Patient-derived organoids for personalized gallbladder cancer modelling and drug screening.

BACKGROUND: Gallbladder carcinoma (GBC) is a relatively rare but highly aggressive cancer with late clinical detection and a poor prognosis. However, the lack of models with features consistent with human gallbladder tumours has hindered progress in pathogenic mechanisms and therapies. METHODS: We established organoid lines derived from human GBC as well as normal gallbladder and benign gallbladder adenoma (GBA) tissues. The histopathology signatures of organoid cultures were identified by H&E staining, immunohistochemistry and immunofluorescence. The genetic and transcriptional features of organoids were analysed by whole-exome sequencing and RNA sequencing. A set of compounds targeting the most active signalling pathways in GBCs were screened for their ability to suppress GBC organoids. The antitumour effects of candidate compounds, CUDC-101 and CUDC-907, were evaluated in vitro and in vivo. RESULTS: The established organoids were cultured stably for more than 6 months and closely recapitulated the histopathology, genetic and transcriptional features, and intratumour heterogeneity of the primary tissues at the single-cell level. Notably, expression profiling analysis of the organoids revealed a set of genes that varied across the three subtypes and thus may participate in the malignant progression of gallbladder diseases. More importantly, we found that the dual PI3K/HDAC inhibitor CUDC-907 significantly restrained the growth of various GBC organoids with minimal toxicity to normal gallbladder organoids. CONCLUSIONS: Patient-derived organoids are potentially a useful platform to explore molecular pathogenesis of gallbladder tumours and discover personalized drugs.

Comparison of Drug Inhibitory Effects ([Formula: see text]) in Monolayer and Spheroid Cultures.

Traditionally, the monolayer (two-dimensional) cell cultures are used for initial evaluation of the effectiveness of anticancer drugs. In particular, these experiments provide the [Formula: see text] curves that determine drug concentration that can inhibit growth of a tumor colony by half when compared to the cells grown with no exposure to the drug. Low [Formula: see text] value means that the drug is effective at low concentrations, and thus will show lower systemic toxicity when administered to the patient. However, in these experiments cells are grown in a monolayer, all well exposed to the drug, while in vivo tumors expand as three-dimensional multicellular masses, where inner cells have a limited access to the drug. Therefore, we performed computational studies to compare the [Formula: see text] curves for cells grown as a two-dimensional monolayer and a cross section through a three-dimensional spheroid. Our results identified conditions (drug diffusivity, drug action mechanisms and cell proliferation capabilities) under which these [Formula: see text] curves differ significantly. This will help experimentalists to better determine drug dosage for future in vivo experiments and clinical trials.

Homology Modeling and Virtual Screening Studies of Antigen MLAA-42 Protein: Identification of Novel Drug Candidates against Leukemia-An In Silico Approach.

Monocytic leukemia-associated antigen-42 (MLAA-42) is associated with excessive cell division and progression of leukemia. Thus, human MLAA-42 is considered as a promising target for designing of new lead molecules for leukemia treatment. Herein, the 3D model of the target was generated by homology modeling technique. The model was then evaluated using various cheminformatics servers. Moreover, the virtual screening studies were performed to explore the possible binding patterns of ligand molecules to MLAA's active site pocket. Thirteen ligand molecules from the ChemBank™ database were identified as they showed good binding affinities, scaffold diversity, and preferential ADME properties which may act as potent drug candidates against leukemia. The study provides the way to identify novel therapeutics with optimal efficacy, targeting MLAA-42.

Atezolizumab for the treatment of breast cancer.

INTRODUCTION: Breast cancer (BC) is the most common cancer diagnosed among women. The development of new personalized therapeutic strategies has reshaped the landscape in this field. However, BC is still the first cause of death among women. Interestingly, several preclinical studies and some clinical evidences are focused their attention on the role of immune system and immunotherapy on cancer control, also in BC. Areas covered: Usually, BC has been considered a not immunogenic tumor for its low mutational load. However, recent studies have evidenced that some subtypes, triple negative and HER-2 positive BC, are 'hot' tumors, thus more immunogenic. Moreover, the presence of immune infiltrate is positively associated with favorable prognosis. Therefore, the use of immune-checkpoint inhibitors seems to be an encouraging treatment option also in BC. Among these drugs, atezolizumab is an anti-PD-L1 monoclonal antibody with a particular structure that reduce antibody-dependent cellular cytotoxicity against T cells, increasing quantitatively and qualitatively the effective response. Expert opinion: The use of immunotherapy is a promising option for BC. However, at the same time it still raises many doubts. Surely, the research and the validation of immune biomarkers can permit to identify patients who more benefit from these drugs.

CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds.

In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action.

