Citations

SummaryAimsFibrillar aggregates of β‐amyloid protein (Aβ) are the main constituent of senile plaques and considered to be one of the causative events in the pathogenesis of Alzheimer's disease (AD). Compounds that could inhibit Aβ fibrils formation, disaggregate preformed Aβ fibrils as well as reduce their associated neurotoxicity might have therapeutic values for treating AD. In this study, the inhibitory effects of bis (heptyl)‐cognitin (B7C), a multifunctional dimer derived from tacrine, on aggregation and neurotoxicity of Aβ1‐40 were evaluated both in vitro and in vivo.MethodsThioflavin T fluorescence assay was carried out to evaluate Aβ aggregation, MTT and Hoechst‐staining assays were performed to investigate Aβ‐associated neurotoxicity. Fluorescent probe DCFH‐DA was used to estimate the accumulation of intracellular reactive oxygen stress (ROS). Morris water maze was applied to determine learning and memory deficits induced by intracerebroventricular infusion of Aβ in rats.ResultsB7C (0.1–10 μM), but not tacrine, effectively inhibited Aβ fibrils formation and disaggregated preformed Aβ fibrils following co‐incubation of B7C and Aβ monomers or preformed fibrils, respectively. In addition, B7C markedly reduced Aβ fibrils‐associated neurotoxicity in SH‐SY5Y cell line, as evidenced by the increase in cell survival, the decrease in Hoechst‐stained nuclei and in intracellular ROS. Most encouragingly, B7C (0.1 and 0.2 mg/kg), 10 times more potently than tacrine (1 and 2 mg/kg), inhibited memory impairments after intracerebroventricular infusion of Aβ in rats, as evidenced by the decrease in escape latency and the increase in the spatial bias in Morris water maze test along with upregulation of choline acetyltransferase activity and downregulation of acetylcholinesterase activity.ConclusionThese findings provide not only novel molecular insight into the potential application of B7C in treating AD, but also an effective approach for screening anti‐AD agents.

Purpose Phase II clinical trials inform go/no-go decisions for proceeding to phase III trials, and appropriate end points in phase II trials are critical for facilitating this decision. Phase II solid tumor trials have traditionally used end points such as tumor response defined by Response Evaluation Criteria for Solid Tumors (RECIST). We previously reported that absolute and relative changes in tumor measurements demonstrated potential, but not convincing, improvement over RECIST to predict overall survival (OS). We have evaluated the metrics by using additional measures of clinical utility and data from phase III trials. Methods Resampling methods were used to assess the clinical utility of metrics to predict phase III outcomes from simulated phase II trials. In all, 2,000 phase II trials were simulated from four actual phase III trials (two positive for OS and two negative for OS). Cox models for three metrics landmarked at 12 weeks and adjusted for baseline tumor burden were fit for each phase II trial: absolute changes, relative changes, and RECIST. Clinical utility was assessed by positive predictive value and negative predictive value, that is, the probability of a positive or negative phase II trial predicting an effective or ineffective phase III conclusion, by prediction error, and by concordance index (c-index). Results Absolute and relative change metrics had higher positive predictive value and negative predictive value than RECIST in five of six treatment comparisons and lower prediction error curves in all six. However, differences were negligible. No statistically significant difference in c-index across metrics was found. Conclusion The absolute and relative change metrics are not meaningfully better than RECIST in predicting OS.

Transforming growth factor β (TGFβ) is a pleiotropic protein secreted from essentially all cell types and primary tissues. While TGFβ’s actions reflect the activity of a number of signaling networks, the primary mediator of TGFβ responses are the Smad proteins. Following receptor activation, these cytoplasmic proteins form hetero-oligomeric complexes that translocate to the nucleus and affect gene transcription. Here, through biological, biochemical, and immunofluorescence approaches, sorting nexin 9 (SNX9) is identified as being required for Smad3-dependent responses. SNX9 interacts with phosphorylated (p) Smad3 independent of Smad2 or Smad4 and promotes more rapid nuclear delivery than that observed independent of ligand. Although SNX9 does not bind nucleoporins Nup153 or Nup214 or some β importins (Imp7 or Impβ), it mediates the association of pSmad3 with Imp8 and the nuclear membrane. This facilitates nuclear translocation of pSmad3 but not SNX9.

