Authors: Luka Hiti, Tijana Markovič, Mitja Lainscak, Jerneja Farkaš Lainščak, Emil Pal & Irena Mlinarič-Raščan

Article

A cytokine storm is marked by excessive pro-inflammatory cytokine release, and has emerged as a key factor in severe COVID-19 cases – making it a critical therapeutic target. However, its pathophysiology was poorly understood, which hindered effective treatment. SARS-CoV-2 initially disrupts angiotensin signalling, promoting inflammation through ACE-2 downregulation. Some patients’ immune systems then fail to shift from innate to adaptive immunity, suppressing interferon responses and leading to excessive pyroptosis and neutrophil activation. This amplifies tissue damage and inflammation, creating a pro-inflammatory loop. The result is the disruption of Th1/Th2 and Th17/Treg balances, lymphocyte exhaustion, and extensive blood clotting. Cytokine storm treatments include glucocorticoids to suppress the immune system, monoclonal antibodies to neutralize specific cytokines, and JAK inhibitors to block cytokine receptor signalling. However, the most effective treatment options for mitigating SARS-CoV-2 infection remain vaccines as a preventive measure and antiviral drugs for the early stages of infection. This article synthesizes insights into immune dysregulation in COVID-19, offering a framework to better understand cytokine storms and to improve monitoring, biomarker discovery, and treatment strategies for COVID-19 and other conditions involving cytokine storms.

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Authors: Evelyn Hewitt, Gauthier Bouche, Alexandre Costa Alencar, Stephen J Bigelsen, Raluca Radu, Violeta Stoyanova-Beninska, Alfredo Carrato, Federica Valsecchi, Alicia Soler Cantón, Helene G van der Meer, María Laura García Bermejo, Alfredo Budillon, Luca Cardone, Ilse Rooman, Hans Platteeuw, Judit Baijet & Claudia Fuchs

Article

Pancreatic cancer (PC) remains one of the most challenging malignancies to treat. Current therapeutic options are unsatisfactory, and there is an urgent need for more effective and less toxic drugs to improve the dismal prognosis of PC. In recent years, drug repurposing (DR) has emerged as an attractive strategy to identify novel treatments for PC by leveraging existing drugs approved for other indications. Through the use of electronic medical records, Artificial Intelligence, study of metabolic pathways, signalling pathways, and many other approaches, it has become much easier in recent years to identify potential novel uses for old drugs. Although policy, funding and research attention in this area are steadily growing, major challenges to efficient and effective patient-centric DR in PC need to be addressed. These include but are not limited to regulatory, financial and funding barriers and the lack of coordination and collaboration among several sectors and stakeholders. To explore the opportunities and challenges associated with DR in PC, a one-day multi-stakeholder meeting was held on 14^th of November 2023 in Brussels, Belgium as part of the REMEDi4ALL project. This meeting provided a platform for researchers, clinicians, industry representatives, funders, regulatory experts, and patient advocates to discuss and propose actions to optimize and accelerate DR in PC. Insights from this meeting support the potential of DR to enhance PC treatment options while highlighting the importance of systemic and supportive changes in the regulatory, policy and funding landscapes, interdisciplinary collaboration, data sharing, and patient involvement in driving therapeutic innovation. This summary highlights key outcomes and recommendations from the meeting in informing future efforts to advance DR initiatives in the context of PC.

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Authors: Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth, Shantanu Singh, Jordi Carreras-Puigvert, Nigel Greene, Andreas Bender & Anne E. Carpenter

Article

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell’s state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting’s ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

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Authors: Aleksandr Ianevski, Kristen Nader, Kyriaki Driva, Wojciech Senkowski, Daria Bulanova, Lidia Moyano-Galceran, Tanja Ruokoranta, Heikki Kuusanmäki, Nemo Ikonen, Philipp Sergeev, Markus Vähä-Koskela, Anil K. Giri, Anna Vähärautio, Mika Kontro, Kimmo Porkka, Esa Pitkänen, Caroline A. Heckman, Krister Wennerberg & Tero Aittokallio

