Anticancer medicines in development: assessment of bioactivity profiles within the National Cancer Institute anticancer screening data

Muti-target rationale design of novel substituted N-phenyl-2-((6-phenylpyridazin-3-yl)thio)acetamide candidates as telomerase/JAK1/STAT3/TLR4 inhibitors: In vitro and in vivo investigations

In this work, additional effort was applied to design new BIBR1532-based analogues with potential inhibitory activity against telomerase and acting as multitarget antitumor candidates to overcome the resistance problem. Therefore, novel substituted N-phenyl-2-((6-phenylpyridazin-3-yl)thio)acetamide candidates (4a-n) were synthesized. Applying the lead optimization strategy of the previously designed compound 8e; compound 4l showed an improved telomerase inhibition of 64.95 % and a superior growth inhibition of 79 % suggesting its potential use as a successful "multitarget-directed drug" for cancer therapy. Accordingly, compound 4l was further selected to evaluate its additional JAK1/STAT3/TLR4 inhibitory potentials. Compound 4l represented a very promising JAK1 inhibitory potential with a 0.46-fold change, compared to that of pacritinib reference standard (0.33-fold change). Besides, it showed a superior STAT3-inhibitory potential with a 0.22-fold change compared to sorafenib (0.33-fold change). Additionally, compound 4l downregulated TLR4 protein expression by 0.81-fold change compared to that of resatorvid (0.29-fold change). Also, molecular docking was performed to investigate the binding mode and affinity of the superior candidate 4l towards the four target receptors (telomerase, JAK1, STAT3, and TLR4). Furthermore, the therapeutic potential of compound 4l as an antitumor agent was additionally explored through in vivo studies involving female mice implanted with Solid Ehrlich Carcinoma (SEC). Remarkably, compound 4l led to prominent reductions in tumor size and mass. Concurrent enhancements in biochemical, hematologic, histopathologic, and immunohistochemical parameters further confirmed the suppression of angiogenesis and inflammation, elucidating additional mechanisms by which compound 4l exerts its anticancer effects.

Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence

The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.

In Silico Mixed Ligand/Structure-Based Design of New CDK-1/PARP-1 Dual Inhibitors as Anti-Breast Cancer Agents

CDK-1 and PARP-1 play crucial roles in breast cancer progression. Compounds acting as CDK-1 and/or PARP-1 inhibitors can induct cell death in breast cancer with a selective synthetic lethality mechanism. A mixed treatment by means of CDK-1 and PARP-1 inhibitors resulted in radical breast cancer cell growth reduction. Inhibitors with a dual target mechanism of action could arrest cancer progression by simultaneously blocking the DNA repair mechanism and cell cycle, resulting in advantageous monotherapy. To this aim, in the present work, we identified compound 645656 with a significant affinity for both CDK-1 and PARP-1 by a mixed ligand- and structure-based virtual screening protocol. The Biotarget Predictor Tool was used at first in a Multitarget mode to filter the large National Cancer Institute (NCI) database. Then, hierarchical docking studies were performed to further screen the compounds and evaluate the ligands binding mode, whose putative dual-target mechanism of action was investigated through the correlation between the antiproliferative activity data and the target proteins' (CDK-1 and PARP-1) expression pattern. Finally, a Molecular Dynamics Simulation confirmed the high stability of the most effective selected compound 645656 in complex with both PARP-1 and CDK-1.