Abstract Summary: One central theme of modern high-throughput genomic data analysis is to identify relevant genomic features as well as build up a predictive model based on selected features for various tasks such as personalized medicine. Correlating the large number of ‘omics’ features with a certain phenotype is particularly challenging due to small sample size (n) and high dimensionality (p). To address this small n, large p problem, various forms of sparse regression models have been proposed by exploiting the sparsity assumption. Among these, network-constrained sparse regression model is of particular interest due to its ability to utilize the prior graph/network structure in the omics data. Despite its potential usefulness for omics data analysis, no efficient R implementation is publicly available. Here we present an R software package ‘glmgraph’ that implements the graph-constrained regularization for both sparse linear regression and sparse logistic regression. We implement both the L1 penalty and minimax concave penalty for variable selection and Laplacian penalty for coefficient smoothing. Efficient coordinate descent algorithm is used to solve the optimization problem. We demonstrate the use of the package by applying it to a human microbiome dataset, where phylogeny structure among bacterial taxa is available. Availability and implementation: ‘glmgraph’ is implemented in R and C++ Armadillo and publicly available under CRAN. Contact: chen.jun2@mayo.edu or hongzhe@upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Background Intra-tumor heterogeneity is the driving force behind disease progression and metastasis, drug resistance, and relapse in cancer patients. Defining personalized treatment regimens and increasing the arsenal of targeted therapies available will provide the best chance of prolonging survival and finding curative treatments. Recent work by our group and others has shed light on aberrant de novo lipogenesis as a feature of many cancers including kidney, breast, and lung cancer. Of the molecules involved in fatty acid metabolism, stearoyl-CoA desaturase 1 (SCD1) is frequently over-expressed. Targeted inhibition of SCD1 demonstrates a loss of tumor cell viability in a variety of cancer models. While there currently are specific small molecule inhibitors for SCD1 none are under investigation as anti-cancer therapeutics. In order to evaluate the efficacy of SCD1 inhibitors against ccRCC in both a preclinical and eventually a clinical setting we sought to generate unique compounds and test their biological efficacy. Methods Utilizing an innovative in silico approach, we designed new inhibitors for SCD1 via a scaffold hopping approach while searching for different “core” scaffolds. Critical interaction moieties from the chemical R-groups were held fixed, while generated cores were rapidly scanned for best-fit criteria (shape, docking, pharmacophore fit (QSAR)) into our Z-matrix. Following de novo compound generation and synthesis, we implemented iterative analog generation for the top hits. Using this method and diversity of chemical space, we pursued three divergent species of compounds into the nanomolar regime. Biological testing of the novel compounds included high-throughput proliferative-based screening, oleic acid rescue, and evaluation of the endoplasmic stress response. Enzymatic inhibition of SCD1 was evaluated by LC-MS. Preclinical pharmacokinetics was established using a range of doses administered intravenously and orally. Anti-tumor response was evaluated in an ectopic model of renal carcinoma, both as a single agent and in combination with SOC. Results A cohort of potential unique small molecule inhibitors against SCD1 were designed and synthesized. Of these, a select group of compounds demonstrated potent and selective enzyme inhibition and reduced tumor growth in in vitro and in vivo models of ccRCC. These novel inhibitors reliably reproduced activation of ER stress markers, authenticating this biological response as a harbinger for successful abrogation of SCD1 activity. Conclusions We have established an effective, expedited, and economical format for in silico modeling of novel agents based on a scaffold-hopping methodology. Our findings provide compelling evidence supporting the therapeutic benefit of SCD1 inhibitors for clinical use as broad-spectrum anti-tumor agents alone or in combination with SOC. Citation Format: Christina A. Von Roemeling, Thomas R. Caulfield, Derek C. Radisky, Ilah Bok, Laura A. Marlow, James Miller, Mojda Sidiqi, Anthony B. Pinkerton, Winston W. Tan, Amy L. Lane, Han W. Tun, John A. Copland. Accelerated drug discovery platform yields synthesis of novel stearoyl-CoA desaturase 1 inhibitors that demonstrate anti-tumor efficacy in several models of aggressive cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4459. doi:10.1158/1538-7445.AM2015-4459