Drug Repurposing Research Articles

Repurposing the antipsychotic drug penfluridol for cancer treatment (Review).

Cancer is one of the most prevalent diseases and the leading cause of death worldwide. Despite the improved survival rates of cancer in recent years, the current available treatments often face resistance and side effects. Drug repurposing represents a cost‑effective and efficient alternative to cancer treatment. Recent studies revealed that penfluridol (PF), an antipsychotic drug, is a promising anticancer agent. In the present study, a scoping review was conducted to ascertain the anticancer properties of PF. For this, a literature search was performed using the Scopus, PubMed and Web of Science databases with the search string 'penfluridol' AND 'cancer'. A total of 23 original articles with in vivo and/or in vitro studies on the effect of PF on cancer were included in the scoping review. The outcome of the analysis demonstrated the anticancer potential of PF. PF significantly inhibited cell proliferation, metastasis and invasion while inducing apoptosis and autophagy in vivo and across a spectrum of cancer cell lines, including breast, lung, pancreatic, glioblastoma, gallbladder, bladder, oesophageal, leukaemia and renal cancers. However, research on PF derivatives with high anticancer activities and reduced neurological side effects may be necessary.

Computer-Aided Identification and Design of Ligands for Multi-Targeting Inhibition of a Molecular Acute Myeloid Leukemia Network.

Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission. Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously. A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3-8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties. Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target-drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML.

Target Fisher: A Consensus Structure-Based Target Prediction Tool, and its Application in the Discovery of Selective MAO-B Inhibitors.

In this work we introduce Target Fisher, a consensus structure-based target prediction tool that integrates molecular docking and machine learning with the aim to aid in the identification of potential biological targets and the optimization of the use of bioassays. Target Fisher uses per-residue energy decomposition profiles extracted from docking poses as fingerprints to train target-specific machine learning models. It provides predictions for a curated set of 37 protein targets, covering a diverse range of biological entities, and offers a user-friendly interface accessible via a web server (https://gqc.quimica.unlp.edu.ar/targetfisher/). In this sense, Target Fisher is a valuable tool to aid organic and medicinal chemistry groups in target identification, drug discovery and drug repurposing. As a case study, we demonstrate the efficacy of Target Fisher by screening a small library of assorted natural products for targets relevant to neurodegenerative diseases, which resulted in the identification and experimental validation of selective inhibitors of monoamine oxidase B (MAO-B).

An explainable foundation model for drug repurposing.

An explainable foundation model for drug repurposing.

In silico drug repurposing approach to predict most effective HAART for HIV drug resistance variants prevalent in the Indian HIV-positive population.

HIV epidemics still exist as a major global public health burden, especially in middle- and low-income countries. Given the lack of approved vaccines, antiretroviral therapy (ART) remains the primary approach to reduce the mortality and morbidity linked to this disease. Effective treatment for HIV-1 requires the simultaneous administration of multiple drugs. However, the virus can show resistance to antiretroviral drugs, resulting in treatment failure. Therefore, this study focused on assessing the prevalence of mutations within the Indian HIV-positive population. After assessing the data, we intended to identify the most effective highly active ART (HAART) regimens for individuals with drug-resistant variants. Furthermore, our analysis revealed a spectrum of HIV mutations, with varying effects on protein stability. The significance of this analysis lies in its potential to optimize HAART selection for HIV-positive individuals by accounting for both prevalence and stability-altering mutations. By considering mutation effects on protein stability, we can modify treatment regimens, increasing the likelihood of therapy success and diminishing the risk of resistance. Moreover, this study contributes to the broader field of drug repurposing, offering insights into the rational design of antiretroviral therapies.

Drug repurposing and phenotypic screening: innovative strategies for treating ultra-rare disorders.

Drug repurposing and phenotypic screening: innovative strategies for treating ultra-rare disorders.

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and preprocessing of data on ingredients and targets, we employ an innovative autoencoder framework to fuse multiple drug (target) topological similarity matrices. Additionally, we employ denoising blocks, sparse blocks, and stacked blocks to extract crucial features from the similarity matrix, reducing noise and enhancing accuracy across diverse datasets. Our results indicate that the CWI-DTI model shows improved performance compared to several existing state-of-the-art methods on the datasets tested in both Western and Chinese medicine databases. The findings of this study hold immense promise for advancing DTI prediction in Chinese and Western medicine, thus fostering more efficient drug discovery and repurposing endeavors. Our model is available at https://github.com/WANG-BIN-LAB/CWIDTI.

The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing.

