Pre-clinical study of IRDye800CW-nimotuzumab formulation, stability, pharmacokinetics, and safety.

BACKGROUND: Epidermal growth factor receptor (EGFR) is a target for cancer therapy as it is overexpressed in a wide variety of cancers. Therapeutic antibodies that bind EGFR are being evaluated in clinical trials as imaging agents for positron emission tomography and image-guided surgery. However, some of these antibodies have safety concerns such as infusion reactions, limiting their use in imaging applications. Nimotuzumab is a therapeutic monoclonal antibody that is specific for EGFR and has been used as a therapy in a number of countries. METHODS: Formulation of IRDye800CW-nimotuzumab for a clinical trial application was prepared. The physical, chemical, and pharmaceutical properties were tested to develop the specifications to determine stability of the product. The acute and delayed toxicities were tested and IRDye800CW-nimotuzumab was determined to be non-toxic. Non-compartmental pharmacokinetics analysis was used to determine the half-life of IRDye800CW-nimotuzumab. RESULTS: IRDye800CW-nimotuzumab was determined to be non-toxic from the acute and delayed toxicity study. The half-life of IRDye800CW-nimotuzumab was determined to be 38 ± 1.5 h. A bi-exponential analysis was also used which gave a t1/2 alpha of 1.5 h and t1/2 beta of 40.8 h. CONCLUSIONS: Here, we show preclinical studies demonstrating that nimotuzumab conjugated to IRDye800CW is safe and does not exhibit toxicities commonly associated with EGFR targeting antibodies.

IFHDS: Intelligent Framework for Securing Healthcare BigData.

Big data has become one of the most imperative technologies for collecting, handling and analysing enormous volumes of data in a high-performance environment. Enterprise healthcare organizations needs high compute power for the large volume of sensitive data, as well as large storage for storing both data and results, preferably in the cloud. However, security and privacy of patient data have become a critical issue that restricts many healthcare services from using cloud services to their optimal level. Therefore, this issue has limited healthcare organizations from migrating patient data to a cloud storage, because the cloud operators have chance to access sensitive data without the owner's permission. This paper proposes an intelligent security system called Intelligent Framework for Healthcare Data Security (IFHDS). IFHDS enables to secure and process large-scale data using column-based approach with less impact on the performance of data processing. The intelligent framework intends masking personal data and to encrypt sensitive data only. The proposed IFHDS splits sensitive data into multiple parts according to sensitivity level, where each part is stored separately over distributed cloud storage. Splitting data based on sensitivity level prevents cloud provider to break complete record of data if succeeds to decrypt part of data. The experimental results confirm that the proposed system secure the sensitive patient data with an acceptable computation time compared to recent security approaches.

Correction to: IFHDS: Intelligent Framework for Securing Healthcare BigData.

The original version of this article unfortunately contained a mistake. All algorithms are missing in the online PDF version. The original version has been corrected.

Synthetic Modular Antibody Construction by Using the SpyTag/SpyCatcher Protein-Ligase System.

Efforts to engineer recombinant antibodies for specific diagnostic and therapy applications are time consuming and expensive, as each new recombinant antibody needs to be optimized for expression, stability, bio-distribution, and pharmacokinetics. We have developed a new way to construct recombinant antibody-like "devices" by using a bottom-up approach to build them from well-behaved discrete recombinant antibody domains or "parts". Studies on antibody structure and function have identified antibody constant and variable domains with specific functions that can be expressed in isolation. We used the SpyTag/SpyCatcher protein ligase to join these parts together, thereby creating devices with desired properties based on summed properties of parts and in configurations that cannot be obtained by using genetic engineering. This strategy will create optimized recombinant antibody devices at reduced costs and with shortened development times.

A Single-Framework Synthetic Antibody Library Containing a Combination of Canonical and Variable Complementarity-Determining Regions.

