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In this work the authors use their contour integral method to derive the double integral given by $\int_{0}^{\infty}\int_{0}^{\infty}\frac{x^{m-1} y^{m+\frac{q}{2}-1} \log ^k(a x y)}{\left(x^q+1\right)^2 \left(y^q+1\right)^2}dxdy$ in terms of the Lerch function. This integral formula is then used to derive closed solutions in terms of fundamental constants and special functions. There are some useful results relating double integrals of certain kinds of functions to ordinary integrals for which we know no general reference. Thus a table of integral pairs is given for interested readers. All the results in this work are new.

In applied sciences it is always useful to improve the catalogue of definite integrals available in tables. In this paper the authors derived definite integrals used in research concerned with perturbation analysis of single Langmuir solitons, proving the Riemann Hypothesis and the Goldbach conjecture and the LPM effect in sequential bremsstrahlung. We also derived new results and summarized these formulae in a table of integrals.

We present a method using contour integration to derive definite integrals and their associated infinite sums which can be expressed as a special function. We give a proof of the basic equation and some examples of the method. The advantage of using special functions is their analytic continuation which widens the range of the parameters of the definite integral over which the formula is valid. We give as examples definite integrals of logarithmic functions times a trigonometric function. In various cases these generalizations evaluate to known mathematical constants such as Catalan’s constant and π.

This current paper is a table of definite integrals involving hyperbolic and logarithmic functions expressed in terms of the incomplete gamma function and fundamental constants. All the results in this work are new.

A definite integral involving the power square root of an algebraic function is derived in terms of the Lerch function. A table consisting of mostly new results is given for easy reading. The majority of the results in this work are new.

This paper contains new explicit computations of some integrals containing elementary functions, such as powers, logarithms, exponentials. In this work the authors use their contour integral method to derive a double integral connected to the modified Bessel function of the second kind $K_{\nu}(z)$ and express it in terms of the Lerch function. A table of integral pairs is given for interested readers. The majority of the results in this work are new.

In this work the authors derive the Stieltjes transform of the logarithmic function in terms of the Lerch function. This transform is used to derive closed form solutions involving fundamental constants and special functions. Specifically we derive the definite integral given by\[\int_{0}^{\infty} \frac{(1-b x)^m \log ^k(c (1-b x))+(b x+1)^m \log ^k(c (b x+1))}{a+x^2}dx\]where $a,b,c,m$ and $k$ are general complex numbers subject to the restrictions given in connection with the formulas.

Bierens de haan (1867) evaluated a definite integral involving the cotangent function and this result was also listed in Gradshteyn and Ryzhik (2007). The objective of this present note is to use this integral along with Cauchy's integral formula to derive a definite logarithmic integral in terms of the Lerch function. We will use this integral formula to produce a table of known and new results in terms of special functions and thereby expanding the list of definite integrals in both text books.

In this manuscript, the authors derive a formula for the double Laplace transform expressed in terms of the Lerch Transcendent. The log term mixes the variables so that the integral is not separable except for special values of k. The method of proof follows the method used by us to evaluate single integrals. This transform is then used to derive definite integrals in terms of fundamental constants, elementary and special functions. A summary of the results is produced in the form of a table of definite integrals for easy referencing by readers.

Tuberculosis (TB) continues to claim the lives of around 1.7 million people per year. Most concerning are the reports of multidrug drug resistance. Paradoxically, this global health pandemic is demanding new therapies when resources and interest are waning. However, continued tuberculosis drug discovery is critical to address the global health need and burgeoning multidrug resistance. Many diverse classes of antitubercular compounds have been identified with activity in vitro and in vivo. Our analyses of over 100 active leads are representative of thousands of active compounds generated over the past decade, suggests that they come from few chemical classes or natural product sources. We are therefore repeatedly identifying compounds that are similar to those that preceded them. Our molecule-centered cheminformatics analyses point to the need to dramatically increase the diversity of chemical libraries tested and get outside of the historic Mtb property space if we are to generate novel improved antitubercular leads.

