Mining the NCI anticancer drug discovery databases: genetic function approximation for the QSAR study of anticancer ellipticine analogues

Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D₂ modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([³H]-NPA) at D₂ receptors (D₂R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [³H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D₂R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.

FlavoDb: a web-based chemical repository of flavonoid compounds

There are many online resources that focus on chemical diversity of natural compounds, but only handful of resources exist that focus solely on flavonoid compounds and integrate structural and functional properties; however, extensive collated flavonoid literature is still unavailable to scientific community. Here we present an open access database 'FlavoDb' that is focused on providing physicochemical properties as well as topological descriptors that can be effectively implemented in deducing large scale quantitative structure property models of flavonoid compounds. In the current version of database, we present data on 1, 19,400 flavonoid compounds, thereby covering most of the known structural space of flavonoid class of compounds. Moreover, effective structure searching tool presented here is expected to provide an interactive and easy-to-use tool for obtaining flavonoid-based literature and allied information. Data from FlavoDb can be freely accessed via its intuitive graphical user interface made available at following web address: http://bioinfo.net.in/flavodb/home.html.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c _j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c _j formed from >70 different biological activity parameters ( c₀), >300 different drug targets ( c₁), >230 cell lines ( c₂), and 5 organisms of assay ( c₃) or organisms of the target ( c₄). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.

Computational fishing of new DNA methyltransferase inhibitors from natural products

DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis have been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs with novel chemotypes as DNMTis, 47 compounds with known activity against these enzymes were used to build a LDA-based QSAR model for active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed to identify potential DNMTis on 800 NPs from NatProd Collection. 447 selected compounds were docked on two human DNA methyltransferase (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina and Surflex-Dock, prioritizing according to their score values, contact patterns at 4 Å and molecular diversity. Six consensus NPs were identified as virtual hits against DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin and centaurein. This method is an innovative computational strategy for identifying DNMTis, useful in the identification of potent and selective anticancer drugs.

Computational Search for Possible Mechanisms of 4-Thiazolidinones Anticancer Activity: The Power of Visualization

Public databases of NCI-60 tumor cell line screen results and measurements of molecular targets in the NCI-60 panel give the opportunity to assign possible anticancer mechanism to compounds with positive outcome from antitumor assay. Here, the novel protocol of NCI databases mining where inferences are based on the visualization is presented and utilized with the aim to identify putative biological routes of 4-thiazolidinones anticancer effect. As a result, highly potent 4-thiazolidinone-pyrazoline-isatin conjugates show the similarity of activity patterns with puromycin and CBU-028 and their pattern is also highly correlated with fraction of methylated CpG sites in CD34, AF5q31 and SYK. Several compounds from this group show strong negative correlation with fraction of methylated CpG sites in HOXA5. Thiopyrano[2,3-d][1,3]thiazol-2-ones bearing naphtoquinone fragment were found to possess the same activity pattern as fusarubin does. But none of the studied 4-thiazolidinone derivatives has activity fingerprint similar to standard anticancer agents. The obtained results bring medicinal chemistry closer to the understanding of basic nature of 4-thiazolidinones effect on cancer cells.

Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli

Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation).

Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1

S6K1 has emerged as a potential target for the treatment for obesity, type II diabetes and cancer diseases. Discovery of S6K1 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening method that involves pharmacophore hypothesis, genetic function approximation (GFA) model, and molecular docking technology has been used to discover S6K1 inhibitors especially with novel scaffolds. The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors. Then, the molecular docking method was carried out to re-filter these screened compounds. Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.

QSPR Study on Electric Spark Sensitivity of Nitro Arenes

A quantitative structure-property relationship (QSPR) model was proposed for predicting electric spark sensitivity of 39 nitro arenes. The genetic function approximation (GFA) was employed to select the descriptors that have significant contribution to electric spark sensitivity from various descriptors and for fitting the relationship existed between the selected 8 descriptors and electric spark sensitivity. The correlation coefficients (R2) together with correlation coefficient of the leave-one-out cross validation (Q2CV) of the model are 0.924 and 0.873, respectively. The model is highly statistically significant, and the robustness as well as internal prediction capability of which is satisfactory. The results show that the predicted electric spark sensitivity values are in good agreement with the experimental data.

