Molecules. 2024 Jan 29;29(3):635. doi: 10.3390/molecules29030635.

ABSTRACT

Repurposing is a universal mechanism for innovation, from the evolution of feathers to the invention of Velcro tape. Repurposing is particularly attractive for drug development, given that it costs more than a billion dollars and takes longer than ten years to make a new drug from scratch. The COVID-19 pandemic has triggered a large number of drug repurposing activities. At the same time, it has highlighted potential pitfalls, in particular when concessions are made to the target product profile. Here, we discuss the pros and cons of drug repurposing for infectious diseases and analyze different ways of repurposing. We distinguish between opportunistic and rational approaches, i.e., just saving time and money by screening compounds that are already approved versus repurposing based on a particular target that is common to different pathogens. The latter can be further distinguished into divergent and convergent: points of attack that are divergent share common ancestry (e.g., prokaryotic targets in the apicoplast of malaria parasites), whereas those that are convergent arise from a shared lifestyle (e.g., the susceptibility of bacteria, parasites, and tumor cells to antifolates due to their high rate of DNA synthesis). We illustrate how such different scenarios can be capitalized on by using examples of drugs that have been repurposed to, from, or within the field of anti-infective chemotherapy.

PMID:38338378 | DOI:10.3390/molecules29030635

Photochem Photobiol Sci. 2024 Feb 9. doi: 10.1007/s43630-024-00535-4. Online ahead of print.

ABSTRACT

Squamous cell carcinoma represents the second most common type of keratinocyte carcinoma with ultraviolet radiation (UVR) making up the primary risk factor. Oral photoprotection aims to reduce incidence rates through oral intake of photoprotective compounds. Recently, drug repurposing has gained traction as an interesting source of chemoprevention. Because of their reported photoprotective properties, we investigated the potential of bucillamine, carvedilol, metformin, and phenformin as photoprotective compounds following oral intake in UVR-exposed hairless mice. Tumour development was observed in all groups in response to UVR, with only the positive control (Nicotinamide) demonstrating a reduction in tumour incidence (23.8%). No change in tumour development was observed in the four repurposed drug groups compared to the UV control group, whereas nicotinamide significantly reduced carcinogenesis (P = 0.00012). Metformin treatment significantly reduced UVR-induced erythema (P = 0.012), bucillamine and phenformin increased dorsal pigmentation (P = 0.0013, and P = 0.0005), but no other photoprotective effect was observed across the repurposed groups. This study demonstrates that oral supplementation with bucillamine, carvedilol, metformin, or phenformin does not affect UVR-induced carcinogenesis in hairless mice.

PMID:38337129 | DOI:10.1007/s43630-024-00535-4

Curr Opin Struct Biol. 2024 Feb 8;85:102776. doi: 10.1016/j.sbi.2024.102776. Online ahead of print.

ABSTRACT

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.

PMID:38335558 | DOI:10.1016/j.sbi.2024.102776

Arch Pharm (Weinheim). 2024 Feb 9:e2300661. doi: 10.1002/ardp.202300661. Online ahead of print.

ABSTRACT

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

PMID:38335311 | DOI:10.1002/ardp.202300661

J Biomol Struct Dyn. 2024 Feb 9:1-15. doi: 10.1080/07391102.2024.2313161. Online ahead of print.

ABSTRACT

The dengue virus (DENV) infects approximately 400 million people annually worldwide causing significant morbidity and mortality. Despite advances in understanding the virus life cycle and infectivity, no specific treatment for this disease exists due to the lack of therapeutic drugs. In addition, vaccines available currently are ineffective with severe side effects. Therefore, there is an urgent need for developing therapeutics suitable for effective management of DENV infection. In this study, we adopted a drug repurposing strategy to identify new therapeutic use of existing FDA approved drug molecules to target DENV2 non-structural proteins NS3 and NS5 using computational approaches. We used Drugbank database molecules for virtual screening and multiple docking analysis against a total of four domains, the NS3 protease and helicase domains and NS5 MTase and RdRp domains. Subsequently, MD simulations and MM-PBSA analysis were performed to validate the intrinsic atomic interactions and the binding affinities. Furthermore, the internal dynamics in all four protein domains, in presence of drug molecule binding were assessed using essential dynamics and free energy landscape analyses, which were further coupled with conformational dynamics-based clustering studies and cross-correlation analysis to map the regions that exhibit these structural variations. Our comprehensive analysis identified tolcapone, cefprozil, delavirdine and indinavir as potential inhibitors of NS5 MTase, NS5 RdRp, NS3 protease and NS3 helicase functions, respectively. These high-confidence candidate molecules will be useful for developing effective anti-DENV therapy to combat dengue infection.Communicated by Ramaswamy H. Sarma.

