U.S. Food and Drug Administration

Background Campylobacter spp. have been reported as a common cause of gastroenteritis in humans in many countries. However, in Brazil there is insufficient data to estimate the impact of Campylobacter in public health. In light of the importance of this foodborne pathogen, the aim of this study was to perform comparative analyses on 80 Brazilian Campylobacter coli genomes isolated from human feces, animals, the environment, and food. Methods include Average Nucleotide Identity (ANI), Gegenees, genomic plasticity, presence of pathogenicity, resistance, and metabolic islands. In addition, virulence analysis in Galleria mellonella were also performed for 18 selected C. coli strains. Results The ANI values confirmed that all strains belonged to the C. coli species. Phylogenetic analyses demonstrated the evolutionary relationships among the studied strains, highlighting the genetic diversity among them. The differences in shared and deleted regions of the studied genomes were demonstrated, with 16 genomic islands identified, including 4 metabolic islands, 4 resistance islands, and 8 pathogenicity islands. We detected genes associated with chemotaxis, exotoxin production, antimicrobial resistance, stress response, defense mechanisms, and intracellular survival among these islands, highlighting the pathogenic potential of these strains. Two strains isolated from human and one from animal showed high virulence, killing 100% of Galleria mellonella larvae. Two strains isolated from the environment and two isolated from food killed 70–90% of the larvae and were classified as virulent. Three strains isolated from animal, two from human, two from the environment and one from food killed 30% to 60% of the larvae and were considered of intermediate virulence. Campylobacter jejuni ATCC 33291, one strain isolated from human and one from food killed 10 to 20% of the larvae and were considered of low virulence. One strain isolated from food did not kill any larvae and was considered avirulent. Conclusions The results obtained highlighted the genetic diversity, pathogenic and virulence potential of many of the C. coli strains studied, contributing for a more complete characterization of this important pathogen recognized as a cause of human gastroenteritis.

Introduction: LGBTQ+ populations and people who smoke face stigma. This may lead to distancing oneself from smoking-related stigma by becoming phantom smokers (i.e., reporting smoking, but not identifying as a smoker). We explored correlates of phantom cigarette smoking among LGBTQ+ young adults. Methods: Participants were U.S. young adults (18 to 24 years) who identified as LGBTQ+, reported any past 30-day cigarette smoking and had a valid response for smoker self-identification (unique N= 5,545). We incorporated data from participants who completed one or more of the seven surveys from FDA's This Free Life campaign evaluation (February 2016-July 2019). Multivariable panel regression models with unweighted data examined phantom smoking correlates. Results: Over 60% of the sample were phantom smokers. Compared with self-identified smokers, phantom smokers were younger, more likely to be gay men than lesbian/gay women, and more likely to be non-Hispanic White than non-Hispanic Black, Hispanic or non-Hispanic people of other races/ethnicities. Phantom smokers were more likely to have a college plus education (vs. high school or less) and report past 30-day alcohol use. Phantom smokers smoked on fewer of the past 30 days and were less likely to report positive cessation attitudes, nicotine dependence, and current e-cigarette or other tobacco product use. Conclusions: This is the first known study to explore factors associated with phantom smoking among LGBTQ+ young adults. Over half of young adult smokers were phantom smokers. Tobacco education for LGBTQ+ populations should consider phantom smoking and cessation implications to tailor content for phantom and self-identified smokers. Implications: We examined predictors of phantom smoking (current smoking but denying smoker identity) among LGBTQ+ young adult smokers, which has not yet been explored among this population. Phantom (vs. self-identified) smokers were less likely to be lesbian/gay women (vs. gay men) or from a racial/ethnic minority group and more likely to report past 30-day alcohol use. Phantom smokers reported less tobacco use, lower nicotine dependence, and less favorable cessation attitudes. Phantom smokers comprised most smokers in our sample. Findings suggest the importance of addressing this unique aspect of LGBTQ+ smoking in research, clinical settings, and tailored tobacco public education messages.

