Adaptive control methods for the dose individualisation of anticancer agents

Protective effect of metformin on methotrexate induced reduction of rat hippocampal neural stem cells and neurogenesis

Adult neurogenesis is a process in which the adult neural stem cells produce newborn neurons that are implicated in terms of learning and memory. Methotrexate (MTX) is a chemotherapeutic drug, which has a negative effect on memory and hippocampal neurogenesis in animal models. Metformin is an antidiabetic drug with strong antioxidant capacities. We found that metformin ameliorates MTX induced deteriorations of memory and hippocampal neurogenesis in adult rats. In this study, we focus to investigate neural stem cells, biomarkers of apoptosis, and the protein for synaptogenesis, which involves in the transcription factors of the hippocampus in rats that received metformin and MTX. Male Sprague-Dawley rats were composed of control, MTX, metformin, and MTX+metformin groups. MTX (75 mg/kg, i.v.) was given on days 7 and 14, whereas metformin (200 mg/kg, i.p.) was given for 14 days. Hippocampal neural stem cells in the subgranular zone (SGZ) were quantified using immunofluorescence staining of Sox2 and nestin. Protein expression including PSD95, Casepase-3, Bax, Bcl-2, CREB, and pCREB were determined using Western blotting. MTX-treated rats displayed decreases in Sox2 and nestin-positive cells in the SGZ. Increases in Caspase-3 and Bax levels and decreases in PSD95, Bcl-2, CREB, and pCREB protein expressions in the hippocampus were also detected. However, these negative impacts of MTX were ameliorated by co-treatment with metformin. These consequences postulate that metformin has a potential to increase neural stem cells, synaptic plasticity, decreased apoptotic activities, and transcription factors, resulting in upregulation of hippocampal neurogenesis in MTX-treated rats.

Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr

Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model‐informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP‐BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, “test” models with different features were coded, for example, first‐order and zero‐order absorption, lag time, time‐varying covariates, Michaelis–Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter‐individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP‐BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven “real” previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose‐related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP‐BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

AIMS

Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes.

METHODS

We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles.

RESULTS

The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration.

CONCLUSION

We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making.

Metformin alleviates memory and hippocampal neurogenesis decline induced by methotrexate chemotherapy in a rat model

Methotrexate (MTX) is a chemotherapeutic drug commonly used to treat cancers that has an adverse effect on patients' cognition. Metformin is a primary treatment for type 2 diabetes mellitus that can pass through the blood-brain barrier. Metformin has neuroprotective actions, which can improve memory. In the present study, we examined the ability of metformin in MTX chemotherapy-generated cognitive and hippocampal neurogenesis alterations. Male Sprague-Dawley rats were allocated into control, MTX, metformin, preventive, and throughout groups. MTX (75 mg/kg/day) was given intravenously on days 7 and 14 of the study. Metformin (200 mg/kg/day) was injected intraperitoneally for 14 days. Some of the MTX-treated rats received co-treatment with metformin once a day for either 14 (preventive) or 28 days (throughout). After treatment, memory ability was evaluated using novel object location and novel object recognition tests. Ki67 (proliferating cells), BrdU (survival cells), and doublecortin (immature neurons, DCX) positive cells in the subgranular zone (SGZ) of the hippocampal dentate gyrus were quantified. We found that reductions of cognition, the number of proliferating and survival cells and immature neurons in the SGZ were ameliorated in the co-treatment groups, which suggests that metformin can prevent memory and hippocampal neurogenesis impairments induced by MTX in adult rats.

Melatonin protects against methotrexate-induced memory deficit and hippocampal neurogenesis impairment in a rat model

Methotrexate (MTX) is a chemotherapy agent linked to cognitive deficits in cancer patients received chemotherapy treatment. MTX decreases cell proliferation in the hippocampus, which is concomitant with cognitive deficits in animal models. The present study aimed to investigate the disadvantages of MTX on cognition associated with cell division, survival, and immature neurons involved in hippocampal neurogenesis, as well as the practical neuroprotective effects of melatonin. Male Sprague Dawley rats were given two injections of MTX (75 mg/kg) on days 8 and 15 followed by Leucovorin (LCV, 6 mg/kg) at hours 18, 26, 42, 50 via i.p. injection. Some rats received co-treatment with melatonin (8 mg/kg, i.p. injection) for 15 days before and during MTX administration (preventive), 15 days after MTX administration (recovery), or both (30 days total; throughout). Hippocampal-dependent memory was examined using novel objection location (NOL) and novel object recognition (NOR) tests. Cell division, survival and immature neurons in the subgranular zone (SGZ) in the hippocampus were evaluated using immunofluorescence staining. Rats given MTX/LCV were found to have cognitive memory deterioration based on the NOL and NOR tests. Moreover, reductions in cell division, cell survival, and the numbers of immature neurons were detected in the MTX/LCV group when compared to the controls. This damage was not observed in rats in the preventive, recovery, or throughout groups. These findings reveal that melatonin has the potential to diminish the negative effects of MTX on memory and neurogenesis. This also indicates the benefit of melatonin co-administration in patients who undergo chemotherapy treatment.

Whole-body Imaging of Cell Death Provides a Systemic, Minimally Invasive, Dynamic, and Near-real Time Indicator for Chemotherapeutic Drug Toxicity

PURPOSE

Response to toxicity in chemotherapies varies considerably from tissue to tissue and from patient to patient. An ability to monitor the tissue damage done by chemotherapy may have a profound impact on treatment and prognosis allowing for a proactive management in understanding and mitigating such events. For the first time, we investigated the feasibility of using whole-body imaging to map chemotherapeutic drug-induced toxicity on an individual basis.

