Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.

GLUT4 localisation with the plasma membrane is unaffected by an increase in plasma free fatty acid availability

BACKGROUND

Insulin-stimulated glucose uptake into skeletal muscle occurs via translocation of GLUT4 from intracellular storage vesicles to the plasma membrane. Elevated free fatty acid (FFA) availability via a lipid infusion reduces glucose disposal, but this occurs in the absence of impaired proximal insulin signalling. Whether GLUT4 localisation to the plasma membrane is subsequently affected by elevated FFA availability is not known.

METHODS

Trained (n = 11) and sedentary (n = 10) individuals, matched for age, sex and body mass index, received either a 6 h lipid or glycerol infusion in the setting of a concurrent hyperinsulinaemic-euglycaemic clamp. Sequential muscle biopsies (0, 2 and 6 h) were analysed for GLUT4 membrane localisation and microvesicle size and distribution using immunofluorescence microscopy.

RESULTS

At baseline, trained individuals had more small GLUT4 spots at the plasma membrane, whereas sedentary individuals had larger GLUT4 spots. GLUT4 localisation with the plasma membrane increased at 2 h (P = 0.04) of the hyperinsulinemic-euglycemic clamp, and remained elevated until 6 h, with no differences between groups or infusion type. The number of GLUT4 spots was unchanged at 2 h of infusion. However, from 2 to 6 h there was a decrease in the number of small GLUT4 spots at the plasma membrane (P = 0.047), with no differences between groups or infusion type.

CONCLUSION

GLUT4 localisation with the plasma membrane increases during a hyperinsulinemic-euglycemic clamp, but this is not altered by elevated FFA availability. GLUT4 appears to disperse from small GLUT4 clusters located at the plasma membrane to support glucose uptake during a hyperinsulinaemic-euglycaemic clamp.

Advancing the utilization of real-world data and real-world evidence in clinical pharmacology and translational research-Proceedings from the ASCPT 2023 preconference workshop

Real-world data (RWD) and real-world evidence (RWE) are now being routinely used in epidemiology, clinical practice, and post-approval regulatory decisions. Despite the increasing utility of the methodology and new regulatory guidelines in recent years, there remains a lack of awareness of how this approach can be applied in clinical pharmacology and translational research settings. Therefore, the American Society of Clinical Pharmacology & Therapeutics (ASCPT) held a workshop on March 21st, 2023 entitled "Advancing the Utilization of Real-World Data (RWD) and Real-World Evidence (RWE) in Clinical Pharmacology and Translational Research." The work described herein is a summary of the workshop proceedings.

Training the next generation of pharmacometric modelers: a multisector perspective

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

The future of rare disease drug development: the rare disease cures accelerator data analytics platform (RDCA-DAP)

Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients. The future of rare disease drug development will hopefully rely on increased data sharing and collaboration among the entire rare disease ecosystem. One path to achieving this outcome has been the development of the rare disease cures accelerator, data analytics platform (RDCA-DAP) funded by the US FDA and operationalized by the Critical Path Institute. FDA intentions were clearly focused on improving the quality of rare disease regulatory applications by sponsors seeking to develop treatment options for various rare disease populations. As this initiative moves into its second year of operations it is envisioned that the increased connectivity to new and diverse data streams and tools will result in solutions that benefit the entire rare disease ecosystem and that the platform becomes a Collaboratory for engagement of this ecosystem that also includes patients and caregivers.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.

Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy

Pregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted. Part of the challenge is the uncertain risk potential that pregnant women represent in the absence of timely and costly toxicology and developmental pharmacology studies, which only partly mitigate such risks. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed. Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Funding for translational research in pregnancy is limited but will fill some of these gaps, especially when joined with ongoing clinical trials in pregnancy that also fill certain knowledge gaps, especially biomarker and end point evaluation across pregnancy states linked to clinical outcomes. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real-world data sources and complimentary artificial intelligence/machine learning approaches. The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.

Supporting Prospective Pregnancy Trials via Modeling and Simulation: Lessons From the Past and Recommendations for the Future

Despite the increasing awareness and guidance to support drug research and development in the pregnant population, there is still a high unmet medical need and off-label use in the pregnant population for mainstream, acute, chronic, rare disease, and vaccination/prophylactic use. There are many obstacles to enrolling the pregnant population in a study, ranging from ethical considerations, the complexity of the pregnancy stages, postpartum, fetus-mother interaction, and drug transfer to breast milk during lactation and impacts on neonates. This review will outline the common challenges of incorporating physiological differences in the pregnant population and historical but noninformative practice in a past clinical trial in pregnant women that led to labeling difficulties. The recommendations of different modeling approaches, such as a population pharmacokinetic model, physiologically based pharmacokinetic modeling, model-based meta-analysis, and quantitative system pharmacology modeling, are presented with some examples. Finally, we outline the gaps in the medical need for the pregnant population by classifying various types of diseases and some considerations that exist to support the use of medicines in this area. Ideas on the potential framework to support clinical trials and collaboration examples are also presented that could also accelerate understanding of drug research and medicine/prophylactics/vaccines in the pregnant population.

Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor's own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery.

Natural History and Real‐World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data

The 21^st Century Cures Act requires FDA to expand its use of real-world evidence (RWE) to support approval of previously approved drugs for new disease indications and post-marketing study requirements. To address this need in neonates, the FDA and the Critical Path Institute (C-Path) established the International Neonatal Consortium (INC) to advance regulatory science and expedite neonatal drug development. FDA recently provided funding for INC to generate RWE to support regulatory decision making in neonatal drug development. One study is focused on developing a validated definition of bronchopulmonary dysplasia (BPD) in neonates. BPD is difficult to diagnose with diverse disease trajectories and few viable treatment options. Despite intense research efforts, limited understanding of the underlying disease pathobiology and disease projection continues in the context of a computable phenotype. It will be important to determine if: 1) a large, multisource aggregation of real-world data (RWD) will allow identification of validated risk factors and surrogate endpoints for BPD, and 2) the inclusion of these simulations will identify risk factors and surrogate endpoints for studies to prevent or treat BPD and its related long-term complications. The overall goal is to develop qualified, fit-for-purpose disease progression models which facilitate credible trial simulations while quantitatively capturing mechanistic relationships relevant for disease progression and the development of future treatments. The extent to which neonatal RWD can inform these models is unknown and its appropriateness cannot be guaranteed. A component of this approach is the critical evaluation of the various RWD sources for context-of use (COU)-driven models. The present manuscript defines a landscape of the data including targeted literature searches and solicitation of neonatal RWD sources from international stakeholders; analysis plans to develop a family of models of BPD in neonates, leveraging previous clinical trial experience and real-world patient data is also described.

Role of Disease Progression Models in Drug Development

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.

Innovations in Therapy Development for Rare Diseases Through the Rare Disease Cures Accelerator-Data and Analytics Platform

Rare diseases impact the lives of an estimated 350 million people worldwide, and yet about 90% of rare diseases remain without an approved treatment. New technologies have become available, such as gene and oligonucleotide therapies, that offer great promise in treating rare diseases. However, progress toward the development of therapies to treat these diseases is hampered by a limited understanding of the course of each rare disease, how changes in disease progression occur and can be effectively measured over time, and challenges in designing and running clinical trials in diseases where the natural history is poorly characterized. Data that could be used to characterize the natural history of each disease has often been collected in various ways, including in electronic health records, patient-report registries, clinical natural history studies, and in past clinical trials. However, each data source contains a limited number of subjects and different data elements, and data is frequently kept proprietary in the hands of the study sponsor rather than shared widely across the rare disease community. The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) is an FDA-funded effort to overcome these persistent challenges. By aggregating data across all rare diseases and making that data available to the community to support understanding of rare disease natural history and inform drug development, RDCA-DAP aims to accelerate the regulatory approval of new therapies. RDCA-DAP curates, standardizes, and tags data across rare disease datasets to make it findable within the database, and contains a built-in analytics platform to help visualize, interpret, and use it to support drug development. RDCA-DAP will coordinate data and tool resources across non-profit, commercial, and for-profit entities to serve a diverse array of rare disease stakeholders that includes academic researchers, drug developers, FDA reviewers and of course patients and their caregivers. Drug development programs utilizing the RDCA-DAP will be able to leverage existing data to support their efforts and reach definitive decisions on the efficacy of their therapeutics more efficiently and more rapidly than ever.

High intramuscular triglyceride turnover rates and the link to insulin sensitivity: influence of obesity, type 2 diabetes and physical activity

Large intramuscular triglyceride (IMTG) stores in sedentary, obese individuals have been linked to insulin resistance, yet well-trained athletes exhibit high IMTG levels whilst maintaining insulin sensitivity. Contrary to previous assumptions, it is now known that IMTG content per se does not result in insulin resistance. Rather, insulin resistance is caused, at least in part, by the presence of high concentrations of harmful lipid metabolites, such as diacylglycerols and ceramides in muscle. Several mechanistic differences between obese sedentary individuals and their highly trained counterparts have been identified, which determine the differential capacity for IMTG synthesis and breakdown in these populations. In this review, we first describe the most up-to-date mechanisms by which a low IMTG turnover rate (both breakdown and synthesis) leads to the accumulation of lipid metabolites and results in skeletal muscle insulin resistance. We then explore current and potential exercise and nutritional strategies that target IMTG turnover in sedentary obese individuals, to improve insulin sensitivity. Overall, improving IMTG turnover should be an important component of successful interventions that aim to prevent the development of insulin resistance in the ever-expanding sedentary, overweight and obese populations. Novelty: A description of the most up-to-date mechanisms regulating turnover of the IMTG pool. An exploration of current and potential exercise/nutritional strategies to target and enhance IMTG turnover in obese individuals. Overall, highlights the importance of improving IMTG turnover to prevent the development of insulin resistance.