The nonsteroidal anti-inflammatory drug (NSAID) sulindac demonstrates attractive anticancer activity, but the toxicity resulting from cyclooxygenase (COX) inhibition and the suppression of physiologically important prostaglandins precludes its long-term, high dose use in the clinic for cancer prevention or treatment. While inflammation is a known tumorigenic driver, evidence suggests that sulindac’s antineoplastic activity is partially or fully independent of its COX inhibitory activity. One COX-independent target proposed for sulindac is cyclic guanosine monophosphate phosphodiesterase (cGMP PDE) isozymes. Sulindac metabolites, i.e., sulfide and sulfone, inhibit cGMP PDE enzymatic activity at concentrations comparable with those associated with cancer cell growth inhibitory activity. Additionally, the cGMP PDE isozymes PDE5 and PDE10 are overexpressed during the early stages of carcinogenesis and appear essential for cancer cell proliferation and survival based on gene silencing experiments. Here, we describe a novel amide derivative of sulindac, sulindac sulfide amide (SSA), which was rationally designed to eliminate COX-inhibitory activity while enhancing cGMP PDE inhibitory activity. SSA was 68-fold and 10-fold less potent than sulindac sulfide (SS) in inhibiting COX-1 and COX-2, respectively, but 10-fold more potent in inhibiting growth and inducing apoptosis in breast cancer cells. The pro-apoptotic activity of SSA was associated with inhibition of cGMP PDE activity, elevation of intracellular cGMP levels, and activation of cGMP-dependent protein kinase (PKG) signaling, as well as the inhibition of β-catenin/Tcf transcriptional activity. SSA displayed promising in vivo anticancer activity, resulting in a 57% reduction in the incidence and a 62% reduction in the multiplicity of tumors in the N-methyl-N-nitrosourea (MNU)-induced model of breast carcinogenesis. These findings provide strong evidence for cGMP/PKG signaling as a target for breast cancer prevention or treatment and the COX-independent anticancer properties of sulindac. Furthermore, this study validates the approach of optimizing off-target effects by reducing the COX-inhibitory activity of sulindac for future targeted drug discovery efforts to enhance both safety and efficacy.

A 109-membered library of 5′-substituted cytidine analogs was synthesized, via funding through the NIH Roadmap Initiative and the Pilot Scale Library (PSL) Program. Reaction core compounds contained −NH2 (2) and −COOH (44 and 93) groups that were coupled to a diversity of reactants in a parallel, solution phase format to produce the target library. The assorted reactants included −NH2, −CHO, −SO2Cl, and −COOH functional groups, and condensation with the intermediate core materials 2 and 44 followed by acidic hydrolysis produced 3–91 in good yields and high purity. Linkage of the amino terminus of d-phenylalanine methyl ester to the free 5′-COOH of 44 and NaOH treatment led to core library −COOH precursor 93. In a libraries from libraries approach, compound 93 served as the vital building block for our unique library of dipeptidyl cytidine analogs 94–114 through amide coupling of the −COOH group with numerous commercial amines followed by acidic deprotection. Initial screening of the complete final library...

This manuscript concerns two definite integrals that could be connected to the Bose-Einstein and the Fermi-Dirac functions in the integrands, separately, with numerators slightly modified with a difference in two expressions that contain the Fourier kernel multiplied by a polynomial and its complex conjugate. In this work, we use our contour integral method to derive these definite integrals, which are given by ∫0∞ie−imx(log(a)−ix)k−eimx(log(a)+ix)k2eαx−1dx and ∫0∞ie−imx(log(a)−ix)k−eimx(log(a)+ix)k2eαx+1dx in terms of the Lerch function. We use these two definite integrals to derive formulae by Erdéyli and Watson. We derive special cases of these integrals in terms of special functions not found in current literature. Special functions have the property of analytic continuation, which widens the range of computation of the variables involved.

We present a method using contour integration to derive definite integrals and their associated infinite sums which can be expressed as a special function. We give a proof of the basic equation and some examples of the method. The advantage of using special functions is their analytic continuation, which widens the range of the parameters of the definite integral over which the formula is valid. We give as examples definite integrals of logarithmic functions times a trigonometric function. In various cases these generalizations evaluate to known mathematical constants, such as Catalan’s constant C and π.

