Drug-Associated Rhabdomyolysis in the FDA Adverse Event Reporting System (FAERS): A Disproportionality Analysis

arXiv:2511.00093 · q-bio.OT · Submitted 2025-10-30 · Read on arXiv

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Introduction to the show: ident: Genomics Radio. Generated commentary on the latest computational biology and genomics papers.

Ines: We're looking at the paper: "Drug-Associated Rhabdomyolysis in the FDA Adverse Event Reporting System (FAERS)".

Marcus: Here's the gist of the paper. The gist: This study systematically identified and quantified risks for drug-induced rhabdomyolysis using real-world data from the FDA Adverse Event Reporting System (FAERS) and proposed a data-driven risk mitigation framework.

Ines: 1.

Marcus: The study conducted a retrospective pharmacovigilance study using the FAERS database from Q1 2005 to Q1 2025.

Ines: 2.

Title and authors: Ines: So we’re looking at a paper titled Drug-Associated Rhabdomyolysis in the FDA Adverse Event Reporting System (FAERS): A Disproportionality Analysis. Marcus, what’s the main idea here in one sentence?

Marcus: Basically, they used real-world data from FAERS from two thousand five to two thousand twenty-five to systematically find and count the risks of drug-induced rhabdomyolysis across fifty-four different drugs <ref:2511.00093#pg1>. It’s a big pharmacovigilance study trying to build a risk mitigation framework based on what actually happened in the wild.

Yuki: From my perspective, it’s interesting because it moves beyond just listing known risky drugs and tries to quantify the unknown risks that might be hidden in the data. I wonder if this real-world evidence can actually support those novel signals they find.

Ines: Exactly, Yuki. The paper digs deep into how they detect these risks using a two-stage analysis, starting with a Reporting Odds Ratio or ROR and then moving to a LASSO-optimized logistic regression for adjusted odds ratios or aORs. That’s the engineering side of it trying to isolate the true association.

Marcus: Yeah, so they didn't just look at raw numbers; they controlled for confounders in that multivariate model, which is crucial when you're dealing with real patient data where things like age or sex can mess with the signal. They tuned their lambda parameter using ten-fold cross-validation to pick the best fit for their model <ref:2511.00093#pg1>.

Yuki: That tuning process is important because it helps ensure that what they find isn't just noise, but a statistically stable association when you look at different subsets of the data. It grounds the finding in statistical rigor before we even talk about the biology.

Ines: The paper then identified some really potent risks for known agents like gemfibrozil with an aOR of one hundred seventy-three point six seven and statins, where lovastatin has an aOR of ninety-seven point two zero and simvastatin is at eighty-five point one two, which shows how strong the established risks are <ref:2511.00093#pg1>.

Marcus: And it’s not just those established drugs; they found strong novel risk signals for agents that currently don't have warnings, specifically levetiracetam with an aOR of eleven point zero two and donepezil with an aOR of eight point nine zero, which is what I find particularly concerning for regulatory bodies right now <ref:2511.00093#pg1>.

Yuki: That’s where the population geneticist perspective comes in—those novel signals could point to underlying mechanisms that we haven't fully mapped yet in terms of how different populations react to those specific agents. It suggests the biology is more complex than just looking at existing labels.

Title and authors: Ines: And they quantified a significant labeling gap: sixty-one point one percent of drugs with a statistically significant DIR risk lack a corresponding warning on their official U <ref:2511.00093#pg1>.S. drug labels, which is a huge finding because it shows where the safety communication is falling short of the scientific discovery.

Marcus: It’s like the data is screaming one thing, but the official documentation isn't catching up to that urgency, and that gap quantification helps show just how big that communication failure actually is in terms of drug classes.

Yuki: When you look at sixty-one point one percent of drugs lacking warnings, it really puts into perspective how much reliance we have on the official labels versus the real-world pharmacovigilance data they collected <ref:2511.00093#pg1>. It suggests a need for much more agile regulatory action to keep up with this scientific discovery.

