Biomed Pharmacother. 2023 Sep 21;167:115513. doi: 10.1016/j.biopha.2023.115513. Online ahead of print.

ABSTRACT

Immune effector cells in the microenvironment tend to be depleted or remodeled, unable to perform normal functions, and even promote the malignant characterization of tumors, resulting in the formation of immunosuppressive microenvironments. The strategy of reversing immunosuppressive microenvironment has been widely used to enhance the tumor immunotherapy effect. Signal transducer and activator of transcription 3 (STAT3) was found to be a crucial regulator of immunosuppressive microenvironment formation and activation as well as a factor, stimulating tumor cell proliferation, survival, invasiveness and metastasis. Therefore, regulating the immune microenvironment by targeting the STAT3 oncogenic pathway might be a new cancer therapy strategy. This review discusses the pleiotropic effects of STAT3 on immune cell populations that are critical for tumorigenesis, and introduces the novel strategies targeting STAT3 oncogenic pathway for cancer immunotherapy. Lastly, we summarize the conventional drugs used in new STAT3-targeting anti-tumor applications.

PMID:37741251 | DOI:10.1016/j.biopha.2023.115513

Lancet Neurol. 2023 Oct;22(10):890-899. doi: 10.1016/S1474-4422(23)00285-5.

ABSTRACT

BACKGROUND: Drug repurposing could provide novel treatment options for Duchenne muscular dystrophy. Because tamoxifen-an oestrogen receptor regulator-reduced signs of muscular pathology in a Duchenne muscular dystrophy mouse model, we aimed to assess the safety and efficacy of tamoxifen in humans as an adjunct to corticosteroid therapy over a period of 48 weeks.

METHODS: We did a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial at 12 study centres in seven European countries. We enrolled ambulant boys aged 6·5-12·0 years with a genetically confirmed diagnosis of Duchenne muscular dystrophy and who were on stable corticosteroid treatment for more than 6 months. Exclusion criteria included ophthalmological disorders, including cataracts, and haematological disorders. We randomly assigned (1:1) participants using an online randomisation tool to either 20 mg tamoxifen orally per day or matched placebo, stratified by centre and corticosteroid intake. Participants, caregivers, and clinical investigators were masked to treatment assignments. Tamoxifen was taken in addition to standard care with corticosteroids, and participants attended study visits for examinations every 12 weeks. The primary efficacy outcome was the change from baseline to week 48 in scores on the D1 domain of the Motor Function Measure in the intention-to-treat population (defined as all patients who fulfilled the inclusion criteria and began treatment). This study is registered with ClinicalTrials.gov (NCT03354039) and is completed.

FINDINGS: Between May 24, 2018, and Oct 14, 2020, 95 boys were screened for inclusion, and 82 met inclusion criteria and were initially enrolled into the study. Three boys were excluded after initial screening due to cataract diagnosis or revoked consent directly after screening, but before randomisation. A further boy assigned to the placebo group did not begin treatment. Therefore, 40 individuals assigned tamoxifen and 38 allocated placebo were included in the intention-to-treat population. The primary efficacy outcome did not differ significantly between tamoxifen (-3·05%, 95% CI -7·02 to 0·91) and placebo (-6·15%, -9·19 to -3·11; 2·90% difference, -3·02 to 8·82, p=0·33). Severe adverse events occurred in two participants: one participant who received tamoxifen had a fall, and one who received placebo suffered a panic attack. No deaths or life-threatening serious adverse events occurred. Viral infections were the most common adverse events.

INTERPRETATION: Tamoxifen was safe and well tolerated, but no difference between groups was reported for the primary efficacy endpoint. Slower disease progression, defined by loss of motor function over time, was indicated in the tamoxifen group compared with the placebo group, but differences in outcome measures were neither clinically nor statistically significant. Currently, we cannot recommend the use of tamoxifen in daily clinical practice as a treatment option for boys with Duchenne muscular dystrophy due to insufficient clinical evidence.

FUNDING: Thomi Hopf Foundation, ERA-Net, Swiss National Science Foundation, Duchenne UK, Joining Jack, Duchenne Parent Project, Duchenne Parent Project Spain, Fondation Suisse de Recherche sur les Maladies Musculaires, Association Monegasque contre les Myopathies.

