Lina A. Dahabiyeh

Bio: Lina A. Dahabiyeh is an academic researcher from University of Jordan. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 8, co-authored 24 publications receiving 173 citations. Previous affiliations of Lina A. Dahabiyeh include University of Nottingham.

Combining ligand-based pharmacophore modeling, quantitative structure-activity relationship analysis and in silico screening for the discovery of new potent hormone sensitive lipase inhibitors.

Abstract: Hormone sensitive lipase (HSL) has been recently implicated in diabetes and obesity, prompting attempts to discover new HSL inhibitors. Toward this end, we explored the pharmacophoric space of HSL inhibitors using four diverse sets of compounds. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of yielding a self-consistent and predictive quantitative structure−activity relationship (QSAR) (r = 0.822, n = 99, F = 11.1, rLOO2 = 0.521, rPRESS2 against 23 external test inhibitors = 0.522). Interestingly, two pharmacophoric models emerged in the QSAR equation suggesting at least two binding modes. These pharmacophores were employed to screen the National Cancer Institute (NCI) list of compounds and our in-house built database of established drugs and agrochemicals. Active hits included the safe herbicidal agent bifenox (IC50 = 0.43 μM) and the nonsteroidal anti-inflammatory nap...

Perspectives on RNA Vaccine Candidates for COVID-19

Abstract: With the current outbreak caused by SARS-CoV-2, vaccination is acclaimed as a public health priority. Rapid genetic sequencing of SARS-CoV-2 has triggered the scientific community to search for effective vaccines. Collaborative approaches from research institutes and biotech companies have acknowledged the use of viral proteins as potential vaccine candidates against COVID-19. Nucleic acid (DNA or RNA) vaccines are considered the next generation vaccines as they can be rapidly designed to encode any desirable viral sequence including the highly conserved antigen sequences. RNA vaccines being less prone to host genome integration (cons of DNA vaccines) and anti-vector immunity (a compromising factor of viral vectors) offer a great potential as a front-runner for universal COVID-19 vaccine. The proof of concept for RNA-based vaccines has already been proven in humans, and the prospects for commercialization are very encouraging as well. With the emergence of COVID-19, mRNA-1273, a mRNA vaccine developed by Moderna, Inc. was the first to enter human trials, with the first volunteer receiving the dose within 10 weeks after SARS-CoV-2 genetic sequencing. The recent interest in mRNA vaccines has been fueled by the state of the art technologies that enhance mRNA stability and improve vaccine delivery. Interestingly, as per the “Draft landscape of COVID-19 candidate vaccines” published by the World Health Organization (WHO) on 12th November, 2020, 6 potential RNA based COVID-19 vaccines are in different stages of clinical trials, of them, 2 are already in Phase III clinical trial. On the other hand, another 22 potential candidates are undergoing preclinical investigations. This review will shed light on the rationality of the RNA as a platform for vaccine development against COVID-19, highlighting the possible pros and cons, lessons learned from the past and the future prospects.

Identification of potential COVID-19 main protease inhibitors using structure-based pharmacophore approach, molecular docking and repurposing studies

Abstract: The current outbreak of novel coronavirus (COVID-19) infections urges the need to identify potential therapeutic agents. Therefore, the repurposing of FDA-approved drugs against today's diseases involves the use of de-risked compounds with potentially lower costs and shorter development timelines. In this study, the recently resolved X-ray crystallographic structure of COVID-19 main protease (Mpro) was used to generate a pharmacophore model and to conduct a docking study to capture antiviral drugs as new promising COVID-19 main protease inhibitors. The developed pharmacophore successfully captured five FDA-approved antiviral drugs (lopinavir, remdesivir, ritonavir, saquinavir and raltegravir). The five drugs were successfully docked into the binding site of COVID-19 Mpro and showed several specific binding interactions that were comparable to those tying the co-crystallized inhibitor X77 inside the binding site of COVID-19 Mpro. Three of the captured drugs namely, remdesivir, lopinavir and ritonavir, were reported to have promising results in COVID-19 treatment and therefore increases the confidence in our results. Our findings suggest an additional possible mechanism of action for remdesivir as an antiviral drug inhibiting COVID-19 Mpro. Additionally, a combination of structure-based pharmacophore modeling with a docking study is expected to facilitate the discovery of novel COVID-19 Mpro inhibitors.

Dexamethasone-Induced Perturbations in Tissue Metabolomics Revealed by Chemical Isotope Labeling LC-MS analysis.

Abstract: Dexamethasone (Dex) is a synthetic glucocorticoid (GC) drug commonly used clinically for the treatment of several inflammatory and immune-mediated diseases. Despite its broad range of indications, the long-term use of Dex is known to be associated with specific abnormalities in several tissues and organs. In this study, the metabolomic effects on five different organs induced by the chronic administration of Dex in the Sprague-Dawley rat model were investigated using the chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS) platform, which targets the amine/phenol submetabolomes. Compared to controls, a prolonged intake of Dex resulted in significant perturbations in the levels of 492, 442, 300, 186, and 105 metabolites in the brain, skeletal muscle, liver, kidney, and heart tissues, respectively. The positively identified metabolites were mapped to diverse molecular pathways in different organs. In the brain, perturbations in protein biosynthesis, amino acid metabolism, and monoamine neurotransmitter synthesis were identified, while in the heart, pyrimidine metabolism and branched amino acid biosynthesis were the most significantly impaired pathways. In the kidney, several amino acid pathways were dysregulated, which reflected impairments in several biological functions, including gluconeogenesis and ureagenesis. Beta-alanine metabolism and uridine homeostasis were profoundly affected in liver tissues, whereas alterations of glutathione, arginine, glutamine, and nitrogen metabolism pointed to the modulation of muscle metabolism and disturbances in energy production and muscle mass in skeletal muscle. The differential expression of multiple dipeptides was most significant in the liver (down-regulated), brain (up-regulation), and kidney tissues, but not in the heart or skeletal muscle tissues. The identification of clinically relevant pathways provides holistic insights into the tissue molecular responses induced by Dex and understanding of the underlying mechanisms associated with their side effects. Our data suggest a potential role for glutathione supplementation and dipeptide modulators as novel therapeutic interventions to mitigate the side effects induced by Dex therapy.