Synthetic antibody libraries have been used to generate antibodies with favorable biophysical and pharmacological properties. Here, we describe the design, construction, and validation of a phage-displayed antigen-binding fragment (Fab) library built on a modified trastuzumab framework with four fixed and two diversified complementarity-determining regions (CDRs). CDRs L1, L2, H1, and H2 were fixed to preserve the most commonly observed "canonical" CDR conformation preferred by the modified trastuzumab Fab framework. The library diversity was engineered within CDRs L3 and H3 by use of custom-designed trinucleotide phosphoramidite mixes and biased towards human antibody CDR sequences. The library contained ≈7.6 billion unique Fabs, and >95 % of the library correctly encoded both diversified CDR sequences. We used this library to conduct selections against the human epidermal growth factor receptor-3 extracellular domain (HER3-ECD) and compared the CDR diversity of the naïve library and the anti-HER3 selection pool by use of next-generation sequencing. The most commonly observed CDR combination isolated, named Her3-3, was overexpressed and purified in Fab and immunoglobulin G (IgG) formats. Fab HER3-3 bound to HER3-ECD with a KD value of 2.14 nm and recognized cell-surface HER3. Although HER3-3 IgG bound to cell-surface HER3, it did not inhibit the proliferation of HER3-positive cells. Near-infrared imaging showed that Fab HER3-3 selectively accumulated in a murine HER3-postive xenograft, thus providing a lead for the development of HER3 imaging probes.

Transcytosis-Targeting Peptide: A Conductor of Liposomal Nanoparticles through the Endothelial Cell Barrier.

The ultimate goal in the area of drug-delivery systems is the development of a nanoparticle that can penetrate the endothelial cell monolayer for the targeting of tissue parenchyma. In the present study, we identify a transcytosis-targeting peptide (TTP) that permits polyethyleneglycol (PEG)-modified liposomes (PEG-LPs) to penetrate through monolayers of brain-derived endothelial cells. These endothelial cells were layered on a gelatin nanofiber sheet, a nanofiber meshwork that allows the evaluation of transcellular transport of nanosized particles (ca. 100 nm). Systematic modification of the sequences results in the identification of the consensus sequence of TTP as L(R/K)QZZZL, where Z denotes hydrophilic amino acids (R/K/S and partially D). The TTP-modified liposomes are bound on the heparin sulfate proteoglycan, and are then taken up via lipid raft-mediated endocytosis. Subsequent intracellular imaging of the particles reveals a unique intracellular sorting of TTP-modified PEG liposomes (TTP-PEG-LPs); namely the TTP-LPs are not localized with the lysosomes, whereas this co-localization is dominant in the unmodified PEG liposomes (PEG-LPs). The in vivo endothelial penetration of liposomes in adipose tissue is conferred by the dual modification of the particles with TTP and tissue-targeting ligands. This technology promises innovations in intravenously available delivery system to tissue parenchyma.

Endocytosis of gene delivery vectors: from clathrin-dependent to lipid raft-mediated endocytosis.

The ideal nonviral vector delivers its nucleic acid cargo to a specific intracellular target. Vectors enter cells mainly through endocytosis and are distributed to various intracellular organelles. Recent advances in microscopy, lipidomics, and proteomics confirm that the cell membrane is composed of clusters of lipids, organized in the form of lipid raft domains, together with non-raft domains that comprise a generally disordered lipid milieu. The binding of a nonviral vector to either region can determine the pathway for its endocytic uptake and subsequent intracellular itinerary. Given this model of the cell membrane structure, endocytic pathways should be reclassified in relation to lipid rafts. In this review, we attempt to assess the currently recognized endocytic pathways in mammalian cells. The endocytic pathways are classified in relation to the membrane regions that make up the primary endocytic vesicles. This review covers the well-recognized clathrin-mediated endocytosis (CME), phagocytosis, and macropinocytosis in addition to the less addressed pathways that take place in lipid rafts. These include caveolae-mediated, flotillin-dependent, GTPase regulator associated with focal adhesion kinase-1 (GRAF1)-dependent, adenosine diphosphate-ribosylation factor 6 (Arf6)-dependent, and RhoA-dependent endocytic pathways. We summarize the regulators associated with each uptake pathway and methods for interfering with these regulators are discussed. The fate of endocytic vesicles resulting from each endocytic uptake pathway is highlighted.

EEG seizure detection: concepts, techniques, challenges, and future trends.

A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.

Evaluation of nimotuzumab Fab2 as an optical imaging agent in EGFR positive cancers.

