Population-scale identification of differential adverse events before and during a pandemic

Leveraging pharmacovigilance data to predict population-scale toxicity profiles of checkpoint inhibitor immunotherapy

Immune checkpoint inhibitor (ICI) therapies have made considerable advances in cancer immunotherapy, but the complex and diverse spectrum of ICI-induced toxicities poses substantial challenges to treatment outcomes and computational analysis. Here we introduce DySPred, a dynamic graph convolutional network-based deep learning framework, to map and predict the toxicity profiles of ICIs at the population level by leveraging large-scale real-world pharmacovigilance data. DySPred accurately predicts toxicity risks across diverse demographic cohorts and cancer types, demonstrating resilience in small-sample scenarios and revealing toxicity trends over time. Furthermore, DySPred consistently aligns the toxicity-safety profiles of small-molecule antineoplastic agents with their drug-induced transcriptional alterations. Our study provides a versatile methodology for population-level profiling of ICI-induced toxicities, enabling proactive toxicity monitoring and timely tailoring of treatment and intervention strategies in the advancement of cancer immunotherapy.

Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network

Accurate prediction of Adverse Drug Reactions (ADRs) at the patient level is essential for ensuring patient safety and optimizing healthcare outcomes. Traditional machine learning-based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. In this study, a novel framework called Precise Adverse Drug Reaction (PreciseADR) for patient-level ADR prediction is proposed. The approach effectively integrates relations between patients and ADRs, and harnesses the power of heterogeneous Graph Neural Networks (GNNs) to address the limitations of traditional methods. Specifically, a heterogeneous graph representation of patients is constructed, encompassing nodes that represent patients, diseases, drugs, and ADRs. By leveraging edges in the graph, crucial connections are captured such as a patient being affected by diseases, taking specific drugs, and experiencing ADRs. Next, a GNN-based model is utilized to learn latent representations of the patient nodes and facilitate the propagation of information throughout the graph structure. By employing patient embeddings that consider their diseases and drugs, potential ADRs can be accurately predicted. The PreciseADR is dedicated to effectively capturing both local and global dependencies within the heterogeneous graph, allowing for the identification of subtle patterns and interactions that play a significant role in ADRs. To evaluate the performance of the approach, extensive experiments are conducted on a large-scale real-world healthcare dataset with adverse reports from the FDA Adverse Event Reporting System (FAERS). Experimental results demonstrate that the PreciseADR achieves superior predictive performance in identifying patient-level ADRs, surpassing the strongest baseline by 3.2% in AUC score and by 4.9% in Hit@10.

Evaluation of anticancer therapy-related dermatologic adverse events: Insights from Food and Drug Administration's Adverse Event Reporting System dataset

BACKGROUND

New anticancer therapies have improved patient outcomes but associated dermatologic adverse events (AEs) may cause morbidity and treatment discontinuation. A comprehensive estimation of associations between cancer drugs and skin AEs is lacking.

METHODS

This study utilized the Food and Drug Administartion (FDA)'s Adverse Event Reporting System database (January 2013-September 2022), with 3,399,830 reports involving 3084 drugs and 16,348 AEs. A nearest neighbor matching model was employed to select 10 controls for each case report, utilizing the cosine similarity of demographic and AE severity factors to minimize false positives/negatives.

RESULTS

There were 10,698 unique anticancer drugs (n = 212) to skin AE (n = 873) pairs, of which 676 had significant reporting odds ratios (ROR) > 1, comprising 113 drugs and 144 AEs. The minimum ROR was 1.25, and 50% of associations displayed a ROR >10. The most common were rash (51 agents) and dry skin (28 drugs). Methotrexate induced the most distinct AEs (34), then mechlorethamine (33), and vemurafenib (24). Targeted therapies accounted for 49% of pairs, cytotoxic chemotherapies for 35.9%, and immunotherapies for 11%.

CONCLUSIONS

A total of 113 anticancer drugs were identified as significantly associated with skin AEs, most frequently rash and dry skin. Data are likely under-reported but enable quick postmarketing identification of skin toxicity signals.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BACKGROUND

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

METHODS

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

RESULTS

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

CONCLUSIONS

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

Background

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

Methods

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

Results

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

Conclusions

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.