Drug-induced rhabdomyolysis: from systems pharmacology analysis to biochemical flux

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Fluvastatin-induced myofibrillar damage is associated with elevated ROS, and impaired fatty acid oxidation, and is preceded by mitochondrial morphological changes

Previously, we showed that fluvastatin treatment induces myofibrillar damage and mitochondrial phenotypes in the skeletal muscles of Drosophila. However, the sequential occurrence of mitochondrial phenotypes and myofibril damage remains elusive. To address this, we treated flies with fluvastatin for two and five days and examined their thorax flight muscles using confocal microscopy. In the two-day fluvastatin group, compared to the control, thorax flight muscles exhibited mitochondrial morphological changes, including fragmentation, rounding up and reduced content, while myofibrils remained organized in parallel. In the five-day fluvastatin treatment, not only did mitochondrial morphological changes become more pronounced, but myofibrils became severely disorganized with significantly increased thickness and spacing, along with myofilament abnormalities, suggesting myofibril damage. These findings suggest that fluvastatin-induced mitochondrial changes precede myofibril damage. Moreover, in the five-day fluvastatin group, the mitochondria demonstrated elevated H2O2 and impaired fatty acid oxidation compared to the control group, indicating potential mitochondrial dysfunction. Surprisingly, knocking down Hmgcr (Drosophila homolog of HMGCR) showed normal mitochondrial respiration in all parameters compared to controls or five-day fluvastatin treatment, which suggests that fluvastatin-induced mitochondrial dysfunction might be independent of Hmgcr inhibition. These results provide insights into the sequential occurrence of mitochondria and myofibril damage in statin-induced myopathy for future studies.

Exploring the association between selective serotonin reuptake inhibitors and rhabdomyolysis risk based on the FDA pharmacovigilance database

Rhabdomyolysis is a syndrome potentially fatal and has been associated with selective serotonin reuptake inhibitors (SSRIs) treatment in a few case reports. Herein, we purpose to establish the correlation between SSRIs use and rhabdomyolysis using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. We conducted an analysis on reports that were submitted to the FAERS database during the period between January 1, 2004, and December 31, 2022. Four algorithms, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), were employed to quantify the signals of rhabdomyolysis associated with SSRIs. In total, 16,011,277 non-duplicated reports were obtained and analyzed. Among 33,574 reports related to rhabdomyolysis, SSRIs were classified as primary suspected drug in 889 cases. Disproportionality analysis identified a positive signal between rhabdomyolysis and SSRIs (ROR: 2.86, 95% CI 2.67-3.05; PRR: 2.84, χ²: 1037.16; IC_0.25 = 1.39; EBGM_0.5 = 2.64). Among six SSRIs, fluvoxamine had the strongest signal (ROR: 11.64, 95% CI 8.00-16.93; PRR: 11.38, χ²: 265.51; IC_0.25 = 2.41; EBGM_0.5 = 8.31), whereas no significant signal of rhabdomyolysis was detected for paroxetine (ROR: 1.83, 95% CI 1.55-2.15; PRR: 1.82, χ²: 53.82; IC_0.25 = 0.73; EBGM_0.5 = 1.59). After excluding cases co-administered with statins, the signal of rhabdomyolysis associated with SSRIs remains significant. Our analysis reveals that there are differences in safety signals among six SSRIs in respect to the risk of rhabdomyolysis, with fluvoxamine displaying the highest risk signal, while paroxetine did not show a significant signal. Given the potentially lethal nature of rhabdomyolysis, healthcare professionals should inform patients of the potential risk of rhabdomyolysis associated with SSRIs prior to initiating treatment.

Combating Viral Diseases in the Era of Systems Medicine

Viruses can cause many diseases resulting in disabilities and death. Fortunately, advances in systems medicine enable the development of effective therapies for treating viral diseases, of vaccines to prevent viral infections, as well as of diagnostic tools to mitigate the risk and reduce the death toll. Characterizing the SARS-CoV-2 gene sequence and the role of its spike protein in infection informs development of small molecule drugs, antibodies, and vaccines to combat infection and complication, as well as to end the pandemic. Drug repurposing of small molecule drugs is a viable strategy to combat viral diseases; the key concepts include (1) linking a drug candidate's pharmacological network to its pharmacodynamic response in patients; (2) linking a drug candidate's physicochemical properties to its pharmacokinetic characteristics; and (3) optimizing the safe and effective dosing regimen within its therapeutic window. Computational integration of drug-induced signaling pathways with clinical outcomes is useful to inform selection of potential drug candidates with respect to safety and effectiveness. Key pharmacokinetic and pharmacodynamic principles for computational optimization of drug development include a drug candidate's Cminss/IC95 ratio, pharmacokinetic characteristics, and systemic exposure-response relationship, where Cminss is the trough concentration following multiple dosing. In summary, systems medicine approaches play a vital role in global success in combating viral diseases, including global real-time information sharing, development of test kits, drug repurposing, discovery and development of safe, effective therapies, detection of highly transmissible and deadly variants, and development of vaccines.

