An in silico method has been developed that permits the binary differentiation between pure liquids causing serious eye damage or eye irritation, and pure liquids with no need for such classification, according to the UN GHS system. The method is based on the finding that the Hansen Solubility Parameters (HSP) of a liquid are collectively important predictors for eye irritation. Thus, by applying a two-tier approach in which in silico predicted pKa values (firstly) and a trained model based solely on in silico-predicted HSP data (secondly) were used, we have developed, and validated, a fully in silico approach for predicting the outcome of a Draize test (in terms of UN GHS Cat. 1/Cat. 2A/Cat. 2B or UN GHS No Cat.) with high validation set performance (sensitivity = 0.846, specificity = 0.818, balanced accuracy = 0.832) using SMILES only. The method is applicable to pure non-ionic liquids with molecular weight below 500 g/mol, fewer than six hydrogen bond donors (e.g. nitrogen–hydrogen or oxygen–hydrogen bonds) and fewer than eleven hydrogen bond acceptors (e.g. nitrogen or oxygen atoms). Due to its fully in silico characteristics, this method can be applied to pure liquids that are still at the desktop design stage and not yet in production.

Objectives: New drugs and methods to efficiently fight carbapenem-resistant gram-negative pathogens are sorely needed. In this study, we characterized the preclinical pharmacokinetics (PK) and pharmacodynamics of the clinical stage drug candidate apramycin in time kill and mouse lung infection models. Based on in vitro and in vivo data, we developed a mathematical model to predict human efficacy. Methods: Three pneumonia-inducing gram-negative species Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae were studied. Bactericidal kinetics were evaluated with time-kill curves; in vivo PK were studied in healthy and infected mice, with sampling in plasma and epithelial lining fluid after subcutaneous administration; in vivo efficacy was measured in a neutropenic mouse pneumonia model. A pharmacokinetic-pharmacodynamic model, integrating all the data, was developed and simulations were performed. Results: Good lung penetration of apramycin in epithelial lining fluid (ELF) was shown (area under the curve (AUC)ELF/AUCplasma = 88%). Plasma clearance was 48% lower in lung infected mice compared to healthy mice. For two out of five strains studied, a delay in growth (∼5 h) was observed in vivo but not in vitro. The mathematical model enabled integration of lung PK to drive mouse PK and pharmacodynamics. Simulations predicted that 30 mg/kg of apramycin once daily would result in bacteriostasis in patients. Discussion: Apramycin is a candidate for treatment of carbapenem-resistant gram-negative pneumonia as demonstrated in an integrated modeling framework for three bacterial species. We show that mathematical modelling is a useful tool for simultaneous inclusion of multiple data sources, notably plasma and lung in vivo PK and simulation of expected scenarios in a clinical setting, notably lung infections. © 2022 The Author(s)

In the past 15 years, close to 1000 of new psychoactive substances (NPS) have been reported in Europe and globally. At the time of identification, data on safety, toxicity and carcinogenic potential of many NPS are not available or very limited. To work more efficiently, a strategy and collaboration between the Public Health Agency of Sweden (PHAS) and the National Board of Forensic Medicine was established involving in vitro receptor activity assays to demonstrate neurological activity of NPS. This report summarizes the first results on the synthetic cannabinoid receptor agonists (SCRAs), and subsequent actions taken by PHAS. A total of 18 potential SCRAs were selected by PHAS for in vitro pharmacological characterization. 17 compounds could be acquired and investigated for their activity on the human cannabinoid-1 (CB1) receptors expressed together with the AequoScreen system in CHO-K1 cells. Dose-response curves were established using eight different concentrations in triplicates at three occasions with JWH-018 as reference. For the MDMB-4en-PINACA, MMB-022, ACHMINACA, ADB-BUTINACA, 5F-CUMYL-PeGACLONE, 5C-AKB48, NM-2201, 5F-CUMYL-PINACA, JWH-022, 5Cl-AB-PINACA, MPhP-2201, 5F-AKB57 the half maximal effective concentration values ranged from 2.2 nM (5F-CUMYL-PINACA) to 171 nM (MMB-022). EG-018 and 3,5-AB-CHMFUPPYCA were none-active. The results contributed to 14 of these compounds being scheduled as narcotics in Sweden. In conclusion, many of the emerging SCRAs are potent activators of the CB1 receptor in vitro, although some lack activity or are partial agonists. The new strategy proved useful when data on psychoactive effects of the SCRAs under investigation were not available or limited. © 2023 The Authors

A new series of dual low nanomolar benzothiazole inhibitors of bacterial DNA gyrase and topoisomerase IV were developed. The resulting compounds show excellent broad-spectrum antibacterial activities against Gram-positive Enterococcus faecalis, Enterococcus faecium and multidrug resistant (MDR) Staphylococcus aureus strains [best compound minimal inhibitory concentrations (MICs): range, <0.03125-0.25 μg/mL] and against the Gram-negatives Acinetobacter baumannii and Klebsiella pneumoniae (best compound MICs: range, 1-4 μg/mL). Lead compound 7a was identified with favorable solubility and plasma protein binding, good metabolic stability, selectivity for bacterial topoisomerases, and no toxicity issues. The crystal structure of 7a in complex with Pseudomonas aeruginosa GyrB24 revealed its binding mode at the ATP-binding site. Expanded profiling of 7a and 7h showed potent antibacterial activity against over 100 MDR and non-MDR strains of A. baumannii and several other Gram-positive and Gram-negative strains. Ultimately, in vivo efficacy of 7a in a mouse model of vancomycin-intermediate S. aureus thigh infection was also demonstrated. © 2023 The Authors. 