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

Aim: Experimental and epidemiological studies and clinical trials suggest that nonsteroidal anti-inflammatory drugs possess antitumor potential. Sulindac, a widely used nonsteroidal anti-inflammatory drug, can prevent adenomatous colorectal polyps and colon cancer, especially in patients with familial adenomatous polyposis. Sulindac sulfide amide (SSA) is an amide-linked sulindac sulfide analog that showed in vivo antitumor activity in a human colon tumor xenograft model. Results/methodology: A new analog series with heterocyclic rings such as oxazole or thiazole at the C-2 position of sulindac was prepared and screened against prostate, colon and breast cancer cell lines to probe the effect of these novel substitutions on the activity of sulindac analogs. Conclusion: In general, replacement of the amide function of SSA analogs had a negative impact on the cell lines tested. A small number of hits incorporating rigid oxazole or thiazole groups in the sulindac scaffold in place of the amide linkage show comparable activity to our lead agent SSA., Graphical abstract

Background:Sulindac belongs to the chemically diverse family of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) that effectively prevent adenomatous colorectal polyps and colon cancer, especially in patients with familial adenomatous polyposis. Sulindac sulfide amide (SSA), an amide analog of sulindac sulfide, shows insignificant COX-related activity and toxicity while enhancing anticancer activityin vitroand demonstratingin vivoxenograft activity.Objective:Develop structure-activity relationships in the sulindac amine series and identify analogs with promising anticancer activities.Method:A series of sulindac amine analogs were designed and synthesized and then further modified in a “libraries from libraries” approach to produce amide, sulfonamide and N,N-disubstituted sulindac amine sub-libraries. All analogs were screened against three cancer cell lines (prostate, colon and breast).Results:Several active compounds were identifiedviain vitrocancer cell line screening with the most potent compound (26) in the nanomolar range.Conclusion:Compound26and analogs showing the most potent inhibitory activity may be considered for further design and optimization efforts as anticancer hit scaffolds.

As part of an ongoing program to study the anticancer activity of non-steroidal anti-inflammatory drugs (NSAIDs) through generating diversity libraries of multiple NSAID scaffolds, we synthesized a series of NSAID amide derivatives and screened these sets against three cancer cell lines (prostate, colon and breast) and Wnt/β-catenin signaling. The evaluated amide analog libraries show significant anticancer activity/cell proliferation inhibition, and specific members of the sets show inhibition of Wnt/β-catenin signaling.

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitroMtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivoMtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app.

While browsing through the famous book of Bierens de Haan, we came across a table with some very interesting integrals. These integrals also appeared in the book of Gradshteyn and Ryzhik. Derivation of these integrals are not listed in the current literature to best of our knowledge. The derivation of such integrals in the book of Gradshteyn and Ryzhik in terms of closed form solutions is pertinent. We evaluate several of these definite integrals of the form &int, 0&infin, (a+y)k&minus, (a&minus, y)keby&minus, 1dy, &int, y)keby+1dy, &int, y)ksinh(by)dy and &int, (a+y)k+(a&minus, y)kcosh(by)dy in terms of a special function where k, a and b are arbitrary complex numbers.

Under the aegis of the Pilot Scale Library Program of the NIH Roadmap Initiative, a new library of propan-1-amine containing aza acyclic nucleosides was designed and prepared, and we now report a diverse set of 157 purine, pyrimidine, and 1,2,4-triazole- N-acetamide analogues. These new nucleoside analogues were prepared in a parallel high throughput solution-phase format. A set of diverse amines was reacted with several nucleobase N-propaldehydes utilizing reductive amination with sodium triacetoxyborohydride coupling to produce a small and diverse aza acyclic nucleoside library. All reactions were performed using 24-well reaction blocks and an automatic reagent-dispensing platform under an inert atmosphere. Final targets were purified on an automated system using solid sample loading prepacked cartridges and prepacked silica gel columns. All compounds were characterized by NMR and HRMS and were analyzed for purity by HPLC prior to submission to the Molecular Libraries Small Molecule Repository (MLSMR). Initial screening through the Molecular Libraries Probe Production Centers Network (MLPCN) demonstrated diverse and interesting biological activities.

