U.S. Food and Drug Administration
  • Washington, D.C., Maryland, United States
Recent publications
Background The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a Patient-Reported Outcome Measure (PROM) used to evaluate the health status of patients with heart failure (HF) but has predominantly been tested in settings serving predominately white, male, and economically well-resourced populations. We sought to examine the acceptability of the shorter version of the KCCQ (KCCQ-12) among racially and ethnically diverse patients receiving care in an urban, safety-net setting. Methods We conducted cognitive interviews with a diverse population of patients with heart failure in a safety net system to assess their perceptions of the KCCQ-12. We conducted a thematic analysis of the qualitative data then mapped themes to the Capability, Opportunity, Motivation Model of Behavior framework. Results We interviewed 18 patients with heart failure and found that patients broadly endorsed the concepts of the KCCQ-12 with minor suggestions to improve the instrument’s content and appearance. Although patients accepted the KCCQ-12, we found that the instrument did not adequately measure aspects of health care and quality of life that patients identified as being important components of managing their heart failure. Patient-important factors of heart failure management coalesced into three main themes: social support, health care environment, and mental health. Conclusions Patients from this diverse, low-income, majority non-white population experience unique challenges and circumstances that impact their ability to manage disease. In this study, patients were receptive to the KCCQ-12 as a tool but perceived that it did not adequately capture key health components such as mental health and social relationships that deeply impact their ability to manage HF. Further study on the incorporation of social determinants of health into PROMs could make them more useful tools in evaluating and managing HF in diverse, underserved populations.
The COVID-19 pandemic has led to increased usage of hand sanitizer products by the public to prevent the spread of COVID-19 and decrease the likelihood of acquiring the disease. The increase in demand has also led to an increase in the number of manufacturers. This work describes the FDA's Center for Drug Evaluation and Research (CDER) laboratories efforts to develop tests to assess the quality of hand sanitizer products containing ethanol or isopropanol as the primary active ingredient. The products were evaluated for the active ingredient content and determination of the 12 impurities listed in the FDA Hand Sanitizer Temporary Guidance, followed by a spike recovery assay performed to verify the test results. Extensive method development was conducted including an investigation into the stability of ethanol, isopropanol, and the 12 impurities. Stability and kinetic studies confirmed the instability of acetal in acidic liquid hand sanitizer products during spike recovery assay testing. The headspace GC-MS method was validated according to ICH Q2 (R1) guidelines and the spike recovery assay was validated using three concentrations of standards for the drug product. During method application, six liquid hand sanitizer products were tested and all were determined to have ethanol or isopropanol above 70% v/v. Two liquid hand sanitizer products were determined to contain acetaldehyde as an impurity above the FDA recommended safety levels. Supplementary information: The online version contains supplementary material available at 10.1186/s41120-021-00049-8.
Objective To determine how additional explanatory text (context) about drug side effects in a patient medication information handout affected comprehension and perceptions of risk and efficacy. Methods We conducted an online experiment with a national sample of 1,119 U.S. adults with rheumatoid arthritis and related conditions, sampled through random-digit dialing, address-based sampling, and online ads. We randomized participants to receive one of several versions of a patient information handout for a fictitious drug, either with or without additional context, then measured comprehension and other outcomes. Results Additional qualitative context about warnings and side effects resulted in lower comprehension of side effect information, but not information about uses of the drug or warnings. The effect of additional context on risk perceptions depended on whether the medication handout was delivered online or through the mail. Those who received a hardcopy of the handout with additional context had higher perceived risk of side effects than those who saw the version without additional context. Conclusion More clarifying information is not always better and may lead to cognitive overload, inhibiting comprehension. Practice implications Additional research should further explore effects of context in online vs. hard-copy formats before practice implications can be determined.
