7196 research outputs found
Sort by
Micelle-Dependent Spontaneous Formation of Gold(I) in Nanodendritic Chloride-Bridged Particles with Catalytic Activity for Cyclization of Alkynylanilines in an Aqueous Environment
No full textA novel proline-based amphiphile featuring a thiourea functional group has been synthesized for the purpose of controlled
reduction of Au(III) to Au(I) and subsequent stabilization of the resulting nanomaterial. This study elucidates the significant
role played by the newly developed amphiphile, along with chloride ions, in imparting a dendritic morphology to the resulting nanomaterial.Such morphology is pivotal for the catalytic activity of Au(I), as demonstrated in the cyclization reaction of alkynylanilines, conducted in water as a benign reaction medium. The structural characterization of the nanomaterial revealed intriguing associations between dendrimers and nanomicelles, as observed through cryogenic transmission electron microscopy. Further insights into the structures of dendritic Au are provided by scanning transmission electron microscopy-high angle annular dark field imaging and high-resolution transmission electron microscopy imaging. Additionally, spectroscopic analyses, including X-ray photoelectron and X-ray absorption spectroscopy, corroborate the presence of Au in the +1 oxidation state within the dendritic nanomaterial. The metalamphiphile binding is further supported by X-ray absorption fine structure fitting. Control cyclic voltammetry analysis confirms the amphiphile's mediation of Au(III) to Au(I) reduction. The dendritic morphology is significantly influenced by nucleophilic bromide ions, which disrupt bridging linkages formed by chloride ions. Under such conditions, the catalytic activity is adversely affected
Cure as a treatment target in rheumatoid arthritis and systemic sclerosis-achievable aim or mission impossible? FOREUM stimulates new industry-academia collaboration.
No full textNo abstrac
UNIQUE: A Framework for Uncertainty Quantification Benchmarking.
No full textMachine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or experimental testing. However, no ML model is robust or predictive in all real-world scenarios. Therefore, uncertainty quantification (UQ) in ML predictions has gained importance in recent years. Many investigations have focused on developing methodologies that provide accurate uncertainty estimates for ML-based predictions. Unfortunately, there is no UQ strategy that consistently provides robust estimates about model's applicability on new samples. Depending on the dataset, prediction task, and algorithm, accurate uncertainty estimations might be unfeasible to obtain. Moreover, the optimum UQ metric also varies across applications, and previous investigations have shown a lack of consistency across benchmarks. Herein, the UNIQUE (UNcertaInty QUantification bEnchmarking) framework is introduced to facilitate a comparison of UQ strategies in ML-based predictions. This Python library unifies the benchmarking of multiple UQ metrics, including the calculation of nonstandard UQ metrics (combining information from the dataset and model), and provides a comprehensive evaluation. In this framework, UQ metrics are evaluated for different application scenarios, e.g., eliminating the predictions with the lowest confidence or obtaining a reliable uncertainty estimate for an acquisition function. Taken together, this library will help to standardize UQ investigations and evaluate new methodologies
Population modeling of nilotinib exposure versus longitudinal BCR::ABL1 response in patients with chronic phase chronic myeloid leukemia using a semi-mechanistic disease model
No full textBackground: This study evaluated the exposure-efficacy relationship of nilotinib and longitudinal BCR::ABL1 levels in patients with newly diagnosed Philadelphia chromosome–positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) and those who are imatinib-resistant or intolerant using a semi-mechanistic model.
Methods: The analysis included 489 CML-CP patients from three nilotinib trials (NCT00109707; NCT00471497; NCT01043874) with duration of follow-up ranging from 2 to 9 years. The semi-mechanistic disease model of CML-CP consisted of quiescent leukemic stem cells (q), proliferating drug-susceptible (p) and resistant (r) bone marrow cells. Drug effect on the elimination of p cells was characterized by an Emax model based on the individual daily AUC0-24h simulated using their empirical Bayes estimates from a population pharmacokinetic model. The influence of line of therapy was evaluated on model parameters and its impact was investigated through simulations of the major molecular response (MMR) rate, defined as the proportion of the simulated profiles that achieved BCR::ABL1 level of ≤0.1% at 48 and 96 weeks of treatment.
