Cost Of Drug Development Research Articles

Drug-target binding affinity prediction based on power graph and word2vec

BackgroundDrug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.ResultsCompared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed.ConclusionsIn this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.

Use of Clinical Trial Characteristics to Estimate Costs of New Drug Development

Despite their importance to patients, health, and industry, the magnitude of investments in drug research and development (R&D) remain nebulous. New policies require more granular and transparent R&D cost estimates to better balance incentives for innovation and returns to developers. To estimate per-drug R&D costs using a novel, reproduceable approach and to describe firm-level R&D costs per discrete unit of R&D activity (1 patient-month). This economic evaluation used cross-sectional data to estimate 2014 to 2019 costs per patient-month. Costs per patient-month were calculated using data from 268 US publicly traded drug developers, contributing 1311 firm-year observations, that were highest ranked by assets or market capitalization, after exclusions. Per-drug costs were calculated from all R&D activity through approval for a cohort of 38 new drugs approved by the US Food and Drug Administration in 2019. Data were analyzed from January 2022 to July 2024. R&D activity, measured in terms of clinical trial patient-months. This study used a 2-step approach to estimate R&D costs, first allocating firm-year-level total R&D spending across similarly aggregated patient-months, and then aggregating these incremental costs to estimate drug-level R&D costs per new drug. Among 268 developers assessed, 20 large firms accounted for 80.8% of all patient-months and had 27.4% lower mean and 26.7% lower median costs per patient-month compared with other firms. Each 1% increase in patient-months was associated with a 0.9% increase in R&D costs. R&D costs per new drug were highly skewed, with a lower median (IQR), at $708 million ($247 million to $1.42 billion) than mean (SD), at $1.31 ($1.92) billion, after adjusting for the cost of capital and discontinued products. Without these adjustments, direct costs per new drug were a median (IQR) of $150 ($67.6-$453) million and a mean (SD) $369 of ($684) million. While estimated R&D costs varied in sensitivity analyses, mean costs were always substantially greater than median costs. This economic evaluation found median per-drug R&D costs toward the lower end of the range from prior studies, with a mean closer to the middle of the existing range despite the broad scope of included costs. These findings suggest parallel development across indications, adjustment for discontinued products, and a small number of expensive development programs are particularly important drivers of R&D costs.

Therapeutic role of aripiprazole in cartilage defects explored through a drug repurposing approach

Articular cartilage has a limited regenerative capacity, resulting in poor spontaneous healing of damaged tissue. Despite various scientific efforts to enhance cartilage repair, no single method has yielded satisfactory results. With rising drug development costs, drug repositioning has emerged as a viable alternative. This study aimed to identify a drug capable of improving cartilage defects by analyzing chondrogenesis-related microarray data from the Gene Expression Omnibus (GEO) public database. We utilized datasets GSE69110, GSE107649, GSE111822, and GSE116173 to identify genes associated with cartilage differentiation, employing StringTie for differential gene expression analysis and extracting drug data from the Drug-Gene Interaction database. Additionally, we aimed to verify the cartilage regeneration potential of the identified drug through experiments using cellular and animal models. We evaluated the effects of aripiprazole on adipose-derived mesenchymal stem cells (ADMSCs) and chondrocytes using qRT-PCR and a 3D pellet culture system. In vivo, we assessed cartilage restoration by combining aripiprazole with a scaffold and implanting it into artificially induced cartilage defects in Sprague-Dawley rats. Subsequent mRNA sequencing provided insights into the mechanistic pathways involved. Our results showed that aripiprazole significantly increased mRNA expression of COL2A1 and SOX9, markers of chondrogenesis, and promoted chondrogenic condensation in vitro. Furthermore, aripiprazole effectively enhanced cartilage regeneration in the rat model. KEGG pathway and Gene Ontology Biological Processes (GOBP) analyses of the mRNA sequencing data revealed that aripiprazole upregulated genes related to ribosomes and cytoplasmic translation, thereby facilitating chondrogenesis. In conclusion, our findings suggest that aripiprazole is a promising candidate for improving damaged cartilage, offering a novel approach to cartilage regeneration.

Applications of pharmacometrics in drug development.

