Generating the Evidence Base for Convalescent Plasma Use for a New Infectious Disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) swept across the world in the waning months of 2019 and emerged as the cause of the coronavirus disease 19 (COVID-19) pandemic in early 2020. The use of convalescent plasma (CP) for prior respiratory pandemics provided a strong biological rationale for the rapid deployment of COVID-19 convalescent plasma (CCP) in early 2020 when no validated treatments or prior immunity existed. CCP is an antiviral agent, with its activity against SARS-CoV-2 stemming from specific antibodies elicited by the virus. Early efforts to investigate the efficacy of CCP in randomized clinical trials (RCTs) that targeted hospitalized patients with COVID-19 did not demonstrate the overall efficacy of CCP despite signals of benefit in certain subgroups, such as those treated earlier in disease. In contrast, studies adhering to the principles of antibody therapy in their study design, choice of patient population, and product qualification, i.e., those that administered high levels of specific antibody during the viral phase of disease in immunocompromised or very early in immunocompetent individuals, demonstrated benefits. In this chapter, we leverage the knowledge gained from clinical studies of CCP for COVID-19 to propose a framework for future studies of CP for a new infectious disease. This framework includes obtaining high-quality CP and designing clinical studies that adhere to the principles of antibody therapy to generate a robust evidence base for using CP.

Effect of case and control definitions on genome-wide association study (GWAS) findings.

Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self-reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end-results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases-glaucoma, migraine, and iron-deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end-results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS.

The impact of different strategies for modeling associations between medications at low doses and health outcomes: a simulation study and practical application to postpartum opioid use.

Pharmacoepidemiological studies commonly examine the association between drug dose and adverse health outcomes. In situations where no safe dose exists, the choice of modeling strategy can lead to identification of an apparent safe low dose range in the presence of a non-linear relationship or due to the modeling strategy forcing a linear relationship through a dose of 0. We conducted a simulation study to assess the performance of several regression approaches to model the drug dose-response curve at low doses in a setting where no safe range exists, including the use of a (1) linear dose term, (2) categorical dose term, and (3) natural cubic spline terms. Additionally, we introduce and apply an expansion of prior work related to modeling dose-response curves at low and infrequently used doses in the setting of no safe dose ("spike-at-zero" and "slab-and-spline"). Furthermore, we demonstrate and empirically assess the use of these regression strategies in a practical scenario examining the association between the dose of the initial postpartum opioid prescribed after vaginal delivery and the subsequent total dose of opioids prescribed in the entire postpartum period among a cohort of opioid-naïve women with a vaginal delivery enrolled in a State Medicaid program (2007-2014).

Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples.

The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people are more likely to transmit a pathogen than others. Here we present an estimator to quantify differential transmission, as measured by the ratio of reproductive numbers between people with different characteristics, using transmission pairs linked by molecular data, along with a sample size calculation for this estimator. We also provide extensions to our method to correct for imperfect identification of transmission linked pairs, overdispersion in the transmission process, and group imbalance. We validate this method via simulation and provide tools to implement it in an R package, phylosamp.

The Healthy User Effect in Pharmacoepidemiology.

The healthy user effect is a well-recognized bias in the field of pharmacoepidemiology and can be expected to overstate the effect of a preventive intervention when comparing long term users or "adherers" to non-users. Similar to the healthy worker effect observed in occupational epidemiology, the healthy user effect can be separated into a healthy initiator effect (baseline confounding) and a healthy adherer effect (selection bias). Restriction approaches and new user designs that implicitly condition on the indication and, similar healthy behaviors or health status can often mitigate the healthy initiator effect (confounding) or healthy adherer effect (selection bias) at the start of a study. Addressing the healthy adherer effect due to continued conditioning on adherence over the duration of a study is more challenging as methods to mitigate it require the ability to predict adherence, which is often difficult using databases common in pharmacoepidemiologic research. Here, we describe the healthy user effect, with supporting examples, and describe study design approaches available to pharmacoepidemiologists to mitigate the potential for bias.

Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations.

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations.

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Synthesizing regulatory guidance for demonstrating preclinical efficacy and translating promising cell therapies to early phase clinical trials: a scoping review.

