Current Medicinal Chemistry - Volume 28, Issue 38, 2021

Background: Alzheimer's disease (AD) is a very common neurodegenerative disorder in individuals over 65 years of age; however, younger individuals can also be affected due to brain damage. Introduction: The general symptoms of this disease include progressive loss of memory, changes in behavior, deterioration of thinking, and gradual loss of ability to perform daily activities. According to the World Health Organization, dementia has affected more than 50 million people worldwide, and it is estimated that there are 10 million new cases per year, of which 70% are due to AD. Methods: This paper reported a review of scientific articles available on the internet which discuss in silico analyzes such as molecular docking, molecular dynamics, and quantitative structure-activity relationship (QSAR) of different classes of natural products and their derivatives published from 2016 onwards. In addition, this work reports the potential of fermented papaya preparation against oxidative stress in AD. Results: This research reviews the most recent studies on AD, computational analysis methods used in proposing new bioactive compounds and their possible molecular targets, and finally, the molecules or classes of natural products involved in each study. Conclusion: Thus, studies like this can orientate new research works on neurodegenerative diseases, especially AD.

Background: Despite continued efforts to develop new treatments, there is an urgent need to discover new drug leads to treat tumors exhibiting primary or secondary resistance to existing drugs. Cell cultures derived from patient-derived orthotopic xenografts are promising pre-clinical models to better predict drug response in cancer recurrence. Objective: The aim of the study was to investigate the relationship between the physiochemical properties of drugs and their in vitro potency as well as identifying chemical scaffolds biasedtowards selectivity or promiscuity of such drugs. Methods: The bioactivities of 158 drugs screened against cell cultures derived from 30 cancer orthotopic patient-derived xenograft (O-PDX) models were considered. Drugs were represented by physicochemical descriptors and chemical structure fingerprints. Supervised learning was employed to model the relationship between features and in vitro potency. Results: Drugs with in vitro potency for alveolar rhabdomyosarcoma and osteosarcoma tend to have a higher number of rings, two carbon-hetero bonds and halogens. Selective and promiscuous scaffolds for these phenotypic targets were identified. Highly-predictive models of in vitro potency were obtained across these 30 targets, which can be applied to unseen molecules via a webserver (https://rnewbie.shinyapps.io/Shobek-master). Conclusion: It is possible to identify privileged chemical scaffolds and predict the in vitro potency of unseen molecules across these 30 targets This information and models should be helpful to select which molecules to screen against these primary cultures of pediatric solid tumors.

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others lead to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction and analysis of virus-host interactions. Our review also covers the perspectives of using the machine learning approaches for antiviral research including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses and some others.

It is more pressing than ever to reduce the time and costs for the development of lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the development of large-scale multimodal predictive models for virtual drug screening. Recently, deep generative models have emerged as a powerful tool to explore the chemical space and raise hopes to expedite the drug discovery process. Following this progress in chemocentric approaches for generative chemistry, the next challenge is to build multimodal conditional generative models that leverage disparate knowledge sources when mapping biochemical properties to target structures. Here, we call the community to bridge drug discovery more closely with systems biology when designing deep generative models. Complementing the plethora of reviews on the role of DL in chemoinformatics, we specifically focus on the interface of predictive and generative modelling for drug discovery. Through a systematic publication keyword search on PubMed and a selection of preprint servers (arXiv, biorXiv, chemRxiv, and medRxiv), we quantify trends in the field and find that molecular graphs and VAEs have become the most widely adopted molecular representations and architectures in generative models, respectively. We discuss progress on DL for toxicity, drug-target affinity, and drug sensitivity prediction and specifically focus on conditional molecular generative models that encompass multimodal prediction models. Moreover, we outline future prospects in the field and identify challenges such as the integration of deep learning systems into experimental workflows in a closed-loop manner or the adoption of federated machine learning techniques to overcome data sharing barriers. Other challenges include, but are not limited to interpretability in generative models, more sophisticated metrics for the evaluation of molecular generative models, and, following up on that, community-accepted benchmarks for both multimodal drug property prediction and property-driven molecular design.