The last two decades have witnessed profound changes in how advanced computational tools can help leverage tons of data to improve our knowledge, and ultimately reduce cost and increase productivity in drug development. Pharmacometrics has demonstrated its impact through model-informed drug development (MIDD) approaches. It is now an indispensable component throughout the whole continuum of drug discovery, development, regulatory review, and approval. Today, applications of pharmacometrics are common in designing better trials and accelerating evidence-based decisions. Newly emerging technologies, especially those from data and computer sciences, are being integrated with existing computational tools used in the pharmaceutical industry at a remarkably fast pace. The new challenges faced by the pharmacometrics community are not what or how to contribute, but which optimal MIDD strategy should be adopted to maximize its value in the decision-making process. While we are embracing new innovative approaches and tools, this article discusses how a variety of existing modeling tools, with differentiated advantages and focus, can work in concert to inform drug development.

Exploring novel drug targets for erectile dysfunction through plasma proteome with genome.

Currently, the treatment and prevention of erectile dysfunction (ED) remain highly challenging. This study conducted a systematic druggable genome-wide Mendelian randomization (MR) analysis to identify potential therapeutic targets for ED. A proteome-wide MR approach was employed to investigate the causal effects of plasma proteins on ED. Subsequently, summary data-based MR (SMR) analysis was performed to identify potential drug targets for ED. Enrichment analysis and protein-protein interaction (PPI) networks revealed the functional characteristics and biological relevance of these potential therapeutic targets. Drug prediction and molecular docking studies were conducted to validate the pharmacological activity of these identified targets. Finally, a systematic MR analysis was conducted to assess upstream intervention factors, such as lifestyles and diseases, associated with these targets, providing insights for the prevention and treatment of ED. This study identified several potential therapeutic targets for ED. Proteome-wide MR analysis revealed that 126 genetically predicted plasma proteins were causally associated with ED. SMR analysis indicated that TMEM9 was associated with an increased risk of ED, while MDH1, NQO1, QDPR, ARL4D, TAGLN2, and PPP1R14A were associated with a decreased risk of ED. These potential targets were primarily enriched in metabolic and redox-related biological processes. Molecular docking indicated that the predicted drugs had favorable binding affinities with the proteins, further confirming the pharmacological value of these targets. Finally, 6 plasma proteins (MDH1, NQO1, QDPR, ARL4D, TAGLN2, and TMEM9) could be modulated by lifestyle- and disease-related factors. This study provides new insights into the etiology and potential drug targets of ED and contributes to the development of more effective treatments for ED and reducing the cost of drug development. This is a systematic and extensive study exploring the causal relationship between plasma proteins and ED, which helps to provide a comprehensive perspective to understand the role of potential targets in ED. However, we did not conduct this study in different types of ED or different stages of ED progression. In summary, this study identified 7 plasma proteins causally associated with ED and provided new insights into the etiology and potential drug targets for ED.

In medicine today – Cancer and the prorogation of clinical reality in favour of probabilistic outcomes

Abstract How essential is the randomised trial in cancer therapeutics? Is it conceivable that prospective randomised clinical trials in cancer medicine could, one day, safely be abandoned in favour of Phase II studies supplemented with real-world data from cancer centres, without detriment to the progress of cancer therapeutics or the welfare of cancer sufferers? If feasible, such practice could reduce the cost of new drug development, while freeing clinical investigators from the ethical burden of random allocation of treatment or indeed providing no therapy with the administration of a placebo to cancer sufferers.

Pharma in The Digital Era: The Role of Artificial Intelligence in Drug Development

The integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a transformative era, where technology reshapes traditional drug development processes. This paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug discovery, testing, and approval. Existing methods in drug development often suffer from high costs, extended timelines, and high attrition rates in clinical trials, posing significant barriers to timely patient care. To address these issues, we propose the Drug Development based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and machine learning models. The DD-AI framework enhances the identification and optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, simulate their interactions, and streamline the clinical trial process by identifying the most promising candidates. Implementing the DD-AI framework has demonstrated substantial improvements in reducing drug development costs and timelines while increasing the accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach. The findings underscore AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.

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.

Pharmacological Potential and Electrochemical Characteristics of Typha angustifolia Pollen

Typha angustifolia L. (TA) pollen has been utilized as a traditional Chinese medicine for treating various internal and external traumas. Moreover, bioactive compounds possess diverse pharmacological activities. This study aims to evaluate the antiviral properties of TA based on its ability to generate bioenergy, capable of inhibiting viruses. TA pollens were extracted using water and ethanol solvents. These extracts were utilized to identify the phytochemical contents and correlate with the antioxidant activity via 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) assays. HPLC analysis was conducted to identify its electron-shuttling compositions. The bioenergy-generating characteristics were determined via microbial fuel cells. The water extract (TA-W) showed higher antioxidant activity due to a higher phenolic and flavonoid content compared to the ethanol extract (TA-E). Quercetin-3-O-(2G-α-L-rhamnosyl)-rutinoside, quercetin-3-O-neohesperidoside, and quercetin are the electron shuttles (ES) identified out of the 11 compounds. TA obtained a 1.39 ± 0.10 amplification factor of power generation that indicates potential bioenergy-generating and associated antiviral characteristic properties. The findings may provide a foundation for developing antiviral medications specifically designed to target virus-related diseases, while minimizing the risk of drug toxicity and reducing the costs of drug development.