PMID:38334186 | DOI:10.1080/07391102.2024.2313161

Mult Scler. 2024 Feb 9:13524585241227119. doi: 10.1177/13524585241227119. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing knowledge about multiple sclerosis (MS) pathophysiology has reinforced the need for an improved description of disease phenotypes, connected to disease biology. Growing evidence indicates that complex diseases constitute phenotypical and genetic continuums with "simple," monogenic disorders, suggesting shared pathomechanisms.

OBJECTIVES: The objective of this study was to depict a novel MS phenotypical framework leveraging shared physiopathology with Mendelian diseases and to identify phenotype-specific candidate drugs.

METHODS: We performed an enrichment testing of MS-associated variants with Mendelian disorders genes. We defined a "MS-Mendelian network," further analyzed to define enriched phenotypic subnetworks and biological processes. Finally, a network-based drug screening was implemented.

RESULTS: Starting from 617 MS-associated loci, we showed a significant enrichment of monogenic diseases (p < 0.001). We defined an MS-Mendelian molecular network based on 331 genes and 486 related disorders, enriched in four phenotypic classes: neurologic, immunologic, metabolic, and visual. We prioritized a total of 503 drugs, of which 27 molecules active in 3/4 phenotypical subnetworks and 140 in subnetwork pairs.

CONCLUSION: The genetic architecture of MS contains the seeds of pathobiological multiplicities shared with immune, neurologic, metabolic and visual monogenic disorders. This result may inform future classifications of MS endophenotypes and support the development of new therapies in both MS and rare diseases.

PMID:38333907 | DOI:10.1177/13524585241227119

Sci Rep. 2024 Feb 8;14(1):3246. doi: 10.1038/s41598-024-53316-5.

ABSTRACT

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

PMID:38332162 | DOI:10.1038/s41598-024-53316-5

Nat Commun. 2024 Feb 8;15(1):1173. doi: 10.1038/s41467-024-45241-y.

ABSTRACT

Respiratory syncytial virus (RSV) is a common cause of acute lower respiratory tract infection in infants, older adults and the immunocompromised. Effective directly acting antivirals are not yet available for clinical use. To address this, we screen the ReFRAME drug-repurposing library consisting of 12,000 small molecules against RSV. We identify 21 primary candidates including RSV F and N protein inhibitors, five HSP90 and four IMPDH inhibitors. We select lonafarnib, a licensed farnesyltransferase inhibitor, and phase III candidate for hepatitis delta virus (HDV) therapy, for further follow-up. Dose-response analyses and plaque assays confirm the antiviral activity (IC50: 10-118 nM). Passaging of RSV with lonafarnib selects for phenotypic resistance and fixation of mutations in the RSV fusion protein (T335I and T400A). Lentiviral pseudotypes programmed with variant RSV fusion proteins confirm that lonafarnib inhibits RSV cell entry and that these mutations confer lonafarnib resistance. Surface plasmon resonance reveals RSV fusion protein binding of lonafarnib and co-crystallography identifies the lonafarnib binding site within RSV F. Oral administration of lonafarnib dose-dependently reduces RSV virus load in a murine infection model using female mice. Collectively, this work provides an overview of RSV drug repurposing candidates and establishes lonafarnib as a bona fide fusion protein inhibitor.

PMID:38332002 | DOI:10.1038/s41467-024-45241-y

Acta Trop. 2024 Feb 6:107143. doi: 10.1016/j.actatropica.2024.107143. Online ahead of print.

