Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes.

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study.

Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti-TTK bioactivities. One hit of novel chemotype showed reasonable dose-response curve with experimental IC50 of 1.0 µM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.

Plants as a Source of Anticancer Agents: From Bench to Bedside.

Cancer is the second leading cause of death after cardiovascular diseases. Conventional anticancer therapies are associated with lack of selectivity and serious side effects. Cancer hallmarks are biological capabilities acquired by cancer cells during neoplastic transformation. Targeting multiple cancer hallmarks is a promising strategy to treat cancer. The diversity in chemical structure and the relatively low toxicity make plant-derived natural products a promising source for the development of new and more effective anticancer therapies that have the capacity to target multiple hallmarks in cancer. In this review, we discussed the anticancer activities of ten natural products extracted from plants. The majority of these products inhibit cancer by targeting multiple cancer hallmarks, and many of these chemicals have reached clinical applications. Studies discussed in this review provide a solid ground for researchers and physicians to design more effective combination anticancer therapies using plant-derived natural products.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study.

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signalling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Assessing health-related behaviors among Jordanian children during COVID-19 pandemic: A cross-sectional study.

Background: The coronavirus disease identified in 2019 (COVID-19) led to extreme actions being taken by the governments to restrict the spread of this virus. Closure of schools, sport clubs and playgrounds were among these actions; children had to stay indoors and were not allowed to pursue their normal lifestyle activities. Objectives: To assess the differences in health-related behaviors among Jordanian school-aged children (6-16 years) before and during COVID-19 quarantine and to evaluate public's perception of the role of pharmacists regarding children's health-related behaviors management. Methods: A cross-sectional study was conducted from August 2020 to January 2021 using an anonymous web-based survey. The survey was developed based on previously published surveys. Evaluation of the validity and reliability of the survey were conducted by a professional committee of clinical pharmacists and a statistician. Results: A total of 230 children, aged 9.02± 2.977 participated in the study. Physical activity and healthy balanced meals decreased (less than 1 hr or 1-3 hrs/week vs 2 meals/day, p= <0.001), whereas daily screen time (1-3 hrs/week vs 4-6 hrs/week, p= <0.001), sleep hours (8-9 hrs/day vs 10-11 hrs/day, p= <0.001) and the ingestion of unhealthy snacks had increased (1-2 meals/day vs. 2-3 meals/day, p=<0.001). A positive perception of pharmacists' role during the pandemic was revealed. Conclusion: The present study showed that a significant change in children's health-related behavior happened during the COVID-19 pandemic. Such changes can lead to social, physical and mental health problems. The public perceived community pharmacists as trusted health care professionals during the pandemic.

The first detection of Pneumocystis jirovecii in asthmatic patients post-COVID-19 in Jordan.

Pneumocystis jirovecii pneumonia (PCP), caused by fungal species named Pneumocystis jirovecii, is a frequent opportunistic infection in those with human immunodeficiency virus (HIV) infection. However, PCP has been documented in immunocompetent patients. This study aims to determine if P. jirovecii detection occurs in asthma patients following coronavirus disease 2019 (COVID-19) in a Jordanian cohort. Also, to evaluate a method of TaqMan quantitative polymerase chain reaction (qPCR) assay to detect P. jirovecii, from sputum samples. The nasopharyngeal swabs were used to detect SARS-CoV-2 and sputum samples were tested for P. jirovecii using real time qPCR assay. Beta-tubulin (BT) and Dihydrofolate reductase (DHFR) genes were the directed targets of P. jirovecii. The results showed that the mean qPCR efficiencies of BT and DHFR were 96.37% and 100.13%, respectively. Three out of 31 included patients (9.7%) had a positive P. jirovecii. All of the three patients had used oral corticosteroids (OCS) in the last two months due asthma exacerbation and were treated with OCS for COVID-19. This is the first study based in Jordan to demonstrate that P. jirovecii and COVID-19 can co-exist and that it is important to maintain a broad differential diagnosis, especially in immunocompromised patients. Chronic lung disease can be a risk factor for the P. jirovecii colonization possibly due to corticosteroid's immunosuppression.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning.

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand-receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure-activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand-receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC50 value of 57 nM.

Assessing health-related behaviors among Jordanian children during COVID-19 pandemic: A cross-sectional study

Background: The coronavirus disease identified in 2019 (COVID-19) led to extreme actions being taken by the governments to restrict the spread of this virus. Closure of schools, sport clubs and playgrounds were among these actions; children had to stay indoors and were not allowed to pursue their normal lifestyle activities. Objectives: To assess the differences in health-related behaviors among Jordanian school-aged children (6-16 years) before and during COVID-19 quarantine and to evaluate public’s perception of the role of pharmacists regarding children’s health-related behaviors management. Methods: A cross-sectional study was conducted from August 2020 to January 2021 using an anonymous web-based survey. The survey was developed based on previously published surveys. Evaluation of the validity and reliability of the survey were conducted by a professional committee of clinical pharmacists and a statistician. Results: A total of 230 children, aged 9.02± 2.977 participated in the study. Physical activity and healthy balanced meals decreased (less than 1 hr or 1-3 hrs/week vs 2 meals/day, p= <0.001), whereas daily screen time (1-3 hrs/week vs 4-6 hrs/week, p= <0.001), sleep hours (8-9 hrs/day vs 10-11 hrs/day, p= <0.001) and the ingestion of unhealthy snacks had increased (1-2 meals/day vs. 2-3 meals/day, p=<0.001). A positive perception of pharmacists’ role during the pandemic was revealed. Conclusion: The present study showed that a significant change in children’s healthrelated behavior happened during the COVID-19 pandemic. Such changes can lead to social, physical and mental health problems. The public perceived community pharmacists as trusted health care professionals during the pandemic. (AU)

Ligand-based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti- COVID-19 Treatments.