Considerations for addressing anti-vaccination campaigns: How did we get here and what can we do about it?

A course on vaccine development asked students to write a blog addressing general anti‐vaccination strategies and their significance today, in the context of the resistance seen against novel SARS‐CoV‐2 mRNA vaccines. This perspective explores how and why these efforts are successful at reducing vaccine uptake and why, for the most part, efforts to combat the movement have been unsuccessful. This summary of the collective view of the class provides recommendations for combatting current and future campaigns of misinformation. It is hoped that this perspective will serve as a call to action for clinical pharmacologists and translational scientists to do their part to educate the lay community and promote the science in an open and transparent manner to ensure that current and future vaccines fulfill their potential.

Status Toward the Implementation of Precision Dosing in Children

Precision dosing is progressing beyond the conceptual and proof-of-concept stages toward implementation. As the availability of dosing algorithms, tools, and platforms increases, so do the investment in technology services and actual implementation of clinical services offering these solutions to patients. Nowhere is this needed more than in pediatric populations, which are still reliant on adult drug development and bridging strategies to support dosing, often in the absence of actual dose-finding studies in the target pediatric population. Still, there is more work to be done to ensure that proper governance of these services is maintained, and that sustainability of these early implementations is guided by new science as it evolves and meaningful outcome data to confirm that such services deliver on both clinical and economic return on investment. In addition, the field should ensure that all approaches beyond a therapeutic drug monitoring-driven, pharmacokinetic-centric approach should be considered as the tools and services evolve, especially when pediatric-specific pharmacokinetic/pharmacodyamic and pharmacogenetic data are available and shown to be useful to guide dosing. This review evaluates current pediatric precision dosing efforts, highlighting their utility, longevity, and sustainability and assesses the current process for implementing such approaches examining current barriers that stand in the way of broader implementation and the stakeholders that must engage to ensure its ultimate success.

Model-Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children

One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to adolescents. Model-informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model-informed drug development to define the right dose for children and provides suggestions for future development.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

Serum amoxicillin levels in young infants (0-59 days) with sepsis treated with oral amoxicillin

BACKGROUND

WHO recommends simplified antibiotics for young infants with sepsis in countries where hospitalisation is not feasible. Amoxicillin provides safe, Gram-positive coverage. This study was done to determine pharmacokinetics, drug disposition and interpopulation variability of oral amoxicillin in this demographic.

METHODS

Young infants with signs of sepsis enrolled in an oral amoxicillin/intramuscular gentamicin treatment arm of a sepsis trial in Karachi, Pakistan, were studied. Limited pharmacokinetic (PK) sampling was performed at 0, 2-3 and 6-8 hours following an index dose of oral amoxicillin. Plasma concentrations were determined by high-performance liquid chromatography/mass spectrometry. Values of ≥2 mg/L were considered as the effect threshold, given the regional minimal inhibitory concentration (MIC) of resistant Streptococcus pneumoniae. RESULTS: Amoxicillin concentrations were determined in 129 samples from 60 young infants. Six of 44 infants had positive blood cultures with predominant Gram-positive organisms. Forty-four infants contributing blood at ≥2 of 3 specified timepoints were included in the analysis. Mean amoxicillin levels at 2-3 hours (11.6±9.5 mg/L, n=44) and 6-8 hours (16.4±9.3 mg/L, n=20) following the index dose exceeded the MIC for amoxicillin (2.0 mg/L) against resistant S. pneumoniae strains. Of 20 infants with three serum levels, 7 showed a classic dose-exposure profile and 13 showed increasing concentrations with time, implying delayed absorption or excretion.

CONCLUSION

Amoxicillin concentrations in sera of young infants following oral administration at 75-100 mg/kg/day daily divided doses exceeds the susceptibility breakpoint for >50% of a 12-hour dosing interval.Oral amoxicillin may hold potential as a safe replacement of parenteral ampicillin in newborn sepsis regimens, including aminoglycosides, where hospitalisation is not feasible.

TRIAL REGISTRATION NUMBER

NCT01027429.

Risk assessment of therapeutic agents under consideration to treat COVID-19 in paediatric patients and pregnant women

AIM

Repurposing strategies to address the COVID-19 pandemic have been accelerated. As both pregnant and paediatric patients are likely to be excluded from most planned investigations, the list of repurposed options and the available data on these drugs and vaccines provide a baseline risk assessment and identify gaps for targeted investigation.

METHODS

Clinical trials have been searched and reviewed; 23 repurposed drugs and drug combinations and nine candidate vaccines have been assessed regarding the availability of relevant data in paediatrics and pregnant women and to evaluate expected or unanticipated risk.

