About 80% of the consumers worldwide use herbal medicine (HMs) or other natural products. The percentage may vary significantly (7%-55%) among pregnant women, depending upon social status, ethnicity, and cultural traditions. This manuscript discusses the most common HMs used by pregnant women, and the potential interactions of HMs with conventional drugs in some medical conditions that occur during pregnancy (e.g., hypertension, asthma, epilepsy). It also includes an examination of the characteristics of pregnant HM consumers, the primary conditions for which HMs are taken, and a discussion related to the potential toxicity of HMs taken during pregnancy. Many cultures have used HMs in pregnancy to improve wellbeing of the mother and/or baby, or to help decrease nausea and vomiting, treat infection, ease gastrointestinal problems, prepare for labor, induce labor, or ease labor pains. One of the reasons why pregnant women use HMs is an assumption that HMs are safer than conventional medicine. However, for pregnant women with pre-existing conditions like epilepsy and asthma, supplementation of conventional treatment with HMs may further complicate their care. The use of HMs is frequently not reported to healthcare professionals. Providers are often not questioning HM use, despite little being known about the HM safety and HM-drug interactions during pregnancy. This lack of knowledge on potential toxicity and the ability to interact with conventional treatments may impact both mother and fetus. There is a need for education of women and their healthcare professionals to move away from the idea of HMs not being harmful. Healthcare professionals need to question women on whether they use any HMs or natural products during pregnancy, especially when conventional treatment is less efficient and/or adverse events have occurred as herbaldrug interactions could be the reason for these observations. Additionally, more preclinical and clinical studies are needed to evaluate HM efficacy and toxicity.

Pregnant and breastfeeding women have been rendered therapeutic orphans as they have been historically excluded from clinical trials. Labelling for most approved drugs does not provide information about safety and efficacy during pregnancy. This lack of data is mainly due to ethico-legal challenges that have remained entrenched in the post-diethylstilbestrol and thalidomide era, and that have led to pregnancy being viewed in the clinical trial setting primarily through a pharmacovigilance lens. Policy considerations that encourage and/or require the inclusion of pregnant or lactating women in clinical trials may address the current lack of available information. However, there are additional pragmatic strategies, such the employment of pharmacometric tools and the introduction of innovative clinical trial designs, which could improve knowledge about the safety and efficacy of medication use during pregnancy and lactation. This paper provides a broad overview of the pharmacoepidemiology of drugs used during pregnancy and lactation, and offers recommendations for regulators and researchers in academia and industry to increase the available pharmacokinetic and -dynamic understanding of medication use in pregnancy.

Practitioners commonly use amikacin in patients with cystic fibrosis. Establishment of the pharmacokinetics of amikacin in adults with cystic fibrosis may increase the efficacy and safety of therapy. This study was aimed to establish the population pharmacokinetics of amikacin in adults with cystic fibrosis. We used serum concentration data obtained during routine therapeutic drug monitoring and explored the influence of patient covariates on drug disposition. We performed a retrospective chart review to collect the amikacin dosing regimens, serum amikacin concentrations, blood sampling times, and patient characteristics for adults with cystic fibrosis admitted for treatment of acute pulmonary exacerbations. Amikacin concentrations were retrospectively collected for 49 adults with cystic fibrosis, and 192 serum concentrations were available for analysis. A population pharmacokinetic model was developed using nonlinear mixed-effects modeling with the first-order conditional estimation method. A two-compartment model with first-order elimination best described amikacin pharmacokinetics. Creatinine clearance and weight were identified as significant covariates for clearance and the volume of distribution, respectively, in the final model. Residual variability was modeled using a proportional error model. Typical estimates for clearance, central and peripheral volumes of distribution, and intercompartmental clearance were 3.06 liters/h, 14.4 liters, 17.1 liters, and 0.925 liters/h, respectively. The pharmacokinetics of amikacin in individuals with cystic fibrosis seems to differ from those in individuals without cystic fibrosis. However, further investigations are needed to confirm these results and, thus, the need for variations in amikacin dosing. Future pharmacodynamic studies will potentially establish the optimal amikacin dosing regimens for the treatment of acute pulmonary exacerbations in adult patients with CF.