Cytotoxicity of Carvotacetones from Sphaeranthus africanus Against Cancer Cells and Their Potential to Induce Apoptosis

Three carvotacetones (1 - 3: ) isolated from Sphaeranthus africanus were screened in 60 cancer cell lines at the National Cancer Institute (NCI) within the Developmental Therapeutics Program (DTP). At the concentration of 10^-5 M, compound 1: (3,5-diangeloyloxy-7-hydroxycarvotacetone) turned out to be the most active compound against ACHN and UO-31 renal cancer cell lines with growth percent values of - 100% (all cells dead). Compound 2: (3-angeloyloxy-5-[2″,3″-epoxy-2″-methylbutanoyloxy]-7-hydroxycarvotacetone) showed strong effects in SK-MEL-5 melanoma and ACHN renal cancer cells with inhibition values of 93% and 97%, respectively. Compound 3: (3-angeloyloxy-5-[3″-chloro-2″-hydroxy-2″-methylbutanoyloxy]-7-hydroxy-carvotacetone) exhibited a quite strong effect on renal cancer cells with a growth inhibitory effect of 96% against ACHN and UO-31 cells. When treated with five different concentrations of 1: (1 × 10^-8, 1 × 10^-7, 1 × 10^-6, 1 × 10^-5, and 1 × 10^-4 M), HOP-92 cells were found to be most sensitive with GI₅₀, TGI, and LC₅₀ values of 0.17, 0.40, and 0.96 µM, respectively. When using the ApoTox-Glo triplex assay to evaluate the apoptosis inducing effects of seven carvotacetones isolated from S. africanus in CCRF-CEM cells, compounds 1:  - 6: increased caspase-3/7 activity with 1, 2: , and 4: (3-angeloyloxy-5,7-dihydroxycarvotacetone) exhibiting the highest activitiy, indicating induction of caspase-dependent apoptosis.

Artificial intelligence for drug discovery: Resources, methods, and applications

Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.

Terminators or Guardians? Design, Synthesis, and Cytotoxicity Profiling of Chalcone-Sulfonamide Hybrids

With a "less is more" philosophy, a series of 15 chalcone-sulfonamide hybrids were designed anticipating synergistic anticancer activity. The aromatic sulfonamide moiety was included as a known direct inhibitor of carbonic anhydrase IX activity through its zinc chelating property. The chalcone moiety was incorporated as an electrophilic stressor to indirectly inhibit carbonic anhydrase IX cellular activity. Screening by the Developmental Therapeutics Program of the National Cancer Institute, NCI-60, revealed that 12 derivatives were potent inhibitors of cancer cell growth in multiple cell lines and were promoted to the five-dose screen. The cancer cell growth inhibition profile indicated sub- to two-digit micromolar potency (GI₅₀ down to 0.3 μM and LC₅₀ as low as 4 μM) against colorectal carcinoma cells, in particular. Unexpectedly, most compounds demonstrated low to moderate potency as direct inhibitors of carbonic anhydrase catalytic activity in vitro, with 4d being the most potent, having an average Ki value of 4 μM. Compound 4j showed ca. six-fold selectivity to carbonic anhydrase IX over the other tested isoforms in vitro. Cytotoxicity of both 4d and 4j in live HCT116, U251, and LOX IMVI cells under hypoxic conditions confirmed their targeting of carbonic anhydrase activity. Elevation of oxidative cellular stress was stipulated from the increase in Nrf2 and ROS levels in 4j-treated colorectal carcinoma, HCT116, cells compared to the control. Compound 4j arrested the cell cycle of HCT116 cells at the G1/S phase. In addition, both 4d and 4j showed up to 50-fold cancer cell selectivity compared to the non-cancerous HEK293T cells. Accordingly, this study presents 4d and 4j being new, synthetically accessible, simplistically designed derivatives as potential candidates to be further developed as anticancer therapeutics.

Schweinfurthin induces ICD without ER stress and caspase activation

Our previous study showed that one of the schweinfurthin compounds, 5’-methoxyschweinfurthin G (MeSG), not only enhances the anti-tumor effect of anti-PD1 antibody in the B16F10 murine melanoma model, but also provokes durable, protective anti-tumor immunity. Here we further investigated the mechanisms by which MeSG treatment induces immunogenic cell death (ICD). MeSG induced significant cell surface calreticulin (CRT) exposure in a time and concentration dependent manner as well as increased phagocytosis of tumor cells by dendritic cells in vitro. Interestingly, this CRT exposure differs from the canonical pathway in several aspects. MeSG does not cause ER stress and does not require PERK to induce CRT exposure. Caspase inhibitors partially rescue cells from MeSG-induced apoptosis, but fail to reduce CRT exposure. MeSG does not cause ERp57 exposure and the absence of ERp57 expression does not reduce CRT exposure. Finally, an intact ER to Golgi transport system is required for this phenomenon. These results lend support to the development of the schweinfurthin family as drugs to enhance clinical response to immunotherapy and highlight the need for additional research on the mechanisms of ICD induction.