PMID:37739572 | DOI:10.1016/S1474-4422(23)00285-5

Bioimpacts. 2023;13(5):373-382. doi: 10.34172/bi.2023.24180. Epub 2023 Jan 7.

ABSTRACT

INTRODUCTION: Machine learning methods, coupled with a tremendous increase in computer power in recent years, are promising tools in modern drug design and drug repurposing.

METHODS: Machine learning predictive models, publicly available at chemosophia.com, were used to predict the bioactivity of recently synthesized platinum(IV) complexes against different kinds of diseases and medical conditions. Two novel QSAR models based on the BiS algorithm are developed and validated, capable to predict activities against the SARS-CoV virus and its RNA dependent RNA polymerase.

RESULTS: The internal predictive power of the QSAR models was tested by 10-fold cross-validation, giving cross-R2 from 0.863 to 0.903. 38 different activities, ranging from antioxidant, antibacterial, and antiviral activities, to potential anti-inflammatory, anti-arrhythmic and anti-malarial activity were predicted for a series of eighteen platinum(IV) complexes.

CONCLUSION: Complexes 1, 3 and 13 have high generalized optimality criteria and are predicted as potential SARS-CoV RNA dependent RNA polymerase inhibitors.

PMID:37736338 | PMC:PMC10509740 | DOI:10.34172/bi.2023.24180

J Dermatol Sci. 2023 Aug 29:S0923-1811(23)00184-6. doi: 10.1016/j.jdermsci.2023.08.007. Online ahead of print.

NO ABSTRACT

PMID:37735021 | DOI:10.1016/j.jdermsci.2023.08.007

Front Pharmacol. 2023 Sep 5;14:1255586. doi: 10.3389/fphar.2023.1255586. eCollection 2023.

ABSTRACT

Introduction: Among skin cancers, melanoma has a high mortality rate. Recent advances in immunotherapy, particularly through immune checkpoint modulation, have improved the clinical treatment of melanoma. Maltol has various bioactivities, including anti-oxidant and anti-inflammatory properties, but the anti-melanoma property of maltol remains underexplored. The aim of this work is to explore the anti-melanoma potential of maltol through regulating immune checkpoints. Methods: The immune checkpoint PD-L1 was analyzed using qPCR, immunoblots, and immunofluorescence. Melanoma sensitivity towards T cells was investigated via cytotoxicity, cell viability, and IL-2 assays employing CTLL-2 cells. Results: Maltol was found to reduce melanin contents, tyrosinase activity, and expression levels of tyrosinase and tyrosinase-related protein 1. Additionally, maltol suppressed the proliferative capacity of B16F10 and induced cell cycle arrest. Maltol increased apoptotic rates by elevating cleaved caspase-3 and PARP. The co-treatment with maltol and cisplatin revealed a synergistic effect on inhibiting growth and promoting apoptosis. Maltol suppressed IFN-γ-induced PD-L1 and cisplatin-upregulated PD-L1 by attenuating STAT1 phosphorylation, thereby enhancing cisplatin's cytotoxicity against B16F10. Maltol augmented sensitivity to CTLL-2 cell-regulated melanoma destruction, leading to an increase in IL-2 production. Discussion: These findings demonstrate that maltol restricts melanoma growth through the downregulation of PD-L1 and elicits T cell-mediated anti-cancer responses, overcoming PD-L1-mediated immunotherapy resistance of cisplatin. Therefore, maltol can be considered as an effective therapeutic agent against melanoma.

PMID:37731735 | PMC:PMC10508342 | DOI:10.3389/fphar.2023.1255586

Comput Struct Biotechnol J. 2023 Sep 9;21:4446-4455. doi: 10.1016/j.csbj.2023.09.007. eCollection 2023.

ABSTRACT

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

PMID:37731599 | PMC:PMC10507583 | DOI:10.1016/j.csbj.2023.09.007

Sci Rep. 2023 Sep 20;13(1):15629. doi: 10.1038/s41598-023-42903-7.