ABSTRACT

leishmaniasis is an endemic disease in more than 90 countries, constituting a relevant public health problem. Limited treatment options, increase in resistance, and therapeutic failure are important aspects for the discovery of new treatment options. Drug repurposing may accelerate the discovery of antileishmanial drugs. Recent tests indicating the in vitro potential of antimalarials Leishmania resulted in the design of this study. This study aimed at evaluating the susceptibility of Leishmania (L.) amazonensis to chloroquine (CQ) and quinine (QN), alone or in combination with amphotericin B (AFT) and pentamidine (PTN). In the in vitro tests, first, we evaluated the growth inhibition of 50% of promastigotes (IC50) and cytotoxicity for HepG2 and THP-1 cells (CC50). The IC50 values of AFT and PNT were below 1 µM, while the IC50 values of CQ and QN ranged between 4 - 13 µM. Concerning cytotoxicity, CC50 values ranged between 7 and 30 µM for AFT and PNT, and between 22 to 157 µM for the antimalarials. We also calculated the Selectivity Index (SI), where AFT and PTN obtained the highest values, while the antimalarias obtained values between 5-12. Both antimalarials were additive (ƩFIC 1.05 - 1.8) in combination with AFT and PTN. For anti-amastigote activity, the drugs obtained the following ICA50 values: AFT (0.26 µM), PNT (2.09 µM), CQ (3.77 µM) and QN (24.5 µM). In the in vivo tests, we observed that the effective dose for the death of 50% of parasites (ED50) of AFT and CQ were 0.63 mg/kg and 27.29 mg/kg, respectively. When combining CQ with AFT, a decrease in parasitemia was observed, being statistically equal to the naive group. For cytokine quantification, it was observed that CQ, despite presenting anti-inflammatory activity was effective at increasing the production of IFN-γ. Overall, our data indicate that chloroquine will probably be a candidate for repurposing and use in drug combination therapy.

PMID:38331084 | DOI:10.1016/j.actatropica.2024.107143

Bioorg Chem. 2024 Feb 4;144:107176. doi: 10.1016/j.bioorg.2024.107176. Online ahead of print.

ABSTRACT

Repurposing drugs can significantly reduce the time and costs associated with drug discovery and development. However, many drug compounds possess intrinsic fluorescence, resulting in aberrations such as auto-fluorescence, scattering and quenching, in fluorescent high-throughput screening assays. To overcome these drawbacks, time-resolved technologies have received increasing attention. In this study, we have developed a rapid and efficient screening platform based on time-resolved emission spectroscopy in order to screen for inhibitors of the DNA repair enzyme, uracil-DNA glycosylase (UDG). From a database of 1456 FDA/EMA-approved drugs, sodium stibogluconate was discovered as a potent UDG inhibitor. This compound showed synergistic cytotoxicity against 5-fluorouracil-resistant cancer cells. This work provides a promising future for time-resolved technologies for high-throughput screening (HTS), allowing for the swift identification of bioactive compounds from previously overlooked scaffolds due to their inherent fluorescence properties.

PMID:38330721 | DOI:10.1016/j.bioorg.2024.107176

Toxicol Appl Pharmacol. 2024 Feb 5:116844. doi: 10.1016/j.taap.2024.116844. Online ahead of print.

ABSTRACT

Glioblastoma multiforme (GBM) is the most common, aggressive, and fatal primary malignant brain tumor in adults. The therapeutic efficacy of temozolomide (TMZ) is limited owing to frequent treatment resistance. The latter is in part related to the overexpression of redox systems such as the thioredoxin system. This system is fundamental for cell survival and proliferation, regulating hypoxia inducible factor-1alpha (HIF-1α) activity, in turn controlling vascular endothelial growth factor (VEGF), which is indispensable for tumor invasiveness, angiogenesis and microenvironment maintenance. HIF-1α can also be regulated by the signal transducer and activator of transcription 3 (STAT3), an oncogene stimulated by pro-inflammatory cytokines and growth factors. The thioredoxin system has several known inhibitors including mercury compounds such as Thimerosal (TmHg) which readily crosses the blood-brain barrier (BBB) and accumulates in the brain. Though previously used in various applications epidemiological evidence on TmHg's neurotoxicity is lacking. The objective of this study was to verify whether thimerosal is a suitable candidate for hard repurposing to control glioblastoma; therefore, the effects of this molecule were evaluated in human GBM (U87) cells. Our novel results show that TmHg decreased cellular viability (>50%) and migration (up to 90% decrease in wound closure), reduced thioredoxin reductase (TrxR/TXNRD1) and thioredoxin (Trx) activity, and increased reactive oxygen species (ROS) generation. Moreover, TmHg reduced HIF-1α expression (35%) as observed by immunofluorescence. Co-exposure of U87 cells to TmHg and TMZ reduced HIF-1α, VEGF, and phosphorylated STAT3. Consequently, TmHg alone or combined with chemotherapeutic drugs can reduce neoangiogenesis and ameliorate glioblastoma progression and treatment.

PMID:38325586 | DOI:10.1016/j.taap.2024.116844

J Cardiovasc Pharmacol. 2024 Feb 5. doi: 10.1097/FJC.0000000000001546. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared to humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models (LLM), such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including post-publication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of LLMs, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to impact it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.