RESULTS

Thirteen of the repurposed drugs or drug combinations are indicated for use in paediatrics in some age category albeit for indications other than COVID-19; 10 of these are indicated for use in pregnant women. Even in cases where these drugs are indicated in the populations, source data from which safety and or dosing could be extrapolated for use in COVID-19 is sparse. Vaccine trials are ongoing and generally exclude pregnant women; only in a few instances have paediatric subgroups been planned for enrolment. Data from individual case studies and RWD may suggest that subpopulations of both paediatric patients and pregnant women may be more at risk, particularly those in an increased inflammatory state.

CONCLUSION

In conjunction with more prospective collaboration, plans are evolving to ensure that we will be better prepared to address similar situations especially in paediatrics and pregnant women where experience is limited and actual practice relies heavily on leveraging data from other populations and indications.

A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat

Gaucher’s disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first‐line SRT approved for GD1, on treatment‐naïve or patients with ERT‐stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype‐phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

Financial toxicity among breast cancer survivors with health insurance

12073

Background: Cancer treatment and its sequelae have been associated with financial toxicity in breast cancer survivors, particularly those who have no health insurance. However, the prevalence of financial toxicity in the insured survivors, and the underlying factors are not well understood. Methods: Breast cancer survivors attending a survivorship clinic (University of Alabama at Birmingham) completed a survey assessing demographics, financial toxicity (i.e., material resources; food/housing/energy insecurity), and health-related quality of life (HRQL: SF-36). Clinical characteristics were abstracted from medical records. A multivariable logistic regression model was developed to understand factors associated with financial toxicity; the model included survivor age, race, socioeconomic status, insurance type, marital status, cancer stage, time since diagnosis, current medications, and physical and mental domains of HRQL. Results: The 368 participants (1% male; 67% white, 25% African American, 8% other) were a median of 61y of age (range, 33-86y) and 4.3y post-diagnosis (1-34y) at survey completion; 90% had stage 0-II disease; 34% were single (not currently married/partnered); type of health insurance included private/military (57%), Medicare (39%), and Medicaid/self-pay (4%). Overall, 31% reported financial toxicity; 26% endorsed not being able to live at current standard of living > 2 mo. if they lost all current sources of income; 6% endorsed energy insecurity, 5% endorsed food insecurity, and 4% endorsed housing insecurity. In a multivariable model, financial toxicity was associated with age ≤60y at survey (Odds Ratio [OR] 5.1; 95% confidence interval [CI] 2.0-13.3); household income < $50K/y (OR 5.3; 95%CI 2.5-11.2); being single (OR 2.6; 95%CI 1.3-5.4); and lower physical (OR 2.6; 95%CI 1.2-5.4) and mental (OR 2.2; 95%CI 1.2-4.3) HRQL. Cancer stage, race, time from diagnosis, and insurance type were not associated with financial toxicity. The prevalence of financial toxicity among survivors who were single, ≤60y at survey, and with household income < $50k/y was 79.3%, compared with 6.7% among those who were older, married/partnered, and with higher income. Conclusions: Financial toxicity is prevalent among insured breast cancer survivors several years after cancer diagnosis, and is exacerbated among the younger survivors who are single, with low household income, and endorse poorer physical and mental quality of life. These findings inform the need to develop interventions to mitigate financial toxicity among at-risk breast cancer survivors.

Pediatric Extrapolation in Type 2 Diabetes: Future Implications of a Workshop

Extrapolation from adults to youth with type 2 diabetes (T2D) is challenged by differences in disease progression and manifestation. This manuscript presents the results of a mock-team workshop focused on examining the typical team-based decision process used to propose a pediatric development plan for T2D addressing the viability of extrapolation. The workshop was held at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) in Orlando, Florida on March 21, 2018.

A Pediatric Covariate Function for CYP3A-Mediated Midazolam Clearance Can Scale Clearance of Selected CYP3A Substrates in Children

Recently a framework was presented to assess whether pediatric covariate models for clearance can be extrapolated between drugs sharing elimination pathways, based on extraction ratio, protein binding, and other drug properties. Here we evaluate when a pediatric covariate function for midazolam clearance can be used to scale clearance of other CYP3A substrates. A population PK model including a covariate function for clearance was developed for midazolam in children aged 1–17 years. Commonly used CYP3A substrates were selected and using the framework, it was assessed whether the midazolam covariate function accurately scales their clearance. For eight substrates, reported pediatric clearance values were compared numerically and graphically with clearance values scaled using the midazolam covariate function. For sildenafil, clearance values obtained with population PK modeling based on pediatric concentration-time data were compared with those scaled with the midazolam covariate function. According to the framework, a midazolam covariate function will lead to systemically accurate clearance scaling (absolute prediction error (PE) < 30%) for CYP3A substrates binding to albumin with an extraction ratio between 0.35 and 0.65 when binding < 10% in adults, between 0.05 and 0.55 when binding > 90%, and with an extraction ratio ranging between these values when binding between 10 and 90%. Scaled clearance values for eight commonly used CYP3A substrates were reasonably accurate (PE < 50%). Scaling of sildenafil clearance was accurate (PE < 30%). We defined for which CYP3A substrates a pediatric covariate function for midazolam clearance can accurately scale plasma clearance in children. This scaling approach may be useful for CYP3A substrates with scarce or no available pediatric PK information.