Missing or erroneous information is a common problem in the analysis of pharmacokinetic (PK) data. This may present as missing or inaccurate dose level or dose time, drug concentrations below the analytical limit of quantification, missing sample times, or missing or incorrect covariate information. Several methods to handle problematic data have been evaluated, though no single, broad set of recommendations for commonly occurring errors has been published. In this tutorial, we review the existing literature and present the results of our simulation studies that evaluated common methods to handle known data errors to bridge the remaining gaps and expand upon the existing knowledge. This tutorial is intended for any scientist analyzing a PK dataset with missing or apparently erroneous data. The approaches described herein may also be useful for the analysis of nonclinical PK data. Overview Data from clinical trials is frequently incomplete, particularly datasets collected during large, late phase trials, during routine clinical patient care or follow-up visits. Portions of data may be missing or inaccurate due to factors such as study site noncompliance, patient noncompliance, inappropriate sample handling, data entry errors, and analytical problems. How "problematic" data are handled can impact its interpretation, especially when data used for population pharmacokinetic (PPK) modeling contains missing or erroneous data. Prior to beginning an analysis, pharmacometricians often spend a large portion of time dealing with problematic data. During data cleaning (data quality assurance), the first step is to identify missing or problematic data. Concentration-time data and dosing records are often the primary concern, but other issues, such as missing or questionable covariate data, must also be considered. Once issues/discrepancies are identified, the next challenge is to evaluate frequency of occurrence of each type of problem and the associated reason to establish appropriate methods for handling these erroneous data. Prior studies have addressed handling of specific types of problematic data, though no set of broad recommendations spanning the various types of problematic data have been previously presented. Accepted Article This article is protected by copyright. All rights reserved Through review of published methods, simulation of data sets with known errors, and evaluation using different methods for handling these errors, this tutorial aims to provide guidance for dealing with problematic clinical (and some non-clinical) concentration vs. time, dosing, and covariate data. This tutorial is intended to be utilized by scientists analyzing pharmacokinetic data with either missing data or where apparently questionable or erroneous data is present. Although data quality assurance (QA) and control (QC) are essential to successful modeling, this tutorial assumes the dataset has already undergone appropriate QC or was assembled from locked, clean data. Basic assessments include exploratory data analysis by plotting and...

Amikacin plays a key role in the treatment of severe hospital-acquired infections with Gram-negative bacteria. Therapeutic use of amikacin is challenged by high inter-individual variability (IIV) combined with a narrow therapeutic spectrum. Pediatric patients represent a particularly fragile population where adequate dosing is crucial yet challenging to achieve due significant IIV associated with developmental processes and other factors. The current review provides an overview of parametric population pharmacokinetic analyses of amikacin in pediatric patients and associated patient-specific determinants of IIV. We searched PubMed for parametric population pharmacokinetic analyses of amikacin in pediatric patients. Information on patient population, study design, pharmacokinetic model characteristics, and identified patient-specific predictors of IIV was collected. Comparative analyses across studies were conducted to characterize quantitative differences reported for different studies and patient populations. Eight eligible publications were identified, of which six analyses involved neonates up to 3 months of age and two studies investigated older pediatric patients (age 2-17 years). Most commonly included covariates were current body weight for both clearance and volume of distribution, followed by age-related covariates on clearance in neonatal studies (four of six models). Quantitative comparisons of different models reported generally showed similar developmental effects in neonatal populations. The present review provides a comprehensive overview of parametric population pharmacokinetic studies for amikacin. Future studies could address the knowledge gap of patients between 3 months and 2 years of age. Furthermore, systematic studies of additional potential predictors for IIV (e.g., sepsis, inflammatory markers, renal function biomarkers) could be of relevance to address the significant IIV remaining after inclusion of the most commonly identified covariates.

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AIMSWe aimed to compare the performance of renal function and age as predictors of inter-individual variability (IIV) in clearance of amikacin in neonates through parallel development of population pharmacokinetic (PK) models and their associated impact on optimal dosing regimens. METHODSAmikacin concentrations were retrospectively collected for 149 neonates receiving amikacin (post-natal age (PNA) between 4-89 days). Two population PK models were developed in parallel, considering at least as predictors current body weight (WT), in combination with either creatinine clearance (CL cr ) or age descriptors. Using stochastic simulations for both renal function or agebased dosing, we identified optimal dosing strategies that were based on attainment of optimal peak-(PCC) and trough target concentration coverage (TCC) windows associated with efficacy and toxicity. RESULTSThe CL cr and age-based population PK models both included current body weight (WT) on CL, central distribution volume and intercompartmental clearance, in combination with either CL cr or PNA as predictors for IIV of clearance (CL). The WT-CL cr model explained 6.9% more IIV in CL compared with the WT-PNA model. Both models successfully described an external dataset (n = 53) of amikacin PK. The simulation analysis of optimal dose regimens suggested similar performance of either CL cr or PNA based dosing. CONCLUSIONCL cr predicted more IIV in CL, but did not translate into clinically relevant improvements of target concentrations. Our optimized dose regimens can be considered for further evaluation to optimize initial treatment with amikacin. British Journal of Clinical Pharmacology WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• The population pharmacokinetics of amikacin have been established in various neonatal populations.• Current dosing guidelines for amikacin in neonates are based on weight and/or age descriptors.• Renal function markers have not yet been reported as potential predictors, even though amikacin is a renally excreted antibiotic. WHAT THIS STUDY ADDS• Creatinine clearance (CL cr ) predicted 6.9% more of the inter-individual variability of amikacin clearance than post-natal age (PNA) in neonates.• A systematic evaluation of optimal dose regimens based on CL cr and PNA was performed and novel optimal dose regimens were proposed, which could be considered further to optimize initial treatment with amikacin based on body weight and PNA or CL cr .• First dose administration of amikacin based on CL cr and PNA results in comparable attainment of target trough and peak concentrations, when using optimized dose regimens.