MTXPK.org: A Clinical Decision Support Tool Evaluating High-Dose Methotrexate Pharmacokinetics to Inform Post-Infusion Care and Use of Glucarpidase

Methotrexate (MTX), an antifolate, is administered at high doses to treat malignancies in children and adults. However, there is considerable interpatient variability in clearance of high‐dose (HD) MTX. Patients with delayed clearance are at an increased risk for severe nephrotoxicity and life‐threatening systemic MTX exposure. Glucarpidase is a rescue agent for severe MTX toxicity that reduces plasma MTX levels via hydrolysis of MTX into inactive metabolites, but is only indicated when MTX concentrations are > 2 SDs above the mean excretion curve specific for the given dose together with a significant creatinine increase (> 50%). Appropriate administration of glucarpidase is challenging due to the ambiguity in the labeled indication. A recent consensus guideline was published with an algorithm to provide clarity in when to administer glucarpidase, yet clinical interpretation of laboratory results that do not directly correspond to the algorithm prove to be a limitation of its use. The goal of our study was to develop a clinical decision support tool to optimize the administration of glucarpidase for patients receiving HD MTX. Here, we describe the development of a novel 3‐compartment MTX population pharmacokinetic (PK) model using 31,672 MTX plasma concentrations from 772 pediatric patients receiving HD MTX for the treatment of acute lymphoblastic leukemia and its integration into the online clinical decision support tool, MTXPK.org. This web‐based tool has the functionality to utilize individualized demographics, serum creatinine, and real‐time drug concentrations to predict the elimination profile and facilitate model‐informed administration of glucarpidase.

Systematic Review of Pharmacogenetic Factors That Influence High-Dose Methotrexate Pharmacokinetics in Pediatric Malignancies

Methotrexate (MTX) is a mainstay therapeutic agent administered at high doses for the treatment of pediatric and adult malignancies, such as acute lymphoblastic leukemia, osteosarcoma, and lymphoma. Despite the vast evidence for clinical efficacy, high-dose MTX displays significant inter-individual pharmacokinetic variability. Delayed MTX clearance can lead to prolonged, elevated exposure, causing increased risks for nephrotoxicity, mucositis, seizures, and neutropenia. Numerous pharmacogenetic studies have investigated the effects of several genes and polymorphisms on MTX clearance in an attempt to better understand the pharmacokinetic variability and improve patient outcomes. To date, several genes and polymorphisms that affect MTX clearance have been identified. However, evidence for select genes have conflicting results or lack the necessary replication and validation needed to confirm their effects on MTX clearance. Therefore, we performed a systematic review to identify and then summarize the pharmacogenetic factors that influence high-dose MTX pharmacokinetics in pediatric malignancies. Using the PRISMA guidelines, we analyzed 58 articles and 24 different genes that were associated with transporter pharmacology or the folate transport pathway. We conclude that there is only one gene that reliably demonstrates an effect on MTX pharmacokinetics: SLCO1B1.

Clinical implementation of pharmacogenetics and model-informed precision dosing to improve patient care

Providing maximal therapeutic efficacy without toxicity is a universal goal of rational drug therapy. However, substantial between-patient variability in drug response often impedes such successful treatments and brings the necessity of tailoring drug dose to individual needs for more precise therapy. In many cases plenty of patient characteristics, such as body size, genetic makeup and environmental factors, need to be taken into consideration to find the optimal dose in clinical practice. A pharmacokinetics and pharmacodynamics (PK/PD) model-informed approach offers integration of various patient information to provide an expectation of drug response and derive practical dose estimates to support clinicians' dosing decisions. Such an approach was pioneered in the late 1970s, but its broad clinical acceptance and implementation have been hampered by the lack of widespread computer technology, including user-friendly software tools. This has significantly changed in recent years. With the advent of electronic health records (EHRs) and the ubiquity of user-friendly software tools, we now experience a convergence of clinical information, pharmacogenetics, systems pharmacology and pharmacometrics, and technology. Advanced pharmacometrics research is now more appliable and implementable to improve health care. This article presents examples of successful development and implementation of pharmacogenetics-guided and PK/PD model-informed decision support to facilitate precision dosing, including the development of an EHR-embedded decision support tool. Through the integration of clinical decision support tools in EHRs, clinical pharmacometrics support can be brought directly to the clinical team and the bedside.