KnoMol: A Knowledge-Enhanced Graph Transformer for Molecular Property Prediction.

Molecular property prediction (MPP) techniques are pivotal in reducing drug development costs by preemptively predicting bioactivity and ADMET properties. Despite the application of numerous deep learning approaches, enhancing the representational capacity of these models remains a significant challenge. This paper presents a novel knowledge-based Transformer framework, KnoMol, designed to improve the understanding of molecular structures. KnoMol integrates expert chemical knowledge into the Transformer, emulating the analytical methods of medicinal chemists. Additionally, the multiperspective attention mechanism provides a more precise way to represent ring systems. In the evaluation experiments, KnoMol achieved state-of-the-art performance on both MoleculeNet and small-scale data sets, surpassing existing models in terms of accuracy and generalization. Further research indicated that the incorporation of knowledge significantly reduces KnoMol's reliance on data volumes, offering a solution to the challenge of data scarcity. Moreover, KnoMol identified several new inhibitors of HER2 in a case study, demonstrating its value in real-world applications. Overall, this research not only provides a powerful tool for MPP but also serves as a successful precedent for embedding knowledge into Transformers, with positive implications for computer-aided drug discovery and the development of MPP algorithms.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

Assessing the causal relationship between plasma proteins and osteoporosis: novel insights into pathological mechanisms and therapeutic implications.

This study aims to identify potential therapeutic targets for OP using summary data-based Mendelian randomization (SMR) and colocalization analysis methods. Furthermore, we seek to explore the biological significance and pharmacological value of these drug targets. To identify potential therapeutic targets for OP, we conducted SMR and colocalization analysis. Plasma protein (pQTL, exposure) data were sourced from the study by Ferkingstad et al. (n = 35,559). Summary statistics for bone mineral density (BMD, outcome) were obtained from the GWAS Catalog (n = 56,284). Additionally, we utilized enrichment analysis, protein-protein interaction (PPI) network analysis, drug prediction, and molecular docking to further analyze the biological significance and pharmacological value of these drug targets. In the SMR analysis, while 20 proteins showed significance, only 8 potential drug targets (GCKR, ERBB3, CFHR1, GPN1, SDF2, VTN, BET1L, and SERPING1) received support from colocalization (PP.H4 > 0.8). These proteins are closely associated with immune function in terms of biological significance. Molecular docking also demonstrated favorable binding of drugs to proteins, consistent with existing structural data, further substantiating the pharmacological value of these targets. The study identified 8 potential drug targets for OP. These prospective targets are believed to have a higher chance of success in clinical trials, thus aiding in prioritizing OP drug development and reducing development costs.

Novel drug design and repurposing: An opportunity to improve translational research in cardiovascular diseases?

Drug repurposing is defined as the use of approved therapeutic drugs for indications different from those for which they were originally designed. Repositioning diminishes both the time and cost for drug development by omitting the discovery stage, the analysis of absorption, distribution, metabolism, and excretion routes, as well as the studies of the biochemical and physiological effects of a new compound. Besides, drug repurposing takes advantage of the increased bioinformatics knowledge and availability of big data biology. There are many examples of drugs with repurposed indications evaluated in in vitro studies, and in pharmacological, preclinical, or retrospective clinical analyses. Here, we briefly review some of the experimental strategies and technical advances that may improve translational research in cardiovascular diseases. We also describe exhaustive research from basic science to clinical studies that culminated in the final approval of new drugs and provide examples of successful drug repurposing in the field of cardiology.

Integrative Approaches in Non-Small Cell Lung Cancer Management: The Role of Radiotherapy.