Molecular-targeted imaging probes can be used with a variety of imaging modalities to detect diseased tissues and guide their removal. EGFR is a useful biomarker for a variety of cancers, because it is expressed at high levels relative to normal tissues. Previously, we showed the anti-EGFR antibody nimotuzumab can be used as a positron emission tomography and fluorescent imaging probe for EGFR positive cancers in mice. These imaging probes are currently in clinical trials for PET imaging and image-guided surgery, respectively. One issue with using antibody probes for imaging is their long circulation time and slow tissue penetration, which requires patients to wait a few days after injection before imaging or surgery, multiple visits and longer radiation exposure. Here, we generated a Fab2 fragment of nimotuzumab, by pepsin digestion and labeled it with IRDye800CW to evaluate its optical imaging properties. The Fab2 had faster tumor accumulation and clearance in mice relative to the nimotuzumab IgG. The fluorescent signal peaked at 2 h post injection and remained high until 6 h post injection. The properties of the Fab2 allow a higher signal to background to be obtained in a shorter time frame, reducing the wait time for imaging after probe infusion.

Effect of the anchor in polyethylene glycol-lipids on the transfection activity of PEGylated cationic liposomes encapsulating DNA.

The use of polyethylene glycol (PEG)-modified lipids (PEG-lipids) as a component of cationic liposomes impairs the cytoplasmic delivery of the encapsulated cargos by reducing endosomal escape. While this results in a loss of gene expression of encapsulated plasmid DNA, PEG-modification is useful in that it permits the formation of small, stabilized particles. In the present study, the dilemma associated with the use of PEG was overcome by modifying liposomes with stearylated INF7 (STR-INF7), a membrane fusion-independent destabilizer of endosomes, and substituting hydrophobic lipid-anchors in the PEG-lipid. The cationic liposomes modified with a series of PEG-lipids showed a drastically impaired transgene expression. However, the incorporation of STR-INF7 recovered the gene expression, and this was found to be mainly dependent on the type of PEG lipid-anchor used. Of note, the fold increase in transfection activity was highest in cholesterol-anchored PEG (>100-fold), whose enhanced endosomal escape was followed by imaging techniques. These data suggest that the structure of lipid-anchors in PEG affects the action of the peptides for inducing of endosomal escape.

Copyright protection of deep neural network models using digital watermarking: a comparative study.

Nowadays, deep learning achieves higher levels of accuracy than ever before. This evolution makes deep learning crucial for applications that care for safety, like self-driving cars and helps consumers to meet most of their expectations. Further, Deep Neural Networks (DNNs) are powerful approaches that employed to solve several issues. These issues include healthcare, advertising, marketing, computer vision, speech processing, natural language processing. The DNNs have marvelous progress in these different fields, but training such DNN models requires a lot of time, a vast amount of data and in most cases a lot of computational steps. Selling such pre-trained models is a profitable business model. But, sharing them without the owner permission is a serious threat. Unfortunately, once the models are sold, they can be easily copied and redistributed. This paper first presents a review of how digital watermarking technologies are really very helpful in the copyright protection of the DNNs. Then, a comparative study between the latest techniques is presented. Also, several optimizers are proposed to improve the accuracy against the fine-tuning attack. Finally, several experiments are performed with black-box settings using several optimizers and the results are compared with the SGD optimizer.

Imaging Immune Cells Using Fc Domain Probes in Mouse Cancer Xenograft Models.

Tracking immune responses is complex due to the mixture of cell types, variability in cell populations, and the dynamic environment. Tissue biopsies and blood analysis can identify infiltrating and circulating immune cells; however, due to the dynamic nature of the immune response, these are prone to sampling errors. Non-invasive targeted molecular imaging provides a method to monitor immune response, which has advantages of providing whole-body images, being non-invasive, and allowing longitudinal monitoring. Three non-specific Fc-containing proteins were labeled with near-infrared dye IRDye800CW and used as imaging probes to assess tumor-infiltrating immune cells in FaDu and A-431 xenograft models. We showed that Fc domains localize to tumors and are visible by fluorescent imaging. This tumor localization appears to be based on binding tumor-associated immune cells and some xenografts showed higher fluorescent signals than others. The Fc domain alone bound to different human immune cell types. The Fc domain can be a valuable research tool to study innate immune response.

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis.