Antiproliferative Activity Predictor: A New Reliable In Silico Tool for Drug Response Prediction against NCI60 Panel

In vitro antiproliferative assays still represent one of the most important tools in the anticancer drug discovery field, especially to gain insights into the mechanisms of action of anticancer small molecules. The NCI-DTP (National Cancer Institute Developmental Therapeutics Program) undoubtedly represents the most famous project aimed at rapidly testing thousands of compounds against multiple tumor cell lines (NCI60). The large amount of biological data stored in the National Cancer Institute (NCI) database and many other databases has led researchers in the fields of computational biology and medicinal chemistry to develop tools to predict the anticancer properties of new agents in advance. In this work, based on the available antiproliferative data collected by the NCI and the manipulation of molecular descriptors, we propose the new in silico Antiproliferative Activity Predictor (AAP) tool to calculate the GI₅₀ values of input structures against the NCI60 panel. This ligand-based protocol, validated by both internal and external sets of structures, has proven to be highly reliable and robust. The obtained GI₅₀ values of a test set of 99 structures present an error of less than ±1 unit. The AAP is more powerful for GI₅₀ calculation in the range of 4-6, showing that the results strictly correlate with the experimental data. The encouraging results were further supported by the examination of an in-house database of curcumin analogues that have already been studied as antiproliferative agents. The AAP tool identified several potentially active compounds, and a subsequent evaluation of a set of molecules selected by the NCI for the one-dose/five-dose antiproliferative assays confirmed the great potential of our protocol for the development of new anticancer small molecules. The integration of the AAP tool in the free web service DRUDIT provides an interesting device for the discovery and/or optimization of anticancer drugs to the medicinal chemistry community. The training set will be updated with new NCI-tested compounds to cover more chemical spaces, activities, and cell lines. Currently, the same protocol is being developed for predicting the TGI (total growth inhibition) and LC₅₀ (median lethal concentration) parameters to estimate toxicity profiles of small molecules.

Visible-Light-Induced Cascade Cyclization of 3-(2-(Ethynyl)phenyl)quinazolinones to Phosphorylated Quinolino[2,1-b]quinazolinones

3-(2-(Ethynyl)phenyl)quinazolinones were designed and synthesized as a class of novel and efficient skeletons for phosphorylation/cyclization reactions. Under visible light irradiation, a series of phosphorylated quinolino[2,1-b]quinazolinones (35 examples, up to 87% yield) were first synthesized from 3-(2-(ethynyl)phenyl)quinazolinones and diarylphosphine oxides by using 4CzIPN as a photocatalyst under mild conditions. This reaction was also applicable under sunlight irradiation. Moreover, the reaction efficiency could be significantly improved under continuous-flow conditions.

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method

Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone.

Synthesis, anticancer activity and mechanism of action of new phthalimido-1,3-thiazole derivatives

In this work, 22 new compounds were obtained and evaluated for their cytotoxic activity on peripheral blood mononuclear cells (PBMC) and eight different tumor cell lines. All compounds displayed IC₅₀ values above 100 μM when assayed against PBMCs. The cytotoxic assays in tumor cell lines revealed that sub-series of phthalimido-bis-1,3-thiazoles (5a-f) exhibited the best anti-tumor activity profile, presenting viability values below 59 %. As a result, the IC₅₀ value was calculated for compounds 5a-f and 4c, and compounds 5b and 5e were selected for further assays due to their best IC₅₀s. Considering the results presented by the sub-series 5a-f, the importance of the 1,3-thiazole ring in improving the anti-tumor activity was pointed out. Together, the results highlighted the anti-tumor activity of phthalimido-bis-1,3-thiazole derivatives.