Statin-Associated Myopathy: Emphasis on Mechanisms and Targeted Therapy

Hyperlipidemia is a major risk factor for cardiovascular morbidity and mortality. Statins are the first-choice therapy for dyslipidemias and are considered the cornerstone of atherosclerotic cardiovascular disease (ASCVD) in both primary and secondary prevention. Despite the statin-therapy-mediated positive effects on cardiovascular events, patient compliance is often poor. Statin-associated muscle symptoms (SAMS) are the most common side effect associated with treatment discontinuation. SAMS, which range from mild-to-moderate muscle pain, weakness, or fatigue to potentially life-threatening rhabdomyolysis, are reported by 10% to 25% of patients receiving statin therapy. There are many risk factors associated with patient features and hypolipidemic agents that seem to increase the risk of developing SAMS. Due to the lack of a "gold standard", the diagnostic test for SAMS is based on a clinical criteria score, which is independent of creatine kinase (CK) elevation. Mechanisms that underlie the pathogenesis of SAMS remain almost unclear, though a high number of risk factors may increase the probability of myotoxicity induced by statin therapy. Some of these, related to pharmacokinetic properties of statins and to concomitant therapies or patient characteristics, may affect statin bioavailability and increase vulnerability to high-dose statins.

Risk factors and future directions for preventing and diagnosing exertional rhabdomyolysis

Exertional rhabdomyolysis may occur when an individual is subjected to strenuous physical exercise. It is occasionally associated with myoglobinuria (i.e. "cola-colored" urine) alongside muscle pain and weakness. The pathophysiology of exertional rhabdomyolysis involves striated muscle damage and the release of cellular components into extracellular fluid and bloodstream. This can cause acute renal failure, electrolyte abnormalities, arrhythmias and potentially death. Exertional rhabdomyolysis is observed in high-performance athletes who are subjected to intense, repetitive and/or prolonged exercise but is also observed in untrained individuals and highly trained or elite groups of military personnel. Several risk factors have been reported to increase the likelihood of the condition in athletes, including: viral infection, drug and alcohol abuse, exercise in intensely hot and humid environments, genetic polymorphisms (e.g. sickle cell trait and McArdle disease) and epigenetic modifications. This article reviews several of these risk factors and proposes screening protocols to identify individual susceptibility to exertional rhabdomyolysis as well as the relevance of proteomics for the evaluation of potential biomarkers of muscle damage.

Statin-Related Myotoxicity: A Comprehensive Review of Pharmacokinetic, Pharmacogenomic and Muscle Components

Statins are a cornerstone in the pharmacological prevention of cardiovascular disease. Although generally well tolerated, a small subset of patients experience statin-related myotoxicity (SRM). SRM is heterogeneous in presentation; phenotypes include the relatively more common myalgias, infrequent myopathies, and rare rhabdomyolysis. Very rarely, statins induce an anti-HMGCR positive immune-mediated necrotizing myopathy. Diagnosing SRM in clinical practice can be challenging, particularly for mild SRM that is frequently due to alternative aetiologies and the nocebo effect. Nevertheless, SRM can directly harm patients and lead to statin discontinuation/non-adherence, which increases the risk of cardiovascular events. Several factors increase systemic statin exposure and predispose to SRM, including advanced age, concomitant medications, and the nonsynonymous variant, rs4149056, in SLCO1B1, which encodes the hepatic sinusoidal transporter, OATP1B1. Increased exposure of skeletal muscle to statins increases the risk of mitochondrial dysfunction, calcium signalling disruption, reduced prenylation, atrogin-1 mediated atrophy and pro-apoptotic signalling. Rare variants in several metabolic myopathy genes including CACNA1S, CPT2, LPIN1, PYGM and RYR1 increase myopathy/rhabdomyolysis risk following statin exposure. The immune system is implicated in both conventional statin intolerance/myotoxicity via LILRB5 rs12975366, and a strong association exists between HLA-DRB1*11:01 and anti-HMGCR positive myopathy. Epigenetic factors (miR-499-5p, miR-145) have also been implicated in statin myotoxicity. SRM remains a challenge to the safe and effective use of statins, although consensus strategies to manage SRM have been proposed. Further research is required, including stringent phenotyping of mild SRM through N-of-1 trials coupled to systems pharmacology omics- approaches to identify novel risk factors and provide mechanistic insight.

A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy

Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

A scientist engineer's contribution to therapeutic discovery and development

An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging.

