Cancer Drug Discovery Research Articles

Reinstating apoptosis using putative Bcl-xL natural product inhibitors: Molecular docking and ADMETox profiling investigations

ObjectivesWhile a fine balance in the pro-apoptotic and anti-apoptotic family members of the B-cell lymphoma-2 (Bcl-2) protein family represents a normal signaling profile, a tilt in balance towards anti-apoptotic family members has fortified different forms of cancers with survival advantage and resistance against treatment. Induction of apoptosis is a key therapeutic approach in cancer drug discovery, and the inhibition of the anti-apoptotic B cell lymphoma extra-large (Bcl-xL) is a long-standing clinical target for cancer therapy. In this study, we combined computer-aided approaches to report putative binders for this target. MethodsBefore our virtual screening campaign, we conducted a redocking experiment strategy of the x-ray bound inhibitor of the Bcl-xL protein with some of the available docking software at our disposal to determine the software with the best efficiency for this screening. iGEMDOCK emerged to reproduce the x-ray crystallographic information and was used to dock the library of ligand, which was developed from diverse literature reporting compounds with anti-apoptotic profiles through the Bcl-2 family. ResultsOf the compounds in the library, alpha-mangostin and oubain scored as hits with binding energy values of −123.025 kcal/mol and −122.271 kcal/mol, respectively, which is more than −120.8 kcal/mol observed by the standard. ConclusionsThese compounds revealed a more binding affinity potential than ABT-737, which is a standard inhibitor of the protein. In addition, these scaffolds not only interact with relevant and hotspot residues for the inhibition of Bcl-xL but also possess good pharmacokinetic and excellent toxicity, an endpoint that should be considered for further testing and drug development.

DDDR-19. NEXT-GENERATION PI3K INHIBITOR FOR GLIOBLASTOMA TREATMENT

Abstract High-grade gliomas are especially problematic because of the limited treatment options available to patients. The phosphoinositide 3-kinase (PI3K) pathway has become a significant research target within the field of cancer drug discovery due to the pathway’s promotion of cell proliferation and inhibition of apoptosis. While many new PI3K inhibitory medications show promise in preventing cancer growth, many are toxic because they inhibit multiple PI3K catalytic subunit isoforms making these drugs ineffective treatment options. Our novel peptide drug, Selectide-18, overcomes this toxicity by specifically targeting the p110β isoform of PI3K, which was shown to be the most impactful isoform in many cancer types, including several high-grade gliomas. We designed Selectide-18 after discovering an 18-residue motif unique to p110β. Selectide-18 is a mimetic peptide modeled after this motif. Our in silico analyses show that our drug works by binding to the PI3K regulatory subunit, p85α, thus preventing the binding of catalytic subunit p110β and leaving PI3K inactive. These findings were supported by coimmunoprecipitation. Using a PI3K cell-free kinase assay, we confirmed that our drug specifically targets p110β and does not inhibit p110α, p110δ, or p110γ. The IC50 of Selectide-18 was determined to be approximately 100nM using the same PI3K cell-free kinase assay. We tested several drugs in an MTS viability assay, including Selectide-18, a scrambled 18-residue negative control peptide, and PI3K inhibitor GSK2636771 to compare levels of cancer cell viability. Selectide-18 successfully killed glioblastoma cells within lines with high p110β activity. The drug had no effect in cell lines with low p110β activity. In conclusion, Selectide-18 is explicitly tailored for p110β-high gliomas and has shown promise to treat high-grade glioma effectively. We believe this model of peptide drug can usher in a new generation of p110β specific PI3K inhibitors.

DNA Binding and Anticancer Properties of New Pd(II)-Phosphorus Schiff Base Metal Complexes.

DNA has become the target of metal complexes in cancer drug discovery. Due to the side effects of widely known cisplatin and its derivative compounds, alternative metal-based drug discovery studies are still ongoing. In this study, the DNA-binding ability of Pd(II) and Pt(II) complexes of four phosphorus Schiff base ligands and four hydrazonoic-phosphines are investigated by using in silico analyses. Phosphorus Schiff base-Pd(II) complexes encoded as B1 and B2 with the best DNA-binding potential are synthesized and characterized. The DNA-binding potentials of these two new Pd(II) complexes are also investigated experimentally, and their antitumor properties are demonstrated in vitro in A549, MCF7, HuH7, and HCT116 cancer cells. The mechanisms of these metal complexes that kill the cells mentioned above in different activities are elucidated by flow cytometry apoptosis analysis and colony formation analysis The in silico binding energies of these two new palladium complexes ΔG (B1): -4.51 and ΔG (B2): -6.04 kcal/mol, and their experimental DNA-binding constants were found as Kb (B1): 4.24 × 105, Kb (B2): 4.98 × 105). The new complexes, which show different antitumor effects in different cells, are the least effective in HuH7 liver cells, while they showed the best antitumor properties in HCT116 colon cancer cells.