Off-Target-Based Design of Selective HIV-1 PROTEASE Inhibitors

The approval of the first HIV-1 protease inhibitors (HIV-1 PRIs) marked a fundamental step in the control of AIDS, and this class of agents still represents the mainstay therapy for this illness. Despite the undisputed benefits, the necessary lifelong treatment led to numerous severe side-effects (metabolic syndrome, hepatotoxicity, diabetes, etc.). The HIV-1 PRIs are capable of interacting with "secondary" targets (off-targets) characterized by different biological activities from that of HIV-1 protease. In this scenario, the in-silico techniques undoubtedly contributed to the design of new small molecules with well-fitting selectivity against the main target, analyzing possible undesirable interactions that are already in the early stages of the research process. The present work is focused on a new mixed-hierarchical, ligand-structure-based protocol, which is centered on an on/off-target approach, to identify the new selective inhibitors of HIV-1 PR. The use of the well-established, ligand-based tools available in the DRUDIT web platform, in combination with a conventional, structure-based molecular docking process, permitted to fast screen a large database of active molecules and to select a set of structure with optimal on/off-target profiles. Therefore, the method exposed herein, could represent a reliable help in the research of new selective targeted small molecules, permitting to design new agents without undesirable interactions.

Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction

Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.

Discovery of a Pyrimidinedione Derivative with Potent Inhibitory Activity against Mycobacterium tuberculosis Ketol-Acid Reductoisomerase

New drugs aimed at novel targets are urgently needed to combat the increasing rate of drug-resistant tuberculosis (TB). Herein, the National Cancer Institute Developmental Therapeutic Program (NCI-DTP) chemical library was screened against a promising new target, ketol-acid reductoisomerase (KARI), the second enzyme in the branched-chain amino acid (BCAA) biosynthesis pathway. From this library, 6-hydroxy-2-methylthiazolo[4,5-d]pyrimidine-5,7(4H,6H)-dione (NSC116565) was identified as a potent time-dependent inhibitor of Mycobacterium tuberculosis (Mt) KARI with a K_i of 95.4 nm. Isothermal titration calorimetry studies showed that this inhibitor bound to MtKARI in the presence and absence of the cofactor, nicotinamide adenine dinucleotide phosphate (NADPH), which was confirmed by crystal structures of the compound in complex with closely related Staphylococcus aureus KARI. It is also shown that NSC116565 inhibits the growth of H37Ra and H37Rv strains of Mt with MIC₅₀ values of 2.93 and 6.06 μm, respectively. These results further validate KARI as a TB drug target and show that NSC116565 is a promising lead for anti-TB drug development.

Study of the Relationship between Sigma Receptor Expression Levels and Some Common Sigma Ligand Activity in Cancer Using Human Cancer Cell Lines of the NCI-60 Cell Line Panel

Sigma (σ) receptors have attracted great interest since they are implicated in various cellular functions and biological processes and diseases, including various types of cancer. The receptor family consists of two subtypes: sigma-1 (σ1) and sigma-2 (σ2). Both σ receptor subtypes have been proposed as therapeutic targets for various types of cancers, and many studies have provided evidence that their selective ligands (agonists and antagonists) exhibit antiproliferative and cytotoxic activity. Still, the precise mechanism of action of both σ receptors and their ligands remains unclear and needs to be elucidated. In this study, we aimed to simultaneously determine the expression levels of both σ receptor subtypes in several human cancer cell lines. Additionally, we investigated the in vitro antiproliferative activity of some widely used σ1 and σ2 ligands against those cell lines to study the relationship between σ receptor expression levels and σ ligand activity. Finally, we ran the NCI60 COMPARE algorithm to further elucidate the cytotoxic mechanism of action of the selected σ ligands studied herein.