EXPERIMENTAL DESIGN

In a preclinical proof-of-concept, rats were treated with a single clinical dose of cyclophosphamide, methotrexate, or cisplatin. In vivo whole-body imaging data were acquired using ^99mTc-duramycin, which identifies dead and dying cells as an unambiguous marker for tissue injury in susceptible organs. Imaging results were cross-validated using quantitative ex vivo measurements and histopathology and compared with standard blood and serum panels for toxicology.

RESULTS

The in vivo whole-body imaging data detected widespread changes, where spatially heterogeneous toxic effects were identified across different tissues, within substructures of organs, as well as among different individuals. The signal changes were consistent with established toxicity profiles of these chemotherapeutic drugs. Apart from generating a map of susceptible tissues, this in vivo imaging approach was more sensitive compared with conventional blood and serum markers used in toxicology. Also, repeated imaging during the acute period after drug treatment captured different kinetics of tissue injury among susceptible organs in males and females.

CONCLUSIONS

This novel and highly translational imaging approach shows promise in optimizing therapeutic decisions by detecting and managing drug toxicity on a personalized basis.Toxicity to normal tissues is a significant limitation in chemotherapies. This work demonstrated an in vivo imaging-based approach for characterizing toxicity-induced tissue injury in a systemic, dynamic, and near-real time fashion. This novel approach shows promise in optimizing therapeutic decisions by monitoring drug toxicity on a personalized basis.

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.

A Nonparametric Method to Optimize Initial Drug Dosing and Attainment of a Target Exposure Interval: Concepts and Application to Busulfan in Pediatrics

The traditional approach for model-based initial dosing is based on the use of a single vector of typical population parameters for targeting a specific exposure. This approach is theoretically ill-suited for targeting a range of exposure. The objective of this work was to develop a general approach for optimal (OPT) targeting of a drug exposure interval. After methodological purposes, we applied our method to the busulfan case. We used a nonparametric population pharmacokinetic model of intravenous busulfan to estimate the individual pharmacokinetic parameters of 163 bone marrow-transplanted children. Then, an array of 151 doses of busulfan ranging from 0.5 to 2 mg/kg was simulated a priori in each patient. For each dose, 29 possible busulfan plasma concentration profiles, corresponding to the nonparametric prior, each associated with a probability, were obtained. The multiple-model-based, OPT dose was identified as the dose maximizing the a priori probability of achieving the busulfan target area under the concentration-time curve (AUC). Two AUC targets were considered: 900-1500 (conventional) or <1500 µM min^-1. Finally, the OPT dose was individually simulated in each patient. We compared the ability of this method to achieve the target exposure interval with that of three other traditional model-based methods and one based on the non-parametric approach. When targeting the busulfan conventional AUC range, the OPT dose provided better attainment than the best of the three other methods after one dose (82.2 vs. 41.7 %, p < 0.005), two doses (79.1 vs. 65.0 %, p < 0.005), and at the end of therapy (80.4 vs. 76.7 %, p < 0.42). The approach provided a balanced distribution between under- (10.4 %) and overexposure (9.2 %), while other approaches showed higher rates of underexposure (≥19 %). When targeting an AUC <1500 µM min, the OPT dose was successful in minimizing overexposure as 0 % of children showed simulated AUC >1500 µM min^-1. Our approach has been designed to optimize the targeting of an exposure interval. When applied to busulfan in children, it outperformed the traditional model-based dosing approach, with earlier and better achievement of busulfan target AUC. The approach can be applied for OPT dosing of many drugs, when the target objective is an interval.

Personalised dosing of medicines for children

Objectives

Doses for most drugs are determined from population-level information, resulting in a standard ?one-size-fits-all’ dose range for all individuals. This review explores how doses can be personalised through the use of the individuals’ pharmacokinetic (PK)-pharmacodynamic (PD) profile, its particular application in children, and therapy areas where such approaches have made inroads.

Key findings

The Bayesian forecasting approach, based on population PK/PD models that account for variability in exposure and response, is a potent method for personalising drug therapy. Its potential utility is even greater in young children where additional sources of variability are observed such as maturation of eliminating enzymes and organs. The benefits of personalised dosing are most easily demonstrated for drugs with narrow therapeutic ranges such as antibiotics and cytotoxics and limited studies have shown improved outcomes. However, for a variety of reasons the approach has struggled to make more widespread impact at the bedside: complex dosing algorithms, high level of technical skills required, lack of randomised controlled clinical trials and the need for regulatory approval.

Summary

Personalised dosing will be a necessary corollary of the new precision medicine initiative. However, it faces a number of challenges that need to be overcome before such an approach to dosing in children becomes the norm.

Therapeutic drug monitoring of cancer chemotherapy

Therapeutic drug monitoring is not routinely used for chemotherapy agents. There are Several reasons, but one major drawback is the lack of established therapeutic Concentration ranges. Combination chemotherapy makes the establishment of Therapeutic ranges for individual drugs difficult, the concentration-effect relationship for a single drug may not be the same as when that drug is used in a drug combination. Pharmacokinetic optimization protocols for many classes of cytotoxic compounds exist in specialized centers, and some of these protocols are now part of large multicentre trials. Nonetheless, TDM clearly has the potential to improve the clinical use of chemotherapy gents, most of which have very narrow therapeutic indices and highly variable pharmacokinetics. A substantial body of literature accumulating during the past 15 years demonstrates relationships between systemic exposure to various chemotherapy agents and their toxic or therapeutic effects. This article reviews TDM concepts in addition to tools based on pharmacokinetic modeling of chemotherapy agents. The administered dose of chemotherapy agents is sometimes adjusted individually using either a priori or a posteriori methods. These models can only be applied by using the same dose and schedule as the original study. Bayesian estimation offers more flexibility in blood sampling times and, owing to its precision and to the amount of information provided is the method of choice for ensuring that a given patient benefits from the desired systemic exposure. Moreover, the role and application of Pharmacogenetics as a tool for individualizing chemotherapy is discussed highlighting the agents and mechanisms that have been well studied and defined and their relevance to clinical practice. Finally, this paper address issues critical to the optimal use of TDM in a clinical setting, and the role of clinical pharmacist in this regard. In addition, it discusses future developments in this field that can contribute to improving cancer chemotherapy In terms of patient outcome and survival. J Oncol Pharm Practice (2007) 13: 207—221.