The coronavirus is an irresistible virus that generally influences the respiratory framework. It has an effective impact on the global economy specifically, on the financial movement of stock markets. Recently, an accurate stock market prediction has been of great interest to investors. A sudden change in the stock movement due to COVID -19 appearance causes some problems for investors. From this point, we propose an efficient system that applies sentiment analysis of COVID-19 news and articles to extract the final impact of COVID-19 on the financial stock market. In this paper, we propose a stock market prediction system that extracts the stock movement with the COVID spread. It is important to predict the effect of these diseases on the economy to be ready for any disease change and protect our economy. In this paper, we apply sentimental analysis to stock news headlines to predict the daily future trend of stock in the COVID-19 period. Also, we use machine learning classifiers to predict the final impact of COVID-19 on some stocks such as TSLA, AMZ, and GOOG stock. For improving the performance and quality of future trend predictions, feature selection and spam tweet reduction are performed on the data sets. Finally, our proposed system is a hybrid system that applies text mining on social media data mining on the historical stock dataset to improve the whole prediction performance. The proposed system predicts stock movement for TSLA, AMZ, and GOOG with average prediction accuracy of 90%, 91.6%, and 92.3% respectively.

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals.

Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

Comparison between Hybrid, Reverse Hybrid, and Non-Bismuth Levofloxacin Quadruple Regimens for Helicobacter Pylori Infection in Egypt: A Randomized Trial.

BACKGROUND The prevalence of Helicobacter pylori (H. pylori) in developing countries is 50.8%, with the highest occurrence presented in Africa (79.1%). It increases the risk of chronic gastritis, peptic ulcer, cancer of the stomach, and lymphoma. The effect of standard treatment for H. pylori eradication is below 80%, and evaluation of alternative lines of treatment is needed. We aimed to compare the hybrid, reverse hybrid, and levofloxacin quadruple therapies as first-line therapy in Egypt. METHODS This was a randomized interventional trial done in the clinics affiliated with the Internal Medicine Department. 330 individuals were selected according to the inclusion criteria. They were divided into three groups: group 1 (110 subjects who received a reverse hybrid regimen), group 2 (110 subjects who received a hybrid regimen), and group 3 (110 subjects who received a non-bismuth levofloxacin quadruple regimen). RESULTS Group 3 had a significantly lower eradication rate of 82.7% versus 92.7% and 91.8% in groups 1 and 2, respectively. There were non-significant differences in the incidence rates of adverse events among the three groups. CONCLUSION Both the reverse hybrid and hybrid groups had good eradication rates in the Egyptian population, but non-bismuth levofloxacin quadruple therapy did not obtain a sufficient eradication rate.

Development and preclinical evaluation of cixutumumab drug conjugates in a model of insulin growth factor receptor I (IGF-1R) positive cancer.

Overexpression of insulin growth factor receptor type 1 (IGF-1R) is observed in many cancers. Antibody drug conjugates (ADCs) with PEGylated maytansine (PEG6-DM1) show promise in vitro. We developed PEG6-DM1 ADCs with low and high drug to antibody ratios (DAR) using an anti-IGF-1R antibody cixutumumab (IMC-A12). Conjugates with low (cixutumumab-PEG6-DM1-Low) and high (cixutumumab-PEG6-DM1-High) DAR as 3.4 and 7.2, respectively, were generated. QC was performed by UV spectrophotometry, HPLC, bioanalyzer, and biolayer-interferometry. We compared the in vitro binding and internalization rates of the ADCs in IGF-1R-positive MCF-7/Her18 cells. We radiolabeled the ADCs with 111In and used microSPECT/CT imaging and ex vivo biodistribution to understand their in vivo behavior in MCF-7/Her18 xenograft mice. The therapeutic potential of the ADC was studied in vitro and in mouse xenograft. Internalization rates of all ADCs was high and increased over 48 h and EC50 was in the low nanomolar range. MicroSPECT/CT imaging and ex vivo biodistribution showed significantly lower tumor uptake of 111In-cixutumumab-PEG6-DM1-High compared to 111In-cixutumumab-PEG6-DM1-Low and 111In-cixutumumab. Cixutumumab-PEG6-DM1-Low significantly prolonged the survival of mice bearing MCF-7/Her18 xenograft compared with cixutumumab, cixutumumab-PEG6-DM1-High, or the PBS control group. Cixutumumab-PEG6-DM1-Low ADC was more effective. The study highlights the potential utility of cixutumumab-ADCs as theranostics against IGF-1R positive cancers.

Near infrared imaging of epidermal growth factor receptor positive xenografts in mice with domain I/II specific antibody fragments.