Issue Information

Molecular OncologyVolume 16, Issue 21 p. 3755-3756 Issue InformationOpen Access Issue Information First published: 02 November 2022 https://doi.org/10.1002/1878-0261.13008AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Renaissance for cancer drug discovery. Cover art by Sofija Grabuloska, based on photo taken by Ruzhica Bogeksa and a Shutterstock image. Read also Editorial by Matilde Murga and Oscar Fernandez-Capetillo, pp 3757–3760. Volume16, Issue21Thematic issue: Drug discovery for cancer therapyNovember 2022Pages 3755-3756 RelatedInformation

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases.

Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach.

Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches. We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer. All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes. Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer. The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance.

Prevention of breast cancer with the breast self-examination method (BSE) in the Negeri Lima village, Leihitu District, Maluku Province

The number of incidences and deaths from cancer is increasing every year around the world. However, currently available cancer treatment methods still have weaknesses, the discovery of cancer drugs is still very important. The purpose of this study was to determine the effect of the level of public knowledge about breast cancer with BSE behavior on housewives in Negeri Lima Village, Maluku Province. The design of this research is descriptive analytic with a cross-sectional approach. The study was conducted in May 2022. The population in this study were residents of the village of Negeri Lima. Sampling using the method with 50 respondents. The tool used in this research is a questionnaire. The analysis technique used is Chi Square analysis. The results showed that the influence of the level of knowledge about breast cancer on housewives was mostly in the good category of 60.5%. BSE behavior in housewives is mostly in the category of not doing BSE by 40%. The results of the Chi Square analysis obtained the calculated X2 value of 55.40 with a p value of 0.000 (p <0.05). The close relationship between the level of knowledge of breast cancer with conscious behavior has a strong category indicated by a coefficient of 0.50. The conclusion of this study is that there is a relationship between the level of knowledge of mothers about breast cancer and the BSE behavior.

EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers

The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.

Novel mechanism of napabucasin, a naturally derived furanonaphthoquinone: apoptosis and autophagy induction in lung cancer cells through direct targeting on Akt/mTOR proteins

BackgroundAkt and mTOR are aberrantly activated in cancers and targeting these proteins are interesting for cancer drug discovery. Napabucasin (NB), a phytochemical compound, has been reported as potential anti-cancer agent, however, Akt and mTOR targeting mechanisms remain unclear. MethodApoptosis induction was investigated by Hoechst 33342/PI double staining and annexin V/PI staining with flowcytometry. Autophagy was evaluated by monodansylcadaverine staining and Western blot analysis. Binding affinity of NB and essential signaling proteins (PI3K, Akt, and mTOR) was investigated using molecular docking and confirmed by Western blot analysis.ResultA structure modification from changing methyl moiety of acetyl group of NB to hydroxyl moiety of carboxyl group of NB derivative (napabucasin-acid or NB-acid) greatly affected the compound activities. NB showed more potent anti-cancer activity. NB reduced cell viability with an approximately 20 times lower IC50 and inhibited the colony formation capacity much more than NB-acid treated cells. NB induced cell apoptosis, which was accompanied by decrease Bcl‑2 and Mcl-1 and clevage of PARP, while NB-acid show lesser effect on Mcl-1. NB was found to strongly induce autophagy indicated by acidic vesicle staining and the LC3B conversion. Interestingly, computational molecular docking analysis further demonstrated that NB directly bound to Akt and mTOR (complex 1 and 2) proteins at their critical sites indicating that NB targets the upstream regulators of apoptosis and autophagy. The docking results were confirmed by decrease of p-Akt/Akt, p-mTOR/mTOR, and c-Myc a downstream target of Akt protein levels.ConclusionResults show for the first time that NB exerts an anti-cancer activity through the direct interaction to Akt and mTOR proteins. The methyl moiety of acetyl group of NB is required for its potent anti-cancer activities. These data encourage further development of NB compounds for Akt and mTOR driven cancers.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture.