Background The small patient populations inherent to rare genetic diseases present many challenges to the traditional drug development paradigm. One major challenge is generating sufficient data in early phase studies to inform dose selection for later phase studies and dose optimization for clinical use of the drug. However, optimizing the benefit-risk profile of drugs through appropriate dose selection during drug development is critical for all drugs, including those being developed to treat rare diseases. Recognizing the challenges of conducting dose finding studies in rare disease populations and the importance of dose selection and optimization for successful drug development, we assessed the dose-finding studies and analyses conducted for drugs recently approved for rare genetic diseases. Results Of the 40 marketing applications for new molecular entity (NME) drugs and biologics approved by the United States Food and Drug Administration for rare genetic diseases from 2015 to 2020, 21 (53%) of the development programs conducted at least one dedicated dose-finding study. In addition, the majority of drug development programs conducted clinical studies in healthy subjects and included population pharmacokinetic and exposure–response analyses; some programs also conducted clinical studies in patient populations other than the disease for which the drug was initially approved. The majority of primary endpoints utilized in dedicated dose-finding studies were biomarkers, and the primary endpoint of the safety and efficacy study matched the primary endpoint used in the dose finding study in 9 of 13 (69%) drug development programs where primary study endpoints were assessed. Conclusions Our study showed that NME drug development programs for rare genetic diseases utilize multiple data sources for dosing information, including studies in healthy subjects, population pharmacokinetic analyses, and exposure–response analyses. In addition, our results indicate that biomarkers play a key role in dose-finding studies for rare genetic disease drug development programs. Our findings highlight the need to develop study designs and methods to allow adequate dose-finding efforts within rare disease drug development programs that help overcome the challenges presented by low patient prevalence and other factors. Furthermore, the frequent reliance on biomarkers as endpoints for dose-finding studies underscores the importance of biomarker development in rare diseases.
Background Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes. Main text Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications. Conclusion The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.
Background The insights gleaned from patient-reported outcomes (PROs) have implications across the healthcare ecosystem, from clinical investigations to evaluate the safety and effectiveness of medical devices to clinical care and reimbursement decisions. The U.S. Food and Drug Administration’s (FDA) Center for Devices and Radiological Health (CDRH) hosted a public meeting in September 2020 discussing how PROs can be used in medical device evaluation throughout the total product life cycle, as well as methods for developing and modifying PRO instruments to ensure they are fit-for-purpose. This commentary presents key points of discussion from the meeting, providing insight into the increased interest in PRO data to support medical product development while also exploring future opportunities of incorporating PRO data throughout healthcare. Main Body Thoughtful use of fit-for-purpose PRO instruments to integrate the patient’s voice into clinical care paradigms, medical device development, regulatory decisions, and reimbursement and coverage decisions were emphasized throughout the meeting. Existing PRO instruments may be used if the context of use is appropriate. Modifications to an existing PRO instrument may also be explored to ensure the instrument is fit-for-purpose in a new context of use. Development of a novel PRO instrument may be necessary to capture attributes in a new patient population or application. Multi-stakeholder collaborations, of which patients are a key component, create efficiencies in the development and modification of PRO instruments. Conclusion Continued multi-stakeholder collaborations bringing together researchers, clinicians, patients, regulators, and payers are critical to further advance the inclusion of the patient voice incorporating PRO instruments throughout the healthcare ecosystem in an efficient manner that is least burdensome to patients.
There have been limited studies focused on validation of swine microRNAs (miRNA) with mRNA targets. The objective of this study was to validate a defined set of targets using artificial miRNA mimics transfected into cell lines to confirm specific targets of endogenous miRNAs after administration of Escherichia coli lipopolysaccharide (LPS). Sixteen hours after mimic transfection of 3D4/21 cell lines, the cells were stimulated with 1 μg/ml LPS or phosphate-buffered saline (PBS). The cells were harvested and collected at 0, 1, 3, and 8 h post administration. The selected genes DAD1, IL8, and ESR, which are involved in known pathways of inflammation. and are predicted or validated human targets of either miR-146a, let-7a, or miR-22-3p. These were then evaluated by quantitative real-time-PCR (qRT-PCR) to verify microRNA-mRNA interaction in swine. Using the ROX reference dye, mRNA changes in expression were assessed using the comparative CT Method (ΔΔCT method) for normalization against the PBS control group. DAD1 and ESR1 were negatively regulated by miR-22-3p and miR-146a-5p, respectively in 3D4/21 cells after LPS stimulation. However, miR-146a-5p may play an indirect positive regulatory role of both DAD1 and IL8 mRNA expression. Furthermore, we found an inverse relationship between LPS stimulation compared with the let-7a-5p overexpression with DAD1. Our inflammation study provides new evidence on the roles and predicted targets of miR-146a, let-7a, and miR-22-3p in swine.
