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.

Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as ‘misinformation’ and ‘other.’ We fine-tuned five BERT models (BERT_BASE, BERT_LARGE, BERTweet-base, BERTweet-COVID-19, and BERTweet-large) using a general COVID-19 rumor dataset or a garlic-specific dataset. Accuracy and F1 score were calculated to evaluate the performance of the models. The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0.647. BERT models fine-tuned with the garlic-specific dataset showed better performance. BERTweet models achieved accuracy of 0.897–0.911, while BERT_BASE and BERT_LARGE achieved accuracy of 0.887–0.897. BERTweet-large showed the best performance with maximum accuracy of 0.911 and an F1 score of 0.894. Thus, BERT models showed good performance in classifying misinformation. The results of our study will help detect misinformation related to garlic and COVID-19 on Twitter.

Background: COVID-19 messenger RNA (mRNA) vaccines have demonstrated efficacy and effectiveness in preventing symptomatic COVID-19, while being relatively safe in trial studies. However, vaccine breakthrough infections have been reported. Objective: This study aims to identify risk factors associated with COVID-19 breakthrough infections among fully mRNA-vaccinated individuals. Methods: We conducted a series of observational retrospective analyses using the electronic health records (EHRs) of the Columbia University Irving Medical Center/New York Presbyterian (CUIMC/NYP) up to September 21, 2021. New York City (NYC) adult residences with at least 1 polymerase chain reaction (PCR) record were included in this analysis. Poisson regression was performed to assess the association between the breakthrough infection rate in vaccinated individuals and multiple risk factors—including vaccine brand, demographics, and underlying conditions—while adjusting for calendar month, prior number of visits, and observational days in the EHR. Results: The overall estimated breakthrough infection rate was 0.16 (95% CI 0.14-0.18). Individuals who were vaccinated with Pfizer/BNT162b2 (incidence rate ratio [IRR] against Moderna/mRNA-1273=1.66, 95% CI 1.17-2.35) were male (IRR against female=1.47, 95% CI 1.11-1.94) and had compromised immune systems (IRR=1.48, 95% CI 1.09-2.00) were at the highest risk for breakthrough infections. Among all underlying conditions, those with primary immunodeficiency, a history of organ transplant, an active tumor, use of immunosuppressant medications, or Alzheimer disease were at the highest risk. Conclusions: Although we found both mRNA vaccines were effective, Moderna/mRNA-1273 had a lower incidence rate of breakthrough infections. Immunocompromised and male individuals were among the highest risk groups experiencing breakthrough infections. Given the rapidly changing nature of the SARS-CoV-2 pandemic, continued monitoring and a generalizable analysis pipeline are warranted to inform quick updates on vaccine effectiveness in real time.

Lee SW, et al. Allergy Asthma Immunol Res. 2023 Mar;15(2):231-245. https://doi.org/10.4168/aair.2023.15.2.231

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are approved for the treatment of human epidermal growth factor receptor 2 (HER-2)-negative, hormone receptor-positive breast cancer. The cardiovascular toxicity of CDK4/6 inhibitors is not well understood. This study aims to profile the cardiac events associated with CDK4/6 inhibitors. Reports from 2015Q1 to 2024Q1 were obtained from the FDA Adverse Event Reporting System (FAERS). Reports identifying palbociclib, ribociclib, and abemaciclib as the primary suspect were examined for cardiovascular toxicity, including hypertension, cardiac failure, cardiomyopathy, arrhythmia, myocardial infarction, and myocarditis. Signal detection was performed using the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). A total of 69,139 reports were analyzed. The median time to adverse events was 69 days (interquartile range [IQR], 18–260 days). Of these, 2065 reports documented cardiac adverse events. Ribociclib and QT prolongation were re-confirmed as a signal (PRR 8.43, ROR 8.65, IC025 2.86). Hypertension and cardiac failure were the most frequently reported cardiovascular toxicities. This study demonstrates that the use of CDK4/6 inhibitors is associated with cardiovascular adverse events, such as heart failure and hypertension. Further research is needed to understand the mechanisms and risk factors contributing to the cardiovascular toxicity of CDK4/6 inhibitors.

Objective: This study aimed to assess the potential of PARP inhibitors to prevent cardiotoxicity. Methods: First, a re-analysis and update of a previously published study was conducted. Additional searches were conducted of the PubMed and Cochrane Central Register of Controlled Trials databases on 2 June 2023. After the selection process, the pooled odds ratio (OR) for cardiac adverse events (AEs) was calculated. Second, the FAERS database was examined for 10 frequently co-administered anticancer agents. The reporting odds ratio (ROR) was calculated based on the occurrence of cardiac AEs depending on the co-administration of PARP inhibitors. Results: Seven studies were selected for the meta-analysis. Although not statistically significant, co-administration of PARP inhibitors with chemotherapy/bevacizumab decreased the risk of cardiac AEs (Peto OR = 0.61; p = 0.36), while co-administration with antiandrogens increased the risk of cardiac AEs (Peto OR = 1.83; p = 0.18). A total of 19 cases of cardiac AEs were reported with co-administration of PARP inhibitors in the FAERS database. Co-administration of PARP inhibitors with chemotherapy/bevacizumab significantly decreased the risk of cardiac AEs (ROR = 0.352; 95% confidence interval (CI), 0.194–0.637). On the other hand, for antiandrogens co-administered with PARP inhibitors, the ROR was 3.496 (95% CI, 1.539–7.942). The ROR for immune checkpoint inhibitors co-administered with PARP inhibitors was 0.606 (95% CI, 0.151–2.432), indicating a non-significant effect on cardiac AEs. Conclusion: This study reports that PARP inhibitors show cardioprotective effects when used with conventional anticancer agents.