Neutrophil-guided dosing of anthracycline-cyclophosphamide-containing chemotherapy in patients with breast cancer: a feasibility study

The aim of this study was to investigate whether neutrophil-guided dose escalation of anthracycline-cyclophosphamide-containing chemotherapy (ACC) for breast cancer is feasible, in order to optimize outcome. Breast cancer patients planned for 3-weekly ACC were enrolled in this study. The first treatment cycle was administered in a standard BSA-adjusted dose. The absolute neutrophil count was measured at baseline and at day 8, 11 and 15 after administration of ACC. For patients with none or mild (CTC grade 0-2) neutropenia and no other dose-limiting toxicity, we performed a 10-25 % dose escalation of the second cycle with the opportunity to a further 10-25 % dose escalation of the third cycle. Thirty patients were treated in the adjuvant setting with either FE100C (n = 23) or AC (n = 4), or in the palliative setting with FAC (n = 3). Two out of 23 patients (9 %) treated with FEC did not develop grade 3-4 neutropenia after the first treatment cycle. Dose escalation was performed in these two patients (30 % in one and 15 % in the other patient). During dose escalation, there were no complications like febrile neutropenia. No patients treated with FAC or AC could be escalated, since all of them developed grade 3-4 neutropenia. We conclude that asymptomatic grade 3-4 neutropenia is likely to be achieved in the majority of patients with breast cancer treated with ACC according to presently advocated BSA-based dose levels. Escalation of currently advocated ACC doses without G-CSF, with a target of grade 3-4 neutropenia, is feasible, but only possible in a small proportion of patients. EudraCT 2010-020309-33.

The effect of ABCG2 genotype on the population pharmacokinetics of sunitinib in patients with renal cell carcinoma

BACKGROUND

Sunitinib, a multitargeted tyrosine kinase inhibitor, offers favorable therapeutic outcomes to patients with advanced renal cell carcinoma. However, to maximize the clinical benefits, an effective therapeutic management strategy with dose optimization is essential. The objectives of this analysis were to describe the pharmacokinetics (PK) of sunitinib by a population PK approach and to quantitatively evaluate the effect of potential predictive factors including ABCG2 genotype on the PK of sunitinib.

METHODS

Plasma concentration-time profiles at 3 consecutive days including a total of 245 sunitinib plasma concentrations were available from 19 Japanese patients with renal cell carcinoma. Blood samples were collected on days 2, 8, and 15 after the start of the therapy. Population PK analysis was performed using NONMEM 7.2. Body weight, gender, and genotype of ABCG2 421C>A were evaluated as potential covariates. Interoccasion variability (IOV) among the 3 sampling days was also assessed as a random effect parameter.

RESULTS

The sunitinib PK profiles were best described by a 1-compartment model with first-order absorption. The ABCG2 421C>A genotype was identified as a significant covariate for the prediction of oral clearance (CL/F). No significant improvement in model fit was observed by including body weight and/or gender. A systematic difference in estimated population CL/F was observed between days 2 and 8, which was quantified as approximately 30% decrease over time. This difference was described as a covariate for CL/F in the model. IOV included as a random effect parameter significantly improved the model fit.

CONCLUSIONS

This analysis provides a population PK model of sunitinib with the ABCG2 421C>A genotype as a predictive covariate for CL/F. It also suggests that IOV and change of CL/F over time need to be considered to predict the sunitinib PK more accurately. These findings will be implemented to optimize the pharmacotherapy of sunitinib.

Optimal sampling strategy development methodology using maximum a posteriori Bayesian estimation

Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility.

Analysis of anticancer drugs: a review

In the last decades, the number of patients receiving chemotherapy has considerably increased. Given the toxicity of cytotoxic agents to humans (not only for patients but also for healthcare professionals), the development of reliable analytical methods to analyse these compounds became necessary. From the discovery of new substances to patient administration, all pharmaceutical fields are concerned with the analysis of cytotoxic drugs. In this review, the use of methods to analyse cytotoxic agents in various matrices, such as pharmaceutical formulations and biological and environmental samples, is discussed. Thus, an overview of reported analytical methods for the determination of the most commonly used anticancer drugs is given.

Fluoxetine reverses the memory impairment and reduction in proliferation and survival of hippocampal cells caused by methotrexate chemotherapy

RATIONALE

Adjuvant cancer chemotherapy can cause long-lasting, cognitive deficits. It is postulated that these impairments are due to these drugs targeting neural precursors within the adult hippocampus, the loss of which has been associated with memory impairment.

OBJECTIVES

The present study investigates the effects of the chemotherapy, methotrexate (MTX) on spatial working memory and the proliferation and survival of the neural precursors involved in hippocampal neurogenesis, and the possible neuroprotective properties of the antidepressant fluoxetine.

