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Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development. [ABSTRACT FROM AUTHOR]

The use of Bayesian methodology to design and analyze pediatric efficacy trials is one of the possible options to reduce their sample size. This reduction of the sample size results from the use of an informative prior for the parameters of interest. In most of the applications, the principle of 'information borrowing' from adults' trials is applied, which means that the informative prior is constructed using efficacy results in adult of the drug under investigation. This implicitly assumes similarity in efficacy between the selected pediatric dose and the efficacious dose in adults. The goal of this article is to propose a method to construct prior distribution for the parameter of interest, not directly constructed from the efficacy results of the efficacious dose in adult patients but using pharmacodynamic modeling of a bridging biomarker using early phase pediatric data. When combined with a model bridging the biomarker with the clinical endpoints, the prior is constructed using a variational method after simulation of the parameters of interest. A use case application illustrates how the method can be used to construct a realistic informative prior. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In a rapidly growing and aging population, heart failure (HF) has become recognised as a public health concern that imposes high economic and societal costs worldwide. HF management stems from the use of highly cost-effective angiotensin converting enzyme inhibitors (ACEi) and ß-blockers to the use of newer drugs such as sodium-glucose cotransporter-2 inhibitors (SGLT2i), ivabradine, and vericiguat. Modelling studies of pharmacological treatments that report on cost effectiveness in HF is important in order to guide clinical decision making. Multiple costeffectiveness analysis of dapagliflozin for heart failure with reduced ejection fraction (HFrEF) suggests that it is not only cost-effective and has the potential to improve long-term clinical outcomes, but is also likely to meet conventional cost-effectiveness thresholds in many countries. Similar promising results have also been shown for vericiguat while a cost effectiveness analysis (CEA) of empagliflozin has shown cost effectiveness in HF patients with Type 2 diabetes. Despite the recent FDA approval of dapagliflozin and empagliflozin in HF, it might take time for these SGLT2i to be widely used in real-world practice. A recent economic evaluation of vericiguat found it to be cost effective at a higher cost per QALY threshold than SGLT2i. However, there is a lack of clinical or realworld data regarding whether vericiguat would be prescribed on top of newer treatments or in lieu of them. Sacubitril/valsartan has been commonly compared to enalapril in cost effectiveness analysis and has been found to be similar to that of SGLT2i but was not considered a cost-effective treatment for heart failure with reduced ejection fraction in Thailand and Singapore with the current economic evaluation evidences. In order for more precise analysis on cost effectiveness analysis, it is necessary to take into account the income level of various countries as it is certainly easier to allocate more financial resources for the intervention, with greater effectiveness, in high- and middleincome countries than in low-income countries. This review aims to evaluate evidence and cost effectiveness studies in more recent HF drugs i.e., SGLT2i, ARNi, ivabradine, vericiguat and omecamtiv, and gaps in current literature on pharmacoeconomic studies in HF. [ABSTRACT FROM AUTHOR]

Epstein-Barr virus (EBV) establishes a lifelong latent infection in healthy humans, kept under immune control by cytotoxic T cells (CTLs). Following paediatric haematopoetic stem cell transplantation (HSCT), a loss of immune surveillance leads to opportunistic outgrowth of EBV-infected cells, resulting in EBV reactivation, which can ultimately progress to post-transplant lymphoproliferative disorder (PTLD). The aims of this study were to identify risk factors for EBV reactivation in children in the first 100 days post-HSCT and to assess the suitability of a previously reported mathematical model to mechanistically model EBV reactivation kinetics in this cohort. Retrospective electronic data were collected from 56 children who underwent HSCT at Great Ormond Street Hospital (GOSH) between 2005 and 2016. Using EBV viral load (VL) measurements from weekly quantitative PCR (qPCR) monitoring post-HSCT, a multivariable Cox proportional hazards (Cox-PH) model was developed to assess time to first EBV reactivation event in the first 100 days post-HSCT. Sensitivity analysis of a previously reported mathematical model was performed to identify key parameters affecting EBV VL. Cox-PH modelling revealed EBV seropositivity of the HSCT recipient and administration of anti-thymocyte globulin (ATG) pre-HSCT to be significantly associated with an increased risk of EBV reactivation in the first 100 days post-HSCT (adjusted hazard ratio (AHR) = 2.32, P = 0.02; AHR = 2.55, P = 0.04). Five parameters were found to affect EBV VL in sensitivity analysis of the previously reported mathematical model. In conclusion, we have assessed the effect of multiple covariates on EBV reactivation in the first 100 days post-HSCT in children and have identified key parameters in a previously reported mechanistic mathematical model that affect EBV VL. Future work will aim to fit this model to patient EBV VLs, develop the model to account for interindividual variability and model the effect of clinically relevant covariates such as rituximab therapy and ATG on EBV VL. [ABSTRACT FROM AUTHOR]