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Abstract PO029: Targeting non-canonical Hippo pathway in NRF2-mutant liver cancer

Abstract NRF2 is an “undruggable” oncogenic transcription factor recurrently mutated in solid tumors such as liver and lung cancers. Our recent study demonstrates that NRF2 acts by coordinating the redox and metabolic stress responses as well as drug resistance programs in liver and other cancers. Developing an NRF2 inhibitor is a major goal of cancer drug discovery. Here, we identify the non-canonical Hippo pathway protein serine/threonine kinase 38 (STK38/NDR1) as a new NRF2 kinase that is a requirement for its stability and function. STK38 directly phosphorylates NRF2 at two sites (S33 and T559), and both sites contribute to full NRF2 activation. Loss of STK38 disables the redox response in NRF2 driven cancers and leads to tumor regression in vivo. Conversely, STK38 can also activate NRF2 and promote de novo liver cancer development in mice. This oncogenic effect is reflected in low frequency (3%) genomic amplifications in human liver cancers. Importantly, inhibition of the upstream STK38-activating mammalian Hippo kinases STK3/4 (MST2/1) by XMU-MP-1 inactivates STK38-NRF2 regulatory axis in vitroand in vivo. More importantly, XMU-MP-1 produces single agent activity against NRF2-driven liver and lung cancers in vivo in primary and patient derived xenograft (PDX)-based mouse models. In addition, we performed an in-silicoscreen and identified TAE-684 as an STK38 inhibitor that was subsequently confirmed to block STK38 activity in an in vitro kinase assay. TAE-684 treatment resulted in significant growth impairment of NRF2-mutant PDXs in vivo but no activity was observed in NRF2 wildtype counterparts. Together, these results uncover a surprising role of Hippo-related kinase STK38 in NRF2 activation and point to a paradoxical vulnerability in NRF2-driven cancers. Citation Format: Viraj Sanghvi, Aleksander Rust, Josef Leibold, Scott Lowe, Agnes Viale, John Chodera, Ronald C. Hendrickson, Elisa de Stanchina, Hans-Guido Wendel. Targeting non-canonical Hippo pathway in NRF2-mutant liver cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr PO029.

Identification of Novel AXL Kinase Inhibitors Using Ligand-Based Pharmacophore Screening and Molecular Dynamics Simulations

AXL kinase is a promising target in novel drug discovery for cancer. A ligand-based pharmacophore model was generated with the Pharmit web server. Its inbuilt PubChem molecule database was screened and led to 408 candidate molecules. Docking of the AXL kinase active sites with the identified list of candidate molecules was carried out with Autodock Vina docking software. This resulted in four compounds selected for further investigation. Molecular dynamics simulation of two ligands (PubChem-122421875 and PubChem-78160848) showed considerable binding with AXL kinase. From the MM-PBSA binding free energies investigation, the PubChem-122421875 (G = −179.3 kJ/mol) and PubChem-78160848 (G = −208.3 kJ/mol) ligands had favorable protein-ligand complex stability and binding free energy. Hence, PubChem-122421875 and PubChem-78160848 molecules identified in this work could be a potent starting point for developing novel AXL kinase inhibitor molecules.

AMTDB: A comprehensive database of autophagic modulators for anti-tumor drug discovery.

Autophagy, originally described as a mechanism for intracellular waste disposal and recovery, has been becoming a crucial biological process closely related to many types of human tumors, including breast cancer, osteosarcoma, glioma, etc., suggesting that intervention of autophagy is a promising therapeutic strategy for cancer drug development. Therefore, a high-quality database is crucial for unraveling the complicated relationship between autophagy and human cancers, elucidating the crosstalk between the key autophagic pathways, and autophagic modulators with their remarkable antitumor activities. To achieve this goal, a comprehensive database of autophagic modulators (AMTDB) was developed. AMTDB focuses on 153 cancer types, 1,153 autophagic regulators, 860 targets, and 2,046 mechanisms/signaling pathways. In addition, a variety of classification methods, advanced retrieval, and target prediction functions are provided exclusively to cater to the different demands of users. Collectively, AMTDB is expected to serve as a powerful online resource to provide a new clue for the discovery of more candidate cancer drugs.

