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Chlorhexidine (CHX) represents one of the most commonly used antiseptics in dentistry and other medical–pharmaceutical fields due to its broad-spectrum antimicrobial activity. However, the potential toxic events arising from its common use in practice has become a subject of increasing concern. Thus, the present study was designed to investigate the potential toxicity of CHX digluconate at concentrations covering its antibacterial properties (0.0002–0.2%) in HGF primary gingival fibroblasts, HaCaT immortalized human keratinocytes, and JB6 Cl 41-5a epidermal cells, as well as its irritant action in ovo. Our results indicate that CHX exerted a concentration- and time-dependent cytotoxicity in all cell lines, which was evidenced by the reduction in cell viability, number, and confluence, damaged cell membrane integrity, impaired cell morphology, and specific apoptotic nuclear shape. The highest cytotoxicity was caused by CHX digluconate 0.02% and 0.2%, concentrations, at which an irritant effect on the chorioallantoic membrane was also observed. The novel findings revealed in this research contribute to the overall safety profile of CHX and stand as a basis for further investigations in this regard. [ABSTRACT FROM AUTHOR]

Blueberry fruits have been widely explored for their rich composition of bioactive compounds with recognized health benefits. In contrast, blueberry pruning waste (BPW), generated during the pruning stages of blueberries, has been typically overlooked, even though it can represent a potential source of natural antioxidants. This study aims to characterize the value-added compounds extracted from BPW using green techniques, namely microwave-assisted and subcritical water extraction. The total phenolic content ranged from 157 ± 5 to 335 ± 12 mg GAE/g dw, while the radical scavenging activity determined by a DPPH assay varied from 223 ± 21 to 453 ± 21 mg Trolox equivalents/g dw. Additionally, to ensure the safe application of BPW and its extracts, a screening of pesticides and several environmental contaminants was conducted. Chlorpyrifos-methyl was quantified at a concentration of 4.27 µg/kg in a Bluecrop variety collected in 2019; however, none of the studied compounds were found in the extracts. Despite the presence of a pesticide, this level was below the maximum residue limits for blueberry crops. The results of this study demonstrated the potential of this agro-industrial residue as a natural source of bioactive compounds with high antioxidant activity for food industry applications. [ABSTRACT FROM AUTHOR]

Biological effect-based monitoring is essential for predicting or alerting to a possible deterioration in drinking water quality. In the present study, a reporter gene assay based on oxidative stress–mediated Pgst-4::GFP induction in the Caenorhabditis elegans strain VP596 (VP596 assay) was assessed for its applicability in evaluating drinking water safety and quality. This assay was used to measure the oxidative stress response in VP596 worms exposed to six ubiquitous components (As3+, Al3+, F−, NO3−-N, CHCl3, and residual chlorine) in drinking water, eight mixtures of these six components designed through orthogonal design, ninety-six unconcentrated water samples from source to tap water in two supply systems, and organic extracts (OEs) of twenty-five selected water samples. Pgst-4::GFP fluorescence was not induced by Al3+, F−, NO3−-N, and CHCl3, and was significantly enhanced by As3+ and residual chlorine only at concentrations higher than their respective drinking water guideline levels. Pgst-4::GFP induction was not detected in any of the six-component mixtures. Induction of Pgst-4::GFP was observed in 9.4% (3/32) of the source water samples but not in the drinking water samples. However, a notable induction effect was revealed in the three OEs of drinking water, with a relative enrichment factor of 200. These results suggest that the VP596 assay has limited utility for screening drinking water safety by testing unconcentrated water samples; however, it offers a supplemental in vivo tool for prioritizing water samples for an enhanced quality assessment, monitoring pollutant removal performance by drinking water treatment plants, and evaluating water quality in water supplies. [ABSTRACT FROM AUTHOR]

Blueberry fruits have been widely explored for their rich composition of bioactive compounds with recognized health benefits. In contrast, blueberry pruning waste (BPW), generated during the pruning stages of blueberries, has been typically overlooked, even though it can represent a potential source of natural antioxidants. This study aims to characterize the value-added compounds extracted from BPW using green techniques, namely microwave-assisted and subcritical water extraction. The total phenolic content ranged from 157 ± 5 to 335 ± 12 mg GAE/g dw, while the radical scavenging activity determined by a DPPH assay varied from 223 ± 21 to 453 ± 21 mg Trolox equivalents/g dw. Additionally, to ensure the safe application of BPW and its extracts, a screening of pesticides and several environmental contaminants was conducted. Chlorpyrifos-methyl was quantified at a concentration of 4.27 µg/kg in a Bluecrop variety collected in 2019; however, none of the studied compounds were found in the extracts. Despite the presence of a pesticide, this level was below the maximum residue limits for blueberry crops. The results of this study demonstrated the potential of this agro-industrial residue as a natural source of bioactive compounds with high antioxidant activity for food industry applications.