"Danya Glabau, Food Allergy Advocacy: Parenting and the Politics of Care" is an ethnography that explores the role of food allergy activism in the United States. The book argues that food allergy advocacy perpetuates White affluence and heteronormative family relations as conditions for health and well-being. It also examines how mainstream biomedical interventions hold mothers responsible for managing allergies in the home. The book highlights the gendered work of allergy management and the capitalist interests that shape the treatment of allergies. It concludes by calling for social changes that prioritize the safety of all individuals rather than relying on narrow biomedical fixes. [Extracted from the article]

This article analyzes the twenty-first-century adoption of the hormonal contraceptive pill in Japan, where it was not legalized until 1999. Drawing on fieldwork conducted in Tokyo, this research combines close readings of popular health media with ethnographic analysis to argue that in the context of a society suffering from low fertility, reproductive health actors, including pharmaceutical representatives, medical experts, and the government, construct the Pill as an everyday therapeutic that rehabilitates women's reproductive systems so that they may have children in the future. In a society experiencing growing labor insecurity and accelerating inequality, reproductive health professionals present women's reproductive systems as having become damaged through overwork and stress. As such, the Pill's new appeal lies in its identity as a therapeutic for hormonal regulation. By eliminating the contingencies of menstruation that interfere with women's workplace productivity while keeping their reproductive systems healthy, the Pill, I argue, extends women's productive and reproductive labor. Advancing material approaches to gender analysis through a feminist technoscience studies perspective, this research shows how the Pill's uses as a multipurpose drug beyond merely birth control disciplines women's bodies on a molecular level according to new gendered expectations that women must both work and reproduce. [ABSTRACT FROM AUTHOR]

Pharmacokinetics‐matched digital electrocardiogram data (n = 503 measurements from 180 patients) collected in a first‐in‐human, multi‐part, dose‐escalation (from 80 to 800 mg) and dose expansion (at 480 mg) phase 1 study in patients with advanced solid malignancies, were used to assess potential risk of QT prolongation associated with the AKT inhibitor capivasertib. The relationship between plasma drug concentrations and baseline‐adjusted Fridericia‐corrected QT (ΔQTcF) values was estimated using a prespecified linear mixed‐effects model. The model provided an unbiased reproduction of the experimental data set, estimating a small but positive correlation between capivasertib concentration and ΔQTcF. At the expected therapeutic dose (400 mg twice daily) the predicted mean ΔQTcF at the steady state maximum concentration was 3.97 ms with an upper limit of the 90% CI of 5.07 ms; below the 10 ms limit proposed by ICH E14 guidance. This analysis suggests that capivasertib is not expected to present a clinically significant risk for QT prolongation that is associated with pro‐arrhythmic effects. [ABSTRACT FROM AUTHOR]

Designing Motherhood: Things that Make and Break Our Births By Michelle Millar Fisher and Amber Winick. In reconsidering the provenance of such objects, I Designing Motherhood i recognizes women inventors like Donovan and Margaret Crane, the young graphic designer who made the first home pregnancy test in the 1970s but remained unknown until recently. [Extracted from the article]

Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V2ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non‐ordinary‐differential‐equation‐based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V2ACHER facilitates consistent, intuitive, single‐plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross‐functional development teams and facilitating confident use in support of decisions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. Objective: Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. Methods: A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. Results: From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. Conclusions: The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles. [ABSTRACT FROM AUTHOR]

Joining the Food and Drug Administration/University of Maryland Center of Excellence in Regulatory Science and Innovation Workshop to discuss and identify solutions to optimize pediatric drug development and, in particular, to address the question as to whether we are ready to incorporate pediatric ontogeny into modeling was the opportunity to share learnings, confront ideas, and present examples of studies performed in industry and academia. This was not only the opportunity to reflect on the experience and the knowledge so far within the current regulatory framework but also to look at the future and explore new and future approaches as well as best practices with the use of modeling and simulation and extrapolation as part of pediatric development. [ABSTRACT FROM AUTHOR]

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment. [ABSTRACT FROM AUTHOR]

Joining the Food and Drug Administration/University of Maryland Center of Excellence in Regulatory Science and Innovation Workshop to discuss and identify solutions to optimize pediatric drug development and, in particular, to address the question as to whether we are ready to incorporate pediatric ontogeny into modeling was the opportunity to share learnings, confront ideas, and present examples of studies performed in industry and academia. This was not only the opportunity to reflect on the experience and the knowledge so far within the current regulatory framework but also to look at the future and explore new and future approaches as well as best practices with the use of modeling and simulation and extrapolation as part of pediatric development. [ABSTRACT FROM AUTHOR]

Balancing antitumor efficacy with toxicity is a significant challenge, and drug‐induced myelosuppression is a common dose‐limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further. [ABSTRACT FROM AUTHOR]

The accuracy of physiologically based pharmacokinetic (PBPK) model prediction in children, especially those younger than 2 years old, has not been systematically evaluated. The aim of this study was to characterize the pediatric predictive performance of the PBPK approach for 10 drugs extensively metabolized by CYP1A2 (theophylline), CYP2C8 (desloratidine, montelukast), CYP2C9 (diclofenac), CYP2C19 (esomeprazole, lansoprazole), CYP2D6 (tramadol), and CYP3A4 (itraconazole, ondansetron, sufentanil). Model performance in children was evaluated by comparing simulated plasma concentration–time profiles with observed clinical results for each drug and age group. PBPK models reasonably predicted the pharmacokinetics of desloratadine, diclofenac, itraconazole, lansoprazole, montelukast, ondansetron, sufentanil, theophylline, and tramadol across all age groups. Collectively, 58 out of 67 predictions were within 2‐fold and 43 out of 67 predictions within 1.5‐fold of observed values. Developed PBPK models can reasonably predict exposure in children age 1 month and older for an array of predominantly CYP metabolized drugs. [ABSTRACT FROM AUTHOR]

Drugs can affect the cardiovascular (CV) system either as an intended treatment or as an unwanted side effect. In both cases, drug-induced cardiotoxicities such as arrhythmia and unfavourable hemodynamic effects can occur, and be described using mathematical models; such a model informed approach can provide valuable information during drug development and can aid decision-making. However, in order to develop informative models, it is vital to understand CV physiology. The aims of this tutorial are to present (1) key background biological and medical aspects of the CV system, (2) CV electrophysiology, (3) CV safety concepts, (4) practical aspects of development of CV models and (5) regulatory expectations with a focus on using model informed and quantitative approaches to support nonclinical and clinical drug development. In addition, we share several case studies to provide practical information on project strategy (planning, key questions, assumptions setting, and experimental design) and mathematical models development that support decision-making during drug discovery and development. [ABSTRACT FROM AUTHOR]

Structural identifiability is an often overlooked, but essential, prerequisite to the experiment design stage. The application of structural identifiability analysis to models of myelosuppression is used to demonstrate the importance of its considerations. It is shown that, under certain assumptions, these models are structurally identifiable and so drug and system specific parameters can truly be separated. Further it is shown via a meta-analysis of the literature that because of this the reported system parameter estimates for the “Friberg” or “Uppsala” model are consistent in the literature. [ABSTRACT FROM AUTHOR]

