Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches

Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach

The COVID-19 pandemic has created a global health crisis, driving the need for the rapid identification of potential therapeutics. In this study, we used the Zinc database to screen the world-approved including FDA-approved 5903 drugs for repurposing as potential COVID-19 treatments targeting the main protease 3CL of SARS-CoV-2. We performed molecular docking using Autodock-Vina to check the efficacy of drug molecules. To enhance the efficiency of drug repurposing approach, we modeled the binding affinities using several machine learning regression approaches for QSAR modeling such as decision tree, extra trees, MLP, KNN, XGBoost, and gradient boosting. The computational results demonstrated that Decision Tree Regression (DTR) model has improved statistical measures of R2 and RMSE. These simulated results helped to identify drugs with high binding affinity and favorable binding energies. From the statistical analysis, we shortlisted 13 promising drugs with their respective Zinc IDs (ZINC000003873365, ZINC000085432544, ZINC000203757351, ZINC000085536956, ZINC000085536990, ZINC000008214470, ZINC000261494640, ZINC000169344691, ZINC000094303244, ZINC000095618608, ZINC000095618689, ZINC000095618743, and ZINC000253684767) within the range of -15.1 kcal/mol to -12.7 kcal/mol. Further, we analyzed the physiochemical properties of these selected drugs with respect to their best binding interaction to specific target protease. Our study has provided an efficient framework for drug repurposing against COVID-19. This highlights the potential of combining molecular docking with machine learning regression approaches to accelerate the identification of potential therapeutic candidates.Comment: 30 Page

ARTIFICIAL INTELLIGENCE IN TACKLING CORONAVIRUS AND FUTURE PANDEMICS

SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) was initially tested in Wuhan City, China, in December 2019 and had a devastating impact worldwide, exterminating more than 6 million people as of September 2022. It became the biggest worldwide health crisis since the 1918 influenza outbreak. Viruses generally mutate randomly, so predicting how SARS-CoV-2 will transform over the next few months or years and which forms will predominate is impossible. The possibilities for virus mutation, in theory, are practically endless. Enabling researchers to determine which antibodies have the potential to be most effective against existing and future variations could help machine learning to assist in drug discovery. In the COVID-19 pandemic, AI has benefited four key areas: diagnosis, clinical decision-making for public health, virtual assistance, and therapeutic research. This study conducted a discourse analysis and textual evaluation of AI (deep learning and machine learning) concerning the COVID-19 outbreak. Further, this study also discusses the latest inventions that can be very helpful in future pandemic detection. COVID-19 has already changed our lives, and in the future, we might be able to deal with pandemics like this with the help of AI. This review has also emphasized the legal implications of AI in the battle against COVID-19

An update on novel approaches for diagnosis and treatment of SARS-CoV-2 infection

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health and economic crisis worldwide which united global efforts to develop rapid, precise, and cost-efficient diagnostics, vaccines, and therapeutics. Numerous multi-disciplinary studies and techniques have been designed to investigate and develop various approaches to help frontline health workers, policymakers, and populations to overcome the disease. While these techniques have been reviewed within individual disciplines, it is now timely to provide a cross-disciplinary overview of novel diagnostic and therapeutic approaches summarizing complementary efforts across multiple fields of research and technology. Accordingly, we reviewed and summarized various advanced novel approaches used for diagnosis and treatment of COVID-19 to help researchers across diverse disciplines on their prioritization of resources for research and development and to give them better a picture of the latest techniques. These include artificial intelligence, nano-based, CRISPR-based, and mass spectrometry technologies as well as neutralizing factors and traditional medicines. We also reviewed new approaches for vaccine development and developed a dashboard to provide frequent updates on the current and future approved vaccines

Drug repositioning: current scenario and future prospective for rewriting saga of drug development

Drug development is a process that demands huge investment of resources and time with only 1 drug candidate successful in reaching market among 10,000 screened taking time duration of 10-15 years and millions of dollars. This high attrition rates discourage investors and researchers. The pharmaceutical industry is shifting its attention away from de novo drug research and towards discovering novel targets and indications for already-approved drugs. In order to accelerate the drug development process with reduced risk of failure and relatively lower costs, pharmaceutical companies have adopted drug repositioning as an alternative. Therefore, a good strategy for drug development would be drug repositioning or drug repurposing, which is to identify, investigate, and exploit new therapeutic uses of already-available, on-market drugs, as well as those that have been withdrawn due to toxicities or that remain on shelves in various stages of development. The outbreak of SARS-COV-19 shows that humanity is constantly vulnerable to epidemics and new microbial attacks and that there is no time to create disease-specific therapies. Consequently, it would seem advantageous to use what is already accessible. Novel therapeutic indications that have previously been approved by the market can reduce investment costs significantly in terms of money, resources, and most importantly, time, as long as they meet PKPD and toxicity standards. Sponsors and pharmaceutical corporations get enthusiastic about additional investments and initiatives related to drug development as a consequence. The upcoming therapeutic revolution, especially with the aid of artificial intelligence, is indicated by the successful applications of several already-available drugs against COVID-19 and the various phases of repurposed drugs against TB, colorectal cancer, Alzheimer’s disease, cervical cancer, and Parkinsonism