Investigation on nanoparticle based combination therapy for targeted cancer treatment

“The current treatment methods in cancer are associated with toxicity in healthy tissues, partial therapeutic response, drug resistance and finally recurrence of the disease. The cancer drugs are challenged by non-specific binding, undesired toxicity in healthy cells, low therapeutic index and finally poor therapeutic outcome. In this work, a targeted nanoscale therapeutic system Antibody Drug Nanoparticle (ADN) was engineered to selectively inhibit the breast cancer cell growth with reduced toxicity in healthy cells. The ADNs were designed by synthesizing rod shaped anoparticles using pure chemotherapeutic drug and covalently conjugating a therapeutic monoclonal antibody (mAb) on the surface of the drug nanorods. The rod shaped nanosized formulation of ADNs significantly enhanced the aqueous phase stability and therapeutic payload of the system while the conjugated mAb was utilized for specific targeting of breast cancer cells. The designed ADN was effective for active targeting and synergistic inhibition of breast cancer cells. The mechanisms of actions of ADN was investigated at the cellular, molecular and genetic levels in cancer cells. The engineered AND synergistically inhibited the growth of \u3e 80% of the human epidermal growth factor receptor 2 (HER2) - positive breast cancer cells in vitro. The cell cycle and protein expression analysis showed that ADN arrested the cellular growth for a prolonged time and induced a programmed cell death mechanism in HER2-positive breast cancer cells in vitro. Finally, the gene regulatory analysis showed the genetic mechanisms of programmed cell death regulation induced by ADN in breast cancer cell lines”--Abstract, page iv

Coupled Thermal-Hydraulic-Mechanical (THM) modelling of underground gas storage – A case study from the Molasse Basin, South Germany

Thermal-hydraulic-mechanical (THM) models of gas storage in porous media provide valuable information for various applications. The range of these applications varies from prediction of ground surface displacements, determination of stress path changes, and maximum reservoir pressure to storage capacity for maintaining fault stability and overburden integrity. The study, conducted in collaboration with research institutes and storage companies in Germany, addresses the numerical modelling of geomechanical effects caused by the storage of methane in a depleted gas field. The geomechanical assessment focuses on a former gas reservoir in the Bavarian Molasse Basin east of Munich, for which a hypothetical conversion into underground gas storage (UGS) is considered. The target reservoir is of Late Oligocene age, i.e., the Chattian Hauptsand with three gas bearing layers having a total thickness of 85 m. The reservoir formation is highly porous with an average porosity of 23% and permeability is in the range between 20 mD and 80 mD. The reservoir has produced natural gas from 1958 till 1978 and has been in a shut-in phase ever since. The storage operations require precise understanding of reservoir mechanics and stresses; therefore, the selected methodology helps to analyze these issues in detail. The geomechanical analysis is performed with the help of a state-of-the-art THM model with the following objectives: (1) analyze the variation of principal stress field induced by the field activities (2) analyze the effective stress changes with changing pore pressure in short-term as well as long-term using hypothetical injection-production schedule cases (3) prediction of ground surface displacements over the field, (4) analyze the possible reactivation of faults and fractures as well as the safe storage capacity of the reservoir; and (5) thermal stress changes with injection of colder foreign gas in underground reservoir. The methodology comprises 1D mechanical earth modelling (MEM) to calculate elastic properties as well as a first estimate for the vertical and horizontal stresses at well locations by using log data. This modelling phase provide complete analyses of log, core and laboratory data which leads to detailed 1D MEM of all the wells available for case study reservoir. This information is then used to populate a 3D finite element MEM) which has been built from seismic data and comprises not only the reservoir but the entire overburden up to the earth’s surface as well as part of the underburden. The size of this model is 30 × 24 × 5 km3 and 3D property modelling has been done by applying geostatistical approach for property inter-/extrapolation. The behavior of pore pressure in the field has been derived from dynamic fluid flow simulation through history matching for the production and subsequent shut-down phases of the field. Subsequently, changes in the pore pressure field during injection-production and subsequent shut-down phases are analyzed for weekly and seasonal loading and unloading scenario cases. The resulting pore pressure changes are coupled with 3D geomechanical model in order to have complete understanding of stress changes during these operations. In two scenario cases, the surplus electricity in Germany from renewable energy sources such as solar and wind from the year 2017 is considered. It results that the German surplus electricity can be stored in underground gas storage facilities with a Power-to-Gas (PtG) concept and that the stored gas can be reused again. Additionally, fault reactivation and thermal stress analyses are also performed on THM model in order to evaluate maximum threshold (injection) pressure as well as safe storage capacity of the reservoir. The fault reactivation already occurs at 1.25 times the initial reservoir pressure which provides a safe storage rate of 100,000-150,000 m3/day in the case study reservoir. The validated THM model is ready to be used for analyzing new wells for future field development and testing further arbitrary injection-production schedules, among others. The methodology can be applied on to any UGS facility not only in German Molasse Basin but anywhere in the world