Results: The final disease model was based on a truncated 3-year data that characterized the bi-phasic pattern of BCR::ABL1 transcript profiles. Line of therapy was a significant covariate of the drug kill effect, p and r cells. Simulations of BCR::ABL1 time course predicted MMR rates at 48 weeks and 96 weeks for both nilotinib 300 and 400 mg twice-daily of 66-71% and 77-82% in first-line, and 34-39% and 46-54% in second-line, respectively. Results are consistent with observed MMR rates in the respective trials.
Conclusions: The current disease model was developed using time-course of BCR::ABL1 transcript profiles of nilotinib in first- and second-line CML-CP. The ability to distinguish molecular response between lines of therapy is demonstrated using model-based analysis. These nilotinib information enable the extrapolation of novel TKI’s (e.g., asciminib) response to other lines of therapy in patients with CML-CP
[18F]NP3-627, a Candidate PET Imaging Agent Targeting the NLRP3 Inflammasome in the Central Nervous System.
No full textWe describe the identification of a candidate positron emission tomography (PET) imaging agent for the NLRP3 protein. NLRP3 plays a critical role in the immune system and has proven a difficult target for the development of imaging agents due to its low and cell-specific expression profile. A recently described series of pyridazine-based inhibitors, with improved permeability and brain-penetration properties, was used as a starting point for the development of a suitable PET imaging agent. Optimization of affinity, non-specific binding and pharmacokinetic properties led to the identification of aminopyridazine (R)-2-(6-((1-cyclopropylpiperidin-3-yl)amino)pyridazin-3-yl)-5-fluoro-3-methylphenol (17 b), which meets the preclinical profile of a successful imaging agent, and whose tritiated version demonstrated excellent specificity in a radioligand saturation binding assay, confirming its imaging potential.18F labeling led to [18F]NP3-627, the proposed PET imaging agent
Efficient internalization of nano architectured 177Lu-hyaluronic acid@ zirconium-based metal-organic framework for the treatment of neuroblastoma: Unravelling toxicity, stability, radiolabelling and bio-distribution
No full textZirconium-based metal-organic frameworks (UiO-66) have gained considerable attention owing to their versatile application. In the present research, UiO-66 was synthesized via a defect engineering approach, and its toxicity profile was explored. The synthesized nanomaterial was extensively characterized via spectroscopic methods such as FTIR and Raman spectroscopy, which confirmed the formation of the framework. X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to determine the crystallinity, shape and size of the nanoformulations. Thermal gravimetric analysis, 1H NMR spectroscopy and Brunauer–Emmett–Teller (BET) surface area analysis were used to identify the differences between pristine and defective UiO-66. Furthermore, the synthesized MOF was exposed to various pH conditions, serum protein and DMEM. Drug loading and release studies were evaluated using 5-fluorouracil as a model anticancer drug. The synthesized MOFs were modified with hyaluronic acid via mussel-inspired polymerization to increase their uptake and stability. More importantly, the toxicity of the nanoformulation was investigated via various toxicity studies, such as hemolysis assays and cell viability assays, and was further supported by in vivo acute and subacute toxicity data obtained from Wistar rats. Radiolabelling and bio-distribution studies were also performed using 177Lu to explore the bio-distribution profile of UiO-6
DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.
No full textAfter initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the evolution of the compound's concentration over time, typically in rodents' blood or plasma. From this concentration-time (C-t) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived. An early estimation of compounds' PK offers the promise of reducing animal studies and cycle times by selecting and designing molecules with increased chances of success at the PK stage. Even though C-t curves are the major readout from a PK study, most machine learning-based prediction efforts have focused on the derived PK parameters instead of C-t profiles, likely due to the lack of approaches to model the underlying ADME mechanisms. Herein, a novel deep learning approach termed DeepCt is proposed for the prediction of C-t curves from the compound structure. Our methodology is based on the prediction of an underlying mechanistic compartmental PK model, which enables further simulations, and predictions of single- and multiple-dose C-t profiles
Understanding Voriconazole Metabolism: A Middle-Out Physiologically-Based Pharmacokinetic Modelling Framework Integrating In Vitro and Clinical Insights.