Investigating the correlations among the chemical structures, bioactivity profiles and molecular targets of small molecules

MOTIVATION

Most of the previous data mining studies based on the NCI-60 dataset, due to its intrinsic cell-based nature, can hardly provide insights into the molecular targets for screened compounds. On the other hand, the abundant information of the compound-target associations in PubChem can offer extensive experimental evidence of molecular targets for tested compounds. Therefore, by taking advantages of the data from both public repositories, one may investigate the correlations between the bioactivity profiles of small molecules from the NCI-60 dataset (cellular level) and their patterns of interactions with relevant protein targets from PubChem (molecular level) simultaneously.

RESULTS

We investigated a set of 37 small molecules by providing links among their bioactivity profiles, protein targets and chemical structures. Hierarchical clustering of compounds was carried out based on their bioactivity profiles. We found that compounds were clustered into groups with similar mode of actions, which strongly correlated with chemical structures. Furthermore, we observed that compounds similar in bioactivity profiles also shared similar patterns of interactions with relevant protein targets, especially when chemical structures were related. The current work presents a new strategy for combining and data mining the NCI-60 dataset and PubChem. This analysis shows that bioactivity profile comparison can provide insights into the mode of actions at the molecular level, thus will facilitate the knowledge-based discovery of novel compounds with desired pharmacological properties.

AVAILABILITY

The bioactivity profiling data and the target annotation information are publicly available in the PubChem BioAssay database (ftp://ftp.ncbi.nlm.nih.gov/pubchem/Bioassay/).

Restoration of DNA-binding and growth-suppressive activity of mutant forms of p53 via a PCAF-mediated acetylation pathway

Tumor-derived mutant forms of p53 compromise its DNA binding, transcriptional, and growth regulatory activity in a manner that is dependent upon the cell-type and the type of mutation. Given the high frequency of p53 mutations in human tumors, reactivation of the p53 pathway has been widely proposed as beneficial for cancer therapy. In support of this possibility p53 mutants possess a certain degree of conformational flexibility that allows for re-induction of function by a number of structurally different artificial compounds or by short peptides. This raises the question of whether physiological pathways for p53 mutant reactivation also exist and can be exploited therapeutically. The activity of wild-type p53 is modulated by various acetyl-transferases and deacetylases, but whether acetylation influences signaling by p53 mutant is still unknown. Here, we show that the PCAF acetyl-transferase is down-regulated in tumors harboring p53 mutants, where its re-expression leads to p53 acetylation and to cell death. Furthermore, acetylation restores the DNA-binding ability of p53 mutants in vitro and expression of PCAF, or treatment with deacetylase inhibitors, promotes their binding to p53-regulated promoters and transcriptional activity in vivo. These data suggest that PCAF-mediated acetylation rescues activity of at least a set of p53 mutations. Therefore, we propose that dis-regulation of PCAF activity is a pre-requisite for p53 mutant loss of function and for the oncogenic potential acquired by neoplastic cells expressing these proteins. Our findings offer a new rationale for therapeutic targeting of PCAF activity in tumors harboring oncogenic versions of p53.

Molecular modeling and QSAR studies of a set of indole and benzimidazole derivatives as H₄ receptor antagonists

Histamine is an important biogenic amine, which acts with a group of four G-protein coupled receptors (GPCRs), namely H(1) to H(4) (H(1)R - H(4)R) receptors. The actions of histamine at H(4)R are related to immunological and inflammatory processes, particularly in pathophysiology of asthma, and H(4)R ligands having antagonistic properties could be helpful as antiinflammatory agents. In this work, molecular modeling and QSAR studies of a set of 30 compounds, indole and benzimidazole derivatives, as H(4)R antagonists were performed. The QSAR models were built and optimized using a genetic algorithm function and partial least squares regression (WOLF 5.5 program). The best QSAR model constructed with training set (N = 25) presented the following statistical measures: r (2) = 0.76, q (2) = 0.62, LOF = 0.15, and LSE = 0.07, and was validated using the LNO and y-randomization techniques. Four of five compounds of test set were well predicted by the selected QSAR model, which presented an external prediction power of 80%. These findings can be quite useful to aid the designing of new anti-H(4) compounds with improved biological response.