Escitalopram and Sertraline Population Pharmacokinetic Analysis in Pediatric Patients

BACKGROUND AND OBJECTIVE

Escitalopram and sertraline are commonly prescribed for anxiety and depressive disorders in children and adolescents. The pharmacokinetics (PK) of these medications have been evaluated in adults and demonstrate extensive variability, but studies in pediatric patients are limited. Therefore, we performed a population PK analysis for escitalopram and sertraline in children and adolescents to characterize the effects of demographic, clinical, and pharmacogenetic factors on drug exposure.

METHODS

A PK dataset was generated by extracting data from the electronic health record and opportunistic sampling of escitalopram- and sertraline-treated psychiatrically hospitalized pediatric patients aged 5-18 years. A population PK analysis of escitalopram and sertraline was performed using NONMEM. Concentration-time profiles were simulated using MwPharm++ to evaluate how covariates included in the final models influence medication exposure and compared to adult therapeutic reference ranges.

RESULTS

The final escitalopram cohort consisted of 315 samples from 288 patients, and the sertraline cohort consisted of 265 samples from 255 patients. A one-compartment model with a proportional residual error model best described the data for both medications. For escitalopram, CYP2C19 phenotype and concomitant CYP2C19 inhibitors affected apparent clearance (CL/F), and normalizing CL/F and apparent volume of distribution (V/F) to body surface area (BSA) improved estimations. The final escitalopram model estimated CL/F and V/F at 14.2 L/h/1.73 m2 and 428 L/1.73 m2, respectively. For sertraline, CYP2C19 phenotype and concomitant CYP2C19 inhibitors influenced CL/F, and empirical allometric scaling of patient body weight on CL/F and V/F was significant. The final sertraline model estimated CL/F and V/F at 124 L/h/70 kg and 4320 L/70 kg, respectively. Normalized trough concentrations (Ctrough) for CYP2C19 poor metabolizers taking escitalopram were 3.98-fold higher compared to normal metabolizers (151.1 ng/mL vs 38.0 ng/mL, p < 0.0001), and normalized Ctrough for CYP2C19 poor metabolizers taking sertraline were 3.23-fold higher compared to normal, rapid, and ultrarapid metabolizers combined (121.7 ng/mL vs 37.68 ng/mL, p < 0.0001). Escitalopram- and sertraline-treated poor metabolizers may benefit from a dose reduction of 50-75% and 25-50%, respectively, to normalize exposure to other phenotypes.

CONCLUSION

To our knowledge, this is the largest population PK analysis of escitalopram and sertraline in pediatric patients. Significant PK variability for both medications was observed and was largely explained by CYP2C19 phenotype. Slower CYP2C19 metabolizers taking escitalopram or sertraline may benefit from dose reductions given increased exposure.

Insights from a pharmacometric analysis of HDMTX in adults with cancer: Clinically relevant covariates for application in precision dosing

AIMS

High-dose methotrexate (HDMTX) is an essential part of the treatment of several adult and paediatric malignancies. Despite meticulous supportive care during HDMTX administration, severe toxicities, including acute kidney injury (AKI), may occur contributing to patient morbidity. Population pharmacokinetics provide a powerful tool to predict time to clear HDMTX and adjust subsequent doses. We sought to develop and validate pharmacokinetic models for HDMTX in adults with diverse malignancies and to relate systemic exposure with the occurrence of severe toxicity.

METHODS

Anonymized, de-identified data were provided from 101 US oncology practices that participate in the Guardian Research Network, a non-profit clinical research consortium. Modelled variables included clinical, laboratory, demographic and pharmacological data. Population pharmacokinetic analysis was performed by means of nonlinear mixed effects modelling using MonolixSuite.

RESULTS

A total of 693 HDMTX courses from 243 adults were analysed, of which 62 courses (8.8%) were associated with stage 2/3 acute kidney injury (43 stage 2, 19 stage 3). A three-compartment model adequately fitted the data. Time-dependent serum creatinine, baseline serum albumin and allometrically scaled bodyweight were clinically significant covariates related to methotrexate clearance. External evaluation confirmed a satisfactory predictive performance of the model in adults receiving HDMTX. Dose-normalized methotrexate concentration at 24 and 48 hours correlated with AKI incidence.

CONCLUSION

We developed a population pharmacometric model that considers weight, albumin and time-dependent creatinine that can be used to guide supportive care in adult patients with delayed HDMTX elimination.