Introduction Although the popularity of oral nicotine products (ONPs) such as ZYN and On! is growing globally, there is limited research on their marketing and advertising. This report describes recent ONP marketing communication to retailers. Promotion to retailers can provide insight into new product flavours and styles, as well as future marketing strategies targeting consumers. Methods We obtained all unique ONP print and online advertisements (ads) (N=50) targeted towards US businesses between January 2016 and August 2022 from Vivvix (formerly Numerator Ad Intel). Two independent reviewers coded for type of ONP, brand, nicotine strength(s), flavour(s), slogan(s), claim(s) and frequency of each component. Results Most ads featured nicotine pouches alone (52%), while 22% featured a mix of ONPs including pouches, tablets and lozenges. By brand, Rogue constituted 36% of ads, followed by Zyn and On! (16% each). Most (82%) ads featured at least one cooling flavour and 48% displayed at least one fruit flavour. Wintergreen flavour appeared most frequently (48%). Most (72%) ads contained a slogan, which frequently highlighted convenience of use (eg, ‘Chew on this Anywhere… Anytime’), bypassing current restrictions on other tobacco and nicotine products use (eg, ‘Laughs at no smoking signs’) and highlighting big profit margins from sales of ONPs for retailers (eg, ‘small pouches big margins’). Conclusion This analysis provides insight into tobacco companies’ strategies for increasing ONP endorsement among retailers. Strategies include appealing to profitability, emphasising convenience of product use and primarily promoting non-tobacco flavours. These findings highlight new trends in ONP products and marketing tactics and identify important areas to monitor to inform tobacco marketing regulations.

The resistance-nodulation-cell division (RND) superfamily of multidrug efflux systems are important players in mediating antibiotic resistance in gram-negative pathogens. Campylobacter jejuni , a major enteric pathogen, utilizes an RND-type transporter system, CmeABC, as the primary mechanism for extrusion of various antibiotics. Recently, a functionally potent variant of CmeABC (named RE-CmeABC) emerged in clinical Campylobacter isolates, conferring enhanced resistance to multiple antibiotic classes. Despite the clinical importance of RE-CmeABC, the molecular mechanisms for its functional gain and its evolutionary trajectory remain unknown. Here, we demonstrated that amino acid substitutions in RE-CmeB (inner membrane transporter), but not in RE-CmeA (periplasmic protein) and RE-CmeC (outer membrane protein), in conjunction with a nucleotide mutation in the promoter region of the efflux operon, are responsible for the functional gain of the multidrug efflux system. We also showed that RE- cmeABC is emerging globally and distributed in genetically diverse C. jejuni strains, suggesting its possible spread by horizontal gene transfer. Notably, many of RE- cmeABC harboring isolates were associated with the human host including strains from large disease outbreaks, indicating the clinical relevance and significance of RE-CmeABC. Evolutionary analysis indicated that RE- cmeB likely originated from Campylobacter coli , but its expansion mainly occurred in C. jejuni, possibly driven by antibiotic selection pressure. Additionally, RE- cmeB , but not RE- cmeA and RE- cmeC , experienced a selective sweep and was progressing to be fixed during evolution. Together, these results identify a mutation-based mechanism for functional gain in RE-CmeABC and reveal the key role of RE-CmeB in facilitating Campylobacter adaptation to antibiotic selection.

Acute respiratory failure (ARF) associated with antipsychotic use has been documented through case reports and population-based studies. To assess whether the recent use of antipsychotics is associated with an increased risk of ARF in U.S. Medicare beneficiaries with chronic obstructive pulmonary disease. Case-crossover study conducted among U.S. Fee-for-Service Medicare beneficiaries with chronic obstructive pulmonary disease hospitalized with ARF, from January 1, 2007, through December 31, 2019. Oral antipsychotics. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) for ARF requiring invasive mechanical ventilation associated with the use of antipsychotics in the case period (days -14 to -1) compared to the control period (days -75 to -88). We identified 145,018 cases (mean age 69.4 years, 57.2% female). Of these, 2,003 had antipsychotic use only during the case period and 1,728 only during the control period. The aOR of ARF within 14 days after antipsychotic use was 1.13 (95% CI, 1.06, 1.20). The risk increased with increasing age, being statistically significant in patients ages 75–84 years (aOR: 1.37 [95% CI, 1.17, 1.60]) and 85 + years (aOR: 1.50 [95% CI, 1.20, 1.89]), but not in beneficiaries under 75 years of age (aOR 18–49 years: 1.01 [95% CI, 0.85, 1.20]; 50–64 years: 1.03 [95% CI, 0.92, 1.15]; 65–74 years: 1.12 [95% CI, 0.98, 1.27]). Recent antipsychotic use by Medicare beneficiaries with chronic obstructive pulmonary disease was associated with an increased risk of ARF in those aged 75 years and older.

