Words:
  • Modelling and simulation
  • chronopharmacokinetics
  • translational research
  • cancer chemotherapy
  • therapeutic drug monitoring
  • 5-FU prodrug
  • pharmacometrics
  • review

5-Fluorouracil (5-FU) has been used to treat patients for approximately 60 years and remains a cornerstone of colorectal cancer chemotherapy. The mechanism underlying the pharmacodynamic (PD) action of 5-FU involves its conversion to fluoro-deoxyuridine monophosphate (FdUMP), which subsequently inhibits thymidylate synthase via the formation of a ternary complex comprising FdUMP, thymidylate synthase, and 5,10-methylene tetrahydrofolate in tumour cells, consequently inhibiting DNA synthesis. 5-FU is also incorporated into RNA and prevents protein synthesis (1). After it reaches the blood circulation, over 80% of administered 5-FU is metabolized by dihydropyrimidine dehydrogenase (DPD), which is a rate-limiting enzyme, in the liver (2, 3).

An appropriate 5-FU administration schedule has been developed to improve clinical responses in the clinical oncology setting. A meta-analysis showed that the infusion schedule of 5-FU is superior to the bolus administration schedule with respect to response rate and overall survival (4). Currently, bolus plus long-term infusion schedules, such as the folinic acid/5-FU/irinotecan (FOLFIRI) and the folinic acid/5-FU/oxaliplatin (FOLFOX) regimens, are standard approaches for treating adjuvant or metastatic colorectal cancer. However, infusion regimens involve long hospital stays for patients and require catheterization. To overcome these shortcomings of infusion therapy, orally available prodrugs of 5-FU have been developed, such as capecitabine, tegafur/gimeracil/oteracil (S-1), and tegafur/uracil (UFT). The capecitabine plus oxaliplatin (XELOX), S-1 plus oxaliplatin, and UFT plus leucovorin regimens are accepted for clinical chemotherapy of colorectal cancer.

Various administration schedules have been developed for 5-FU-based chemotherapy; however, there are large inter- and intra-individual pharmacokinetic (PK) variabilities which are important contributors to clinical treatment failure, and the method for determining the optimal 5-FU dose is debatable. The standard approach for 5-FU dose determination is based on the body surface area (BSA); however, this approach leads to large, approximately 100-fold variability, inter-individual variability in plasma 5-FU level (5-7). Many clinical studies have shown that PK-guided dose adjustments of 5-FU can improve clinical efficacies and reduce toxicity (8-10). Therapeutic drug monitoring (TDM) of the plasma 5-FU level is recommended for personalization of 5-FU dosing to obtain adequate systemic exposure, leading to improved clinical efficacy and lesser adverse effects. In 2018, the academic members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology reviewed the use of TDM for 5-FU infusion and strongly recommended TDM of 5-FU in clinical practice (11). While TDM of 5-FU is an important tool for determining the optimal dose for individual patients and obtaining appropriate systemic exposure, additional challenges still remain for achieving personalized 5-FU-based chemotherapy and further improved clinical outcomes. During dose adjustments by TDM, for instance, the 5-FU dose in the first cycle must be determined on the basis of BSA, whereas the doses in all subsequent cycles are adjusted on the basis of the plasma 5-FU level achieved. Therefore, the optimum target concentration can only be achieved after some chemotherapy cycles are completed. Moreover, several patient characteristics (e.g. sex, age, body weight, tumour type, cancer stage, DPD phenotype and activity level, and co-administered drugs) and circadian fluctuations of plasma 5-FU concentration make it difficult to adequately estimate drug exposure and to determine individual dose setting during TDM. When dose-limiting toxicities are observed, empirical dose reduction or treatment discontinuation is required, which leads to treatment failure. Therefore, an approach that would provide the best quantitative prediction of drug exposure, therapeutic response, and toxicities on the basis of the plasma 5-FU concentration needs to be developed.

In recent years, the pharmacometrics approach has attracted widespread interest in the field of oncology for achieving personalized medicine in clinical practice (12). Pharmacometrics is defined as “the science of developing and applying mathematical and statistical methods to (a) characterize, understand, and predict a drug’s pharmacokinetic and pharmacodynamic behaviour; (b) quantify uncertainty of information about that behaviour; and (c) rationalize data-driven decision making in the drug development process and pharmacotherapy” (13). Pharmacometric modelling and simulation techniques can help understand the relationship between drug exposure and the subsequent effects, which is particularly important for determining the appropriate dose setting for each patient (14). Almost all anticancer agents have narrow therapeutic windows, and there are large inter- and intra-individual variabilities in drug exposure. A balance between drug efficacy and adverse events is required for obtaining the desired clinical outcomes, especially in the field of oncology. A mathematical method such as the use of PK– PD/toxicodynamic (PK-PD/TD) models as part of the pharmacometrics approach would be valuable for predicting the time course of profiles of plasma drug level and PD/TD responses such that the optimal dose schedule can be prescribed for the patient in order to maximize drug efficacy and minimize toxicity. Close collaboration between oncological physicians and pharmacometricians could lead to better prognosis on application of pharmacometrics in a clinical dose setting.

In this review, recent pharmacometrics approaches for the personalization of 5-FU-based chemotherapy in patients with cancer have been summarized. We have discussed pharmacometrics-related studies describing and simulating both 5-FU exposure and effects in various dose settings. This review focuses only on the PK-PD/TD model approach related to drug responses, including antitumor effects and dose-limiting toxicities, although there are many studies on classic compartmental PK models. Future perspectives on applying pharmacometrics to routinely collected clinical data and personalized medicine in order to optimize drug targeting are also discussed.

PK Model for Evaluating Circadian Variation

A circadian rhythm is observed in plasma 5-FU concentration during long-term infusion in clinical studies (15). Circadian fluctuations of plasma 5-FU level during constant infusion may be a major factor leading to incorrect PK and PD/TD estimation by the pharmacometrics approach; therefore, the current review also focused on the pharmacometrics approach in relation to the circadian rhythm of 5-FU PK. The circadian alterations in 5-FU PK might be derived from circadian variations in DPD activity and contribute to large inter- and intra-individual variations in plasma 5-FU concentration (16, 17). Conflicting results have been reported for the time points for peak and trough levels during the day (15). Harris et al. reported that plasma 5-FU levels obtained over a 24-h period reached peak values at 11:00 h and trough values at 23:00 h in patients who had cancer and were receiving continuous 5-FU infusion (300 mg m–2 d–1) (18). However, Metzger et al. reported that peak values were obtained at 04:00 h and trough values at 13:00 h in patients after 5-FU infusion (600 mg m–2 d–1) (19). Table I shows a previously reported PK model for evaluating and describing circadian changes in plasma 5-FU concentration. To the best of our knowledge, only one study has described a clinical PK model taking into account the circadian rhythm in plasma 5-FU concentration. Bressolle et al. defined the circadian model by the sum of two cosine cyclic components of 12- and 24-h periods and described circadian variations in 5-FU clearance (20). This circadian model was developed using 562 5-FU concentration datasets obtained for 65 patients and validated using another 104 datasets obtained for 20 patients. Analysis of this model revealed that the peak period for the plasma 5-FU concentration was approximately 04:00 h, consistent with previous results obtained by Metzger et al. (19). Moreover, the model was able to estimate 5-FU PK parameters for individual patients, thereby contributing to optimization of the dose regimen of each patient. On the basis of these findings, a chronomodulated chemotherapy regimen has been proposed (21-23). However, clinical application of chronomodulation of 5-FU dosing is limited in the current standard regimen, which may be attributable to difficulties in estimating the circadian rhythm of each patient.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table I.