Plant triterpenoids with bond-missing skeletons: biogenesis, distribution and bioactivity

Covering: up to December 2018 The polycyclic ABCD(E) framework of triterpenoids can miss a single endocyclic C-C bond as a result of a modification of the cyclization cascade that triggers their formation (interrupted- or diverted cascades), or can be the result of post-cyclization ring cleavage by late-stage oxidative modifications (seco-triterpenoids). Because of mechanistic and biogenetic differences, ring opening associated with loss of a skeletal fragment, as in nor-seco-triterpenoids (limonoids, quassinoids), will not be covered, nor will compounds where ring opening is part of a fragmentation cascade or of a multiple diversion from it. Even with these limitations, 342 bond-missing triterpenoids could be retrieved from the literature, with transversal distribution in the plant kingdom. Their structural diversity translates into a variety of biological targets, with dominance of potential applications in the realm of cancer, neuroprotection, and anti-infective therapy. In addition to the bioactivity and chemotaxonomic relevance of bond-missing triterpenoids, current knowledge on the genetic basis of interrupted- and diverted oxidosqualene cyclases will be summarized. This untapped source of enzymes could be useful to selectively modify triterpenoids by metabolic engineering, circumventing the bottlenecks of their isolation (poor yield or inadequate supply chain) to explore new areas of their chemical space.

A Novel Class of N-Sulfonyl and N-Sulfamoyl Noscapine Derivatives that Promote Mitotic Arrest in Cancer Cells

Noscapine displays weak anticancer efficacy and numerous research efforts have attempted to generate more potent noscapine analogues. These modifications included the replacement of the N-methyl group in the 6'-position with a range of substituents, where N-ethylcarbamoyl substitution was observed to possess enhanced anticancer activity. Herein, we describe advances in this area, namely the synthesis and pharmacological evaluation of a series of N-sulfonyl and N-sulfamoyl noscapine derivatives. A number of these sulfonyl-containing noscapinoids demonstrated improved activities compared to noscapine. ((R)-5-((S)-4,5-Dimethoxy-1,3-dihydroisobenzofuran-1-yl)-4-methoxy-6-((1-methyl-1H-imidazol-4-yl)sulfonyl)-5,6,7,8-tetrahydro[1,3]dioxolo[4,5-g]isoquinoline) (14 q) displayed sub-micromolar activities of 560, 980, 271 and 443 nM against MCF-7, PANC-1, MDA-MB-435 and SK-MEL-5 cells, respectively. This antiproliferative effect was also maintained against drug-resistant NCI/Adr^RES cells despite high expression of the multidrug efflux pump, P-glycoprotein.

Exploration of (hetero)aryl Derived Thienylchalcones for Antiviral and Anticancer Activities

BACKGROUND

Search for new antiviral and anticancer agents are essential because of the emergence of drug resistance in recent years. In continuation of our efforts in identifying the new small molecule antiviral and anticancer agents, we identified chalcones as potent antiviral and anticancer agents.

OBJECTIVE

With the aim of identifying the broad acting antiviral and anticancer agents, we discovered substituted aryl/heteroaryl derived thienyl chalcones as antiviral and anticancer agents.

METHOD

A focused set of thienyl chalcone derivaties II-VI was screened for selected viruses Hepatitis B virus (HBV), Herpes simplex virus 1 (HSV-1), Human cytomegalovirus (HCMV), Dengue virus 2 (DENV2), Influenza A (H1N1) virus, MERS coronavirus, Poliovirus 1 (PV 1), Rift Valley fever (RVF), Tacaribe virus (TCRV), Venezuelan equine encephalitis virus (VEE) and Zika virus (ZIKV) using the National Institute of Allergy and Infectious Diseases (NIAID)'s Division of Microbiology and Infectious Diseases (DMID) antiviral screening program. Additionally, a cyclopropylquinoline derivative IV has been screened for 60 human cancer cell lines using the Development Therapeutics Program (DTP) of NCI.

