Drug Product Labeling Research Articles

An Accurate and Fast 31P qNMR Assay Method for Oligonucleotide Therapeutics.

Chemically modified nucleic acid molecules have been developed as oligonucleotide therapeutics, and its assay is critical in quality assurance. The common DNA/RNA quantification method using UV-260 nm can lack accuracy because of structure modifications and the possible formation of higher-order structure (HOS). Additionally, process-associated water and counterions affect the accuracy in gravimetric analysis. Thus, to improve accuracy, efficiency, and flexibility, in this work a fast (<1 h) externally referenced 31P quantitative-NMR (qNMR) method was developed. The qNMR assay results agreed within 1-5% of the UV-260 nm results for the single-stranded DNA standards, confirming the method accuracy. Next, an NMR and UV comparison study was performed on intact oligonucleotide drug products. The 31P qNMR method showed 7 ± 2%, 8 ± 1%, and 12 ± 1% lower concentration values compared with drug product labels for eteplirsen, inotersen, and inclisiran, respectively. Meanwhile the UV-260 nm results showed 28 ± 3%, 10 ± 3%, and 10 ± 1% lower concentrations than the label for the same three drugs. The agreement between NMR and UV for phosphorothioate (PS)-based inotersen and mostly phosphodiester (PO)-based inclisiran suggest that the labeled concentration may have been obtained using different extinction coefficients. The underestimate of UV results for eteplirsen, which has a phosphorodiamidate morpholino oligomer (PMO) structure, suggests that the UV-260 nm extinction coefficient may need to be re-established for the PMO based oligonucleotide. Therefore, the 31P qNMR method could be a primary assay method for the oligonucleotide drug and reference standard.

Administration sequences in single-day chemotherapy regimens for breast cancer: a comprehensive review from a practical perspective.

Breast cancer is one of the most prevalent malignant tumors globally, posing a severe threat to human life and health. Chemotherapy, a cornerstone in the treatment of breast cancer, often overlooks the sequence of drug administration within single-day regimens. This study aims to explore the impact of drug administration order on the efficacy and toxicity of combination chemotherapy protocols for breast cancer. Through a comprehensive review and analysis based on current evidence from evidence-based medicine, we delved into how the order of drug administration affects both efficacy and toxicity. We systematically classified and analyzed commonly used combination drug regimens, providing graded recommendations and a reasoned analysis to offer valuable references for clinical decision-making. Our findings indicate that the sequence of drug administration in complex combination chemotherapy protocols is not arbitrary but necessitates multifaceted considerations. Rational drug sequencing can maximize synergistic effects between drugs, thereby augmenting therapeutic efficacy while effectively mitigating drug-related adverse effects. Additionally, some drug labels and clinical trials have explicitly highlighted the therapeutic benefits of specific drug sequences. This study underscores the importance of considering the sequence of drug administration in clinical practice. It is recommended to prioritize the sequential drug administration according to official drug product labeling, while also considering factors such as the administration sequence from large randomized controlled trials, cell proliferation kinetics specific to cancer types, drug interactions, chronopharmacology, drug irritability, clinical experiences, and patient preferences. By taking these factors into account, the goal is to maximize treatment efficacy and minimize the occurrence of adverse reactions.

To Dilute or Not to Dilute: Nominal Titer Dosing for Genetic Medicines.

Recombinant adeno-associated virus (AAV) has become a popular platform for many gene therapy applications. The strength of AAV-based products is a critical quality attribute that affects the efficacy of the drug and is measured as the concentration of vector genomes, or physical titer. Because the dosing of patients is based on the titer measurement, it is critical for manufacturers to ensure that the measured titer of the drug product is close to the actual concentration of the batch. Historically, dosing calculations have been performed using the measured titer, which is reported on the drug product label. However, due to recent regulatory guidance, sponsors are now expected to label the drug product with nominal or "target" titer. This new expectation for gene therapy products can pose a challenge in the presence of process and analytical variability. In particular, the manufacturer must decide if a dilution of the drug substance is warranted at the drug product stage to bring the strength in line with the nominal value. In this paper, we present two straightforward statistical methods to aid the manufacturer in the dilution decision. These approaches use the understanding of process and analytical variability to compute probabilities of achieving the desired drug product titer. We also provide an approach for determining an optimal assay replication strategy for achieving the desired probability of meeting drug product release specifications.

