Food And Drug Administration Labeling Research Articles

ADME Gene-Related Pharmacogenomic Labeling of FDA-Approved Drugs: Comparison with Clinical Pharmacogenetics Implementation Consortium (CPIC) Evidence Levels.

Pharmacogenomics (PGx) can facilitate the transition to patient-specific drug regimens and thus improve their efficacy and reduce toxicity. The aim of this study was to evaluate the overlap of PGx classification for drug absorption, distribution, metabolism, and elimination (ADME)-related genes in the U.S. Food and Drug Administration (FDA) PGx labeling and in the Clinical Pharmacogenetics Implementation Consortium (CPIC) database. FDA-approved drugs and PGx labeling for ADME genes were identified in the CPIC database. Drugs were filtered by their association with ADME (pharmacokinetics)-related genes, PGx FDA labeling class, and CPIC evidence level. FDA PGx labeling was classified as either actionable, informative, testing recommended, or testing required, and varying CPIC evidence levels as either A, B, C, or D. From a total of 442 ADME and non-ADME gene-drug pairs in the CPIC database, 273, 55, and 48 pairs were excluded for lack of FDA labeling, mixed CPIC evidence level provisional classification, and non-ADME gene-drug pairs, respectively. The 66 ADME gene-drug pairs were classified into the following categories: 10 (15%) informative, 49 (74%) actionable, 6 (9%) testing recommended, and 1 (2%) testing required. CYP2D6 was the most prevalent gene among the FDA PGx labeling. From the ADME gene-drug pairs with both FDA and CPIC PGx classification, the majority of the drugs were for depression, cancer, and pain medications. The ADME gene-drug pairs with FDA PGx labeling considerably overlap with CPIC classification; however, a large number of ADME gene-drug pairs have only CPIC evidence levels but not FDA classification. PGx actionable labeling was the most common classification, with CYP2D6 as the most prevalent ADME gene in the FDA PGx labeling. Health professionals can impact therapeutic outcomes via pharmacogenetic interventions by analyzing and reconciling the FDA labels and CPIC database.

Statin Drug-Drug Interactions: Pharmacokinetic Basis of FDA Labeling Recommendations and Comparison Across Common Tertiary Clinical Resources.

Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)-based drug-drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK-based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding "Dose Adjustment" and "Avoid Coadministration" recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource.

Including the term ‘tobacco-free nicotine’ in the nicotine addiction warning label mandated by the US Food and Drug Administration alters risk perceptions and use intentions

BackgroundThe US Food and Drug Administration (FDA) requires a warning label on nicotine e-cigarettes and pouches: ‘This product contains nicotine. Nicotine is an addictive chemical’. Some brands marketing synthetic nicotine...

Clinical benefit, development, innovation, trials, epidemiology, and price for cancer drugs and indications with multiple special FDA designations.

This study analyzes the development, US Food and Drug Administration (FDA) approval, benefits, innovation, trials, epidemiology, and price of cancer drugs with multiple special designations: orphan, fast track, accelerated approval, priority review, and breakthrough therapy. In total, 355 FDA-approved cancer drug indications with 841 special designations were identified (2012-2022). Trial, epidemiology, and price data were collected from FDA labels, the Global Burden of Disease study, and Medicare and Medicaid. The association between efficacy outcomes and indications' number of special designations were compared in meta-analyses. Median development times were 7.3, 7.8, and 5.4 months (P = .027) for drugs with 0 to 1, 2 to 3, and 4 to 5 special designations, respectively. Multiple special designations were associated with higher biotechnological and clinical innovation. Median patient enrollment in trials were 615, 471, 398, 168, 104, and 120 (P < .001) for indications with 0 to 5 special designations. Drugs for rare diseases supported by open-label phase 1/2 trials of single-arm design were granted more special designations. Hazard ratios for overall survival (0.80 vs 0.73 vs 0.73 vs 0.69 vs 0.56 vs 0.52; P = .003) and progression-free survival (0.70 vs 0.61 vs 0.59 vs 0.44 vs 0.37 vs 0.67; P < .001) substantially declined while tumor response increased with more special designations. Mean monthly prices increased for drugs with 0 to 4 but not 5 special designations ($21 596 vs $14 753 vs $32 410 vs $41 240 vs $38 703 vs $19 184). Multiple special designations are associated with faster clinical development and greater benefits for patients with unmet needs but also with nonrobust trial evidence and a tendency toward higher drug prices.