Antibody-Drug Nanoparticle Induces Synergistic Treatment Efficacies in HER2 Positive Breast Cancer Cells

Chemotherapeutic drugs suffer from non-specific binding, undesired toxicity, and poor blood circulation which contribute to poor therapeutic efficacy. In this study, antibody–drug nanoparticles (ADNs) are engineered by synthesizing pure anti-cancer drug nanorods (NRs) in the core of nanoparticles with a therapeutic monoclonal antibody, Trastuzumab on the surface of NRs for specific targeting and synergistic treatments of human epidermal growth factor receptor 2 (HER2) positive breast cancer cells. ADNs were designed by first synthesizing ~ 95 nm diameter × ~ 500 nm long paclitaxel (PTX) NRs using the nanoprecipitation method. The surface of PTXNRs was functionalized at 2′ OH nucleophilic site using carbonyldiimidazole and conjugated to TTZ through the lysine residue interaction forming PTXNR-TTZ conjugates (ADNs). The size, shape, and surface charge of ADNs were characterized using scanning electron microscopy (SEM), SEM, and zeta potential, respectively. Using fluorophore labeling and response surface analysis, the percentage conjugation efficiency was found \u3e 95% with a PTX to TTZ mass ratio of 4 (molar ratio ≈ 682). In vitro therapeutic efficiency of PTXNR-TTZ was evaluated in two HER2 positive breast cancer cell lines: BT-474 and SK-BR-3, and a HER2 negative MDA-MB-231 breast cancer cell using MTT assay. PTXNR-TTZ inhibited \u3e 80% of BT-474 and SK-BR-3 cells at a higher efficiency than individual PTX and TTZ treatments alone after 72 h. A combination index analysis indicated a synergistic combination of PTXNR-TTZ compared with the doses of single-drug treatment. Relatively lower cytotoxicity was observed in MCF-10A human breast epithelial cell control. The molecular mechanisms of PTXNR-TTZ were investigated using cell cycle and Western blot analyses. The cell cycle analysis showed PTXNR-TTZ arrested \u3e 80% of BT-474 breast cancer cells in the G2/M phase, while \u3e 70% of untreated cells were found in the G0/G1 phase indicating that G2/M arrest induced apoptosis. A similar percentage of G2/M arrested cells was found to induce caspase-dependent apoptosis in PTXNR-TTZ treated BT-474 cells as revealed using Western blot analysis. PTXNR-TTZ treated BT-474 cells showed ~ 1.3, 1.4, and 1.6-fold higher expressions of cleaved caspase-9, cytochrome C, and cleaved caspase-3, respectively than untreated cells, indicating up-regulation of caspase-dependent activation of apoptotic pathways. The PTXNR-TTZ ADN represents a novel nanoparticle design that holds promise for targeted and efficient anti-cancer therapy by selective targeting and cancer cell death via apoptosis and mitotic cell cycle arrest