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. The pathological hallmark of PD is the appearance of intraneuronal cytoplasmic α-synuclein (α-Syn) aggregation, called Lewy bodies. α-Syn aggregation is deeply involved in the pathogenesis of PD. Oxidative stress is also associated with the progression of PD. In the present study, to investigate whether a hypoxia-inducible factor (HIF)-prolyl hydroxylase (PH) inhibitor, FG-4592 (also called roxadustat), has neuroprotective effects against α-Syn-induced neurotoxicity, we employed a novel α-Syn stably expressing cell line (named α-Syn-N2a cells) utilizing a piggyBac transposon system. In α-Syn-N2a cells, oxidative stress and cell death were induced by α-Syn, and FG-4592 showed significant protection against this neurotoxicity. However, FG-4592 did not affect α-Syn protein levels. FG-4592 triggered heme oxygenase-1 (HO-1) expression downstream of HIF-1α in a concentration-dependent manner. In addition, FG-4592 decreased the production of reactive oxygen species possibly via the activation of HO-1 and subsequently suppressed α-Syn-induced neurotoxicity. Moreover, FG-4592 regulated mitochondrial biogenesis and respiration via the induction of the peroxisome proliferator-activated receptor-γ coactivator-1α. As FG-4592 has various neuroprotective effects against α-Syn and is involved in drug repositioning, it may have novel therapeutic potential for PD.

PMID:37731009 | DOI:10.1038/s41598-023-42903-7

Breast Cancer Res Treat. 2023 Sep 20. doi: 10.1007/s10549-023-07107-7. Online ahead of print.

ABSTRACT

PURPOSE: This research focused on the identification of herbal compounds as potential anti-cancer drugs, especially for breast cancer, that involved the recognition of Notch downstream targets NOTCH proteins (1-4) specifically expressed in breast tumours as biomarkers for prognosis, along with P53 tumour antigens, that were used as comparisons to check the sensitivity of the herbal bio-compounds.

METHODS: After investigating phytochemical candidates, we employed an approach for computer-aided drug design and analysis to find strong breast cancer inhibitors. The present study utilized in silico analyses and protein docking techniques to characterize and rank selected bio-compounds for their efficiency in oncogenic inhibition for use in precise carcinomic cell growth control.

RESULTS: Several of the identified phytocompounds found in herbs followed Lipinski's Rule of Five and could be further investigated as potential medicinal molecules. Based on the Vina score obtained after the docking process, the active compound Epigallocatechin gallate in green tea with NOTCH (1-4) and P53 proteins showed promising results for future drug repurposing. The stiffness and binding stability of green tea pharmacological complexes were further elucidated by the molecular dynamic simulations carried out for the highest scoring phytochemical ligand complex.

CONCLUSION: The target-ligand complex of green tea active compound Epigallocatechin gallate with NOTCH (1-4) had the potential to become potent anti-breast cancer therapeutic candidates following further research involving wet-lab experiments.

PMID:37726449 | DOI:10.1007/s10549-023-07107-7

J Biomol Struct Dyn. 2023 Sep 18:1-15. doi: 10.1080/07391102.2023.2258968. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Drug repositioning is a process of finding new therapeutic applications for existing drugs. One of the methods in drug repositioning is to use the side-effect profile of a drug to identify a new therapeutic indication. The drugs with similar side-effects may act on similar biological targets and could affect the same biochemical process. In this study, we explored the Food and Drug Administration-approved drugs using PROMISCUOUS database to find those that have adverse effects profile comparable with the ligands being studied or used to treat AD. Here, we found that the ropinirole, a dopamine receptor agonist, shared a maximum number of side-effects with the drugs proven beneficial for treating AD. Furthermore, molecular modelling demonstrated that ropinirole exhibited strong binding affinity (-9.313 kcal/mol) and best ligand efficiency (0.49) with sigma-1 receptor. Here, we observed that the quaternary amino group of ropinirole is essential for binding with sigma-1 receptor. Molecular dynamic simulation indicated that the movement of the carboxy-terminal helices (α4/α5) could play a major role in the receptor's physiological functions. The neurotoxicity induced by Aβ25-35 in SH-SY5Y cells was reduced by ropinirole at concentrations 10, 30, and 50 µM. The effect on spatial learning and memory was examined in mice with Aβ25-35 induced memory deficit using the radial arm maze. Ropinirole (10 and 20 mg/kg) significantly improved the short and long-term memories in the radial arm maze test. Our results suggest that ropinirole has the potential to be repositioned for AD treatment.Communicated by Ramaswamy H. Sarma.