RESULTS

All thienyl chalcone derivatives II-VI displayed moderate to excellent antiviral activity towards several viruses tested. Compounds V and VI were turned out be active compounds towards human cytomegalovirus for both normal strain (AD169) as well as resistant isolate (GDGr K17). Particularly, cyano derivative V showed very high potency (EC50: <0.05 µM) towards AD169 strain of HCMV compared to standard drug Ganciclovir (EC50: 0.12 µM). Additionally, it showed moderate activity in the secondary assay (AD169; EC50: 2.30 µM). The cyclopropylquinoline derivative IV displayed high potency towards Rift Valley fever virus (RVFV) and Tacaribe virus (TCRV) towards Rift Valley fever virus (RVFV). The cyclopropylquinoline derivative IV is nearly 28 times more potent in our initial in vitro visual assay (EC50: 0.39 µg/ml) and nearly 17 times more potent in neutral red assay (EC50: 0.71 μg/ml) compared to the standard drug Ribavirin (EC50: 11 µg/ml; visual assay and EC50: 12 µg/ml; neutral red assay). It is nearly 12 times more potent in our initial in vitro visual assay (EC50: >1 µg/ml) and nearly 8 times more potent in neutral red assay (EC50: >1.3 µg/ml) compared to the standard drug Ribavirin (EC50: 12 µg/ml; visual assay and EC50: 9.9 µg/ml; neutral red assay) towards Tacaribe virus (TCRV). Additionally, cyclopropylquinoline derivative IV has shown strong growth inhibitory activity towards three major cancers (colon, breast, and leukemia) cell lines and moderate growth inhibition shown towards other cancer cell lines screened.

CONCLUSION

Compounds V and VI were demonstrated viral inhibition towards Human cytomegalovirus, whereas cyclopropylquinoline derivative IV towards Rift Valley fever virus and Tacaribe virus. Additionally, cyclopropylquinoline derivative IV has displayed very good cytotoxicity against colon, breast and leukemia cell lines in vitro.

Bioactivity of a Rice Bran-Derived Peptide and its Sensory Evaluation and Storage Stability in Orange Juice

A pentapeptide prepared from rice bran demonstrated growth inhibition on human lung, liver, breast, and colon cancer cell lines. The objectives of this study were to evaluate the human prostate cancer growth inhibition by the pentapeptide and its 6-mo storage stability by incorporating spray-dried orange juice, and determining sensory acceptability. The pentapeptide showed inhibition of human prostate cancer cells by 45% at 460 μg/mL concentration. When incorporated in spray-dried orange juice, and reconstituted with water and tested, there was an approximately 10% degradation of the peptide at 620 μg/mL concentration under refrigerated conditions over a 6 mo storage period, whereas at ambient temperature the degradation was 30%. Larger degradation was observed when 240 or 460 μg/mL pentapeptide was used. Overall, consumer panelists liked sensory aspect of the reconstituted pentapeptide incorporated orange juice beverage. Also consumer panelists liked the color and mouthfeel attributes, their hedonic impression of flavor attribute was slightly low due to unpalatable bitter note caused by the presence of the peptide. Incorporation of the pentapeptide in spray-dried orange juice has the potential to serve as a functional food ingredient that can offer health benefits to consumers. It is possible that the structural instability can be minimized by encapsulation.