Batch‐to‐batch variability in inhalation powder has been identified as a potential challenge in the development of generic versions. This study explored the impact of batch‐to‐batch variability on the probability of establishing pharmacokinetic (PK) bioequivalence (BE) in a two‐sequence, two‐period (2 × 2) crossover study. A model‐based parametric simulation approach was employed, incorporating batch‐to‐batch variability through the relative bioavailability (RBA) ratio. In the absence of batch variability, recruiting a total of 48 subjects in a 2 × 2 crossover study with the reference formulation resulted in a 95% probability of concluding BE. However, this probability decreased to 80% with a 5% batch difference in RBA and further declined to 30% with a 10% batch difference. With a 10% batch difference, the required number of subjects to achieve an 80% probability of concluding BE increased to 84. When considering product differences between the reference and the test formulations, an additional 10% batch difference reduced the study power from 97% to 30% for a T/R bioavailability ratio of 100% in a 2 × 2 crossover study with 48 subjects. As a result, the substantial impact of batch‐to‐batch variability on the study power and type I error of the PK BE study may pose significant challenges for the development of generic Advair Diskus due to its degree of PK batch‐to‐batch variability. Therefore, alternative PK BE study designs and guidelines are needed to adequately address the influence of batch‐to‐batch variability in products like Advair Diskus.

Retinal pigment epithelium (RPE) cells are essential for normal retinal function. Morphological defects in these cells are associated with a number of retinal neurodegenerative diseases. Owing to the cellular resolution and depth-sectioning capabilities, individual RPE cells can be visualized in vivo with adaptive optics-optical coherence tomography (AO-OCT). Rapid, cost-efficient, and objective quantification of the RPE mosaic’s structural properties necessitates the development of an automated cell segmentation algorithm. This paper presents a deep learning-based method with partial annotation training for detecting RPE cells in AO-OCT images with accuracy better than human performance. We have made the code, imaging datasets, and the manual expert labels available online.

A fundamental goal of evaluating the performance of a clinical model is to ensure it performs well across a diverse intended patient population. A primary challenge is that the data used in model development and testing often consist of many overlapping, heterogeneous patient subgroups that may not be explicitly defined or labeled. While a model’s average performance on a dataset may be high, the model can have significantly lower performance for certain subgroups, which may be hard to detect. We describe an algorithmic framework for identifying subgroups with potential performance disparities (AFISP), which produces a set of interpretable phenotypes corresponding to subgroups for which the model’s performance may be relatively lower. This could allow model evaluators, including developers and users, to identify possible failure modes prior to wide-scale deployment. We illustrate the application of AFISP by applying it to a patient deterioration model to detect significant subgroup performance disparities, and show that AFISP is significantly more scalable than existing algorithmic approaches.

Modeling and simulation have emerged as indispensable tools in various fields, revolutionizing our ability to understand, analyze, and predict complex systems. This paper explores the fundamental concepts, methods, and applications of modeling and simulation in drug development for rare diseases. At its core, modeling involves constructing simplified representations of real-world phenomena, while simulation involves executing these models to observe system behavior under different conditions. Together, they offer a virtual laboratory for researchers, engineers, and decision-makers to explore and experiment without physical constraints. The applications of modeling and simulation are vast and diverse, spanning science, engineering, healthcare, economics, and beyond. In drug development, they facilitate dose optimization, treatment personalization, clinical trial design, safety evaluation, and regulatory decision support. By complementing traditional methods and leveraging available data, modeling and simulation accelerate the drug development process and improve decision-making. This paper delves into various modeling approaches, including statistical and mechanistic models, and highlights case studies showcasing their efficacy in drug development. It emphasizes the importance of model assumptions, rigorous validation, and transparent result interpretation. Additionally, the paper discusses opportunities for collaboration and integration of different modeling techniques, as well as the role of machine learning and artificial intelligence in advancing drug development for rare diseases. Overall, this paper underscores the critical role of modeling and simulation in addressing the unique challenges of drug development for rare diseases. It advocates for continued collaboration between disciplines and emphasizes the need for transparent communication, robust validation, and careful interpretation of results to advance the field and ultimately improve patient outcomes.