Article

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

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Authors: Alfredo Budillon, Alessandra Leone, Eugenia Passaro, Lucrezia Silvestro, Francesca Foschini, Federica Iannelli, Maria Serena Roca, Marina Macchini, Francesca Bruzzese, Maria Laura Garcia Bermejo, Mercedes Rodriguez Garrote, Giampaolo Tortora, Michele Milella, Michele Reni, Claudia Fuchs, Eve Hewitt, Christine Kubiak, Elena Di Gennaro, Diana Giannarelli & Antonio Avallone

Article

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.

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Authors: Srijit Seal, Dominic Williams, Layla Hosseini-Gerami, Manas Mahale, Anne E Carpenter, Ola Spjuth & Andreas Bender

Article

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation.

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Authors: Jordi Carreras-Puigvert & Ola Spjuth

Article

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI’s role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI’s potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI’s role in drug discovery.

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Authors: Aron Schulman, Juho Rousu, Tero Aittokallio & Ziaurrehman Tanoli

Article

Drug–target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets.This study introduces an attention-based method (called as MMAtt-DTA) to predict drug–target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P < 0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185 676 drug–target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation >0.57 (P < 0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications.

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Authors: Aleksandr Ianevski, Aleksandr Kushnir, Kristen Nader, Mitro Miihkinen, Henri Xhaard, Tero Aittokallio & Ziaurrehman Tanoli

Article

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug–disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug–disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

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Authors: Claudia Fuchs, Peter A. C. ‘t Hoen, Annelieke R. Müller, Friederike Ehrhart & Clara D. M. Van Karnebeek

Article

Rett syndrome (RTT) and Rett-like syndromes [i.e., CDKL5 deficiency disorder (CDD) and FOXG1-syndrome] represent rare yet profoundly impactful neurodevelopmental disorders (NDDs). The severity and complexity of symptoms associated with these disorders, including cognitive impairment, motor dysfunction, seizures and other neurological features significantly affect the quality of life of patients and families. Despite ongoing research efforts to identify potential therapeutic targets and develop novel treatments, current therapeutic options remain limited. Here the potential of drug repurposing (DR) as a promising avenue for addressing the unmet medical needs of individuals with RTT and related disorders is explored. Leveraging existing drugs for new therapeutic purposes, DR presents an attractive strategy, particularly suited for neurological disorders given the complexities of the central nervous system (CNS) and the challenges in blood-brain barrier penetration. The current landscape of DR efforts in these syndromes is thoroughly examined, with partiuclar focus on shared molecular pathways and potential common drug targets across these conditions.

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Authors: Shady Adnan Awad, Olli Dufva, Jay Klievink, Ella Karjalainen, Aleksandr Ianevski, Paavo Pietarinen, Daehong Kim, Swapnil Potdar, Maija Wolf, Kourosh Lotfi, Tero Aittokallio, Krister Wennerberg, Kimmo Porkka, & Satu Mustjok

Article

BCR::ABL1-independent pathways contribute to primary resistance to tyrosine kinase inhibitor (TKI) treatment in chronic myeloid leukemia (CML) and play a role in leukemic stem cell persistence. Here, we perform ex vivo drug screening of CML CD34⁺ leukemic stem/progenitor cells using 100 single drugs and TKI-drug combinations and identify sensitivities to Wee1, MDM2, and BCL2 inhibitors. These agents effectively inhibit primitive CD34⁺CD38⁻ CML cells and demonstrate potent synergies when combined with TKIs. Flow-cytometry-based drug screening identifies mepacrine to induce differentiation of CD34⁺CD38⁻ cells. We employ genome-wide CRISPR-Cas9 screening for six drugs, and mediator complex, apoptosis, and erythroid-lineage-related genes are identified as key resistance hits for TKIs, whereas the Wee1 inhibitor AZD1775 and mepacrine exhibit distinct resistance profiles. KCTD5, a consistent TKI-resistance-conferring gene, is found to mediate TKI-induced BCR::ABL1 ubiquitination. In summary, we delineate potential mechanisms for primary TKI resistance and non-BCR::ABL1-targeting drugs, offering insights for optimizing CML treatment.