3-Deoxyschweinfurthin B Lowers Cholesterol Levels by Decreasing Synthesis and Increasing Export in Cultured Cancer Cell Lines

The schweinfurthins have potent antiproliferative activity in multiple glioblastoma multiforme (GBM) cell lines; however, the mechanism by which growth is impeded is not fully understood. Previously, we demonstrated that the schweinfurthins reduce the level of key isoprenoid intermediates in the cholesterol biosynthetic pathway. Herein, we describe the effects of the schweinfurthins on cholesterol homeostasis. Intracellular cholesterol levels are greatly reduced in cells incubated with 3-deoxyschweinfurthin B (3dSB), an analog of the natural product schweinfurthin B. Decreased cholesterol levels are due to decreased cholesterol synthesis and increased cholesterol efflux; both of these cellular actions can be influenced by liver X-receptor (LXR) activation. The effects of 3dSB on ATP-binding cassette transporter 1 levels and other LXR targets are similar to that of 25-hydroxycholesterol, an LXR agonist. Unlike 25-hydroxycholesterol, 3dSB does not act as a direct agonist for LXR α or β. These data suggest that cholesterol homeostasis plays a significant role in the growth inhibitory activity of the schweinfurthins and may elucidate a mechanism that can be targeted in human cancers such as GBM.

Calcium and P-glycoprotein independent synergism between schweinfurthins and verapamil

Schweinfurthins are intriguing natural products with anti-cancer activities and as yet incompletely understood mechanisms of action. We investigated whether inhibitors of P-glycoprotein (Pgp), in a manner analogous to other natural products, might enhance schweinfurthins' growth inhibitory actions by increasing intracellular schweinfurthin levels. Both the schweinfurthin-sensitive glioblastoma multiforme cell line SF-295 and relatively insensitive lung carcinoma cell line A549 were treated with 2 schweinfurthin analogs: 3-deoxyschweinfurthin B-p-nitro bis-stilbene (3dSB-PNBS) and 5'-methylschweinfurthin G (methyl-G). There was a synergistic enhancement of growth inhibition with the combination of the Pgp inhibitor verapamil and both analogs in SF-295 cells. Methyl-G, verapamil, and the combination did not result in alterations to intracellular calcium concentration. Verapamil increased the intracellular concentration of 3dSB-PNBS in both SF-295 and A549 cells in a Pgp-independent manner. Methyl-G, verapamil, and the combination do not result in increased ER stress. Methyl-G increased the intracellular concentration of a known Pgp substrate, Rhodamine 123 in SF-295 cells. Reduction of cellular cholesterol leads to the accumulation of Pgp substrates, as Pgp requires cholesterol for proper function. Since 3dSB enhances lovastatin-induced upregulation of the cholesterol efflux pump ABCA1, it is intriguing that co-treatment with cholesterol rescued the methyl-G-induced increase in Rhodamine 123 intracellular concentration. These studies support the hypothesis that verapamil potentiates the schweinfurthin growth inhibitory effect by increasing its intracellular concentration.

Matching the power of high throughput screening to the chemical diversity of natural products

Covering up to 2013. Application of high throughput screening technologies to natural product samples demands alterations in assay design as well as sample preparation in order to yield meaningful hit structures at the end of the campaign.

Perspective: Opportunities in recalcitrant, rare and neglected tumors

The ‘Recalcitrant Cancer Research Act of 2012’ defines recalcitrant cancers as having a 5-year survival rate of <20% and estimated to cause the death of at least 30,000 individuals in the US each year. Cancers specifically mentioned in the act are lung and pancreatic cancers. In addition to recalcitrant tumors, rare tumors are often neglected in the drug discovery arena. Sarcomas are ~1% of cancers. The NCI Specialized Programs of Research Excellence (SPOREs) provide disease-focused cancer center grants specifically to accelerate the impact of laboratory research on the treatment of patients. There are 3 SPOREs focused on pancreatic cancer, 7 SPOREs focused on lung cancer and 1 SPORE focused on sarcoma. Through the Developmental Therapeutics Program (DTP), NCI maintains the infrastructure and expertise for the operation of cell-free and cell-based high-, medium- and low-throughput assays. The current effort is on sarcoma, SCLC and pancreatic lines. The DTP functional genomics laboratory provides molecular analyses including gene expression microarrays, exon arrays, microRNA arrays, multiplexing gene assays, plus others as tools to identify potential drug targets and to determine the role of selected genes in the mechanism(s) of drug action and cellular responses to stressors. The DTP tumor microenvironment laboratory focuses on the discovery of targets and the development of therapeutic strategies targeting the tumor microenvironment and physiological abnormalities of tumors resulting from environmental factors or alterations in metabolic enzymes. The DTP maintains a group focused on determining the mechanism(s) of action and identifying potential surrogate markers of activity for select compounds integrating proteomics, transcriptomics and molecular biology platforms. In conclusion, the NCI has active SPORE programs and an internal effort focused on recalcitrant, rare and neglected cancers which are generating data toward improving treatment of these difficult diseases.

