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.

Influence of N-acetyltransferase 2 (NAT2) genotype/single nucleotide polymorphisms on clearance of isoniazid in tuberculosis patients: a systematic review of population pharmacokinetic models.

PURPOSE: Significant pharmacokinetic variabilities have been reported for isoniazid across various populations. We aimed to summarize population pharmacokinetic studies of isoniazid in tuberculosis (TB) patients with a specific focus on the influence of N-acetyltransferase 2 (NAT2) genotype/single-nucleotide polymorphism (SNP) on clearance of isoniazid. METHODS: A systematic search was conducted in PubMed and Embase for articles published in the English language from inception till February 2022 to identify population pharmacokinetic (PopPK) studies of isoniazid. Studies were included if patient population had TB and received isoniazid therapy, non-linear mixed effects modelling, and parametric approach was used for building isoniazid PopPK model and NAT2 genotype/SNP was tested as a covariate for model development. RESULTS: A total of 12 articles were identified from PubMed, Embase, and hand searching of articles. Isoniazid disposition was described using a two-compartment model with first-order absorption and linear elimination in most of the studies. Significant covariates influencing the pharmacokinetics of isoniazid were NAT2 genotype, body weight, lean body weight, body mass index, fat-free mass, efavirenz, formulation, CD4 cell count, and gender. Majority of studies conducted in adult TB population have reported a twofold or threefold increase in isoniazid clearance for NAT2 rapid acetylators compared to slow acetylators. CONCLUSION: The variability in disposition of isoniazid can be majorly attributed to NAT2 genotype. This results in a trimodal clearance pattern with a multi-fold increase in clearance of NAT2 rapid acetylators compared to slow acetylators. Further studies exploring the generalizability/adaptability of developed PopPK models in different clinical settings are required.

Influence of N-acetyltransferase 2 polymorphisms and clinical variables on liver function profile of tuberculosis patients.

BACKGROUND: Single nucleotide polymorphisms (SNPs) in the N-acetyltransferase 2 (NAT2) gene as well as several other clinical factors can contribute to the elevation of liver function test values in tuberculosis (TB) patients receiving antitubercular therapy (ATT). RESEARCH DESIGN AND METHODS: A prospective study involving dynamic monitoring of the liver function tests among 130 TB patients from baseline to 98 days post ATT initiation was undertaken to assess the influence of pharmacogenomic and clinical variables on the elevation of liver function test values. Genomic DNA was extracted from serum samples for the assessment of NAT2 SNPs. Further, within this study population, we conducted a case control study to identify the odds of developing ATT-induced drug-induced liver injury (DILI) based on NAT2 SNPs, genotype and phenotype, and clinical variables. RESULTS: NAT2 slow acetylators had higher mean [90%CI] liver function test values for 8-28 days post ATT and higher odds of developing DILI (OR: 2.73, 90%CI: 1.05-7.09) than intermediate acetylators/rapid acetylators. CONCLUSION: The current study findings provide evidence for closer monitoring among TB patients with specific NAT2 SNPs, genotype and phenotype, and clinical variables, particularly between the period of more than a week to one-month post ATT initiation for better treatment outcomes.

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.

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.

Population Pharmacokinetics of Hydroxychloroquine Sulfate in Healthcare Workers, Given for Prophylaxis Against Coronavirus Disease 2019 (COVID-19) in India.

Healthcare workers (HCWs) and frontline workers were recommended hydroxychloroquine (HCQ) 400 mg twice a day on day 1, followed by 400 mg once weekly for the next 7 weeks, as prophylaxis against COVID-19. There was limited information on the population pharmacokinetics (popPK) of HCQ in an Indian setting when administered for prophylaxis against COVID-19, and hence this study was proposed. It was a multicentric prospective study conducted at 3 sites in India wherein HCWs who were already on HCQ prophylaxis, who were about to start prophylaxis or who had stopped the prophylaxis for any reason were enrolled. Each participant gave 2 to 6 blood samples at different time points and whole-blood HCQ concentrations were assayed using liquid chromatography with tandem mass spectrometry (LC MS/MS). popPK analysis was performed using PUMAS 1.1.0. A total of N = 338 blood samples from N = 121 participants were included in the popPK analysis. A 2-compartment structural model with linear elimination was able to explain the observed data. Body weight was found to be a significant covariate influencing drug clearance. The final model was assessed using goodness-of-fit plots, a visual predictive check and a bootstrap, all of which confirmed that the model was appropriate. Simulations based on the current regimen showed that trough values were below the half-maximal effective concentration (EC50) of 0.7 µmol against COVID-19. A new weight-based dosage regimen was proposed to maintain the trough concentration above the EC50 threshold.

Population pharmacokinetic-pharmacodynamic modeling of acetaminophen in preterm neonates with hemodynamically significant patent ductus arteriosus.

BACKGROUND: Pharmacokinetics (PK) of intravenous acetaminophen has not been assessed in preterm neonates with hemodynamically significant patent ductus arteriosus (PDA). Moreover, there is a lack of data evaluating the association between PK and pharmacodynamics (PD) of acetaminophen in hemodynamically significant PDA. Hence, we performed a population PK-PD modeling of acetaminophen in preterm neonates with hemodynamically significant PDA. METHODS: A prospective, observational study was carried out in preterm neonates with hemodynamically significant PDA receiving intravenous acetaminophen (15 mg/kg six hourly) for maximum of nine days. The diameter of the ductus arteriosus was measured using General Electric Vivid 7® (echocardiography) and was the PD measure. The PK-PD modeling was performed using Monolix 2019R2. We performed Monte Carlo (MC) simulations to determine the probability of ductus arteriosus closure from first to the ninth day of acetaminophen treatment. RESULTS: Fifty-five neonates were recruited. A one-compartment model with first-order elimination described well the PK of acetaminophen. Clearance (CL) and volume of distribution (Vd) for typical neonate weighing 0.98 kg was 0.0452 L/h and 1.18 L, respectively. A combination of an Imax model with effect compartment and an exponential disease progression model described well the PD of acetaminophen. The average baseline diameter of the ductus arteriosus (E0) was 2.53 mm while IC50 was 0.477 µg/mL. The disease progression rate constant (Kprog) and effect compartment transfer rate constant (ke0) were 0.00425 h-1 and 0.000103 h-1, respectively. MC simulations of the current dosing regimen revealed a probability of 73.7% ductus arteriosus closure compared to 83.8% with 20 mg/kg six hourly dose. CONCLUSION: The PK-PD model developed can be used for dosing acetaminophen in premature neonates with hemodynamically significant PDA. Intravenous dose of 20 mg/kg intravenously every six hours is likely to provide a better therapeutic effect than the existing dosing regimen.