Predictive performance of population pharmacokinetic models of imatinib in chronic myeloid leukemia patients.

BACKGROUND AND AIM: Chronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram. METHODS: A systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions. RESULTS: Eight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models. CONCLUSION: Menon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.

Validated HPLC method for ceftriaxone from dried blood spots for pharmacokinetic studies and therapeutic drug monitoring in neonatal population.

Background: Pharmacokinetic evaluation is essential for the precise dosing of ceftriaxone in neonates. There is a need for developing a sensitive, affordable and convenient analytical method that can estimate ceftriaxone from dried blood spot (DBS) samples of neonates. Method: An HPLC-UV method was developed and validated as per ICH M10 for ceftriaxone from DBS and plasma using an Inertsil-ODS-3V column with gradient elution. DBS samples were extracted with methanol. Clinical validation was performed using neonatal samples. Results: The developed plasma- and DBS-based-HPLC method were linear from 2-700 µg/ml and 2-500 µg/ml, respectively, for ceftriaxone. Bland-Altman analysis indicated a strong interconvertibility between the plasma and DBS assays. Conclusion: Observed concentrations in clinical samples were comparable to the predicted concentrations, proving the clinical validity of the method.

Neonatal meningitis is usually treated with the drug ceftriaxone. It is essential to determine the amount of ceftriaxone in the blood for precise dosing that is safe and effective in newborn children. Dried blood spot (DBS) sampling from a heel prick is a less invasive sample collection method for newborn children than withdrawing blood from a vein. In this study we developed a robust analytical method based on a commonly available analytical platform to measure ceftriaxone in DBS samples. The results of ceftriaxone analysis obtained using the developed DBS-based method were compared with the results of traditional plasma sample analysis to make sure that they are accurate and reproducible. The range of analysis of the method is 2­500 µg/ml, which is suitable for the measurement of ceftriaxone concentration levels in newborns' blood. The effect of varying proportions of red blood cells in whole blood (30­50%) and volume of blood (20­60 µl) applied on the DBS card were not found to influence the results. The method was validated with clinical samples from newborn children and resulted in the expected concentrations. The developed method is affordable and can be used for therapeutic drug monitoring and precise dosing of ceftriaxone in newborn children.

Predictive Performance of Population Pharmacokinetic Models for Amikacin in Term Neonates.

BACKGROUND AND OBJECTIVE: Amikacin is preferred in treating Gram-negative infections in neonates and it has a narrow therapeutic window. The population pharmacokinetic modeling approach can aid in designing optimal dosage regimens for amikacin in neonates. In this study, we attempted to identify the suitable population pharmacokinetic model from the published reports for the study population from an Indian setting. METHODS: Published population pharmacokinetic studies for amikacin in neonates were identified. Data on structural models and typical pharmacokinetic parameters were extracted from the studies. For the clinical study, neonates who met the inclusion criteria were enrolled in the study from the NICU, Kasturba Medical College, Manipal, during Jan 2020 to March 2022. Drug concentrations were estimated, and demographic and clinical data were collected. Identified population pharmacokinetic models were used to predict the amikacin concentrations in neonates. Predicted concentrations were compared against the observed concentrations. Differences between predicted and observed concentrations were quantified using statistical measures. The population pharmacokinetic model, which was able to predict the data well, is considered a suitable model for the study population. Dosing regimens were suggested for neonates using the pharmacometric simulation approach generated by the selected model. RESULTS: A total of 43 plasma samples were collected from 31 neonates. Twelve population pharmacokinetic models were found for amikacin in neonates. The predictive performance of the 12 studies was performed using clinical data. A two-compartment model reported by Illamola et al. predicted the amikacin concentrations better than other models. Illamola et al. reported creatinine clearance and body weight as the significant covariates impacting the pharmacokinetic parameters of amikacin. This model was able to predict the clinical data with 29.97% and 0.686 of relative median absolute prediction error and relative root mean square error, respectively, which is the best among the published models. The Illamola et al. model was selected as the final model to perform pharmacometric simulations for the subjects with different combinations of creatinine clearance and body weight. Dosage regimens were designed to attain target therapeutic concentrations for the virtual subjects and a nomogram was developed. CONCLUSIONS: The population pharmacokinetic model reported by the Illamola et al. model was selected as the final model to explain the clinical data with the lowest relative median absolute prediction error and relative root mean square error when compared with other models. An amikacin nomogram was developed for the neonates whose creatinine clearance and body weight ranged between 10 and 90 mL/min and between 2 and 4 kg, respectively. A developed nomogram can assist clinicians to design an optimal dosage regimen of amikacin for term neonates.