Biopsy-Proven Anticoagulant-Related Nephropathy: A Case Report and Review of the Literature

Anticoagulant-related nephropathy is a type of acute kidney injury that may follow warfarin and other anticoagulants. Anticoagulant-related nephropathy has been shown to be associated with irreversible kidney injury and increased risk for morbidity. Accurate diagnosis and management remain to be challenging. We describe a case of a 62-year-old man with significant cardiac history who presented with impaired kidney function associated with supratherapeutic international normalized ratio. Kidney biopsy findings suggested anticoagulant-related nephropathy

Revisiting the Import Demand Function: A Comparative Analysis

This study attempts to revisit import demand function across three panels of frontier, emerging, and developed economy from 1980 to 2016. Long-run relationship exists among import demand, relative price, exchange rate, and real GDP in economy. Due to increase in real GDP, import demand responds positively across economies. It responds in same direction in short-run in frontier and emerging economies with relative price unlike that of long-run in same economies. However, it responds in same direction with relative price in developed economy. It moves in opposite direction with respect to movement in exchange rate of frontier economy unlike that of developed economy. Next, the behavior of import demand in short-run due to change in exchange rate varies from that of long-run in emerging economy. This study will help to predict the dynamics of import due to change in income level, relative price, and exchange rate at national and international level

Reversible and irreversible adsorption of bare and hybrid silica nanoparticles onto carbonate surface at reservoir condition

© 2020 Southwest Petroleum University Realistic implementation of nanofluids in subsurface projects including carbon geosequestration and enhanced oil recovery requires full understanding of nanoparticles (NPs) adsorption behaviour in the porous media. The physicochemical interactions between NPs and between the NP and the porous media grain surface control the adsorption behavior of NPs. This study investigates the reversible and irreversible adsorption of silica NPs onto oil-wet and water-wet carbonate surfaces at reservoir conditions. Each carbonate sample was treated with different concentrations of silica nanofluid to investigate NP adsorption in terms of nanoparticles initial size and hydrophobicity at different temperatures, and pressures. Aggregation behaviour and the reversibility of NP adsorption onto carbonate surfaces was measured using dynamic light scattering (DLS), scanning electron microscope (SEM) images, energy dispersive X-ray spectroscope (EDS), and atomic force microscope (AFM) measurement. Results show that the initial hydrophilicity of the NP and the carbonate rock surface can influence the NPs adsorption onto the rock surfaces. Typically, oppositely charged NP and rock surface are attracted to each other, forming a mono or multilayers of NPs on the rock. Operation conditions including pressure and temperature have shown minor influence on nano-treatment efficiency. Moreover, DLS measurement proved the impact of hydrophilicity on the stability and adsorption trend of NPs. This was also confirmed by SEM images. Further, AFM results indicated that a wide-ranging adsorption scenario of NPs on the carbonate surface exists. Similar results were obtained from the EDS measurements. This study thus gives the first insight into NPs adsorption onto carbonate surfaces at reservoirs conditions

Software Fault Localization Using N -gram Analysis

Abstract. A major portion of software development effort is spent in testing and debugging. Execution sequence collected in the testing phase can be a rich source of information for locating the fault in the program, but the exact execution sequence of a program, i.e., the actual order of execution of the statements in the program, is seldom used due to the huge volume. In this study, we apply data mining techniques on this data to reduce the debugging time by narrowing down the possible location of the fault. Our method applies N -gram analysis to rank the executable statements of a software by level of suspicion. We conducted three case studies to demonstrate the effectiveness of our proposed method. We also present comparison with other approaches, and illustrate the potential of our method