Summary of pharmacokinetic (PK) model to describe circadian variations in plasma concentration of 5-fluorouracil (5-FU).

Various baseline characteristics in patients, including DPD activity level, may also affect circadian changes in plasma 5-FU levels. Recently, the circadian 5-FU patterns in animals were investigated using a PK model with the cosinor method (24-27) to exclude these potential contributing factors (Figure 1). In rats treated with continuous 5FU infusion (50 mg m–2 h–1) for 48 h, PK model analysis showed that the plasma 5-FU concentration followed a 24-h cosine circadian curve, representing an overall 1.8-fold increase from a nadir to a peak, with a relative amplitude (percentage of mesor) of 28% (27). Additionally, the loading bolus dose before initiating the infusion was found to contribute to circadian variations in plasma 5-FU level (27). These observations from animal studies suggest that in the recently modified regimen that omits bolus 5-FU injection, chronomodulation of dosing may enable sufficient clinical response with minimum toxicities and that timing of blood sampling during TDM procedures should be determined cautiously. These animal study findings indicate that further clinical evaluations using a PK model that takes into account circadian variations in plasma 5-FU level are required for deciding appropriate dosing schedules and blood sampling times in TDM.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Schematic of the pharmacokinetic (PK) model taking into account the circadian rhythm of 5-fluorouracil (5-FU) after continuous infusion or of 5-FU prodrugs. To determine the circadian variations in plasma 5-FU concentrations, the cosinor method is applied in the PK model and 5-FU clearance (CL) is defined using the cosine curve. These models were developed in animal studies. CAP: Capecitabine; CCAP/FT/5-FU: plasma concentration of CAP/tegafur/5-FU; CL: clearance of 5-FU; CLCAP: CL of CAP; CLD: intercompartmental clearance; CLFT: clearance of FT; CL5’-DFUR: CL of 5’-deoxy-5-fluorouridine; FT: tegafur; GI: gastrointestinal tract; Ka: absorption rate constant; Km: conversion rate constant of FT into 5-FU during first-pass metabolism; Ka2: conversion rate constant of the first pass metabolism of FT; UFT: FT/uracil; VCAP/FT/5-FU: distribution volume of CAP/FT/5-FU.

Similar to regimens proposed for continuous 5-FU infusion, chronomodulated regimens using orally available prodrugs of 5-FU, such as capecitabine and UFT, have been proposed for obtaining favourable antitumor effects because they can help avoid 5-FU elimination (28-31). Chronomodulation can easily be achieved in such regimens involving orally available drugs via drug self-administration by patients. However, there are conflicting results regarding the utility of chronomodulated chemotherapy involving oral 5-FU prodrugs. Pilancı et al. evaluated the usefulness of capecitabine morning and noon dosing as part of a first-line XELOX regimen in patients with metastatic colorectal cancer (31). Good clinical response with favourable toxicity profiles was obtained, indicating that chronomodulation of capecitabine dosing may provide a valuable therapeutic option. In contrast, Qvortrup et al. reported that the chronomodulated XELOX regimen did not improve clinical efficacy or reduce toxicity in their patients (29). A recent phase I study showed that there were no circadian variations in exposure to capecitabine and its metabolites (5’-deoxy-5-fluorocytidine and 5’-deoxy-5-fluorouridine), including 5-FU, after continuous chronomodulated administration (32).

To investigate the usability of chronomodulated regimens involving capecitabine, circadian rhythmicity in the PK (i.e. chronopharmacokinetics) of capecitabine and its metabolites was evaluated in rats by using a population PK model with the cosinor method (25). Significant circadian variations were observed in the plasma concentration profiles of capecitabine, 5’-deoxy-5-fluorocytidine and 5’-deoxy-5-fluorouridine, and 5-FU according to the dosing time. Similar to the model for capecitabine, a population PK model taking into account the circadian rhythm showed a circadian pattern of 5-FU clearance after UFT administration to rats (26); such a circadian pattern has not been clearly obtained in clinical studies (33, 34). These animal studies using PK models with circadian rhythm showed that circadian variations in the absorption of prodrugs and their sequential metabolism to 5-FU would also contribute to variations in plasma 5-FU level. These observations suggest that the administration time point for 5-FU prodrugs is a critical factor for achieving appropriate clinical outcomes in patients. A PK model that can help determine circadian variations by the cosinor method can provide evidence to support the development of a suitable dosing strategy for improving antitumor efficacy and minimizing severe toxicities.

PK-PD/TD Model of 5-FU

PK-PD model for antitumor effects. The PK-PD model for determining tumour size profiles after treatment with antitumor agents is a valuable tool for drug development and pre-clinical and clinical studies for establishing personalized medicine. Simeoni et al. successfully developed a PK-PD model of 5-FU for tumour growth dynamics in in vivo animal studies using xenograft models (35); this model is the standard model for evaluating and comparing the degree of antitumor effects in pre-clinical drug development. In this tumour growth model, an exponential tumour growth pattern is described; it assumes that the anticancer treatment inhibits the proliferation of some cells and eventually leads to their death. Inhibition of the tumour growth rate by 5-FU is described as a factor proportional to an index of 5-FU efficacy. A transit compartment model has been applied for describing the delayed antitumor effects of 5-FU; this model allows for prediction of time delay between drug administration and the observed effects. The tumour growth model has been modified according to the cell death mechanism noted after 5-FU exposure. On the basis of Simeoni et al.’s model (35), Sung et al. proposed new PD models connected to the physiologically based pharmacokinetic model in order to describe tumour cell growth after UFT administration: The cell cycle phase– specific model, and the dual-transit compartment model where two cell death pathways exist (36). They found that the dual-transit compartment model explained the tumour growth curves in animals well, suggesting that it can be used to develop dosing strategies and patient-specific 5-FU therapies. The tumour growth model developed by Simeoni et al. is a platform for investigating responses to drug exposure in the oncology field (35); therefore, this model has been used for analysing combination chemotherapy with other antitumor agents (37) and for translational research on 5-FU (38). Daryani et al. scaled a pre-clinical PK-PD model of 5-FU to children and simulated various 5-FU dosing strategies and tumour-growth inhibition in order to determine an appropriate 5-FU dosage for use in a clinical study involving children with ependymoma (38). This translational PK-PD approach is preferable for bridging pre-clinical and clinical studies and can be applied to developing new or optimizing existing dosing strategies for 5-FU.

PK-PD model along with biomarkers for PK and PD estimation for 5-FU. The endogenous DPD substrate uracil is metabolized to dihydrouracil (UH2) in the liver. Because 5-FU is also metabolized by the same pathway, pre-therapeutic assessment of the plasma concentration ratio of UH2 to uracil was proposed as a biomarker for estimating 5-FU clearance before its administration (16). Many clinical and animal studies have shown that the pre-therapeutic UH2/uracil ratio represents a valuable indirect biomarker that shows good correlation with hepatic DPD activities (16, 39, 40), 5-FU clearance (41, 42), and 5-FU-related toxicity (43-45). On the basis of the tumour-growth model developed by Simeoni et al. (35), a PK-PD model involving the UH2/uracil ratio has been developed to analyse plasma 5-FU concentrations and tumour-growth inhibition in a rat model of 5-FU-treated colorectal cancer (46). In this model, the elimination rate constant of 5-FU was estimated using the plasma UH2/uracil ratio before 5-FU treatment, and the estimated values were applied to the PK-PD model. A combination strategy involving predictive biomarkers and model-based estimations of the drug response may aid in determining individual 5-FU dosage.