Prescribing cascades in ambulatory care: A structured synthesis of evidence.

The strength of evidence for specific ambulatory care prescribing cascades, in which a marker drug is used to treat an adverse event caused by an index drug, has not been well characterized. To perform a structured, systematic, and transparent review of the evidence supporting ambulatory care prescribing cascades. Ninety-four potential prescribing cascades identified through a previously published systematic review. Systematic search of the literature to further characterize prescribing cascades. (1) Grading of evidence based on observational studies investigating associations between index and marker drugs, including: Level I-strong evidence [i.e. multiple high-quality studies]; Level II-moderate evidence [i.e. single high-quality study]; Level III-fair evidence [no high-quality studies but one or more moderate-quality studies]; and Level IV-poor evidence [other]. (2) Listing of the adverse event associated with the index drug in the product's United States Food and Drug Administration (FDA) label. (3) Synthesis of the evidence supporting mechanisms linking index drugs and associated adverse events. Of 99 potential cascades, 94 were supported by one or more confirmatory observational studies and were therefore included in this review. The 94 cascades related to 30 types of adverse drug reactions affecting 10 different anatomic/physiologic systems and were investigated by a total of 88 confirmatory studies, including prescription sequential symmetry analysis (n = 51), cohort (n = 30), and case-control (n = 7) studies. Overall, the evidence from observational studies was strong for 18 (19.1%) prescribing cascades, moderate for 61 (64.9%), fair for 13 (13.8%), and poor for 2 (2.1%). Although the evidence supporting mechanisms that link index drugs and associated adverse events was variable, FDA labels included information about the adverse event associated with the index drug for most (n = 86) but not all of the 94 prescribing cascades. Although we identified 18 of 94 prescribing cascades supported by strong clinical evidence and most adverse events associated with index drugs are included in FDA label, the evidentiary basis for prescribing cascades varies, with many requiring further evidence of clinical relevance.

Comparing the FDA's process for health claim submission and the FFC's guidelines for regulating functional foods

Health claims are essential for providing accurate information about the relationship between nutrients and health outcomes to consumers. They facilitate informed decision-making and support individuals in making healthy dietary choices. The U.S. Food and Drug Administration (FDA) regulates health claims to guide consumers in maintaining healthy dietary practices. Health claims on food labels help consumers understand the potential health benefits associated with specific products and make informed choices aligned with their health goals. These claims also promote transparency and trust in the food industry by providing evidence-based information. The process of submitting a health claim to the FDA involves complying with regulatory requirements and providing robust scientific evidence. Scientific evidence plays a crucial role in substantiating health claims and should be based on well-designed clinical trials, epidemiological studies, and mechanistic research. Adhering to FDA labeling requirements ensures the accurate and informative presentation of health claims on food labels. The Functional Food Center (FFC) is working on theoretical aspects of creating ideal functional food (FF) products as well as how these products should be identified with special FF labels. While the FDA focuses on evaluating health claims, the FFC emphasizes the safe and effective use of functional foods. Understanding the FDA's evaluation process and the FFC's guidelines is crucial for researchers, food manufacturers, and policymakers to navigate the regulatory landscape and promote informed consumer choices. Compliance with regulatory guidelines, adherence to scientific standards, and clear communication of health claims contribute to a more informed and health-conscious society.Keywords: Health claims, health outcomes, regulatory requirements, functional foods, health benefits

Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents.