Retrospective analysis of the biopharmaceutics characteristics of solid oral Modified-Release drug products in approved US FDA NDAs designated as Extended-Release or Delayed-Release formulations

BackgroundModified Release (MR) orally administered drugs products [Extended-Release (ER) and Delayed-Release (DR)] differ from Immediate-Release (IR) drug products in their drug release site and/or rate to offer therapeutic advantages. It is important to understand the biopharmaceutics factors that determine how a drug works in the gastrointestinal tract and the various pharmacokinetic properties that determine a drug’s rate of absorption and release in the human body. To better understand the biopharmaceutics characteristics of ER and DR drug products, this study retrospectively analyzed submissions approved by the US Food and Drug Administration (FDA), from 2001 to 2021, and their corresponding review documents. This review work is expected to enhance the readers’ understanding regarding the biopharmaceutics properties that supported approval of these products’ ER claims, as per 21 CFR 320.25(f), and DR claims. MethodsA comprehensive search was conducted using the FDA’s internal New Drug Application (NDA) database for ER and DR oral drug products approved between 2001 and 2021. The search yielded 87 ER applications (23 ER capsules and 64 ER tablets) and 21 DR applications (10 DR capsules, 11 DR tablets) for which electronic records were accessible. These products were analyzed for overall drug product design, dosing frequency compared to the reference (if applicable), degree of fluctuation, dissolution method, and alcohol dose-dumping. ResultsOut of 87 total applications for ER drug products that were assessed, 62% of the ER tablets contained a polymer matrix formulation, and hypromellose (HPMC) was used in 50% of these products. 52% of the ER capsules consisted of polymer beads while about half of the DR drug products contained a non-bead formulation with a combination of polymers. The majority of ER drug products were found to have a reduction in dosing frequency and a decrease in the degree of fluctuation when compared to the IR reference product. The 13 ER drug products that exhibited an increase in degree of fluctuation exhibited general and pharmacodynamic benefits, such as reduced dosing frequency and reduced pill burden. The majority of DR formulations were developed to prevent drug degradation in the stomach, followed by to decrease potential stomach irritation, and lastly for localized release in the colon. The majority of ER drug products had 1:1 ratios of dissolution duration compared to dosing frequency (i.e., the majority of ER drug products had a dissolution duration of 24 h and were dosed every 24 h while those with a dissolution duration of 12 h were dosed every 12 h). The majority of ER applications had single-stage dissolution methods while most DR drug products used biphasic dissolution methods. All of the DR dissolution methods incorporated an acid stage of 2 h and a buffer stage with various timeframes. 53% the DR drug products had a ratio of dissolution duration to dosing frequency of 1:4 (e.g. a dissolution duration of 2 h to a dosing frequency of 8 h) or 1:8 (e.g. a dissolution duration of 2 h to a dosing frequency of 16 h). Of the ER tablets and DR drug products, 72% exhibited no alcohol dose-dumping under in vitro testing conditions. ER capsules, however, did not yield similar results—most of which exhibited alcohol induced dose-dumping. Alcohol dose dumping was mitigated by either in vivo studies or warnings on the drug product label. ConclusionThe results of this study help the reader understand the design, characteristics, and pharmacological advantages of the ER and DR drug products for patient benefit; as well as the regulations governing the FDA’s assessment of ER claims.

Medication Update.

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update.

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update.

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update.

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update.

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update.

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

EP04.01-004 Interim Feedback on Pilot Public Facing Website for Cancer Clinical Trial Patient-Reported Symptom Data: Project Patient Voice

Patient experience data is rarely included in oncology drug labeling. Over the last 10 years, there have been only 13 oncology drug product labels where this data was included. Project Patient Voice (PPV) is a pilot website developed by the US FDA Oncology Center of Excellence to show patient-reported symptom data from select cancer clinical trials of approved products. PPV is intended to be reviewed by patients and caregivers alongside their healthcare providers (HCPs) when discussing treatment.

Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.

The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the public when a medication is suspected to have caused an AE. The FDA monitors these reports to identify drug safety issues that were undetected during the premarket evaluation of these products. These reports contain patient narratives that provide information regarding the AE that needs to be coded using standardized terminology to enable aggregation of reports for further review. Additionally, the FDA collects structured drug product labels (SPLs) that facilitate standardized distribution of information regarding marketed medical products. Manufacturers are currently not required to code labels with associated AEs. Approaches for automated classification of reports by preferred terminology could enhance regulatory efficiency. The goal of this work was to assess the suitability of manually annotated FDA FAERS and SPL data sets to be subjected to predictive modeling. A recurrent neural network (RNN) was proposed as a proof-of-concept model for automated extraction of preferred AE terminology. A separate RNN was fit and cross-validated on two regulatory data sets with varying properties. First, the researchers trained and cross-validated a model on 325 annotated FAERS patient narratives for a sample of AE terms. A model was then trained and validated on a data set of 100 SPLs. Model cross-validation results for product labels demonstrated that the model performed at least as well as more conventional models for all but one of the terms selected based on F1-score. Model results for the FAERS data set were mixed. This work successfully demonstrated a proof-of-concept machine learning approach to automatically detect AEs in several textual regulatory data sets to support post-market regulatory activities. Limited instances of each AE class likely prohibited models from generalizing data effectively. Additional data may permit more robust validation.

Ontology-Based Classification and Analysis of Adverse Events Associated With the Usage of Chloroquine and Hydroxychloroquine.