Anti-lung cancer drug discovery approaches by polysaccharides: an in silico study, quantum calculation and molecular dynamics study

Lung cancer (LC) is one of the major and risky health defects even the serious cause for death in concurrent era. But no potential drugs even chemotherapeutic agents have been discovered with approval of health safety although some non-toxic biological macromolecules, such as polysaccharides and polysaccharide–protein complexes, have obtained as anti-lung cancer properties. This study conveys the anti-lung cancer properties of 45 polysaccharide derivatives collected from PubChem database. Primarily, the PASS prediction was performed to depict their anti-cancer activity, and 37 compounds showed the desired results. Next, the chemical descriptors, such as HOMO, LUMO, softness, and hardness etc, were calculated through the density functional theory (DFT) for quantum properties. Secondly, the auto molecular docking was executed to delineate the protein-ligand interactions, binding ability and inhibition of active sites of proteins. Additionally, the compounds showed docking score more than −6.40 kcal/mol, and the highest binding affinity was at −10.00 kcal/mol even 15 compounds have higher binding score (-8.6 to −10.0) than approved drugs, Gemcitabine. Succeeding, the most common protein residue, VAL 647, was blocked by ligands for the main protein (1X2J). In addition, five protein’s active sites were determined to make the relative study of protein-ligand interactions. As a result, the target docking against five proteins was performed, and it was found that the targeted docking score as the binding affinity is lower than auto docking. Finally, a comparative study between auto docking and targeted docking was performed for the most common five lung cancer proteins founded in three organisms. Communicated by Ramaswamy H. Sarma

Prediction of Driver Gene Matching in Lung Cancer NOG/PDX Models Based on Artificial Intelligence

Patient-derived tumor xenografts (PDXs) are a powerful tool for drug discovery and screening in cancer. However, current studies have led to little understanding of genotype mismatches in PDXs, leading to massive economic losses. Here, we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2% (42/53). Furthermore, 17 clinicopathological features were examined and input in stepwise logistic regression (LR) models based on the lowest Akaike information criterion (AIC), least absolute shrinkage and selection operator (LASSO)-LR, support vector machine (SVM) recursive feature elimination (SVM-RFE), extreme gradient boosting (XGBoost), gradient boosting and categorical features (CatBoost), and the synthetic minority oversampling technique (SMOTE). Finally, the performance of all models was evaluated by the accuracy, area under the receiver operating characteristic curve (AUC), and F1 score in 100 testing groups. Two multivariable LR models revealed that age, number of driver gene mutations, epidermal growth factor receptor ( EGFR ) gene mutations, type of prior chemotherapy, prior tyrosine kinase inhibitor (TKI) therapy, and the source of the sample were powerful predictors. Moreover, CatBoost (mean accuracy = 0.960; mean AUC = 0.939; mean F1 score = 0.908) and the eight-feature SVM-RFE (mean accuracy = 0.950; mean AUC = 0.934; mean F1 score = 0.903) showed the best performance among the algorithms. Meanwhile, application of the SMOTE improved the predictive capability of most models, except CatBoost. Based on the SMOTE, the ensemble classifier of single models achieved the highest accuracy (mean = 0.975), AUC (mean = 0.949), and F1 score (mean = 0.938). In conclusion, we established an optimal predictive model to screen lung cancer patients for non-obese diabetic (NOD)/Shi-scid, interleukin-2 receptor (IL-2R) γ null (NOG)/PDX models and offer a general approach for building predictive models.

Progressing Towards a Human-Centric Approach in Cancer Research.

Despite the advancement in research methodologies and technologies for cancer research, there is a high rate of anti-cancer drug attrition. In this review, we discuss different conventional and modern approaches in cancer research and how human-centric models can improve on the voids conferred by more traditional animal-centric models, thereby offering a more reliable platform for drug discovery. Advanced three-dimensional cell culture methodologies, along with in silico computational analysis form the core of human-centric cancer research. This can provide a holistic understanding of the research problems and help design specific and accurate experiments that could lead to the development of better cancer therapeutics. Here, we propose a new human-centric research roadmap that promises to provide a better platform for cancer research and drug discovery.

Caffeic Acid Phenethyl Ester as a DHODH Inhibitor and Its Synergistic Anticancer Properties in Combination with 5-Fluorouracil in a Breast Cancer Cell Line.