PMID:38323891 | DOI:10.1097/FJC.0000000000001546

Gigascience. 2024 Jan 2;13:giae001. doi: 10.1093/gigascience/giae001.

ABSTRACT

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

PMID:38323677 | DOI:10.1093/gigascience/giae001

Acta Pharm Sin B. 2024 Feb;14(2):729-750. doi: 10.1016/j.apsb.2023.11.004. Epub 2023 Nov 7.

ABSTRACT

Chemotherapy-induced complications, particularly lethal cardiovascular diseases, pose significant challenges for cancer survivors. The intertwined adverse effects, brought by cancer and its complication, further complicate anticancer therapy and lead to diminished clinical outcomes. Simple supplementation of cardioprotective agents falls short in addressing these challenges. Developing bi-functional co-therapy agents provided another potential solution to consolidate the chemotherapy and reduce cardiac events simultaneously. Drug repurposing was naturally endowed with co-therapeutic potential of two indications, implying a unique chance in the development of bi-functional agents. Herein, we further proposed a novel "trilogy of drug repurposing" strategy that comprises function-based, target-focused, and scaffold-driven repurposing approaches, aiming to systematically elucidate the advantages of repurposed drugs in rationally developing bi-functional agent. Through function-based repurposing, a cardioprotective agent, carvedilol (CAR), was identified as a potential neddylation inhibitor to suppress lung cancer growth. Employing target-focused SAR studies and scaffold-driven drug design, we synthesized 44 CAR derivatives to achieve a balance between anticancer and cardioprotection. Remarkably, optimal derivative 43 displayed promising bi-functional effects, especially in various self-established heart failure mice models with and without tumor-bearing. Collectively, the present study validated the practicability of the "trilogy of drug repurposing" strategy in the development of bi-functional co-therapy agents.

PMID:38322326 | PMC:PMC10840436 | DOI:10.1016/j.apsb.2023.11.004

J Transl Med. 2024 Feb 6;22(1):139. doi: 10.1186/s12967-024-04911-7.

ABSTRACT

BACKGROUND: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.

METHODS: By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.

RESULTS: A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.

CONCLUSIONS: The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

PMID:38321543 | DOI:10.1186/s12967-024-04911-7

Neuropsychopharmacology. 2024 Feb 6. doi: 10.1038/s41386-024-01805-6. Online ahead of print.

ABSTRACT

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

PMID:38321095 | DOI:10.1038/s41386-024-01805-6

NPJ Aging. 2024 Feb 6;10(1):12. doi: 10.1038/s41514-024-00138-4.

ABSTRACT

The involvement of cellular senescence in the initiation and propagation of diseases is clearly characterized, making the elimination of senescent cells essential to treat age-related diseases. The development of senolytic drugs demonstrated that targeting these cells limits the deterioration of patients' condition, by inducing apoptosis. Nevertheless, the first generations of senolytics which has been developed displayed their activities through specific mechanisms and demonstrated several limitations during clinical development. However, the rational to eliminate senescent cells remains evident, with the necessity to develop specific therapies in a context of diseases and tissues. The evolutions in the field of drug discovery open the way to a new generation of senolytic therapies, such as immunological approaches (CAR-T cells, Antibody-Drug Conjugated or vaccines), which require preliminary steps of research to identify markers specifically expressed on senescent cells, demonstrating promising specific effects. Currently, the preclinical development of these strategies appears more challenging to avoid strong side effects, but the expected results are commensurate with patients' hopes for treatments. In this review, we highlight the fact that the classical senolytic approach based on drug repurposing display limited efficacy and probably reached its limits in term of clinical development. The recent development of more complex therapies and the extension of interest in the domain of senescence in different fields of research allow to extend the possibility to discover powerful therapies. The future of age-related diseases treatment is linked to the development of new approaches based on cell therapy or immunotherapy to offer the best treatment for patients.

PMID:38321020 | DOI:10.1038/s41514-024-00138-4

OMICS. 2024 Feb 6. doi: 10.1089/omi.2023.0256. Online ahead of print.

