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Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases. The authors review approaches that leverage GWAS to identify opportunities for repurposing existing drugs, including single-loci mapping to drug targets, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization and polygenic scoring.

Working memory (WM), a key component of cognitive functions, is often impaired in psychiatric disorders such as schizophrenia. Through a genome-guided drug repurposing approach, we identified fampridine, a potassium channel blocker used to improve walking in multiple sclerosis, as a candidate for modulating WM. In a subsequent double-blind, randomized, placebo-controlled, crossover trial in 43 healthy young adults (ClinicalTrials.gov, NCT04652557), we assessed fampridine’s impact on WM (3-back d-prime, primary outcome) after 3.5 days of repeated administration (10 mg twice daily). Independently of baseline cognitive performance, no significant main effect was observed (Wilcoxon P  = 0.87, r = 0.026). However, lower baseline performance was associated with higher working memory performance after repeated intake of fampridine compared to placebo (r s  = −0.37, P  = 0.014, n  = 43). Additionally, repeated intake of fampridine lowered resting motor threshold (F(1,37) = 5.31, P  = 0.027, R 2 β = 0.01), the non-behavioral secondary outcome, indicating increased cortical excitability linked to cognitive function. Fampridine’s capacity to enhance WM in low-performing individuals and to increase brain excitability points to its potential value for treating WM deficits.

Background Metastatic pancreatic ductal adenocarcinoma (mPDAC) patients have very poor prognosis highlighting the urgent need of novel treatments. In this regard, repurposing non-oncology already-approved drugs might be an attractive strategy to offer more-effective treatment easily tested in clinical trials. Accumulating evidence suggests that epigenetic deregulation is a hallmark of cancer contributing to treatment resistance in several solid tumors, including PDAC. Histone deacetylase inhibitors (HDACi) are epigenetic drugs we have investigated preclinically and clinically as anticancer agents. Valproic acid (VPA) is a generic low-cost anticonvulsant and mood stabilizer with HDAC inhibitory activity, and anticancer properties also demonstrated in PDAC models. Statins use was reported to be associated with lower mortality risk in patients with pancreatic cancer and statins have been shown to have a direct antitumor effect when used alone or in combination therapy. We recently showed capability of VPA/Simvastatin (SIM) combination to potentiate the antitumor activity of gemcitabine/nab-paclitaxel in vitro and in vivo PDAC preclinical models. Methods/Design VESPA is a patient-centric open label randomized multicenter phase-II investigator-initiated trial, evaluating the feasibility, safety, and efficacy of VPA/SIM plus first line gemcitabine/nab-paclitaxel-based regimens (AG or PAXG) (experimental arm) versus chemotherapy alone (standard arm) in mPDAC patients. The study involves Italian and Spanish oncology centers and includes an initial 6-patients safety run-in-phase. A sample size of 240 patients (120 for each arm) was calculated under the hypothesis that the addition of VPA/SIM to gemcitabine and nab-paclitaxel-based regimens may extend progression free survival from 6 to 9 months in the experimental arm. Secondary endpoints are overall survival, response rate, disease control rate, duration of response, CA 19.9 reduction, toxicity, and quality of life. The study includes a patient engagement plan and complementary biomarkers studies on tumor and blood samples. Conclusions VESPA is the first trial evaluating efficacy and safety of two repurposed drugs in oncology such as VPA and SIM, in combination with standard chemotherapy, with the aim of improving mPDAC survival. The study is ongoing. Enrollment started in June 2023 and a total of 63 patients have been enrolled as of June 2024. Trial registration EudraCT number: 2022-004154-63; ClinicalTrials.gov identifier NCT05821556, posted 2023/04/20.