Chemogenomic analysis identifies Macbecin II as a compound specific for SMAD4-negative colon cancer cells

The tumor suppressor gene, SMAD4, is mutated in approximately 30% of colon cancers. To identify compounds with enhanced potency on cells with a SMAD4-negative context, we combined genomic and cheminformatic analyses of publicly available data relating to the colon cancer cell lines within the NCI60 panel. Two groups of cell lines were identified with either wild-type or negative SMAD4 status. A cheminformatic analysis of the NCI60 screening data was carried out, which led to the identification of 14 compounds that preferentially inhibited cell growth of the SMAD4-negative cell lines. Using cell viability assays, the effect of these compounds was validated on four colon cancer cell lines: HCT-116 and HCT-15 (SMAD4-expressing), and HT-29 and COLO-205 (SMAD4-negative). Our data identified Macbecin II, a hydroquinone ansamycin antibiotic, as having increased potency in the SMAD4-negative cells compared to SMAD4 wild-type cells. In addition, we showed that silencing of SMAD4 using siRNA in HCT-116 enhanced Macbecin II potency. Our results demonstrate that Macbecin II is specifically active in colon cancer cells having a SMAD4-negative background and thus is a potential candidate for further investigation in a drug discovery perspective.

QSAR modeling of a set of pyrazinoate esters as antituberculosis prodrugs

Tuberculosis is an infection caused mainly by Mycobacterium tuberculosis. A first-line antimycobacterial drug is pyrazinamide (PZA), which acts partially as a prodrug activated by a pyrazinamidase releasing the active agent, pyrazinoic acid (POA). As pyrazinoic acid presents some difficulty to cross the mycobacterial cell wall, and also the pyrazinamide-resistant strains do not express the pyrazinamidase, a set of pyrazinoic acid esters have been evaluated as antimycobacterial agents. In this work, a QSAR approach was applied to a set of forty-three pyrazinoates against M. tuberculosis ATCC 27294, using genetic algorithm function and partial least squares regression (WOLF 5.5 program). The independent variables selected were the Balaban index (J), calculated n-octanol/water partition coefficient (ClogP), van-der-Waals surface area, dipole moment, and stretching-energy contribution. The final QSAR model (N = 32, r(2) = 0.68, q(2) = 0.59, LOF = 0.25, and LSE = 0.19) was fully validated employing leave-N-out cross-validation and y-scrambling techniques. The test set (N = 11) presented an external prediction power of 73%. In conclusion, the QSAR model generated can be used as a valuable tool to optimize the activity of future pyrazinoic acid esters in the designing of new antituberculosis agents.

Molecular Modeling and Structure-Activity Relationship of Podophyllotoxin and Its Congeners

A quantitative structure-activity relationship (QSAR) model has been developed between cytotoxic activity and structural properties by considering a data set of 119 podophyllotoxin analogs based on 2D and 3D structural descriptors. A systematic stepwise searching approach of zero tests, a missing value test, a simple correlation test, a multicollinearity test, and a genetic algorithm method of variable selection was used to generate the model. A statistically significant model ( r train2 = 0.906; q cv2 = 0.893) was obtained with the molecular descriptors. The robustness of the QSAR model was characterized by the values of the internal leave-one-out cross-validated regression coefficient ( q cv2) for the training set and r test2 for the test set. The overall root mean square error (RMSE) between the experimental and predicted pIC50 value was 0.265 and r test2 = 0.824, revealing good predictability of the QSAR model. For an external data set of 16 podophyllotoxin analogs, the QSAR model was able to predict the tubulin polymerization inhibition and mechanistically cytotoxic activity with an RMSE value of 0.295 in comparison to experimental values. The QSAR model developed in this study shall aid further design of novel potent podophyllotoxin derivatives.

Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models

Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

Quantitative structure-property relationship studies on amino acid conjugates of jasmonic acid as defense signaling molecules

Jasmonates and related compounds, including amino acid conjugates of jasmonic acid, have regulatory functions in the signaling pathway for plant developmental processes and responses to the complex equilibrium of biotic and abiotic stress. But the molecular details of the signaling mechanism are still poorly understood. Statistically significant quantitative structure-property relationship models (r(2) > 0.990) constructed by genetic function approximation and molecular field analysis were generated for the purpose of deriving structural requirements for lipophilicity of amino acid conjugates of jasmonic acid. The best models derived in the present study provide some valuable academic information in terms of the 2/3D-descriptors influencing the lipophilicity, which may contribute to further understanding the mechanism of exogenous application of jasmonates in their signaling pathway and designing novel analogs of jasmonic acid as ecological pesticides.

Human protein tyrosine phosphatase 1B inhibitors: QSAR by genetic function approximation

Protein tyrosine phosphatase 1B (PTP 1B), a negative regulator of insulin receptor signaling system, has emerged as a highly validated, attractive target for the treatment of non-insulin dependent diabetes mellitus (NIDDM) and obesity. As a result there is a growing interest in the development of potent and specific inhibitors for this enzyme. This quantitative structure-activity relationship (QSAR) study for a series of formylchromone derivatives as PTP lB inhibitors was performed using genetic function approximation (GFA) technique. The QSAR models were developed using a training set of 29 compounds and the predictive ability of the QSAR model was evaluated against a test set of 7 compounds. The internal and external consistency of the final QSAR model was 0.766 and 0.785. The statistical quality of QSAR models was assessed by statistical parameters r2, r2 (crossvalidated r2), r2pred (predictive r2) and lack of fit (LOF) measure. The results indicate that PTP lB inhibitory activity of the formylchromone derivatives is strongly dependent on electronic, thermodynamic and shape related parameters.

Quantitative structure-activity relationship studies on 1-aryl-tetrahydroisoquinoline analogs as active anti-HIV agents

Predictive quantitative structure-activity relationship analysis was developed for a diverse series of recently synthesized 1-aryl-tetrahydroisoquinoline analogs with anti-HIV activities in this study. The conventional 2D-QSAR models were developed by genetic function approximation (GFA) and stepwise multiple linear regression (MLR) with acceptable explanation of 94.9% and 95.5% and good predicted power of 91.7% and 91.7%, respectively. The results of the 2D-QSAR models were further compared with 3D-QSAR model generated by molecular field analysis (MFA), investigating the substitutional requirements for the favorable receptor-drug interaction and quantitatively indicating the important regions of molecules for their activities. The results obtained by combining these methodologies give insights into the key features for designing more potent analogs against HIV.

In vitro differential sensitivity of melanomas to phenothiazines is based on the presence of codon 600 BRAF mutation

The panel of 60 human cancer cell lines (the NCI-60) assembled by the National Cancer Institute for anticancer drug discovery is a widely used resource. We previously sequenced 24 cancer genes in those cell lines. Eleven of the genes were found to be mutated in three or more of the lines. Using a pharmacogenomic approach, we analyzed the relationship between drug activity and mutations in those 11 genes (APC, RB1, KRAS, NRAS, BRAF, PIK3CA, PTEN, STK11, MADH4, TP53, and CDKN2A). That analysis identified an association between mutation in BRAF and the antiproliferative potential of phenothiazine compounds. Phenothiazines have been used as antipsychotics and as adjunct antiemetics during cancer chemotherapy and more recently have been reported to have anticancer properties. However, to date, the anticancer mechanism of action of phenothiazines has not been elucidated. To follow up on the initial pharmacologic observations in the NCI-60 screen, we did pharmacologic experiments on 11 of the NCI-60 cell lines and, prospectively, on an additional 24 lines. The studies provide evidence that BRAF mutation (codon 600) in melanoma as opposed to RAS mutation is predictive of an increase in sensitivity to phenothiazines as determined by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt assay (Wilcoxon P = 0.007). That pattern of increased sensitivity to phenothiazines based on the presence of codon 600 BRAF mutation may be unique to melanomas, as we do not observe it in a panel of colorectal cancers. The findings reported here have potential implications for the use of phenothiazines in the treatment of V600E BRAF mutant melanoma.