Easy access to large quantities of accurate health data is required to understand medical and scientific information in real-time; evaluate public health measures before, during, and after times of crisis; and prevent medical errors. Introducing a system in the USA that allows for efficient access to such health data and ensures auditability of data facts, while avoiding data silos, will require fundamental changes in current practices. Here, we recommend the implementation of standardized data collection and transmission systems, universal identifiers for individual patients and end users, a reference standard infrastructure to support calibration and integration of laboratory results from equivalent tests, and modernized working practices. Requiring comprehensive and binding standards, rather than incentivizing voluntary and often piecemeal efforts for data exchange, will allow us to achieve the analytical information environment that patients need.
The eighth Paediatric Strategy Forum focused on multi-targeted kinase inhibitors (mTKIs) in osteosarcoma and Ewing sarcoma. The development of curative, innovative products in these tumours is a high priority and addresses unmet needs in children, adolescents and adults. Despite clinical and investigational use of mTKIs, efficacy in patients with bone tumours has not been definitively demonstrated. Randomised studies, currently being planned or in progress, in front-line and relapse settings will inform the further development of this class of product. It is crucial that these are rapidly initiated to generate robust data to support international collaborative efforts. The experience to date has generally indicated that the safety profile of mTKIs as monotherapy, and in combination with chemotherapy or other targeted therapy, is consistent with that of adults and that toxicity is manageable. Increasing understanding of relevant predictive biomarkers and tumour biology is absolutely critical to further develop this class of products. Biospecimen samples for correlative studies and biomarker development should be shared, and a joint academic-industry consortium created. This would result in an integrated collection of serial tumour tissues and a systematic retrospective and prospective analyses of these samples to ensure robust assessment of biologic effect of mTKIs. To support access for children to benefit from these novel therapies, clinical trials should be designed with sufficient scientific rationale to support regulatory and payer requirements. To achieve this, early dialogue between academia, industry, regulators, and patient advocates is essential. Evaluating feasibility of combination strategies and then undertaking a randomised trial in the same protocol accelerates drug development. Where possible, clinical trials and development should include children, adolescents, and adults less than 40 years. To respond to emerging science, in approximately 12 months, a multi-stakeholder group will meet and review available data to determine future directions and priorities.
How do consumers perceive risks associated with food contamination? How do they respond to foodborne illness outbreaks and food recalls resulting from food contamination? We report findings from an experiment (N = 1,010) in which participants were exposed to a simulated news report on a food contamination incident that had led to a foodborne illness outbreak and voluntary food recalls. Two characteristics of the food contamination incident were experimentally manipulated - severity (i.e., how serious the consequences of the incident were) and intentionality (i.e., whether the incident was caused by an accident or an intentional act to harm). We found that higher severity generally led to higher risk perceptions and risk-reduction intentions. A contamination incident attributed to an intentional act to harm, as opposed to an accident, caused greater intentions to temporarily reduce consumption of the contaminated food and to seek out more information, but only when incident severity was relatively low. Implications of these findings for effectively communicating food contamination risks are discussed.
On July 26, 2021, the Food and Drug Administration granted approval to pembrolizumab in combination with chemotherapy for neoadjuvant treatment and then continued as a single agent for adjuvant treatment following surgery for patients with high-risk, early-stage triple negative breast cancer (TNBC). Approval was based on results from KEYNOTE-522, an ongoing randomized (2:1) trial evaluating pembrolizumab or placebo in combination with chemotherapy for neoadjuvant treatment and then as a single agent for adjuvant treatment. The co-primary endpoints were pathological complete response (pCR) rate and event free survival (EFS). The trial demonstrated an improvement in pCR and EFS in the pembrolizumab arm compared to the control arm. The number of patients who experienced an EFS event was 123 (16%) and 93 (24%), respectively (HR: 0.63, 95% CI: 0.48-0.82, p=0.00031). Patients on the pembrolizumab arm experienced EFS benefit regardless of tumor PD-L1 status. The absolute pCR rate improvement with the addition of pembrolizumab was 7.5% (95% CI: 1.6%, 13.4%). Among patients receiving pembrolizumab, 44% experienced an immune-related adverse reaction. This article summarizes FDA’s review of pembrolizumab and the data supporting the favorable benefit-risk assessment.
