Xiaoxi Liu

Comparing the performance of FOCE and different expectation-maximization methods in handling complex population physiologically-based pharmacokinetic models.

For the purpose of population pharmacometric modeling, a variety of mathematic algorithms are implemented in major modeling software packages to facilitate the maximum likelihood modeling, such as FO, FOCE, Laplace, ITS and EM. These methods are all designed to estimate the set of parameters that maximize the joint likelihood of observations in a given problem. While FOCE is still currently the most widely used method in population modeling, EM methods are getting more popular as the current-generation methods of choice because of their robustness with more complex models and sparse data structures. There are several versions of EM method implementation that are available in public modeling software packages. Although there have been several studies and reviews comparing the performance of different methods in handling relatively simple models, there has not been a dedicated study to compare different versions of EM algorithms in solving complex PBPK models. This study took everolimus as a model drug and simulated PK data based on published results. Three most popular EM methods (SAEM, IMP and QRPEM) and FOCE (as a benchmark reference) were evaluated for their estimation accuracy and converging speed when solving models of increased complexity. Both sparse and rich sampling data structure were tested. We concluded that FOCE was superior to EM methods for simple structured models. For more complex models and/ or sparse data, EM methods are much more robust. While the estimation accuracy was very close across EM methods, the general ranking of speed (fastest to slowest) was: QRPEM, IMP and SAEM. IMP gave the most realistic estimation of parameter standard errors, while under- and over- estimation of standard errors were observed in SAEM and QRPEM methods.

Optimizing the use of intravenous magnesium sulfate for acute asthma treatment in children.

Asthma is the most common pediatric chronic disease and currently affects 7.1 million children in the United States. Many children experience acute asthma exacerbations. Many children also require hospitalization despite treatment in an emergency department with current standard therapy (corticosteroids, albuterol, and ipratropium). These hospitalizations may be avoided if effective adjunctive therapies can be developed to adequately treat severe exacerbations.

Pharmacodynamic studies of voriconazole: informing the clinical management of invasive fungal infections

ABSTRACT Introduction: Voriconazole is a broad-spectrum antifungal agent commonly used to treat invasive fungal infections (IFI), including aspergillosis, candidiasis, Scedosporium infection, and Fusarium infection. IFI often occur in immunocompromised patients, leading to increased morbidity and mortality. Areas covered: The objective of this review is to summarize the pharmacodynamic properties of voriconazole and to provide considerations for potential optimal dosing strategies. Studies have demonstrated superior clinical response when an AUC/MIC >25 or Cmin/MIC >1 is attained in adult patients, correlating to a trough concentration range as narrow as 2–4.5 mg/L; however, these targets are poorly established in the pediatric population. Topics in this discussion include voriconazole use in multiple age groups, predisposing patient factors for IFI, and considerations for clinicians managing IFI. Expert commentary: The relationship between voriconazole dosing and exposure is not well defined due to the large inter- and intra-subject variability. Development of comprehensive decision support tools for individualizing dosing, particularly in children who require higher dosing, will help to increase the probability of achieving therapeutic efficacy and decrease sub-therapeutic dosing and adverse events.

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug–drug interactions, real-time assessment of pharmacokinetic–safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established.

Evident bias in a paracetamol metabolite population pharmacokinetic model applied to an external dataset

Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah, USA, Department of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA, Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, Utah, USA, Division of Clinical Pharmacology, Children’s National Health System,Washington, DC, USA, Department of Pediatrics, Pharmacology and Physiology, School of Medicine and Health Sciences, GeorgeWashington University, Washington, DC, USA, Intensive Care and Department of Pediatric Surgery, Erasmus Medical Center–Sophia Children’s Hospital, Rotterdam, The Netherlands, and Division of Paediatric Pharmacology and Pharmacometrics, University Children’s Hospital Basel, Basel, Switzerland

Genetic Variation in the Histamine Production, Response, and Degradation Pathway Is Associated with Histamine Pharmacodynamic Response in Children with Asthma