METHODS

Male Lister hooded rats were administered MTX (75 mg/kg, two i.v. doses a week apart) followed by leucovorin rescue (i.p. 18 h after MTX at 6 mg/kg and at 26, 42 and 50 h at 3 mg/kg) and/or fluoxetine (10 mg/kg/day in drinking water for 40 days). Memory was tested using the novel location recognition (NLR) test. Using markers, cell proliferation (Ki67) and survival (bromodeoxyuridine/BrdU), in the dentate gyrus were quantified.

RESULTS

MTX-treated rats showed a cognitive deficit in the NLR task compared with the vehicle and fluoxetine-treated groups. Cognitive ability was restored in the group receiving both MTX and fluoxetine. MTX reduced both the number of proliferating cells in the SGZ and their survival. This was prevented by the co-administration of fluoxetine, which alone increased cell numbers.

CONCLUSIONS

These results demonstrate that MTX induces an impairment in spatial working memory and has a negative long-term effect on hippocampal neurogenesis, which is counteracted by the co-administration of fluoxetine. If translatable to patients, this finding has the potential to prevent the chemotherapy-induced cognitive deficits experienced by many cancer survivors.

In vitro chemosensitivity of head and neck cancer cell lines

Background

Systemic treatment of head and neck squamous cell carcinoma (HNSCC) includes a variety of antineoplastic drugs. However, drug-resistance interferes with the effectiveness of chemotherapy. Preclinical testing models are needed in order to develop approaches to overcome chemoresistance.

Methods

Ten human cell lines were obtained from HNSCC, including one with experimentally-induced cisplatin resistance. Inhibition of cell growth by seven chemotherapeutic agents (cisplatin, carboplatin, 5- fluorouracil, methotrexate, bleomycin, vincristin, and paclitaxel) was measured using metabolic MTT-uptake assay and correlated to clinically-achievable plasma concentrations.

Results

All drugs inhibited cell growth in a concentration-dependent manner with an IC50 comparable to that achievable in vivo. However, response curves for methotrexate were unsatisfactory and for paclitaxel, the solubilizer cremophor EL was toxic. Cross-resistance was observed between cisplatin and carboplatin.

Conclusion

Chemosensitivity of HNSCC cell lines can be determined using the MTT-uptake assay. For DNA-interfering cytostatics and vinca alkaloids this is a simple and reproducible procedure. Determined in vitro chemosensitivity serves as a baseline for further experimental approaches aiming to modulate chemoresistance in HNSCC with potential clinical significance.

Drug exposure: still an excellent biomarker

Relationships between plasma drug concentrations and clinical outcome have been defined for various chemotherapeutic agents, showing that drug exposure may be a marker of toxicity, mainly hematological and gastrointestinal toxicity, as well as efficacy, expressed as tumor response and survival. Biomarkers provide information for guidance in dosing and the minimization of interindividual variation in response. Drug exposure is commonly measured with parameters such as area under the plasma concentration-time curve, steady-state plasma or serum concentrations, peak concentrations and duration above a threshold concentration. In this review, we focus on drugs where pharmacokinetic/pharmacodynamic relationships as well as systemic exposure have been associated to toxicity and/or efficacy in solid and hematological tumors, these include: carboplatin, methotrexate, busulphan, 5-fluorouracil, etoposide, anthracyclines, irinotecan, vinorelbine and docetaxel.

Model-based neutrophil-guided dose adaptation in chemotherapy: evaluation of predicted outcome with different types and amounts of information

One of the most employed approaches to reduce severe neutropenia following anticancer drug regimens is to reduce the consecutive dose in fixed steps, commonly by 25%. Another approach has been to use pharmacokinetic (PK) sampling to tailor dosing, but only rarely have model-based computer approaches utilizing collected PK and/or pharmacodynamic (PD) data been used. A semi-mechanistic model for myelosuppression that can characterize the interindividual and interoccasion variability in the time-course of neutrophils following administration of a wide range of anticancer drugs may be used in a clinical setting for model-based dose individualization. The aim of this study was to compare current stepwise procedures to model-based dose adaptation by simulations, and investigate if the overall dose intensity in the population could be increased without increasing the risk of severe toxicity. The value of various amounts of PK- and/or PD-information was compared to standard dosing strategies using a maximum a posteriori procedure in NONMEM. The results showed that when information on neutrophil counts was available, the additional improvement from PK sampling was negligible. Using neutrophil sampling at baseline and an observation near the predicted nadir increased the number of patients in the target range by 27% in comparison with a one-sided 25% dose adjustment schedule, while keeping the number of patients experiencing severe toxicity at a comparable low level after five courses of treatment. High interindividual variability did not limit the benefit of model-based dose adaptation, whereas high interoccasion variability was predicted to make any dose adaptation method less successful. This study indicates that for successful model-based dose adaptation clinically, there is no need for drug concentration sampling, and that one extra neutrophil measurement in addition to the pre-treatment value is sufficient to limit severe neutropenia while increasing dose intensity.

A tool for neutrophil guided dose adaptation in chemotherapy

Chemotherapy dosing in anticancer treatment is a balancing act between achieving concentrations that are effective towards the malignancy and that result in acceptable side-effects. Neutropenia is one major side-effect of many antitumor agents, and is related to an increased risk of infection. A model capable of describing the time-course of myelosuppression from administered drug could be used in individual dose selection. In this paper we describe the transfer of a previously developed semi-mechanistic model for myelosuppression from NONMEM to a dosing tool in MS Excel, with etoposide as an example. The tool proved capable to solve a differential equation system describing the pharmacokinetics and pharmacodynamics, with estimation performance comparable to NONMEM. In the dosing tool the user provides neutrophil measures from a previous treatment course and request for the dose that results in a desired nadir in the upcoming course through a Bayesian estimation procedure.