BACKGROUND: Regulatory applications for cell therapy face more objections compared to conventional small molecule or biological drugs, leading to delays in market approval and clinical adoption. Increased regulatory objections frequently relate to issues regarding preclinical evidence, such as experimental design of animal studies, selection of animal models, endpoints, and determination of mechanism of action. Synthesis and clarification of the preclinical evidence necessary to demonstrate treatment efficacy and advance into early-phase clinical trials is needed to help researchers avoid regulatory objections. METHODS: We conducted a scoping review in which we searched repositories of the International Council for Harmonisation and all national member organizations (N = 38) for documents related to preclinical studies of cell therapies. Active guidance documents related to cell therapy were included, with no restrictions based on the year or language of publication. Data extraction was conducted in duplicate with conflicts resolved through consensus discussion. RESULTS: From 1215 identified documents, a total of 182 were included and analyzed, with 71% originating from ten major regulatory agencies. The most prevalent preclinical item addressed was the mechanism of action (n = 161, 88% of documents), underscoring its importance in bridging preclinical findings to clinical application. Most documents (n = 140, 77%) emphasized the importance of using clinically relevant preclinical models, though specific recommendations on models of disease were less common (n = 81, 45%). Selection of clinically relevant intervention parameters (n = 136, 75%) and outcome measures (n = 121, 66%) were also frequently recommended, but selection of relevant comparator groups appeared less frequently (n = 35, 19%). Furthermore, robust study design elements such as randomization and blinding were less frequently recommended, appearing in 31% of documents (n = 57). Comparison with clinical trial guidance revealed a significant gap in the rigor of study design recommendations for preclinical research. CONCLUSIONS: Regulatory guidance for preclinical efficacy studies often recommends a strong emphasis on the clinical relevance of animal models, intervention parameters, outcomes, and mechanism of action. Incorporating these recommendations into early preclinical studies should improve the acceptability of preclinical evidence for approval by the relevant national regulators and can be used as a guide to ensure that all evidence that regulators say they expect is efficiently assembled into new clinical trial applications.

Breastfeeding Duration and Cardiometabolic Health during Adolescence: A Longitudinal Analysis.

OBJECTIVE: To investigate the longitudinal association between breastfeeding duration and cardiometabolic health, using repeated measures study design among children and adolescents. STUDY DESIGN: This study included 634 offsprings aged 10 to 21 years (52% female) from the Early Life Exposure in Mexico to Environmental Toxicants birth cohort followed up to four time points during adolescence. Breastfeeding duration was prospectively quantified using questionnaires during early childhood. Cardiometabolic risk factors, body composition, and weight-related biomarkers were assessed as outcomes during adolescent follow-up visits. Sex-stratified linear mixed-effects models were used to model the association between quartiles of breastfeeding duration and outcomes, adjusting for age and additional covariates. RESULTS: Median breastfeeding duration was 7 months (minimum = 0, maximum = 36). Boys in the second quartile (median breastfeeding = 5 months) had lower total fat mass % (ß (SE) -3.2 (1.5) P = .037), and higher lean mass % (3.1 (1.6) P = .049) and skeletal muscle mass % (1.8 (0.8) P = .031) compared with the reference group (median breastfeeding = 2 months). A positive linear trend between breastfeeding duration and trunk lean mass % (0.1 (0.04) P = .035) was found among girls. No association was found with other cardiometabolic indicators. CONCLUSION: Despite sex-specific associations of breastfeeding duration with body composition, there was a lack of substantial evidence for the protective effects of breastfeeding against impaired cardiometabolic health during adolescence among Mexican youth. Further longitudinal studies with a robust assessment of breastfeeding are recommended.

Expert opinion on design and endpoints for studies on catheter ablation of atrial fibrillation.

INTRODUCTION: Catheter ablation of atrial fibrillation (AF) is frequently studied in randomized trials, observational and registry studies. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of future clinical studies on catheter ablation of AF, implement lessons learned from previous studies, and promote a higher degree of consistency across studies. BACKGROUND: Studies on catheter ablation of AF may benefit from well-described definitions of endpoints and consistent methodology and documentation of outcomes related to efficacy, safety and cost-effectiveness. The availably of new, innovative technologies warrants further consideration about their application and impact on study design and the choice of endpoints. Moreover, recent insights gained from AF ablation studies suggest a reconsideration of some methodological aspects. METHODS: A panel of clinical experts on catheter ablation of AF and designing and conducting clinical studies developed an expert opinion on the design and endpoints for studies on catheter ablation of AF. Discussions within the expert panel with the aim to reach consensus on predefined topics were based on outcomes reported in the literature and experiences from recent clinical trials. RESULTS: A comprehensive set of recommendations is presented. Key elements include the documentation of clinical AF, medication during the study, repeated ablations and their effect on endpoint assessments, postablation blanking and the choice of rhythm-related and other endpoints. CONCLUSION: This expert opinion provides guidance and promotes consistency regarding design of AF catheter ablation studies and identified aspects requiring further research to optimize study design and methodology. CONDENSED ABSTRACT: Recent insights from studies on catheter ablation of atrial fibrillation (AF) and the availability of new innovative technologies warrant reconsideration of methodological aspects related to study design and the choice and assessment of endpoints. This expert opinion, developed by clinical experts on catheter ablation of AF provides a comprehensive set of recommendations related to these methodological aspects. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of clinical studies, implement lessons learned from previous studies, and promote a higher degree of consistency across studies.

