Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules.

Generative artificial intelligence algorithms have shown to be successful in exploring large chemical spaces and designing novel and diverse molecules. There has been considerable interest in developing predictive models using artificial intelligence for drug-like properties, which can potentially reduce the late-stage attrition of drug candidates or predict the properties of novel AI-designed molecules. Concurrently, it is important to understand the contribution of functional groups toward these properties and modify them to obtain property-optimized lead compounds. As a result, there is an increasing interest in the development of explainable property prediction models. However, current explainable approaches are mostly atom-based, where, often, only a fraction of a fragment is shown to be significant. To address the above challenges, we have developed a novel domain-aware molecular fragmentation approach termed post-processing of BRICS (pBRICS), which can fragment small molecules into their functional groups. Multitask models were developed to predict various properties, including the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The fragment importance was explained using the gradient-weighted class activation mapping (Grad-CAM) approach. The method was validated on data sets of experimentally available matched molecular pairs (MMPs). The explanations from the model can be useful for medicinal chemists to identify the fragments responsible for poor drug-like properties and optimize the molecule. The explainability approach was also used to identify the reason behind false positive and false negative MMP predictions. Based on evidence from the existing literature and our analysis, some of these mispredictions were justified. We propose that the quantity, quality, and diversity of the training data will improve the accuracy of property prediction algorithms for novel molecules.

Pediatric Endocrinology Milestones 2.0-guide to their implementation.

The Milestones were initiated by the Accreditation Council for Graduate Medical Education (ACGME) to provide a framework for monitoring a trainee's progression throughout residency/fellowship. The Milestones describe stepwise skill progression through six core domains of clinical competency: Patient Care, Medical Knowledge, Interpersonal and Communication Skills, Practice-based Learning and Improvement, Professionalism, and Systems-based Practice. Since their introduction in 2013, several barriers to implementation have emerged. Thus, the ACGME launched the Milestones 2.0 project to develop updated specialty-specific milestones. The Pediatric Endocrinology Milestones 2.0 project aimed to improve upon Milestones 1.0 by addressing common limitations, providing resources for faculty to easily incorporate milestones into their assessment of trainees, and adding sub-competencies in health disparities, patient safety, and physician well-being.This paper reviews the development of the Pediatric Endocrinology Milestones 2.0 including the major changes from Milestones 1.0, development of the Supplemental Guide, and how Milestones 2.0 can be applied at the program level. Although use of the Milestones are required only for ACGME programs, the tools provided in Milestones 2.0 are applicable to fellowship programs worldwide.

Bone Mass Accrual in First Six Months of Life: Impact of Maternal Diabetes, Infant Adiposity, and Cord Blood Adipokines.

The perinatal period is a time of substantial bone mass accrual with many factors affecting long-term bone mineralization. Currently it is unclear what effect maternal gestational/type 2 diabetes has on infant bone mass accrual. This is a prospective study of offspring of Native American and Hispanic mothers with normoglycemia (n = 94) and gestational diabetes or type 2 diabetes (n = 64). Infant anthropometrics were measured at birth, 1, and 6 months of age. Cord blood leptin, high-molecular weight adiponectin (HMWA), pigment epithelium-derived factor (PEDF), vascular epithelium growth factor (VEGF), endoglin, and C-peptide were measured by ELISA. Infants had bone mineral density measurement at 1 month or/and 6 months of age using dual-energy x-ray absorptiometry scan. Mothers with diabetes were older (31 ± 6 years vs 25 ± 4 years) and had higher pre-pregnancy BMI (32.6 ± 5.8 vs 27.2 ± 6.4 kg/m2) than control mothers. Mean HbA1C of mothers with diabetes was 5.9 ± 1.0% compared to 5.1 ± 0.3% in controls early in pregnancy. Infants born to mothers with diabetes (DM-O) were born at a slightly lower gestational age compared to infants born to control mothers (Con-O). There was no difference in total body less head bone mineral content (BMC) or bone mineral density (BMD) between DM-O and Con-O. For both groups together, bone area, BMD, and BMC tracked over the first 6 months of life (r: 0.56, 0.38, and 0.48, respectively). Percent fat was strongly and positively correlated with BMC at 1 month of age (r = 0.44; p < 0.001) and BMC at both 1 and 6 months of age correlated strongly with birth weight. There were no associations between infant bone mass and cord blood leptin, PEDF, or VEGF, while C-peptide had a significant correlation with BMC at 1 and 6 months only in DM-O (p = 0.01 and 0.03, respectively). Infants born to mothers with well-controlled gestational/type 2 diabetes have normal bone mass accrual. Bone mineral content during this time is highly correlated with indices of infant growth and the association of bone mineral indices with percent body fat suggests that bone-fat crosstalk is operative early in life.

