External Evaluation of Two Pediatric Population Pharmacokinetics Models of Oral Trimethoprim and Sulfamethoxazole.

The antibiotic combination trimethoprim (TMP)-sulfamethoxazole (SMX) has a broad spectrum of activity and is used for the treatment of numerous infections, but pediatric pharmacokinetic (PK) data are limited. We previously published population PK (popPK) models of oral TMP-SMX in pediatric patients based on sparse opportunistically collected data (POPS study) (J. Autmizguine, C. Melloni, C. P. Hornik, S. Dallefeld, et al., Antimicrob Agents Chemother 62:e01813-17, 2017, https://doi.org/10.1128/AAC.01813-17). We performed a separate PK study of oral TMP-SMX in infants and children with more-traditional PK sample collection and independently developed new popPK models of TMP-SMX using this external data set. The POPS data set and the external data set were each used to evaluate both popPK models. The external TMP model had a model and error structure identical to those of the POPS TMP model, with typical values for PK parameters within 20%. The external SMX model did not identify the covariates in the POPS SMX model as significant. The external popPK models predicted higher exposures to TMP (median overprediction of 0.13 mg/liter for the POPS data set and 0.061 mg/liter for the external data set) and SMX (median overprediction of 1.7 mg/liter and 0.90 mg/liter) than the POPS TMP (median underprediction of 0.016 mg/liter and 0.39 mg/liter) and SMX (median underprediction of 1.2 mg/liter and 14 mg/liter) models. Nonetheless, both models supported TMP-SMX dose increases in infants and young children for resistant pathogens with a MIC of 1 mg/liter, although the required dose increase based on the external model was lower. (The POPS and external studies have been registered at ClinicalTrials.gov under registration no. NCT01431326 and NCT02475876, respectively.).

Pharmacokinetics of perioperative FVIII in adult patients with haemophilia A: An external validation and development of an alternative population pharmacokinetic model.

INTRODUCTION: Haemophilia A patients require perioperative clotting factor replacement to limit excessive bleeding. Weight-based dosing of Factor VIII (FVIII) does not account for inter-individual pharmacokinetic (PK) variability, and may lead to suboptimal FVIII exposure. AIM: To perform an external validation of a previously developed population PK (popPK) model of perioperative FVIII in haemophilia A patients. METHODS: A retrospective chart review identified perioperative haemophilia A patients at the University of North Carolina (UNC) between April 2014 and November 2019. Patient data was used to externally validate a previously published popPK model proposed by Hazendonk. Based on these validation results, a modified popPK model was developed to characterize FVIII PK in our patients. Dosing simulations were performed using this model to compare FVIII target attainment between intermittent bolus (IB) and continuous infusion (CI) administration methods. RESULTS: A total of 521 FVIII concentrations, drawn from 34 patients, were analysed. Validation analyses revealed that the Hazendonk model did not fully capture FVIII PK in the UNC cohort. Therefore, a modified one-compartment model, with weight and age as covariates on clearance (CL), was developed. Dosing simulations revealed that CI resulted in improved target attainment by 16%, with reduced overall FVIII usage by 58 IU/kg, compared to IB. CONCLUSION: External validation revealed a previously published popPK model of FVIII did not adequately characterize UNC patients, likely due to differences in patient populations. Future prospective studies are needed to evaluate our model prior to implementation into clinical practice.

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30].

Population Pharmacokinetics of Oral Migalastat in Adolescents and Adults With and Without Renal Impairment.

Migalastat is approved for the treatment of Fabry disease (FD) with amenable variants. Objectives were to characterize effects of estimated glomerular filtration rate (eGFR) on oral clearance (CL), predict doses in mild to moderate renal impairment and in pediatric patients with FD, and to improve designs of FD studies. A 2-compartment model was fit to data from 260 subjects with/without FD and iteratively refined with evolving data. FD, eGFR, and weight affected CL, while weight and FD affected volume. Optimal sampling theory was used to choose pharmacokinetic sampling times for pediatric studies. Doses in patients with renal impairment and in pediatrics were determined by targeting exposure in adults receiving migalastat 123 mg every other day. A clinical study was conducted in 20 adolescent patients with FD ≥45 kg. eGFR had the largest effect on CL. Simulations showed that exposures in moderate renal impairment were within phase 2-3 exposures; patients aged 2-17 years require weight-based dosing; and predicted exposures in adolescent patients ≥45 kg receiving migalastat 123 mg every other day were similar to adults (data confirmed in a clinical study). Model-informed drug development optimized dosing and design of clinical studies and supported that no dose adjustments were needed in patients with mild to moderate renal impairment or in adolescent patients ≥45 kg.

Migalastat Tissue Distribution: Extrapolation From Mice to Humans Using Pharmacokinetic Modeling and Comparison With Agalsidase Beta Tissue Distribution in Mice.

Approved therapies for Fabry disease (FD) include migalastat, an oral pharmacological chaperone, and agalsidase beta and agalsidase alfa, 2 forms of enzyme replacement therapy. Broad tissue distribution may be beneficial for clinical efficacy in FD, which has severe manifestations in multiple organs. Here, migalastat and agalsidase beta biodistribution were assessed in mice and modeled using physiologically based pharmacokinetic (PBPK) analysis, and migalastat biodistribution was subsequently extrapolated to humans. In mice, migalastat concentration was highest in kidneys and the small intestine, 2 FD-relevant organs. Agalsidase beta was predominantly sequestered in the liver and spleen (organs unaffected in FD). PBPK modeling predicted that migalastat 123 mg every other day resulted in concentrations exceeding the in vitro half-maximal effective concentration in kidneys, small intestine, skin, heart, and liver in human subjects. However, extrapolation of mouse agalsidase beta concentrations to humans was unsuccessful. In conclusion, migalastat may distribute to tissues that are inaccessible to intravenous agalsidase beta in mice, and extrapolation of mouse migalastat concentrations to humans showed adequate tissue penetration, particularly in FD-relevant organs.