PMID:37723871 | DOI:10.1080/07391102.2023.2258968

Appl Biochem Biotechnol. 2023 Sep 18. doi: 10.1007/s12010-023-04595-7. Online ahead of print.

ABSTRACT

Globally, lung cancer contributes significantly to the public health burden-associated mortality. As this form of cancer is insidious in nature, there is an inevitable diagnostic delay leading to chronic tumor development. Non-small cell lung cancer (NSCLC) constitutes 80-85% of all lung cancer cases, making this neoplasia form a prevalent subset of lung carcinoma. One of the most vital aspects for proper diagnosis, prognosis, and adequate therapy is the precise classification of non-small cell lung cancer based on biomarker expression profiling. This form of biomarker profiling has provided opportunities for improvements in patient stratification, mechanistic insights, and probable druggable targets. However, numerous patients have exhibited numerous toxic side effects, tumor relapse, and development of therapy-based chemoresistance. As a result of these exacting situations, there is a dire need for efficient and effective new cancer therapeutics. De novo drug development approach is a costly and tedious endeavor, with an increased attrition rate, attributed, in part, to toxicity-related issues. Drug repurposing, on the other hand, when combined with computer-assisted systems biology approach, provides alternatives to the discovery of new, efficacious, and safe drugs. Therefore, in this review, we focus on a comparison of the conventional therapy-based chemoresistance mechanisms with the repurposed anti-cancer drugs from three different classes-anti-parasitic, anti-depressants, and anti-psychotics for cancer treatment with a primary focus on NSCLC therapeutics. Certainly, amalgamating these novel therapeutic approaches with that of the conventional drug regimen in NSCLC-affected patients will possibly complement/synergize the existing therapeutic modalities. This approach has tremendous translational significance, since it can combat drug resistance and cytotoxicity-based side effects and provides a relatively new strategy for possible application in therapy of individuals with NSCLC.

PMID:37721630 | DOI:10.1007/s12010-023-04595-7

Neural Regen Res. 2024 Mar;19(3):507-508. doi: 10.4103/1673-5374.380901.

NO ABSTRACT

PMID:37721276 | DOI:10.4103/1673-5374.380901

Future Microbiol. 2023 Sep 18. doi: 10.2217/fmb-2023-0108. Online ahead of print.

ABSTRACT

Tweetable abstract Repurposing existing drugs for fungal infections has demonstrated potential in both in vitro and animal models, but there are still obstacles to overcome for clinical application. #antifungal #drugrepurposing #fungalinfections.

PMID:37721174 | DOI:10.2217/fmb-2023-0108

Front Artif Intell. 2023 Sep 1;6:1237542. doi: 10.3389/frai.2023.1237542. eCollection 2023.

ABSTRACT

MOTIVATION: Tensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.

RESULTS: We developed two bioconductor packages-TDbasedUFE and TDbasedUFEadv-that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO.

AVAILABILITY AND IMPLEMENTATION: TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.org/packages/TDbasedUFEadv, respectively.

PMID:37719083 | PMC:PMC10503044 | DOI:10.3389/frai.2023.1237542

Biochem Pharmacol. 2023 Sep 15:115809. doi: 10.1016/j.bcp.2023.115809. Online ahead of print.

ABSTRACT

Although a great cure rate has been achieved for pediatric BCP-ALL, approximately 15% of patients do not respond to conventional chemotherapy and experience disease relapse. A major effort to improve the cure rates by treatment intensification would result in an undesirable increase in treatment-related toxicity and mortality, raising the need to identify novel therapeutic approaches. High-throughput (HTP) drug screening enables the profiling of patients' responses in vitro and allows the repurposing of compounds currently used for other diseases, which can be immediately available for clinical application. The aim of this study was to apply HTP drug screening to identify potentially effective compounds for the treatment of pediatric BCP-ALL patients with poor prognosis, such as patients with Down Syndrome (DS) or carrying rearrangements involving PAX5 or KMT2A/MLL genes. Patient-derived Xenografts (PDX) samples from 34 BCP-ALL patients (9 DS CRLF2r, 15 PAX5r, 10 MLLr), 7 human BCP-ALL cell lines and 14 hematopoietic healthy donor samples were screened on a semi-automated HTP drug screening platform using a 174 compound library (FDA/EMA-approved or in preclinical studies). We identified 9 compounds active against BCP-ALL (ABT-199/venetoclax, AUY922/luminespib, dexamethasone, EC144, JQ1, NVP-HSP990, paclitaxel, PF-04929113 and vincristine), but sparing normal cells. Ex vivo validations confirmed that the BCL2 inhibitor venetoclax exerts an anti-leukemic effect against all three ALL subgroups at nanomolar concentrations. Overall, this study points out the benefit of HTP screening application for drug repurposing to allow the identification of effective and clinically translatable therapeutic agents for difficult-to-treat childhood BCP-ALL subgroups.

