Search

Until recently, ondansetron was approved for the prevention of nausea and vomiting only in patients older than 2 years. However, as the use of ondansetron in patients younger than 2 years had been documented, characterization of ondansetron pharmacokinetics in this younger pediatric age group was warranted. The pharmacokinetics of intravenously administered ondansetron were evaluated in oncology and surgical patients aged 1-48 months. Pooled data from 124 patients, including 745 pharmacokinetic samples, were analyzed using nonlinear mixed-effects modeling. Ondansetron pharmacokinetics were described by a two-compartment model. Body-size effects on ondansetron disposition were accounted for via standard allometric relationships, normalized to 10.4 kg. A maturation process with a half-life of approximately 4 months was incorporated to describe a decrease in clearance (CL) in infants. Clearance [95% confidence interval (CI)] for a typical patient was 1.53 (1.34-1.78) L/h/kg0.75 with an interindividual variability of 56.8%. Ondansetron CL was reduced by 31%, 53%, and 76% for the typical 6-month-, 3-month-, and 1-month-old patient, respectively. Simulations showed that an ondansetron dose of 0.1 mg/kg in children younger than 6 months produced exposure similar to a 0.15-mg/kg dose in older children. The population pharmacokinetic analysis of ondansetron allows for characterization of individual patients based on body weight and age. It is recommended that patients younger than 4 months receiving ondansetron be closely monitored. [ABSTRACT FROM AUTHOR]

The primary objective of this study was to characterize the drug exposure for children hospitalized in the authors' institution's pediatric intensive care unit for the year 2002. Secondary objectives included the examination of drug utilization differences among various age criteria and the suitability of the most prevalent resources for pediatric dosing guidance. Many of the most commonly prescribed agents in the pediatric intensive care unit fall into the broad categories of pain management/sedation and anti-infectives. Based on the generally narrow windows afforded by each of these drug classes, it is obvious that more, well-defined investigations in critically ill children are warranted. The existing dosing guidance for many of these agents is neither generalizable nor sufficient to accommodate the diversity in pediatric intensive care unit patients, and the current drug monographs fall short of any practical dosing information. [ABSTRACT FROM AUTHOR]

M100240 is a thioester of MDL 100,173, a dual angiotensin-converting enzyme (ACE)/neutral endopeptidase (NEP) inhibitor. Clinical studies have shown that M100240 is capable of decreasing ACE activity and angiotensin II concentrations while increasing plasma renin activity and potentiating the effects of atrial natriuretic peptide. This may result in a unique treatment benefit in disease states characterized by intravascular volume or sodium overload or increased venous pressure. The pharmacokinetics of MDL 100,173 were evaluated in 30 healthy subjects in an open-label, randomized, 2-period crossover design. Subjects received a single oral dose of 50 mg of M100240 administered with a high-fat meal and separately under fasted conditions. Serial plasma concentrations of M100240 and MDL 100,173 were analyzed, and pharmacokinetic parameters were calculated with noncompartmental methods. The intrasubject percent coefficient of variation for MDL 100,173 C(max) and AUC(0-24h) were less than 20%, indicating that this agent is a moderately variable drug. Although AUC(0-24h) was within the protocol-defined range of 80% to 125%, the lower limit of the 90% confidence interval for C(max) fell outside of the 70% to 143% range. Absence of a food effect on the pharmacokinetic profile of 50 mg of M100240 could therefore not be demonstrated. This finding is not surprising based on the documented food effect with the sulfhydryl-containing ACE inhibitor, captopril. Clinical significance of this pharmacokinetic food effect is unlikely, as the magnitude of pharmacodynamic response is probably better correlated with AUC than a single-point determination of C(max). Single oral doses of 50 mg of M100240 were safe and well tolerated under fed and fasted conditions. [ABSTRACT FROM AUTHOR]