Disruptor of Telomeric Silencing 1-Like (DOT1L), the sole histone H3 lysine 79 (H3K79) methyltransferase, is required for leukemogenic transformation in a subset of leukemias bearing chromosomal translocations of the Mixed Lineage Leukemia (MLL) gene, as well as other cancers. Thus, DOT1L is an attractive therapeutic target and discovery of small molecule inhibitors remain of high interest. Herein, we are presenting screening results for a unique focused library of 1,200 nucleoside analogs originally produced under the aegis of the NIH Pilot Scale Library Program. The complete nucleoside set was screened virtually against DOT1L, resulting in 210 putative hits. In vitro screening of the virtual hits resulted in validation of 11 compounds as DOT1L inhibitors clustered into two distinct chemical classes, adenosine-based inhibitors and a new chemotype that lacks adenosine. Based on the developed DOT1L ligand binding model, a structure-based design strategy was applied and a second-generation non-nucleoside DOT1L inhibitors was developed. Newly synthesized compound 25 was the most potent DOT1L inhibitor in the new series with an IC50 of 1.0 μM, showing 40-fold improvement in comparison with hit 9 and exhibiting reasonable on target effects in a DOT1L dependent murine cell line. These compounds represent novel chemical probes with unique non-nucleoside scaffold that bind and compete with the SAM binding site of DOT1L, thus providing foundation for further medicinal chemistry efforts and developing more potent compounds.

Sulindac is a non-steroidal anti-inflammatory drug (NSAID) that has shown significant anticancer activity. Sulindac sulfide amide (1) possessing greatly reduced COX-related inhibition relative to sulindac displayed in vivo antitumor activity that was comparable to sulindac in a human colon tumor xenograft model. Inspired by these observations, a panel of diverse sulindac amide derivatives have been synthesized and their activity probed against three cancer cell lines (prostate, colon and breast). A neutral analog, compound 79 was identified with comparable potency relative to lead 1 and activity against a panel of lymphoblastic leukemia cell lines. Several new series also show good activity relative to the parent (1), including five analogs that also possess nanomolar inhibitory potencies against acute lymphoblastic leukemia cells. Several new analogs identified may serve as anticancer lead candidates for further development.

A small library of fifty-five adenosine peptide analogs was synthesized, under the Pilot Scale Library (PSL) Program of the NIH Roadmap initiative, from 2',3'-O-isopropylideneadenosine-5'-carboxylic acid 2. The coupling of amine or sulfanilamide reactants to the free 5'-carboxylic acid moiety of 2, in automated solution-phase fashion, led after acid-mediated hydrolysis to target compounds 3-57 in good yields and high purity. No marked anticancer or antimalarial activity was noted on preliminary cellular testing. Initial screening through the MLPCN program, however, indicates that these analogs may show diverse and interesting biological activities.

Tuberculosis is a major, neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event data sets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose-response, single-point and dual-event dose-response as well as combined data sets for three distinct data sets from the same laboratory. We used a fourth data set of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose-response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual-event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25 000-350 000 molecules. Our results have implications for justifying further high-throughput screening versus focused testing based on model predictions.

Expansion of a stem-like subpopulation with increased growth and survival potential is thought to drive colorectal tumor growth and progression. We investigated a CD44-positive (CD44((+))) subpopulation with extended growth and survival capacity in the human colon adenoma cell line LT97. This subpopulation expressed elevated levels of fibroblast growth factor 18 (FGF18) and fibroblast growth factor receptor FGFR3-IIIc. Expression levels of the FGFR3-IIIb, which does not bind FGF18, were similar in CD44((+)) and CD44((-)). Addition of FGF18 to the medium or its overexpression from an adenoviral vector increased the colony formation capacity of CD44((+)) threefold, and stimulated phosphorylation of ERK and GSK3β in both total LT97 populations and CD44((+)) cells. FGFR3 signaling blockade by expression of a dominant-negative FGFR3-IIIc mutant led to inhibition of both colony formation and down-stream signaling in the CD44((+)) cells. CD44((-)) cells did not respond. Blockade of the wnt-pathway by a dominant-negative Tcf4-mutant inhibited FGFR3 activation in LT97 cells as well as in HT29 colorectal cancer cells. The chemical wnt-inhibitor sulindac sulfide amide inhibited expression of FGF18 and FGFR3-IIIc and led to inhibition of receptor activation to less than 30% of control treated cells, both in LT97 and HT29 cultures. Our results demonstrate that an FGF18/FGFR3-IIIc autocrine growth and survival loop is up-regulated in a wnt-dependent manner and drives tumor cell growth in a subpopulation of colon adenoma cells. This subpopulation can be regarded as a precursor of colon cancer development and can be targeted for CRC-prevention by blocking either wnt- or FGFR3-signaling.

Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an animal model of the disease is a challenge that takes considerable time and money. As an example, recent years have seen whole-cell phenotypic screens of millions of compounds yielding over 1500 inhibitors of Mycobacterium tuberculosis (Mtb). These must be prioritized for testing in the mouse in vivo assay for Mtb infection, a validated model utilized to select compounds for further testing. We demonstrate learning from in vivo active and inactive compounds using machine learning classification models (Bayesian, support vector machines, and recursive partitioning) consisting of 773 compounds. The Bayesian model predicted 8 out of 11 additional in vivo actives not included in the model as an external test set. Curation of 70 years of Mtb data can therefore provide statistically robust computational models to focus resources on in vivo active small molecule antituberculars. This highlights a cost-effective predictor for in vivo testing elsewhere in other diseases.

A small library of peptidyl adenosine antibiotic analogs was synthesized, under the Pilot Scale Library Program of the NIH Roadmap initiative, from 2',3'-O-isoproylideneadenosine-5'-carboxylic acid 2 in excellent yield. The coupling of the amino terminus of L-2-aminophenylbutyric methyl ester to a free 5'-carboxylic acid moiety of 2 followed by sodium hydroxide treatment led to carboxylic acid analog 4. Hydrolysis of this latter gave unprotected nucleoside analog 5. Intermediate 4 served as the precursor for the preparation of novel peptidyl adenosine analogs 6-18 in good yields and high purity through peptide coupling reactions to diverse amine derivatives. No marked anticancer and antimalaria activity was noted on preliminary cellular testing; however these analogs should be useful candidates for other types of biological activity.

Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested.A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate.In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models.Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.

Tuberculosis, caused by Mycobacterium tuberculosis, remains one of the leading causes of death worldwide despite extensive research, directly observed therapy using multidrug regimens, and the widespread use of a vaccine. The majority of patients harbor the bacterium in a state of metabolic dormancy. New drugs with novel modes of action are needed to target essential metabolic pathways in M. tuberculosis; ATP-competitive enzyme inhibitors are one such class. Previous screening efforts for ATP-competitive enzyme inhibitors identified several classes of lead compounds that demonstrated potent anti-mycobacterial efficacy as well as tolerable levels of toxicity in cell culture. In this report, a probe-based chemoproteomic approach was used to selectively profile the M. tuberculosis ATP-binding proteome in normally growing and hypoxic M. tuberculosis. From these studies, 122 ATP-binding proteins were identified in either metabolic state, and roughly 60% of these are reported to be essential for survival in vitro. These data are available through ProteomeXchange with identifier PXD000141. Protein families vital to the survival of the tubercle bacillus during hypoxia emerged from our studies. Specifically, along with members of the DosR regulon, several proteins involved in energy metabolism (Icl/Rv0468 and Mdh/Rv1240) and lipid biosynthesis (UmaA/Rv0469, DesA1/Rv0824c, and DesA2/Rv1094) were found to be differentially abundant in hypoxic versus normal growing cultures. These pathways represent a subset of proteins that may be relevant therapeutic targets for development of novel ATP-competitive antibiotics.

6-Oxo and 6-thio analogs of purine were prepared based on the initial activity screening of a small, diverse purine library against Mycobacterium tuberculosis (Mtb). Certain 6-oxo and 6-thio-substituted purine analogs described herein showed moderate to good inhibitory activity. N(9)-substitution apparently enhances the anti-mycobacterial activity in the purine series described herein. Several 2-amino and 2-chloro purine analogs were also synthesized that showed moderate inhibitory activity against Mtb.