BACKGROUND: Chemokines are involved in several human diseases and different stages of COVID-19 infection. They play a critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients. METHODS: We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM. CONCLUSION: These results proved the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases for new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

Evaluation and molecular modelling of bis-Schiff base derivatives as potential leads for management of diabetes mellitus.

Developing a medication to cure and manage diabetes mellitus complications is of interest in medicinal chemistry. Toward this end, six bis-biphenyl-salicylaldehyde Schiff base derivatives have been evaluated for their α-glucosidase inhibition, antiglycation and anti-inflammation potentials. Four compounds (compounds 2-5) showed an excellent α-glucosidase inhibitory effect superior to that produced by acarbose. Additionally, the docking study revealed that these compounds are anchored within the binding pocket of α-glucosidase via hydrogen bonding, π-stacking and hydrophobic interactions, comparable to a high number of hydrogen bonding involved in anchoring acarbose. Interestingly, all tested compounds showed varying degrees of antiglycation activity with superior activity for two of them (compound 1 and compound 6) compared to the standard rutin. Moreover, the results indicated an outstanding anti-inflammatory activity for two compounds (compounds 1 and 6) compared to ibuprofen.

Melatonin in Cancer Treatment: Current Knowledge and Future Opportunities.

Melatonin is a pleotropic molecule with numerous biological activities. Epidemiological and experimental studies have documented that melatonin could inhibit different types of cancer in vitro and in vivo. Results showed the involvement of melatonin in different anticancer mechanisms including apoptosis induction, cell proliferation inhibition, reduction in tumor growth and metastases, reduction in the side effects associated with chemotherapy and radiotherapy, decreasing drug resistance in cancer therapy, and augmentation of the therapeutic effects of conventional anticancer therapies. Clinical trials revealed that melatonin is an effective adjuvant drug to all conventional therapies. This review summarized melatonin biosynthesis, availability from natural sources, metabolism, bioavailability, anticancer mechanisms of melatonin, its use in clinical trials, and pharmaceutical formulation. Studies discussed in this review will provide a solid foundation for researchers and physicians to design and develop new therapies to treat and prevent cancer using melatonin.

Design and Synthesis of New JAK1 Inhibitors based on Sulfonamide- Triazine Conjugates.

AIMS: Design of sulfonamide-triazine derivatives as JAK1 inhibitors. BACKGROUND: JAK1 is a kinase involved in different autoimmune diseases. JAK1 inhibitors have shown promising results in treating autoimmune diseases. OBJECTIVES: To design new JAK1 inhibitors based on sulfonamides-triazine conjugates capable of binding interactions comparable to observed interactions anchoring potent crystallographic JAK1 inhibitors. METHODS: The crystallographic structures of 4 diverse nanomolar inhibitors complexed within JAK1 were used to guide the synthesis of new diaminotriazine-sulfonamide-based JAK1 inhibitors. RESULTS: Nineteen compounds have been prepared, some of which exhibited low micromolar IC50 values against JAK1. CONCLUSIONS: Crystallographic complexes of diverse JAK1 inhibitors were successfully used to guide the synthesis of novel sulfonamide-triazine-based low micromolar JAK1 inhibitors.

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

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.

Plant-Derived Natural Products in Cancer Research: Extraction, Mechanism of Action, and Drug Formulation.

Cancer is one of the main causes of death globally and considered as a major challenge for the public health system. The high toxicity and the lack of selectivity of conventional anticancer therapies make the search for alternative treatments a priority. In this review, we describe the main plant-derived natural products used as anticancer agents. Natural sources, extraction methods, anticancer mechanisms, clinical studies, and pharmaceutical formulation are discussed in this review. Studies covered by this review should provide a solid foundation for researchers and physicians to enhance basic and clinical research on developing alternative anticancer therapies.

Pharmacophore modeling of JAK1: A target infested with activity-cliffs.

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC50 of 1.04 µM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Preparation, Physicochemical Characterization and Biological Evaluation of some Hesperidin Metal Complexes.

The ability of hesperidin (HP) to form complexes with five metals; cobalt, nickel, zinc, calcium and magnesium was investigated. The complexation was studied using U.V spectroscopic titration, in methanol as well as aqueous buffer solutions (physiological conditions). Potential complexes were studied by IR and NMR spectroscopy, melting point and their solubility were also evaluated. The interaction of HP and its metal complexes with DNA was investigated by U.V spectroscopy. HP and its potential complexes were also tested for their ability to inhibit alpha amylase and alpha glucosidase enzymes. The results indicated that HP can form 1:1 complexes with cobalt, nickel and zinc in methanolic solution but not in aqueous buffers. Both HP and its metal complexes were found to intercalate DNA, at physiological condition, with preference to GC rich sequences. HP-metal complexes appeared to have higher affinity towards poly A DNA than the free HP. Neither HP nor its complexes exhibited antimicrobial activity against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa or Candida albicans. Results showed that HP has little inhibitory action on glucosidase and amylase enzymes with no obvious effect of complexation on the behavior of free HP. In conclusion HP was shown to form 1:complexes with the studied metal in methanol but not in aqueous buffer solutions. In presence of DNA however, complex formation in aqueous solutions seem to be encouraged with differential effect between the complexes and free HP.