Ines: And they didn't stop there; they developed a three-tiered risk stratification model based on those adjusted odds ratios, putting drugs into Tier one at risk if the aOR is greater than or equal to ten <ref:2511.00093#pg1>.

Marcus: So Tier one includes fourteen drugs, and the most potent ones are gemfibrozil with an aOR of one hundred seventy-three point six seven, lovastatin with an aOR of ninety-seven point two zero, and simvastatin with an aOR of eighty-five point one two, which you can see is where the highest quantified risk lies <ref:2511.00093#pg1>.

Yuki: It’s interesting that they also looked at demographic factors like male sex showing a more than two-fold increase in risk with an aOR of two point one six, and the age relationship being non-linear, with the highest risk in younger adults between twenty and thirty years old with an aOR of one point four four.

Ines: That finding about younger adults having higher risk is something to watch because it suggests a specific biological vulnerability or exposure pattern that we need to investigate further in terms of sequence and structure analysis.

Marcus: They also found that the highest incidence period was within the first thirty days of treatment initiation, with approximately fifty percent of cases happening in that timeframe, meaning the immediate post-treatment window is where you need to be most vigilant <ref:2511.00093#pg1>.

Yuki: That temporal analysis is valuable because it tells us exactly when we should be focusing our safety monitoring efforts when assessing these drugs in a clinical setting for the species as a whole.

Ines: The paper also provided biological plausibility for those novel risks, suggesting mechanisms like levetiracetam binding to SV2A at the neuromuscular junction and its enhancement of cholinergic activity to induce muscle cell over-excitation.

Title and authors: Marcus: That mechanism is what connects the statistical signal of donepezil having an aOR of eight point nine zero—it suggests a specific molecular pathway is driving that risk, which helps us move from correlation to potential causation <ref:2511.00093#pg1>.

Yuki: Linking the statistics to things like binding at the neuromuscular junction gives us something concrete to discuss with other geneticists who study muscle function and how those specific drug-protein interactions might manifest in different genetic backgrounds.

Ines: And they noted that the higher adjusted odds ratio for donepezil compared to a Canadian cohort study’s weighted odds ratio of two point two one is interpreted as reflecting a risk signal enriched with more severe cases due to "notoriety bias."

Marcus: That notoriety bias point is important because it explains why some signals look stronger in one region than another, and it reminds us that the data itself has context that needs to be accounted for before we draw final conclusions.

Yuki: So when we look at this paper on Drug-Associated Rhabdomyolysis in the FDA Adverse Event Reporting System (FAERS): A Disproportionality Analysis, we see a robust quantification of known risks alongside novel signals and a clear map of where the official labeling is missing warnings.

Ines: The conclusion is that the challenge isn't necessarily a lack of evidence, but rather the need for an agile regulatory process to ensure drug safety information keeps pace with scientific discovery.

Marcus: And they propose this framework as a data-driven foundation for developing tiered clinical decision support systems and guiding future regulatory action. It’s a way to use this real-world evidence to proactively enhance prescribing safety.

Yuki: From my view, the implication is that large-scale pharmacovigilance analytics can pinpoint blind spots within current drug safety surveillance, forcing a necessary update in how we communicate risks across the species.

Ines: So we’ve seen how they quantified DIR risk for fifty-four drugs and analyzed demographic variables. This paper provides a robust evidence base to catalyze updates in drug safety communication based on hard data.

Marcus: It successfully replicated known risk gradients within drug classes as a positive control for the analytical method, which gives them confidence in their approach before applying it to the novel signals.

Yuki: Ultimately, this work demonstrates the capacity of real-world evidence analytics to pinpoint blind spots within current drug safety surveillance and provides a foundation for improving how we evaluate these complex interactions.