The US Food and Drug Administration (FDA) regulatory process often involves several reviewers who focus on sets of information related to their respective areas of review. Accordingly, manufacturers that provide submission packages to regulatory agencies are instructed to organize the contents using a structure that enables the information to be easily allocated, retrieved, and reviewed. However, this practice is not always followed correctly; as such, some documents are not well structured, with similar information spreading across different sections, hindering the efficient access and review of all of the relevant data as a whole. To improve this common situation, we evaluated an artificial intelligence (AI)-based natural language processing (NLP) methodology, called Bidirectional Encoder Representations from Transformers (BERT), to automatically classify free-text information into standardized sections, supporting a holistic review of drug safety and efficacy. Specifically, FDA labeling documents were used in this study as a proof of concept, where the labeling section structure defined by the Physician Label Rule (PLR) was used to classify labels in the development of the model. The model was subsequently evaluated on texts from both well-structured labeling documents (i.e., PLR-based labeling) and less- or differently structured documents (i.e., non-PLR and Summary of Product Characteristic [SmPC] labeling.) In the training process, the model yielded 96% and 88% accuracy for binary and multiclass tasks, respectively. The testing accuracies observed for the PLR, non-PLR, and SmPC testing data sets for the binary model were 95%, 88%, and 88%, and for the multiclass model were 82%, 73%, and 68%, respectively. Our study demonstrated that automatically classifying free texts into standardized sections with AI language models could be an advanced regulatory science approach for supporting the review process by effectively processing unformatted documents.

Evaluation of FDA Labeling Changes Related to PREA Safety-Waivers.

The Pediatric Research Equity Act (PREA) gives the US Food and Drug Administration (FDA) authority to require pediatric studies for drug and biologics products under certain circumstances and to waive this requirement in some, or all, pediatric ages. When studies are waived for safety, PREA stipulates the safety issue must be described in labeling. This study assessed the rate of including waiver-related safety information in labeling. FDA databases were reviewed to determine the number of safety-related pediatric study waivers and issued from December 2003 through August 2020, and corresponding labeling to establish when relevant safety information was included. Descriptive comparisons were conducted across Cohort 1: December 2003-2007, Cohort 2: 2008-2011, Cohort 3: 2012-2015, and Cohort 4: 2016-August 2020. One hundred sixteen safety waivers were issued [Cohort 1 (n = 1); Cohort 2 (n = 38), Cohort 3 (n = 37), and Cohort 4 (n = 40)] for 84 unique drugs or biologics. Most (106 of 116; 91%) waiver-related safety issues were described in labeling [Cohort 1 (1 of 1), Cohort 2 (33 of 38), Cohort 3 (33 of 37), and Cohort 4 (39 of 40)]. Safety waivers were most common in patients ≤ 17years (n = 40) and least common in patients ≤ 6months (n = 15). Products for infections (n = 32) were the most common group receiving safety waivers; 17 for non-antiviral anti-infective products including treatments for dermatologic infestations/infections, and 15 for antiviral products. The data confirm that FDA consistently describes waiver-related safety information in drug/biologic product labeling since the inception of PREA in December of 2003.

Clinical evidence and benefit of special FDA designations in anti-cancer drugs.

e13655 Background: Over the past decades, US congress enabled the US Food and Drug Administration (FDA) to facilitate and expedite drug development for serious conditions filling unmet medical needs with five special designations: orphan, fast track, accelerated approval, priority review, and breakthrough therapy. It remains unclear whether multiple designations per indication are redundant. This study analyzes cancer drugs with multiple special designations regarding their FDA approval timelines, evidence, benefit, epidemiology and price. Methods: All anti-cancer drugs and their supplemental indication approvals were identified in the Drugs@FDA database from 2000 to 2022. For each indication, clinical trial characteristics, epidemiologic, and price data were collected from FDA labels, the Global Burden of Disease Study, and Medicare &amp; Medicaid Part B and D, respectively. The association between overall survival (OS) and progression-free survival (PFS) hazard ratios (HR) and drug indications’ number of special designations were compared in meta-analyses and meta-regressions. Results: We identified 170 anti-cancer drugs with FDA approval in 455 indications. The median time to first FDA approval was 13.8 years for drugs with zero, 7.3 years with one, 7.1 years with two, 7.8 with three, 5.8 with four, and 5.3 with five special designations (HR per additional designation: 1.27, 95%CI: 1.09-1.48, p=.002). Drug indications with more special designations were more frequently granted to rare diseases with fewer treatment options supported by smaller and shorter open-label phase 1/2 trials. OS HRs were 0.79 for indications with zero, 0.74 with one, 0.73 with two, 0.69 with three, 0.59 with four, and 0.52 with five special designations (p=.024). On average, each additional special designation was associated with lower HRs for OS (ß=-0.03, p=.002) and PFS (ß=-0.07, p&lt;.001). Each additional special designation was associated with a 19.05% (95%CI: 4.56-35.55, p=.013) higher drug price. Conclusions: The FDA’s special designations are non-redundant. Multiple special designations are associated with faster approval times and a greater efficacy for patients with high unmet needs, while multiple special designations are associated with less robust trial evidence and higher drug prices. This study supports the strategy of special FDA designations for unmet clinical needs.