Treatment guidelines for non-small cell lung cancer (NSCLC) vary by several factors including pathological stage, patient candidacy, and goal of treatment. With many therapeutics and even more combinations available in the NSCLC clinician's toolkit, a multitude of questions remain unanswered vis-a-vis treatment optimization. While some studies have begun exploring the interplay among the many pillars of NSCLC treatment-surgical resection, radiotherapy, chemotherapy, and immunotherapy-the vast number of combinations and permutations of different therapy modalities in addition to the modulation of each constituent therapy leaves much to be desired in a field that is otherwise rapidly evolving. Given NSCLC's high incidence and lethality, the experimentation of synergistic benefits that combinatorial treatment may confer presents a ripe target for advancement and increased understanding without the cost and burden of novel drug development. This review introduces, synthesizes, and compares prominent NSCLC therapies, placing emphasis on the interplay among types of therapies and the synergistic benefits some combinatorial therapies have demonstrated over the past several years.

Causal relationship between complement C1QB and colorectal cancer: a drug target Mendelian randomization study.

Colorectal cancer is influenced by several factors such as unhealthy habits and genetic factors. C1QB has been linked to a number of malignancies. However, uncertainty surrounds the connection between C1QB and CRC. Therefore, this study aimed to explore a bidirectional causal relationship of C1QB as a drug target in CRC through Mendelian randomization (MR) analysis. The GWASs for C1QB and CRC were obtained from the Integrative Epidemiology Unit Open GWAS database. There were five strategies to investigate MR. Sensitivity analysis was carried out via tests for heterogeneity, horizontal pleiotropy and leave-one-out effects to evaluate the dependability of the MR analysis results. Furthermore, colocalization analysis of C1QB and CRC, protein-protein interaction network and drug prediction according to exposure factors as well as phenotype scanning were performed. The results of forward MR analysis demonstrated that C1QB was a risk factor for CRC (OR = 1.104, p = 0.033). However, we did not find a causal relationship between CRC and C1QB (reverse MR). Rs294180 and rs291985 corresponded to the same linkage interval and had the potential to influence C1QB and CRC, respectively. The PPI results demonstrated that C1QB interacted with 10 genes (C1QA, C1QC, C1R, C1S, C2, C4A, C4B, CALR, SERPING1, and VSIG4). Additionally, 21 medications were predicted to match C1QB. Molecular docking data, including for benzo(a)pyrene, 1-naphthylisothiocyanate, calcitriol and medroxyprogesterone acetate, revealed excellent binding for drugs and proteins. Moreover, we identified 29 diseases that were associated with C1QB and related medicines via disease prediction and intersection methods. As a therapeutic target for CRC, phenotypic scanning revealed that C1QB does not significantly affect weight loss, liver cirrhosis, or nonalcoholic fatty liver disease, but might have protective impacts on ovarian cancer and melanoma. The results highlight a causal relationship between C1QB and CRC and imply an oncogenic role for C1QB in CRC, as potential drug targets. Drugs designed to target C1QB have a greater chance of success in clinical trials and are expected to help prioritize CRC drug development and reduce drug development costs. That provided a theoretical foundation and reference for research on CRC and C1QB in MR.

The United States Food and Drug Administration's Platform Technology Designation to Expedite the Development of Drugs.

Drug development costs can be significantly reduced if proven "platform" technologies are allowed to be used without having to validate their use. The most recent US Food and Drug Administration (FDA) guideline brings more clarity, as well as a greater focus on the most complex technologies that can now be used for faster drug development. The FDA has highlights the use of lipid nanoparticles (LNPs) to package and deliver mRNA vaccines, gene therapy, and short (2-20 length) synthetic nucleotides (siRNA). Additionally, monoclonal antibody cell development is targeted. The FDA provides a systematic process of requesting platform status to benefit from its advantages. It brings advanced science and rationality into regulatory steps for the FDA's approval of drugs and biologicals.

Impact of Artificial Intelligence in Drug Discovery and Development

The field of drug discovery and development has been revolutionized by the integration of artificial intelligence (AI) technologies. AI has significantly impacted various stages of the drug development process, including target identification, lead optimization, pharmacokinetics, and toxicity prediction. This review paper provides an overview of the impact of AI in drug discovery and development, highlighting the advancements, challenges, and future prospects. It discusses the application of machine learning, deep learning, and other AI techniques in accelerating the drug discovery process, improving the efficiency of clinical trials, and reducing the overall cost of drug development. Additionally, this review examines the ethical and regulatory considerations associated with the use of AI in drug development. Overall, this paper emphasizes the transformative potential of AI in revolutionizing the pharmaceutical industry and improving patient outcomes

Sales Revenues for New Therapeutic Agents Approved by the United States Food and Drug Administration From 1995 to 2014