Introduction: There is growing knowledge of the wide ranging effects of histamine throughout the body therefore it is important to better understand the effects of this amine in patients with asthma. We aimed to explore the association between histamine pharmacodynamic (PD) response and genetic variation in the histamine pathway in children with asthma. Methods: Histamine Iontophoresis with Laser Doppler Monitoring (HILD) was performed in children with asthma and estimates for area under the effect curve (AUEC), maximal response over baseline (Emax), and time of Emax (Tmax) were calculated using non-compartmental analysis and non-linear mixed-effects model with a linked effect PK/PD model. DNA isolation and genotyping were performed among participants to detect known single nucleotide polymorphisms (SNPs) (n = 10) among genes (HDC, HNMT, ABP1, HRH1, HRH4) within the histamine pathway. General linear model was used to identify associations between histamine related genetic variants and measured histamine PD response parameters. Results: Genotyping and HILD response profiles were completed for 163 children. ABP1 47 C/T, ABP1 4107, and HNMT-1639 C/Twere associated with Emax (ABP1 47 CC genotype mean Emax 167.21 vs. CT/TT genotype mean Emax 139.20, p = 0.04; ABP1 4107 CC genotype mean Emax 141.72 vs. CG/GG genotype mean Emax 156.09, p = 0.005; HNMT-1639 CC genotype mean Emax 132.62 vs. CT/TT genotype mean Emax 155.3, p = 0.02). In a stratified analysis among African American children only, ABP1 and HNMT SNPs were also associated with PD response; HRH4 413 CC genotype was associated with lower Emax, p = 0.009. Conclusions: We show for the first time that histamine pathway genetic variation is associated with measureable changes in histamine response in children with asthma. The variability in histamine response and impact of histamine pathway genotype is important to further explore in patients with asthma so as to improve disease phenotyping leading to more personalized treatments.

Riding (High) into the danger zone: a review of potential differences in chemical exposures in fighter pilots resulting from high altitude and G-forces

ABSTRACT Introduction: When in flight, pilots of high performance aircraft experience conditions unique to their profession. Training flights, performed as often as several times a week, can expose these pilots to altitudes in excess of 15 km (~50,000 ft, with a cabin pressurized to an altitude of ~20,000 ft), and the maneuvers performed in flight can exacerbate the G-forces felt by the pilot. While the pilots specifically train to withstand these extreme conditions, the physiologic stress could very likely lead to differences in the disposition of chemicals in the body, and consequently, dangerously high exposures. Unfortunately, very little is known about how the conditions experienced by fighter pilots affects chemical disposition. Areas covered: The purpose of this review is to present information about the effects of high altitude, G-forces, and other conditions experienced by fighter pilots on chemical disposition. Using this information, the expected changes in chemical exposure will be discussed, using isopropyl alcohol as an example. Expert opinion: There is a severe lack of information concerning the effects of the fighter pilot environment on the pharmacokinetics and pharmacodynamics of chemicals. Given the possibility of exposure prior to or during flight, it is important that these potential effects be investigated further.

Predicting tacrolimus concentrations in children receiving a heart transplant using a population pharmacokinetic model

Objective Immunosuppressant therapy plays a pivotal role in transplant success and longevity. Tacrolimus, a primary immunosuppressive agent, is well known to exhibit significant pharmacological interpatient and intrapatient variability. This variability necessitates the collection of serial trough concentrations to ensure that the drug remains within therapeutic range. The objective of this study was to build a population pharmacokinetic (PK) model and use it to determine the minimum number of trough samples needed to guide the prediction of an individual’s future concentrations. Design, setting and patients Retrospective data from 48 children who received tacrolimus as inpatients at Primary Children’s Hospital in Salt Lake City, Utah were included in the study. Data were collected within the first 6 weeks after heart transplant. Outcome measures Data analysis used population PK modelling techniques in NONMEM. Predictive ability of the model was determined using median prediction error (MPE, a measure of bias) and median absolute prediction error (MAPE, a measure of accuracy). Of the 48 children in the study, 30 were used in the model building dataset, and 18 in the model validation dataset. Results Concentrations ranged between 1.5 and 37.7 µg/L across all collected data, with only 40% of those concentrations falling within the targeted concentration range (12 to 16 µg/L). The final population PK model contained the impact of age (on volume), creatinine clearance (on elimination rate) and fluconazole use (on elimination rate) as covariates. Our analysis demonstrated that as few as three concentrations could be used to predict future concentrations, with negligible bias (MPE (95% CI)=0.10% (−2.9% to 3.7%)) and good accuracy (MAPE (95% CI)=24.1% (19.7% to 27.7%)). Conclusions The use of PK in dose guidance has the potential to provide significant benefits to clinical care, including dose optimisation during the early stages of therapy, and the potential to limit the need for frequent drug monitoring.