Mutant p53 targeting by the low molecular weight compound STIMA-1

Reactivation of mutant p53 in human tumor cells should induce cell death by apoptosis and thus eliminate the tumor. Several small molecules that reactivate mutant p53 have been identified. Here we show that STIMA-1, a low molecular weight compound with some structural similarities to the previously identified molecule CP-31398, can stimulate mutant p53 DNA binding in vitro and induce expression of p53 target proteins and trigger apoptosis in mutant p53-expressing human tumor cells. Human diploid fibroblasts are significantly more resistant to STIMA-1 than mutant or wild type p53-carrying tumor cells. STIMA-1 may provide new insights into possible mechanisms of mutant p53 reactivation and thus facilitate the development of novel anticancer drugs that target mutant p53-carrying tumors.

Comparative QSTR studies for predicting mutagenicity of nitro compounds

Mutagenicity and carcinogenicity are toxicological endpoints which pose a great concern being the major determinants of cancers and tumours. Nitroarenes possess genotoxic properties as they can form various electrophilic intermediates and adducts with biological systems. Different QSTR techniques were employed to develop models for the prediction of mutagenicity of nitroarenes using a diverse set of 197 nitro aromatic and hetero aromatic molecules. The 2D and 3D QSTR methods used for model development gave statistically significant results. The alignment for 3D methods was obtained by maximum common substructures (MCS) approach, by taking the most mutagenic molecule of the dataset as the template. All the QSTR models were developed with the same set of training and test set molecules. The 3D contours and 2D contribution maps along with molecular fingerprints provide useful information about the mutagenic potentials of the molecules. The GFA based model shows thermodynamic and topological descriptors play an important role in characterizing mutagenicity of nitroarenes. Atomic-level thermodynamic descriptor namely AlogP throws light on hydrophobic features and helps to understand the bilinear model. Topological aspects of these classes of compounds were depicted by the fragment fingerprints and Balaban indices obtained from HQSAR and GFA models, respectively. The predictive abilities of 2D and 3D QSTR models may be useful as a vibrant predictive tool to screen out mutagenic nitroarenes and design safer non-mutagenic nitro compounds.

Quantitative structure activity relationship studies on thiourea analogues as influenza virus neuraminidase inhibitors

Influenza virus is a major global threat that impacts the world in one form or another as flu infections. Neuraminidase, one of the targets for these viruses, has recently been exploited in the treatment of these infections. Quantitative structure activity relationship studies were performed on thiourea analogues using spatial, topological, electronic, thermodynamic and E-state indices. Genetic algorithm based genetic function approximation method of variable selection was used to generate the model. Highly statistically significant model was obtained when number of descriptors in the equation was set to 5. The atom type log P and shadow indices descriptors showed enormous contributions to neuraminidase inhibition. The validation of the model was done by cross validation, randomization and external test set prediction. The model gives insight on structural requirements for designing more potent analogues against influenza virus neuraminidase.

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

We present an analysis of current anticancer compounds that are in phase I, II, or III clinical trials and their structural analogues that have been screened in the National Cancer Institute (NCI) anticancer screening program. Bioactivity profiles, measured across the NCI 60 cell lines, were examined for a correspondence between the type of cancer proposed for clinical testing and selective sensitivity to appropriately matched tumor subpanels in the NCI screen. These results find strongest support for using the NCI anticancer screen to select analogue compounds with selective sensitivity to the leukemia, colon, central nervous system, melanoma, and ovarian panels, but not for renal, prostate, and breast panels. These results are extended to applications of two-dimensional structural features to further refine compound selections based on tumor panel sensitivity obtained from tumor screening results.

A novel range based QSAR study of human neuropeptide Y (NPY) Y5 receptor inhibitors

A conventional QSAR study has been carried out using thermodynamic and other descriptors, on a set of arylsulfonamidomethylcyclohexyl derivatives as antagonists of potential obesity drug target human neuropeptide Y Y5 receptor. In addition, a novel range based method was applied to obtain a QSAR model so that the information contained in the compounds for which an approximate value instead of exact value of inhibitory activity was available could be included in the model. Analysis of models suggests that range based model is better in screening biologically active compounds from chemical library. The conventional model is able to predict activity accurately only for active compounds whereas the range based method is better in discriminating active and inactive compounds.