Race and Ethnicity Reporting and Enrollment Disparities in Clinical Trials Leading to FDA Approvals for Breast Cancer Between 2010 and 2020

BackgroundWe determined the race and ethnicity demographics and reporting trends of clinical trials leading to Food and Drug Administration (FDA) approvals for breast cancer. MethodsWe collected enrollment and reporting data from clinical trials leading to FDA novel and new use approvals for breast cancer from 2010 to 2020 from Drugs@FDA, ClinicalTrials.gov, and associated journal manuscripts. Enrollment demographics were compared to the US cancer population estimates obtained using National Cancer Institute-Surveillance, Epidemiology, and End Results and 2010 US Census databases. ResultsSeventeen drugs received approval based on 18 clinical trials with a total enrollment of 12,334. For approvals from 2010 to 2015 and from 2016 to 2020, there was no significant difference in race (80% vs. 91.6%, P = .34) or ethnicity reporting (20% vs. 33.3%, P = .5) on ClinicalTrials.Gov, manuscripts, and FDA labels. For trials that reported race and ethnicity, White, Asian, Black, and Hispanic patients represented 73.8%, 16.4%, 3.7%, and 10.4% of trial participants. Relative to their US cancer incidence, Black (31% of expected) patients were underrepresented compared with White (90% of expected), Hispanic (115%), and Asian (327% of expected) patients. ConclusionWe observed no significant difference in race and ethnicity reporting in pivotal clinical trials leading to FDA approval for breast cancer from 2010 to 2020. Black patients were underrepresented in these pivotal trials relative to White, Hispanic, and Asian patients. Ethnicity reporting remained low throughout the study period. Innovative approaches are needed to ensure equitable benefit of novel therapeutics.

FDA approval, clinical trial evidence, efficacy, epidemiology, and price for non-orphan and ultra-rare, rare, and common orphan cancer drug indications: cross sectional analysis

ObjectiveTo analyze the US Food and Drug Administration (FDA) approval, trials, unmet needs, benefit, and pricing of ultra-rare (<6600 affected US citizens), rare (6600-200 000 citizens), and common (>200 000...

Assessing Readability of FDA-Required Labeling for Breast Implants

Abstract Goals/Purpose In 2021, the United States (US) Food and Drug Administration (FDA) introduced regulations to improve the delivery of breast implant patient education, including new labeling requirements. The new regulations called for a patient decision checklist to be reviewed with a physician ahead of surgery. To ensure educational materials are accessible to most patients, the National Institutes of Health recommend they be written at a sixth to seventh grade level. This study aims to assess the readability of patient-facing FDA labeling for breast implants. Methods/Technique Eleven patient information brochures and seven patient decision checklists were obtained from four breast implant company websites. An example checklist was also obtained from the FDA website. Plain text was extracted from each of the documents. Figures, tables, references, and indices were omitted. The text files were analyzed using four measures of readability validated in clinical materials: Flesch-Kincaid Grade Level, Coleman-Liau Index, Simplified Measure of Gobbledygook (SMOG), and Automated Readability Index (ARI). Results/Complications On average, patient information brochures were 73 pages long (range: 48-92) and included 8 figures or graphics, while patient decision checklists were 5.7 pages long (range: 4-7) and included 0 figures or graphics. Mean readability scores of brochures were a Flesch-Kincaid of 12.7, Coleman-Liau Index of 13.0, SMOG of 14.0, and ARI of 12.7, with an overall mean grade level of 13.1 (13th grade). For decision checklists, results showed a Flesch-Kincaid of 14.2, Coleman-Liau Index of 13.3, SMOG of 15.6, and ARI of 14.8, with an overall grade level of 14.5 (14-15th grade). Comparatively, the sample decision checklist published by the FDA had a Flesch-Kincaid of 13.9, Coleman-Liau Index of 13.0, SMOG of 15.5, and ARI of 14.4, with an overall grade level of 14.2 (14th grade). Conclusion Although patient decision checklists were introduced to improve accessibility of breast implant educational materials, the level at which they are written exceeds the recommendation for the average US adult by several grade levels, suggesting that patients may not fully understand current materials. While shorter than the comprehensive information brochures available, decision checklists were more difficult to read across all readability scales assessed in this study, and neither brochures nor checklists met recommended reading levels on any of the assessed scales. In order to encourage patient understanding of surgical and implant risks and promote the positive health outcomes, the FDA and breast implant companies may consider revising labeling materials.