An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System.

The aim of drug design and development is to produce a drug that can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multistep process where different parameters such as activity and physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method that can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which act as an additional challenge for CNS drug delivery. The contribution of each parameter toward molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.

De Novo Structure-Based Drug Design Using Deep Learning.

In recent years, deep learning-based methods have emerged as promising tools for de novo drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties. Although there have been attempts to develop alternative ways to design target-specific ligand data sets, availability of such data sets remains a challenge while designing molecules against novel target proteins. In this work, we propose a deep learning-based method, where the knowledge of the active site structure of the target protein is sufficient to design new molecules. First, a graph attention model was used to learn the structure and features of the amino acids in the active site of proteins that are experimentally known to form protein-ligand complexes. Next, the learned active site features were used along with a pretrained generative model for conditional generation of new molecules. A bioactivity prediction model was then used in a reinforcement learning framework to optimize the conditional generative model. We validated our method against two well-studied proteins, Janus kinase 2 (JAK2) and dopamine receptor D2 (DRD2), where we produce molecules similar to the known inhibitors. The graph attention model could identify the probable key active site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.

Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning.

In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug design. Yet, these methods face the problem of data scarcity while designing drugs against a novel target. In this work, the potential of deep learning and molecular modeling approaches was leveraged to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of the homologues of the target protein were screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning was used to learn the features of the target-specific dataset. A deep predictive model was utilized to predict the docking scores of newly designed molecules. Both these models were combined using reinforcement learning to design new chemical entities with an optimized docking score. The pipeline was validated by designing inhibitors against the human JAK2 protein, where none of the existing JAK2 inhibitors were used for training. The ability of the method to reproduce existing molecules from the validation dataset and design molecules with better binding energy demonstrates the potential of the proposed approach.

Severe growth failure associated with a novel heterozygous nonsense mutation in the GHR transmembrane domain leading to elevated growth hormone binding protein.

OBJECTIVE: To report a novel mutation in GHR and to characterize a novel mechanism of nonclassical growth hormone insensitivity. CONTEXT: Laron syndrome (LS) is a well-described disorder of growth hormone insensitivity due to mutations in the growth hormone receptor (GHR) that leads to short stature. Biochemically, LS patients classically have elevated levels of growth hormone (GH), but low levels of insulin-like growth factor (IGF)-1, IGF binding protein (IGFBP)-3 and GH binding protein (GHBP). DESIGN: Case presentation with in vitro functional studies. PATIENTS: A young male Caucasian child with short stature was found to have growth hormone insensitivity manifested by elevated levels of GH and GHBP. MEASUREMENTS: Growth hormone stimulation tests revealed baseline GH level of 20.9 µg/L and maximum stimulated GH level of 52.7 µg/L and GHBP level of 4868 pmol/L. GHR gene sequencing revealed a novel heterozygous nonsense mutation (c.800G > A, p.Trp267*) in the transmembrane domain of the receptor. Immunoblot analysis of transfected GHR p.Trp267* in HEK293 revealed inhibition of GH-induced STAT5 signalling that was overcome with increasing doses of recombinant human GH. RESULTS: Using an in vitro model, we show that elevated levels of GHBP inhibit the action of GH. Furthermore, our studies demonstrate that this inhibition by GHBP can be overcome by increasing doses of recombinant human GH. CONCLUSIONS: To our knowledge, this is the first study to demonstrate in vitro that elevated levels of GHBP attenuate the effect of GH and inhibit GH-induced signalling, thereby leading to short stature. Though this inhibition was overcome in vitro with supraphysiologic doses of GH, significantly above endogenously available GH, it remains to be seen whether such an effect can be replicated in vivo.