PK-TD model for myelosuppression. Severe treatment-related toxicity occurs in approximately 10-30% of 5-FU-treated patients (47). Myelosuppression is one of the most frequent dose-limiting toxicities related to this treatment (48). Predicting time-course alterations in blood cell counts after 5-FU treatment in patients helps establish the dosing schedule for each patient, thereby preventing treatment discontinuation. Mathematical PK-PD modelling can help determine the relationship between drug exposure and myelotoxicities, thereby predicting the onset and degree of myelosuppression due to 5-FU treatment. Semi-physiological PK-PD models of 5-FU for myelosuppression were developed for both rats and humans (Table II) to determine the time course of alterations in blood cell counts after 5-FU administration.

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table II.

Summary of pharmacokinetic-pharmacodynamic/toxicodynamic (PK-PD/TD) model of 5-fluorouracil (5-FU).

View this table:
  • View inline
  • View popup
  • Download powerpoint
Table II.

Summary of pharmacokinetic-pharmacodynamic/toxicodynamic (PK-PD/TD) model of 5-fluorouracil (5-FU).

The Friberg model is a standard and versatile model for investigating anticancer drug–induced myelosuppression (49). The original model was developed to determine time-course alterations in white blood cell counts due to 5-FU. The model consists of three types of compartments: Proliferative cell compartment, transit compartments with maturing cells, and circulating blood cell compartment. To explain the delay in onset of myelosuppression due to 5-FU, a transit compartment model was applied to the original model, that is, the Friberg model. Another feature of the Friberg model is the explanation of regulation of the haematological system by endogenous growth factors and cytokines as a feedback mechanism. This feedback was modelled as the ratio of circulating blood cell counts at baseline divided by the cell counts at time ‘t’ raised to a feedback factor, which allows for description of the rebound of cells (overshoot compared with the baseline) after drug exposure. In clinical practice, the subsequent treatment course is generally initiated before the blood cell counts return to baseline, which makes it difficult to observe the rebound of cells after anticancer drug therapy and limits PD model development from clinical data sets. Therefore, Friberg et al. stated that this myelosuppression model should preferably be developed from animal data (49). The authors tried to extrapolate the time course of alterations in leucocyte counts from rats to those in patients by using the myelosuppression model, while accounting for the differences in drug potency in the two species (50). To determine the alterations in different blood cell counts (thrombocytes and erythrocytes), several modified models were reported for both animals and patients after these studies by Friberg et al. (Figure 2) (51-58). The Friberg model (49) provides a robust platform for analysing myelotoxicity due to chemotherapy involving a prodrug of 5-FU or their combination with other drugs (59).

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Reference schematic of the pharmacokinetic-pharmacodynamic/toxicodynamic (PK-PD/TD) model of 5-fluorouracil (5-FU) for analysing the tumour volume, myelosuppression, erythropenia, and body weight loss after 5-FU treatment. These basic models have been developed using animal data. A1: Amount of 5-FU in the central compartment; A2: amount of 5-FU in the peripheral compartment; BWSS: maximal body weight; C1: 5-FU concentration in the central compartment; C2: 5-FU concentration in the peripheral compartment; Circ: circulating blood cell count; Circ0: baseline value of circulating blood cells; Edrug: drug effects; k1, first-order rate constant of transit; k12: rate constant of central compartment to peripheral compartment; k2, measure of drug potency; k21: rate constant of peripheral compartment to central compartment; kbw,in: rate constant describing the rate of body weight increase; kbw,out: first-order rate constant describing the rate of body weight decrease; kcirc: degradation rate of circulating blood cells; kin: precursor production rate; Km: concentration of 5-FU when the rate of nonlinear elimination is at half its maximum value; kprol: proliferation rate constant determining the rate of cell division; kslope: slope of linear function in drug effect; ktr: first-order rate constant of transit; V1: central volume of distribution of 5-FU; V2: peripheral volume of distribution of 5-FU; Vmax: maximal rate of saturable metabolism; XBW: one compartment of observed body weight; XCirc: one compartment of observed circulating blood cells; Xn: some transit compartments; XPre: precursor production compartment; XProl: one compartment that represented proliferative cells such as stem cells and progenitor cells; γ: power which describes a feedback mechanism from the circulating blood cells; λ0: the rate of exponential tumour growth.

In recent research involving the semi-physiological PK-PD model, a distributed delay approach was applied to model delayed responses in PK-PD studies (56, 60), instead of the traditional transit compartment model approach (49). This traditional model includes a number of different equations and has been widely accepted in describing delayed PD/TD effects, including tumour regression and myelosuppression. Although this classic model approach can adequately capture features of drug effect data, it has some disadvantages, such as the requirement for manual analysis of the preferable number of transit compartments. Moreover, many differential equations in the transit compartment model also need to fit the observed data and are not preferable for use in complex compartment models.

To overcome these disadvantages, a distributed delay approach has been proposed (56, 60). This approach utilizes an ordinary differential equation approximation of the convolution integral with gamma distribution for modelling the delay in drug absorption and the effects of drugs on myeloid cells, thereby avoiding the time-consuming process required for estimating appropriate model equations and parameters. Krzyzansk et al. (56) successfully applied the distributed delay model to previously reported myelosuppression data for FU-treated rats to which the Friberg model had been applied (49). Considering these advantages, instead of the transit compartment model, the distributed delay model should be applied as a standard model in oncology research for analysing the delays in pharmacological effects frequently observed after drug exposure in PK and PD/TD data.

Discussion and Future Perspectives

The current review discusses some modelling and simulation approaches for 5-FU that can be used to analyse drug responses after 5-FU treatment in patients; classic PK models have not been discussed. PK-PD modelling and simulation using clinical data have limitations because of the difficulties faced in collecting tumour size or drug response data from patients, whereas PK-TD model analysis uses routinely collected clinical data (i.e. blood cell counts) and is therefore relatively easy to perform. Moreover, 5-FU is administered along with other anticancer agents such as irinotecan and oxaliplatin and combination chemotherapy can also make it difficult to isolate the PD/TD data for 5-FU. To develop these types of PK-PD/TD models that can be used to analyse tumour size, drug response and combination chemotherapy data, modelling and simulation with animal data are preferable and have been reported as summarized in Table II. However, models developed using animal data cannot be directly extrapolated to clinical practice. To achieve personalized dosing strategies, approaches involving translational PK-PD/TD modelling and simulation across species and model analysis using clinical data based on the results of the animal PK-PD/TD model are required. The approach used by Daryani et al., who performed translational PK-PD modelling and simulation from a pre-clinical to paediatric model (as described in the PK-PD model for the subsection on antitumor effects), may provide a framework for future studies on the translational PK-PD/TD modelling approach for optimal dosing strategies that can reduce toxicity while maintaining the chemotherapeutic effects of 5-FU (38).

Many clinical studies have shown that there are large inter- and intra-individual variabilities in plasma 5-FU level, which contribute to clinical treatment failure (10). Contributors to inter-individual variations include differences in chemotherapeutic regimens, for example, whether bolus dosing was used, and patient characteristics. PK analysis of clinical data revealed that the area under plasma concentration-time profile (AUC: 5-FU exposure) is strongly associated with clinical outcomes, including toxicity and efficacy (11). To date, a target AUC range of 20-30 mg h l–1 in the infusion regimen has been proposed in the TDM procedure, which is simply determined from the steady-state plasma concentration and infusion period of 5-FU (11, 61). However, using this simple method for calculating AUC can lead to over- or underestimation of the values because the circadian concentration is not considered. Simulating the time profiles of both plasma 5-FU concentration and clinical responses from blood sampling data and mathematical approaches, which would enable appropriate dosing modifications for each patient, remains a critical challenge. A PK-PD/TD model that can describe the circadian rhythm of 5-FU PK may aid in realizing this strategy.