The pharmacokinetics and safety of the ketolide telithromycin were evaluated in two separate studies after single and repeat oral dosing in patients with varying degrees of renal impairment and in subjects with normal renal function. The single-dose study was an open-label, nonrandomized, parallel-group design in which all 40 patients received a single oral dose of telithromycin 800 mg. The repeat-dose study was an open-label study with a randomized, balanced, incomplete three-block treatment crossover design. In this study, each of the 36 patients received two of three telithromycin regimens (400, 600, or 800 mg once daily for 5 days), with a washout period of >/= 7 days between treatments. Telithromycin was well tolerated. Adverse events were generally mild in severity, and no serious drug-related adverse events were reported. Plasma exposure to telithromycin (C(max), AUC) showed a tendency to increase with increasing severity of renal impairment in both studies. In patients with severe renal impairment (CL(CR) < 30 mL/min) receiving telithromycin 800 mg in the repeat-dose study, C(max,ss) and AUC((0-24 h)ss) increased 1.5-fold (p < 0.05) to 2.0-fold (p = 0.0005), respectively, compared with healthy subjects. The percentage of dose excreted in urine and renal clearance (CL(R)) of telithromycin was found to decrease significantly with increasing severity of renal impairment in both studies, and CL(R) was found to be independent of telithromycin dose in the repeat-dose study. In conclusion, telithromycin dosage adjustment is not necessary in patients with mild to moderate renal impairment (CL(CR) >/= 30 mL/min). In patients with severe renal impairment (CL(CR) < 30 mL/min), dosage adjustment could be considered. [ABSTRACT FROM AUTHOR]

Optimising the timing of food intake relative to exercise may maximise the effectiveness of free-living exercise programmes on improvements in glycaemic control and cardio-metabolic health. This study aimed to assess the feasibility of a free-living, walking-based exercise programme and determine whether undertaking each exercise session before or after breakfast would most benefit longer-term metabolic health. Thirty-four people living with obesity (43±12 y, BMI 35.1±5.1 kg.m-2) undertook a 12-week walking-based programme, consisting of two continuous (30-60 min at 50% HRmax) and two interval exercise sessions per week (30-60 min, alternating 3 min at 85% HRmax and 3 min at 50% HRmax). Participants were allocated to exercise before (FASTED) or after (FED) breakfast (n = 17 per group). Feasibility (acceptability, adherence and compliance) to the exercise intervention were assessed, as well as changes in anthropometric variables, 24-hour continuous glucose monitoring, serum biochemistry including HbA1c, lipid profile and liver transaminases. Exercise adherence (FASTED: 93±4%, FED: 95±5%) and compliance (FASTED: 85±10%, FED: 88±10%) was high in both groups, and participants described exercise monitoring, programme structure and support as facilitators to this. Body mass, BMI, waist-to-hip ratio and HbA1c decreased similarly between groups (all P<0.01). However, serum ALT concentrations decreased after FASTED (-16± -14%; P = 0.001), but not FED training (-2 ± -4%; P = 0.720). We demonstrate that a free-living walking-based exercise programme, with exercise timed relative to breakfast can achieve high adherence and compliance and improve some anthropometric variables and HbA1c. Whether FASTED exercise can elicit greater improvements in liver health requires further investigation., Competing Interests: No author has any conflict of interest, financial or otherwise, with this study., (Copyright: © 2024 Barrett et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Under US Food and Drug Administration (FDA) grant (2U18FD005320-06), the Critical Path Institute (C-Path) and experienced private sector partners collaborated with global health organizations to create didactic video materials in an e-learning format on model-informed drug development (MIDD) topics relevant to a non-modeling audience. Several multinational pharmaceutical companies contributed case studies illustrating the application of the MIDD approach in practice. Training videos were created and divided into several modules: introducing the MIDD landscape for drug development and regulatory science, a review of various model types used for MIDD, discussions of how models inform drug development and regulatory decisions, future goals of MIDD, and discussions on the interconnectedness of models used for MIDD. Examples and vignettes from stakeholders and thought leaders were included. These educational materials fill a gap between academic and "on the job training" for regulators, academic, and industry scientists, delivering insights and value for those performing modeling and non-modelers reviewing the output of modeling and simulation work. A total of 13 hours of video content is currently available. A small panel of FDA reviewers is currently beta-testing the learning management system (LMS). Future efforts for this MIDD training initiative will include expansion of the content via an expanded and diverse faculty, 1:1 online mentorship sessions, and eventually broader access to this resource consistent with an open science approach and curriculum. The MIDD training LMS can accommodate a diverse learning ecosystem; further development may also accommodate different audiences in the future., (© 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Competing Interests: Disclosures. The author declares no conflicts or financial interest in any product or service mentioned in the manuscript, including grants, equipment, medications, employment, gifts, and honoraria. The author attests to meeting the four criteria recommended by the ICMJE for authorship of this manuscript.

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., Competing Interests: Declaration of competing interest The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

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., (© 2024. The Author(s).)

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., (© 2024 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

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., (© 2023, The American College of Clinical Pharmacology.)

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., (© 2023, The American College of Clinical Pharmacology.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Barrett, Oskoui, Russell and Borens.)