3D shape-based analysis of cell line-specific compound response in cancers

The rapid increase in the volume of high-throughput anticancer chemical screening data requires a better interpretation of the relationships between diverse chemical structures and their varied effects in distinct cancer subtypes. Unexpected compound efficacy or resistance in cancer cells has been difficult to explain, in part because there has been no systematic analysis of compound response profiles in cancer cells with different genotypic backgrounds. In this study, we compared 2D chemical- and 3D shape-based similarity search methods to study the structure-activity relationships of anticancer compounds in a collection of heterogeneous cancer cell lines. The 3D shape-based metric provided better resolution than the 2D chemical topology-based method for identifying compound pairs with similar cellular response profiles. We confirmed that the 3D method exclusively identified compound pairs with different chemical scaffolds that stimulated highly similar cellular responses. The present analyses provide useful guidelines for investigating the lineage- and genotype-specific activities of diverse compounds and their mechanisms of action.

Predicting in vitro drug sensitivity using Random Forests

MOTIVATION

Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART).

RESULTS

This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable.

IMPLEMENTATION

Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.

Discovery of novel Myc-Max heterodimer disruptors with a three-dimensional pharmacophore model

A three-dimensional pharmacophore model was generated utilizing a set of known inhibitors of c-Myc-Max heterodimer formation. The model successfully identified a set of structurally diverse compounds with potential inhibitory activity against c-Myc. Nine compounds were tested in vitro, and four displayed affinities in the micromolar range and growth inhibitory activity against c-Myc-overexpressing cells. These studies demonstrate the applicability of pharmacophore modeling to the identification of novel and potentially more puissant inhibitors of the c-Myc oncoprotein.

Screens, iron, and leukemia

In this issue of Blood, Eberhard and colleagues screen a large library of off-patent agents for down-regulation of the survivin promoter in HeLa cells. Their most specific “hit” was ciclopirox, formerly developed as an antifungal, which was found to have preclinical antileukemic activity possibly via depletion of intracellular iron.

Evaluation of molecular descriptors for antitumor drugs with respect to noncovalent binding to DNA and antiproliferative activity

Background

Small molecules that bind reversibly to DNA are among the antitumor drugs currently used in chemotherapy. In the pursuit of a more rational approach to cancer chemotherapy based upon these molecules, it is necessary to exploit the interdependency between DNA-binding affinity, sequence selectivity and cytotoxicity. For drugs binding noncovalently to DNA, it is worth exploring whether molecular descriptors, such as their molecular weight or the number of potential hydrogen acceptors/donors, can account for their DNA-binding affinity and cytotoxicity.

Results

Fifteen antitumor agents, which are in clinical use or being evaluated as part of the National Cancer Institute's drug screening effort, were analyzed in silico to assess the contribution of various molecular descriptors to their DNA-binding affinity, and the capacity of the descriptors and DNA-binding constants for predicting cell cytotoxicity. Equations to predict drug-DNA binding constants and growth-inhibitory concentrations were obtained by multiple regression following rigorous statistical procedures.

Conclusion

For drugs binding reversibly to DNA, both their strength of binding and their cytoxicity are fairly predicted from molecular descriptors by using multiple regression methods. The equations derived may be useful for rational drug design. The results obtained agree with that compounds more active across the National Cancer Institute's 60-cell line data set tend to have common structural features.