Although TDM of 5-FU can reduce toxicity and improve clinical efficacy in long-term infusion regimens, a TDM strategy has not yet been established for 5-FU prodrugs. To elucidate the relationship between exposure to the drug and its toxic properties, some studies performed PK-TD model analysis of 5-FU prodrugs (51, 57). Recently, Oyaga-Iriarte et al. successfully developed a multicompartmental PK model for capecitabine and its metabolite in patients and determined optimal sampling times for capecitabine during TDM procedures (62). These proposed sampling times will help predict the PK of capecitabine in new patients, enabling dose adjustment. Chronomodulated chemotherapy of 5-FU prodrugs has also been proposed (28-31). To determine the diurnal cycle of PK properties, some circadian models were used in animal research (25, 26). These pharmacometrics data indicate that further clinical studies on treatment efficacy and toxicity during individualized treatment with TDM need to be performed with large patient populations in order to realize the goal of personalized medicine using 5-FU prodrug chemotherapy.

Combination chemotherapy such as the FOLFIRI and FOLFOX regimens are standard approaches for treating colorectal cancer. Recently, the frequency of co-administration of drugs has increased; the folinic acid, 5-FU, irinotecan, and oxaliplatin (FOLFIRINOX) regimen has been used in chemotherapy for colorectal and pancreatic cancer (63). Moreover, supportive therapy for nausea and vomiting requires additional drugs, which complicates drug–drug interaction (64). A PK-PD/TD model that links drug exposure and drug response has been developed for 5-FU, but it has not been used for combination chemotherapy involving 5-FU (63). Developing a PK-PD/TD model for each combination chemotherapeutic regimen remains challenging.

Future studies should apply the PK-PD/TD model as a tool for determining 5-FU dosing in clinical practice. Although the currently used procedure for TDM of 5-FU therapy leads to improvement in efficacies and reduction in toxicity, drug responses cannot be predicted from the plasma 5-FU level. The current dose-adjustment method requires completion of a number of cycles of therapy to achieve a narrow target therapeutic range, and the 5-FU dosage to be used in the first cycle is determined on the basis of an empirical index, namely the BSA (10, 65). PK-PD/TD modelling and simulation can facilitate more appropriate clinical dose setting for each patient via close co-operation between physicians and pharmacometricians. The traditional PK-PD/TD model has been developed using clinical data for large populations in multiple multi-institutional joint studies (Figure 3). However, there is bias with respect to patient background in the data from each clinical hospital (i.e. regionality, chronic disease, or hospitals specializing in dialysis, paediatrics, or transplantation). These different hospital characteristics may generate variabilities in the PK of 5-FU and affect dose setting. Therefore, PK-PD/TD models should be developed using routinely collected medical record data from clinical organizations. Use of a specific model for each clinical organization can help realize the goal of personalized medicine within the hospital. The model should be routinely updated with new patient information. Although the pharmacometrics technique has limitations such as model validations and education of clinical pharmacometricians, we believe that the clinical pharmacometrics approach can aid in determining the appropriate 5-FU dose to improve clinical outcomes.

Figure 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 3.

Summary of traditional and proposed prospective pharmacometrics-based approach for realizing personalized dose setting.

Conclusion

The current review promotes the understanding of the PK-PD/TD model of 5-FU. Personalized medicine involving 5-FU dose setting by using the PK-PD/TD model has not yet been applied in clinical chemotherapy. The review discusses models and current pharmacometrics approaches for personalized medicine, and provides fundamental information for further development of the PK-PD/TD model and platform. This information can help establish rational dosage-based 5-FU dose setting for each combination chemotherapy regimen, which would enable improved prognosis in patients with cancer.

Acknowledgements

The Authors would like to thank Professor Toshiyuki Sakaeda from Kyoto Pharmaceutical University (Kyoto, Japan) for his valuable support and advices during the writing of this review.

Footnotes

  • Authors’ Contributions

    S.K.: Conception and study design, analysis, and interpretation of data, drafting of the article; Y.I.: Collection and interpretation of data, and revision of the article. All Authors approved the final version of the article.

  • This article is freely accessible online.

  • Conflicts of Interest

    The Authors declare no competing interests.

  • Received October 16, 2020.
  • Revision received October 28, 2020.
  • Accepted October 29, 2020.
  • Copyright © 2020 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