The paper's summary: Ines: So we’re looking at how this paper summarizes everything about finding risks for drugs that cause rhabdomyolysis using the FAERS database. It boils down to them systematically hunting for signals across fifty-four different drugs and then creating a risk stratification model based on those adjusted odds ratios.

Marcus: Yeah, so they take this massive stream of real-world reporting data from two thousand five to two thousand twenty-five use the ROR and then LASSO regression to figure out which drugs are truly associated with this muscle damage, controlling for all that extra noise like age or sex.

Ines: And what’s really striking is how they build that three-tiered model—Tier one being high risk because the adjusted odds ratio, or aOR, is over ten. Tier two and tier three are just lower risk categories based on those aOR values.

Marcus: They found these potent associations with known agents like gemfibrozil with an aOR of one hundred seventy-three point sixty-seven and statins like lovastatin at nine seven point twenty, which shows the established risks are incredibly strong when you look at the raw numbers.

Ines: But it’s not just about the old drugs; they also flagged novel signals for things like levetiracetam with an aOR of eleven point zero two and donepezil with an aOR of eight point nine zero, which is what gets my attention as a computational biologist looking at sequence structures.

Marcus: That’s where the cohort data comes in; those novel signals are what we need to check against known biological pathways to see if there's any actual mechanism driving them. They also quantified that labeling gap—sixty-one point one percent of these risky drugs don't even have a warning on their official U.S. drug labels, which is a big communication failure point.

Ines: That lack of labeling information is huge because it shows the scientific discovery isn't matching the regulatory warning system yet, and they even proposed specific biological mechanisms for those novel risks, like levetiracetam binding to SV2A at the neuromuscular junction.

Marcus: And they pointed out that their statistical finding for donepezil was higher than a Canadian study’s odds ratio because of something called notoriety bias, meaning the data itself is skewed by which cases get reported and noticed.

Ines: So what this means for us listening, it’s that the biggest challenge isn't just finding new evidence, but making sure the regulatory process moves as fast as science discovers these novel links before patients are put at risk.

Marcus: That paper sets up a data-driven framework for tiered clinical decision support systems and guiding future regulatory action based on those aOR values, which is how we can start to make prescribing safer in practice.

The paper's improvements: Ines: So we're looking at what they propose to do next after they finish their analysis of the rhabdomyolysis risks in FAERS data. They suggest developing a data-driven risk mitigation framework based on that tiered model we talked about earlier.

Marcus: Right, it’s not just finding the risks; they want to build something practical—a system that can actually help doctors decide if a new drug is too risky based on those adjusted odds ratios.

Ines: They're talking about creating these three-tiered clinical decision support systems where you categorize drugs into high risk, moderate risk, or lower risk using those specific aOR cutoffs we established.

Marcus: That means instead of just reading a list of risks, a clinician gets an alert based on which tier the drug falls into—that’s how it changes for someone who only listens to the show.

Ines: They also suggest using this framework to guide future regulatory action, so when new drugs come out, regulators can use this kind of predictive modeling instead of just reacting after adverse events happen.

Marcus: It sounds like they're moving from just post-market surveillance to a proactive safety management approach where the data informs the label before the drug even hits the market.

Ines: They’re emphasizing that this isn't just about one specific drug, but creating a general method that can be applied across all future adverse event reporting systems, which is a big goal for computational biology.

Marcus: And they stress that this framework needs to keep pace with scientific discovery, meaning the system has to be agile enough to absorb new signals as they pop up in the data stream.

Ines: They also touched on the need for better biological plausibility checks, so future work should focus on linking those statistical signals directly back into specific molecular pathways, like those SV2A interactions we mentioned earlier.

Marcus: So the next step is taking these statistically significant risk tiers and feeding them into something that can predict temporal trends or identify new biomarkers that might explain why a certain drug causes muscle damage in younger adults.

Ines: That moves us from quantifying what *is* happening to predicting what *will* happen, which is where the real computational biology gets interesting.