Background: Peptides and peptide-based therapeutics are biomolecules that demarcate a significant chemical space to bridge small molecules with biological therapeutics, such as antibodies, recombinant proteins, and protein domains. Introduction: Cyclooligopeptides and depsipeptides, particularly cyanobacteria-derived thiazoline-based polypeptides (CTBCs), exhibit a wide array of pharmacological activities due to their unique structural features and interesting bioactions, which furnish them as promising leads for drug discovery. Methods: In the present study, we comprehensively review the natural sources, distinguishing chemistries, and pertinent bioprofiles of CTBCs. We analyze their structural peculiarities counting the mode of actions for biological portrayals which render CTBCs as indispensable sources for emergence of prospective peptide-based therapeutics. In this milieu, metal organic frameworks and their biomedical applications are also briefly discussed. To boot, the challenges, approaches, and clinical status of peptide-based therapeutics are conferred. Results: Based on these analyses, CTBCs can be appraised as ideal drug targets that have always remained a challenge for traditional small molecules, like those involved in protein- protein interactions or to be developed as potential cancer-targeting nanomaterials. Cyclization-induced reduced conformational freedom of these cyclooligopeptides contribute to improved metabolic stability and binding affinity to their molecular targets. Clinical success of several cyclic peptides provokes the large library-screening and synthesis of natural product-like cyclic peptides to address the unmet medical needs. Conclusion: CTBCs can be considered as the most promising lead compounds for drug discovery. Adopting the amalgamation of advanced biological and biopharmaceutical strategies might endure these cyclopeptides to be prospective biomolecules for futuristic therapeutic applications in the coming times.

Background: Molecules with a phenothiazine scaffold have been considered versatile organic structures with a wide variety of biological activities, such as antipsychotic, anticancer, antibacterial, antifungal, antiviral, anti-inflammatory, antimalarial, and trypanocidal, etc. It was first discovered in the 19th century as a histochemical dye, phenothiazine methylene blue. Since then, its derivatives have been studied, showing new activities; moreover, they have also been repurposed. Objective: This review aims to describe the main synthetic routes of phenothiazines and, particularly, the anticancer and antiprotozoal activities reported during the second decade of the 2000s (2010 - 2020). Results: Several studies on phenothiazines against cancer and protozoa have revealed that these compounds show IC50 values in the micromolar and near nanomolar range. The structural analyses have revealed that compounds bearing halogens or electron-withdrawing groups at 2-position have favorable anticancer activity. Phenothiazine dyes have shown a photosensitizing activity against trypanosomatids at a micromolar range. Tetra and pentacyclic azaphenothiazines are structures with a high broad-spectrum anticancer activity. Conclusion: The phenothiazine scaffold is favorable for developing anticancer agents, especially those bearing halogens and electron-withdrawing groups bound at 2-position with enhanced biological activities through a variety of aromatic, aliphatic and heterocyclic substituents in the thiazine nitrogen. Further studies are warranted along these investigation lines to attain more active anticancer and antiprotozoal compounds with minimal to negligible cytotoxicity.

In recent years, polymeric materials with the ability to self-assemble into micelles have been increasingly investigated for application in various fields, mainly in biomedicine. Micellar morphology is interesting in the field of drug transport and delivery since micelles can encapsulate hydrophobic molecules in their nucleus, have active molecules in their outer layer, and due to their nanometric size, can take advantage of the enhanced permeability and retention (EPR) effect, prolong the time in circulation and avoid renal clearance. In addition, nanobioactive molecules (joined in covalent form or by host-host interaction), such as drugs, bioimaging molecules, targeting ligands, “crosslinkable” molecules or bonds, sensitive to internal or external stimuli, can be incorporated into them and showed better activity as anticancer agents, siRNA delivery agents as well as antiviral and antiparasitic compounds. The present work is a review of the information published, which is the most important about the synthesis and biological importance of the confined multivalent cooperation and the ability to modify the dendritic structure, provide the versatility to create and improve the amphiphiles used in the micellar supramolecular field. The most studied structures are the hybrid copolymers formed by the combination of linear polymers and dendrons. However, small dendritic molecules that do not involve linear polymers have also been developed, such as Janus dendrimers, facial dendrons, and dendritic amphiphiles with only one dendron. Amphiphilic dendrimer micelles have achieved efficient and promising results, both in in vitro and in vivo tests, which encourage their research for future application in nanotherapies.