IntroductionA combination of chemotherapy agents is the best choice in breast cancer treatment to increase the patient survival rate. 5-fluorouracil (5-FU) is one of the drugs applied in combination with other drugs to control and delay development of cancer cells. Nevertheless, the occurrence of multidrug resistance and dose-limiting cytotoxicity have limited the efficacy of 5-FU treatment. Therefore, the discovery of new anti-breast cancer drugs should be pursued.ObjectiveTo study potency of a promising naturally derived compound, caffeic acid phenethyl ester (CAPE), for breast cancer treatment in single and combination with 5-FU.MethodsCytotoxicity of CAPE, 5-FU, and 5-FU+CAPE was studied by in vitro MTT experiment in MCF-7 cell line, and RT-PCR analysis was used to evaluate the change in gene expression due to the treatment. Moreover, an enzymatic assay and molecular docking analysis were applied to evaluate the possible mechanism of substance-induced apoptosis.ResultsThe study revealed that a single treatment of CAPE showed cytotoxicity with IC50 6.6 ± 1.0 µM and 6.5 ± 2.9 µM at 24 h and 48 h, respectively. Meanwhile, 5-FU showed cytostatic activity. The 5-FU + CAPE has a synergistic effect at 24 h treatment with a CI = 0.5 and an additive effect at 48 h treatment with CI = 1.0. CAPE was also found to enhances the mRNA expression of caspase-8 and BAX within 6 hours in combination with 5-FU compared to 5-FU treatment alone. Our study reveals a new mechanism of CAPE which is related to the inhibition of human dihydroorotate dehydrogenase (HsDHODH) with an IC50 of 120.7 ± 6.8 µM, by bound to the ubiquinone-binding site of the enzyme and could be responsible for inducing extrinsic and intrinsic apoptosis.ConclusionThis study demonstrated the cytotoxicity of CAPE potential to induce apoptosis of breast cancer MCF-7 cell line single and cytotoxic–cytostatic combination with 5-FU. Therefore, further studies to develop CAPE and its derivatives will be required to discover new candidates for breast cancer agents.

Anticancer drug screening of natural products

Natural products include several diverse compounds that have been found to be effective against cancer. Discovering anticancer compounds in nature is a multistep and complex process that requires pre-clinical and clinical studies. Only a few of the available natural products are used to treat cancer since most of them have very high complexity and low bioavailability. Therefore, the process of anticancer drug discovery requires a straightforward and effective method to assess anticancer activity using in vitro assays. This review summarizes various cell-based assays and techniques used to measure cell viability, migration, and apoptosis, focusing in particular on the principles, mechanisms, advantages, and disadvantages of each assay to provide a preliminary platform for cancer drug discovery.

Germline Aberrations in Pancreatic Cancer: Implications for Clinical Care.

Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis and represents a major public health issue, as both its incidence and mortality are expecting to increase steeply over the next years. Effective screening strategies are lacking, and most patients are diagnosed with unresectable disease precluding the only chance of cure. Therapeutic options for advanced disease are limited, and the treatment paradigm is still based on chemotherapy, with a few rare exceptions to targeted therapies. Germline variants in cancer susceptibility genes—particularly those involved in mechanisms of DNA repair—are emerging as promising targets for PDAC treatment and prevention. Hereditary PDAC is part of the spectrum of several syndromic disorders, and germline testing of PDAC patients has relevant implications for broad cancer prevention. Germline aberrations in BRCA1 and BRCA2 genes are predictive biomarkers of response to poly(adenosine diphosphate–ribose) polymerase (PARP) inhibitor olaparib and platinum-based chemotherapy in PDAC, while mutations in mismatch repair genes identify patients suitable for immune checkpoint inhibitors. This review provides a timely and comprehensive overview of germline aberrations in PDAC and their implications for clinical care. It also discusses the need for optimal approaches to better select patients for PARP inhibitor therapy, novel therapeutic opportunities under clinical investigation, and preclinical models for cancer susceptibility and drug discovery.

Death-Associated Protein Kinase 3 Inhibitors Identified by Virtual Screening for Drug Discovery in Cancer and Hypertension.

Death-associated protein kinase 3 (DAPK3) is a serine/threonine protein kinase that regulates apoptosis, autophagy, transcription, and actin cytoskeleton reorganization. DAPK3 induces morphological alterations in apoptosis when overexpressed, and it is considered a potential drug target in antihypertensive and anticancer drug development. In this article, we report new findings from a structure-guided virtual screening for discovery of phytochemicals that could modulate the elevated expression of DAPK3, and with an eye to anticancer drug discovery. We used the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT), a curated database, as part of the methodology. The potential initial hits were identified based on their physicochemical properties and binding affinity toward DAPK3. Subsequently, various filters for drug likeness followed by interaction analysis and molecular dynamics (MD) simulations for 100 nsec were performed to explore the conformational sampling and stability of DAPK3 with the candidate molecules. Notably, the data from all-atom MD simulations and principal component analysis suggested that DAPK3 forms stable complexes with ketanserin and rotenone. In conclusion, this study supports the idea that ketanserin and rotenone bind to DAPK3, and show stability, which can be further explored as promising scaffolds in drug development and therapeutics innovation in clinical contexts such as hypertension and various types of cancer.