Pregabalin inhibits in vivo and in vitro cytokine secretion and attenuates spleen inflammation in Lipopolysaccharide/Concanavalin A -induced murine models of inflammation

Abstract: Immune system alteration has been implicated in the pathogenesis of chronic pain conditions, epilepsy and generalized anxiety disorder. Targeting cytokines has recently been proposed for the management of such conditions. Pregabalin (PGB) is an antiepileptic agent used for the management of these conditions. However, little is known about its immunomodulatory effects on cytokine secretion in vivo and in vitro. Hence, a mitogen (Lipopolysaccharide [LPS] or Concanavalin A [ConA])-induced murine model of inflammation was used to investigate the effect of PGB on in vivo and in vitro IL-1β, IL-6, TNF-α and IL-2 cytokine secretion using ELISA. In addition, PGB effect on spleen histology, as a lymphoid organ, was examined. Our results revealed that PGB significantly inhibited the secretion of ConA-induced IL-6 secretion, basal and ConA-induced TNF-α and IL-2 secretion in splenocytes in vitro. In vivo, PGB inhibited basal and LPS/ConA-induced IL-6 and TNF-α secretion in addition to LPS-induced IL-1β and ConA-induced IL-2 secretion. Moreover, PGB attenuated mitogen-induced inflammatory changes in the spleen. These findings provide an evidence of the anti-inflammatory properties of PGB on cytokine secretion and lymphoid organ inflammation. This might give insights into the role of PGB in the management of the inflammatory state in PGB-indicated conditions.

Remdesivir as a possible therapeutic option for the COVID-19

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Inhibition of hormone sensitive lipase and pancreatic lipase by Rosmarinus officinalis extract and selected phenolic constituents

Abstract: Rosmarinus officinalis L. (Rosemary) has been long claimed to have hypogylcemic-hypolipidemic dual effects in folkloric medicine. In an effort to explain rosemary's claimed benefits, numerous published studies have investigated an array of pharmacologic activities of the plant including anti-inflammatory, anticarcinogenic and metabolic effects. The question remained, however, as how rosemary would target both plasma lipids and glucose levels simultaneously. A newer mechanism has been suggested, in which targeting the hormone sensitive lipase (HSL) would be the common link between the two metabolic effects. In fact, HSL has been extensively studied for its effects on the metabolic switch between glucose and free fatty acids (FFAs) as an energy source. The current manuscript summarizes a significant amount of work that was undertaken to identify plant species native to Jordan with potential HSL and pancreatic lipase (PL) inhibitory activities. Our results demonstrated in vitroinhibitory effects of R. officnalis on both HSL and PL in a dose dependent manner. Interestingly, the rosemary extract had an IC50 for PL that was several fold lower than the IC50 for HSL, indicating a higher affinity to the former enzyme (13.8 and 95.2 μg/mL for PL and HSL, respectively). In addition, we have compared the inhibitory activities of purified constituents found in rosemary to the parent plant [rosmarinic acid (RA), chlorogenic acid (CA), caffeic acid (CaA) and gallic acid (GA)]. Our results showed that all the tested compounds (RA, CA, CaA, and GA) were able to inhibit the PL and HSL activities in a dose dependent manner, but with different potencies. PL and HSL IC50 values were calculated for each compound and GA was found to be the most potent (IC50 10.1 and 14.5 for PL and HSL, respectively). Further work is necessary to determine whether our in vitro findings would correlate with the in vivo effects. Nonetheless, our results are a first step in fully understanding the long claimed hypoglycemic-hypolipidemic dual effects of rosemary. Simultaneous targeting of both HSL and PL is likely to open the door for a new era in our continuous battle against DM type 2 and its cardiovascular complications. Currently, we are working on identifying the most active constituents of the plant to evaluate a structure-activity relationship which would pave the road for future therapeutic use. Key words: Rosmarinus officinalis, Rosemary, obesity, diabetes mellitus, pancreatic lipase, hormone sensitive lipase, phenolic compounds, rosmarinic acid.

From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

Abstract: A pharmacophore describes the arrangement of molecular features a ligand must contain to efficaciously bind a receptor. Pharmacophore models are developed to improve molecular understanding of ligand–protein interactions, and can be used as a tool to identify novel compounds that fulfil the pharmacophore requirements and have a high probability of being biologically active. Protein structure-based pharmacophores (SBPs) derive these molecular features by conversion of protein properties to reciprocal ligand space. Unlike ligand-based pharmacophore models, which require templates of ligands in their bioactive conformation, SBPs do not depend on ligand information. The current review describes the different steps in the construction of SBPs: (i) protein structure preparation, (ii) binding site detection, (iii) pharmacophore feature definition, and (iv) pharmacophore feature selection. We show that the SBP modeling workflow poses different challenges than ligand-based pharmacophore modeling, including the definition of protein pharmacophore features essential for ligand binding. A comprehensive overview of different SBP modeling and screening methods and applications is provided to illustrate that SBPs can be efficiently used for virtual screening, ligand binding mode prediction, and binding site similarity detection. Our review demonstrates that SBPs are valuable tools for hit and lead optimization, compound library design and target hopping, especially in cases where ligand information is scarce.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19.

Abstract: The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.