PolyRad -- Protection Against Free Radical Damage

The effects of elevated levels of radiation contribute to the instability of pharmaceutical formulations in space compared to those on earth. Existing technologies are ineffective at maintaining the therapeutic efficacies of drugs in space. Thus, there is an urgent need to develop novel space-hardy formulations for preserving the stability and efficacy of drug formulations. This work aims to develop a novel approach for the protection of space pharmaceutical drug molecules from the radiation-induced damage to help extend or at least preserve their structural integrity and potency. To achieve this, free radical scavenging antioxidant, Trolox was conjugated on the surface of poly-lactic-co-glycolic acid (PLGA) nanoparticles for the protection of a candidate drug, melatonin that is used as a sleep aid medication in International Space Station (ISS). Melatonin-PLGA-PLL-Trolox nanoparticle as named as PolyRad was synthesized employing single oil in water (o/w) emulsion solvent evaporation method. PolyRad is spherical in shape and has an average diameter of ~600 nm with a low polydispersity index of 0.2. PolyRad and free melatonin (control) were irradiated by UV light after being exposed to a strong oxidant, hydrogen peroxide (H2O2). Bare melatonin lost ~80% of the active structure of the drug following irradiation with UV light or treatment with H2O2. In contrast, PolyRad protected \u3e 80% of the active structure of melatonin. The ability of PolyRad to protect melatonin structure was also carried out using 0, 1, 5 and 10 Gy gamma radiation. Gamma irradiation showed \u3e 98% active structures of melatonin encapsulated in PolyRads. Drug release and effectiveness of melatonin using PolyRad were evaluated on human umbilical vein endothelial cells (HUVEC) in vitro. Non-irradiated PolyRad demonstrated maximum drug release of ~70% after 72 h, while UV-irradiated and H2O2-treated PolyRad showed a maximum drug release of ~85%. Cytotoxicity of melatonin was carried out using both live/dead and MTT assays. Melatonin, non-radiated PolyRad and irradiated PolyRad inhibited the viability of HUVEC in a dose-dependent manner. Cell viability of melatonin, PolyRad alone without melatonin (PolyRad carrier control), non-radiated PolyRad, and irradiated PolyRad were ~98, 87, 75 and 70%, respectively at a concentration ~ 0.01 mg/ ml (10 μg/ ml). Taken together, PolyRad nanoparticle provides an attractive formulation platform for preventing damage to pharmaceutical drugs in potential space mission applications

DRPO : A Deep Learning Technique for Drug Response Prediction in Oncology Cell Lines

With the invention of high-throughput screening technologies, innumerable drug sensitivity data for thousands of cancer cell lines and hundreds of compounds have been produced. Computational analysis of these data has opened a new horizon in the development of novel anti-cancer drugs. Previous deep-learning approaches to predict drug sensitivity showed drawbacks due to the casual integration of genomic features of cell lines and compound chemical features. The challenges addressed include the intricate interplay of diverse molecular features, interpretability of complex deep learning models, and the optimization of drug combinations for synergistic effects. Through the utilization of normalized discounted cumulative gain (NDCG) and root mean squared error (RMSE) as evaluation metrics, the models aim to concurrently assess the ranking quality of recommended drugs and the accuracy of predicted drug responses. The integration of the DRPO model into cancer drug response prediction not only tackles these challenges but also holds promise in facilitating more effective, personalized, and targeted cancer therapies. This paper proposes a new deep learning model, DRPO, for efficient integration of genomic and compound features in predicting the half maximal inhibitory concentrations (IC50). First, matrix factorization is used to map the drug and cell line into latent ’pharmacogenomic’ space with cell line-specific predicted drug responses. Using these drug responses, we next obtained the essential drugs using a Normalized Discounted Cumulative Gain (NDCG) score. Finally, the essential drugs and genomic features are integrated to predict drug sensitivity using a deep model. Experimental results with RMSE 0.39 and NDCG 0.98 scores on Genomics of drug sensitivity in cancer (GDSC1) datasets show that our proposed approach has outperformed the previous approaches, including DeepDSC, CaDRRes, and KMBF. These good results show great potential to use our new model to discover novel anti-cancer drugs for precision medicine