The X-factor in ART: does the use of assisted reproductive technologies influence DNA methylation on the X chromosome?

BACKGROUND: Assisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate sex-specific DNAm differences on the X chromosome. The discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). To verify our results from the MoBa cohort, we used an external cohort of 149 ART and 58 non-ART neonates from the Australian 'Clinical review of the Health of adults conceived following Assisted Reproductive Technologies' (CHART) study. The Illumina EPIC array was used to measure DNAm in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ('XWASs' hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. Additionally, we ran an analogous model to our main model on the external CHART dataset. RESULTS: In the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental DNAm-dependent features as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. CONCLUSIONS: Genes that co-localized with the significant CpGs and DMRs associated with ART are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism). These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to those naturally conceived.

Estimating waiting time for compatible blood: A Negative Binomial approach.

OBJECTIVES: Screening blood units for compatibility constitutes a Bernoulli series. Estimating the number of units needed to be screened represents a classic waiting time problem that may be resolved using the Negative Binomial Distribution. The currently recommended method for estimating the number of units screened, n, to find a required number of compatible units, r, with a given probability, p, is n = r/p. This coincides with the mean of the Negative Binomial Distribution so that the actual number of units screened will often be underestimated by the current method. METHODS: The cumulative distribution function of the Negative Binomial Distribution provides the probability of success (compatibility), F(n;r,p), as a function of the number of trials performed (attempted crossmatches), n, the probability of success on each trial, p, and the number of successes (compatible units) required, r. Choosing a threshold cumulative probability sufficiently high, such as F ~ 0.9, for example, will provide confidence that the projected number of units screened will be underestimated less often (~10% of the time). RESULTS: With F ≥ 0.9, the estimated number of attempted crossmatches ranges from 1.3 to 2.3 times as many as the number calculated by the current method. As a rule of thumb approximately 1.6 times the current estimated number provides a similar estimate (n ~ 1.6∙r/p). CONCLUSIONS: Waiting time underestimation will be reduced significantly by using the Negative Binomial Distribution solution and should be accompanied by improved customer satisfaction.

Impact of Digitally Enabled Peer Support Interventions on Diabetes Distress and Depressive Symptoms in People Living with Type 1 Diabetes: A Systematic Review.

PURPOSE OF REVIEW: To explore the impact of digitally-enabled peer support interventions on diabetes distress and depression for individuals living with Type 1 Diabetes (T1D). RECENT FINDINGS: We synthesized the results of nine key studies from a review of 3,623 English-language articles published between January 2012 and January 2024. Three studies demonstrated significant reductions in diabetes distress, and two studies reported reductions in depression. Data were analyzed using a narrative approach, including thematic synthesis. This process was structured around the Behavior Change Wheel framework Effective interventions shared several common features such as (1) involved participatory development approaches, (2) included diabetes education, (3) lasted over a longer time, (4) designed with a psychological framework, and (5) utilized peer mentors. Studies showed that digitally-enabled peer support has the potential to improve diabetes distress and depression among people living with T1D despite heterogeneity in intervention approaches. Moreover, designing interventions with certain features may enhance key psychosocial outcomes.

How to study psychological mechanisms of mania? A systematic review on the methodology of experimental studies on manic mood dysregulation of leading theories on bipolar disorder.

INTRODUCTION: Although there are several psychological theories on bipolar disorders (BD), the empirical evidence on these theories through experimental studies is still limited. The current study systematically reviews experimental methods used in studies on the main theories of BD: Reward Hypersensitivity Theory (RST) or Behavioral Activation System (BAS), Integrative Cognitive Model (ICM), Positive Emotion Persistence (PEP), Manic Defense theory (MD), and Mental Imagery (MI). The primary aim is to provide an overview of the used methods and to identify limitations and suggest areas of improvement. METHODS: A systematic search of six databases until October 2023 was conducted. Study selection involved two independent reviewers extracting data on experimental study design and methodology. RESULTS: A total of 84 experimental studies were reviewed. BAS and RST were the most frequently studied theories. The majority of these experimental studies focus on mechanisms of reward sensitivity. Other important elements of the reviewed theories, such as goal setting and-attainment, situation selection (avoidance or approach), activation, affective/emotional reactivity, and regulatory strategies, are understudied. Self-report and neuropsychological tasks are most often used, while mood induction and physiological measures are rarely used. CONCLUSION: There is a need for more consensus on the operationalization of psychological theories of mania. Standardization of test batteries could improve comparability among studies and foster a more systematic approach to experimental research. Research on affective (activated) states is still underrepresented in comparison with studies on trait vulnerabilities.