A systematic review of limited sampling strategies for platinum agents used in cancer chemotherapy

Despite evidence in the literature suggesting that a strong correlation exists between the pharmacokinetic parameters and pharmacodynamic effect of anticancer agents, many of these agents are still dosed by body surface area. Therapeutic drug monitoring with the aim of pharmacokinetic-guided dosing would not only maintain target concentrations associated with efficacy but may potentially minimise the likelihood of dose-related systemic toxicities. The pharmacokinetic parameter that displays the best correlation with the pharmacodynamics of anticancer drugs is the area under the plasma concentration-time curve (AUC). However, accurate determination of the AUC requires numerous blood samples over an extended interval, which is not feasible in clinical practice. Therefore, limited sampling strategies (LSSs) have been proposed as a means to accurately and precisely estimate pharmacokinetic parameters with a minimal number of blood samples. LSSs have been developed for many drugs, particularly ciclosporin and other immunosuppressants, as well as for certain anticancer drugs. This systematic review evaluates LSSs developed for the platinum compounds and categorises 18 pertinent citations according to criteria adapted from the US Preventive Services Task Force. Thirteen citations (four level I, six level II-1, three level II-2) pertained to LSSs for carboplatin, four citations (one level II-1, one level II-2, two level III) to cisplatin LSSs, and one citation (level II-2) to nedaplatin. Based on the current evidence, it appears that LSSs may be useful for pharmacokinetic-guided dosage adjustments of carboplatin in both adults and children with cancer. Although some validation studies suggest that LSSs can be extended to different cancer populations or different chemotherapy regimens, other studies dispute this finding. Although the use of LSSs to predict the pharmacokinetic parameters of cisplatin and nedaplatin appear promising, the quality of evidence from published studies does not support routine implementation at this time.LSSs represent one approach in which clinicians can make specific dosage adjustments for individual patients to optimise outcomes. However, the limitations of these strategies must also be taken into consideration. There is also a need for prospective studies to demonstrate that application of LSSs for platinum agents ultimately improves patient response and decreases systemic toxicities.

Real-time dose adjustment of cyclophosphamide in a preparative regimen for hematopoietic cell transplant: a Bayesian pharmacokinetic approach

PURPOSE

Dose-related toxicity of cyclophosphamide may be reduced and therapeutic efficacy may be improved by pharmacokinetic sampling and dose adjustment to achieve a target area under the curve (AUC) for two of its metabolites, hydroxycyclophosphamide (HCY) and carboxyethylphosphoramide mustard (CEPM). To facilitate real-time dose adjustment, we developed open-source code within the statistical software R that incorporates individual data into a population pharmacokinetic model.

EXPERIMENTAL DESIGN

Dosage prediction performance was compared to that obtained with nonlinear mixed-effects modeling using NONMEM in 20 cancer patients receiving cyclophosphamide. Bayesian estimation of individual pharmacokinetic parameters was accomplished from limited (i.e., five samples over 0-16 hours) sampling of plasma HCY and CEPM after the initial cyclophosphamide dose. Conditional on individual pharmacokinetics, simulations of the AUC of both HCY and CEPM were provided for a range of second doses (i.e., 0-100 mg/kg cyclophosphamide).

RESULTS

The results compared favorably with NONMEM and returned accurate predictions for AUCs of HCY and CEPM with comparable mean absolute prediction error and root mean square prediction error. With our method, the mean absolute prediction error and root mean square prediction error of AUC CEPM were 11.0% and 12.8% and AUC HCY were 31.7% and 44.8%, respectively.

CONCLUSIONS

We developed dose adjustment software that potentially can be used to adjust cyclophosphamide dosing in a clinical setting, thus expanding the opportunity for pharmacokinetic individualization of cyclophosphamide. The software is simple to use (requiring no programming experience), reads individual patient data directly from an Excel spreadsheet, and runs in less than 5 minutes on a desktop PC.

In vitro methods in human drug biotransformation research: implications for cancer chemotherapy

Anticancer drugs have a complex pharmacological and toxicological profile with a narrow therapeutic index. It is therefore critical to understand the factors that contribute to the marked intersubject variability in the pharmacokinetics and pharmacodynamics often observed with anticancer compounds. Since hepatic and extra-hepatic drug metabolism represents a major drug disposition pathway, extensive efforts are made to thoroughly investigate metabolism of anticancer compounds during the pre-clinical and clinical development phases as well as to address issues encountered during the clinical use of an approved drug. In recent years there has been a significant paradigm shift in pre-clinical/non-clinical drug metabolism studies. Most importantly, this has included a reduced reliance on animal models and increased use of human tissues (i.e. human liver microsomes and other cellular fractions, primary culture of human hepatocytes, cDNA expressed human-specific enzymes and cell-based reporter assays). Typically, experiments are performed using these tools to identify the phase I and/or phase II enzymes involved in metabolism of the drug/investigational agent and for metabolic fingerprinting. Additionally, issues pertaining to the rate, extent and mechanism(s) of the inhibition or induction of the metabolic pathways are also investigated. These studies provide important clues about various aspects of the disposition of a therapeutic agent including first-pass metabolism, elimination half-life, overall bioavailability and the potential for drug-drug interactions. The methodologies used for in vitro assessment of drug metabolism and their applications to drug development and clinical therapeutics with special emphasis on anticancer drugs are reviewed in this manuscript.

Population Pharmacokinetics of High-Dose Methotrexate in Children with Acute Lymphoblastic Leukaemia

OBJECTIVE

To develop and a priori validate a methotrexate population pharmacokinetic model in children with acute lymphoblastic leukaemia (ALL), receiving high-dose methotrexate followed by folinic acid rescue, identifying the covariates that could explain part of the pharmacokinetic variability of methotrexate.