A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data. Mania first constructs a drug-drug similarity network through integrating multiple heterogeneous data sources, including drug sensitivity, drug chemical structure, and perturbation assays. It then learns a compact vector representation for each drug to simultaneously encode its structural and pharmacogenomic properties. Extensive experiments demonstrate that Mania achieves substantially improved performance in both MoAs and targets prediction, compared to predictions based on individual data sources as well as a state-of-the-art integrative method. Moreover, Mania identifies drugs that target frequently mutated cancer genes, which provides novel insights into drug repurposing.

Ameliorating the antitumor activity of lenalidomide using PLGA nanoparticles for the treatment of multiple myeloma

Abstract Lenalidomide (LND) is an anti-cancer drug and an effective derivative of thalidomide used for multiple myeloma therapy. Because of its poor solubility in water, LND is known to cause low oral bioavailability (below 33%), and as a direct consequence of this, the dosing frequency is extended thus increasing risk of toxicity. To improve its bioavailability and sustained release, the present study aims to formulate polymeric nanoparticles (NPs) for LND using [Poly (lactic-co-glycolic acid)] (PLGA) as a polymer. The polymeric NPs were evaluated for particle size, SEM, XRD, drug content, entrapment efficiency (EE), in vitro release studies and in vivo bioavailability studies in rats. The formulated NPs possessed a size of 179±0.9 nm and a zeta potential of -24.4 ± 0.2 mV. The drug loading and EE of the optimized formulation was 32 ± 0.37 % and 78 ± 0.92% respectively. After oral administration of LND PLGA-NPs, the relative bioavailability was enhanced about 3.67-fold compared to LND. This study demonstrates the novel drug delivery for LND with PLGA-NPs as effective drug delivery system for sustained delivery of LND.

MicroC(3): an ex vivo microfluidic cis-coculture assay to test chemosensitivity and resistance of patient multiple myeloma cells.

Chemosensitivity and resistance assays (CSRAs) aim to direct therapies based upon ex vivo responses of patient tumor cells to chemotherapeutic drugs. However, successful CSRAs are yet to be developed. Here, we exposed primary CD138(+) multiple myeloma (MM) cells to bortezomib, a clinical proteasome inhibitor, in microfluidic-cis-coculture (MicroC(3)) incorporating the patient's own CD138(-) tumor-companion mononuclear cells to integrate some of the patients' own tumor microenvironment components in the CSRA design. Statistical clustering techniques segregated MicroC(3) responses into two groups which correctly identified all seventeen patients as either clinically responsive or non-responsive to bortezomib-containing therapies. In contrast, when the same patient MM samples were analyzed in the absence of the CD138(-) cells (monoculture), the tumor cell responses did not segregate into clinical response clusters. Thus, MicroC(3) identified bortezomib-therapy MM patient responses making it a viable CSRA candidate toward enabling personalized therapy.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.

Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

Synthesis and bioevaluation of diphyllin glycosides as novel anticancer agents.

A series of diphyllin glycosides were synthesized from diphyllin by phase transfer catalysis glycosylation, deprotection, and etherification, and the structures were established by (1) H NMR, (13) C NMR, and HRMS. These glycosides were evaluated for their in vitro cytotoxicity against HCT-116, A549, and A549T cancer cell lines by MTT assay, and most of them were cytotoxic at submicromolar concentrations. They were also effective against the paclitaxel-resistant cell line A549T. The kDNA decatenation assay indicated that most of these compounds inhibited topoisomerase IIα-mediated kDNA decatenation. In addition, the in vitro tubulin polymerization study showed that compounds 5 and 6 had antimicrotubule activity with a paclitaxel-like mode of action. Taken together, these results suggest that these diphyllin glycosides act on both TopoII and tubulin.

Importance of retention data from affinity and reverse-phase high-performance liquid chromatography on antitumor activity prediction of imidazoacridinones using QSAR strategy.

Quantitative structure-activity relationships (QSAR) studies for prediction of cytotoxic and antitumor activity of imidazoacridinones (IA) based on experimentally obtained high-performance liquid chromatography (HPLC) retention data and calculated parameters using computational (molecular modeling) medicinal chemistry methods were proposed. The RP-HPLC and affinity-HPLC chromatographic techniques with four diversified HPLC systems applying columns with octadecylsilanes (C18), phosphatidylcholine (IAM), as well as α(1)-glycoprotein (AGP) and albumin (HSA) were used for the determination of the retention constants logk and logk(w) which characterize lipophilicity and protein affinity of IA. Moreover, molecular modeling studies were performed using HyperChem and Dragon software's, and structural descriptors were calculated and subsequently used. The QSAR equations using multiple linear regression (MLR) analysis method were derived which indicated that in vivo antileukemia activity of IA depends on cytotoxic activity against leukemia cells, whereas this cytotoxic activity depends on logk and logk(w) parameters obtained on all HPLC systems. Moreover, the QSRR equations were derived and indicated that logk and logk(w) parameters depend on calculated non-empirical structural parameters. The predictive power of obtained QSAR and QSRR equations allowed the prediction of cytotoxic and antitumor activity of IA and also their HPLC retention parameters. Finally, the equations can be used for prediction of antileukemia activity of IA without the necessity of carrying out experimental measurements.