Assessment of FDA-Approved Drugs Not Recommended for Use or Reimbursement in Other Countries, 2017-2020

Drug expenditures in the US are higher than in any other country and are projected to continue increasing, so US health systems may benefit from evaluating international regulatory and reimbursement decision-making of new drugs. To evaluate regulatory decisions and health technology assessments (HTAs) in Australia, Canada, and the UK regarding new drugs approved by the US Food and Drug Administration (FDA) in 2017 through 2020, as well as to estimate the US cost per patient per year for drugs receiving negative recommendations. In this cross-sectional study, recommendations issued by agencies in Australia, Canada, and the UK were collected for new drugs approved by the FDA in 2017 through 2020. All data were current as of May 31, 2022. Authorizations and HTAs in selected countries. All FDA-approved drugs were matched by active ingredient to decision summary reports published by drug regulators and HTA agencies in Australia, Canada, and the UK. Regulatory approval concordance and reasons for negative recommendations were assessed using descriptive statistics. For drugs not recommended by an international agency, the annual US drug cost per patient was estimated from FDA labeling and wholesale acquisition costs. The FDA approved 206 new drugs in 2017 through 2020, of which 162 (78.6%) were granted marketing authorization by at least 1 other regulatory agency at a median (IQR) delay of 12.1 (17.7) months following US approval. Conversely, 5 FDA-approved drugs were refused marketing authorization by an international regulatory agency due to unfavorable benefit-to-risk assessments. An additional 42 FDA-approved drugs received negative reimbursement recommendations from HTA agencies in Australia, Canada, or the UK due to uncertainty of clinical benefits or unacceptably high prices. The median (IQR) US cost of the 47 drugs refused authorization or not recommended for reimbursement by an international agency was $115 281 ($166 690) per patient per year. Twenty drugs were for oncology indications, and 36 were approved by the FDA through expedited regulatory pathways or the Orphan Drug Act. This cross-sectional study assessed reasons for which drugs recently approved by the FDA were refused marketing authorization or not recommended for public reimbursement in other countries. Drugs with limited international market presence may require close examination by US health care professionals and health systems.

80P Efficacy, FDA approval, innovativeness, clinical evidence, and price of breast, ovarian, endometrial, and cervical cancer drugs

This study analyzes the innovativeness, clinical evidence, and efficacy of new drug indications with US Food and Drug Administration (FDA) approval for breast, ovarian, endometrial, and cervical cancer. We identified 26 drugs with FDA approval in 55 gynecological cancer indications from 2000 to 2021. For each indication, we collected drug, indication, and clinical trial characteristics from FDA labels. Hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), and relative risk (RR) for tumor response were meta-analyzed. Monthly treatment costs were calculated for US patients covered under Medicare & Medicaid. Of 55 indications, 36 received approval for breast, 12 for ovarian, 3 for endometrial, and 4 for ovarian cancer. Drugs acted via a targeted (84%), immune-regulating (13%), and cytotoxic (4%) mechanism of action. Approval was granted to 34 (62%) small-molecules, 16 (29%) antibodies, and 5 (9.1%) antibody-drug conjugates. Indications were predominantly first-or second-line (82%) combination therapies (62%) with a biomarker (84%). Indications frequently received the orphan (24%), fast track (20%), accelerated approval (27%), priority review (82%), and breakthrough therapy (40%) designation. Approval was mostly supported by double-blind (51%) phase 3 (75%) randomized-controlled (82%) trials enrolling a median of 521 patients (IQR: 302-724) for 3.8 years (IQR: 2.5-5.2). On average, new drugs HRs were 0.79 for OS (95%CI: 0.75-0.82) and 0.55 for PFS (95%CI: 0.50-0.60), whereas tumor response was 1.48 (95%CI: 1.37-1.60). Novel drugs increased patient survival by a median of 3.20 months for OS (IQR, 2.0-4.90 months) and 4.75 months for PFS (IQR, 2.40-7.10 months). Greater OS benefits were observed for drugs with a biomarker (HR: 0.76 vs. 0.84, p=.034). In 2022, Gynecological cancer drugs cost an average of $13,053.30 per month. Over the past 22 years, the approval of innovative and effective drugs transformed the landscape of gynecological cancer treatments. Especially biomarker-guided targeted therapies resulted in substantial improvements in OS and PFS. However, rising drug prices pose a challenge to healthcare systems and patient access.