Epidermal growth factor receptor (EGFR) is a transmembrane cell surface receptor that is frequently overexpressed and/or mutated in many cancers. Therapies targeting EGFR have poor outcomes due to the lack of reliable diagnostic tests to monitor EGFR. Current in vitro EGFR diagnostic methods are invasive, requiring biopsies, which limits tumor sampling and availability. EGFR molecular imaging provides non-invasive whole-body images capable of detecting primary tumors and metastases, which can be used to diagnose and monitor response to therapy. Methods: We evaluated properties of two anti-EGFR fragments, 8708 and 8709, as molecular-targeted imaging probes. 8708 and 8709 are anti-EGFR antigen binding fragments (Fabs) that recognize domain I/II of EGFR, which is distinct from epitopes recognized by current anti-EGFR therapeutic antibodies. We used complementarity determining region sequences from 8708 and 8709 Fabs to generate an anti-EGFR IgG and (scFv)2 and scFv-Fc antibody fragments. We expressed, purified, and labeled the IgG and fragments with IRDye800CW and used them to image EGFR-positive and -negative xenografts in CD-1 nude mice. 8709 scFv-Fc was also tested for competitive binding with the therapeutic anti-EGFR antibody nimotuzumab and for quantifying ratios of EGFR and EGFRvIII deletion mutant. Results: IRDye800CW-labeled 8708 (scFv)2 and 8709 scFv-Fc imaging probes showed high levels of accumulation and good retention in EGFR-positive xenografts, with peak accumulation occurring at 24 and 48 hours post injection, respectively. IRDye680RD-labeled 8709 scFv-Fc did not compete with IRDye800CW-labeled nimotuzumab for EGFR binding as assayed by flow cytometry using an EGFR-positive cell line. IRDye680RD-labeled 8709 scFv-Fc and IRDye800CW-labeled nimotuzumab used in combination were able to determine the ratio of cells expressing EGFR and a deletion mutant EGFRvIII. Conclusion: IRDye800CW-labeled 8708 (scFv)2 and 8709 scFv-Fc had desirable binding affinities, clearance times, and tumor accumulation to be used for imaging in combination with current EGFR targeted therapies. This study highlights the potential for using 8708 (scFv)2 and 8709 scFv-Fc as EGFR diagnostic and therapy monitoring tools.

Site-Specific Fluorescent Labeling of Antibodies and Diabodies Using SpyTag/SpyCatcher System for In Vivo Optical Imaging.

PURPOSE: Construction of antibody-based, molecular-targeted optical imaging probes requires the labeling of an antibody with a fluorophore. The most common method for doing this involves non-specifically conjugating a fluorophore to an antibody, resulting in poorly defined, heterogeneous imaging probes that often have suboptimal in vivo behavior. We tested a new strategy to site-specific label antibody-based imaging probes using the SpyCatcher/SpyTag protein ligase system. PROCEDURES: We used the SpyCatcher/SpyTag protein ligase system to site specifically label nimotuzumab, an anti-EGFR antibody and an anti-HER3 diabody. To prevent the labeling from interfering with antigen binding, we introduced the SpyTag and SpyCatcher at the C-terminus of the antibody and diabody, respectively. Expression and binding properties of the C-terminal antibody-SpyTag and diabody-SpyCatcher fusions were similar to the antibody and diabody, indicating that the SpyTag and SpyCatcher fusions were well tolerated at this position. Site-specific labeling of the antibody and diabody was performed in two steps. First, we labeled the SpyCatcher with IRDye800CW-Maleimide and the SpyTag with IRDye800CW-NHS. Second, we conjugated the IRDye800CW-SpyCatcher and the IRDye800CW-SpyTag to the antibody or diabody, respectively. We confirmed the affinity and specificity of the IRDye800CW-labeled imaging probes using biolayer interferometry and flow cytometry. We analyzed the in vivo biodistribution and tumor accumulation of the IRDye800CW-labeled nimotuzumab and anti-HER3 diabody in nude mice bearing xenografts that express EGFR and HER3, respectively. RESULTS: Expression and binding properties of the C-terminal antibody-SpyTag and diabody-SpyCatcher fusions were similar to the antibody and diabody, indicating that the SpyTag and SpyCatcher fusions were well tolerated at this position. We confirmed the affinity and specificity of the IRDye800CW-labeled imaging probes using biolayer interferometry and flow cytometry. We analyzed the in vivo biodistribution and tumor accumulation of the IRDye800CW-labeled nimotuzumab and anti-HER3 diabody in nude mice bearing xenografts that express EGFR and HER3, respectively. Site-specifically IRDye800CW-labeled imaging probes bound to their immobilized targets, cells expressing these targets, and selectively accumulated in xenografts. CONCLUSIONS: These results highlight the ease and utility of using the modular SpyTag/SpyCatcher protein ligase system for site-specific fluorescent labeling of protein-based imaging probes. Imaging probes labeled in this manner will be useful for optical imaging applications such as image-guided surgery and have broad application for other imaging modalities.