Background and Purpose: Whole body physiologically based pharmacokinetic (PBPK) models have been increasingly applied in drug development to describe kinetic events of therapeutic agents in animals and humans. The advantage of such modelling is the ability to incorporate vast amounts of physiological information, such as organ blood flow and volume, to ensure that the model is as close to reality as possible.Experimental Approach: Previous PBPK model development of enantiomers of a series of seven racemic β-blockers, namely, acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol and propranolol, together with S-timolol in rat was based on tissue and blood concentration data at steady state. Compounds were administered in several cassettes with the composition mix and blood and tissue sampling times determined using a D-optimal design.Key Results: Closed-loop PBPK models were developed initially based on the application of open loop forcing function models to individual tissues and compounds. For the majority of compounds and tissues, distribution kinetics was adequately characterized by perfusion rate-limited models. For some compounds in the testes and gut, a permeability rate-limited distribution model was required to best fit the data. Parameter estimates of the tissue-to-blood partition coefficient through fitting of individual enantiomers and of racemic pair were generally in agreement and also concur with those from previous steady-state experiments.Conclusions and Implications: PBPK modelling is a very powerful tool to aid drug discovery and development of therapeutic agents in animals and humans. However, careful consideration of the assumptions made during the modelling exercise is essential. [ABSTRACT FROM AUTHOR]

Background The role of anti-viral therapy in prevention of hepatocellular carcinoma (HCC) recurrence is to be defined. Aim To investigate the role of anti-viral therapy in prevention of tumour recurrence after curative treatment of hepatitis B virus (HBV)-related HCC. Methods A systematic electronic search on keywords including HCC and different anti-viral therapies was performed through eight electronic databases, including Medline, EMBASE and Cochrane Databases. The primary outcome was HCC recurrence after curative treatment of HBV-related HCC. The secondary outcomes were mortality related to HCC, mortality related to liver failure and the overall mortality. Results Nine cohort studies were included with a total number of 551 patients: 204 patients with anti-viral treatment group and 347 patients without anti-viral treatment (control group). There was significant difference in the incidence of HCC recurrence in favour of the anti-viral treatment group (55% vs. 58%; odds risk (OR) = 0.59, 95% CI 0.35-0.97, P = 0.04). The risk of HCC was reduced by 41% in the anti-viral treatment group. There were also significant differences in favour of anti-viral treatment group in terms of liver-related mortality (0% vs. 8%; OR = 0.13, 95% CI 0.02-0.69, P = 0.02) and overall mortality (38% vs. 42%; OR = 0.27, 95% CI 0.14-0.50, P < 0.001). Conclusions Anti-viral therapy has potential beneficial effects after the curative treatment of HBV-related hepatocellular carcinoma in terms of tumour recurrence, liver-related mortality and overall survival. Anti-viral therapy should be considered after curative treatment of hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

Occupational risk factors of carpal tunnel syndrome (CTS) are popular current research targets, with main emphasis put on wrist posture and dynamics. In this study, we do not intend to pinpoint individual occupations, but aim to identify high risk wrist postures and actions which may occur across various occupations. It is hoped that prevention can thus be instituted in a general population by directing at the particular causative wrist actions rather than exclusively targeting isolated occupations. We performed a case-control study with 166 cases and 111 controls recruited from different hospitals in Hong Kong in 2004. All cases and controls completed the survey on their general health condition, smoking status, wrist posture and motion as well as psychosocial status at the time of diagnosis of CTS. Frequent flexion OR = 4.436 (95% CI: 1.833–10.734), frequent extension OR = 2.691 (95% CI: 1.106–6.547) of the wrist were found to be associated with CTS. Frequent sustained forceful motion of the wrist OR = 2.588 (95% CI: 1.144–5.851) was also found to be associated with CTS. Neutral wrist position and repetitive wrist motion were not associated with CTS. Adjustment was made for age, sex, BMI, smoking and psychosocial stress. Our study confirms that frequent flexion, extension and sustained force of the wrist increase the risk of developing CTS. [ABSTRACT FROM AUTHOR]