References

  1. ↵
    1. Longley DB,
    2. Harkin DP and
    3. Johnston PG
    : 5-Fluorouracil: Mechanisms of action and clinical strategies. Nat Rev Cancer 3(5): 330-338, 2003. PMID: 12724731. DOI: 10.1038/nrc1074
    OpenUrlCrossRefPubMed
  2. ↵
    1. Pinedo HM and
    2. Peters GF
    : Fluorouracil: biochemistry and pharmacology. J Clin Oncol. 6(10): 1653-1664, 1988. PMID: 3049954. DOI: 10.1200/JCO.1988.6.10.1653
    OpenUrlAbstract/FREE Full Text
  3. ↵
    1. Naguib FNM,
    2. El Kouni MH and
    3. Cha S
    : Enzymes off Uracil Catabolism in Normal and Neoplastic Human Tissues. Cancer Res 45(11 pt 1): 5405-5412, 1985. PMID: 3931905.
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Piedbois P
    : Efficacy of intravenous continuous infusion of fluorouracil compared with bolus administration in advanced colorectal cancer. J Clin Oncol 16(1): 301-308, 1998. PMID: 9440757. DOI: 10.1200/JCO.1998.16.1.301
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Fety R,
    2. Rolland F,
    3. Barberi-Heyob M,
    4. Hardouin A,
    5. Campion L,
    6. Conroy T,
    7. Merlin JL,
    8. Rivière A,
    9. Perrocheau G,
    10. Etienne MC and
    11. Milano G
    : Clinical impact of pharmacokinetically-guided dose adaptation of 5-fluorouracil: results from a multicentric randomized trial in patients with locally advanced head and neck carcinomas. Clin Cancer Res 4(9): 2039-2045, 1998. PMID: 9748117.
    OpenUrlAbstract
    1. Baker SD,
    2. Verweij J,
    3. Rowinsky EK,
    4. Donehower RC,
    5. Schellens JH,
    6. Grochow LB and
    7. Sparreboom A
    : Role of body surface area in dosing of investigational anticancer agents in adults, 1991-2001. J Natl Cancer Inst 94(24): 1883-1888, 2002. PMID: 12488482. DOI: 10.1093/jnci/94.24.1883
    OpenUrlCrossRefPubMed
  6. ↵
    1. Undevia SD,
    2. Gomez-Abuin G and
    3. Ratain MJ
    : Pharmacokinetic variability of anticancer agents. Nat Rev Cancer 5(6): 447-458, 2005. PMID: 15928675. DOI: 10.1038/nrc1629
    OpenUrlCrossRefPubMed
  7. ↵
    1. Gamelin E,
    2. Boisdron-Celle M,
    3. Delva R,
    4. Regimbeau C,
    5. Cailleux PE,
    6. Alleaume C,
    7. Maillet ML,
    8. Goudier MJ,
    9. Sire M,
    10. Person-Joly MC,
    11. Maigre M,
    12. Maillart P,
    13. Fety R,
    14. Burtin P,
    15. Lortholary A,
    16. Dumesnil Y,
    17. Picon L,
    18. Geslin J,
    19. Gesta P,
    20. Danquechin-Dorval E,
    21. Larra F and
    22. Robert J
    : Long-term weekly treatment of colorectal metastatic cancer with fluorouracil and leucovorin: Results of a multicentric prospective trial of fluorouracil dosage optimization by pharmacokinetic monitoring in 152 patients. J Clin Oncol 16(4): 1470-1478, 1998. PMID: 9552054. DOI: 10.1200/JCO.1998.16.4.1470
    OpenUrlAbstract/FREE Full Text
    1. Milano G,
    2. Etienne MC,
    3. Renée N,
    4. Thyss A,
    5. Schneider M,
    6. Ramaioli A and
    7. Demard F
    : Relationship between fluorouracil systemic exposure and tumor response and patient survival. J Clin Oncol 12(6): 1291-1295, 1994. PMID: 8201391. DOI: 10.1200/JCO.1994.12.6.1291
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Saif MW,
    2. Choma A,
    3. Salamone SJ and
    4. Chu E
    : Pharmacokinetically guided dose adjustment of 5-fluorouracil: A rational approach to improving therapeutic outcomes. J Natl Cancer Inst 101(22):1543-1552, 2009. PMID: 8201391. DOI: 10.1200/JCO.1994.12.6.1291
    OpenUrlCrossRefPubMed
  9. ↵
    1. Beumer JH,
    2. Chu E,
    3. Allegra C,
    4. Tanigawara Y,
    5. Milano G,
    6. Diasio R,
    7. Kim TW,
    8. Mathijssen RH,
    9. Zhang L,
    10. Arnold D,
    11. Muneoka K,
    12. Boku N and
    13. Joerger M
    : Therapeutic drug monitoring in oncology: International Association of Therapeutic Drug Monitoring and Clinical Toxicology Recommendations for 5-Fluorouracil Therapy. Clin Pharmacol Ther 105(3): 598-613, 2019. PMID: 29923599. DOI: 10.1002/cpt.1124
    OpenUrlCrossRef
  10. ↵
    1. Buil-Bruna N,
    2. López-Picazo JM,
    3. Martín-Algarra S and
    4. Trocóniz IF
    : Bringing model-based prediction to oncology clinical practice: A review of pharmacometrics principles and applications. Oncologist 21(2): 220-232, 2016. PMID: 26668254. DOI: 10.1634/theoncologist.2015-0322.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Ette EI and
    2. Williams PJ
    : Pharmacometrics: The Science of Quantitative Pharmacology. John Wiley & Sons, Hoboken, USA, pp. 6, 2007.
  12. ↵
    1. Solans BP,
    2. Garrido MJ and
    3. Trocóniz IF
    : Drug exposure to establish pharmacokinetic-response relationships in oncology. Clin Pharmacokinet 59(2): 123-135, 2020. PMID: 31654368. DOI: 10.1007/s40262-019-00828-3
    OpenUrlCrossRef
  13. ↵
    1. Fleming GF,
    2. Schumm P,
    3. Friberg G,
    4. Ratain MJ,
    5. Njiaju UO and
    6. Schilsky RL
    : Circadian variation in plasma 5-fluorouracil concentrations during a 24 hour constant-rate infusion. BMC Cancer 15: 69, 2015. PMID: 25885822. DOI: 10.1186/s12885-015-1075-6.
    OpenUrlCrossRef
  14. ↵
    1. Jiang H,
    2. Lu J and
    3. Ji J
    : Circadian rhythm of dihydrouracil/uracil ratios in biological fluids: A potential biomarker for dihydropyrimidine dehydrogenase levels. Br J Pharmacol 141(4): 616-623, 2004. PMID: 14744810. DOI: 10.1038/sj.bjp.0705651
    OpenUrlCrossRefPubMed
  15. ↵
    1. Kuwahara A,
    2. Yamamori M,
    3. Nishiguchi K,
    4. Okuno T,
    5. Chayahara N,
    6. Miki I,
    7. Tamura T,
    8. Kadoyama K,
    9. Inokuma T,
    10. Takemoto Y,
    11. Nakamura T,
    12. Kataoka K and
    13. Sakaeda T
    : Effect of dose-escalation of 5-fluorouracil on circadian variability of its pharmacokinetics in Japanese patients with stage III/IVa esophageal squamous cell carcinoma. Int J Med Sci 7(1): 48-54, 2010. PMID: 20151048. DOI: 10.7150/ijms.7.48
    OpenUrlCrossRefPubMed
  16. ↵
    1. Harris BE,
    2. Song R,
    3. Soong SJ and
    4. Diasio RB
    : Relationship between dihydropyrimidine dehydrogenase activity and plasma 5-fluorouracil levels with evidence for circadian variation of enzyme activity and plasma drug levels in cancer patients receiving 5-fluorouracil by protracted continuous infusion. Cancer Res 50(1): 197-201, 1990. PMID: 2293556.
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Metzger G,
    2. Massari C,
    3. Etienne MC,
    4. Comisso M,
    5. Brienza S,
    6. Touitou Y,
    7. Milano G,
    8. Bastian G,
    9. Misset JL and
    10. Lévi F
    : Spontaneous or imposed circadian changes in plasma concentrations of 5-fluorouracil coadministered with folinic acid and oxaliplatin: Relationship with mucosal toxicity in patients with cancer. Clin Pharmacol Ther 56(2):190-201, 1994. PMID: 8062496. DOI: 10.1038/clpt.1994.123
    OpenUrlCrossRefPubMed
  18. ↵
    1. Bressolle F,
    2. Joulia JM,
    3. Pinguet F,
    4. Ychou M,
    5. Astre C,
    6. Duffour J and
    7. Gomeni R
    : Circadian rhythm of 5-fluorouracil population pharmacokinetics in patients with metastatic colorectal cancer. Cancer Chemother Pharmacol 44(4): 295-302, 1999. PMID: 10447576. DOI: 10.1007/s002800050980
    OpenUrlCrossRefPubMed
  19. ↵
    1. Altinok A,
    2. Lévi F and
    3. Goldbeter A
    : Identifying mechanisms of chronotolerance and chronoefficacy for the anticancer drugs 5-fluorouracil and oxaliplatin by computational modeling. Eur J Pharm Sci 36(1): 20-38, 2009. PMID: 19041394. DOI: 10.1016/j.ejps.2008.10.024
    OpenUrlCrossRefPubMed
    1. Lévi F,
    2. Karaboué A,
    3. Etienne-Grimaldi MC,
    4. Paintaud G,
    5. Focan C,
    6. Innominato P,
    7. Bouchahda M,
    8. Milano G and
    9. Chatelut E
    : Pharmacokinetics of irinotecan, oxaliplatin and 5-fluorouracil during hepatic artery chronomodulated infusion: A translational European OPTILIV study. Clin Pharmacokinet 56(2): 165-177, 2017. PMID: 27393140. DOI: 10.1007/s40262-016-0431-2
    OpenUrlCrossRef
  20. ↵
    1. Hill RJW,