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Mechanism of drug resistance in HIV-1 protease subtype C in the presence of Atazanavir.

AIDS is one of the deadliest diseases in the history of humankind caused by HIV. Despite the technological development, curtailing the viral infection inside human host still remains a challenge. Therapies such as HAART uses a combination of drugs to inhibit the viral activity. One of the important targets includes HIV protease and inhibiting its activity will minimize the production of mature structural proteins. However, the genetic diversity and the occurrence of drug resistant mutations adds complexity to effective drug design. In this study, we aimed at understanding the drug binding mechanism of one such subtype, namely subtype C and its insertion variant L38HL. We performed multiple molecular dynamics simulations along with binding free energy analysis of wild-type and L38HL bound to Atazanavir (ATV). From the analysis, we revealed that the insertion alters the hydrogen bond and hydrophobic interaction networks. The alterations in the interaction networks increase flexibility at the hinge-fulcrum interface. Further, the effects of these changes affect flap tip curling. Moreover, the changes in the hinge-fulcrum-cantilever interface alters the concerted motion of the functional regions leading to change in the direction of flap movement thus causing a subtle change in the active site volume. Additionally, formation of intramolecular hydrogen bonds in the ATV docked to L38HL restricted the movement of R1 and R2 groups thereby altering the interactions. Overall, the changes in the flexibility of flap together with the changes in the active site volume and compactness of the ligand provide insights for increased binding affinity of ATV with L38HL.

Invited Mini Review Metabolic Bone Disease of Prematurity: Overview and Practice Recommendations.

Metabolic bone disease of prematurity (MBDP) is defined by undermineralization of the preterm infant skeleton arising from inadequate prenatal and postnatal calcium (Ca) and phosphate (PO4) accretion. Severe MBDP can be associated with rickets and fractures. Despite advances in neonatal nutrition, MBDP remains prevalent in premature infants due to inadequate mineral accretion ex-utero. There also remain significant knowledge gaps regarding best practices for monitoring and treatment of MBDP among neonatologists and pediatric endocrinologists. Preventing and treating MBDP can prevent serious consequences including rickets or pathologic fractures. Postnatal monitoring to facilitate early recognition of MBDP is best done by first-tier laboratory screening by measuring serum calcium, phosphorus, and alkaline phosphatase to identify infants at risk. If these labs are abnormal, further studies including assessing parathyroid hormone and/or tubular resorption of phosphate can help differentiate between Ca and PO4 deficiency as primary etiologies to guide appropriate treatment with mineral supplements. Additional research into optimal mineral supplementation for the prevention and treatment of MBDP is needed to improve long-term bone health outcomes and provide a fuller evidence base for future treatment guidelines.

R-SIM: A Database of Binding Affinities for RNA-small Molecule Interactions.

Ribonucleic acids (RNAs) are involved in a multitude of crucial cellular functions by acting as a central conduit for information transfer. Due to their essential and versatile functional roles in the cell, RNAs have also been implicated in multiple disease conditions of therapeutic relevance including cancers, bacterial and viral infections and neurodegenerative disorders. Recently, several approaches have emerged to tap into the potentially unexplored regions of the druggable genome, which refers to the genes and gene products that are focused during drug development. For example, considering RNAs as viable alternative therapeutic targets for drug development can potentially expand the range of therapeutic targets. Consequently, the availability of adequate binding affinity measurements for RNA-small molecule interactions is essential to understand target selectivity and design more potent RNA-targeting drug-like molecules. To facilitate this growing need, we have curated a database of experimentally validated RNA-small molecule interactions, called RNA-Small molecule Interaction Miner (R-SIM). Each entry in R-SIM provides comprehensive information on sequence, structure and classification of the RNA target, various physicochemical properties of the small molecule, binding affinity value and corresponding experimental conditions, three-dimensional structure (experimental or modelled) of the RNA-small molecule complex, and the literature source for the data. It also provides a user-friendly web interface with several options for search, display, sorting, visualization, download and upload of the data. R-SIM is freely available at: https://web.iitm.ac.in/bioinfo2/R_SIM/index.html. We envisage that R-SIM has several potential applications in understanding and accelerating the development of novel RNA-targeted small molecule therapeutics.