Overview of FDA Drug Approval and Labeling

The U.S. Food and Drug Administration (FDA) regulates a variety of products, including medical, food, and tobacco products. Prior to the creation of the FDA, there were few protections to the public around the contents and sale of food and pharmaceuticals. Over time, legislation was passed and amended that ensured drugs and biologics undergo extensive review by multidisciplinary teams to provide assurance that marketed products are safe and effective for their intended use. The FDA-approved drug labeling is the primary tool for communicating essential information regarding the safe and effective use of a drug product. As such, providers should be familiar with the format of the prescribing information because it is a valuable source of information, particularly prior to prescribing a new drug for the first time. Although there are clinical circumstances in which off-label drug use may be warranted, prescribing drugs off-label involves a context of use that has not undergone the FDA's rigorous evaluation of the benefit-risk assessment.

Patient-reported outcomes labeling for oncology drugs: Multidisciplinary perspectives on current status and future directions.

Introduction: Regulatory agencies encourage the incorporation of the patient voices throughout clinical drug development. Patient-Reported Outcomes (PROs) offer one way of doing this and their use has markedly increased in many therapeutic areas, particularly oncology, in recent years. However, few oncology drug labels include PRO data and those which do, offer little consistency. Objective: To provide multidisciplinary perspectives (patient, pharmaceutical industry, PRO researcher, regulatory expert) on PRO data in oncology drug labels. Methods: PRO data in the labels of drugs approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA) for oncology indications between 2010 and 2020 were critically reviewed by authors who provided their insights on the advantages and disadvantages/gaps. Results: Forty-six oncology drugs included PRO data in their labels. Differences were observed between FDA and EMA PRO labeling (e.g., PRO concept, use of tables and graphs to display PROs or reference to clinical meaningfulness). In providing their perspectives on the number and nature of PROs in labels, authors noted limitations including: the low proportion of oncology drugs with PRO labeling, limited PRO information in labels, lack of patient-friendly language, and potential bias towards positive outcomes. Lack of consistency within- and between-agencies was noted. Conclusion: Despite regulatory agencies' commitment to incorporate patient voices in regulatory decisions, availability of PRO information is limited in oncology drug labels. While several PRO guidance documents are available from regulatory and Health Technology Assessment agencies, harmonization of PRO guidance for labeling inclusion around the world is needed to better inform prescribers and consequently their patients in the process of shared medical decisions.

(Even Off-Label) Cochlear Implantation in Single-Sided Deafness and Asymmetric Hearing Loss Results in Measurable Objective and Subjective Benefit.