    2. Innominato PF,
    3. Lévi F and
    4. Ballesta A
    : Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy. PLoS Comput Biol 16(1): e1007218, 2020. PMID: 31986133. DOI: 10.1371/journal.pcbi.1007218
    OpenUrlCrossRef
  21. ↵
    1. Kobuchi S,
    2. Ito Y,
    3. Nakano Y and
    4. Sakaeda T
    : Population pharmacokinetic modelling and simulation of 5-fluorouracil incorporating a circadian rhythm in rats. Xenobiotica 46(7): 597-604, 2016. PMID: 26503235. DOI: 10.3109/00498254.2015.1100767
    OpenUrlCrossRef
  22. ↵
    1. Kobuchi S,
    2. Yazaki Y,
    3. Ito Y and
    4. Sakaeda T
    : Circadian variations in the pharmacokinetics of capecitabine and its metabolites in rats. Eur J Pharm Sci 112: 152-158, 2018. PMID: 29175408. DOI: 10.1016/j.ejps.2017.11.021
    OpenUrlCrossRef
  23. ↵
    1. Kobuchi S,
    2. Ito Y,
    3. Takamatsu D and
    4. Sakaeda T
    : Circadian variations in the pharmacokinetics of the oral anticancer agent tegafur-uracil (UFT) and its metabolites in rats. Eur J Pharm Sci 123: 452-458, 2018. PMID: 30077713. DOI: 10.1016/j.ejps.2018.08.004
    OpenUrlCrossRef
  24. ↵
    1. Kobuchi S,
    2. Matsumura E,
    3. Ito Y and
    4. Sakaeda T
    : Population pharmacokinetic model-based evaluation of circadian variations in plasma 5-fluorouracil concentrations during long-term infusion in rats: A comparison with oral anticancer prodrugs. J Pharm Sci 109(7): 2356-2361, 2020. PMID: 32311368. DOI: 10.1016/j.xphs.2020.04.005
    OpenUrlCrossRef
  25. ↵
    1. Santini D,
    2. Vincenzi B,
    3. Schiavon G,
    4. Di Seri M,
    5. Virzí V,
    6. Spalletta B,
    7. Caricato M,
    8. Coppola R and
    9. Tonini G
    : Chronomodulated administration of oxaliplatin plus capecitabine (XELOX) as first line chemotherapy in advanced colorectal cancer patients: phase II study. Cancer Chemother Pharmacol 59(5): 613-620, 2007. PMID: 16944151. DOI: 10.1007/s00280-006-0302-x
    OpenUrlCrossRefPubMed
  26. ↵
    1. Qvortrup C,
    2. Jensen BV,
    3. Fokstuen T,
    4. Nielsen SE,
    5. Keldsen N,
    6. Glimelius B,
    7. Bjerregaard B,
    8. Mejer J,
    9. Larsen FO and
    10. Pfeiffer P
    : A randomized study comparing short-time infusion of oxaliplatin in combination with capecitabine XELOX(30) and chronomodulated XELOX(30) as first-line therapy in patients with advanced colorectal cancer. Ann Oncol 21(1): 87-91, 2010. PMID: 19622596. DOI: 10.1093/annonc/mdp272
    OpenUrlCrossRefPubMed
  27. ↵
    1. Akgun Z,
    2. Saglam S,
    3. Yucel S,
    4. Gural Z,
    5. Balik E,
    6. Cipe G,
    7. Yildiz S,
    8. Kilickap S,
    9. Okyar A and
    10. Kaytan-Saglam E
    : Neoadjuvant chronomodulated capecitabine with radiotherapy in rectal cancer: A phase II brunch regimen study. Cancer Chemother Pharmacol 74(4): 751-756, 2014. PMID: 25102935. DOI: 10.1007/s00280-014-2558-x
    OpenUrlCrossRef
  28. ↵
    1. Pilancı KN,
    2. Saglam S,
    3. Okyar A,
    4. Yucel S,
    5. Pala-Kara Z,
    6. Ordu C,
    7. Namal E,
    8. Ciftci R,
    9. Iner-Koksal U and
    10. Kaytan-Saglam E
    : Chronomodulated oxaliplatin plus Capecitabine (XELOX) as a first line chemotherapy in metastatic colorectal cancer: A phase II Brunch regimen study. Cancer Chemother Pharmacol 78(1): 143-150, 2016. PMID: 27270460. DOI: 10.1007/s00280-016-3067-x
    OpenUrlCrossRef
  29. ↵
    1. Roosendaal J,
    2. Jacobs BAW,
    3. Pluim D,
    4. Rosing H,
    5. de Vries N,
    6. van Werkhoven E,
    7. Nuijen B,
    8. Beijnen JH,
    9. Huitema ADR,
    10. Schellens JHM and
    11. Marchetti S
    : Phase I pharmacological study of continuous chronomodulated capecitabine treatment. Pharm Res 37(5): 89, 2020. PMID: 32382808. DOI: 10.1007/s11095-020-02828-6
    OpenUrlCrossRef
  30. ↵
    1. Muggia FM,
    2. Wu X,
    3. Spicer D,
    4. Groshen S,
    5. Jeffers S,
    6. Leichman CG,
    7. Leichman L and
    8. Chan KK
    : Phase I and pharmacokinetic study of oral UFT, a combination of the 5-fluorouracil prodrug tegafur and uracil. Clin Cancer Res 2(9): 1461-1467, 1996. PMID: 9816321.
    OpenUrlAbstract
  31. ↵
    1. Etienne-Grimaldi MC,
    2. Cardot JM,
    3. François E,
    4. Renée N,
    5. Douillard JY,
    6. Gamelin E and
    7. Milano G
    : Chronopharmacokinetics of oral tegafur and uracil in colorectal cancer patients. Clin Pharmacol Ther 83(3): 413-415, 2008. PMID: 17637782. DOI: 10.1038/sj.clpt.6100297
    OpenUrlCrossRefPubMed
  32. ↵
    1. Simeoni M,
    2. Magni P,
    3. Cammia C,
    4. De Nicolao G,
    5. Croci V,
    6. Pesenti E,
    7. Germani M,
    8. Poggesi I and
    9. Rocchetti M
    : Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res 64(3): 1094-1101, 2004. PMID: 14871843. DOI: 10.1158/0008-5472.can-03-2524
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Sung JH,
    2. Dhiman A and
    3. Shuler ML
    : A combined pharmacokinetic-pharmacodynamic (PK-PD) model for tumor growth in the rat with UFT administration. J Pharm Sci 98(5): 1885-1904, 2009. PMID: 18803264. DOI: 10.1002/jps.21536
    OpenUrlCrossRefPubMed
  34. ↵
    1. Terranova N,
    2. Germani M,
    3. Del Bene F and
    4. Magni P
    : A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination. Cancer Chemother Pharmacol 72(2): 471-482, 2013. PMID: 23812004. DOI: 10.1007/s00280-013-2208-8
    OpenUrlCrossRef
  35. ↵
    1. Daryani VM,
    2. Patel YT,
    3. Tagen M,
    4. Turner DC,
    5. Carcaboso AM,
    6. Atkinson JM,
    7. Gajjar A,
    8. Gilbertson RJ,
    9. Wright KD and
    10. Stewart CF
    : Translational pharmacokinetic-pharmacodynamic modeling and simulation: Optimizing 5-fluorouracil dosing in children with pediatric ependymoma. CPT Pharmacometrics Syst Pharmacol 5(4): 211-221, 2016. PMID: 27104090. DOI: 10.1002/psp4.12075
    OpenUrlCrossRef
  36. ↵
    1. Kobuchi S,
    2. Ito Y,
    3. Okada K,
    4. Imoto K,
    5. Kuwano S and
    6. Takada K
    : Pre-therapeutic assessment of plasma dihydrouracil/uracil ratio for predicting the pharmacokinetic parameters of 5-fluorouracil and tumor growth in a rat model of colorectal cancer. Biol Pharm Bull 36(6): 907-916, 2013. PMID: 23575271. DOI: 10.1248/bpb.b12-00819
    OpenUrlCrossRef
  37. ↵
    1. Kobuchi S,
    2. Kuwano S,
    3. Imoto K,
    4. Okada K,
    5. Nishimura A,
    6. Ito Y,
    7. Shibata N and
    8. Takada K
    : A predictive biomarker for altered 5-fluorouracil pharmacokinetics following repeated administration in a rat model of colorectal cancer. Biopharm Drug Dispos 34(7): 365-376, 2013. PMID: 23836081. DOI: 10.1002/bdd.1851
    OpenUrlCrossRef
  38. ↵
    1. Gamelin E,
    2. Boisdron-Celle M,
    3. Guérin-Meyer V,
    4. Delva R,
    5. Lortholary A,
    6. Genevieve F,
    7. Larra F,
    8. Ifrah N and
    9. Robert J
    : Correlation between uracil and dihydrouracil plasma ratio, fluorouracil (5-FU) pharmacokinetic parameters, and tolerance in patients with advanced colorectal cancer: A potential interest for predicting 5-FU toxicity and determining optimal 5-FU dosage. J Clin Oncol 17(4): 1105, 1999. PMID: 10561167. DOI: 10.1200/JCO.1999.17.4.1105
    OpenUrlAbstract/FREE Full Text
  39. ↵
    1. Jiang H,
    2. Lu J,
    3. Jiang J and
    4. Hu P.
    Important role of the dihydrouracil/uracil ratio in marked interpatient variations of fluoropyrimidine pharmacokinetics and pharmacodynamics. J Clin Pharmacol 44(11): 1260-1272, 2004. PMID: 15496644. DOI: 10.1177/0091270004268911.
    OpenUrlCrossRefPubMed
  40. ↵
    1. Sistonen J,
    2. Büchel B,
    3. Froehlich TK,
    4. Kummer D,
    5. Fontana S,
    6. Joerger M,
    7. van Kuilenburg AB and
    8. Largiadèr CR
    : Predicting 5-fluorouracil toxicity: DPD genotype and 5,6-dihydrouracil:uracil ratio. Pharmacogenomics 15(13): 1653-1666, 2014. PMID: 25410891. DOI: 10.2217/pgs.14.126
    OpenUrlCrossRef
    1. Zhou ZW,
    2. Wang GQ,
    3. Wan de S,
    4. Lu ZH,
    5. Chen YB,
    6. Li S,
    7. Chen G and
    8. Pan ZZ