A library of eighty one adenosine antibiotic analogs was prepared under the Pilot Scale Library Program of the NIH Roadmap initiative from 5′-amino-5′-deoxy-2′,3′-O-isopropylidene-adenosine 3. Diverse aldehyde, sulfonyl chloride and carboxylic acid reactant sets were condensed to 3, in solution-phase fashion, leading after acid-mediated hydrolysis to the targeted compounds in good yields and high purity. No marked anti-tuberculosis or anticancer activity was noted on preliminary cellular testing, but these nucleoside analogs should be useful candidates for other types of biological activity.

We report the chemical synthesis of a series of disaccharides of arabinofuranose with a glycosidic sulfur linker as mimics of the acceptor for arabinofuranosyltransferases with and without using any activator to avoid any complex reactions. These analogs were tested for in vitro activity against MTB strain H37Ra and 3 MAC clinical isolates. MICs were determined using a colorimetric microdilution broth assay. Bactericidal activity was studied with kill curves over a period of seven days. Intracellular activity against MTB H37Ra was determined in the Mono Mac 6 (MM6) human monocytic cell line.

Silibinin is a natural compound isolated from milk thistle seed extracts, and has traditionally been used as a hepatoprotectant. A number of studies have also established the cancer therapeutic and chemopreventive role of silibinin in both in vitro and in vivo models. The low density lipoprotein receptor-related protein-6 (LRP6) is an essential Wnt co-receptor for the Wnt/β-catenin pathway and represents a promising target for cancer prevention and therapy. In the present study, we found that silibinin was able to repress endogenous LRP6 expression and block Wnt3A-induced LRP6 phosphorylation and Wnt/β-catenin signaling activation in HEK293 cells. Importantly, silibinin was also able to suppress endogenous LRP6 expression and phosphorylation and block Wnt/β-catenin signaling in prostate cancer PC-3 and DU-145 cells and breast cancer MDA-MB-231 and T-47D cells. Mechanistically, silibinin inhibited LRP6 promoter activity and decreased LRP6 mRNA levels in prostate and breast cancer cells. Finally, we demonstrated that silibinin displayed anticancer activity with IC(50) values comparable to those shown to suppress LRP6 expression and Wnt/β-catenin signaling activities in prostate and breast cancer cells. Our data indicate that silibinin is a novel small molecule Wnt/β-catenin signaling inhibitor by suppressing Wnt co-receptor LRP6 expression at the transcription level, and that the anti-cancer activity of silibinin is associated with its inhibitory effect on Wnt/LRP6 signaling.

Introduction of nitrogen atom into the benzene ring of a previously identified HCV replication (replicase) benzofuran inhibitor 2, resulted in the discovery of the more potent pyridofuran analogue 5. Subsequent introduction of small alkyl and alkoxy ligands into the pyridine ring resulted in further improvements in replicon potency. Replacement of the 4-chloro moiety on the pyrimidine core with a methyl group, and concomitant monoalkylation of the C-2 amino moiety resulted in the identification of several inhibitors with desirable characteristics. Inhibitor 41, from the monosubstituted pyridofuran and inhibitor 50 from the disubstituted series displayed excellent potency, selectivity (GAPDH/MTS CC(50)) and PK parameters in all species studied, while the selectivity in the thymidine incorporation assay (DNA·CC(50)) was low.

Based on a previously identified HCV replication (replicase) inhibitor 1 , SAR efforts were conducted around the pyrimidine core to improve the potency and pharmacokinetic profile of the inhibitors. A benzothiazole moiety was found to be the optimal substituent at the pyrimidine 5-position. Due to potential reactivity concern, the 4-chloro residue was replaced by a methyl group with some loss in potency and enhanced rat in vivo profile. Extensive investigations at the C-2 position resulted in identification of compound 16 that demonstrated very good replicon potency, selectivity and rodent plasma/target organ concentration. Inhibitor 16 also demonstrated good plasma levels and oral bioavailability in dogs, while monkey exposure was rather low. Chemistry optimization towards a practical route to install the benzothiazole moiety resulted in an efficient direct C–H arylation protocol.