METHODS

The study was carried out in 49 children (aged 6 months to 17 years) who received high-dose methotrexate (3 g/m(2) per course) in long-term treatment. In an index group (37 individuals; 1236 methotrexate plasma concentrations), a population pharmacokinetic model was developed using a nonlinear mixed-effects model. The remaining patients' data (12 individuals; 278 methotrexate plasma concentrations) were used for model validation. Age, sex, total bodyweight (TBW), height, body surface area, lowest urine pH during infusion, serum creatinine, ALT, AST, folinic acid dose and length of rescue were analysed as possible covariates. The final predictive performance of the pharmacokinetic model was tested using standardised mean prediction errors.

RESULTS

The final population pharmacokinetic model (two-compartmental) included only age and total bodyweight as influencing clearance (CL) and volume of distribution of central compartment (V(1)). For children aged < or =10 years: CL (L/h) = 0.287 . TBW(0.876); V(1) (L) = 0.465 . TBW, and for children aged >10 years: CL (L/h) = 0.149 . TBW; V(1) (L) = 0.437 . TBW. From the base to the final model, the inter-individual variabilities for CL and V(1) were significantly reduced in both age groups (30-50%). The coefficients of variation of the pharmacokinetic parameters were <30%, while residual and inter-occasional coefficients maintained values close to 40%. Validation of the proposed model revealed the suitability of the model.

CONCLUSION

A methotrexate population pharmacokinetic model has been developed for ALL children. The proposed model could be used in Bayesian algorithms with a limited sampling strategy to estimate the systemic exposure of individual patients to methotrexate and adapt both folinic acid rescue and methotrexate dosing accordingly.

Chemotherapy-induced anaemia during adjuvant treatment for breast cancer: development of a prediction model

BACKGROUND

At present, oncologists prescribe chemotherapy according to standard dose schedules, and as a result many patients develop serious, dose-limiting toxic effects such as anaemia. We aimed to develop a prediction model for anaemia in patients with breast cancer who were receiving adjuvant chemotherapy.

METHODS

We reviewed medical records of 331 patients who had received adjuvant chemotherapy for breast cancer. Patients were divided randomly into a derivation sample (n=221) and internal-validation sample (n=110). An external sample of 119 patients enrolled onto the control group of a randomised trial of epoetin alfa was used to validate the model further. Multivariable logistic regression was applied to develop the initial model. We then developed a risk-scoring system, ranging from 0 (low risk) to 50 (high risk), based on the final regression variables. A receiver operating characteristic (ROC) curve analysis was done to measure the accuracy of the scoring system when applied to both validation samples.

FINDINGS

The risk of anaemia increased as the pretreatment haemoglobin concentration decreased and was reduced with successive chemotherapy cycles. Risk was also predicted by a platelet count of 200x10(9) cells/L or less before chemotherapy, age 65 years or older, type of adjuvant chemotherapy, and use of prophylactic antibiotics. ROC analysis had acceptable areas under the curve of 0.88 for the internal-validation sample and 0.84 for the external validation sample. A risk score of > or = 24 to < 25 before chemotherapy was identified as the optimum cut-off for maximum sensitivity (83.5%) and specificity (92.3%) of the prediction model.

INTERPRETATION

The application and continued refinement of this prediction model will help oncologists to identify patients at risk of developing anaemia during chemotherapy for breast cancer, and might enhance patient-centred care by the application of anaemia treatment in a proactive and appropriate way.

Pharmacokinetic variability of anticancer agents

The translation of advances in cancer biology to drug discovery can be complicated by pharmacokinetic variation between individuals and within individuals, and this can result in unpredictable toxicity and variable antineoplastic effects. Previously unrecognized variables (such as genetic polymorphisms) are now known to have a significant impact on drug disposition. How can the pharmacokinetic variability of anticancer agents be reduced? This will require the understanding of correlations between pharmacokinetics and treatment outcomes, the identification of relevant patient parameters, mathematical modelling of individual and population pharmacokinetics, and the development of algorithms that will tailor doses to the individual patient.

Individualised Cancer Chemotherapy: Strategies and Performance of Prospective Studies on Therapeutic Drug Monitoring with Dose Adaptation

Therapeutic drug monitoring (TDM) is increasingly used in clinical practice for the optimisation of drug treatment. Although pharmacokinetic variability is an established factor involved in the variation of therapeutic outcome of many chemotherapeutic agents, the use of TDM in the field of oncology has been limited thus far. An important reason for this is that a therapeutic index for most anticancer agents has not been established; however, in the last 20 years, relationships between plasma drug concentrations and clinical outcome have been defined for various chemotherapeutic agents. Several attempts have been made to use these relationships for optimising the administration of chemotherapeutics by applying pharmacokinetically guided dosage. These prospective studies, individualising chemotherapy dose during therapy based on measured drug concentrations, are discussed in this review. We focus on the way a target value is defined, the methodologies used for dose adaptation and the way the performance of the dose-adaptation approach is evaluated. Furthermore, attention is paid to the results of the studies and the applicability of the strategies in clinical practice. It can be concluded that TDM may contribute to improving cancer chemotherapy in terms of patient outcome and survival and should therefore be further investigated.

Maximum a posteriori Bayesian estimation of epirubicin clearance by limited sampling

AIMS

To develop a limited sampling strategy for estimation of epirubicin clearance.