SLCO1B1 *15 allele is associated with methotrexate-induced nausea in pediatric patients with inflammatory bowel disease

Low-dose methotrexate (MTX) is an immunosuppressant used to treat inflammatory bowel disease (IBD). SLCO1B1 genetic variation has been associated with delayed MTX clearance and increased toxicity. The purpose of this study was to evaluate the association between SLCO1B1 genetic variation and MTX-induced nausea in children with IBD. We performed a single center retrospective chart analysis of 278 patients who were prescribed MTX for IBD. Two hundred two patients had banked DNA and were genotyped for three SLCO1B1 single nucleotide polymorphisms (SNPs; rs4149056, rs2306283, and rs11045819). Diplotypes were determined by combining the SNPs into *1, *4, *5, *14, *15, and *37 alleles. Incidence of nausea was abstracted from clinician notes. Prescriptions and demographics were extracted from the medical record. The cohort was 69.8% boys, 89.1% White, and 87.6% had a diagnosis of Crohn's disease with a mean age of 16.0 (± 3.8) years. MTX-induced nausea was noted in 34% of the cohort. MTX-induced nausea was associated with the number of reduced-function *15 alleles (p = 0.034) and occurred 2.26 times more often in patients with at least one *15 allele who did not initiate MTX treatment with concomitant ondansetron (p = 0.034). MTX-induced nausea was significantly independently associated with SLCO1B1 diplotype (p = 0.006) after controlling for MTX dose group and concomitant ondansetron. Our data demonstrate that the SLCO1B1 *15 allele is associated with MTX-induced nausea in pediatric patients with IBD. Additionally, *15 allele carriers could benefit from a dose reduction of MTX to reduce exposure and treatment initiation with concomitant ondansetron to reduce nausea.

Toxicity profile of high-dose methotrexate in young children with central nervous system tumors

High-dose methotrexate (HD-MTX) is used in the treatment of children with central nervous system (CNS) tumors; however, toxicity information is limited. We characterized toxicities following 102 administrations of HD-MTX (4.6-13.5 g/m2) infused over 4 or 24 h in 38 children with a CNS tumor before 6 years of age (2010-2020). Delayed clearance of methotrexate occurred following 24% of infusions. Common Terminology Criteria for Adverse Events v5 grade 2-3 mucositis was observed in 47% of individuals, Grade 4 neutropenia in 76%, and grade 3-4 thrombocytopenia in 58%. No neurotoxicity was observed. HD-MTX can be safely used with supportive care and monitoring.

Influence of albumin and methotrexate clearance on high-dose methotrexate-induced mucositis

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Background: Methotrexate (MTX) is a cornerstone of therapy for several types of cancer. HDMTX-induced mucositis can impact quality of life and delay subsequent chemotherapy. Objective: The objective of this study was to understand the factors increasing risk for mucositis in patients receiving HDMTX in a multicenter cohort. Methods: A multi-center retrospective study collected data on pediatric and young adult patients ages 0-32 years at diagnosis who received at least one dose of HDMTX (>500 mg/m2) at Children’s Healthcare of Atlanta or Cincinnati Children’s Hospital Medical Center from January 2010 through December 2020. Demographic and clinical variables were manually abstracted, while medications and lab values were electronically pulled from the electronic medical record. Common Terminology Criteria for Adverse Events v5 was used to identify the presence and grade of mucositis after HDMTX administration. Institutional review board approval was obtained at each site. Chi-square and t-tests, as appropriate, were calculated using GraphPad Prism v9. MTX concentrations were fit to a published pharmacokinetic model to estimate clearance using NONMEM. Results: The cohort is described in Table. The rate of mucositis was highest after the first HDMTX administration (54.6%), when the MTX clearance is slowest (30.1%<10 L/hr/1.73m2 and albumin was lowest (14.3%<2.5g/dL). Mucositis was significantly more likely in patients with hypoalbuminemia and in patients with MTX clearance <10 L/hr/1.73m2 (OR 1.75 (CI: 1.503 to 2.028), p<0.0001 and OR 1.43 (CI: 1.236 to 1.653), p<0.0001, respectively). Conclusions: Mucositis after administration of HDMTX is common, with 72% of patients experiencing at least grade 1 mucositis and a third of HDMTX administrations leading to mucositis. Chart abstraction is ongoing at a third hospital to increase the sample size, particularly those of Hispanic/Latino ethnicity, in order to investigate differences by race and ethnicity. In addition, current analyses are evaluating the impact of changes in supportive care on HDMTX-induced mucositis and other adverse events. The results of this study will demonstrate who is at highest risk for adverse events so supportive care can be tailored during this potentially toxic chemotherapy.[Table: see text]

Effects of obesity on the pharmacology of proton pump inhibitors: current understanding and future implications for patient care and research

INTRODUCTION

In the United States, obesity affects approximately ⅖ adults and ⅕ children, leading to increased risk for comorbidities, like gastroesophageal reflux disease (GERD), treated increasingly with proton pump inhibitors (PPIs). Currently, there are no clinical guidelines to inform PPI dose selection for obesity, with sparse data regarding whether dose augmentation is necessary.

