Assessment of in vitro and in vivo activities in the National Cancer Institute's anticancer screen with respect to chemical structure, target specificity, and mechanism of action

In Silico, In Vitro, and In Vivo Antitumor and Anti-Inflammatory Evaluation of a Standardized Alkaloid-Enriched Fraction Obtained from Boehmeria caudata Sw. Aerial Parts

In the context of the cancer-inflammation relationship and the use of natural products as potential antitumor and anti-inflammatory agents, the alkaloid-enriched fraction of Boehmeriacaudata (BcAEF) aerial parts was evaluated. In vitro antiproliferative studies with human tumor cell lines showed high activity at low concentrations. Further investigation on NCI-H460 cells showed an irreversible effect on cell proliferation, with cell cycle arrest at G2/M phase and programmed cell death induction. Molecular docking studies of four alkaloids identified in BcAEF with colchicine’s binding site on β-tubulin were performed, suggesting (−)-C (15R)-hydroxycryptopleurine as the main inductor of the observed mitotic death. In vivo studies showed that BcAEF was able to reduce Ehrlich tumor volume progression by 30 to 40%. Checking myeloperoxidase activity, BcAEF reduced neutrophils migration towards the tumor. The in vivo anti-inflammatory activity was evaluated by chemically induced edema models. In croton oil-induced ear edema and carrageenan (CG)-induced paw edema models, BcAEF reduced edema around 70 to 80% together with inhibition of activation and/or migration of neutrophils to the inflammatory area. All together the results presented herein show BcAEF as a potent antitumor agent combining antiproliferative and anti-inflammatory properties, which could be further explored in (pre)clinical studies.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Antiproliferative activity of novel derivative of thiopyran on breast and colon cancer lines and DNA binding

Dimethyl-5-acetyl-4-methyl-6-(4-methylphenylimino)-6H-thiopyran-2,3-dicarboxylate (4) has been synthesized and its antiproliferative activity is reported here. Compound 4 inhibited the growth of human colon cancer cell line HCT-15 with an IC(50) value of 3.5 μM and of breast cancer cell line MCF-7 with an IC(50) value of 1.5 μM in a dose/time-dependent manner using a sulforhodamine B assay. Moreover, suppression of clonogenic activity occurred after exposure to 4 at a concentration of 4 μM for HCT-15 and 1.7 μM for MCF-7. The results also showed tumor cell invasion through matrigel and cell adhesion. The effect of ligand complexation on DNA structure led to overall affinity constant of K(4-DNA)=9.8×10(4) M(-1).

Antiprotozoal, anticancer and antimicrobial activities of dihydroartemisinin acetal dimers and monomers

Nine dihydroartemisinin acetal dimers (6-14) with diversely functionalized linker units were synthesized and tested for in vitro antiprotozoal, anticancer and antimicrobial activity. Compounds 6, 7 and 11 [IC(50): 3.0-6.7 nM (D6) and 4.2-5.9 nM (W2)] were appreciably more active than artemisinin (1) [IC(50): 32.9 nM (D6) and 42.5 nM (W2)] against the chloroquine-sensitive (D6) and chloroquine-resistant (W2) strains of the malaria parasite, Plasmodium falciparum. Compounds 10, 13 and 14 displayed enhanced anticancer activity in a number of cell lines compared to the control drug, doxorubicin. The antifungal activity of 7 and 12 against Cryptococcus neoformans (IC(50): 0.16 and 0.55 microM, respectively) was also higher compared to the control drug, amphotericin B. The antileishmanial and antibacterial activities were marginal. A number of dihydroartemisinin acetal monomers (15-17) and a trimer (18) were isolated as byproducts from the dimer synthesis and were also tested for biological activity.

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.

Data mining the NCI60 to predict generalized cytotoxicity

Elimination of cytotoxic compounds in the early and later stages of drug discovery can help reduce the costs of research and development. Through the application of principal components analysis (PCA), we were able to data mine and prove that approximately 89% of the total log GI 50 variance is due to the nonspecific cytotoxic nature of substances. Furthermore, PCA led to the identification of groups of structurally unrelated substances showing very specific toxicity profiles, such as a set of 45 substances toxic only to the Leukemia_SR cancer cell line. In an effort to predict nonspecific cytotoxicity on the basis of the mean log GI 50, we created a decision tree using MACCS keys that can correctly classify over 83% of the substances as cytotoxic/noncytotoxic in silico, on the basis of the cutoff of mean log GI 50 = -5.0. Finally, we have established a linear model using least-squares in which nine of the 59 available NCI60 cancer cell lines can be used to predict the mean log GI 50. The model has R (2) = 0.99 and a root-mean-square deviation between the observed and calculated mean log GI 50 (RMSE) = 0.09. Our predictive models can be applied to flag generally cytotoxic molecules in virtual and real chemical libraries, thus saving time and effort.

Connecting chemosensitivity, gene expression and disease

Omics-based investigations offer potentially powerful readouts that might be useful for probing the underlying biology of normal and diseased states, identifying novel therapeutic targets and proposing relevant markers for designing treatment strategies. A vital component of these investigations involves a systematic analysis of gene expression and chemosensitivity data in the context of disease states and small molecule probes into the function of targets responsible for a disease phenotype. Systematic analysis of chemical and pharmacogenetics data offers a possible means to identify novel, small-molecule, potentially therapeutic, agents that affect the phenotype of a particular target. Elegantly simple in concept, the covariation of genetic and chemosensitivity readouts provide a hypothetical link for relating compounds through genomic expression profiles to underlying biology.

Characterization of microRNA expression levels and their biological correlates in human cancer cell lines

MicroRNAs are small noncoding RNAs that function by regulating target gene expression posttranscriptionally. They play a critical role in developmental and physiologic processes and are implicated in the pathogenesis of several human diseases including cancer. We examined the expression profiles of 241 human microRNAs in normal tissues and the NCI-60 panel of human tumor-derived cell lines. To quantify microRNA expression, we employed a highly sensitive technique that uses stem-loop primers for reverse transcription followed by real-time PCR. Most microRNAs were expressed at lower levels in tumor-derived cell lines compared with the corresponding normal tissue. Agglomerative hierarchical clustering analysis of microRNA expression revealed four groups among the NCI-60 cell lines consisting of hematologic, colon, central nervous system, and melanoma tumor-derived cell lines clustered in a manner that reflected their tissue of origin. We identified specific subsets of microRNAs that provide candidate molecular signatures characteristic of the tumor-derived cell lines belonging to these four clusters. We also identified specific microRNA expression patterns that correlated with the proliferation indices of the NCI-60 cell lines, and we developed evidence for the identification of specific microRNAs as candidate oncogenes and tumor suppressor genes in different tumor types. Our results provide evidence that microRNA expression patterns may mark specific biological characteristics of tumors and/or mediate biological activities important for the pathobiology of malignant tumors. These findings call attention to the potential of microRNAs to provide etiologic insights as well as to serve as both diagnostic markers and therapeutic targets for many different tumor types.

Data reduction and representation in drug discovery

Pre-clinical drug discovery relies increasingly on huge volumes of inter-related multivariate data. To make sense of these data and enable quality decision-making based on this plethora of information they must be presented in an interpretable form. Reducing the dimensionality of the data often leaves a data set that is too complex to interpret readily, so intuitive visualization methods are needed. Bioinformatics has provided much of the impetus for visualizing complex data, the cheminformatics community has been aggressive with the data-reduction problem. The increasing appreciation of the inter-related multifactorial nature of pre-clinical drug discovery makes visualization a burgeoning and active field that spans biosciences, mathematics and visual psychology.