Study on the 2-Phenylchroman-4-One Derivatives and their anti-MERS-CoV Activities

Ferrero G, Adelhelm P. A Practical Guide for Using Electrochemical Dilatometry as Operando Tool in Battery and Supercapacitor Research

Lithium-ion batteries and related battery concepts show an expansion and shrinkage ("breathing") of the electrodes during cell cycling. The dimensional changes of an individual electrode or a complete cell can be continuously measured by electrochemical dilatometry (ECD). The obtained data provides information on the electrode/cell reaction itself but can be also used to study side reactions or other relevant aspects, e.g., how the breathing is influenced by the electrode binder and porosity. The method spans over a wide measurement range and allows the determination of macroscopic as well as nanoscopic changes. It has also been applied to supercapacitors. The method has been developed already in the 1970s but recent advancements and the availability of commercial setups have led to an increasing interest in ECD. At the same time, there is no "best practice" on how to evaluate the data and several pitfalls exist that can complicate the comparison of literature data. This review highlights the recent development and future trends of ECD and its use in battery and supercapacitor research. A practical guide on how to evaluate the data is provided along with a discussion on various factors that influence the measurement results.

Synthesis and biological evaluation of 2‐benzylaminoquinazolin ‐4( 3 H )‐one derivatives as a potential treatment for SARS‐CoV ‐2

Despite the continuing global crisis caused by coronavirus disease 2019 (COVID‐19), there is still no effective treatment. Therefore, we designed and synthesized a novel series of 2‐benzylaminoquinazolin‐4(3H)‐one derivatives and demonstrated that they are effective against SARS‐CoV‐2. Among the synthesized derivatives, 7‐chloro‐2‐(((4‐chlorophenyl)(phenyl)methyl)amino)quinazolin‐4(3H)‐one (Compound 39) showed highest anti‐SARS‐CoV‐2 activity, with a half‐maximal inhibitory concentration value greater than that of remdesivir (IC₅₀ = 4.2 μM vs. 7.6 μM, respectively), which gained urgent approval from the U.S. Food and Drug Administration. In addition, Compound 39 showed good results in various assays measuring metabolic stability, human ether a‐go‐go, Cytochromes P450 (CYPs) inhibition, and plasma protein binding (PPB), and showed better solubility and pharmacokinetics than our previous work.

Recent Development of Flavonoids with Various Activities

Flavonoids, a series of compounds with C6-C3-C6 structure, mostly originate from plant metabolism. Flavonoids have shown beneficial effects on many aspects of human physiology and health. Recently, many flavonoids with various activities have been discovered, which has led to more and more studies focusing on their physiological and pharmacodynamic activities. The anti-cancer and anti-viral activities especially have attracted the attention of many researchers. Therefore, the discovery and development of flavonoids as anti-disease drugs has great potential and may make significant contribution to fighting diseases. This review focus on the discovery and development of flavonoids in medicinal chemistry in recent years.

Discovery of a subtype-selective, covalent inhibitor against palmitoylation pocket of TEAD3

The TEA domain (TEAD) family proteins (TEAD1‒4) are essential transcription factors that control cell differentiation and organ size in the Hippo pathway. Although the sequences and structures of TEAD family proteins are highly conserved, each TEAD isoform has unique physiological and pathological functions. Therefore, the development and discovery of subtype selective inhibitors for TEAD protein will provide important chemical probes for the TEAD-related function studies in development and diseases. Here, we identified a novel TEAD1/3 covalent inhibitor (DC-TEADin1072) with biochemical IC₅₀ values of 0.61 ± 0.02 and 0.58 ± 0.12 μmol/L against TEAD1 and TEAD3, respectively. Further chemical optimization based on DC-TEAD in 1072 yielded a selective TEAD3 inhibitor DC-TEAD3in03 with the IC₅₀ value of 0.16 ± 0.03 μmol/L, which shows 100-fold selectivity over other TEAD isoforms in activity-based protein profiling (ABPP) assays. In cells, DC-TEAD3in03 showed selective inhibitory effect on TEAD3 in GAL4-TEAD (1–4) reporter assays with the IC₅₀ value of 1.15 μmol/L. When administered to zebrafish juveniles, experiments showed that DC-TEAD3in03 reduced the growth rate of zebrafish caudal fins, indicating the importance of TEAD3 activity in controlling proportional growth of vertebrate appendages.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Design, synthesis and biological evaluation of 2-aminoquinazolin-4(3H)-one derivatives as potential SARS-CoV-2 and MERS-CoV treatments

Despite the rising threat of fatal coronaviruses, there are no general proven effective antivirals to treat them. 2-Aminoquinazolin-4(3H)-one derivatives were newly designed, synthesized, and investigated to show the inhibitory effects on SARS-CoV-2 and MERS-CoV. Among the synthesized derivatives, 7-chloro-2-((3,5-dichlorophenyl)amino)quinazolin-4(3H)-one (9g) and 2-((3,5-dichlorophenyl)amino)-5-hydroxyquinazolin-4 (3H)-one (11e) showed the most potent anti-SARS-CoV-2 activities (IC₅₀ < 0.25 μM) and anti-MERS-CoV activities (IC₅₀ < 1.1 μM) with no cytotoxicity (CC₅₀ > 25 μM). In addition, both compounds showed acceptable results in metabolic stabilities, hERG binding affinities, CYP inhibitions, and preliminary PK studies.

Identification of 4-anilino-6-aminoquinazoline derivatives as potential MERS-CoV inhibitors

New therapies for treating coronaviruses are urgently needed. A series of 4-anilino-6-aminoquinazoline derivatives were synthesized and evaluated to show high anti-MERS-CoV activities. N4-(3-Chloro-4-fluorophenyl)-N6-(3-methoxybenzyl)quinazoline-4,6-diamine (1) has been identified in a random screen as a hit compound for inhibiting MERS-CoV infection. Throughout optimization process, compound 20 was found to exhibit high inhibitory effect (IC50 = 0.157 μM, SI = 25) with no cytotoxicity and moderate in vivo PK properties.