ABSTRACT

High-grade gliomas (HGGs) are extremely aggressive primary brain tumors with high mortality rates. Despite notable progress achieved by clinical research and biomarkers emerging from proteomics studies, efficacious drugs and therapeutic targets are limited. This study used targeted proteomics, in silico molecular docking, and simulation-based drug repurposing to identify potential drug candidates for HGGs. Importantly, we performed multiple reaction monitoring (MRM) on differentially expressed proteins with putative roles in the development and progression of HGGs based on our previous work and the published literature. Furthermore, in silico molecular docking-based drug repurposing was performed with a customized library of FDA-approved drugs to identify multitarget-directed ligands. The top drug candidates such as Pazopanib, Icotinib, Entrectinib, Regorafenib, and Cabozantinib were explored for their drug-likeness properties using the SwissADME. Pazopanib exhibited binding affinities with a maximum number of proteins and was considered for molecular dynamic simulations and cell toxicity assays. HGG cell lines showed enhanced cytotoxicity and cell proliferation inhibition with Pazopanib and Temozolomide combinatorial treatment compared to Temozolomide alone. To the best of our knowledge, this is the first study combining MRM with molecular docking and simulation-based drug repurposing to identify potential drug candidates for HGG. While the present study identified five multitarget-directed potential drug candidates, future clinical studies in larger cohorts are crucial to evaluate the efficacy of these molecular candidates. The research strategy and methodology used in the present study offer new avenues for innovation in drug discovery and development which may prove useful, particularly for cancers with low cure rates.

PMID:38320249 | DOI:10.1089/omi.2023.0256

Inflammation. 2024 Feb 6. doi: 10.1007/s10753-023-01939-7. Online ahead of print.

ABSTRACT

The intracellular sensor protein complex known as the NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) inflammasome plays a crucial role in regulating inflammatory diseases by overseeing the production of interleukin (IL)-1β and IL-18. Targeting its abnormal activation with drugs holds significant promise for inflammation treatment. This study highlights LCZ696, an angiotensin receptor-neprilysin inhibitor, as an effective suppressor of NLRP3 inflammasome activation in macrophages stimulated by ATP, nigericin, and monosodium urate. LCZ696 also reduces caspase-11 and GSDMD activation, lactate dehydrogenase release, propidium iodide uptake, and the extracellular release of NLRP3 and apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) in ATP-activated macrophages, suggesting a potential mitigation of pyroptosis. Mechanistically, LCZ696 lowers mitochondrial reactive oxygen species and preserves mitochondrial integrity. Importantly, it does not significantly impact NLRP3, proIL-1β, inducible nitric oxide synthase, cyclooxygenase-2 expression, or NF-κB activation in lipopolysaccharide-activated macrophages. LCZ696 partially inhibits the NLRP3 inflammasome through the induction of autophagy. In an in vivo context, LCZ696 alleviates NLRP3-associated colitis in a mouse model by reducing colonic expression of IL-1β and tumor necrosis factor-α. Collectively, these findings suggest that LCZ696 holds significant promise as a therapeutic agent for ameliorating NLRP3 inflammasome activation in various inflammatory diseases, extending beyond its established use in hypertension and heart failure treatment.

PMID:38319541 | DOI:10.1007/s10753-023-01939-7

Transl Gastroenterol Hepatol. 2024 Jan 18;9:2. doi: 10.21037/tgh-23-40. eCollection 2024.

ABSTRACT

BACKGROUND: The World Health Organization (WHO)'s Essential Medicines List (EML) plays an important role in advocating for access to key treatments for conditions affecting people in all geographic settings. We applied our established drug repurposing methods to one EML agent, N-acetylcysteine (NAC), to identify additional uses of relevance to the global health community beyond its existing EML indication (acetaminophen toxicity).

METHODS: We undertook a phenome-wide association study (PheWAS) of a variant in the glutathione synthetase (GSS) gene in approximately 35,000 patients to explore novel indications for use of NAC, which targets glutathione. We then evaluated the evidence regarding biologic plausibility, efficacy, and safety of NAC use in the new phenotype candidates.

RESULTS: PheWAS of GSS variant R418Q revealed increased risk of several phenotypes related to non-acetaminophen induced acute liver failure (ALF), indicating that NAC may represent a therapeutic option for treating this condition. Evidence review identified practice guidelines, systematic reviews, clinical trials, retrospective cohorts and case series, and case reports. This evidence suggesting benefit of NAC use in this subset of ALF patients. The safety profile of NAC in this literature was also concordant with existing evidence on safety of this agent in acetaminophen-induced ALF.

CONCLUSIONS: This body of literature indicates efficacy and safety of NAC in non-acetaminophen induced ALF. Given the presence of NAC on the EML, this medication is likely to be available across a range of resource settings; promulgating its use in this novel subset of ALF can provide healthcare professionals and patients with a valuable and safe complement to supportive care for this disease.

PMID:38317753 | PMC:PMC10838616 | DOI:10.21037/tgh-23-40