Abstract Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.

Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed. [ABSTRACT FROM AUTHOR]

Introduction: Investigations show that the scattering of accidents in individuals is not the same, and three quarters of all accident occur to one quarter of accident prone individuals. The aim of the study was to design and validate a questionnaire for screening out the accident prone individuals. Methods: This study in 5 stages of item generation, content validity assessment, reliability, construct validity, and criterion validity was performed among 230 workers who had and who had not experienced accident in 19 industries of Isfahan, Iran. For data analysis SPSS and Amos 22 were used. Results: 195 items were identified during item generation stage. In content validity evaluation stage 13 items were removed, 15 items were modified and 79 items were merged and convered to 14 items. Internal consistency (Cronbachchr('39')s alpha) of the remained items was 0.86. The results of confirmed validation showed that comparative fit index was acceptable. (CFI=0.99, GFI=0.96, RMSEA=0.066, RMR= 0.022). T test showed that the mean scores of the workers who had accident were significantly higher than the workers who had not experienced accident (p

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Introduction Preclinical in vivo QT measurement as a proarrhythmia essay is expensive and not reliable enough. The aim of the present study was to develop a sensitive, cost-effective, Langendorff perfused guinea pig heart model for proarrhythmia safety screening. Methods Low concentrations of dofetilide and cisapride (inhibitors of the rapid delayed rectifier potassium current, I Kr ) were tested alone and co-perfused with HMR-1556 (inhibitor of the slow delayed rectifier potassium current, I Ks ) in Langendorff perfused guinea pig hearts. The electrocardiographic rate corrected QT (QTc) interval, the T peak –T end interval and the beat-to-beat variability and instability (BVI) of the QT interval were determined in sinus rhythm. Results Dofetilide and HMR-1556 alone or co-perfused, prolonged the QTc interval by 20 ± 2%, 10 ± 1% and 55 ± 10%, respectively. Similarly, cisapride and HMR-1556 alone or co-perfused, prolonged the QTc interval by 11 ± 3%, 11 ± 4% and 38 ± 6%, respectively. Catecholamine-induced fast heart rate abolished the QTc prolonging effects of the I Kr inhibitors, but augmented the QTc prolongation during I Ks inhibition. None of the drug perfusions increased significantly the T peak –T end interval and the sinus BVI of the QT interval. Discussion I Ks inhibition increased the QTc prolonging effect of I Kr inhibitors in a super-additive (synergistic) manner, and the QTc interval was superior to other proarrhythmia biomarkers measured in sinus rhythm in isolated guinea pig hearts. The effect of catecholamines on the QTc facilitated differentiation between I Kr and I Ks inhibitors. Thus, QTc measurement in Langendorff perfused guinea pig hearts with pharmacologically attenuated repolarization reserve and periodic catecholamine perfusion seems to be suitable for preclinical proarrhythmia screening. [ABSTRACT FROM AUTHOR]

Development of new drugs requires a thorough investigation of efficacy and safety of pharmaceuticals. The potential risks and benefits of drugs used in chemotherapy are carefully considered such that the benefits of using a new drug outweigh the risks in terms of the side effects caused by the drug. Damage to normal cells, tissues, organs and/or the whole organism is a big concern. Several tests are now routinely performed and are required for drug approval by various regulatory agencies around the globe. The primary goals of such preclinical safety evaluation of drugs are: (1) to identify an initial safe starting dose and subsequent dose escalation scheme to humans; (2) to identify potential target organs of toxicity and reversibility of toxicity; (3) to identify potential damage to the genetic material (genotoxicity); and (4) to identify parameters of clinical monitoring. In this paper, various models for genotoxicity assays are presented. These include: Ames assay, in vitro chromosome aberration assay and an in vivo micronucleus assay. New technologies, such as DNA adduct formation, DNA strand breakage, apoptotic changes, p53 gene expression and transgenic animal models, are also considered. [ABSTRACT FROM AUTHOR]

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.