7196 research outputs found
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torchsurv: a lightweight package for deep survival analysis
No full texttorchsurv is a Python package that serves as a companion tool for survival modeling
within the PyTorch framework. It offers functionalities for computing log-likelihoods of
common survival models and evaluating their predictive performance. torchsurv distin-
guishes itself by providing a simple and accessible programming interface with PyTorch
backend. Its lightweight design, requiring minimal input specifications and avoiding re-
strictive survival modelparameterizations, allows efficient model implementation for high-
dimensional input data. torchsurv is designed to support users, not to make them jump
through hoops by providing a simple yet rich set of survival tools
Editorial: Incorporating Phase 0 microdosing as a powerful tool into a new vision of drug development.
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6-Hydroxy Picolinohydrazides Promoted Cu(I)-Catalyzed Hydroxylation Reaction in Water: Machine-Learning Accelerated Ligands Design and Reaction Optimization.
No full textHydroxylated (hetero)arenes are privileged motifs in natural products, materials, small-molecule pharmaceuticals and serve as versatile intermediates in synthetic organic chemistry. Herein, we report an efficient Cu(I)/6-hydroxy picolinohydrazide-catalyzed hydroxylation reaction of (hetero)aryl halides (Br, Cl) in water. By establishing machine learning (ML) models, the design of ligands and optimization of reaction conditions were effectively accelerated. The N-(1,3-dimethyl-9H- carbazol-9-yl)-6-hydroxypicolinamide (L32, 6-HPA-DMCA) demonstrated high efficiency for (hetero)aryl bromides, promoting hydroxylation reactions with a minimal catalyst loading of 0.01 mol % (100 ppm) at 80 °C to reach 10000 TON; for substrates containing sensitive functional groups, the catalyst loading needs to be increased to 3.0 mol % under near-room temperature conditions. N-(2,7-Di-tert-butyl-9H-carbazol-9-yl)-6-hydroxypicolinamide (L42, 6-HPA-DTBCA) displayed superior reaction activity for chloride substrates, enabling hydroxylation reactions at 100 °C with 2-3 mol % catalyst loading. These represent the state of art for both lowest catalyst loading and temperature in the copper-catalyzed hydroxylation reactions. Furthermore, this method features a sustainable and environmentally friendly solvent system, accommodates a wide range of substrates, and shows potential for developing robust and scalable synthesis processes for key pharmaceutical intermediates
Drug interaction studies of cabamiquine:ganaplacide combination against hepatic Plasmodium berghei
No full textNew antimalarial combination therapies with novel modes of action are required to counter the emergence and spread of Plasmodium drug resistance against existing therapeutics. Here, we present a study to evaluate the preventive activity of a combination of clinical antimalarial drug candidates, cabamiquine and ganaplacide, that have multi-stage activity against the liver and blood stages of Plasmodium infection. Cabamiquine (DDD107498, M5717) inhibits parasite protein synthesis and ganaplacide (KAF156) inhibits protein trafficking, blocks the establishment of new permeation pathways, and causes endoplasmic reticulum expansion. The pharmacodynamic parameters of a combination of the two compounds were assessed employing a pharmacometrics approach in conjunction with in vitro-in silico checkerboard analysis. The in vitro study was performed on a previously established 3D infection platform based on hepatic cell lines that sustain infection with rodent P. berghei parasites. The in vivo efficacy of this drug combination was assessed against the liver stage of P. berghei strain mCherry (Anka-Luci-GFP) ICB. Our results show that the combination of both drugs does not affect hepatocyte cell viability at the tested concentrations, and our analysis indicates that these drugs do not interfere with their respective mode of action. The drug-combination was fully effective in preventing the appearance of blood stage parasites when a systemic plasma Cav0-24/EC50 ratio >2 for ganaplacide and >5 for cabamiquine was achieved. These findings demonstrate that chemoprevention using a combination of cabamiquine and ganaplacide has the potential to target the asymptomatic liver stage of Plasmodium infection and prevent the development of parasitemia
An evaluation of six techniques for measuring porosity of ribbons produced by roller compaction.