Conclusion: Ines: So we've covered how this paper analyzes FAERS data to find risks for drug-induced rhabdomyolysis, including those novel signals and the risk stratification tiers they built based on adjusted odds ratios.

Marcus: Yeah, it boils down to using a rigorous statistical pipeline—ROR followed by LASSO regression—to create a usable framework that goes beyond just listing known risky drugs.

Ines: The implication for us is that we have a concrete blueprint for how to use real-world evidence to guide regulatory communication and build safer clinical decision support tools.

Marcus: It's about moving toward proactive safety management, using these quantitative risk metrics rather than just waiting for more adverse event reports to appear.

Yuki: From my perspective, this work is important because it shows how the species-wide data from FAERS can be used to uncover risks that might not be apparent in smaller cohort studies, even considering demographic factors like sex and age.

Ines: Exactly, Yuki. The stratification model allows us to focus our attention on the highest risk tier drugs, which is a much more targeted way to improve safety messaging for healthcare providers.

Marcus: They also highlighted the labeling gap—that sixty-one point one percent of risky drugs lack warnings—which is a clear call to action for regulators regarding how safety information needs to be updated.

Yuki: It suggests that population geneticists and pharmacologists need to collaborate more closely so we can link those statistical signals back into actual biological mechanisms, which is what the authors pointed toward with the SV2A example.

Ines: That’s right, so this paper lays a foundation for linking complex statistical analyses directly to molecular pathways in a way that helps us understand the biology behind these risks.

Marcus: Moving forward, we'll be looking at how this framework integrates with predictive modeling to see if it can forecast risk within specific patient subgroups based on their clinical history.

Yuki: That sounds like the next big step—taking these quantified risks and using them to inform personalized safety strategies for different parts of our population.

Ines: It’s a powerful demonstration of how large-scale pharmacovigilance analytics can pinpoint blind spots within current drug safety surveillance, especially when dealing with complex adverse events like rhabdomyolysis.

Marcus: So that's the wrap on "Drug-Associated Rhabdomyolysis in the FDA Adverse Event Reporting System (FAERS): A Disproportionality Analysis."

q-bio.OT

Submitted: 2025-10-30

Updated: 2026-10-20

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 78/100

The gist: The gist: This study systematically identified and quantified risks for drug-induced rhabdomyolysis using real-world data from the FDA Adverse Event Reporting System (FAERS) and proposed a

Key concepts

Reporting Odds Ratio (ROR): This is an initial statistical tool used to quickly detect potential risks by comparing the rate of rhabdomyolysis reports for specific drugs against other adverse events reported in the FAERS database. A high ROR suggests a drug might be associated with this serious side effect.
Adjusted Odds Ratio (aOR): This metric is used to determine the independent risk of rhabdomyolysis for a drug after statistically controlling for other factors like age or sex. It helps isolate the true association between a specific drug and the adverse event, removing confounding variables.
Labeling Gap: This refers to the discrepancy between drugs that show a statistically significant risk of rhabdomyolysis in real-world data and those that have corresponding warnings on their official U.S. drug labels. The study found 61.1% of these high-risk drugs lacked warnings, indicating a major safety communication issue.

Terminology

FDA Adverse Event Reporting System — 2× in this paper

Summary

The gist: This study systematically identified and quantified risks for drug-induced rhabdomyolysis using real-world data from the FDA Adverse Event Reporting System (FAERS) and proposed a data-driven risk mitigation framework.