Physiologically Based Pharmacokinetic Approach to Determine Dosing on Extracorporeal Life Support: Fluconazole in Children on ECMO

Extracorporeal life support (e.g., dialysis, extracorporeal membrane oxygenation (ECMO)) can affect drug disposition, placing patients at risk for therapeutic failure. In this population, dose selection to achieve safe and effective drug exposure is difficult. We developed a novel and flexible approach that uses physiologically based pharmacokinetic (PBPK) modeling to translate results from ECMO ex vivo experiments into bedside dosing recommendations. To determine fluconazole dosing in children on ECMO, we developed a PBPK model, which was validated using fluconazole pharmacokinetic (PK) data in adults and critically ill infants. Next, an ECMO compartment was added to the PBPK model and parameterized using data from a previously published ex vivo study. Simulations using the final ECMO PBPK model reasonably characterized observed PK data in infants on ECMO, and the model was used to derive dosing in children on ECMO across the pediatric age spectrum. This approach can be generalized to other forms of extracorporeal life support (ECLS), such as dialysis.

Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.

Higher Midazolam Clearance in Obese Adolescents Compared with Morbidly Obese Adults

BACKGROUND

The clearance of cytochrome P450 (CYP) 3A substrates is reported to be reduced with lower age, inflammation and obesity. As it is unknown what the overall influence is of these factors in the case of obese adolescents vs. morbidly obese adults, we studied covariates influencing the clearance of the CYP3A substrate midazolam in a combined analysis of data from obese adolescents and morbidly obese adults.

METHODS

Data from 19 obese adolescents [102.7 kg (62-149.5 kg)] and 20 morbidly obese adults [144 kg (112-186 kg)] receiving intravenous midazolam were analysed, using population pharmacokinetic modelling (NONMEM 7.2). In the covariate analysis, the influence of study group, age, total body weight (TBW), developmental weight (WTfor age and length) and excess body weight (WTexcess = TBW - WTfor age and length) was evaluated.

RESULTS

The population mean midazolam clearance was significantly higher in obese adolescents than in morbidly obese adults [0.71 (7%) vs. 0.44 (11%) L/min; p < 0.01]. Moreover, clearance in obese adolescents increased with TBW (p < 0.01), which seemed mainly explained by WTexcess, and for which a so-called 'excess weight' model scaling WTfor age and length to the power of 0.75 and a separate function for WTexcess was proposed.

DISCUSSION

We hypothesise that higher midazolam clearance in obese adolescents is explained by less obesity-induced suppression of CYP3A activity, while the increase with WTexcess is explained by increased liver blood flow. The approach characterising the influence of obesity in the paediatric population we propose here may be of value for use in future studies in obese adolescents.

Leveraging Big Data in Pediatric Development Programs: Proceedings From the 2016 American College of Clinical Pharmacology Annual Meeting Symposium

This article discusses the use of big data in pediatric drug development. The article covers key topics discussed at the ACCP annual meeting symposium in 2016 including the extent to which big data or real-world data can inform clinical trial design and substitute for efficacy and safety data typically obtained in clinical trials. The current states of use, opportunities, and challenges with the use of big data in future pediatric drug development are discussed.

Antiinflammatory effects of aprepitant coadministration with cART regimen containing ritonavir in HIV-infected adults

BACKGROUND

HIV-infected individuals, even well controlled with combined antiretroviral therapy (cART), have systemic inflammation and comorbidities. Substance P (SP) is an undecapeptide, which mediates neurotransmission and inflammation through its cognate neurokinin 1 receptor (NK1R). Plasma SP levels are elevated in HIV-infected individuals. The FDA-approved antiemetic aprepitant, an NK1R antagonist, has anti-HIV effects and antiinflammatory actions. We evaluated the safety, pharmacokinetics, and antiinflammatory properties of aprepitant in HIV-positive individuals receiving cART.

METHODS

We conducted a phase 1B study of 12 HIV-positive individuals on a ritonavir-containing regimen (HIV viral load less than 40 copies/ml and CD4 > 400 cells/μl). Participants received open-label aprepitant 375 mg per day for 28 days and were followed for an additional 30 days. Changes in plasma levels of proinflammatory markers were assessed using flow cytometry, ELISA, luminex, and SOMAscan assays.