No full textRibbon porosity is a critical parameter to monitor in the roller compaction process. In this study, six techniques for measuring the porosity of solid compacts, i.e., manually by caliper (Caliper), X-ray microtomography (µCT), off-line near-infrared spectroscopy (NIR), laser triangulation (Laser), mercury intrusion porosimetry (MIP), and GeoPyc, were compared using a set of rectangular ribblets of microcrystalline cellulose (MCC). These ribblets, which were compressed at 8-130 MPa on a compaction simulator, exhibited porosities over the range of 0.09 - 0.52. Subsequently, porosities of MCC ribbons made on a roller compactor at specific roll forces of 1.8 kN/cm and 8.8 kN/cm were measured. The Caliper method is convenient for samples with a simple shape but not suitable for real ribbons. The accuracy of GeoPyc measurement relies on accurate conversion factor (unit in cm3/mm), sample shape and size, and sufficient sample volume percentage in the medium. The µCT data is more accurate at lower porosities ( 0.4). The Laser method has good accuracy and is more reproducible compared to other methods in the ribblets measurement. The NIR method is fast, which makes it suitable for in-line monitoring of changes in ribbon quality, but porosity quantification is sensitive to sample presentation, such as surface curvature and roughness. These insights could assist in the choice of the most appropriate method for monitoring ribbon porosity to guide the development and optimization of a roller compaction process for a given formulation
Explainable artificial intelligence for targeted protein degradation predictions
No full textDefining structure-activity relationships (SAR) is a central task in medicinal chemistry. Apart from optimizing activity against the target of interest, off-target activities and other properties need to be balanced to ensure a suitable property profile, which is an exceptional challenge in drug design. Machine learning (ML) can identify structural patterns in large compound collections that are correlated to biological activity or other molecular properties. Such ML-based SAR modeling has the potential of greatly assisting in compound optimization. However, the black-box character of most ML models has limited their application to help establishing SAR hypotheses. Explainable ML or, more generally, explainable artificial intelligence (XAI) aims at “opening the black box” by estimating how model inputs – e.g., chemical structures – contribute to model predictions. Although a variety of model interpretation methods have been proposed, XAI for medicinal chemistry is still an active field of research and XAI strategies are still dominated by proofs of concept rather than by practical applications in drug discovery programs. Moreover, with the advent of new modalities, the applicability of ML and XAI models remains under-investigated. Herein, we present a novel application of XAI methods to targeted protein degradation (TPD) predictions. We report a case study of ML-based SAR modeling with explainable predictions for Cereblon (CRBN) glues for GSPT1 (G1 to S phase transition 1). XAI results were able to mirror expert knowledge based on structural data. Importantly, quantitative evaluations showed the ability of our ML/XAI workflow to accurately describe TPD activity cliffs across different data sets. These findings support use of our proposed XAI strategy to help rationalizing model predictions and illustrates how XAI methods can be exploited to balance SAR across different targets even for drug discovery programs focusing on the new modality of TPDs
Modelling of patient journey in chronic spontaneous urticaria: Increasing awareness and education by shorten patients' disease journey in Germany.
No full textBACKGROUND
Chronic spontaneous urticaria (CSU) is both physically and emotionally stressful, and guideline recommendations are often not optimally implemented in clinical practice. The objective of this study was to provide an overview on the patient journey in CSU and to develop a mathematical model based on solid data.
METHODS
The journey of CSU patients in Germany was traced through literature review and expert meetings that included medical experts, pharmacists and representatives of patient organizations. The current situation's main challenges in the patient journey (education, collaboration and disease management) were discussed in depth. Then, a probabilistic model was developed in a co-creation approach to simulate the impact of three potential improvement strategies: (1) patient education campaign, (2) medical professional education programme and (3) implementation of a disease management programme (DMP).
RESULTS
Chronic spontaneous urticaria patients are severely burdened by delays in diagnosis and optimal medical care. Our simulation indicates that in Germany, it takes on average of 3.8 years for patients to achieve disease control in Germany. Modelling all three optimization strategies resulted in a reduction to 2.5 years until CSU symptom control. On a population level, the proportion of CSU patients with disease control increased from 44.2% to 58.1%.