Purpose of ReviewRepurposing established medicines for a new therapeutic indication potentially has important global and societal impact. The high costs and slow pace of new drug development have increased interest in more cost-effective repurposed drugs, particularly in the cancer arena. The conventional drug development pathway and evidence framework are not designed for drug repurposing and there is currently no consensus on establishing the evidence base before embarking on a large, resource intensive, potential practice changing phase III randomised controlled trial (RCT). Numerous observational studies have suggested a potential role for statins as a repurposed drug for cancer chemoprevention and therapy, and we review the strength of the cumulative evidence here.Recent FindingsIn the setting of cancer, a potential repurposed drug, like statins, typically goes through a cyclical history, with initial use for several years in another disease setting, prior to epidemiological research identifying a possible chemo-protective effect. However, further information is required, including review of RCT data in the initial disease setting with exploration of cancer outcomes. Additionally, more contemporary methods should be considered, such as Mendelian randomization and pharmaco-epidemiological research with “target” trial design emulation using electronic health records. Pre-clinical and traditional observational data potentially support the role of statins in the treatment of cancer; however, randomised trial evidence is not supportive. Evaluation of contemporary methods provides little added support for the use of statin therapy in cancer.SummaryWe provide complementary evidence of alternative study designs to enable a robust critical appraisal from a number of sources of the go/no-go decision for a prospective phase III RCT of statins in the treatment of cancer.

Background The clinical presentations of Parkinson’s disease (PD), a chronic neurodegenerative condition, include bradykinesia, hypokinesia, stiffness, resting tremor, and postural instability. Recently, neuroinflammation is involved in pathogenesis of PD. Application of nonsteroidal anti-inflammatory drugs captured attention to treat these neuroinflammation. Aim To investigate the possible effectiveness of celecoxib in patients with PD treated with conventional treatment. Methods Sixty outpatients who fulfilled the inclusion requirements for PD were enrolled in this randomized, prospective, and controlled study. The patients were allocated into two groups at random ( n  = 30); the control group received standard PD treatment, consisting of levodopa/carbidopa, and the celecoxib group received standard PD treatment plus celecoxib. A neurologist evaluated each patient at the beginning of the treatment and after 6 months. Assessment of Unified Parkinson’s disease rating scale (UPDRS) for each patient. Before and after treatment, α -synuclein (α-Syn), tumor necrosis factor alpha (TNF-α), Toll like receptors-4 (TLR-4), nuclear factor erythroid 2–related factor 2 (Nrf-2) and brain-derived neurotropic factor (BDNF) were assessed. Paired and unpaired t tests were used to assess statistical significance within and between groups respectively. Results The celecoxib group exhibited a significant and statistical reduction in the level of measured parameters by unpaired t test as followed: TLR-4 ( p  = 0.004), TNF-α ( p  = 0.042), and α-Syn ( p  = 0.004) apart from a significant increase in BDNF ( p  = 0.0005) and Nrf-2 ( p  = 0.004), in comparison with the control group. Also, UPDRS was significantly decreased in celecoxib group ( p  < 0.05). Conclusion Celecoxib could be a promising adjuvant therapy in managing patients with PD. Trial registration number NCT05962957.

As the coronavirus disease 19 (COVID-19) global pandemic rages across the globe, the race to prevent and treat this deadly disease has led to the “off-label” repurposing of drugs such as hydroxychloroquine and lopinavir/ritonavir, which have the potential for unwanted QT-interval prolongation and a risk of drug-induced sudden cardiac death. With the possibility that a considerable proportion of the world’s population soon could receive COVID-19 pharmacotherapies with torsadogenic potential for therapy or postexposure prophylaxis, this document serves to help health care professionals mitigate the risk of drug-induced ventricular arrhythmias while minimizing risk of COVID-19 exposure to personnel and conserving the limited supply of personal protective equipment.

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application. Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members. Of all the data in the world today, 90% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context. In biomedical research, data is largely fragmented and stored in databases that typically do not “talk” to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition. Himmelstein et al. used a computational approach to analyze 50,000 data points – including drugs, diseases, genes and symptoms – from 19 different public databases. This approach made it possible to create more than two million relationships among the data points, which could be used to develop models that predict which drugs currently in use by doctors might be best suited to treat any of 136 common diseases. For example, Himmelstein et al. identified specific drugs currently used to treat depression and alcoholism that could be repurposed to treat smoking addition and epilepsy. These findings provide a new and powerful way to study drug repurposing. While this work was exclusively performed with public data, an expanded and potentially stronger set of predictions could be obtained if data owned by pharmaceutical companies were incorporated. Additional studies will be needed to test the predictions made by the models.

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.