METHODS

The data set comprised 1051 concentrations measured in 105 patients with advanced or metastatic breast cancer treated with epirubicin alone. Ten limited sampling designs comprising two or three blood samples were proposed, taken at times identified by D-optimality from population pharmacokinetic parameter estimates. The data set was then truncated to include the sampling times for each of the designs. MAP Bayesian estimates of clearance were generated for each design and compared with clearance estimates obtained using all the data. The limited sampling designs were also validated using a separate data set obtained from 18 patients with either breast cancer or hepatocellular carcinoma. The sensitivity of the best limited sampling designs to sample time recording errors of 0-10% or 10-20% was then assessed using a simulated data set including 200 patients.

RESULTS

The optimum sampling times were: end of the injection and 18 min, 40 min, 3 h, 10 h and 48 h after the start of the injection. The best three-sample design included samples at 40 min, 3 h and 48 h and gave unbiased estimates of clearance with an imprecision of 9.1% (95% CI 7.3, 10.5). The best two sample design included samples at 3 and 48 h and gave unbiased estimates of clearance with an imprecision of 12.4% (95% CI 9.6, 14.6). Using the validation data set, these two and three sample designs gave unbiased estimates of clearance with an imprecision of 5.6% (95% CI 3.7, 7.0) and 4.2% (95% CI 2.6, 5.3), respectively. Simulations that included 0-10% or 10-20% errors in the recording of the blood sampling times had negligible effects on the bias and imprecision of clearance estimates.

CONCLUSIONS

Limited sampling designs have been identified and validated that estimate epirubicin clearance with adequate precision and without bias from two or three blood samples. These designs also allow flexibility in blood sample collection and are robust with regard to sample time recording errors.

Development of an optimal sampling strategy for clinical pharmacokinetic studies of the novel anthracycline disaccharide analogue MEN-10755

AIM

MEN-10755 is a novel anthracycline analogue that has shown an improved therapeutic efficacy over doxorubicin in animal models, especially in gynaecological and lung cancers and is currently under clinical development for the treatment of solid tumours. The aim of the project was to develop an optimal sampling strategy for MEN-10755 to provide an efficient basis for future pharmacokinetic/pharmacodynamic investigations.

METHODS

Data from 24 patients who participated in a phase I clinical pharmacokinetic study of MEN-10755 administered as a short i.v. infusion were included. Individual pharmacokinetic values were calculated by fitting the plasma concentration data to a two-compartment model using nonlinear least-squared regression (KINFIT, Ed 3.5). Population pharmacokinetic analysis was carried out using (a) the traditional standard two-stage method (STS) based on all data (KINFIT-ALL), (b) the iterative two-stage Bayesian (IT(2)B) population modelling algorithm (KINPOP), and (c) the STS method using KINFIT and using four optimally timed plasma concentrations (KINFIT-OSS4). Determinant (D) optimal sampling strategy (OSS) was used to evaluate the four most information-rich sampling times. The pharmacokinetic parameters V(c) (l), k(el) (h(-1)), k(12) (h(-1)) and k(21) (h(-1)) calculated using KINPOP served as a model for calculation of four D-optimal sampling times. D-optimal sampling data sets were analysed using KINFIT-OSS4 and compared with the population model obtained by the traditional standard two-stage approach for all data sets (KINFIT-ALL).

RESULTS

The optimal sampling times were: the end of the infusion, and 1.5 h, 3.8 h and 24 h after the start of the infusion. The four-point D-optimal sampling design determined in this study gave individual parameter estimates close to the basic standard estimates using the full data set.

CONCLUSION

Because accurate estimates of pharmacokinetic parameters were achieved, the four-point D-optimal sampling design may be very useful in future studies with MEN-10755.

Pharmacology of anticancer drugs in the elderly population

Modifications to bodily functions and physiology are known to occur with age. These changes can have a considerable impact on the pharmacokinetic processes of absorption, distribution, metabolism and excretion and the pharmacodynamic properties of administered drugs. For many drugs with a high therapeutic index, this will be clinically unimportant, but for anticancer drugs, which usually have a low therapeutic index, these pharmacological changes can lead to dramatic consequences, such as excessive drug concentrations and unacceptable toxicity, or subtherapeutic drug concentrations and ineffective treatment. Despite the increased susceptibility of the elderly to these changes, doses are rarely adapted on the basis of pharmacokinetics and pharmacodynamics, with the exception of changes secondary to altered renal function. Until recently, only a few large prospective randomised trials have provided evidence-based data for dose adaptations in elderly patients. However, with increasing knowledge of the pharmacokinetics of anticancer drugs, advances in the knowledge of pharmacokinetic behaviour with aging, and documented efficacy and toxicity data in the elderly population, it is possible to highlight aspects of prescribing anticancer drugs in the elderly. In general, and for most drugs, age itself is not a contraindication to full-dose chemotherapy. The main limiting factors are comorbidity and poor functional status, which may be present in a significant number of the elderly population. Elderly patients with cancer are part of the daily practice of oncologists, but currently clinicians can often only estimate whether dose modification is advantageous for the elderly. This review attempts to elucidate the factors that can influence the pharmacokinetics of anticancer drugs frequently used in the elderly, and the clinical or biochemical parameters that form the basis for dose adjustments with age.

Bayesian estimation of methotrexate pharmacokinetic parameters and area under the curve in children and young adults with localised osteosarcoma

BACKGROUND

Methotrexate is the most efficient anticancer drug in osteosarcoma. It requires individual exposure monitoring because of the high doses used, its wide interpatient pharmacokinetic variability and the existence of demonstrated relationships between efficacy, toxicity and serum drug concentrations.

OBJECTIVE

To develop a maximum a posteriori (MAP) Bayesian estimator able to predict individual pharmacokinetic parameters and exposure indices such as area under the curve (AUC) for methotrexate from a few blood samples, in order to prevent toxicity and facilitate further studies of the relationships between efficacy and exposure.