CONCLUSIONS
In principle, effective CSU medications and a disease-specific guideline are available. However, implementation of recommendations is lagging in practice. The approach of quantitative modelling of the patient journey validates obstacles and shows a clear effect of multiple interventions on the patient journey. The data generated by our simulation can be used to identify strategies for improving patient care. Our approach might helping in understanding and improving the management of patients beyond CSU
Cardiomyocyte crosstalk with endothelium modulates cardiac structure, function, and ischemia-reperfusion injury susceptibility through erythropoietin.
No full textErythropoietin (EPO) exerts non-canonical roles beyond erythropoiesis that are developmentally, structurally, and physiologically relevant for the heart as a paracrine factor. The role for paracrine EPO signalling and cellular crosstalk in the adult is uncertain. Here, we provided novel evidence showing cardiomyocyte restricted loss of function in in adult mice induced hyper-compensatory increases in expression by adjacent cardiac endothelial cells via HIF-2α independent mechanisms. These hearts showed concentric cellular hypertrophy, elevated contractility and relaxation, and greater resistance to ischemia-reperfusion injury. Voluntary exercise capacity compared to control hearts was improved independent of any changes to whole-body metabolism or blood O content or delivery (i.e., hematocrit). Our findings suggest cardiac EPO had a localized effect within the normoxic heart, which was regulated by cell-specific EPO-reciprocity between cardiomyocytes and endothelium. Within the heart, hyper-compensated end
Discovery of WRN inhibitor HRO761 with synthetic lethality in MSI cancers
No full textThe Werner syndrome RecQ helicase WRN was identified as a synthetic lethal target in cancer cells with microsatellite instability (MSI) by several genetic screens1,2,3,4,5,6. Despite advances in treatment with immune checkpoint inhibitors7,8,9,10, there is an unmet need in the treatment of MSI cancers11,12,13,14. Here we report the structural, biochemical, cellular and pharmacological characterization of the clinical-stage WRN helicase inhibitor HRO761, which was identified through an innovative hit-finding and lead-optimization strategy. HRO761 is a potent, selective, allosteric WRN inhibitor that binds at the interface of the D1 and D2 helicase domains, locking WRN in an inactive conformation. Pharmacological inhibition by HRO761 recapitulated the phenotype observed by WRN genetic suppression, leading to DNA damage and inhibition of tumour cell growth selectively in MSI cells in a p53-independent manner. Moreover, HRO761 led to WRN degradation in MSI cells but not in microsatellite-stable cells. Oral treatment with HRO761 resulted in dose-dependent in vivo DNA damage induction and tumour growth inhibition in MSI cell- and patient-derived xenograft models. These findings represent preclinical pharmacological validation of WRN as a therapeutic target in MSI cancers. A clinical trial with HRO761 (NCT05838768) is ongoing to assess the safety, tolerability and preliminary anti-tumour activity in patients with MSI colorectal cancer and other MSI solid tumours
Automated Carcinogenic Potency Categorization Approach for Nitrosamine Drug Substance-related Impurities
No full textNitrosoamines are known carcinogens that can form during chemical syntheses. Recently released health authority guidelines have defined acceptable intake limits for various nitrosoamines, requiring improved risk assessment approaches. We have developed a web-based application that autonomously predicts the nitrosoamine potency category of compounds from their SMILES notation as high, moderate, or low risk per health authority limits. An automated prediction algorithm categorized nitrosoamine potency based on the encoded molecular structure. The application was trained and validated on datasets of compounds with known nitrosoamine formation potential. It provides instant potency screening to identify high risk nitrosoamine formations. Making these predictions widely accessible enables chemists to proactively mitigate nitrosoamine hazards during process development. This represents a step toward greener, safer chemicals and processes in alignment with public health priorities. The web-based interface and focus on regulatory compliance differentiates this tool from prior in silico models. User testing demonstrates its utility for accelerating low-risk chemical design. This predictive toxicology approach could be extended to other compound classes. Overall, the application integrates computational and regulatory science to advance environmental and human health priorities