AREAS COVERED

We provide a review of available literature regarding the pharmacokinetics (PK), pharmacodynamics (PD), and/or metabolism of PPIs in children and adults with obesity, as a step toward informing PPI dose selection.

EXPERT OPINION

Published PK data in adults and children are limited to first-generation PPIs and point toward reduced apparent oral drug clearance in obesity, with equipoise regarding obesity impact on drug absorption. Available PD data are sparse, conflicting, and limited to adults. No studies are available to inform the PPI PK→PD relationship in obesity and if/how it differs compared to individuals without obesity. In the absence of data, best practice may be to dose PPIs based on CYP2C19 genotype and lean body weight, so as to avoid systemic overexposure and potential toxicities, while monitoring closely for efficacy.

Toward pharmacogenetic SLCO1B1-guided dosing of methotrexate in arthritis using a murine Slco1b2 knockout model

Low‐dose methotrexate (MTX) is a first‐line therapy for the treatment of arthritis. However, there is considerable interindividual variability in MTX exposure following standard dosing. Polymorphisms in SLCO1B1 significantly effect MTX clearance, altering therapeutic response. One decreased function variant, rs4149056 (c.521T>C, Val174Ala), slows MTX clearance and in vitro uptake of MTX. This phenotype was recapitulated in a mouse model using a knockout (KO) of the murine orthologue, Slco1b2. Our objective was to investigate the impact of this phenotype on the pharmacokinetics and therapeutic outcomes of low‐dose MTX in a murine model of collagen‐induced arthritis (CIA). We evaluated response to MTX in mice with CIA using wildtype (WT), heterozygous, and KO Slco1b2 mice on a DBA1/J background. Arthritis was macroscopically evaluated daily to quantify disease progression. Mice received 2 mg/kg or a pharmacogenetically guided MTX dose subcutaneously 3 times a week for 2 weeks. MTX concentrations were collected at the end of the study and exposure (day*µM) was estimated using a two‐compartment model. Mice displayed a seven‐fold range in MTX exposure and revealed a significant exposure‐response relationship (p = 0.0027). KO mice receiving the 2 mg/kg dosing regimen had 2.3‐fold greater exposure to MTX (p < 0.0001) and a 66% reduction in overall disease progression (p = 0.011) compared to WT mice. However, exposure and response were equivalent when pharmacogenetically guided dosing was used. These studies demonstrate that an exposure‐response relationship exists for MTX and that Slco1b2 genotype affects MTX exposure and therapeutic response. Such evidence supports the use of SLCO1B1‐pharmacogenetic dosing of low‐dose MTX for patients with arthritis.

Early therapeutic drug monitoring of methotrexate and its association with acute kidney injury: A retrospective cohort study

INTRODUCTION

High-dose methotrexate (HDMTX) use can be limited by the development of acute kidney injury (AKI). Early AKI detection is paramount to prevent further renal injury and irreversible toxicities. This study sought to determine whether early elimination patterns of MTX would be useful as a biomarker of AKI in HDMTX treatment.

METHODS

This retrospective cohort study included two sites that collected ≥2 MTX levels within 16 h from completion of MTX infusion. Early levels were tagged and MTX elimination half-life (t½) were calculated from combinations of two of three different early time periods. Receiver operating characteristic (ROC) curves were synthesized for each elimination t½ (biomarker) with respect to AKI and delayed methotrexate elimination (DME); the biomarker with the highest area under the ROC curve (AUC) was tested in a multiple variable logistic regression model.

RESULTS

Data from 169 patients who received a total of 556 courses of HDMTX were analyzed. ROC analysis revealed MTX elimination t½ calculated from the second and third time periods had the highest AUC for AKI at 0.62 (interquartile range [IQR] 0.56-0.69) and DME at 0.86 (IQR 0.73-1.00). After adjusting for age, sex, dose (mg/m2), infusion duration, HDMTX course, and baseline estimated glomerular filtration rate, it remained significant for AKI with an OR of 1.29 and 95% confidence interval of 1.03-1.65.

CONCLUSION

Early MTX elimination t½ measured within 16 h of infusion completion was significantly associated with the development of AKI and serves as an early clearance biomarker that may identify patients who benefit from increased hydration, augmented leucovorin rescue, and glucarpidase administration.