Background: It is unclear whether angiotensin-converting enzyme inhibitors (ACEIs) in combination with angiotensin-receptor blockers (ARBs) are superior to ACEIs or ARBs alone in the treatment of nondiabetic chronic kidney disease (CKD). The present meta-analysis was designed to assess the efficacy and safety of ACEIs in combination with ARBs in nondiabetic CKD. Methods: The PubMed, Embase, and Cochrane Library databases were searched to identify randomized controlled trials (RCTs) published prior to March 2020. A random-effects model was used to calculate the effect sizes of eligible studies. Results: The present meta-analysis of 20 RCTs encompassing 1,398 patients with nondiabetic CKD demonstrated that ACEIs in combination with ARBs were superior to ACEIs or ARBs alone in reducing urine albumin excretion (SMD, -0.69; 95% CI, -1.13 to -0.25; P=0.002), urine protein excretion (SMD, -0.34; 95% CI, -0.46 to -0.23; P<0.001), and blood pressure (systolic blood pressure: WMD, -1.43; 95% CI, -2.42 to -0.44; P=0.005; diastolic blood pressure: WMD, -1.85; 95% CI, -2.67 to -1.04; P<0.001) without decreasing glomerular filtration rate (SMD, -0.07; 95% CI, -0.20 to 0.06; P=0.30) or increasing incidences of hyperkalaemia (RR, 1.70; 95% CI, 0.47 to 6.11; P=0.42) and hypotension (RR, 1.80; 95% CI, 0.67 to 4.86; P=0.25). Conclusion: Compared with ACEIs or ARBs alone, ACEIs in combination with ARBs are effective and safe in the treatment of nondiabetic CKD. ACEIs combined with ARBs may be a better choice to reduce proteinuria as long as they can be tolerated.

Aims: This study aims to verify if miR-30e-5p targets Beclin1 (BECN1), a key regulator of autophagy, and investigate the function of miR-30e-5p and Beclin1 through mediating autophagy and apoptosis in contrast-induced acute kidney injury (CIAKI). Methods: Human renal tubular epithelial HK-2 cells were treated with Urografin to construct a cell model of CI-AKI. Real-time reverse transcription-polymerase chain reaction was used to detect gene expression. The dual-luciferase reporting assay and endogenous validation were used to verify targeting and regulating function. The expressions of protein were detected using Western blot. Cell proliferation was detected using methylthiazolyldiphenyl- tetrazolium bromide (MTT) assay. Cell apoptosis was detected using terminal- deoxynucleoitidyl transferase mediated nick end labeling assay, and autophagy was detected using transmission electron microscopy. Results: HK-2 cells exposed to Urografin for 2 h induced a significant increase in miR-30e-5p. miR-30e-5p had a targeting effect on Beclin1. Moreover, Urografin exposure can enhance cell apoptosis by increasing caspase 3 gene expression and inhibiting autophagy, which was induced by decreased Beclin1 expression regulated by miR-30e-5p, thereby resulting in renal cell injury. Downregulation of miR-30e-5p or upregulation of Beclin1 restored cell vitality by promoting autophagy and suppressing apoptosis in Urografin-treated cells. Conclusion : Urografin increased the expression of miR-30e-5p in HK-2 cells and thus decreased Beclin1 levels to inhibit autophagy, but induced apoptosis, which may be the mechanism for CI-AKI.