Methodology

The study conducted a retrospective pharmacovigilance study using the FAERS database from Q1 2005 to Q1 2025.
A two-stage analysis involved initial signal detection using the Reporting Odds Ratio (ROR), followed by a LASSO-optimized multivariate logistic regression to calculate adjusted odds ratios (aORs) for 54 target drugs while controlling for confounders.
Signal detection was assessed using a two-by-two contingency table to compare the frequency of rhabdomyolysis reports versus other adverse events, with a statistically significant signal defined by criteria including a lower limit of the ROR 95% confidence interval greater than 1, more than 100 reported cases, and an adjusted p-value less than 0.01 based on Fisher’s exact test with Bonferroni correction.
To quantify the independent associations between each drug and rhabdomyolysis while controlling for confounding variables, a multivariate logistic regression model was developed, implemented with LASSO regression for automated variable selection.
The optimal penalization parameter (λ) was tuned using a 10-fold cross-validation procedure to select the value (lambda.min) that yielded the maximum area under the receiver operating characteristic (ROC) curve (AUC).
Drug label analysis involved systematically reviewing official U.S. drug labels for all 54 target drugs as available on DailyMed to investigate the labeling gap.

Key Findings and Risk Stratification

**: The analysis confirmed potent DIR risks for known agents, such as gemfibrozil (aOR 173.67) and statins (lovastatin aOR 97.20, simvastatin aOR 85.12). Furthermore, strong novel risk signals were identified for drugs currently lacking warnings, most notably levetiracetam (aOR 11.02) and donepezil (aOR 8.90). A significant labeling gap was quantified, showing that 61.1% of drugs with a statistically significant DIR risk lack a corresponding warning in U.S. drug labels. The study developed a three-tiered risk stratification model based on adjusted odds ratios (aORs). Tier 1 (High Risk; aOR ≥ 10) included 14 drugs, with gemfibrozil (aOR = 173.67), lovastatin (aOR = 97.20), and simvastatin (aOR = 85.12) showing the most potent associations. Tier 2 (Moderate Risk; 5 ≤ aOR < 10) comprised 18 drugs, and Tier 3 (Lower Risk; aOR < 5) comprised the remaining 22 drugs. Demographic covariates were also significant predictors, with male sex associated with a more than two-fold increase in risk (aOR = 2.16; p < 0.001). Age exhibited a non-linear relationship, with the highest risk observed in younger adults (20-30 years group; aOR = 1.44; p < 0.001). Female patients accounted for 34.2% of cases and peak incidence was among working-age adults (18-64.9 years) at 44.0%. Clinical outcomes underscored the severity, with hospitalization emerging as the predominant consequence, affecting 50.3% of patients. Time-to-onset analysis showed that approximately 50% of cases occurred within the first 30 days of drug exposure. The highest risk period was within 30 days of treatment initiation, with diminishing incremental risk thereafter. A striking 33 (61.1%) of the 54 drugs with a confirmed significant risk did not contain any warning or mention of rhabdomyolysis in their official U.S. drug labels. The study also provided biological plausibility for novel risks, proposing mechanisms such as levetiracetam binding to SV2A and donepezil inhibiting acetylcholinesterase (AChE). The higher adjusted odds ratio for donepezil (aOR = 8.90) compared to a Canadian cohort study's weighted odds ratio (OR = 2.21) is interpreted as reflecting a risk signal enriched with more severe cases due to notoriety bias. The findings suggest that the challenge is not a lack of evidence, but the need for a more agile regulatory process to ensure drug safety information keeps pace with scientific discovery. The proposed framework provides a data-driven foundation for developing tiered clinical decision support systems, enhancing prescribing safety, and guiding future regulatory action. The study successfully replicated known risk gradients within drug classes as a positive control for the analytical method. The FDA's commitment to maintaining the FAERS database greatly enhances the understanding and safety of pharmaceutical interventions. The authors conclude that this large-scale pharmacovigilance study has successfully leveraged real-world data to quantify the rhabdomyolysis risk for 54 drugs and demographic variables. The research demonstrates the capacity of large-scale RWE analytics to pinpoint blind spots within current drug safety surveillance. The study provides a robust evidence base to catalyze updates in drug safety communication. The authors acknowledge that their findings and interpretations are their own and do not necessarily represent the views of the FDA. The FAERS database used in the study was available at https://www.fda.gov/drugs/surveillance/fdas-adverse-event-reporting-system-faers. The authors declare that no funds were received for this research. The FAERS database uesd in the study was available at https://www.fda.gov/drugs/surveillance/fdas-adverse-event-reporting-system-faers. The authors declare that no funds were received for this research. The FDA Data Fields Description (2024) is referenced in the data source description. The MedDRA MSSO (Medical Dictionary for Regulatory Activities, 2024) is referenced in the data source description. The U.S. Department of Health & Human Services DailyMed (2025) is referenced in the drug label analysis section. The FDA Framework for FDA’s Real-World Evidence Program (2024) is referenced in the discussion on regulatory action. The European Medicines Agency Pharmacovigilance Overview (2024) is referenced in the discussion on regulatory action. The FDA Adverse Event Reporting System (FAERS) is referenced in the introduction as a resource for post-marketing safety surveillance. The FDA Adverse Event Reporting System (FAERS) collects spontaneously reported cases from physicians, pharmacists, nurses, and consumers. The FDA Adverse Event Reporting System (FAERS) has been increasingly used by researchers to evaluate associations between drugs and adverse events. The FDA Adverse Event Reporting System (FAERS) is a comprehensive pharmacovigilance database that collects voluntary reports of adverse drug events from healthcare professionals, consumers, and manufacturers worldwide <ref:2511.