Anti-glioma effect and safety of docetaxel-loaded nanoemulsion.

Docetaxel, an inhibitor of microtubule depolymerization, has been used for many malignancies. Due to its toxicity and the non-selective distribution of its commercial formulation, Taxotere®, new formulations with less toxicity and tumor targeting need to be explored. For its safety and ease of factory scale production, nanoemulsion, was selected to encapsulate docetaxel. The particle size of docetaxel loaded nanoemulsion (DNE) was 72.3 nm, the average zeta potential was -6.38 mV, the encapsulation efficiency was 93.1% and the drug loading capacity was 2.87%. Although DNE presented similar antiproliferation effects on both U87 cells and bEnd.3 cells, its in vivo toxicity was significantly lower than Taxotere®. In vivo fluorescent imaging suggested nanoemulsions loaded with a fluorescent probe could distribute to the brain and accumulate at the glioma site. The pharmacological experiments also confirmed that the DNE could target glioma sites and prolong the median survival time of mice with gliomas. In conclusion, DNE is a new, less toxic, drug formulation that is effective for brain glioma therapy.

Correlation of drug-induced apoptosis assay results with oncologist treatment decisions and patient response and survival.

BACKGROUND: An observational prospective nonblinded clinical trial was performed to determine the effect of a drug-induced apoptosis assay results on treatments planned by oncologists. METHODS: Purified cancer cells from patient biopsies were placed into the MiCK (Microculture Kinetic) assay, a short-term culture, which determined the effects of single drugs or combinations of drugs on tumor cell apoptosis. An oncologist received the assay results before finalizing the treatment plan. Use of the MiCK assay was evaluated and correlated with patient outcomes. RESULTS: Forty-four patients with successful MiCK assays from breast cancer (n = 16), nonsmall cell lung cancer (n = 6), non-Hodgkin lymphoma (n = 4), and others were evaluated. Four patients received adjuvant chemotherapy after MiCK, and 40 received palliative chemotherapy with a median line of therapy of 2. Oncologists used the MiCK assay to determine chemotherapy (users) in 28 (64%) and did not (nonusers) in 16 patients (36%). In users receiving palliative chemotherapy, complete plus partial response rate was 44%, compared with 6.7% in nonusers (P < .02). The median overall survival was 10.1 months in users versus 4.1 months in nonusers (P = .02). Relapse-free interval was 8.6 months in users versus 4.0 months in nonusers (P < .01). CONCLUSIONS: MiCK assay results are frequently used by oncologists. Outcomes appear to be statistically superior when oncologists use chemotherapy based on MiCK assay results compared with when they do not use the assay results. When available to oncologists, MiCK assay results help to determine patient treatment plans.

Oral microparticulate vaccine for melanoma using M-cell targeting.

Cancer vaccines are limited in their use, because of their inability to mount a robust anti-tumor immune response. Thus, targeting M-cells in the small intestine, which are responsible for entry of many pathogens, will be an attractive way to elicit a strong immune response toward particulate antigens. Therefore, in the present investigation, we demonstrated that efficient oral vaccination against melanoma antigens could be accomplished by incorporating the antigens in an albumin-based microparticle with a ligand AAL (Aleuria aurantia lectin) targeted specifically to M-cells. The oral microparticulate vaccine effectively protected the mice from subcutaneous challenge with tumor cells in prophylactic settings. The animals were vaccinated with antigen microparticles having a size range of around 1-1.25 µm where one prime and four booster doses were administered every 14 days over 10 weeks of duration, followed by challenge with live tumor cells, which showed complete tumor protection after oral vaccination. With the inclusion of ligand in the microparticles, we observed significantly higher IgG titers (1565 µg/mL) as compared to the microparticle formulations without AAL (872 µg/mL). This data suggests that ligand loaded microparticles may have the potential to target antigens to M-cells for an efficient oral vaccination.

Synthesis of 1-substituted 3-aryl-5-aryl(hetaryl)-2-pyrazolines and study of their antitumor activity.

Three series of novel 1,3,5-trisubstituted 2-pyrazoline derivatives containing thiophene and benzodioxol moieties as potential antitumor agents were synthesized. The in vitro antitumor activity of the obtained compounds was determined at the National Cancer Institute (NCI). The 5-(benzo[d][1,3]dioxol-5-yl)-3-(4-methoxyphenyl)-4,5-dihydro-1H-pyrazole-1-carbothioamide (9a) is the most prominent of the compounds due to its remarkable activity toward leukemia (RPMI-8226), renal cancer (UO-31) and prostate cancer (DU-145) cell lines with GI(50) values of 1.88, 1.91 and 1.94 µM, respectively.