RESULTS

The mean peak aprepitant plasma concentration was 30.7 ± 15.3 μg/ml at day 14 and 23.3 ± 12.3 μg/ml at day 28. Aprepitant treatment resulted in decreased plasma SP levels and affected 176 plasma proteins (56 after FDR) and several metabolic pathways, including inflammation and lipid metabolism. No change in soluble CD163 was observed. Aprepitant treatment was associated with a moderate increases in total and HDL cholesterol and affected select hematologic and metabolic markers, which returned to baseline levels 30 days after aprepitant treatment was stopped. There were 12 mild and 10 moderate adverse events (AE).

CONCLUSIONS

Aprepitant is safe and well tolerated. The antiinflammatory properties of aprepitant make it a possible adjunctive therapy for comorbid conditions associated with HIV infection.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT02154360).

FUNDING

This research was funded by NIH UO1 MH090325, P30 MH097488, and PO1 MH105303.

Exposure Matching for Extrapolation of Efficacy in Pediatric Drug Development

During drug development, matching adult systemic exposures of drugs is a common approach for dose selection in pediatric patients when efficacy is partially or fully extrapolated. This is a systematic review of approaches used for matching adult systemic exposures as the basis for dose selection in pediatric trials submitted to the US Food and Drug Administration (FDA) between 1998 and 2012. The trial design of pediatric pharmacokinetic (PK) studies and the pediatric and adult systemic exposure data were obtained from FDA publicly available databases containing reviews of pediatric trials. Exposure-matching approaches that were used as the basis for pediatric dose selection were reviewed. The PK data from the adult and pediatric populations were used to quantify exposure agreement between the 2 patient populations. The main measures were the pediatric PK studies' trial design elements and drug systemic exposures (adult and pediatric). There were 31 products (86 trials) with full or partial extrapolation of efficacy with an available PK assessment. Pediatric exposures had a range of mean C_max and AUC ratios (pediatric/adult) of 0.63 to 4.19 and 0.36 to 3.60, respectively. Seven of the 86 trials (8.1%) had a predefined acceptance boundary used to match adult exposures. The key PK parameter was consistently predefined for antiviral and anti-infective products. Approaches to match exposure in children and adults varied across products. A consistent approach for systemic exposure matching and evaluating pediatric PK studies is needed to guide future pediatric trials.

Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative Systems Pharmacology Simulators

Quantitative Systems Pharmacology (QSP) modeling is increasingly used as a quantitative tool for advancing mechanistic hypotheses on the mechanism of action of a drug, and its pharmacological effect in relevant disease phenotypes, to enable linking the right drug to the right patient. Application of QSP models relies on creation of virtual populations for simulating scenarios of interest. Creation of virtual populations requires 2 important steps, namely, identification of a subset of model parameters that can be associated with a phenotype of disease and development of a sampling strategy from identified distributions of these parameters. We improve on existing sampling methodologies by providing a means of representing the structural relationship across model parameters and describing propagation of variability in the model. This gives a robust, systematic method for creating a virtual population. We have developed the Linear-In-Flux-Expressions (LIFE) method to simulate variability in patient pharmacokinetics and pharmacodynamics using relationships between parameters at baseline to create a virtual population. We demonstrate the importance of this methodology on a model of cholesterol metabolism. The LIFE methodology brings us a step closer toward improved QSP simulators through enhanced capture of the observed variability in drug and disease clinical data.

A Quantitative Systems Pharmacology Platform to Investigate the Impact of Alirocumab and Cholesterol-Lowering Therapies on Lipid Profiles and Plaque Characteristics

Reduction in low-density lipoprotein cholesterol (LDL-C) is associated with decreased risk for cardiovascular disease. Alirocumab, an antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly reduces LDL-C. Here, we report development of a quantitative systems pharmacology (QSP) model integrating peripheral and liver cholesterol metabolism, as well as PCSK9 function, to examine the mechanisms of action of alirocumab and other lipid-lowering therapies, including statins. The model predicts changes in LDL-C and other lipids that are consistent with effects observed in clinical trials of single or combined treatments of alirocumab and other treatments. An exploratory model to examine the effects of lipid levels on plaque dynamics was also developed. The QSP platform, on further development and qualification, may support dose optimization and clinical trial design for PCSK9 inhibitors and lipid-modulating drugs. It may also improve our understanding of factors affecting therapeutic responses in different phenotypes of dyslipidemia and cardiovascular disease.

Reducing the maternal dietary intake of indigestible and slowly absorbed short-chain carbohydrates is associated with improved infantile colic: a proof-of-concept study

BACKGROUND

To investigate if a low fermentable oligo-, di- and mono-saccharides and polyols (FODMAP) diet consumed by breastfeeding mothers may be associated with reduced symptoms of infantile colic.