To quantify objective and subjective outcomes in cochlear implant (CI) recipients with asymmetric hearing loss, including single-sided deafness (SSD) whose candidacy was determined on an ear-specific basis when word recognition was 50% or less. Retrospective case review. Tertiary referral center. The effectiveness of CI in cases of SSD and asymmetric hearing loss (AHL) has been described in terms of tinnitus suppression, improved speech recognition in quiet and noise, enhanced localization ability, and improved quality of life. However, CI is not yet routinely offered as a top option or standard of care for these individuals. Recent Food and Drug Administration (FDA) labeling limits aided word recognition in the ear to be implanted to only 5% in cases of AHL/SSD, which is significantly poorer than 40 to 50%, which is often referenced in cases of bilateral hearing loss. Anecdotal experience suggests that patients with much better preoperative word recognition than 5% can benefit from CI. We conducted a retrospective chart review of all adult CI candidates, with one ear exhibiting consonant-nucleus-consonant (CNC) word recognition scores at least 50% and one ear not meeting CI candidacy (i.e., CNC word recognition >50%). Outcome variables of interest included word and sentence recognition and subjective handicap questionnaires (hearing, tinnitus, dizziness) and the Speech Spatial Qualities questionnaire. Statistically and clinically significant improvement in speech understanding (word, sentence, sentence in noise) was noted for both the SSD and AHL groups in the implanted ear. There were statistically and clinically significant subjective improvements noted for both groups on the Hearing Handicap Inventory, the Tinnitus Handicap Inventory, and the Speech Spatial Qualities questionnaire by 1 month after activation. There were no significant differences between the AHL and SSD groups on either objective or subjective measures of the implanted ear. Individual word understanding improved for the majority of recipients across both groups and is not dependent on meeting the FDA criteria of less than 5%. Cochlear implantation is a viable option with measurable objective and perceived benefits for recipients with preoperative aided CNC word scores exceeding current FDA labeling. There is no significant difference between the AHL and SSD groups, suggesting that candidacy and outcome expectations should be set based on the ear to be implanted alone, without regard for the ability of the better hearing ear.

Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information

Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.

Risk for Postmarket Black Box Warnings in FDA-Approved Monoclonal Antibodies.

ObjectiveTo estimate the potential risk for a future postmarket black box warning (BBW) of US Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) because of the importance for medical clinicians to understand mAb risks and benefits, including unknown future risks, especially for recently approved mAbs.MethodsThe complete dates of the study were March 16, 2020, through May 12, 2021. We searched the FDALabel database online and reviewed the scientific literature to determine current and previous FDA-approved mAbs as of March 2020. The BBWs and initial FDA-issued safety warnings were identified. The BBWs were categorized as premarket or postmarket. For mAbs with specific postmarket BBWs, previous FDA labels were evaluated to identify the presence or absence of an initial corresponding specific FDA warning.ResultsIn March 2020, a total of 83 mAbs had FDA approval; 33 had BBWs (27 premarket and 13 postmarket BBWs). Of these 33 mAbs, 55 individual specific BBWs existed (36 premarket and 19 postmarket specific warnings). On average, the specific BBWs occurred in the postmarket period at a rate of 3.4% (19/562) per year. Most (73.7%; 14/19) specific postmarket BBWs were preceded by an FDA warning in a median time of 3.61 (interquartile range, 1.36-5.78) years. Specific postmarket BBWs not preceded by a specific FDA product label warning occurred at an average rate of 0.9% (5/562) per year.ConclusionSpecific postmarket BBWs occurred in FDA-approved mAbs at a rate of 3.4% per year. Specific postmarket BBWs not preceded by a specific FDA product label warning had a rate of 0.9% per year.

Anticancer Drugs Approved by the US Food and Drug Administration From 2009 to 2020 According to Their Mechanism of Action

Both novel and next-in-class cancer drugs have a role in oncology, but the relative development of each is understudied. To characterize the mechanisms of action of anticancer drugs approved by the US Food and Drug Administration (FDA) between 2009 and 2020, noting how many approvals were based on a new mechanism of action vs next-in-class approvals. This cross-sectional study included all anticancer drugs approved by the FDA from January 2009 to December 2020. The mechanism of action of each drug was extracted from FDA labels. Supportive-care treatments were excluded. Name of drug approved, date of approval, indication, tumor type, mechanism of action, broad pharmaceutical class, and biological target. Approvals considering all tumor types and each tumor type separately were classified in 3 nonoverlapping categories: new mechanism of action, next in class, or subsequent approval. The number of all approvals each year; the number of approvals based on a new mechanism of action, either by drug (considering all tumor types) or by indication (considering tumor types separately); and the frequency of these numbers over time. Overall, 332 approvals were included. Between 2009 and 2020, there was an increase in the total number of approvals from 8 to 57. We found that 209 approvals (63%) were for a next-in-class indication in a new tumor type (84 [25%]) or a subsequent indication of the same drug in the same tumor type (195 [59%]). When considering each tumor type separately, 123 approvals (37%) were based on a new mechanism of action. In this study, approvals based on a new mechanism of action represented a minority of all approvals. Further consideration of incentives for drug development are needed to prioritize novel or highly innovative and transformative anticancer drugs.