AutoPLP: A Padlock Probe Design Pipeline for Zoonotic Pathogens.

Emergence of novel zoonotic infections among the human population has increased the burden on global healthcare systems to curb their spread. To meet the evolutionary agility of pathogens, it is essential to revamp the existing diagnostic methods for early detection and characterization of the pathogens at the molecular level. Padlock probes (PLPs), which can leverage the power of isothermal nucleic acid amplification techniques (NAAT) such as rolling circle amplification (RCA), are known for their high sensitivity and specificity in detecting a diverse pathogen panel of interest. However, due to the complexity involved in deciding the target regions for PLP design and the need for optimization of multiple experimental parameters, the applicability of RCA has been limited in point-of-care testing for pathogen detection. To address this gap, we have developed a novel and integrated PLP design pipeline named AutoPLP, which can automate the probe design process for a diverse pathogen panel of interest. The pipeline is composed of three modules which can perform sequence data curation, multiple sequence alignment, conservation analysis, filtration based on experimental parameters (Tm, GC content, and secondary structure formation), and in silico probe validation via potential cross-hybridization check with host genome. The modules can also take into account the backbone and restriction site information, appropriate combinations of which are incorporated along with the probe arms to design a complete probe sequence. The potential applications of AutoPLP are showcased through the design of PLPs for the detection of rabies virus and drug-resistant strains of Mycobacterium tuberculosis.

Understanding Drug Resistance of Wild-Type and L38HL Insertion Mutant of HIV-1 C Protease to Saquinavir.

Acquired immunodeficiency syndrome (AIDS) is one of the most challenging infectious diseases to treat on a global scale. Understanding the mechanisms underlying the development of drug resistance is necessary for novel therapeutics. HIV subtype C is known to harbor mutations at critical positions of HIV aspartic protease compared to HIV subtype B, which affects the binding affinity. Recently, a novel double-insertion mutation at codon 38 (L38HL) was characterized in HIV subtype C protease, whose effects on the interaction with protease inhibitors are hitherto unknown. In this study, the potential of L38HL double-insertion in HIV subtype C protease to induce a drug resistance phenotype towards the protease inhibitor, Saquinavir (SQV), was probed using various computational techniques, such as molecular dynamics simulations, binding free energy calculations, local conformational changes and principal component analysis. The results indicate that the L38HL mutation exhibits an increase in flexibility at the hinge and flap regions with a decrease in the binding affinity of SQV in comparison with wild-type HIV protease C. Further, we observed a wide opening at the binding site in the L38HL variant due to an alteration in flap dynamics, leading to a decrease in interactions with the binding site of the mutant protease. It is supported by an altered direction of motion of flap residues in the L38HL variant compared with the wild-type. These results provide deep insights into understanding the potential drug resistance phenotype in infected individuals.

De novo design of anti-tuberculosis agents using a structure-based deep learning method.

Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.

An interpretable machine learning model for selectivity of small-molecules against homologous protein family.

Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.

Comorbidities and quality of life in Australian men and women with diagnosed and undiagnosed high-risk obstructive sleep apnea.