Diagnosis testing has become a crucial component of evidence-based patient care. In rare and pediatric disease, because of the limited understanding on the natural history of the diseases and inherently heterogeneous in the diseases, delay in correctly diagnosing patients has been reported as one of the major obstacles to patients. Another major challenge is the lack of clinically meaningful endpoints. In this chapter, the steps to achieve medical consensus of either diagnosis or endpoints are described in detail. The medical diagnosis consensus or selecting the right endpoints depend on effective multi-stake holders’ collaboration to best utilize the available information or generate the fit-for-purpose evidence. The statistical evaluation of the diagnosis and the endpoints are provided. Successful implementations in case studies are included.

The ICH E9 (R1) addendum, introduced in 2019, implements statistical thinking in clinical trial by providing a structured framework to define estimands, enhancing collaboration between clinicians and statisticians, and facilitating regulator-sponsor interactions. This chapter explicates the practical application of the estimand framework in Clinical Outcome Assessments (COA), with a focus on addressing intercurrent events that could impact the interpretability of clinical trials. While ICH E9 (R1) lists common intercurrent events, it does not exhaustively cover events such as noncompliance and decisions by patients or physicians, which are also pivotal. The chapter delves into the nuances of choosing appropriate strategies for such events, covering the hypothetical and principal stratum strategies, particularly relevant to pediatric and rare diseases. It also examines the application of estimands in Bayesian approaches and the intricacies of population-level summaries. By discussing the determination of intercurrent events, defining treatment effects, and the selection of suitable estimand strategies, this chapter advances a comprehensive understanding of the estimand framework’s application in clinical trial design and analysis, ultimately aiming to refine the estimation of treatment effects and facilitate regulatory approval processes.

Rare disease drug development is complex due to the challenges of trial design with small populations, incomplete or unknown information about the natural history of the condition and the degree of unmet medical needs. The natural history studies and the patient’s voice are critical components to rare disease drug development. In recent years, there is also strong regulatory support to incorporate natural history and patient voice into rare disease drug development. Multi-stakeholders including dedicated patients, their caregivers and families, physicians, scientists, healthcare professionals, patient advocates, biopharmaceutical companies and regulatory agencies need to work together to advance the science to understand the disease and develop the better treatments to patients to resolve what matters to them the most. In this chapter, how the natural history studies can be designed, conducted, or leveraged is discussed. Various methods and considerations focusing on patient-focused drug development (PFDD), especially to the clinical outcome assessment (COA), are described in detail. Additionally, how electronic health records and digital technology play a role in natural history studies are discussed. One successful case example is illustrated incorporating natural history studies and/or patient voice into the drug development and led to a drug approval.

The emerging of master protocols is a paradigm shift for drug developers, regulatory agencies, policy makers, payers, health-care providers, and the patients. The innovative trial design framework offers advantages and gains efficiencies over traditional clinical trial designs by using common operating framework, common control when possible, and improved matching rates for patients in clinical trial especially when the prevalence of the biomarkers are low. In this chapter, the definition of master protocol and global regulatory guidance is given first. The practical and operational considerations that are not covered in the global regulatory guidance are provided. Statistical considerations according to different master protocol designs are described in detail, separately for exploratory and confirmatory settings. The master protocol has been implemented successfully and improved the patient care in treating patients with rare cancers and pediatric cancers. One of those successful examples is discussed in the chapter.