TGI-Simulator: a visual tool to support the preclinical phase of the drug discovery process by assessing in silico the effect of an anticancer drug.

This paper presents TGI-Simulator, a software tool designed to show, through a 2-D graphical animation, the simulated time effect of an anticancer drug on a tumor mass by exploiting the well-known Tumor Growth Inhibition (TGI) model published by Simeoni et al. [1]. Simeoni TGI model is a mathematical model routinely used by pharma companies and researchers during the drug development process. The application is based on a Java graphical user interface (GUI) including a self installing differential equation solver implemented in Matlab together with an optimization algorithm that performs model identification via Weighted Least Squares (WLS). However, it can graphically show also the simulation results obtained within other scientific software tools, if they are preventively stored into a suitable ASCII file. The tool would be a valid support also for researchers with no specific skills in scientific calculations and in pharmacokinetic and pharmacodynamic modeling but daily involved in pharma companies drug development processes at different levels. The availability of a movie with a temporal varying 2-D iconographic representation is an original instrument to communicate results and learn Simeoni TGI model and its potential application in preclinical studies.

Synthesis and cytotoxic evaluation of some new phthalazinylpiperazine derivatives.

A new series of 1,4-disubstituted phthalazinylpiperazine derivatives 7a-f, 12a-f and 20a-f were designed and synthesized in order to develop potent and selective antitumor agents. The target compounds were screened for their cytotoxic activities against A549, HT-29 and MDA-MB-231 cancer cell lines in vitro. Among them, compounds 7a-f exhibited excellent selectivity for MDA-MB-231 with IC(50) values ranging from 0.013 µM to 0.079 µM. The most promising compound, 7e (IC(50) = 2.19 µM, 2.19 µM, 0.013 µM), was 9.3, 10, and 4.9 × 10(3) times more active than vatalanib (IC(50) = 20.27 µM, 21.96 µM, 63.90 µM), respectively.

Multivitamin and mineral supplementation in 1,2-dimethylhydrazine induced experimental colon carcinogenesis and evaluation of free radical status, antioxidant potential, and incidence of ACF.

This study was performed to determine the chemopreventive and antioxidant status of multivitamin and mineral (0.01% in drinking water, ad libitum) supplements in 1,2-dimethylhydrazine (DMH)-induced experimental colon carcinogenesis. Experimental colon carcinogenesis was induced in male albino Wistar rats by injecting DMH (20 mg·(kg body mass)(-1)) once weekly for 15 consecutive weeks, and administering a multivitamin supplement in 3 regimes (initiation, post-initiation, and entire experimental period) for 32 weeks. We studied lipid peroxidation products (thiobarbituric acid reactive substances, lipid hydroperoxides, conjugated dienes) in the circulation and in the tissues, antioxidant status (superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, and non-enzymatic antioxidant-reduced glutathione) of the tissues, aberrant crypt foci (ACF), and histopathological alterations. DMH-induced rats had an increase in lipid peroxidation products and a lower antioxidant status compared with control animals. Multivitamin and mineral supplementation during the initiation, post-initiation, and the entire study period significantly decreased the levels of lipid peroxidation products in circulation and colonic tissues, significantly elevated the activities of the antioxidant enzymes and reduced glutathione to near normalcy in DMH-induced rats. The incidence of ACF was reduced by [corrected] 84.1% in rats supplemented with multivitamin and minerals for the entire study and prevented the colonic tissue from histopathological alterations induced by DMH.

In vitro cytotoxic activity of Aesculus indica against breast adenocarcinoma cell line (MCF-7) and phytochemical analysis.

Aesculus indica (Linn.) (Sapindaceae) is an ethanobotanically important plant specie traditionally used against rheumatism, skin and vein complaints. Cytotoxic potential of Aesculus indica crude leaf extract and its fractions was investigated against MCF-7 cell line. Crude extract of Aesculus indica was prepared in methanol by maceration technique. Crude extract was fractionated into four organic and one aqueous fraction on polarity basis. MTT assay was used to evaluate the reduction of viability of MCF-7 breast cancer cell line. Cell viability was inhibited by Aesculus indica crude extract in a dose dependent manner ranging from 34.2% at 10 µg/ml to 94% at 500µg/ml. Activity was found in an ascending order from hexane showing 29.8% inhibition to aqueous fraction indicating maximum inhibition, 60%. Phytochemical analysis of crude and fractionated extracts revealed presence of flavonoids, saponins, coumarins and tannins upto varying degrees. Methanol and aqueous fraction of methanol extract of Aesculus indica can be good source of cytotoxic compounds.