Improvements for AI systems

System can perform drug safety risk quantification using real-world data by applying a two-stage analysis involving initial signal detection using the Reporting Odds Ratio (ROR) followed by a LASSO-optimized multivariate logistic regression to calculate adjusted odds ratios (aORs) for 54 target drugs.
The improved AI system can identify novel, clinically significant risks for drugs lacking official warnings, specifically highlighting signals like levetiracetam (aOR 11.02) and donepezil (aOR 8.90).
It can automatically develop a three-tiered risk stratification model to categorize drugs into Tier 1 (High Risk; aOR ≥ 10), Tier 2 (Moderate Risk; 5 ≤ aOR < 10), and Tier 3 (Lower Risk; aOR < 5) based on adjusted odds ratios.
The system can quantify the labeling gap by automatically checking official drug labels against real-world evidence, identifying that 61.1% of drugs with a statistically significant DIR risk lack a corresponding warning in U.S. drug labels.
It can incorporate biological plausibility into risk assessment by linking statistical signals to mechanisms, such as proposing a causal chain for levetiracetam: levetiracetam, through its binding to SV2A at the neuromuscular junction (NMJ) and its enhancement of cholinergic activity, may induce muscle cell over-excitation.
The AI can perform predictive temporal analysis by analyzing time-to-onset distributions, noting that Approximately 50% of cases occurred within the first 30 days of drug exposure, indicating the highest risk period is early post-initiation.

Abstract

This study aimed to systematically identify and quantify risks for drug-induced rhabdomyolysis (DIR) using real-world data and to propose an evidence-based risk mitigation framework. We conducted a retrospective pharmacovigilance study using the FDA Adverse Event Reporting System (FAERS) database from Q1 2005 to Q1 2025. A two-stage analysis involved initial signal detection using the Reporting Odds Ratio (ROR), followed by a LASSO-optimized multivariate logistic regression to calculate adjusted odds ratios (aORs) for 54 target drugs while controlling for confounders. Our analysis confirmed potent DIR risks for known agents, such as gemfibrozil (aOR 173.67) and statins (lovastatin aOR 97.20, simvastatin aOR 85.12). Crucially, we identified strong, novel risk signals for drugs currently lacking warnings, most notably levetiracetam (aOR 11.02) and donepezil (aOR 8.90). A significant "labeling gap" was quantified: 61.1% of drugs with a statistically significant DIR risk lack a corresponding warning in U.S. drug labels. We subsequently developed a three-tiered risk stratification model. The proposed framework provides a data-driven foundation for developing tiered clinical decision support systems, enhancing prescribing safety, and guiding future regulatory action to bridge the identified evidence-to-labeling gap.