Clinical covariates that improve the description of high dose methotrexate pharmacokinetics in a diverse population to inform MTXPK.org

The MTXPK.org webtool was launched in December 2019 and was developed to facilitate model-informed supportive care and optimal use of glucarpidase following the administration of high-dose methotrexate (HDMTX). One limitation identified during the original development of the MTXPK.org tool was the perceived generalizability because the modeled population comprised solely of Nordic pediatric patients receiving 24-h infusions for the treatment of acute lymphoblastic leukemia. The goal of our study is to describe the pharmacokinetics of HDMTX from a diverse patient population (e.g., races, ethnicity, indications for methotrexate, and variable infusion durations) and identify meaningful factors that account for methotrexate variability and improve the model's performance. To do this, retrospectively analyzed pharmacokinetic and toxicity data from pediatric and adolescent young adult patients who were receiving HDMTX (>0.5 g/m2 ) for the treatment of a cancer diagnosis from three pediatric medical centers. We performed population pharmacokinetic modeling referencing the original MTXPK.org NONMEM model (includes body surface area and serum creatinine as covariates) on 1668 patients, 7506 administrations of HDMTX, and 30,250 concentrations. Our results support the parameterizations of short infusion duration (<8 h) and the presence of Down syndrome on methotrexate clearance, the parameterization of severe hypoalbuminemia (<2.5 g/dL) on the intercompartmental clearance (Q2 and Q3), and the parameterization of pleural effusion on the volume of distribution (V1 and V2). These novel parameterizations will increase the generalizability of the MTXPK.org model once they are added to the webtool.

Assessment of Dosing Strategies for Pediatric Drug Products

Pediatric drug dosing is challenged by the heterogeneity of developing physiology and ethical considerations surrounding a vulnerable population. Often, pediatric drug dosing leverages findings from the adult population; however, recent regulatory efforts have motivated drug sponsors to pursue pediatric-specific programs to meet an unmet medical need and improve pediatric drug labeling. This paradigm is further complicated by the pathophysiological implications of obesity on drug distribution and metabolism and the roles that body composition and body size play in drug dosing. Therefore, we sought to understand the landscape of pediatric drug dosing by characterizing the dosing strategies from drug products recently approved for pediatric indications identified using FDA Drug Databases and analyze the impact of body size descriptors (age, body surface area, weight) on drug pharmacokinetics for several selected antipsychotics approved in pediatric patients. Our review of these pediatric databases revealed a dependence on body size-guided dosing, with 68% of dosing in pediatric drug labelings being dependent on knowing either the age, body surface area, or weight of the patient to guide dosing for pediatric patients. This dependence on body size-guided dosing drives the need for special consideration when dosing a drug in overweight and obese patients. Exploratory pharmacokinetic analyses in antipsychotics illustrate possible effects of drug exposure when applying different dosing strategies for this class of drugs. Future efforts should aim to further understand the pediatric drug dosing and obesity paradigm across pediatric age ranges and drug classes to optimize drug development and clinical care for this patient population.

Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing

There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.

CYP2D6 Phenotype Influences Pharmacokinetic Parameters of Venlafaxine: Results from a Population Pharmacokinetic Model in Older Adults with Depression

In this study, we aimed to improve upon a published population pharmacokinetic (PK) model for venlafaxine (VEN) in the treatment of depression in older adults, then investigate whether CYP2D6 metabolizer status affected model-estimated PK parameters of VEN and its active metabolite O-desmethylvenlafaxine. The model included 325 participants from a clinical trial in which older adults with depression were treated with open-label VEN (maximum 300 mg/day) for 12 weeks and plasma levels of VEN and O-desmethylvenlafaxine were assessed at weeks 4 and 12. We fitted a nonlinear mixed-effect PK model using NONMEM to estimate PK parameters for VEN and O-desmethylvenlafaxine adjusted for CYP2D6 metabolizer status and age. At both lower doses (up to 150 mg/day) and higher doses (up to 300 mg/day), CYP2D6 metabolizers impacted PK model-estimated VEN clearance, VEN exposure, and active moiety (VEN + O-desmethylvenlafaxine) exposure. Specifically, compared with CYP2D6 normal metabolizers, (i) CYP2D6 ultra-rapid metabolizers had higher VEN clearance; (ii) CYP2D6 intermediate metabolizers had lower VEN clearance; (iii) CYP2D6 poor metabolizers had lower VEN clearance, higher VEN exposure, and higher active moiety exposure. Overall, our study showed that including a pharmacogenetic factor in a population PK model could increase model fit, and this improved model demonstrated how CYP2D6 metabolizer status affected VEN-related PK parameters, highlighting the importance of genetic factors in personalized medicine.