Factors Influencing Pharmacokinetics of Tamoxifen in Breast Cancer Patients: A Systematic Review of Population Pharmacokinetic Models.

BACKGROUND: Tamoxifen is useful in managing breast cancer and it is reported to have significant variability in its pharmacokinetics. This review aimed to summarize reported population pharmacokinetics studies of tamoxifen and to identify the factors affecting the pharmacokinetics of tamoxifen in adult breast cancer patients. METHOD: A systematic search was undertaken in Scopus, Web of Science, and PubMed for papers published in the English language from inception to 20 August 2022. Studies were included in the review if the population pharmacokinetic modeling was based on non-linear mixed-effects modeling with a parametric approach for tamoxifen in breast cancer patients. RESULTS: After initial selection, 671 records were taken for screening. A total of five studies were selected from Scopus, Web of Science, PubMed, and by manual searching. The majority of the studies were two-compartment models with first-order absorption and elimination to describe tamoxifen and its metabolites' disposition. The CYP2D6 phenotype and CYP3A4 genotype were the main covariates that affected the metabolism of tamoxifen and its metabolites. Other factors influencing the drug's pharmacokinetics included age, co-medication, BMI, medication adherence, CYP2B6, and CYP2C19 genotype. CONCLUSION: The disposition of tamoxifen and its metabolites varies primarily due to the CYP2D6 phenotype and CYP3A4 genotype. However, other factors, such as anthropometric characteristics and menopausal status, should also be addressed when accounting for this variability. All these studies should be externally evaluated to assess their applicability in different populations and to use model-informed dosing in the clinical setting.

Pharmacometric approach to assist dosage regimen design in neonates undergoing therapeutic hypothermia.

BACKGROUND: Therapeutic hypothermia (TH) is the treatment of choice for neonates diagnosed with perinatal asphyxia (PA). Dosing recommendations of various therapeutic agents including antimicrobials were not specifically available for the neonates undergoing TH. METHODS: A systematic search methodology was used to identify pharmacokinetic (PK) studies of antimicrobials during TH. Antimicrobials with multiple PK studies were identified to create a generalizable PK model. Pharmacometric simulations were performed using the PUMAS software platform to reproduce the results of published studies. A suitable model that could reproduce the results of all other published studies was identified. With the help of a generalizable model, an optimal dosage regimen was designed considering the important covariates of the identified model. RESULTS: With the systematic search, only gentamicin had multiple PK reports during TH. A generalizable model was identified and the model predictions could match the reported/observed concentrations of publications. Birth weight and serum creatinine were the significant covariates influencing the PK of gentamicin in neonates. A dosage nomogram was designed using pharmacometric simulations to maintain gentamicin concentrations below 10 µg/mL at peak and below 2 µg/mL at trough. CONCLUSIONS: A generalizable PK model for gentamicin during TH in neonates was identified. Using the model, a dosing nomogram for gentamicin was designed. IMPACT: Dosing guidelines for antimicrobials during TH in neonates is lacking. This is the first study to identify the generalizable model for gentamicin during TH in neonates. Nomogram, proposed in the study, will aid the clinicians to individualize gentamicin dosing regimen for neonates considering the birth weight and serum creatinine.