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Authors: Yojana Gadiya, Simran Shetty, Martin Hoffmann-Apitius, Philip Gribbon & Andrea Zaliani 

Article

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

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Authors: Paul Arundel, Nick Bishop

Article

There are no licensed treatments for children with osteogenesis imperfecta. Children currently receive off-label treatment with bisphosphonates, without any consistent approach to dose, drug or route of administration. Meta-analyses suggest that anti-fracture efficacy of such interventions is equivocal. New therapies are undergoing clinical trials, and it is likely that one or more will receive marketing authorisation within the next three to five years. The long-term outcome from such interventions will need to be studied carefully well beyond the period over which the clinical trials are conducted, and a consistent approach to the collection of data in this regard will be needed as a major collaborative effort.

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Authors: Srijit Seal, Jordi Carreras-Puigvert, Shantanu Singh, Anne E. Carpenter, Ola Spjuth & Andreas Bender

Article

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. Overall, BioMorph space offers a biologically relevant way to interpret the cell morphological features derived using software such as CellProfiler and to generate hypotheses for experimental validation.

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Authors: Srijit Seal, Ola Spjuth, Layla Hosseini-Gerami, Miguel García-Ortegón, Shantanu Singh, Andreas Bender & Anne E. Carpenter

Article

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10–14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development

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Authors: Anneliene Hechtelt Jonker, Daniel O’Connor, Maria Cavaller-Bellaubi, Christine Fetro, Maria Gogou, Peter A. C. ‘T Hoen, Martin de Kort, Heather Stone, Nivedita Valentine &Anna Maria Gerdina Pasmooij

Article

Repurposing is one of the key opportunities to address the unmet rare diseases therapeutic need. Based on cases of drug repurposing in small population conditions, and previous work in drug repurposing, we analyzed the most important lessons learned, such as the sharing of clinical observations, reaching out to regulatory scientific advice at an early stage, and public-private collaboration. In addition, current upcoming trends in the field of drug repurposing in rare diseases were analyzed, including the role these trends could play in the rare diseases’ ecosystem. Specifically, we cover the opportunities of innovation platforms, the use of real-world data, the use of artificial intelligence, regulatory initiatives in repurposing, and patient engagement throughout the repurposing project. The outcomes from these emerging activities will help progress the field of drug repurposing for the benefit of patients, public health and medicines development.

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Authors: Yingjia Chen, Liye He, Aleksandr Ianevski, Pilar Ayuda-Durán, Swapnil Potdar, Jani Saarela, Juho J. Miettinen, Sari Kytölä, Susanna Miettinen, Mikko Manninen, Caroline A. Heckman, Jorrit M. Enserink, Krister Wennerberg & Tero Aittokallio

Article

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient’s responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

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Authors: Maria Kuzikov, Jeanette Reinshagen, Johanna Huchting, Andrea Zaliani, Philip Gribbon

Poster

Drug repurposing plays a pivotal role in addressing the therapeutic gaps for rare and neglected diseases by leveraging existing medications for ew indications. This approach accelerates the drug development process, offering a cost‐effective and efficient way to repurpose established drugs to meet the urgent medical needs of populations often overlooked by traditional pharmaceutical research. Fraunhofer ITMP creates an accessible value chain of compound libraries, assays, life science informatic to enable scientist and industry to facilitate their drug repurposing projects.