Objectives: To characterize individual participant level response distributions to acute monotherapy for major depressive disorder in randomized, placebo controlled trials submitted to the US Food and Drug Administration from 1979 to 2016. Design: Individual participant data analysis. Population: 232 randomized, double blind, placebo controlled trials of drug monotherapy for major depressive disorder submitted by drug developers to the FDA between 1979 and 2016, comprising 73 388 adult and child participants meeting the inclusion criteria for efficacy studies on antidepressants. Main outcome measures: Responses were converted to Hamilton Rating Scale for Depression (HAMD17) equivalent scores where other measures were used to assess efficacy. Multivariable analyses examined the effects of age, sex, baseline severity, and year of the study on improvements in depressive symptoms in the antidepressant and placebo groups. Response distributions were analyzed with finite mixture models. Results: The random effects mean difference between drug and placebo favored drug (1.75 points, 95% confidence interval 1.63 to 1.86). Differences between drug and placebo increased significantly (P<0.001) with greater baseline severity. After controlling for participant characteristics at baseline, no trends in treatment effect or placebo response over time were found. The best fitting model of response distributions was three normal distributions, with mean improvements from baseline to end of treatment of 16.0, 8.9, and 1.7 points. These distributions were designated Large, Non-specific, and Minimal responses, respectively. Participants who were treated with a drug were more likely to have a Large response (24.5% v 9.6%) and less likely to have a Minimal response (12.2.% v 21.5%). Conclusions: The trimodal response distributions suggests that about 15% of participants have a substantial antidepressant effect beyond a placebo effect in clinical trials, highlighting the need for predictors of meaningful responses specific to drug treatment.
Robust estimation of exposure response analysis relies on correct specification of the model structure with traditional parametric approach. However, the assumptions of the handcrafted model may not always hold or verifiable. Here, we conducted a simulation study to assess the performance of machine learning-based techniques in exposure–response (E–R) analysis where data were generated by a complicated nonlinear system under one dose level. Two analysis options involving machine learning were evaluated. The first option was based on marginal structural model with inverse probability weighting, where machine learning (ML) was employed to improve the performance of propensity score estimation. The simulation results showed that propensity score predicted by ML was more robust than traditional multinomial logistic regression in terms of adjusting the confounding effects and unbiasedly estimating the E–R relationship. The second option estimated the E–R relationship by employing artificial neural network as a universal function approximator to the data generating mechanism, without the requirement of accurately hand-crafting the whole simulation system. The results demonstrated that the trained network was able to correctly predict the treatment effects across a certain range of adjacent dose levels. In contrast, traditional regression provided biased predictions, even when all confounders were included in the model. Our study demonstrated that ML may serve as a powerful tool for pharmacometrics analysis with its prediction flexibility in a nonlinear system and its capacity of approximating the ground truth.
Preclinical in vitro and in vivo methods to study bacterial interactions with dermal fillers and infection pathogenesis are lacking. In this work, first in vitro methods to assess protein biofouling and effective pore size of commercial dermal fillers, including degradable hyaluronic acid (HA)‐based fillers and other semi‐degradable or permanent fillers (non‐HA), were developed. The results were then related to Staphylococcus aureus (S. aureus) adhesion rates in vitro. HA fillers had less protein sorption than non‐HA fillers and overall had smaller effective pore sizes. The properties correlated with levels of bacterial adhesion, where the control glass surface had the most rapid increase in bacterial cell adhesion, with a slope of 0.29 cm−2 min−1, three unique non‐HA fillers had intermediate adhesion with slopes of 0.11 and 0.06 cm−2 min−1, and three unique HA fillers had the least adhesion with slopes of 0.02, 0.02, and 0.01 cm−2 min−1. S. aureus had greater motility on the HA fillers than on non‐HA fillers. Next, a mouse model for dermal filler biofilm and infection was developed. Mice were inoculated with a controlled amount of bioluminescent bacteria (Xen36 S. aureus) and polyacrylamide hydrogels of different stiffness were injected. In vivo bioluminescence was monitored longitudinally for 35 days to ensure that lasting colonization was established. The inoculum was optimized to achieve adequate bioluminescent signal, and bacterial bioburden over time and inter‐animal variability in bioburden were determined. These in vitro and in vivo approaches can be used for future studies of antimicrobial interventions for dermal fillers.