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., (© 2022 PTC Therapeutics Inc, Critical Path Institute and Pfizer Inc. The Journal of Clinical Pharmacology published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

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., Competing Interests: TK, JB, and EJ were employed by the Metrum Research Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Barrett, Cala Pane, Knab, Roddy, Beusmans, Jordie, Singh, Davis, Romero, Padula, Thebaud and Turner.)

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., (© 2022. The Drug Information Association, Inc.)

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2022 Axcella Therapeutics and Critical Path Institute. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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.

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., (© 2020 British Pharmacological Society.)

Background and Aim: An elimination-rechallenge dietary approach targeting naturally-occurring bioactive chemicals has been proposed to alleviate functional gastrointestinal symptoms. A major focus of this approach is salicylates. This study aimed to address the potential role of dietary salicylates in the induction of symptoms in patients with irritable bowel syndrome (IBS)., Methods: A pilot, double-blind, randomized, cross-over trial of 2-week low- versus high-salicylate diets (6.6 and 27.9 g/day salicylate, respectively) was undertaken. All foods were provided containing minimal quantities of other potential food triggers. Gastrointestinal and extraintestinal symptoms were measured daily using a 100-mm visual-analogue-scale., Results: Ten participants with IBS completed the study, including one with known aspirin-sensitivity. Overall, no differences in symptoms were observed ( P  = 0.625; Friedman test). However, clear symptom provocation was seen in the aspirin-sensitive participant, with all abdominal symptoms and tiredness worsening during the high-salicylate diet. A similar trend was seen in another participant, where abdominal symptoms gradually worsened during the high-salicylate diet., Conclusions: These results provide some evidence that food-related salicylates may influence the genesis of symptoms in a subset of patients with IBS. A larger cohort is needed to determine the incidence of salicylate-sensitivity and further evaluate the diet as a potential therapeutic target.The protocol was registered at www.anzctr.org.au (ACTRN12620001250921)., (© 2021 The Authors. JGH Open published by Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

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., (© 2021, The American College of Clinical Pharmacology.)

Background and Aim: Limited data are available on the effects of fermentable fiber in altering intestinal pH and transit to predict efficacy-based delivery profiles of pH-dependent mesalamine coatings in ulcerative colitis (UC). This study aimed to examine regional pH and transit after acute changes in fermentable fiber intake in quiescent UC patients and their effects on drug release systems., Methods: In a randomized, double-blind study, 18 patients with quiescent UC and 10 healthy controls were supplied meals high (13 g) or low (≤ 2 g) in fermentable fiber and subsequently ingested a wireless pH-motility capsule. After a ≥ 3-day washout, they crossed over to the other diet. Measurements of intestinal pH and transit were used to predict drug release for the various pH-dependent coatings., Results: Increasing fermentable fiber intake lowered overall (median 6.2 [6.1-6.7] vs low: 6.9 [range or interquartile range: 6.4-7.4]; P = 0.01) and distal pH (7.8 [7.3-8.1] vs 8.2 [8.0-8.5]; P = 0.04) in controls. In UC patients, only cecal pH was decreased (high: 5.1 [4.8-5.5] vs low: 5.5 [5.3-5.7]; P < 0.01). Colonic transit in the UC cohort varied widely after a low-fiber intake but tended to normalize after the high fermentable fiber intake. Hypothetical coating dissolution profiles were heterogeneous in UC patients, with a multi-matrix delayed release system having the highest likelihood of patients (20-40%) with incomplete dissolution, and predominant small intestinal dissolution predicted for Eudragit L (94% patients) and S (44-69%)., Conclusions: Patients with quiescent UC have abnormalities in intestinal pH and transit in response to acute changes in fermentable fiber intake. These have potentially detrimental effects on predicted luminal release patterns of pH-dependent 5-aminosalicylic acid release systems., (© 2020 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

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., (© 2021, The American College of Clinical Pharmacology.)