Metronomic chemotherapy (MC), long-term continuous administration of anticancer drugs, is gaining attention as an alternative to the traditional maximum tolerated dose (MTD) chemotherapy. By combining MC with other treatments, the therapeutic efficacy is enhanced while minimizing toxicity. MC employs multiple mechanisms, making it a versatile approach against various cancers. However, drug resistance limits the long-term effectiveness of MC, necessitating ongoing development of anticancer drugs. Traditional drug discovery is lengthy and costly due to processes like target protein identification, virtual screening, lead optimization, and safety and efficacy evaluations. Drug repurposing (DR), which screens FDA-approved drugs for new uses, is emerging as a cost-effective alternative. Both experimental and computational methods, such as protein binding assays, in vitro cytotoxicity tests, structure-based screening, and several types of association analyses (Similarity-Based, Network-Based, and Target Gene), along with retrospective clinical analyses, are employed for virtual screening. This review covers the mechanisms of MC, its application in various cancers, DR strategies, examples of repurposed drugs, and the associated challenges and future directions.

A Snapshot on Clinical Trials on Zotarolimus DES: A Repurposing Drug against Myocardial Infarction

AbstractMyocardial infarction is the permanent death (necrosis) of heart muscle caused by a lack of oxygen for an extended period of time (ischemia). Since the very first percutaneous transluminal coronary angioplasty was done 40 years ago, the science of interventional cardiology has advanced dramatically. The above progression began with a balloon catheter attached to a fixed wire, advancing to bare-metal stents, first-generation drug-eluting stents (DES), second- and third-generation biodegradable polymer-based DES. Ultimately, bioabsorbable stents are still in development. Zotarolimus is a new pharmacologic therapy with a similar reprint address; for example, the antirestenosis agent sirolimus has recently been investigated as part of a stent platform combined with a biomimetic phosphorylcholine and BioLINX polymer for its biological activity, as well as its usefulness as an antiproliferative agent. Several clinical trials have been conducted to assess the safety and efficacy of the zotarolimus drug-eluting stent (Z-DES). Medtronic Medical Devices, a global leader in medical technology (United States), and Abbot Global Health Care and Research began clinical trials on the Z-DES in 2002 and will continue through 2021 (INDIA). Endeavor (E-Zes), Resolute (R-Zes), Resolute Integrity (I-Zes), and Resolute Onyx are the different forms of Z-DES that Medtronic has tested in clinical trials. In comparison to other DES, Z-DES was found to be safe in these clinical trials done across multiple nations and in a diverse demographic. This review helps understand the overall clinical trial of Z-DES.

Mechanism-free repurposing of drugs for C9orf72-related ALS/FTD using large-scale genomic data

Repeat expansions in the C9orf72 gene are the most common genetic cause of (ALS) and frontotemporal dementia (FTD). Like other genetic forms of neurodegeneration, pinpointing the precise mechanism(s) by which this mutation leads to neuronal death remains elusive, and this lack of knowledge hampers the development of therapy for C9orf72-related disease. We used an agnostic approach based on genomic data (n= 41,273 ALS and healthy samples, and n= 1,516 C9orf72 carriers) to overcome these bottlenecks. Our drug-repurposing screen, based on gene- and expression-pattern matching and information about the genetic variants influencing onset age among C9orf72 carriers, identified acamprosate, a γ-aminobutyric acid analog, as a potentially repurposable treatment for patients carrying C9orf72 repeat expansions. We validated its neuroprotective effect in cell models and showed comparable efficacy to riluzole, the current standard of care. Our work highlights the potential value of genomics in repurposing drugs insituations where the underlying pathomechanisms are inherently complex. VIDEO ABSTRACT.

Pharmacoproteogenomic approach identifies on-target kinase inhibitors for cancer drug repositioning.

Drug repositioning of approved drugs offers advantages over de novo drug development for a rare type of cancer. To efficiently identify on-target drugs from clinically successful kinase inhibitors in cancer drug repositioning, drug screening and molecular profiling of cell lines are essential to exclude off-targets. We developed a pharmacoproteogenomic approach to identify on-target kinase inhibitors, combining molecular profiling of genomic features and kinase activity, and drug screening of patient-derived cell lines. This study examined eight patient-derived giant cell tumor of the bone (GCTB) cell lines, all of which harbored a signature mutation of H3-3A but otherwise without recurrent copy number variants and mutations. Kinase activity profiles of 100 tyrosine kinases with a three-dimensional substrate peptide array revealed that nine kinases were highly activated. Pharmacological screening of 60 clinically used kinase inhibitors found that nine drugs directed at 29 kinases strongly suppressed cell viability. We regarded ABL1, EGFR, and LCK as on-target kinases; among the two corresponding on-target kinase inhibitors, osimertinib and ponatinib emerged as on-target drugs whose target kinases were significantly activated. The remaining 26 kinases and seven kinase inhibitors were excluded as off-targets. Our pharmacoproteomic approach enabled the identification of on-target kinase inhibitors that are useful for drug repositioning.