The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications.

The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains patients' expression of effectiveness and adverse drug events associated with psychiatric medications. The PsyTAR was generated in four phases. In the first phase, a sample of 891 drugs reviews posted by patients on an online healthcare forum, "askapatient.com", was collected for four psychiatric drugs: Zoloft, Lexapro, Cymbalta, and Effexor XR. For each drug review, patient demographic information, duration of treatment, and satisfaction with the drugs were reported. In the second phase, sentence classification, drug reviews were split to 6009 sentences, and each sentence was labeled for the presence of Adverse Drug Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF), and Others (not applicable). In the third phases, entities including ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792 mentions) were identified and extracted from the sentences. In the four phases, all the identified entities were mapped to the corresponding UMLS Metathesaurus concepts (916) and SNOMED CT concepts (755). In this phase, qualifiers representing severity and persistency of ADRs, WDs, SSIs, and DIs (e.g., mild, short term) were identified. All sentences and identified entities were linked to the original post using IDs (e.g., Zoloft.1, Effexor.29, Cymbalta.31). The PsyTAR dataset can be accessed via Online Supplement #1 under the CC BY 4.0 Data license. The updated versions of the dataset would also be accessible in https://sites.google.com/view/pharmacovigilanceinpsychiatry/home.

Identifying the Underlying Factors Associated With Patients' Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews.

BACKGROUND: Nonadherence to antidepressants is a major obstacle to deriving antidepressants' therapeutic benefits, resulting in significant burdens on the individuals and the health care system. Several studies have shown that nonadherence is weakly associated with personal and clinical variables but strongly associated with patients' beliefs and attitudes toward medications. Patients' drug review posts in online health care communities might provide a significant insight into patients' attitude toward antidepressants and could be used to address the challenges of self-report methods such as patients' recruitment. OBJECTIVE: The aim of this study was to use patient-generated data to identify factors affecting the patient's attitude toward 4 antidepressants drugs (sertraline [Zoloft], escitalopram [Lexapro], duloxetine [Cymbalta], and venlafaxine [Effexor XR]), which in turn, is a strong determinant of treatment nonadherence. We hypothesized that clinical variables (drug effectiveness; adverse drug reactions, ADRs; perceived distress from ADRs, ADR-PD; and duration of treatment) and personal variables (age, gender, and patients' knowledge about medications) are associated with patients' attitude toward antidepressants, and experience of ADRs and drug ineffectiveness are strongly associated with negative attitude. METHODS: We used both qualitative and quantitative methods to analyze the dataset. Patients' drug reviews were randomly selected from a health care forum called askapatient. The Framework method was used to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. Then, 4 annotators coded the drug reviews at the sentence level using the analytical framework. After managing missing values, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude. RESULTS: A total of 892 reviews posted between February 2001 and September 2016 were analyzed. Most of the patients were females (680/892, 76.2%) and aged less than 40 years (540/892, 60.5%). Patient attitude was significantly (P<.001) associated with experience of ADRs, ADR-PD, drug effectiveness, perceived lack of knowledge, experience of withdrawal, and duration of usage, whereas oth age (F4,874=0.72, P=.58) and gender (χ24=2.7, P=.21) were not found to be associated with patient attitudes. Moreover, modeling the relationship between variables and attitudes showed that drug effectiveness and perceived distress from adverse drug reactions were the 2 most significant factors affecting patients' attitude toward antidepressants. CONCLUSIONS: Patients' self-report experiences of medications in online health care communities can provide a direct insight into the underlying factors associated with patients' perceptions and attitudes toward antidepressants. However, it cannot be used as a replacement for self-report methods because of the lack of information for some of the variables, colloquial language, and the unstructured format of the reports.

Bayesian Estimation of Vancomycin Pharmacokinetics in Obese Children: Matched Case-Control Study.

PURPOSE: The study objective was to compare different body size descriptors that best estimate vancomycin Vd and clearance (CL). METHODS: Patients between 3 months and 21 years old who received vancomycin for ≥48 hours from 2003 to 2011 were evaluated in this matched case-control study. Cases had body mass index in the ≥85th percentile; controls were nonobese individuals who were matched by age and baseline serum creatinine (SCr). Using a 1-compartment model with first-order kinetics, Bayesian post hoc individual Vd and CL were estimated. FINDINGS: Analysis included 87 matched pairs with 389 vancomycin serum concentrations. Median ages were 10.0 (interquartile range [IQR], 4.8-15.2) years for cases (overweight and obese children) and 10.2 (IQR, 4.5-14.8) years for controls (normal-weight children). Median weights were 44.0 (IQR, 23.4-78.1) kg for cases and 31.3 (IQR, 16.8-47.1) kg for controls. Mean (SD) for the baseline SCr values were also similar between the groups: 0.51 (0.22) (IQR, 0.34-0.67) mg/dL and 0.48 (0.20) (IQR, 0.30-0.60) mg/dL for the cases and controls, respectively. Actual weight and allometric weight (ie, weight(0.75)) were used in the final model to estimate Vd and CL, respectively. The mean Vd and CL, based on weight, for cases were lower than controls by 0.012 L/kg and 0.014 L/kg/h, respectively. IMPLICATIONS: In obese children, actual weight and allometric weight are reasonable, convenient estimations of body fat to use for estimating vancomycin Vd and CL, respectively. However, these pharmacokinetic differences between obese children and those with normal weights are small and may not likely to be clinically relevant in dose variation.