RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

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.

Optimizing Drug Combinations for T-PLL: Restoring DNA Damage and P53-Mediated Apoptotic Responses.

T-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, i.e. the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL's pathobiology fostered the identification of actionable vulnerabilities: (i) altered epigenetics, (ii) defective DNA damage responses, (iii) aberrant cell-cycle regulation, and (iv) deregulated pro-survival pathways, including TCR and JAK/STAT signaling. To further develop related pre-clinical therapeutic concepts, we studied inhibitors of (H)DACs, BCL2, CDK, MDM2, and clas-sical cytostatics, utilizing (a) single-agent and combinatorial compound testing in 20 well-characterized and molecularly-profiled primary T-PLL (validated by additional 42 cases), and (b) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single-agents and combinations (in vitro and in mice) in-cluded Cladribine, Romidepsin ((H)DAC), Venetoclax (BCL2), and/or Idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance towards MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activa-tion and down-stream signals (including enhanced accessibility of target-gene chromatin re-gions), in particular synergy with insults by Cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

ScType enables fast and accurate cell type identification from spatial transcriptomics data.

SUMMARY: The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation. AVAILABILITY AND IMPLEMENTATION: scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.

Integrated drug profiling and CRISPR screening identify BCR::ABL1-independent vulnerabilities in chronic myeloid leukemia.

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.

The combination of pleconaril, rupintrivir, and remdesivir efficiently inhibits enterovirus infections in vitro, delaying the development of drug-resistant virus variants.

Enteroviruses are a significant global health concern, causing a spectrum of diseases from the common cold to more severe conditions like hand-foot-and-mouth disease, meningitis, myocarditis, pancreatitis, and poliomyelitis. Current treatment options for these infections are limited, underscoring the urgent need for effective therapeutic strategies. To find better treatment option we analyzed toxicity and efficacy of 12 known broad-spectrum anti-enterovirals both individually and in combinations against different enteroviruses in vitro. We identified several novel, synergistic two-drug and three-drug combinations that demonstrated significant inhibition of enterovirus infections in vitro. Specifically, the triple-drug combination of pleconaril, rupintrivir, and remdesivir exhibited remarkable efficacy against echovirus (EV) 1, EV6, EV11, and coxsackievirus (CV) B5, in human lung epithelial A549 cells. This combination surpassed the effectiveness of single-agent or dual-drug treatments, as evidenced by its ability to protect A549 cells from EV1-induced cytotoxicity across seven passages. Additionally, this triple-drug cocktail showed potent antiviral activity against EV-A71 in human intestinal organoids. Thus, our findings highlight the therapeutic potential of the pleconaril-rupintrivir-remdesivir combination as a broad-spectrum treatment option against a range of enterovirus infections. The study also paves the way towards development of strategic antiviral drug combinations with virus family coverage and high-resistance barriers.