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Authors: Alfredo Budillon, Maria Serena Roca, Federica Iannelli, Veronica Barile, Rossella Migliorino, Eugenia Passaro, Cristina Testa, Laura Addi, Laura Grumetti, Tania Moccia, Carlo Vitagliano, Lucrezia Silvestro, Francesca Foschini, Carmine Carbone, Lorenzo Priori, Maddalena Fratelli, Laura Fiorenza, Maria Laura Garcia Bermejo, Bruno Sainz Jr, Francesca Bruzzese, Diana Giannarelli, Mercedes Rodriguez Garrote, Giampaolo Tortora, Michele Milella, Michele Reni, Elena Di Gennaro, Christine Kubiak, Eve Scott, Claudia Fuchs, Alessandra Leone, Antonio Avallone

Poster

Despite advances in cancer therapies, metastatic pancreatic ductal adenocarcinoma (mPDAC) patients have poor prognosis with median progression-free survival of the standard first-line gemcitabine/nab-paclitaxel-based chemotherapy (AG) not exceeding 7 months, suggesting the urgent need of novel treatments. We propose an innovative therapeutic strategy, based on the repurposing of valproic acid (VPA), an anticonvulsant with histone deacetylase inhibitor activity, and simvastatin (SIM), a generic cholesterol-lowering drug, in combination with AG. We tested antitumoral effects of VPA/SIM combination, alone or in association with chemotherapy in vitro in PDAC models demonstrating a strong synergistic anti-proliferative and pro-apoptotic effect of the combined treatment, in human and murine PDAC cells including patient-derived xenograft cells. Synergistic antitumor interaction was further observed as impairment of clonogenic capability as well as growth inhibition in 3D models, such as fibroblast/tumor cell microtissues and patient derived-organoids. Antitumor effect was confirmed in vivo on both heterotopic and orthotropic/metastatic xenograft PDAC models in nude mice. Transcriptomic analysis performed on tumor tissues from in vivo experiments revealed that VPA/SIM combination regulate several protumorigenic pathways through TGF-β and YAP signaling modulation as well as non-coding RNA. Overall, we proposed a novel and affordable combination therapy, based on two orally safe and generic drugs, to sensitize a widely employed first-line treatment in poor prognosis mPDAC patients, providing the rationale for investigating VPA/SIM plus AG-based chemotherapy in a multicentric randomized phase-2 study (VESPA trial; EudraCT 2022-004154-63; NCT05821556), that was launched in mPDAC with PFS as primary end-point. This trial, planned as patient-centric since the research design in collaboration with patients associations, is actively recruiting in Italian and Spanish centers.

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Authors: Filippo Lunghini, Anna Fava, Vincenzo Pisapia, Francesco Sacco, Daniela Iaconis, Carmine Talarico, Davide Graziani, Andrea Rosario Beccari

Poster

Repurposing refers to the identification of novel therapeutic applications for existing drugs, encompassing a range of common, challenging, and rare diseases. Conversely, polypharmacology entails the interaction of drug molecules with numerous targets across various therapeutic indications and diseases. Drugs exhibiting multi-targeting potential, hold significant appeal for repurposing, due to their ability to synergistically engage with multiple targets. In silico predictions are crucial for drug repurposing due to their ability to efficiently fill in sparse drug-targets annotations and suggesting new repurposing hypothesis. Herein we introduce ProfhEX, a suite of ligand-based AI-driven machine learning models for small molecules feature estimation, including PhysChem and ADME properties, multi-target interactions and potential liabilities. By leveraging a comprehensive knowledge base of >5M experimental data, ProfhEX currently predicts >30 ADME properties and drug-target interactions for >600 therapeutically relevant targets. The first version of ProfhEX, which focused on anticipating important liability profiles such as cardio-, neuro- and liver toxicities has already been published [1]. A further key feature of ProfhEX is the ability to identify potential metabolic pathway and automatically estimate drug’s most probable metabolites, enabling the evaluation of more complex polypharmacological interactions and therefore strengthen the discovery process. ProfhEX offers researchers a comprehensive tool to assess the safety and efficacy of potential drug candidates, and in the context of the R4ALL project, can can be a valuable tool for the correct in silico evaluation and characterization of drugs that could enter the drug repurposing process. The web application is free and reachable here: https://profhex.exscalate.eu/ [1] https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00728-6

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