Drug-Induced Rhabdomyolysis Atlas (DIRA) for idiosyncratic adverse drug reaction management

Drug-induced rhabdomyolysis (DIR) is an idiosyncratic and fatal adverse drug reaction (ADR) characterized in severe muscle injuries accompanied by multiple-organ failure. Limited knowledge regarding the pathophysiology of rhabdomyolysis is the main obstacle to developing early biomarkers and prevention strategies. Given the lack of a centralized data resource to curate, organize, and standardize widespread DIR information, here we present a Drug-Induced Rhabdomyolysis Atlas (DIRA) that provides DIR-related information, including: a classification scheme for DIR based on drug labeling information; postmarketing surveillance data of DIR; and DIR drug property information. To elucidate the utility of DIRA, we used precision dosing, concomitant use of DIR drugs, and predictive modeling development to exemplify strategies for idiosyncratic ADR (IADR) management.

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

Computed Biological Relations among Five Select Treatment-Related Organ/Tissue Toxicities

Drug toxicity presents a major challenge in drug development and patient care. We set to build upon previous works regarding select drug-induced toxicities to find common patterns in the mode of action of the drugs associated with these toxicities. In particular, we focused on five disparate organ toxicities, peripheral neuropathy (PN), rhabdomyolysis (RM), Stevens-Johnson syndrome/toxic epidermal necrosis (SJS/TEN), lung injury (LI), and heart contraction-related cardiotoxicity (CT), and identified biological commonalities between and among the toxicities in terms of pharmacological targets and nearest neighbors (indirect effects) using the hyper-geometric test and a distance metric of Spearman correlation. There were 20 significant protein targets associated with two toxicities and 0 protein targets associated with three or more toxicities. Per Spearman distance, PN was closest to SJS/TEN compared to other pairs, whereas the pairs involving RM were more different than others excluding RM. The significant targets associated with RM outnumbered those associated with every one of the other four toxicities. Enrichment analysis of drug targets that are expressed in corresponding organ/tissues determined proteins that should be avoided in drug discovery. The identified biological patterns emerging from the mode of action of these drugs are statistically associated with these serious toxicities and could potentially be used as predictors for new drug candidates. The predictive power and usefulness of these biological patterns will increase with the database of these five toxicities. Furthermore, extension of our approach to all severe adverse reactions will produce useful biological commonalities for reference in drug discovery and development.

Statin-Induced Myopathy Is Associated with Mitochondrial Complex III Inhibition

Cholesterol-lowering statins effectively reduce the risk of major cardiovascular events. Myopathy is the most important adverse effect, but its underlying mechanism remains enigmatic. In C2C12 myoblasts, several statin lactones reduced respiratory capacity and appeared to be strong inhibitors of mitochondrial complex III (CIII) activity, up to 84% inhibition. The lactones were in general three times more potent inducers of cytotoxicity than their corresponding acid forms. The Qo binding site of CIII was identified as off-target of the statin lactones. These findings could be confirmed in muscle tissue of patients suffering from statin-induced myopathies, in which CIII enzyme activity was reduced by 18%. Respiratory inhibition in C2C12 myoblasts could be attenuated by convergent electron flow into CIII, restoring respiration up to 89% of control. In conclusion, CIII inhibition was identified as a potential off-target mechanism associated with statin-induced myopathies.

Parsing interindividual drug variability: an emerging role for systems pharmacology

There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype-drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug-specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically-orientated systems biology and pharmacometrics. Pharmacologically-orientated systems biology utilizes high throughput omics technologies, including next-generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data-derived models of sufficient detail will be required to help realize the goal of precision medicine.

Systems pharmacological analysis of drugs inducing stevens-johnson syndrome and toxic epidermal necrolysis

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are serious cutaneous adverse reactions. We mined the approved labels in Drugs@FDA, identified the SJS/TEN list of 259 small molecular drugs and biologics, and conducted systems pharmacological network analyses. Pharmacological network analysis revealed that drugs with treatment-related SJS and/or TEN are pharmacologically diverse and that the largest subnetwork is associated with antiepileptic drugs and their pharmacological targets. Our pharmacological network analysis identified CTNNB1 [catenin (cadherin-associated protein), beta 1, 88 kDa] as a significant intermediator. This protein is involved in maintaining the functional integrity of the epithelium through regulating cell growth and adhesion between cells in various organs, including the skin. Leveraging a publicly accessible genome-wide transcriptional expression database, we found that human leukocyte antigen-related (HLA) genes were significantly perturbed by various SJS/TEN-inducing drugs. Notably, carbamazepine (CBZ) perturbed several HLA genes, among which HLA-DQB1*0201 was reportedly shown to be associated with CBZ-induced SJS/TEN in caucasians. In short, systems analysis by leveraging a publicly accessible knowledge base and databases could produce meaningful results for further mechanistic investigation. Our study sheds light on the utility of systems pharmacology analysis for gaining insight into clinical drug toxicity.