METHODS

Methotrexate population pharmacokinetics were estimated by a retrospective analysis of concentration data from 40 children and young adults by using the nonparametric expectation maximisation method NPEM. A linear two-compartment model with elimination from the central compartment was assumed. Individual pharmacokinetic parameters and AUC were subsequently estimated in 30 other young patients, using MAP Bayesian estimation as implemented in two programs, ADAPT II and an inhouse program Winphar((R)).

RESULTS

The pharmacokinetic parameters used in the model were the volume of the central compartment (V(1)) and the transfer constants (k(10), k(12) and k(21)). The mean values (with percentage coefficient of variation) obtained were: 18.24L (54.1%) and 0.41 (42.3%), 0.0168 (68.7%), and 0.1069 (61.3%) h(-1), respectively. Bayesian forecasting enabled nonbiased estimation of AUC and systemic clearance using a schedule with two sampling times (6 and 24 hours after the beginning of the infusion) and either program. Collection of a third sample at 4 hours improved the precision.

CONCLUSION

The Bayesian adaptive method developed herein allows accurate estimation of individual exposure to methotrexate and can easily be used in clinical practice.

Up-regulation of MDR1 function and expression by cisplatin in LLC-PK1 cells

To examine whether cisplatin affects the multidrug transporter MDR1/P-glycoprotein in the kidneys, the effects of cisplatin on cell sensitivity to an anticancer drug, MDR1 function and expression were examined by assessing the growth inhibition by the MDR1 substrate paclitaxel, the uptake and efflux of the MDR1 substrate Rhodamine123 and the level of MDR1 mRNA, respectively. Porcine kidney epithelial LLC-PK1 cells were used, as they have a structure and function similar to those of renal proximal tubular cells and physiologically express low levels of MDR1. The growth inhibitory curve of LLC-PK1 cells by paclitaxel was shifted to a higher concentration range by pretreatment with 1 micro M cisplatin for 48 h. The uptake and efflux of Rhodamine123 were significantly reduced and enhanced, respectively, by pretreatment with 1 micro M cisplatin for 48 h. This enhanced efflux was suppressed by the representative MDR1 substrate/inhibitor ciclosporin. The expression of MDR1 mRNA was increased by the existence of cisplatin for 48 h. These observations taken together suggested that the transient exposure to cisplatin could cause the up-regulation of MDR1 in LLC-PK1 cells.

Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs

Over the last 10 years, proofs of the clinical interest of therapeutic drug monitoring (TDM) of certain anticancer drugs have been established. Numerous studies have shown that TDM is an efficient tool for controlling the toxicity of therapeutic drugs, and a few trials have even demonstrated that it can improve their efficacy. This article critically reviews TDM tools based on pharmacokinetic modelling of anticancer drugs. The administered dose of anticancer drugs is sometimes adjusted individually using either a priori or a posteriori methods. The most frequent clinical application of a priori formulae concerns carboplatin and allows the computation of the first dose based on biometrical and biological data such as weight, age, gender, creatinine clearance and glomerular filtration rate. A posteriori methods use drug plasma concentrations to adjust the subsequent dose(s). Thus, nomograms allowing dose adjustment on the basis of blood concentration are routinely used for 5-fluorouracil given as long continuous infusions. Multilinear regression models have been developed, for example for etoposide, doxorubicin. carboplatin, cyclophosphamide and irinotecan, to predict a single exposure variable [such as area under concentration-time curve (AUC)] from a small number of plasma concentrations obtained at predetermined times after a standard dose. These models can only be applied by using the same dose and schedule as the original study. Bayesian estimation offers more flexibility in blood sampling times and, owing to its precision and to the amount of information provided, is the method of choice for ensuring that a given patient benefits from the desired systemic exposure. Unlike the other a posteriori methods, Bayesian estimation is based on population pharmacokinetic studies and can take into account the effects of different individual factors on the pharmacokinetics of the drug. Bayesian estimators have been used to determine maximum tolerated systemic exposure thresholds (e.g. for topotecan or teniposide) as well as for the routine monitoring of drugs characterized by a very high interindividual pharmacokinetic variability such as methotrexate or carboplatin. The development of these methods has contributed to improving cancer chemotherapy in terms of patient outcome and survival and should be pursued.

Bayesian population model of methotrexate to guide dosage adjustments for folate rescue in patients with breast cancer

BACKGROUND

Methotrexate (MTX) infusions may induce severe side-effects, and alkaline hydration along with folinic acid rescue is a common way to reduce such toxic risks. The purpose of this study was to develop an adaptive rescue strategy based upon the early detection of patients with impaired MTX elimination.

METHODS AND RESULTS

In this study, we propose a simple population-based Bayesian approach for predicting MTX plasma concentration from a limited number of samples, so as to adapt both duration and dosage of the rescue agent to be used next. Ten kinetic profiles obtained after 10 courses of MTX (1.5 g/m2) in seven patients with inflammatory breast cancer were used to establish the population pharmacokinetic parameters (Cl, 8.16 L/h; t1/2, 12.7 h). This population was next involved in the Bayesian estimation of MTX individual pharmacokinetic parameters from only two blood samples (T24 and T36 h), thus allowing one to forecast the elimination of this drug by predicting MTX levels at 48 h. According to the MTX concentrations predicted during the elimination phase, folinic acid rescue was then prolonged in patients likely to be overexposed.

CONCLUSION

The Bayesian estimation presented in this study was an easy and convenient method to efficiently detect patients with impaired MTX elimination in routine clinical practice. This information enabled the introduction of strategies for minimizing the risk of severe drug toxicity.