Therapeutic potential of nimotuzumab PEGylated-maytansine antibody drug conjugates against EGFR positive xenograft.

Nimotuzumab is a humanized anti-epidermal growth factor receptor I (EGFR) monoclonal antibody. We have developed antibody drug conjugates (ADCs) with nimotuzumab conjugated to PEGylated-maytansine (PEG6-DM1). We generated conjugates with low (nimotuzumab-PEG6-DM1-Low: DAR = 3.5) and high (nimotuzumab-PEG6-DM1-High: DAR = 7.3) drug to antibody ratios (DAR). Quality control was performed using UV spectrophotometry, size exclusion HPLC, bioanalyzer, biolayer interferometry (BLI), and flow cytometry in EGFR-positive DLD-1, MDA-MB-468 (high density EGFR), and HT-29 (very low EGFR density) cells. Control antibody drug conjugates were developed using a human anti-maltose binding protein (MBP) antibody. BLI showed that the binding of nimotuzumab-PEG6-DM1-Low and nimotuzumab-PEG6-DM1-High was slightly but significantly affected by conjugation of the drug (nimotuzumab KD 0.89 ± 0.02 nM < nimotuzumab-PEG6-DM1-Low KD 1.94 ± 0.02 nM < nimotuzumab-PEG6-DM1-High KD 3.75 ± 0.03 nM). In vitro cytotoxicity was determined following incubation of cells with the immunoconjugates and IC50 values were determined. Nimotuzumab-PEG6-DM1-Low and nimotuzumab-PEG6-DM1-High were used to treat EGFR positive KRAS mutant DLD-1 colorectal cancer xenograft. DLD-1 cells were transduced with a red fluorescent protein (iRFP702) to allow the use of near infrared imaging (NIR) for tumor response monitoring. In vitro potency correlated with the number of drugs on antibody, with nimotuzumab-PEG6-DM1-High showing higher activity than nimotuzumab-PEG6-DM1-Low. Three doses (15 mg/kg) of the ADCs prolonged the survival of DLD-1-iRFP-702 tumor bearing mice as monitored by NIR. Nimotuzumab-PEG6-DM1-Low resulted in 4/6 complete cure while nimotuzumab-PEG6-DM1-High resulted in 2/5 complete cure. The novel ADCs were very effective in a colorectal cancer model in vivo.

Elderly Kidney Transplant Recipients: Single-Center Experience in the Middle East.

OBJECTIVES: The number of renal transplants in elderly patients is increasing as age per se does not constitute a contraindication to transplant. We compared renal transplant outcomes in elderly recipients versus a group of middle-aged patients. MATERIALS AND METHODS: Our retrospective casecontrolled study compared elderly transplant recipients (n = 252; > 60 y old) with a matched cohort of younger adult recipients (n = 710; between 40 and 60 years old) who underwent renal transplant at the Hamed Al-Essa Organ Transplant Center of Kuwait between 2000 and 2014. Demographic characteristics, comorbidities, complications after transplant, and graft and patient outcomes were compared between groups. RESULTS: There were 252 elderly kidney transplant recipients (mean age of 65.5 ± 4.8 y; 59.52% males) and 710 younger adult patients (mean age of 49.3 ± 5.5 years; 61.4% males). Most donors were males in their thirties. Deceased donors predominated in the younger adult group, whereas living unrelated donors predominated in the elderly group (P < .05). Diabetes represented the most common cause of endstage kidney disease. Younger patients tended to receive heavier induction therapy but comparable maint enance immunosuppression. Posttransplant diabetes was higher in younger patients; however, there were more elderly patients with micro- and macroangiopathies (P < .05). No significant differences were shown between groups with regard to patient or graft survival (P > .05). This could be attributed to a significantly higher number of patients with cardiovascular risks, less rejection episodes, and higher number of malignancies in the elderly group (P < .05). CONCLUSIONS: Due to relatively less potent immunosuppression, elderly patients experienced lower rejection rates and better graft survival; however, patient survival was lower due to higher cardiovascular risk factors. Older patients should not be discouraged from living-donor renal transplant. Targeted research studies on protocols for the elderly are needed.