Randomized controlled trials: not always the "gold standard" for evidence in obstetrics and gynecology.

Randomized controlled trials are considered the "gold standard" for therapeutic interventions, and it is not uncommon for sweeping changes in medical practice to follow positive results from such trials. However, randomized controlled trials are not without their limitations. Physicians frequently view randomized controlled trials as infallible, whereas they tend to dismiss evidence derived from sources other than randomized controlled trials as less credible or reliable. In several situations in obstetrics and gynecology, there are no randomized controlled trials to help guide the clinician. In these circumstances, it is important to evaluate the entire body of evidence including observational studies, rather than dismiss interventions altogether simply because no randomized controlled trials exist. Randomized controlled trials and observational studies should be viewed as complementary rather than at odds with each other. Some reversals in widely adopted clinical practice have recently been implemented following subsequent studies that contradicted the outcomes of major randomized controlled trials. The most notable of these was the withdrawal from the market of 17-hydroxyprogesterone caproate for preterm birth prevention. Such reversals could potentially have been averted if the inherent limitations of randomized controlled trials were carefully considered before implementing these universal practice changes. This Clinical Opinion underscores the limitations of an exclusive reliance on randomized controlled trials while disregarding other evidence in determining how best to care for patients. Solutions are proposed that advocate that clinicians adopt a more balanced perspective that considers the entirety of the available medical evidence and the individual patient characteristics, needs, and wishes.

Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.

Multilevel Longitudinal Functional Principal Component Model.

Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10-100 Hz or minute-level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity-related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods.

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement.

This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

The "what, why, and how?" of story completion in health services research: a scoping review.

BACKGROUND: The story completion method provides a different way of doing qualitative research. We note the emergent popularity of this method in health-related research, while much remains to be negotiated in terms of best practices for such studies. This scoping review aims to provide a synthesis on how researchers have used the story completion method in health services research. We offer implications for research and practice for further discussion by the scholarly community. METHODS: We used the JBI methodology for scoping reviews. Six databases were searched for published literature till March 1, 2023: Medline, Embase, CINAHL, PsycINFO, SAGE Journals Online databases, and SAGE Research Methods. We included primary studies of any study design using the story completion method in health services research. RESULTS: A total of 17 studies were included. Findings suggest that the story completion method is useful for research on sensitive topics, and affords the use of comparative study designs and large sample sizes which may be difficult with conventional qualitative research methods. More than 80% of included studies used story completion as the sole method. However, the data collected from this method were limited in terms of the inferences that can be drawn; and richness of participant responses may vary widely. Less than 30% of included studies reported piloting of the story stems. Most studies were conducted online and analyzed qualitatively, though the story stem design and sample size varied widely. CONCLUSION: The story completion method, with its attendant affordances for larger sample sizes, comparative study designs, and streamlined data collection is an innovative and useful stand-alone or adjunct qualitative method for health services research.

Bioequivalence trials for the approval of generic drugs in Saudi Arabia: a descriptive analysis of design aspects.

BACKGROUND: This retrospective analysis aimed to comprehensively review the design and regulatory aspects of bioequivalence trials submitted to the Saudi Food and Drug Authority (SFDA) since 2017. METHODS: This was a retrospective, comprehensive analysis study. The Data extracted from the SFDA bioequivalence assessment reports were analyzed for reviewing the overall design and regulatory aspects of the successful bioequivalence trials, exploring the impact of the coefficient of variation of within-subject variability (CVw) on some design aspects, and providing an in-depth assessment of bioequivalence trial submissions that were deemed insufficient in demonstrating bioequivalence. RESULTS: A total of 590 bioequivalence trials were included of which 521 demonstrated bioequivalence (440 single active pharmaceutical ingredients [APIs] and 81 fixed combinations). Most of the successful trials were for cardiovascular drugs (84 out of 521 [16.1%]), and the 2 × 2 crossover design was used in 455 (87.3%) trials. The sample size tended to increase with the increase in the CVw in trials of single APIs. Biopharmaceutics Classification System Class II and IV drugs accounted for the majority of highly variable drugs (58 out of 82 [70.7%]) in the study. Most of the 51 rejected trials were rejected due to concerns related to the study center (n = 21 [41.2%]). CONCLUSION: This comprehensive analysis provides valuable insights into the regulatory and design aspects of bioequivalence trials and can inform future research and assist in identifying opportunities for improvement in conducting bioequivalence trials in Saudi Arabia.