Model-informed drug development (MIDD) is a powerful approach to support drug development and regulatory review. There is a rich history of MIDD applications at the U.S. Food and Drug Administration (FDA). MIDD applications span across the life cycle of the development of new drugs, generics, and biologic products. In new drug development, MIDD approaches are often applied to inform clinical trial design including dose selection/optimization, aid in the evaluation of critical regulatory review questions such as evidence of effectiveness, and development of policy. In the biopharmaceutics space, we see a trend for increasing role of computational modeling to inform formulation development and help strategize future in vivo studies or lifecycle plans in the post approval setting. As more information and knowledge becomes available pre-approval, quantitative mathematical models are becoming indispensable in supporting generic drug development and approval including complex generic drug products and are expected to help reduce overall time and cost. While the application of MIDD to inform the development of cell and gene therapy products is at an early stage, the potential for future application of MIDD include understanding and quantitative evaluation of information related to biological activity/pharmacodynamics, cell expansion/persistence, transgene expression, immune response, safety, and efficacy. With exciting innovations on the horizon, broader adoption of MIDD is poised to revolutionize drug development for greater patient and societal benefit.
Background Most perinatal and neonatal deaths occur in low- and middle-income countries (LMICs), yet, quality data on burden of adverse outcomes of pregnancy is limited in such countries. Methods A network of 21 maternity units, across seven countries, undertook surveillance for low birthweight, preterm birth, small for gestational age (SGA), stillbirths, congenital microcephaly, in-hospital neonatal deaths, and neonatal infections in a cohort of over 85,000 births from May 2019 - August 2020. For each outcome, site-specific rates per 1,000 livebirths (or per 1,000 total births for stillbirth) and 95% confidence intervals (CI) were calculated. Descriptive sensitivity analysis was conducted to gain insight regarding underreporting of four outcomes at 16 sites. Findings Estimated rates varied across countries and sites, ranging between 43·3-329·5 and 21·4-276·6/1000 livebirths for low birthweight and preterm birth respectively and 11·8-81/1,000 livebirths for SGA. No cases of congenital microcephaly were reported by three sites while the highest estimated rate was 13/1,000 livebirths. Neonatal infection and neonatal death rates varied between 1·8-73 and 0-59·9/1000 livebirths respectively while stillbirth rates ranged between 0-57·1/1000 total births across study sites. Results from the sensitivity analysis confirmed the underreporting of congenital microcephaly and SGA in our study. Interpretation Our study establishes site-specific baseline rates for important adverse perinatal and neonatal outcomes and addresses a critical evidence gap towards improved monitoring of benefits and risks of emerging pregnancy and neonatal interventions. Funding The study was sponsored by the World Health Organization with funding from the Bill and Melinda Gates Foundation.
The National Panel of Tobacco Consumer Studies (TCS Panel) is a probability-based panel of about 4,000 U.S. adult cigarette, cigar, and smokeless tobacco users developed by the U.S. Food and Drug Administration’s Center for Tobacco Products to conduct observational and experimental studies to inform tobacco regulatory activities. This paper describes the methods and characteristics of the current panel. The TCS Panel employed a stratified 4-stage sample design and in-person screening of U.S. sampled households. Selected eligible adults participated in an enrollment interview and completed a baseline survey assessing tobacco use behaviors to enroll in the Panel; 3,893 individuals were enrolled from September 2016–August 2017. Replenishment occurred from July 2019–December 2019 with 2,260 new members, for a current panel of 3,929 members. Demographic and tobacco use characteristics of the current panel were analyzed in 2020. Most demographic characteristics of the TCS Panel are similar to those of U.S. tobacco users in the 2018 National Health Interview Survey, suggesting a lack of systematic bias in the Panel. Small, but statistically significant, differences were observed in the proportion of 18- to 25-year-olds; high school diploma and bachelor’s degree/higher; never married and married (p < 0.05 for all). The TCS Panel appears to be representative of U.S. cigarette, cigar, and smokeless tobacco users; such panels can be a feasible method for conducting tobacco regulatory science research. The TCS Panel has been used to field studies examining purchasing behaviors, receipt and use of free samples/coupons, and the impact of a hypothetical tobacco product standard.
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2,195 members
Girish Ramachandran
  • Center for Biologics Evaluation and Research
Scott Reza Jafarian Kerman
  • Center for Drug Evaluation and Research
Abu Hasanat Md. Zulfiker
  • Center for Biologics Evaluation and Research
Yellela S.R. Krishnaiah
  • Center for Drug Evaluation and Research
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