    : The dihydrouracil/uracil ratios in plasma and toxicities of 5-fluorouracil-based adjuvant chemotherapy in colorectal cancer patients. Chemotherapy 53(2): 127-131, 2007. PMID: 17308379. DOI: 10.1159/000099984
    OpenUrlCrossRefPubMed
  41. ↵
    1. Boisdron-Celle M,
    2. Remaud G,
    3. Traore S,
    4. Poirier AL,
    5. Gamelin L,
    6. Morel A and
    7. Gamelin E
    : 5-Fluorouracil-related severe toxicity: A comparison of different methods for the pretherapeutic detection of dihydropyrimidine dehydrogenase deficiency. Cancer Lett 249(2): 271-282, 2007. PMID: 17064846. DOI: 10.1016/j.canlet.2006.09.006
    OpenUrlCrossRefPubMed
  42. ↵
    1. Kobuchi S,
    2. Ito Y,
    3. Okada K,
    4. Imoto K,
    5. Kuwano S and
    6. Takada K
    : Pharmacokinetic/pharmacodynamic modeling of 5-fluorouracil by using a biomarker to predict tumor growth in a rat model of colorectal cancer. J Pharm Sci 102(6): 2056-2067, 2013. PMID: 23592368. DOI: 10.1002/jps.23547
    OpenUrlCrossRef
  43. ↵
    1. Meulendijks D,
    2. Henricks LM,
    3. Jacobs BAW,
    4. Aliev A,
    5. Deenen MJ,
    6. de Vries N,
    7. Rosing H,
    8. van Werkhoven E,
    9. de Boer A,
    10. Beijnen JH,
    11. Mandigers CMPW,
    12. Soesan M,
    13. Cats A and
    14. Schellens JHM
    : Pretreatment serum uracil concentration as a predictor of severe and fatal fluoropyrimidine-associated toxicity. Br J Cancer 116(11): 1415-1424, 2017. PMID: 28427087. DOI: 10.1038/bjc.2017.94
    OpenUrlCrossRef
  44. ↵
    1. Garg MB,
    2. Lincz LF,
    3. Adler K,
    4. Scorgie FE,
    5. Ackland SP and
    6. Sakoff JA.
    Predicting 5-fluorouracil toxicity in colorectal cancer patients from peripheral blood cell telomere length: A multivariate analysis. Br J Cancer 107(9): 1525-1533, 2012. PMID: 22990653. DOI: 10.1038/bjc.2012.421
    OpenUrlCrossRefPubMed
  45. ↵
    1. Friberg LE,
    2. Freijs A,
    3. Sandström M and
    4. Karlsson MO
    : Semiphysiological model for the time course of leukocytes after varying schedules of 5-fluorouracil in rats. J Pharmacol Exp Ther 295(2): 734-740, 2000. PMID: 11046112.
    OpenUrlAbstract/FREE Full Text
  46. ↵
    1. Friberg LE,
    2. Sandström M and
    3. Karlsson MO
    : Scaling the time-course of myelosuppression from rats to patients with a semiphysiological model. Invest New Drugs 28(6): 744-753, 2010. PMID: 19711011. DOI: 10.1007/s10637-009-9308-7
    OpenUrlCrossRefPubMed
  47. ↵
    1. Zandvliet AS,
    2. Siegel-Lakhai WS,
    3. Beijnen JH,
    4. Copalu W,
    5. Etienne-Grimaldi MC,
    6. Milano G,
    7. Schellens JH and
    8. Huitema AD
    : PK/PD model of indisulam and capecitabine: Interaction causes excessive myelosuppression. Clin Pharmacol Ther 83(6): 829-839, 2008. PMID: 17851564. DOI: 10.1038/sj.clpt.6100344
    OpenUrlCrossRefPubMed
    1. Kobuchi S,
    2. Ito Y,
    3. Hayakawa T,
    4. Kuwano S,
    5. Baba A,
    6. Shinohara K,
    7. Nishimura A,
    8. Shibata N and
    9. Takada K
    : Semi-physiological pharmacokinetic-pharmacodynamic modeling and simulation of 5-fluorouracil for the whole time course of alterations in leukocyte, neutrophil and lymphocyte counts in rats. Xenobiotica 44(9): 804-818, 2014. PMID: 24650147. DOI: 10.3109/00498254.2014.900588
    OpenUrlCrossRef
    1. Kobuchi S,
    2. Ito Y and
    3. Sakaeda T
    : Population pharmacokinetic-pharmacodynamic modeling of 5-fluorouracil for toxicities in rats. Eur J Drug Metab Pharmacokinet 42(4): 707-718, 2017. PMID: 27889876. DOI: 10.1007/s13318-016-0389-3
    OpenUrlCrossRef
    1. Kobuchi S,
    2. Ito Y,
    3. Hayakawa T,
    4. Nishimura A,
    5. Shibata N,
    6. Takada K and
    7. Sakaeda T
    : Semi-physiological pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation of 5-fluorouracil for thrombocytopenia in rats. Xenobiotica 45(1): 19-28, 2015. PMID: 25050790. DOI: 10.3109/00498254.2014.943335
    OpenUrlCrossRef
    1. Kobuchi S,
    2. Ito Y,
    3. Hayakawa T,
    4. Nishimura A,
    5. Shibata N,
    6. Takada K and
    7. Sakaeda T
    : Pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation of 5-fluorouracil for erythropenia in rats. J Pharmacol Toxicol Methods 70(2): 134-144, 2014. PMID: 25072509. DOI: 10.1016/j.vascn.2014.07.007
    OpenUrlCrossRef
  48. ↵
    1. Krzyzanski W
    : Ordinary differential equation approximation of gamma distributed delay model. J Pharmacokinet Pharmacodyn 46(1): 53-63, 2019. PMID: 30617672. DOI: 10.1007/s10928-018-09618-z
    OpenUrlCrossRef
  49. ↵
    1. Sáez-Belló M,
    2. Mangas-Sanjuán V,
    3. Martínez-Gómez MA,
    4. López-Montenegro Soria MÁ,
    5. Climente-Martí M and
    6. Merino-Sanjuán M
    : Evaluation of ABC gene polymorphisms on the pharmacokinetics and pharmacodynamics of capecitabine in colorectal patients: Implications for dosing recommendations. Br J Clin Pharmacol, 2020. PMID: 32559325. DOI: 10.1111/bcp.14441
    OpenUrlCrossRef
  50. ↵
    1. Arshad U,
    2. Ploylearmsaeng SA,
    3. Karlsson MO,
    4. Doroshyenko O,
    5. Langer D,
    6. Schömig E,
    7. Kunze S,
    8. Güner SA,
    9. Skripnichenko R,
    10. Ullah S,
    11. Jaehde U,
    12. Fuhr U,
    13. Jetter A and
    14. Taubert M
    : Prediction of exposure-driven myelotoxicity of continuous infusion 5-fluorouracil by a semi-physiological pharmacokinetic-pharmacodynamic model in gastrointestinal cancer patients. Cancer Chemother Pharmacol 85(4): 711-722, 2020. PMID: 32152679. DOI: 10.1007/s00280-019-04028-5
    OpenUrlCrossRef
  51. ↵
    1. Fornari C,
    2. O’Connor LO,
    3. Yates JWT,
    4. Cheung SYA,
    5. Jodrell DI,
    6. Mettetal JT and
    7. Collins TA
    : Understanding hematological toxicities using mathematical modeling. Clin Pharmacol Ther 104(4): 644-654, 2018. PMID: 29604045. DOI: 10.1002/cpt.1080
    OpenUrlCrossRef
  52. ↵
    1. Hu S,
    2. Dunlavey M,
    3. Guzy S and
    4. Teuscher N
    : A distributed delay approach for modeling delayed outcomes in pharmacokinetics and pharmacodynamics studies. J Pharmacokinet Pharmacodyn 45(2): 285-308, 2018. PMID: 29368268. DOI: 10.1007/s10928-018-9570-4
    OpenUrlCrossRef
  53. ↵
    1. Kaldate RR,
    2. Haregewoin A,
    3. Grier CE,
    4. Hamilton SA and
    5. McLeod HL
    : Modeling the 5-fluorouracil area under the curve versus dose relationship to develop a pharmacokinetic dosing algorithm for colorectal cancer patients receiving FOLFOX6. Oncologist 17(3): 296-302, 2012. PMID: 22382460. DOI: 10.1634/theoncologist.2011-0357
    OpenUrlAbstract/FREE Full Text
  54. ↵
    1. Oyaga-Iriarte E,
    2. Insausti A,
    3. Bueno L,
    4. Sayar O and
    5. Aldaz A
    : Mining small routine clinical data: A population pharmacokinetic model and optimal sampling times of capecitabine and its metabolites. J Pharm Pharm Sci 22(1): 112-121, 2019. PMID: 30964613. DOI: 10.18433/jpps30392
    OpenUrlCrossRef
  55. ↵
    1. Deyme L,
    2. Barbolosi D and
    3. Gattacceca F
    : Population pharmacokinetics of FOLFIRINOX: A review of studies and parameters. Cancer Chemother Pharmacol 83(1): 27-42, 2019. PMID: 30446786. DOI: 10.1007/s00280-018-3722-5
    OpenUrlCrossRef
  56. ↵
    1. Schoffelen R,
    2. Lankheet AG,
    3. van Herpen CML,
    4. van der Hoeven JJM,
    5. Desar IME and
    6. Kramers C
    : Drug–drug interactions with aprepitant in antiemetic prophylaxis for chemotherapy. Neth J Med 76(3): 109-114, 2018. PMID: 29667586.
    OpenUrl
  57. ↵
    1. Gamelin E,
    2. Delva R,
    3. Jacob J,
    4. Merrouche Y,
    5. Raoul JL,
    6. Pezet D,
    7. Dorval E,
    8. Piot G,
    9. Morel A and
    10. Boisdron-Celle M
    : Individual fluorouracil dose adjustment based on pharmacokinetic follow-up compared with conventional dosage: results of a multicenter randomized trial of patients with metastatic colorectal cancer. J Clin Oncol 26(13): 2099-2105, 2008. PMID: 18445839. DOI: 10.1200/JCO.2007.13.3934
    OpenUrlAbstract/FREE Full Text
PreviousNext
Back to top