METHODS

Exclusively breastfeeding mothers and their typically-developing healthy infants who met the Wessel Criteria for infantile colic were recruited from the community, to this single-blind, open-label, interventional study. After a 3-day qualifying period, mothers were provided a low FODMAP 7-day diet. On days 5, 6 and 7 mothers completed a Baby Day Diary. At baseline and at the end of the 7-day dietary intervention, breast milk was analysed for FODMAP content and infant faecal samples for pH.

RESULTS

Eighteen breastfeeding mothers (aged 27-40 years) adhered (100%) to the low FODMAP diet. Infants were of gestational age 37-40.3 weeks and aged 2-17 weeks. At entry, crying durations were a mean [95% CI] of 142 [106-61] min and fell by 52 [178-120] min (P = 0.005; ancova). Combined crying-fussing durations fell by 73 [301-223] min (n = 13; P = 0.007), as did crying episodes (P = 0.01) and fussing durations (P = 0.011). Infant sleeping, feeding, or awake-and-content durations did not change. Infant faecal pH did not change. Breast milk lactose content was stable and other known FODMAPs were not detected. At end of study, mothers reported their baby 'is much more content' and 'can be put down without crying'.

CONCLUSIONS

Maternal low FODMAP diet may be associated with a reduction in infant colic symptoms. A randomized controlled study is warranted to determine if a maternal low FODMAP diet is effective in reducing symptoms.

Adding glucose to food and solutions to enhance fructose absorption is not effective in preventing fructose-induced functional gastrointestinal symptoms: randomised controlled trials in patients with fructose malabsorption

BACKGROUND

In healthy individuals, the absorption of fructose in excess of glucose in solution is enhanced by the addition of glucose. The present study aimed to assess the effects of glucose addition to fructose or fructans on absorption patterns and genesis of gastrointestinal symptoms in patients with functional bowel disorders.

METHODS

Randomised, blinded, cross-over studies were performed in healthy subjects and functional bowel disorder patients with fructose malabsorption. The area-under-the-curve (AUC) was determined for breath hydrogen and symptom responses to: (i) six sugar solutions (fructose in solution) (glucose; sucrose; fructose; fructose + glucose; fructan; fructan + glucose) and (ii) whole foods (fructose in foods) containing fructose in excess of glucose given with and without additional glucose. Intake of fermentable short chain carbohydrates (FODMAPs; fermentable, oligo-, di-, monosaccharides and polyols) was controlled.

RESULTS

For the fructose in solution study, in 26 patients with functional bowel disorders, breath hydrogen was reduced after glucose was added to fructose compared to fructose alone [mean (SD) AUC 92 (107) versus 859 (980) ppm 4 h^-1 , respectively; P = 0.034). Glucose had no effect on breath hydrogen response to fructans (P = 1.000). The six healthy controls showed breath hydrogen patterns similar to those with functional bowel disorders. No differences in symptoms were experienced with the addition of glucose, except more nausea when glucose was added to fructose (P = 0.049). In the fructose in foods study, glucose addition to whole foods containing fructose in excess of glucose in nine patients with functional bowel disorders and nine healthy controls had no significant effect on breath hydrogen production or symptom response.

CONCLUSIONS

The absence of a favourable response on symptoms does not support the concomitant intake of glucose with foods high in either fructose or fructans in patients with functional bowel disorders.

Pharmacologic rationale for the NK1R antagonist, aprepitant as adjunctive therapy in HIV

BACKGROUND

Many HIV infected individuals with suppressed viral loads experience chronic immune activation frequently developing neurological impairment designated as HIV associated neurocognitive disorder (HAND). Adjunctive therapies may reduce HIV associated inflammation and therefore decrease the occurrence of HAND.

METHODS

We have conducted in vitro, animal and clinical studies of the neurokinin 1 receptor (NK1R) antagonist aprepitant in HIV/SIV infection.

RESULTS

Aprepitant inhibits HIV infection of human macrophages ex vivo with an ED50 ~ 5 µM. When administered at 125 mg once daily for 12 months to SIV-infected rhesus macaques, aprepitant reduced viral load by approximately tenfold and produced anti-anxiolytic effects. The anti-viral and anti-anxiolytic effects occur at approximately the third month of dosing; and the effects are sustained throughout the duration of drug administration. Protein binding experiments in culture media and animal and human plasma indicate that the free fraction of aprepitant is lower than previously reported supporting usage of higher doses in vivo. The analysis of blood samples from HIV positive individuals treated for 2 weeks with aprepitant at doses up to 375 mg demonstrated reduced levels of pro-inflammatory cytokines including G-CSF, IL-6, IL-8 and TNFα. Decreased pro-inflammatory cytokines may reduce HIV comorbidities associated with chronic inflammation.

CONCLUSIONS

Our results provide evidence for a unique combination of antiretroviral, anti-inflammatory and behavioral modulation properties of aprepitant in vitro and in vivo. These results provide robust support for a clinical exposure target above that recommended for chemotherapy-induced nausea and vomiting. Doses up to 375 mg once daily in HIV-infected patients still elicit sub-therapeutic exposure of aprepitant though effective plasma concentrations can be achievable by proper dose modulation.

