Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Pharmacogenomics (PGx), studying the relationship between drug response and genetic makeup of an individual, is accelerating advances in precision medicine. The FDA includes PGx information in the labeling of approved drugs to better inform on their safety and effectiveness. We herein present a summary of PGx information found in 261 prescription drug labeling documents by querying the publicly available FDALabel database. A total of 362 drug-biomarker pairs (DBPs) were identified. We profiled DBPs using frequency of the biomarkers and their therapeutic classes. Four categories of applications (indication, safety, dosing and information) were discussed according to information in labeling. This analysis facilitates better understanding, utilization and translation of PGx information in drug labeling among researchers, healthcare professionals and the public.

The tobacco-specific nitrosamine NNK [4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone] is found in tobacco products and tobacco smoke. NNK is a potent genotoxin and human lung carcinogen; however, there are limited inhalation data for the toxicokinetics (TK) and genotoxicity of NNK in vivo. In the present study, a single dose of 5 × 10⁻⁵, 5 × 10⁻³, 0.1, or 50 mg/kg body weight (BW) of NNK, 75% propylene glycol (vehicle control), or air (sham control) was administered to male Sprague-Dawley (SD) rats (9–10 weeks age) via nose-only inhalation (INH) exposure for 1 h. For comparison, the same doses of NNK were administered to male SD rats via intraperitoneal injection (IP) and oral gavage (PO). Plasma, urine, and tissue specimens were collected at designated time points and analyzed for levels of NNK and its major metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and tissue levels of DNA adduct O⁶-methylguanine by LC/MS/MS. TK data analysis was performed using a non-linear regression program. For the genotoxicity subgroup, tissues were collected at 3 h post-dosing for comet assay analysis. Overall, the TK data indicated that NNK was rapidly absorbed and metabolized extensively to NNAL after NNK administration via the three routes. The IP route had the greatest systemic exposure to NNK. NNK metabolism to NNAL appeared to be more efficient via INH than IP or PO. NNK induced significant increases in DNA damage in multiple tissues via the three routes. The results of this study provide new information and understanding of the TK and genotoxicity of NNK.

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.