Cancer cell-based genomic and small molecule screens

This chapter focuses on the promising post-genomic technologies being used for discovery of new, safer, and better cancer drugs and drug targets. Since cancer is largely a disease of the cell, usually involving unrestricted cell proliferation as a result of heritable genetic changes such as mutation, this chapter will focus on cell-centric technologies and their utility in addressing major questions in cancer biology. Recent advances in cell-based technology, including phenotypic assays, image-based readouts, primary tumor cell growth and maintenance in vitro, gene and small molecule delivery tools, and automated systems for cell manipulation, provide a novel means to understand the etiology and mechanisms of cancer as never before. In addition to the abundant tool sophistication, many aspects of cancer can be emulated and monitored in cell systems, which makes them ideal vehicles for exploitation to discover new targets and drugs. This chapter will first handle nomenclature and provide a context for a "good drug target" within the framework of the human genome, then overview functional genomic gene-based library screening approaches with specific applications to cancer target discovery. Second, small molecule screening applications will be handled, with an emphasis on the new paradigm of massively parallel screening and resultant multidimensional dataset analysis approaches to identify drug candidates, assign mechanism of action, and address problems in deriving selective and safe chemical entities.

Quantitative structure activity relationship studies of aryl heterocycle-based thrombin inhibitors

A quantitative structure activity relationship (QSAR) analysis has been performed on a data set of 42 aryl heterocycle-based thrombin inhibitors. Several types of descriptors including topological, spatial, thermodynamic, information content and E-state indices were used to derive a quantitative relationship between the anti thrombin activity and structural properties. Genetic algorithm based genetic function approximation method of variable selection was used to generate the model. Best model was developed when number of descriptors in the equation was set to five. Highly statistically significant model was obtained with atom type logP descriptors, logP and Shadow_YZ. The model is not only able to predict the activity of new compounds but also explained the important regions in the molecules in a quantitative manner.

Data mining of NCI's anticancer screening database reveals mitochondrial complex I inhibitors cytotoxic to leukemia cell lines

Mitochondria are principal mediators of apoptosis and thus can be considered molecular targets for new chemotherapeutic agents in the treatment of cancer. Inhibitors of mitochondrial complex I of the electron transport chain have been shown to induce apoptosis and exhibit antitumor activity. In an effort to find novel complex I inhibitors which exhibited anticancer activity in the NCI's tumor cell line screen, we examined organized tumor cytotoxicity screening data available as SOM (self-organized maps) (http://www.spheroid.ncifcrf.gov) at the developmental therapeutics program (DTP) of the National Cancer Institute (NCI). Our analysis focused on an SOM cluster comprised of compounds which included a number of known mitochondrial complex I (NADH:CoQ oxidoreductase) inhibitors. From these clusters 10 compounds whose mechanism of action was unknown were tested for inhibition of complex I activity in bovine heart sub-mitochondrial particles (SMP) resulting in the discovery that 5 of the 10 compounds demonstrated significant inhibition with IC50's in the nM range for three of the five. Examination of screening profiles of the five inhibitors toward the NCI's tumor cell lines revealed that they were cytotoxic to the leukemia subpanel (particularly K562 cells). Oxygen consumption experiments with permeabilized K562 cells revealed that the five most active compounds inhibited complex I activity in these cells in the same rank order and similar potency as determined with bovine heart SMP. Our findings thus fortify the appeal of mitochondrial complex I as a possible anticancer molecular target and provide a data mining strategy for selecting candidate inhibitors for further testing.

Cluster analysis and two-dimensional quantitative structure-activity relationship (2D-QSAR) of Pseudomonas aeruginosa deacetylase LpxC inhibitors

Compounds from a wide variety of structural classes inhibit Pseudomonas aeruginosa deacetylase LpxC. However, a single unified understanding of the relationship between the structures and activities of these compounds still eludes the researchers. We report herein, the development of cluster analysis-based 2D-QSAR models for LpxC inhibition. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and genetic function approximation (GFA) were employed for the development of the QSAR model. The conventional 2D-QSAR model derived for the complete set of three-structural classes had unsatisfactory predictability with a correlation coefficient (r(2)) of 0.703 and a cross-validated correlation coefficient (q(2)) of 0.584. Descriptor-based cluster analysis indicated that the three-structural classes of LpxC inhibitors studied belonged to two clusters. Separate QSAR models for these two clusters showed substantially improved predictability with r(2) values of 0.904 and 0.944 and q(2) values of 0.805 and 0.906, respectively. Thus, we expect that compared to the conventional model, our two QSAR models can be better used to preliminarily screen molecules from a diverse chemical space while searching for novel LpxC inhibitors.

Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases

An integrated, bioinformatic analysis of three databases comprising tumor-cell-based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand-target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCI's database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug-gene-PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand-target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug-protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo-resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo-sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo-responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates.

Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships

Classification methods allow for the development of structure-activity relationship models when the target property is categorical rather than continuous. We describe a classification method which fits descriptor splines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as SFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent modeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately 40% of compounds in each series were assigned to a test set, "cherry-picked" from the complete set such that they lie outside the training set as much as possible. SFGA produced models that were more predictive for all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for all but the MAO set. A similar trend was observed when using training and test sets to which compounds were randomly assigned and when gradually eliminating compounds from the (designed) training set. The stability of models was examined for the random and reduced sets, where stability means that classification statistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced the most stable models, followed by SFGA and RP. We show that a consensus approach that combines all three methods outperforms the single best model for all data sets.

Transcriptomic analysis of the NCI-60 cancer cell lines

Pharmacogenomics aims at molecular subsetting of patients for more effective therapy. Transcriptomic profiling of the 60 human cancer cell lines (the NCI-60) used by the US National Cancer Institute serves that aim because the cells have been treated with > 100,000 chemical compounds over the last 13 years. Patterns of potency can be mapped into molecular structures of the compounds or into molecular characteristics of the cells. We discuss conceptual and experimental aspects of the profiling, as well as a number of bioinformatic computer programs that we have developed for biological interpretation of the profiles.

Structure-activity approach to the identification of environmental estrogens: the MCASE approach

A sizable number of environmental contaminants and natural products have been found to possess hormonal activity and have been termed endocrine-disrupting chemicals. Due to the vast number (estimated at about 58,000) of environmental contaminants, their potential to adversely affect the endocrine system, and the paucity of health effects data associated with them, the U.S. Congress was led to mandate testing of these compounds for endocrine-disrupting ability. Here we provide evidence that a computational structure-activity relationship (SAR) approach has the potential to rapidly and cost effectively screen and prioritize these compounds for further testing. Our models were based on data for 122 compounds assayed for estrogenicity in the ESCREEN assay. We produced two models, one for relative proliferative effect (RPE) and one for relative proliferative potency (RPP) for chemicals as compared to the effects and potency of 17beta-estradiol. The RPE and RPP models achieved an 88 and 72% accurate prediction rate, respectively, for compounds not in the learning sets. The good predictive ability of these models and their basis on simple to understand 2-D molecular fragments indicates their potential usefulness in computational screening methods for environmental estrogens.

Identification of structural components associated with cytostatic activity in MCF-7 but not in MDA-MB-231 cells

The National Cancer Institute's Developmental Therapeutics Program maintains the screening results obtained in 60 standardized cancer cell lines and contained 37,836 compounds for this study. This dataset has shown to be an outstanding resource for the development of structure-activity relationship (SAR) models describing anticancer activity. We report here a novel SAR modeling approach based on a subtractive protocol to develop models that describe cell type-specific molecular descriptors of cytotoxicity. The goal of this approach is to separate features associated with antiproliferative activity to many cell lines from those that effect only a specific cell type. To assess this approach, we developed SAR models for cytostatic activity against the human breast cancer cell lines MCF-7 and MDA-MB-231 and one differential activity model for compounds that were potent cytostatic agents in MCF-7 cells but relatively inactive against MDA-MB-231 cells. The models were between 72 and 84% accurate when challenged with compounds not in the learning sets. Structural features associated with the differential activity model highlighted how the use of this approach can selectively identify chemical moieties associated with potent cytostatic action to MCF-7 but not to MDA-MB-231 cells. We surmise that outgrowth of this method can facilitate the development of SAR models with sufficient resolution and clarity to identify chemical moieties associated with antiproliferative activity to selective individual cancer types while being innocuous to other cell types.

Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data

Genomic studies are producing large databases of molecular information on cancers and other cell and tissue types. Hence, we have the opportunity to link these accumulating data to the drug discovery processes. Our previous efforts at 'information-intensive' molecular pharmacology have focused on the relationship between patterns of gene expression and patterns of drug activity. In the present study, we take the process a step further-relating gene expression patterns, not just to the drugs as entities, but to approximately 27,000 substructures and other chemical features within the drugs. This coupling of genomic information with structure-based data mining can be used to identify classes of compounds for which detailed experimental structure-activity studies may be fruitful. Using a systematic substructure analysis coupled with statistical correlations of compound activity with differential gene expression, we have identified two subclasses of quinones whose patterns of activity in the National Cancer Institute's 60-cell line screening panel (NCI-60) correlate strongly with the expression patterns of particular genes: (i) The growth inhibitory patterns of an electron-withdrawing subclass of benzodithiophenedione-containing compounds over the NCI-60 are highly correlated with the expression patterns of Rab7 and other melanoma-specific genes; (ii) the inhibitory patterns of indolonaphthoquinone-containing compounds are highly correlated with the expression patterns of the hematopoietic lineage-specific gene HS1 and other leukemia genes. As illustrated by these proof-of-principle examples, we introduce here a set of conceptual tools and fluent computational methods for projecting directly from gene expression patterns to drug substructures and vice versa. The analysis is presented in terms of the NCI-60 cell lines and microarray-based gene expression patterns, but the concept and methods are broadly applicable to other large-scale pharmacogenomic database sets as well. The approach (SAT for Structure-Activity-Target) provides a systematic way to mine databases for the design of further structure-activity studies, particularly to aid in target and lead identification.

QSAR of HIV-1 integrase inhibitors by genetic function approximation method

Quantitative structure--activity relationship (QSAR) paradigm, using genetic function approximation (GFA) technique was used to examine the correlations between the calculated physicochemical descriptors and the in vitro activities (3'-processing and 3'-strand transfer inhibition) of a series of human immunodeficiency virus type 1 (HIV-1) integrase inhibitors. Depending on the chemical structure, all molecules were divided into two classes---catechols and noncatechols. Eighty-one molecules were used in the present study and they were divided into training set and test set. The training set in each class consisted of 35 molecules and QSAR models were generated separately for both catechols and noncatechols. Equations were evaluated using internal as well as external test set predictions. Models generated for catechols show that electronic, shape related, and thermodynamic parameters are important whereas for noncatechols, spatial, structural, and thermodynamic properties play an important role for the activity.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) of 3-aryloxazolidin-2-one antibacterials

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for 3-aryloxazolidin-2-one antibacterials were performed using the genetic function approximation algorithm. This study was performed using 60 compounds, in which the QSAR models were developed using a training set of 50 compounds. The in vitro minimum inhibitory concentration (MIC) against Staphylococcus aureus SFCO-1a was used for the study. The predictive ability of the QSAR model was evaluated by using a test set of 10 compounds. The statistical quality of the QSAR models was assessed using statistical parameters r(2), r(2)(cv) (cross-validated r(2)), r(2)(pred) (predictive r(2)) and lack of fit measure (LOF). The results obtained indicate that the antibacterial activity of the 3-aryloxazolidin-2-ones is strongly dependent on electronic factor as expressed by lowest unoccupied molecular orbital energy (LUMO), spatial factor as expressed by density and thermodynamic factors accounted for by molar refractivity and heat of formation. The model is presently being used to design and predict new potent molecules prior to synthesis.

Microbial genomics - new targets, new drugs

Genomics has changed our view of the biological world in the past decade, providing both new information and new tools to characterise biological systems. Over 100 microbial genomes - including many of substantial clinical importance - have been fully or partially sequenced, pushing the search for novel antimicrobial compounds into the post-genomic era. Genomic information and associated new technologies have the potential to revolutionise the drug discovery process. Genomic methods have created a wealth of potential new antimicrobial targets; strategies are evolving to provide validation for these targets before chemical inhibitors are identified. The ability to obtain large amounts of purified target proteins and advances in X-ray crystallography have caused significant increases in available protein structures, which may foreshadow an increased effort in structure-based drug design. The post-genomics strategies used in antimicrobial drug discovery may have application for small molecule drug discovery in numerous therapeutic areas.