No full textVoriconazole (VRC), a broad-spectrum antifungal drug, exhibits nonlinear pharmacokinetics (PK) due to saturable metabolic processes, autoinhibition and metabolite-mediated inhibition on their own formation. VRC PK is also characterised by high inter- and intraindividual variability, primarily associated with cytochrome P450 (CYP) 2C19 genetic polymorphism. Additionally, recent in vitro findings indicate that VRC main metabolites, voriconazole N-oxide (NO) and hydroxyvoriconazole (OHVRC), inhibit CYP enzymes responsible for VRC metabolism, adding to its PK variability. This variability poses a significant risk of therapeutic failure or adverse events, which are major challenges in VRC therapy. Understanding the underlying processes and sources of these variabilities is essential for safe and effective therapy. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) modelling framework that elucidates the complex metabolism of VRC and the impact of its metabolites, NO and OHVRC, on the PK of the parent, leveraging both in vitro and in vivo clinical data in a middle-out approach.A coupled parent-metabolite PBPK model for VRC, NO and OHVRC was developed in a stepwise manner using PK-Sim® and MoBi®. Based on available in vitro data, NO formation was assumed to be mediated by CYP2C19, CYP3A4, and CYP2C9, while OHVRC formation was attributed solely to CYP3A4. Both metabolites were assumed to be excreted via renal clearance, with hepatic elimination also considered for NO. Inhibition functions were implemented to describe the complex interaction network of VRC autoinhibition and metabolite-mediated inhibition on each CYP enzyme.Using a combined bottom-up and middle-out approach, incorporating data from multiple clinical studies and existing literature, the model accurately predicted plasma concentration-time profiles across various intravenous dosing regimens in healthy adults, of different CYP2C19 genotype-predicted phenotypes. All (100%) of the predicted area under the concentration-time curve (AUC) and 94% of maximum concentration (Cmax) values of VRC met the 1.25-fold acceptance criterion, with overall absolute average fold errors of 1.12 and 1.14, respectively. Furthermore, all predicted AUC and Cmax values of NO and OHVRC met the twofold acceptance criterion.This comprehensive parent-metabolite PBPK model of VRC quantitatively elucidated the complex metabolism of the drug and emphasised the substantial impact of the primary metabolites on VRC PK. The comprehensive approach combining bottom-up and middle-out modelling, thereby accounting for VRC autoinhibition, metabolite-mediated inhibition, and the impact of CYP2C19 genetic polymorphisms, enhances our understanding of VRC PK. Moreover, the model can be pivotal in designing further in vitro experiments, ultimately allowing for extrapolation to paediatric populations, enhance treatment individualisation and improve clinical outcomes
Discovery of NP3-253, a potent brain penetrant inhibitor of the NLRP3 Inflammasome
No full textActivation of the NLRP3 inflammasome in response to danger signals is a key innate immune mechanism, and results in the production of the pro-inflammatory cytokines interleukin 1-beta (IL-1β) and interleukin-18 (IL-18), as well as pyroptotic cell death. Aberrant NLRP3 activation has been linked to many acute and chronic conditions ranging from atherosclerosis to Alzheimer’s disease, and cancer, and based on the clinical success of IL-1-targeting therapies, NLRP3 has emerged as an attractive therapeutic target, with the first wave of NLRP3 inhibitors entering clinical trials. Herein, we describe our discovery, characterization, and structure-based optimization of a pyridazine based series of NLRP3 inhibitors initiating from an HTS campaign. The scaffold, exemplified by lead molecule NP3-253 has excellent potency, physicochemical, and PK properties including significant brain penetration. The establishment of PK/PD relationships in the periphery and CNS in mechanistic models facilitate the use of NP3-253 as a tool to further interrogate the biology of NLRP3 in peripheral and neuroinflammatory models
NIBR-LTSi is a selective LATS kinase inhibitor activating YAP signaling and expanding tissue stem cells in vitro and in vivo.
No full textThe YAP/Hippo pathway is an organ growth and size regulation rheostat safeguarding multiple tissue stem cell compartments. LATS kinases phosphorylate and thereby inactivate YAP, thus representing a potential direct drug target for promoting tissue regeneration. Here, we report the identification and characterization of the selective small-molecule LATS kinase inhibitor NIBR-LTSi. NIBR-LTSi activates YAP signaling, shows good oral bioavailability, and expands organoids derived from several mouse and human tissues. In tissue stem cells, NIBR-LTSi promotes proliferation, maintains stemness, and blocks differentiation in vitro and in vivo. NIBR-LTSi accelerates liver regeneration following extended hepatectomy in mice. However, increased proliferation and cell dedifferentiation in multiple organs prevent prolonged systemic LATS inhibition, thus limiting potential therapeutic benefit. Together, we report a selective LATS kinase inhibitor agonizing YAP signaling and promoting tissue regeneration in vitro and in vivo, enabling future research on the regenerative potential of the YAP/Hippo pathway