Crossover designs offer distinct advantages for rare disease drug development, particularly in reducing the required number of subjects for statistically significant results. These designs capitalize on repeated measurements from the same subjects under different treatments, potentially enhancing the study’s efficiency and sensitivity. However, their suitability depends on specific trial characteristics, such as disease stability and reversibility of treatment effects, which must be carefully considered to ensure valid interpretations of results. Technical challenges, including carryover effects, period effects, and treatment-by-period interactions, necessitate robust methodologies for data analysis and interpretation. This chapter provides a comprehensive overview of the mathematical underpinnings of two by two crossover designs, discussing implications for type I error rates and study power. Additionally, it addresses potential issues and solutions related to carryover effects, treatment estimation biases, and study design considerations, offering insights into optimizing crossover designs for rare disease clinical trials. Through meticulous planning and analysis, crossover designs can serve as invaluable tools in advancing drug development for rare diseases, offering opportunities to enhance efficiency and precision in therapeutic evaluation.

Rare diseases present unique challenges in drug development, including limited patient populations, poorly characterized natural histories, and a lack of validated endpoints. To address these challenges, regulatory agencies like the FDA have introduced flexibilities in evidence requirements and provided guidance specifically aimed at rare disease drug development. This paper reviews the regulatory landscape and challenges in rare disease drug development, highlighting the use of accelerated approval pathways and the importance of confirmatory trials. The FDA’s guidance outlines pathways for accelerated approval based on surrogate endpoints, along with the need for confirmatory evidence to demonstrate clinical benefit. Despite progress, over 10,000 rare diseases lack approved treatments, necessitating innovative approaches in drug development. Real-world data (RWD) from patient registries and natural history studies have emerged as valuable resources, aiding in clinical trial planning and endpoint modeling. However, challenges such as data bias and variability require careful consideration. Case examples, such as the approval of Lutathera for neuroendocrine tumors, demonstrate the utility of RWD in supporting regulatory decisions. Nonetheless, ensuring the quality and granularity of RWD remains crucial for unbiased evaluations. In conclusion, while RWD offers potential benefits in rare disease drug development, careful planning, methodological rigor, and collaboration among stakeholders are essential to harness its full potential and accelerate the development of effective treatments for rare diseases.

The use of adaptive designs can be particularly useful or at times necessary when a clinical trial in rare disorders can only enroll a small number of patients. The global regulatory agencies have issued guidance and have shown encouragement on the use of adaptive designs in evaluating scientific evidence for approvals in rare and pediatric diseases. The types and key principles of adaptive designs are illustrated, and those useful designs that are either been used to support rare and pediatric diseases drug approvals or promising to further make the drug development feasible or efficient are discussed in the chapter. Practical considerations are given when implementing adaptive designs to support drug approval. Successful case examples are discussed throughout the chapter.

Advancement in science and technology in the past few decades has not only enabled molecular understanding of the pediatric and rare diseases, but also enriched the possible data collection methods via digital health technologies (DHTs) and integration of siloed data generated through various sources. In the chapter, we first discuss the requirement and validations for use of the DHTs in the regulatory setting. Various emerging practical considerations are illustrated, including extrapolations approaches based on same mechanism of actions (MOAs), artificial intelligence (AI) in clinical trial setting, and decentralized clinical trials. Several use cases in AI are illustrated: success drug repurposing applied during the COVID-19 pandemic, disease classification, and patient monitoring. Additionally, translational relevance of preclinical models to pediatric and rare diseases and potential pharmacodynamic endpoints are included in the considerations.

Pediatric cancer consists of a diverse group of rare diseases. The relatively small population of children with multiple disparate tumor types across various age groups presents a significant challenge for drug development program as compared to those for adults. Furthermore, modern targeted and precision cancer drug development typically are only effective in a subtype of disease, which further shrinks the targeted patient population. As a result, pediatric population is further reduced to multiple but more rare subpopulations, often defined by molecular phenotypes. The traditional design to run 1 drug trial paired with 1 biomarker is not sustainable. High screen-fail rates due to the low frequency of recurrent genomic alternations also adds to the challenges. This calls for innovative design. In this chapter, the case example of NCI-COG Pediatric MATCH trial, designed as a platform trial, is discussed in detail. The NCI-COG Pediatric MATCH trial overcomes many challenges and has created a collaborative framework for efficient collection, processing, and sequencing of refractory pediatric cancers. The study successfully facilitates evaluation of molecular-targeted agents in biomarker positive cohorts comprising a wide spectrum of childhood cancers, from common to ultra-rare. The success of the NCI-COG Pediatric MATCH sets as an example for clinical trials in pediatric and rare diseases.