In this issue

Anticancer Research: 40 (12)
Anticancer Research
Vol. 40, Issue 12
December 2020
  • Table of Contents
  • Table of Contents (PDF)
  • Index by author
  • Back Matter (PDF)
  • Ed Board (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Anticancer Research.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Application of Pharmacometrics of 5-Fluorouracil to Personalized Medicine: A Tool for Predicting Pharmacokinetic–Pharmacodynamic/Toxicodynamic Responses
(Your Name) has sent you a message from Anticancer Research
(Your Name) thought you would like to see the Anticancer Research web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
2 + 1 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Application of Pharmacometrics of 5-Fluorouracil to Personalized Medicine: A Tool for Predicting Pharmacokinetic–Pharmacodynamic/Toxicodynamic Responses
SHINJI KOBUCHI, YUKAKO ITO
Anticancer Research Dec 2020, 40 (12) 6585-6597; DOI: 10.21873/anticanres.14683

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Reprints and Permissions
Share
Application of Pharmacometrics of 5-Fluorouracil to Personalized Medicine: A Tool for Predicting Pharmacokinetic–Pharmacodynamic/Toxicodynamic Responses
SHINJI KOBUCHI, YUKAKO ITO
Anticancer Research Dec 2020, 40 (12) 6585-6597; DOI: 10.21873/anticanres.14683
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • PK Model for Evaluating Circadian Variation
    • PK-PD/TD Model of 5-FU
    • Discussion and Future Perspectives
    • Conclusion
    • Acknowledgements
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Translational Pharmacokinetic-Toxicodynamic Model of Myelosuppression for Dose Optimization in Combination Chemotherapy of Capecitabine and Oxaliplatin from Rats to Humans
  • A Pharmacokinetic-Pharmacodynamic Model Predicts Uracil-tegafur Effect on Tumor Shrinkage and Myelosuppression in a Colorectal Cancer Rat Model
  • Functional Characterization of 12 Dihydropyrimidinase Allelic Variants in Japanese Individuals for the Prediction of 5-Fluorouracil Treatment-Related Toxicity
  • Google Scholar

More in this TOC Section

  • Cytokine-based Cancer Immunotherapy: Challenges and Opportunities for IL-10
  • Proteolytic Enzyme Therapy in Complementary Oncology: A Systematic Review
  • Multimodal Treatment of Primary Advanced Ovarian Cancer
Show more Reviews

Similar Articles

Keywords

  • Modelling and simulation
  • chronopharmacokinetics
  • Translational research
  • cancer chemotherapy
  • therapeutic drug monitoring
  • 5-FU prodrug
  • pharmacometrics
  • review
Anticancer Research

© 2025 Anticancer Research

Powered by HighWire