Multiple methodologies have been developed to identify and predict adverse events (AEs); however, many of these methods do not consider how patient population characteristics, such as diseases, age, and gender, affect AEs seen. In this study, we evaluated the utility of collecting and analyzing AE data related to hydroxychloroquine (HCQ) and chloroquine (CQ) from US Prescribing Information (USPIs, also called drug product labels or package inserts), the FDA Adverse Event Reporting System (FAERS), and peer-reviewed literature from PubMed/EMBASE, followed by AE classification and modeling using the Ontology of Adverse Events (OAE). Our USPI analysis showed that CQ and HCQ AE profiles were similar, although HCQ was reported to be associated with fewer types of cardiovascular, nervous system, and musculoskeletal AEs. According to EMBASE literature mining, CQ and HCQ were associated with QT prolongation (primarily when treating COVID-19), heart arrhythmias, development of Torsade des Pointes, and retinopathy (primarily when treating lupus). The FAERS data was analyzed by proportional ratio reporting, Chi-square test, and minimal case number filtering, followed by OAE classification. HCQ was associated with 63 significant AEs (including 21 cardiovascular AEs) for COVID-19 patients and 120 significant AEs (including 12 cardiovascular AEs) for lupus patients, supporting the hypothesis that the disease being treated affects the type and number of certain CQ/HCQ AEs that are manifested. Using an HCQ AE patient example reported in the literature, we also ontologically modeled how an AE occurs and what factors (e.g., age, biological sex, and medical history) are involved in the AE formation. The methodology developed in this study can be used for other drugs and indications to better identify patient populations that are particularly vulnerable to AEs.

Vehicles for Drug Administration to Children: Results and Learnings from an In-Depth Screening of FDA-Recommended Liquids and Soft Foods for Product Quality Assessment

PurposeMixing with liquids or soft foods is a common procedure to improve acceptability of oral medicines in children but may affect drug stability and the in vivo performance of the administered drug product. The aim of the present study was to obtain an overview of the variability of critical attributes of commonly used vehicles and to identify which vehicle characteristics need to be considered when developing in vitro methods for evaluating product quality.MethodsOne product of each vehicle listed in the FDA draft guidance “Use of Liquids and/or Soft Foods as Vehicles for Drug Administration” was analyzed with regard to composition, calorific content and physicochemical properties.ResultsThe studied vehicles show wide variability, both in composition and physicochemical properties. No correlation was observed between vehicle composition and physicochemical properties. Comparison of results of the present study with previously published data also provided variability in physicochemical properties within individual vehicle types.ConclusionsTo identify acceptable (qualified) vehicles for global drug product labeling, it is important that the vehicles selected for in vitro compatibility screening reflect the variability in composition and essential physicochemical properties of the vehicles recommended on the product label, rather than relying on results obtained with a single vehicle of each type. Future activities will focus on the development of standardized dosing vehicles that can represent key vehicle characteristics in all their variability to ensure reliable risk assessment.

Medication Update

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

A Review of Patient-Reported Outcome Labeling of FDA-Approved New Drugs (2016-2020): Counts, Categories, and Comprehensibility

ObjectivesA review of new drug approvals (NDAs) by the US Food and Drug Administration (FDA) for 2006 to 2015 showed that approximately 20% of new drugs had labeling based on patient-reported outcomes (PROs). The purpose of this study was to review labeling text based on PRO endpoints for NDAs from 2016 to 2020, with a special focus on the comprehensibility of such statements when included. MethodsWe reviewed drug approval reports on the Drugs@FDA web page of the FDA website to determine the number of NDAs from 2016 to 2020. For all identified NDAs, drug approval package and product labels were reviewed. NDAs from 2016 to 2020 were grouped by disease category as per International Classification of Diseases 10th Revision. Data were summarized for diseases that traditionally rely on PROs for evaluating treatment benefit (PRO dependent) and for diseases that traditionally do not rely on PROs (non-PRO dependent). Results were compared with NDAs from 2006 to 2010. ResultsNDAs amounting to 228 were identified from 2016 to 2020, 26.3% of which had labeling statements based on PRO endpoints. From 2006 to 2015 and from 2016 to 2020, PRO labeling statements were included in 46.5% (46 of 99) and 50.0% (47 of 94), respectively, of NDAs for PRO-dependent new molecular entities and in 6.0% (12 of 199) and 9.7% (13 of 199), respectively, of NDAs for non–PRO-dependent new molecular entities. Comprehensibility of labeling statements based on PRO endpoints was judged to be complex in 56.7% of product labels. ConclusionsThe increase in labeling text based on PRO endpoints in product labels is encouraging. However, there is room for improvement on the comprehensibility of labeling statements based on PRO endpoints.

Medication Update

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.

Medication Update

Medication Update alerts readers to new drug approvals and other timely drug information. Healthcare providers should not make clinical decisions from these announcements, as they are not comprehensive. Consult the manufacturer's complete drug product label before prescribing any of the drugs discussed.