Artificial intelligence-driven new drug discovery targeting serine/threonine kinase 33 for cancer treatment

Background Artificial intelligence (AI) is capable of integrating a large amount of related information to predict therapeutic relationships such as disease treatment with known drugs, gene expression, and drug-target binding. AI has gained increasing attention as a promising tool for next-generation drug development. Methods An AI method was used for drug repurposing and target identification for cancer. Among 8 survived candidates after background checking, N-(1-propyl-1H-1,3-benzodiazol-2-yl)-3-(pyrrolidine-1-sulfonyl) benzamide (Z29077885) was newly selected as an new anti-cancer drug, and the anti-cancer efficacy of Z29077885 was confirmed using cell viability, western blot, cell cycle, apoptosis assay in MDA-MB 231 and A549 in vitro. Then, anti-tumor efficacy of Z29077885 was validated in an in vivo A549 xenograft in BALB/c nude mice. Results First, we discovered an antiviral agent, Z29077885, as a new anticancer drug candidate using the AI deep learning method. Next, we demonstrated that Z29077885 inhibits Serine/threonine kinase 33 (STK33) enzymatic function in vitro and showed the anticancer efficacy in various cancer cells. Then, we found enhanced apoptosis via S-phase cell cycle arrest as the mechanism underlying the anticancer efficacy of Z29077885 in both lung and breast cancer cells. Finally, we confirmed the anti-tumor efficacy of Z29077885 in an in vivo A549 xenograft. Conclusions In this study, we used an AI-driven screening strategy to find a novel anticancer medication targeting STK33 that triggers cancer cell apoptosis and cell cycle arrest at the s phase. It will pave a way to efficiently discover new anticancer drugs.This work was supported and funded by Standigm Inc. This work was also supported in part by National Research Foundation (NRF) funded by Korean government (MIST) (No. RS-2023-00208795 and No. RS-2023-00260529 to S.J.O

Does green tea affect postprandial glucose, insulin and satiety in healthy subjects: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Results of epidemiological studies have suggested that consumption of green tea could lower the risk of type 2 diabetes. Intervention studies show that green tea may decrease blood glucose levels, and also increase satiety. This study was conducted to examine the postprandial effects of green tea on glucose levels, glycemic index, insulin levels and satiety in healthy individuals after the consumption of a meal including green tea.</p> <p>Methods</p> <p>The study was conducted on 14 healthy volunteers, with a crossover design. Participants were randomized to either 300 ml of green tea or water. This was consumed together with a breakfast consisting of white bread and sliced turkey. Blood samples were drawn at 0, 15, 30, 45, 60, 90, and 120 minutes. Participants completed several different satiety score scales at the same times.</p> <p>Results</p> <p>Plasma glucose levels were higher 120 min after ingestion of the meal with green tea than after the ingestion of the meal with water. No significant differences were found in serum insulin levels, or the area under the curve for glucose or insulin. Subjects reported significantly higher satiety, having a less strong desire to eat their favorite food and finding it less pleasant to eat another mouthful of the same food after drinking green tea compared to water.</p> <p>Conclusions</p> <p>Green tea showed no glucose or insulin-lowering effect. However, increased satiety and fullness were reported by the participants after the consumption of green tea.</p> <p>Trial registration number</p> <p>NCT01086189</p

Readthrough of Premature Termination Codons in the Adenomatous Polyposis Coli Gene Restores Its Biological Activity in Human Cancer Cells

The APC tumor suppressor gene is frequently mutated in human colorectal cancer, with nonsense mutations accounting for 30% of all mutations in this gene. Reintroduction of the WT APC gene into cancer cells generally reduces tumorigenicity or induces apoptosis. In this study, we explored the possibility of using drugs to induce premature termination codon (PTC) readthrough (aminoglycosides, negamycin), as a means of reactivating endogenous APC. By quantifying the readthrough of 11 nonsense mutations in APC, we were able to identify those giving the highest levels of readthrough after treatment. For these mutations, we demonstrated that aminoglycoside or negamycin treatment led to a recovery of the biological activity of APC in cancer cell lines, and showed that the level of APC activity was proportional to the level of induced readthrough. These findings show that treatment with readthrough inducers should be considered as a potential strategy for treating cancers caused by nonsense mutations APC gene. They also provide a rational basis for identifying mutations responsive to readthrough inducers

Mechanism of trifluorothymidine potentiation of oxaliplatin-induced cytotoxicity to colorectal cancer cells

Oxaliplatin (OHP) is an anticancer agent that acts by formation of Platinum-DNA (Pt-DNA) adducts resulting in DNA-strand breaks and is used for the treatment of colorectal cancer. The pyrimidine analog trifluorothymidine (TFT) forms together with a thymidine phosphorylase inhibitor (TPI) the anticancer drug formulation TAS-102, in which TPI enhances the bioavailability of TFT in vivo. In this in vitro study the combined cytotoxic effects of OHP with TFT were investigated in human colorectal cancer cells as a model for TAS-102 combinations. In a panel of five colon cancer cell lines (WiDr, H630, Colo320, SNU-C4 and SW1116) we evaluated the OHP-TFT drug combinations using the multiple drug–effect analysis with CalcuSyn software, in which the combination index (CI) indicates synergism (CI<0.9), additivity (CI=0.9–1.1) or antagonism (CI>1.1). Drug target analysis was used for WiDr, H630 and SW1116 to investigate whether there was an increase in Pt-DNA adduct formation, DNA damage induction, cell cycle delay and apoptosis. Trifluorothymidine combined with OHP resulted in synergism for all cell lines (all CI<0.9). This was irrespective of schedule in which either one of the drugs was kept at a constant concentration (using variable drug ratio) or when the two drugs were added in a 1 : 1 IC50-based molar ratio. Synergism could be increased for WiDr using sequential drug treatment schedules. Trifluorothymidine increased Pt-DNA adduct formation significantly in H630 and SW1116 (14.4 and 99.1%, respectively; P<0.05). Platinum-DNA adducts were retained best in SW1116 in the presence of TFT. More DNA-strand breaks were induced in SW1116 and the combination increased DNA damage induction (>20%) compared with OHP alone. Exposure to the drugs induced a clear cell-cycle S-phase arrest, but was dose schedule and cell line dependent. Trifluorothymidine (TFT) and OHP both induced apoptosis, which increased significantly for WiDr and SW1116 after TFT–OHP exposure (18.8 and 20.6% respectively; P<0.05). The basal protein levels of ERCC1 DNA repair enzyme were not related to the DNA damage that was induced in the cell lines. In conclusion, the combination of TFT with the DNA synthesis inhibitor OHP induces synergism in colorectal cancer cells, but is dependent on the dose and treatment schedule used

Feature Selection via Chaotic Antlion Optimization

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2- 0917. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente