Research Title: Smart transdermal patch for monitoring of type 2 diabetes

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: Smart transdermal patch for monitoring of type 2 diabetes

Keywords: Smart transdermal patch for monitoring of type 2 diabetes

Research Title: Computational design of a novel inhibitor against COVID

Author: Balakumar Chandrasekarn, Published Year: 2025

Faculty: Pharmacy

Abstract: Background: In recent years, in silico computational approaches have tremendously guided computational medicinal chemists and research scientists to analyze protein structures, kinetics, functions, and molecular interactions of the administered drugs. Objective: This study aimed to identify a novel inhibitor against SARS-CoV-2 using human CD26 and modeled spike protein through suitable in silico approaches. Methods: In this work, molecular docking and molecular dynamics simulation experiments were conducted to gain insights into the binding affinity and stability, respectively. The docked complex of CD26 with modeled spike protein showed higher binding affinity than the complex of CD26 with resolved spike protein due to the existence of strong interactions with the crucial amino acid residues of the target proteins. Results: The results of the molecular dynamics simulation demonstrated that CD26 with the modeled spike protein docked complex showed good stability when compared with the resolved protein. Conclusion: From this computational finding, it was also suggested that the structure was stable and would rapidly guide the discovery of potential inhibitors against COVID-19.

Keywords: Computational design, molecular dynamics simulation, molecular docking, CD26, COVID

Research Title: Computational Pharmaceutics

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: The conventional ways employed in the preformulation, formulation, and in vivo studies are based on trial-and-error experiments, which are time consuming, expensive, and with limited productivity. This has paved the way for computational pharmaceutics, which is the application of computer to pharmaceutical drug delivery and modeling (computer simulation) in order to save energy, time, and costs in the development pharmaceutical drug delivery systems. Computational methods can help in forecasting a molecule’s structure and physicochemical attributes, simulates the motion of molecules and atoms, characterize the substances structurally, dynamically, and energetically, and examine the molecular mechanisms of molecular mechanics-based formulations. In this chapter, we give an overview about computational pharmaceutics, their history, applications, and developments.

Keywords: Computational Pharmaceutics

Research Title: Liposomal formulation for site-specific drug delivery in liver cirrhosis

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: Liposomal formulation for site-specific drug delivery in liver cirrhosis

Keywords: Liposomal formulation for site-specific drug delivery in liver cirrhosis

Research Title: AI-enabled device for Parkinson’s Disease

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: AI-enabled device for Parkinson’s Disease

Keywords: AI-enabled device for Parkinson’s Disease

Research Title: Computer-aided biopharmaceutical model development

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: Any medication that is derived from or partially synthesized from a naturally occurring biological source is referred to as a biopharmaceutical. The research and development of a new medicine is a complex process, and forecasting pharmacokinetics and drug targeting has proven difficult for scientists. During the discovery and development stages, particularly the early ones, it is crucial to understand the biopharmaceutical profile of drugs. A new drug’s introduction into the market is a labor-intensive procedure that is expensive in terms of both time and money. Drug development and discovery are said to take between 10 and 14 years and cost more than $1 billion. By offering a quick and inexpensive method to assess the bioperformance of medications, computational modeling has developed into a vital tool that accelerates the formulation development process. In-silico screening models have been widely used in the research and development of biopharmaceuticals, such as gastrointestinal absorption simulation, in silico computational modeling, monoclonal antibodies, predicting the corneal permeability of some medicinal agents, and the identification of potential compounds to treat diseases. The concepts and advancements of computer-aided biopharmaceuticals are briefly discussed in this chapter.

Keywords: Computer-aided biopharmaceutical model development

Research Title: Mechanisms of Antibacterial Drug Resistance

Author: Balakumar Chandrasekarn, Published Year: 2024

Faculty: Pharmacy

Abstract: Antibacterial resistance is an escalating worldwide public health challenge with substantial ramifications for global well-being. It has emerged as a critical global health concern. As defined by the World Health Organization (WHO), antimicrobial resistance (AMR) denotes the capacity of microorganisms to endure antimicrobial therapies. Resistance mechanisms involve a change in the target site, efflux pumps, PBP mutation, Porin modification, and enzymatic inhibition. Factors influencing antibacterial resistance encompass inappropriate antibiotic use and environmental and biological factors. The misuse and overuse of antibiotics, coupled with the adaptive capabilities of bacteria, contribute to the emergence of resistant strains. This complex issue demands a multifaceted approach that includes responsible antibiotic use, the development of new therapeutic strategies, and enhanced surveillance measures.

Keywords: Antibacterial Drug Resistance

Research Title: Sentiment Analysis of COV19 Impact on Education and Economy Sectors using Arabic Tweets

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: The coronavirus (COVID-19) infected millions of people around the world. Due to the disease's rapid spread, all governments have decided to implement lockdowns. The two key pillars of any nation, education and economy were negatively impacted. This study investigates public opinion on COVID-19 effects on these two key sector by collecting Arabic tweets from Twitter from the MENA region. Sentiment analysis was applied using the collected dataset for each sector separately. We created five cutting-edge deep learning models to apply them in the prediction process. Such models named Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), LSTM plus CNN, BERT, and AraBERT. The outcomes showed that in two datasets, the LSTM with CNN and LSTM provided the greatest performance results in the education data set, while BERT and AraBERT has the best performance in the economy dataset. The results presented that these algorithms created information that helped several government decision-makers make important and wise decisions on the standard of living in the Arab World Country.

Keywords: COVID-19 , Deep learning , Economics , Sentiment analysis , Social networking (online) , Biological system modeling , Education

Research Title: Sentiment Analysis of COV19 Impact on Education and Economy Sectors using Arabic Tweets

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: The coronavirus (COVID-19) infected millions of people around the world. Due to the disease's rapid spread, all governments have decided to implement lockdowns. The two key pillars of any nation, education and economy were negatively impacted. This study investigates public opinion on COVID-19 effects on these two key sector by collecting Arabic tweets from Twitter from the MENA region. Sentiment analysis was applied using the collected dataset for each sector separately. We created five cutting-edge deep learning models to apply them in the prediction process. Such models named Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), LSTM plus CNN, BERT, and AraBERT. The outcomes showed that in two datasets, the LSTM with CNN and LSTM provided the greatest performance results in the education data set, while BERT and AraBERT has the best performance in the economy dataset. The results presented that these algorithms created information that helped several government decision-makers make important and wise decisions on the standard of living in the Arab World Country.

Keywords: COVID-19 , Deep learning , Economics , Sentiment analysis , Social networking (online) , Biological system modeling , Education

Research Title: Predicting Bug Severity Using Machine Learning and Ensemble Learning Techniques

Author: Aya Adnan Oqla Miqdady, Published Year: 2023

2023 14th International Conference on Information and Communication Systems (ICICS),

Faculty: Information Technology

Abstract: Software quality can be adversely affected by various bugs in the system. Bug identification and fixing are part of every software development life cycle. However, in practice, large and long-lived systems may encounter an enormous number of bugs during their lifetime. Additionally, identifying and fixing a large number of software bugs requires a vast amount of allocated resources that may hinder the software budget. Thus, proper management including bug classification and prioritization is needed. Therefore, this paper employs modern Machine Learning techniques to help developers identify and classify bugs based on various factors including their severity level. Six different datasets are used to evaluate the proposed ML models. Features are extracted from bug descriptions using natural language processing techniques like TF-IDF. Evaluated models include K-Nearest Neighbors, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, and various ensemble learning methods. All candidate models are evaluated based on their accuracy for each of the used datasets. Results show that the Neural Networks model exceeds on all six datasets, achieving an accuracy that ranges from 93% to 95%. It also exceeds in other measures as well including precision, recall, and F1-Score. These results indicate that the proposed method performs satisfactorily when identifying bugs in source code.

Keywords: Support vector machines , Source coding , Computer bugs , Neural networks , Software quality , Feature extraction , Software systems