The Emerging Role of Tetrazines in Drug-Activation Chemistries

Traditionally, prodrug activation has been limited to enzymatic triggers or gross physiological aberrations, such as pH, that offer low selectivity and control over dosage. In recent years, the field of prodrug activation chemistry has been transformed by the use of bioorthogonal reactions that can be carried out under biological conditions at sub-millimolar concentrations, with the tetrazine-mediated inverse electron demand Diels–Alder reaction amongst the most recognised. Their high reaction rates, chemoselectivity and excellent biocompatibility make tetrazines ideal small molecules for activating prodrugs. Recently the tetrazine moiety has been used as a prodrug for a pyridazine thus broadening the scope of prodrug systems. This article discusses the concept of using tetrazines as small-molecule activators for prodrugs, and provides an overview of tetrazine-based prodrug systems, with a particular focus on the recently reported prodrug–prodrug activation strategy.     

Advancements in the Development of non-BET Bromodomain Chemical Probes

The bromodomain and extra terminal (BET) family of bromodomain-containing proteins (BCPs) have been the subject of extensive research over the past decade, resulting in a plethora of high-quality chemical probes for their tandem bromodomains. In turn, these chemical probes have helped reveal the profound biological role of the BET bromodomains and their role in disease, ultimately leading to a number of molecules in active clinical development. However, the BET subfamily represents just 8/61 of the known human bromodomains, and attention has now expanded to the biological role of the remaining 53 non-BET bromodomains. Rapid growth of this research area has been accompanied by a greater understanding of the requirements for an effective bromodomain chemical probe and has led to a number of new non-BET bromodomain chemical probes being developed. Advances since December 2015 are discussed, highlighting the strengths/caveats of each molecule, and the value they add toward validating the non-BET bromodomains as tractable therapeutic targets.     

Behavior of 316L stainless steel containing corrosion pits under cyclic loading

The environmental performance of 316L grade stainless steel, in the form of tensile specimens containing a single corrosion pit with various aspect ratios, under cyclic loading in aerated chloride solutions is investigated in this study. Results from environmental tests were compared and contrasted with those obtained using finite element analysis (FEA). Fractography of the failed specimens obtained from experiments revealed that fatigue crack initiation took place at the base of the shallow pit. The crack initiation shifted towards the shoulder and the mouth of the pit for pits of increasing depth. This process is well predicted by FEA, as the strain contour maps show that strain is the highest around the centric strip of the pit. However, for shallow pits, local strain is uniformly distributed around that strip but begins to concentrate more towards the shoulder and the mouth region for increasingly deep pits.     

Failed Neuroprotection of Combined Inhibition of L-Type and ASIC1a Calcium Channels with Nimodipine and Amiloride

Effective pharmacological neuroprotection is one of the most desired aims in modern medicine. We postulated that a combination of two clinically used drugs—nimodipine (L-Type voltage-gated calcium channel blocker) and amiloride (acid-sensing ion channel inhibitor)—might act synergistically in an experimental model of ischaemia, targeting the intracellular rise in calcium as a pathway in neuronal cell death. We used organotypic hippocampal slices of mice pups and a well-established regimen of oxygen-glucose deprivation (OGD) to assess a possible neuroprotective effect. Neither nimodipine (at 10 or 20 µM) alone or in combination with amiloride (at 100 µM) showed any amelioration. Dissolved at 2.0 Vol.% dimethyl-sulfoxide (DMSO), the combination of both components even increased cell damage (p = 0.0001), an effect not observed with amiloride alone. We conclude that neither amiloride nor nimodipine do offer neuroprotection in an in vitro ischaemia model. On a technical note, the use of DMSO should be carefully evaluated in neuroprotective experiments, since it possibly alters cell damage.     

Categorical observers for metamerism

A method is proposed to generate categorical colour observer functions (individual colour matching functions) for any field size based on the CIE 2006 system of physiological observer functions. The method combines proposed categorical observer techniques of Sarkar et al with a physiologically-based individual observer model of Asano et al and a clustering technique to produce the optimal set of categorical observers. The number of required categorical observers varies depending on an application with as many as 50 required to predict individual observers' matches when a laser projector is viewed. However, 10 categorical observers are sufficient to represent colour-normal populations for personalized colour imaging. The proposed and recommended categorical observers represent a robust and inclusive technique to examine and quantify observer metamerism in any application of colorimetry.     

In Silico/In Vitro Hit-to-Lead Methodology Yields SMYD3 Inhibitor That Eliminates Unrestrained Proliferation of Breast Carcinoma Cells

SMYD3 is a lysine methyltransferase that regulates the expression of over 80 genes and is required for the uncontrolled proliferation of most breast, colorectal, and hepatocellular carcinomas. The elimination of SMYD3 restores normal expression patterns of these genes and halts aberrant cell proliferation, making it a promising target for small molecule inhibition. In this study, we sought to establish a proof of concept for our in silico/in vitro hit-to-lead enzyme inhibitor development platform and to identify a lead small molecule candidate for SMYD3 inhibition. We used Schrodinger^® software to screen libraries of small molecules in silico and the five compounds with the greatest predicted binding affinity within the SMYD3 binding pocket were purchased and assessed in vitro in direct binding assays and in breast cancer cell lines. We have confirmed the ability of one of these inhibitors, Inhibitor-4, to restore normal rates of cell proliferation, arrest the cell cycle, and induce apoptosis in breast cancer cells without affecting wildtype cell behavior. Our results provide a proof of concept for this fast and affordable small molecule hit-to-lead methodology as well as a promising candidate small molecule SMYD3 inhibitor for the treatment of human cancer.     

Parameter Estimation for Incompressible Cake Filtration: Advantages of a Modified Fitting Method

There is a widely used linear strategy to determine the parameters specific cake resistance and filter medium resistance in incompressible cake filtration. In this article, it is intended to demonstrate that this strategy has some disadvantages and should be replaced by an alternative nonlinear approach which yields more exact results. Even though the gains in precision are small for most cases, the nonlinear strategy is favored because it involves no extra effort and is grounded in the same physical theory as the original approach. This claim is based on a broad simulation study using noisy data with known parameter values to compare both fitting strategies and judge their accuracies.     

Evaluation of standard and real fire exposures on thermal response of rail car floor assembly

This work developed a computational methodology to evaluate and compare standard fire exposures such as those outlined in ASTM E119 with real fire exposures and determine the difference in the temperature rise of a rail car floor assembly. The real fire exposures simulated in this work were identified in a review of incidents and consisted of a constantly-fed diesel fuel spill, a localized trash fire, and a gasoline spill simulated from a collision of the railcar with an automobile. These realistic fire exposures were applied to a variety of exemplar rail cars representative of single-level and bi-level passenger cars. These floor assembly models exposed to realistic fires were simulated in Fire Dynamics Simulator (FDS). The thermal exposure at the underside of railcar provided by FDS was coupled with a thermal model in ABAQUS, which provided the evolution of temperature in different components of the floor assembly. The standard scenarios were simulated for 2 hours instead of the typical 30 minutes to identify the appropriate exposure duration in ASTM E119, which can better represent a real fire scenario. The average and maximum temperatures predicted at the unexposed surface for both scenarios were compared with the threshold values given in NFPA 130.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.