DTA Atlas: A massive-scale drug repurposing database

The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions. While modern in silico methods enhance scalability, an open database for projected drug-target interactions across the entire human proteome is still lacking. In this work, we introduce an open database of predicted drug-target interactions, termed DTA Atlas, covering the entire human proteome as well as a wide range of marketed drugs, resulting in over 220 million drug-target pairs. The database integrates 4 billion affinity predictions from advanced deep neural networks and offers a user-friendly web interface, enabling users to explore drug-target affinity predictions for the human proteome. To the best of our knowledge, DTA Atlas represents the first comprehensive collection of drug-target binding strength predictions. It is open-source and can serve as an important resource for drug development, drug repurposing, toxicity studies and more.

Conquering Insulin Network Dysfunctions in Alzheimer's Disease: Where Are We Today?

Functional impairments in the brain's insulin and insulin-like growth factor (IGF) signal transduction networks are recognized mediators of dysregulated energy metabolism, a major driver of the Alzheimer's disease (AD) neurodegeneration cascade. AD-associated insulin-deficient and insulin-resistant states mimic those of diabetes mellitus and affect all cell types in the brain. Besides accounting for abundant amyloid-β and hyperphosphorylated tau lesions in AD, insulin/IGF pathway dysfunctions cause cortical atrophy, loss of synaptic plasticity, white matter myelin/oligodendrocyte degeneration, astrocyte and microglial neuroinflammation and oxidative stress, deficits in energy metabolism, mitochondrial dysfunction, and microvascular disease. These same neuropathological processes have been linked to cognitive impairment in type 2 diabetes mellitus, Parkinson's disease, and vascular dementia. Strategies to address metabolic mediators of cognitive impairment have been borrowed from diabetes and other insulin-resistant diseases and leveraged on preclinical AD model data. The repurposing of diabetes drugs led to clinical trials with intranasal insulin, followed by insulin sensitizers including metformin and peroxisome-proliferator-activated receptor agonists, and then incretin mimetics primarily targeting GLP-1 receptors. In addition, other glucose-lowering agents have been tested for their efficacy in preventing cognitive declines. The strengths and limitations of these approaches are discussed. The main conclusion of this review is that we have now arrived at a stage in which it is time to address long-term deficits in trophic factor availability and receptor responsiveness, signaling abnormalities that extend beyond insulin and include IGFs and interconnected pathways, and the need for multi-pronged rather than single-pronged therapeutic targeting to remediate AD and other forms of neurodegeneration.

Auranofin as a Novel Anticancer Drug for Anaplastic Thyroid Cancer.

Background/Objectives: Anaplastic thyroid cancer (ATC) is an aggressive and rare cancer with a poor prognosis, and traditional therapies have limited efficacy. This study investigates drug repositioning, focusing on auranofin, a gold-based drug originally used for rheumatoid arthritis, as a potential treatment for ATC. Methods: Auranofin was identified from an FDA-approved drug library and tested on two thyroid cancer cell lines, 8505C and FRO. Antitumor efficacy was evaluated through gene and protein expression analysis using Western blot, FACS, and mRNA sequencing. In vivo experiments were conducted using subcutaneous injections in nude mice to confirm the anticancer effects of auranofin. Results: Auranofin induced reactive oxygen species (ROS) production and apoptosis, leading to a dose-dependent reduction in cell viability, G1/S phase cell cycle arrest, and altered expression of regulatory proteins. It also inhibited cancer stem cell activity and suppressed epithelial-mesenchymal transition. mRNA sequencing revealed significant changes in the extracellular matrix-receptor interaction pathway, supported by Western blot results. In vivo xenograft models demonstrated strong antitumor activity. Conclusions: Auranofin shows promise as a repurposed therapeutic agent for ATC, effectively inhibiting cell proliferation, reducing metastasis, and promoting apoptosis. These findings suggest that auranofin could play a key role in future ATC treatment strategies.

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder.

Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research. We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD). Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition). The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

The crucial role of drug repositioning in tackling Chagas disease, sleeping sickness, and leishmaniasis

Neglected tropical diseases (NTDs), such as Chagas disease, human African trypanosomiasis (sleeping sickness), and leishmaniasis, disproportionately affect low-income populations in tropical and subtropical regions, leading to high morbidity and mortality rates. Consequently, these diseases, historically overlooked in global health agendas, perpetuate cycles of poverty and impede economic development. Drug repositioning, the repurposing of existing drugs for new therapeutic uses presents a promising strategy by reducing drug development time and cost while leveraging known safety profiles. However, despite its success in other therapeutic areas, this approach remains underutilized for NTDs due to challenges such as a limited drug pool, intellectual property barriers, regulatory complexities, and ethical concerns. Essential strategies to overcome these obstacles include expanding approved drug libraries, fostering multi-sector collaborations, streamlining regulatory processes, and adopting innovative funding models. Collaborative efforts among governments, pharmaceutical companies, research institutions, and non-profit health organizations are crucial to fully unlock the potential of drug repositioning. By working together as a united front, these stakeholders can ultimately transform NTD treatment and improve global health outcomes.

RECAPITULATING THE POST-SURGICAL BRAIN MICROENVIRONMENT OF ATYPICAL TERATOID RHABDOID TUMOURS TO IDENTIFY MEMBRANE PROTEINS FOR TARGETED THERAPY

Abstract AIMS Atypical Teratoid Rhabdoid Tumours (AT/RT) are rare aggressive tumours primarily arising in the hind brain of children under three, conferring median survival of <12 months. Surgery remains the only standard treatment; however tumours recur from post-surgical residual disease. Decellularised human brain cerebellum aim to recapitulate AT/RT post-surgical brain microenvironments which retain extracellular matrix (ECM) ligands, and where AT/RT plasma membrane proteins are hypothesised to represent viable molecular therapy targets. METHOD AT/RT, astrocyte and human brain cerebellum (decellularised and non-decellularised) membrane protein fractions were extracted and analysed using liquid chromatography-mass spectrometry. Proteomic analysis tools were integrated to identify differential expressed proteins and identify shared correlations. RESULTS Decellularised cerebellar ECM exhibited an enriched membrane proteome relative to total proteome, evidence of complete removal of intracellular components. Furthermore, decellularised and non-decellularised cerebellar tissue expressed comparable levels of membrane proteins and protein networks, indicative of ECM ligand retention. AT/RT and astrocyte KEGG Pathway analysis revealed specific oncology-based pathway expression in membrane protein compared to total protein, reinforcing membrane proteins as more visible therapeutic targets. Between the top 20 expressed proteins, ATP1A1 was the only membrane protein shared exclusively between all AT/RT cells and astrocytes, suggesting a critical functional role. Membrane proteins showing shared expression when normalised against each AT/RT cell line included ATP1A1, HSPD1, GNA13 and SLC3A2, indicating biological similarities between AT/RT molecular subtypes, which may offer candidate ubiquitous therapy targets. Membrane proteins expressed similarly in AT/RT-MYC molecular subtypes include ATP1A1, SLC7A5 and EEF1A1. STRING membrane pathway analysis also identified shared proteins between AT/RT and astrocyte cell populations. CONCLUSION AT/RT membrane protein pathways provide ideal therapeutic targets directly amenable for drug repurposing. Future work prioritises proteomic analysis utilising 3D AT/RT spheroids and cerebellum brain tissue to maximise accurately recapitulating the AT/RT post-surgical microenvironment. Cerebellum decellularisation still permits retention of essential proteins and ligands.

In silico Prediction of Pranlukast as a Stabilizer of PD-L1 Homodimers.

Tumors can be targeted by modulating the immune response of the patient. Programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are critical immune checkpoints in cancer biology. The efficacy of certain cancer immunotherapies has been achieved by targeting these molecules using monoclonal antibodies. Small-molecule drugs have also been developed as inhibitors of the PD-1/PD-L1 axis, with a mechanism of action that is distinct from that of antibodies: they induce the formation of PD-L1 homodimers, causing their stabilization, internalization, and subsequent degradation. Drug repurposing is a strategy in which new uses are sought after for approved drugs, expediting their clinical translation based on updated findings. In this study, we generated a pharmacophore model that was based on reported small molecules that targeted PD-L1 and used it to identify potential PD-L1 inhibitors among FDA-approved drugs. We identified 12 pharmacophore-matching compounds, but only 4 reproduced the binding mode of the reference inhibitors in docking experiments. Further characterization by molecular dynamics showed that pranlukast, an antagonist of leukotriene receptors that is used to treat asthma, generated stable and energyfavorable interactions with PD-L1 homodimers and induced homodimerization of recombinant PD-L1. Our results suggest that pranlukast inhibits the PD-1/PD-L1 axis, meriting its repurposing as an antitumor drug.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA, CTSB, LRRK2, DGKQ, CD38 and CD44. Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.