ObjectivesThis study aimed to analyze worldwide sales of new therapeutic agents and to estimate the time it takes for product sales to exceed industry-wide average drug development costs. MethodsData obtained from company reports were analyzed to track worldwide sales of new medicines approved by the US Food and Drug Administration from 1995 to 2014. All sales figures were reported in 2019 US dollars. Kaplan-Meier curves were used to evaluate the time it took for discounted product sales to exceed the average costs associated with developing 1 new drug (accounting for the costs of failed trials), using published estimates of these costs. ResultsBased on data for 361 of 558 new therapeutic agents approved over the study period (median follow-up 13.2 years), mean sales revenue per product was $15.2 billion through the end of 2019; the median was $6.7 billion. These products jointly generated global sales of $5.5 trillion since approval. Revenues were highly skewed, with the 25 best selling products (7%, 25 of 361) accounting for 38% of this amount ($2.1 trillion of $5.5 trillion). Approximately 47% of products had discounted sales that exceeded the estimated industry-wide average costs of development within 5 years of approval, and 75% within 10 years. After attributing potential production, marketing, and other costs, these numbers dropped to 21% of products within 5 years of approval, and 46% within 10 years. ConclusionsSales of new medicines approved from 1995 to 2014 were highly skewed, but many products had net discounted sales that exceeded the industry-wide average costs of development within 10 years of approval. An understanding of how sales revenues accrue in the years after initial approval, alongside data on business costs, can inform discussions about how to incentivize private investment in innovation while ensuring affordable prices for patients and the healthcare system.

Costs of Drug Development and Research and Development Intensity in the US, 2000-2018

ImportanceUnderstanding the cost of drug development can help inform the development of policies to reduce costs, encourage innovation, and improve patient access to drugs.ObjectiveTo estimate the cost of drug development by therapeutic class and trends in pharmaceutical research and development (R&D) intensity over time.Design, Setting, and ParticipantsIn this economic evaluation study, an analytical model of drug development constructed using public and proprietary sources that collectively cover data from 2000 to 2018 was used to estimate the cost of bringing a drug to market, overall and for specific therapeutic classes. The analysis for the study was completed in October 2020.Main Outcomes and MeasuresThree measures of development cost from nonclinical through postmarketing stages were estimated: mean out-of-pocket cost or cash outlay, mean expected cost, and mean expected capitalized cost. Pharmaceutical R&D intensity, defined as the ratio of R&D spending to total sales, from 2008 to 2019, based on the time frame for available data, was also analyzed.ResultsThe estimated mean cost of developing a new drug was approximately $172.7 million (2018 dollars) (range, $72.5 million for genitourinary to $297.2 million for pain and anesthesia), inclusive of postmarketing studies. The cost increased to $515.8 million when cost of failures was included. When the costs of failures and capital were included, the mean expected capitalized cost of drug development increased to $879.3 million (range, $378.7 million for anti-infectives to $1756.2 million for pain and anesthesia); results varied widely by therapeutic class. The pharmaceutical industry as a whole experienced a decline of 15.6% in sales but increased R&D intensity from 11.9% to 17.7% from 2008 to 2019. By contrast, R&D intensity of large pharmaceutical companies increased from 16.6% to 19.3%, whereas sales increased by 10.0% (from $380.0 to $418.0 billion) over the same 2008 to 2019 period, even though the cost of drug development remained relatively stable or may have even decreased.Conclusions and RelevanceIn this economic evaluation of new drug development costs, even though the cost of drug development appears to have remained stable, R&D intensity of large pharmaceutical companies remained relatively unchanged, despite substantial growth in revenues during this period. These findings can inform the design of drug-related policies and their potential impacts on innovation and competition.

Identifying therapeutic target genes for migraine by systematic druggable genome-wide Mendelian randomization

BackgroundCurrently, the treatment and prevention of migraine remain highly challenging. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine.MethodsWe obtained data on druggable genes and screened for genes within brain expression quantitative trait locis (eQTLs) and blood eQTLs, which were then subjected to two-sample MR analysis and colocalization analysis with migraine genome-wide association studies data to identify genes highly associated with migraine. In addition, phenome-wide research, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic drugs.ResultsWe identified 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53), two of which were significant in both blood and brain (HMGCR and TGFB3). The results of phenome-wide research showed that HMGCR was highly correlated with low-density lipoprotein, and TGFB3 was primarily associated with insulin-like growth factor 1 levels.ConclusionsThis study utilized MR and colocalization analysis to identify 21 potential drug targets for migraine, two of which were significant in both blood and brain. These findings provide promising leads for more effective migraine treatments, potentially reducing drug development costs.