Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening-based approaches.

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided.

A comprehensive review of key factors affecting the efficacy of antibody drug conjugate.

Antibody Drug Conjugate (ADC) is an emerging technology to overcome the limitations of chemotherapy by selectively targeting the cancer cells. ADC binds with an antigen, specifically over expressed on the surface of cancer cells, results decrease in bystander effect and increase in therapeutic index. The potency of an ideal ADC is entirely depending on several physicochemical factors such as site of conjugation, molecular weight, linker length, Steric hinderance, half-life, conjugation method, binding energy and so on. Inspite of the fact that there is more than 100 of ADCs are in clinical trial only 14 ADCs are approved by FDA for clinical use. However, to design an ideal ADC is still challenging and there is much more to be done. Here in this review, we have discussed the key components along with their significant role or contribution towards the efficacy of an ADC. Moreover, we also explained about the recent advancement in the conjugation method. Additionally, we spotlit the mode of action of an ADC, recent challenges, and future perspective regarding ADC. The profound knowledge regarding key components and their properties will help in the synthesis or production of different engineered ADCs. Therefore, contributes to develop an ADC with low safety concern and high therapeutic index. We hope this review will improve the understanding and encourage the practicing of research in anticancer ADCs development.

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Giant Meckel's diverticulum associated with a congenital diaphragmatic hernia.

Giant Meckel's diverticulum is a very rare lesion and its association with a congenital diaphragmatic hernia has not been reported previously. We report a case of newborn with a giant Meckel's diverticulum and congenital diaphragmatic hernia. A large round atypical air-filled bowel segment was found by chest radiography preoperatively, and a giant Meckel's diverticulum was located within the left hemithorax during surgery.