Paediatric models in motion: requirements for model-based decision support at the bedside

Optimal paediatric pharmacotherapy is reliant on a detailed understanding of the individual patient including their developmental status and disease state as well as the pharmaceutical agents he/she is receiving for treatment or management of side effects. Our appreciation for size and maturation effects on the pharmacokinetic/pharmacodynamic (PK/PD) phenomenon has improved to the point that we can develop predictive models that permit us to individualize therapy, especially in the situation where we are monitoring drug effects or therapeutic concentrations. The growth of efforts to guide paediatric pharmacotherapy via model-based decision support necessitates a coordinated and systematic approach to ensuring reliable and robust output to caregivers that represents the current standard of care and adheres to governance imposed by the host institution or coalition responsible. Model-based systems which guide caregivers on dosing paediatric patients in a more comprehensive manner are in development at several institutions. Care must be taken that these systems provide robust guidance with the current best practice. These systems must evolve as new information becomes available and ultimately are best constructed from diverse data representing global input on demographics, ethnic / racial diversity, diet and other lifestyle factors. Multidisciplinary involvement at the project team level is key to the ultimate clinical valuation. Likewise, early engagement of clinical champions is also critical for the success of model-based tools. Adherence to regulatory requirements as well as best practices with respect to software development and testing are essential if these tools are to be used as part of the routine standard of care.

Use of Modeling and Simulation in the Design and Conduct of Pediatric Clinical Trials and the Optimization of Individualized Dosing Regimens

Mathematical models of drug action and disease progression can inform pediatric pharmacotherapy. In this tutorial, we explore the key issues that differentiate pediatric from adult pharmacokinetic (PK) / pharmacodynamic (PD) studies, describe methods to calculate the number of participants to be enrolled and the optimal times at which blood samples should be collected, and therapeutic drug monitoring methods for individualizing pharmacotherapy. The development of pediatric-specific drug dosing dashboards is also highlighted, with an emphasis on clinical-relevance and ease of use.

Challenges and considerations for development of therapeutic proteins in pediatric patients

Target specificity and generally good tolerability of therapeutic proteins (TPs) present desirable treatment opportunities for pediatric patients. However, little is known on the ontogeny of processes related to the pharmacokinetics (PK) and disposition of TPs. The science, regulatory requirements and strategy of developing TPs for children are evolving. Our current review of TPs, (with focus on monoclonal antibodies and fusion proteins) that were approved for pediatric use indicates that dose-selection for pediatric pivotal studies is often based on adult PK information alone. This approach might not be sufficient if more complex PK properties than simple linear PK are present. Body weight-based dosing for pediatric patients directly scaled down from adult dosing can lead to under-exposure in young pediatric patients who are usually in the lowest body-weight range. Tiered-fixed dosing can be reasonably effective for TPs in achieving comparable exposure in children over a wide age range. The uniqueness of the pediatric population, the practical challenges in conducting clinical studies in this population, as well as regulations from health authorities warrant including pharmacometrics as an integral component of pediatric drug development. We propose a framework distinct from previous proposals, to guide clinical pharmacology strategy for pediatric drug development specifically for TPs.

Population pharmacokinetics of midazolam and its metabolites in overweight and obese adolescents

AIM

In view of the increasing prevalence of obesity in adolescents, the aim of this study was to determine the pharmacokinetics of the CYP3A substrate midazolam and its metabolites in overweight and obese adolescents.

METHODS

Overweight (BMI for age ≥ 85(th) percentile) and obese (BMI for age ≥ 95(th) percentile) adolescents undergoing surgery received 2 or 3 mg intravenous midazolam as a sedative drug pre-operatively. Blood samples were collected until 6 or 8 h post-dose. Population pharmacokinetic modelling and systematic covariate analysis were performed using nonmem 7.2.

RESULTS

Nineteen overweight and obese patients with a mean body weight of 102.7 kg (62-149.8 kg), a mean BMI of 36.1 kg m(-2) (24.8-55 kg m(-2)), and a mean age of 15.9 years (range 12.5-18.9 years) were included. In the model for midazolam and metabolites, total body weight was not of influence on clearance (0.66 l min(-1) (RSE 8.3%)), while peripheral volume of distribution of midazolam (154 l (11.2%)), increased substantially with total body weight (P < 0.001). The increase in peripheral volume could be explained by excess body weight (WTexcess ) instead of body weight related to growth (WTfor age and length ).

CONCLUSIONS

The pharmacokinetics of midazolam and its metabolites in overweight and obese adolescents show a marked increase in peripheral volume of distribution and a lack of influence on clearance. The findings may imply a need for a higher initial infusion rate upon initiation of a continuous infusion in obese adolescents.