Emerging trends and therapeutic applications of monoclonal antibodies.

Monoclonal antibodies (mAbs) are being used to prevent, detect, and treat a broad spectrum of malignancies and infectious and autoimmune diseases. Over the past few years, the market for mAbs has grown exponentially. They have become a significant part of many pharmaceutical product lines, and more than 250 therapeutic mAbs are undergoing clinical trials. Ever since the advent of hybridoma technology, antibody-based therapeutics were realized using murine antibodies which further progressed into humanized and fully human antibodies, reducing the risk of immunogenicity. Some of the benefits of using mAbs over conventional drugs include a drastic reduction in the chances of adverse reactions, interactions between drugs, and targeting specific proteins. While antibodies are very efficient, their higher production costs impede the process of commercialization. However, their cost factor has been improved by developing biosimilar antibodies, which are affordable versions of therapeutic antibodies. Along with biosimilars, innovations in antibody engineering have helped to design bio-better antibodies with improved efficacy than the conventional ones. These novel mAb-based therapeutics are set to revolutionize existing drug therapies targeting a wide spectrum of diseases, thereby meeting several unmet medical needs. In the future, mAbs generated by applying next-generation sequencing (NGS) are expected to become a powerful tool in clinical therapeutics. This article describes the methods of mAb production, pre-clinical and clinical development of mAbs, approved indications targeted by mAbs, and novel developments in the field of mAb research.

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer.

Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5-10.5%) was high when compared to the number of instances, precision (2.3-9.5%) was high when compared to the number of instances, sensitivity (2.4-12.5%) was high when compared to several instances, the F-score (2-30%) was high when compared to the number of cases, the error rate (0.7-11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets.

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

Synthesis, in vitro and structural aspects of cap substituted Suberoylanilide hydroxamic acid analogs as potential inducers of apoptosis in Glioblastoma cancer cells via HDAC /microRNA regulation.

Glioblastoma multiforme (GBM) is a heterogeneous, aggressive brain cancer characterized by chemo-resistance and cancer stemness. Histone deacetylases (HDACs) are a group of enzymes that regulate chromatin epigenetics which were in turn found to be controlled by microRNAs (miRs). The drug employed in chemotherapy for the treatment of GBM is Temozolomide (TMZ). Unfortunately, many GBM patients exhibit chemo-resistance to this drug. Here we have synthesized various Suberoyl anilide hydroxamic acid (SAHA) analogs with many substitutions at the cap site majority of which not yet studied. These SAHA analogs have exhibited profound cytotoxicity at 2 µM, and 4 µM concentrations in GBM cancer cell line U87MG, and 1 µM, and 2 µM concentrations in breast cancer cell line MCF-7. Surprisingly, these analogs have exhibited cytotoxic effects in chronic lymphoid leukemia cells (Raji) at 64 µM, and 128 µM concentrations due to mutated p53. Among all the synthesized analogs 3-Chloro-SAHA, 3-Chloro-4-fluoro SAHA have exhibited effective cytotoxicity in all cancer cells. These potent analogs inhibited HDAC-8 enzyme activity by 2-folds in U87MG, and MCF-7 cell lines and 7-folds decrease in HDAC-8 activity was observed in Raji cell line. These analogs decreased the expression of HDAC-2, HDAC-3 genes and enhanced the expression of p53 tumor suppressor. Interestingly, these compounds decreased the expression of Rictor, the main component of the mTORC2 complex involved cancer cell metabolism. Furthermore, these molecules have decreased oncogenic microRNA expression such as miR-21 and enhanced the expression of tumor suppressor microRNAs such as miR-143. The HDAC binding ability of these molecules was highly significant and have exhibited the ability to cross blood-brain barrier (BBB), and followed the Lipinski rule of five. Thus, these molecules need to be taken up further to clinics for better therapy against GBM either singly or combination therapy.

Epigenetic modulation and understanding of HDAC inhibitors in cancer therapy.

The role of genetic and epigenetic factors in tumor initiation and progression is well documented. Histone deacetylases (HDACs), histone methyl transferases (HMTs), and DNA methyl transferases. (DNMTs) are the main proteins that are involved in regulating the chromatin conformation. Among these, histone deacetylases (HDAC) deacetylate the histone and induce gene repression thereby leading to cancer. In contrast, histone acetyl transferases (HATs) that include GCN5, p300/CBP, PCAF, Tip 60 acetylate the histones. HDAC inhibitors are potent drug molecules that can induce acetylation of histones at lysine residues and induce open chromatin conformation at tumor suppressor gene loci and thus resulting in tumor suppression. The key processes regulated by HDAC inhibitors include cell-cycle arrest, chemo-sensitization, apoptosis induction, upregulation of tumor suppressors. Even though FDA approved drugs are confined mainly to haematological malignancies, the research on HDAC inhibitors in glioblastoma multiforme and triple negative breast cancer (TNBC) are providing positive results. Thus, several combinations of HDAC inhibitors along with DNA methyl transferase inhibitors and histone methyl transferase inhibitors are in clinical trials. This review focuses on how HDAC inhibitors regulate the expression of coding and non-coding genes with specific emphasis on their anti-cancer potential.