STUDY OBJECTIVES: In a population-based survey, we determined sex differences in health profiles and quality of life between individuals who have a confirmed diagnosis of obstructive sleep apnea (OSA) and those who are at high risk of OSA yet remain undiagnosed. METHODS: An online survey of Australian adults ≥ 18 years (n = 3,818) identified participants with self-reported diagnosed OSA (n = 460) or high-risk, undiagnosed OSA (OSA50 score ≥ 5, n = 1,015). Ever-diagnosed comorbidities, sociodemographics, and quality of life (EQ-5D-5L, Functional Outcomes of Sleep Questionnaire-10) were assessed. RESULTS: Women were more frequently represented in the high-OSA-risk group compared with those with diagnosed OSA (55.5%, n = 563, versus 43%, n = 198; P < .001). In sex-specific logistic regression analyses, diagnosed OSA was associated with increased likelihoods of ≥ 1 cardiovascular condition (odds ratio: 3.0; 95% confidence interval: 2.0-4.5), hypertension (1.9; 1.3-2.8), gout (1.8; 1.1-2.9), and chronic obstructive pulmonary disease (3.8; 2.1-6.9) in men. In women, an association with asthma (2.0; 1.3-3.0) was seen. Diabetes, arthritis, mental health conditions (ever-diagnosed), and all EQ-5D-5L dimensions were associated with an OSA diagnosis regardless of sex, except for EQ-5D-5L anxiety/depression, which was only associated with an OSA diagnosis in women. A diagnosis of OSA was associated with sleepiness-related impairment (lowest quartile of Functional Outcomes of Sleep Questionnaire-10) in men (1.6; 1.01-2.5) and women (2.2; 1.4-3.6). CONCLUSIONS: Sex-specific health conditions may drive diagnosis of OSA; however, clinical suspicion of OSA needs to be increased in men and women. The impaired quality of life and persistent sleepiness in participants with diagnosed OSA observed at a population level requires greater clinical attention. CITATION: Krishnan S, Chai-Coetzer CL, Grivell N, et al. Comorbidities and quality of life in Australian men and women with diagnosed and undiagnosed high-risk obstructive sleep apnea. J Clin Sleep Med. 2022;18(7):1757-1767.

Pharmacologic Weight Management in the Era of Adolescent Obesity.

CONTEXT: Pediatric obesity is a serious health problem in the United States. While lifestyle modification therapy with dietary changes and increased physical activity are integral for the prevention and treatment of mild to moderate obesity in youth, only a modest effect on sustained weight reduction is observed in children and young adults with severe obesity. This underscores the need for additional evidence-based interventions for children and adolescents with severe obesity, including pharmacotherapy, before considering invasive procedures such as bariatric surgery. EVIDENCE ACQUISITION: This publication focuses on recent advances in pharmacotherapy of obesity with an emphasis on medications approved for common and rarer monogenic forms of pediatric obesity. EVIDENCE SYNTHESIS: We review medications currently available in the United States, both those approved for weight reduction in children and "off-label" medications that have a broad safety margin. CONCLUSION: It is intended that this review will provide guidance for practicing clinicians and will encourage future exploration for successful pharmacotherapy and other interventions for obesity in youth.

Statins Induce Locomotion and Muscular Phenotypes in Drosophila melanogaster That Are Reminiscent of Human Myopathy: Evidence for the Role of the Chloride Channel Inhibition in the Muscular Phenotypes.

The underlying mechanisms for statin-induced myopathy (SIM) are still equivocal. In this study, we employ Drosophila melanogaster to dissect possible underlying mechanisms for SIM. We observe that chronic fluvastatin treatment causes reduced general locomotion activity and climbing ability. In addition, transmission microscopy of dissected skeletal muscles of fluvastatin-treated flies reveals strong myofibrillar damage, including increased sarcomere lengths and Z-line streaming, which are reminiscent of myopathy, along with fragmented mitochondria of larger sizes, most of which are round-like shapes. Furthermore, chronic fluvastatin treatment is associated with impaired lipid metabolism and insulin signalling. Mechanistically, knockdown of the statin-target Hmgcr in the skeletal muscles recapitulates fluvastatin-induced mitochondrial phenotypes and lowered general locomotion activity; however, it was not sufficient to alter sarcomere length or elicit myofibrillar damage compared to controls or fluvastatin treatment. Moreover, we found that fluvastatin treatment was associated with reduced expression of the skeletal muscle chloride channel, ClC-a (Drosophila homolog of CLCN1), while selective knockdown of skeletal muscle ClC-a also recapitulated fluvastatin-induced myofibril damage and increased sarcomere lengths. Surprisingly, exercising fluvastatin-treated flies restored ClC-a expression and normalized sarcomere lengths, suggesting that fluvastatin-induced myofibrillar phenotypes could be linked to lowered ClC-a expression. Taken together, these results may indicate the potential role of ClC-a inhibition in statin-associated muscular phenotypes. This study underlines the importance of Drosophila melanogaster as a powerful model system for elucidating the locomotion and muscular phenotypes, promoting a better understanding of the molecular mechanisms underlying SIM.