PMID:37717691 | DOI:10.1016/j.bcp.2023.115809

Sci Data. 2023 Sep 16;10(1):632. doi: 10.1038/s41597-023-02534-z.

ABSTRACT

Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.

PMID:37717042 | DOI:10.1038/s41597-023-02534-z

Oncotarget. 2023 Sep 15;14:807-808. doi: 10.18632/oncotarget.28463.

NO ABSTRACT

PMID:37713333 | DOI:10.18632/oncotarget.28463

Genes Genomics. 2023 Sep 15. doi: 10.1007/s13258-023-01449-8. Online ahead of print.

ABSTRACT

BACKGROUND: Monkeypox is endemic to African region and has become of Global concern recently due to its outbreaks in non-endemic countries. Although, the disease was first recorded in 1970, no monkeypox specific drug or vaccine exists as of now.

METHODS: We applied drug repositioning method, testing effectiveness of currently approved drugs against emerging disease, as one of the most affordable approaches for discovering novel treatment measures. Techniques such as virtual ligand-based and structure-based screening were applied to identify potential drug candidates against monkeypox.

RESULTS: We narrowed down our results to 6 antiviral and 20 anti-tumor drugs that exhibit theoretically higher potency than tecovirimat, the currently approved drug for monkeypox disease.

CONCLUSIONS: Our results indicated that selected drug compounds displayed strong binding affinity for p37 receptor of monkeypox virus and therefore can potentially be used in future studies to confirm their effectiveness against the disease.

PMID:37713070 | DOI:10.1007/s13258-023-01449-8

Drug Dev Res. 2023 Sep 15. doi: 10.1002/ddr.22115. Online ahead of print.

ABSTRACT

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

PMID:37712494 | DOI:10.1002/ddr.22115

Cancer Biol Ther. 2023 Dec 31;24(1):2253586. doi: 10.1080/15384047.2023.2253586.

ABSTRACT

Lung adenocarcinoma is one of the leading causes of cancer-related mortality globally. Various treatment approaches and drugs had little influence on overall survival; thus, new drugs and treatment strategies are needed. Drug repositioning (repurposing) seems a favorable approach considering that developing new drugs needs much more time and costs. We performed a meta-analysis on 6 microarray datasets to obtain the main genes with significantly altered expression in lung adenocarcinoma. Following that, we found major gene clusters and hub genes. We assessed their enrichment in biological pathways to get insight into the underlying biological process involved in lung adenocarcinoma pathogenesis. The L1000 database was explored for drug perturbations that might reverse the expression of differentially expressed genes in lung adenocarcinoma. We evaluated the potential drug combinations that interact the most with hub genes and hence have the most potential to reverse the disease process. A total of 2148 differentially expressed genes were identified. Six main gene clusters and 27 significant hub genes mainly involved in cell cycle regulation have been identified. By assessing the interaction between 3 drugs and hub genes and information gained from previous clinical investigations, we suggested the three possible repurposed drug combinations, Vorinostat - Dorsomorphin, PP-110 - Dorsomorphin, and Puromycin - Vorinostat with a high chance of success in clinical trials.

PMID:37710391 | DOI:10.1080/15384047.2023.2253586

Pharmacol Ther. 2023 Sep 12:108530. doi: 10.1016/j.pharmthera.2023.108530. Online ahead of print.

ABSTRACT

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

PMID:37708996 | DOI:10.1016/j.pharmthera.2023.108530