Nitric oxide (NO) contributes to maintaining normal cardiovascular and renal function. NO is generally formed enzymatically by NO synthase in the vascular endothelium. NO bioactivity can also be attributed to dietary intake of inorganic nitrate, which is abundant in our diet, especially in green leafy vegetables and beets. Ingested nitrate is reduced to nitrite by oral commensal bacteria and further to NO systemically. Previous studies have shown that dialysis, by means of removing nitrate and nitrite from the body, can reduce NO bioactivity. Hence, dietary intervention approaches aimed to boost the nitrate–nitrite–NO pathway may be of benefit in dialysis patients. The purpose of this study was to examine the kinetics of plasma nitrate and nitrite after a single intake of nitrate-rich concentrated beetroot juice (BJ) in adult hemodialysis (HD) patients and in healthy volunteers (HV). Eight HD patients and seven HV participated in this single center, randomized, single-blind, placebo-controlled, crossover study. Each participant received a sequential single administration of active BJ (70 mL 400 mg nitrate) and placebo BJ (70 mL 0 mg nitrate) in a random order separated by a washout period of seven days. For the kinetic analysis, blood samples were collected at different time-points before and up to 44 h after BJ intake. Compared with placebo, active BJ significantly increased plasma nitrate and nitrite levels both in HD patients and HV. The area under the curve and the maximal concentration of plasma nitrate, but not of nitrite, were significantly higher in HD patients as compared with HV. In both groups, active BJ ingestion did not affect blood pressure or plasma potassium levels. Both BJs were well tolerated in all participants with no adverse events reported. Our data provide useful information in planning dietary nitrate supplementation efficacy studies in patients with reduced NO bioactivity. © 2022 by the authors. 

In several countries forest-based biofuels are being developed and to some extent also deployed. Fast pyrolysis bio-oil produced from, for example, sawdust, has now been coprocessed in fluid catalytic cracking refinery units in a number of commercial trials. However, this application is limited to about 10% of the total feed, and coprocessing in conventional fixed bed hydrotreaters is necessary to reach the high potential with this feedstock. Feeding and upgrading of fast pyrolysis bio-oil in a fixed bed reactor configuration is still problematic due to the inherent bio-oil properties. Stabilization of reactive compounds in fast pyrolysis bio-oil and mild hydrotreatment in a separate refining unit prior to refinery integration has therefore been developed the past decade. Another approach, presented here, involves complete dewatering of fast pyrolysis bio-oil by azeotropic distillation using mesityl oxide as the solvent, followed by conversion of the abundant hydroxyl compounds via mixed anhydride esterification methodology using an external source of mixed carboxylic acids of different chain lengths originating from renewable tall oil fatty acids, providing a lipophilic feed component. Dewatering and derivatizing were carried out in reactors up to 50 dm3 with a mass ratio of fast pyrolysis bio-oil to tall oil fatty acid of 10:13. The produced lipophilic oils were miscible with a petroleum light gas oil fraction and exhibited superior stability even after accelerated aging at elevated temperature (80 °C). The derivatized oils were thus mixed with light gas oil, with a proportion of 30 wt % derivatized oil in final blends and hydrotreated continuously in pilot fixed bed reactors for 14 days at 4 operating conditions without plugging or excessive exotherms. The test conditions were varied; the reactor pressure was either 55 or 80 bar, temperature 380 or 400 °C, and liquid hourly space velocity either 1 or 2 h-1 during the hydrotreatment. Successful hydrodeoxygenation and desulfurization were accomplished, whereas an increasing nitrogen concentration could be observed in the liquid products with the particular catalyst and reaction conditions employed. The observed hydrogen consumption (15-20 g/kg feed) was compared with the stoichiometric consumption for direct deoxygenation and with typical consumptions for industrial hydrotreated vegetable oil processing. The measured biogenic carbon content in hydrotreated liquid products (26.7%) agreed extremely well with the calculated biogenic carbon content in the hydrotreating feed (26.6%) that consisted of the blend of derivatized oil and petroleum light gas oil. The overall results are very promising since simple unit operations can be used to produce derivatized fast pyrolysis bio-oils that do not need additional standalone hydrotreating units but can be coprocessed in existing ones

A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLMs), and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff), and P-glycoprotein (P-gp) substrate identification (P-gp substrate) in the software ADMET Predictor (AP) from Simulations Plus. The dataset consisted of a total of 4, 794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R25 0.79), guiding the synthesis of new compounds and for prioritizing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability, and permeability by using artificial neural network (ANN) models in the optional Modeler module of AP. Predictions of the test sets were performed with both the global and the local models, and the R2 values for linear regression for predicted versus measured HLM in vitro intrinsic clearance (CLint) based on logarithmic data were 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and laboratory-specific assay conditions for parameters that require biologic assay systems. .

The rise of artificial intelligence (AI) based algorithms has gained a lot of interest in the pharmaceutical development field. Our study demonstrates utilization of traditional machine learning techniques such as random forest (RF), support-vector machine (SVM), extreme gradient boosting (XGBoost), deep neural network (DNN) as well as advanced deep learning techniques like gated recurrent unit-based DNN (GRU-DNN) and graph neural network (GNN), towards predicting human ether-á-go-go related gene (hERG) derived toxicity. Using the largest hERG dataset derived to date, we have utilized 203,853 and 87,366 compounds for training and testing the models, respectively. The results show that GNN, SVM, XGBoost, DNN, RF, and GRU-DNN all performed well, with validation set AUC ROC scores equals 0.96, 0.95, 0.95, 0.94, 0.94 and 0.94, respectively. The GNN was found to be the top performing model based on predictive power and generalizability. The GNN technique is free of any feature engineering steps while having a minimal human intervention. The GNN approach may serve as a basis for comprehensive automation in predictive toxicology. We believe that the models presented here may serve as a promising tool, both for academic institutes as well as pharmaceutical industries, in predicting hERG-liability in new molecular structures.