Background & Aims: Irritable bowel syndrome (IBS) is a functional disorder that is characterized by gastrointestinal symptoms and that has a major impact on quality of life, resulting in direct and indirect health care costs. The majority of patients with IBS suffer from food intolerances, most commonly related to the consumption of fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAPs). This study aimed to develop and verify the validity and reproducibility of a short food frequency questionnaire (FFQ) to assess typical FODMAP consumption in adults with IBS., Methods: The primary FFQ list consisted of source foods of FODMAPs that contributed at least 10% to the frequency of consumption among 855 adults from a population-based study in the municipality of Campinas in 2014/2015. In addition, source foods of FODMAPs (according to the Monash University Low FODMAP Diet application) and foods commonly consumed by the Brazilian population (according to the FFQ for adults validated in the city of São Paulo) were included. One hundred and five (n = 105) healthy subjects were recruited to respond to the FFQ twice and to respond the 24-h dietary recall (24HR) three times during a 3-month period. The relative validity of the proposed instrument was compared with the average of the three 24HRs, and the reproducibility of the instrument was assessed by comparing both FFQ applications. The following statistical analyses were used for validation and reproducibility: Wilcoxon's test, Spearman's correlation analysis, weighted kappa, Bland Altman's plot and index, and interclass correlation coefficient., Results: The final list of items for the short FFQ included 54 different foods. The foods were organized by FODMAP groups: free fructose, lactose, total oligosaccharides and total polyols, with variations of categories of responses for consumption frequency between 0 and 10 times and the unit of time in days, weeks or months. In the validity analyses, the correlation coefficients ranged from 0.209 (polyols) to 0.652 (lactose) (p < 0.05). There was no correlation between the methods in the fructose and oligosaccharide groups. The lactose group presented good agreement, and the remaining groups had a lack of agreement, with a mean of 15.7%. The Bland-Altman index values were 4.7% (fructose), 3.8% (lactose), 5.7% (oligosaccharides) and 6.6% (polyols). Regarding reproducibility, the interclass and Spearman's correlation coefficients varied from ICC = 0.781 and r = 0.725 (oligosaccharides) to ICC = 0.913 and r = 0.807 (lactose) (p < 0.05), showing strongly reproducible results for lactose and polyols and good results for fructose and oligosaccharides. Accurate agreement between FFQ applications had a mean of 67.3%, and 3.0% showed disagreement between FFQ1 and FFQ2. The weighted kappa coefficient ranged from 0.576 (polyols) to 0.645 (lactose)., Conclusion: The semi-quantitative short FFQ was developed to evaluate the consumption of FODMAPs in adults in São Paulo. The instrument presents good reproducibility for all groups of FODMAPs, good validity for lactose and weaker validity for fructose, polyols and oligosaccharides. As the short FFQ was carefully designed for the study population, its estimates are relatively reliable at the population group level. A future reanalysis of this questionnaire would be useful when the chemical composition data of FODMAPs are available., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Endurance athletes commonly experience lower gastrointestinal (GI) symptoms similar to those of irritable bowel syndrome (IBS). Previous research on the restriction of fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP), a diet-based mitigation strategy initially developed for IBS, has shown promise for application in athlete populations. Athlete's dietary strategies surrounding exercise have not been formally assessed in relation to FODMAP content of foods or sports nutrition products. Additionally, the FODMAP content of athlete's habitual diets has not been examined in larger sample sizes. This research aims to investigate the FODMAP content of endurance athlete diets by examining these three areas, in conjunction with GI symptoms. Dietary habits surrounding exercise and GI symptoms were examined in 430 endurance athletes using a previously validated Endurance Athlete Questionnaire. A subset of athletes ( n = 73) completed a FODMAP-specific food frequency questionnaire for habitual intake. The most commonly reported sports nutrition products were analyzed for FODMAP content using standardized analytical methods. Mean habitual intakes were compared to previous FODMAP studies and medians were compared between those with and without lower GI symptoms. Athletes commonly consumed high FODMAP foods during pre-race dinners and breakfasts, with over 60% reporting specific high FODMAP foods. More frequent nutrition product use, particularly solid, gel/gummy, and homemade products, was often related to increased frequency of GI symptoms. Of the sixteen commonly used sports nutrition products tested, seven were high FODMAP in one serving. All but one of the remaining products became high FODMAP when consumed in multiple servings, as is likely the case during endurance exercise. Average habitual FODMAP intake was 26.1 g (±15.9 g), similar to intakes classified as high FODMAP in previous research on FODMAPs and IBS or GI symptoms. Only 15.1% of athletes consumed a diet that would be considered low in FODMAP. Exploratory analyses showed higher intake of some FODMAP types among athletes exhibiting various lower GI symptoms. Overall, this study demonstrated that FODMAP intake by endurance athletes is high both surrounding exercise and habitually, and may be contributing to GI symptoms experienced during exercise. This information can be utilized when analyzing athlete diets and selecting foods to decrease GI symptoms., Competing Interests: JM works in a department that financially benefits from the sales of a digital application and booklets on the low FODMAP diet. Funds raised contribute to research of the Department of Gastroenterology and to the University. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SS declared a past collaboration with one of the authors JM to the handling editor., (Copyright © 2021 Killian, Muir, Barrett, Burd and Lee.)