Integrated ML-Based Strategy Identifies Drug Repurposing for Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) affects an estimated global population of around 3 million individuals. IPF is a medical condition with an unknown cause characterized by the formation of scar tissue in the lungs, leading to progressive respiratory disease. Currently, there are only two FDA-approved small molecule drugs specifically for the treatment of IPF and this has created a demand for the rapid development of drugs for IPF treatment. Moreover, denovo drug development is time and cost-intensive with less than a 10% success rate. Drug repurposing currently is the most feasible option for rapidly making the drugs to market for a rare and sporadic disease. Normally, the repurposing of drugs begins with a screening of FDA-approved drugs using computational tools, which results in a low hit rate. Here, an integrated machine learning-based drug repurposing strategy is developed to significantly reduce the false positive outcomes by introducing the predock machine-learning-based predictions followed by literature and GSEA-assisted validation and drug pathway prediction. The developed strategy is deployed to 1480 FDA-approved drugs and to drugs currently in a clinical trial for IPF to screen them against "TGFB1", "TGFB2", "PDGFR-a", "SMAD-2/3", "FGF-2", and more proteins resulting in 247 total and 27 potentially repurposable drugs. The literature and GSEA validation suggested that 72 of 247 (29.14%) drugs have been tried for IPF, 13 of 247 (5.2%) drugs have already been used for lung fibrosis, and 20 of 247 (8%) drugs have been tested for other fibrotic conditions such as cystic fibrosis and renal fibrosis. Pathway prediction of the remaining 142 drugs was carried out resulting in 118 distinct pathways. Furthermore, the analysis revealed that 29 of 118 pathways were directly or indirectly involved in IPF and 11 of 29 pathways were directly involved. Moreover, 15 potential drug combinations are suggested for showing a strong synergistic effect in IPF. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating IPF and other related conditions.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer.

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

Network-based drug repurposing for HPV-associated cervical cancer.

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis.

Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney disease (CKD) and end-stage renal failure. Despite extensive research, there have been many clinical trial failures, and there is currently no effective treatment to cure renal fibrosis. This demonstrates the necessity of more effective therapies and better preclinical models to screen potential drugs for TIF. In this study, we investigated the antifibrotic effect of the machine learning-based repurposed drug, lubiprostone, validated through an advanced proximal tubule on a chip system and in vivo UUO mice model. Lubiprostone significantly downregulated TIF biomarkers including connective tissue growth factor (CTGF), extracellular matrix deposition (Fibronectin and collagen), transforming growth factor (TGF-ß) downstream signaling markers especially, Smad-2/3, matrix metalloproteinase (MMP2/9), plasminogen activator inhibitor-1 (PAI-1), EMT and JAK/STAT-3 pathway expression in the proximal tubule on a chip model and UUO model compared to the conventional 2D culture. These findings suggest that the proximal tubule on a chip model is a more physiologically relevant model for studying and identifying potential biomarkers for fibrosis compared to conventional in vitro 2D culture and alternative of an animal model. In conclusion, the high throughput Proximal tubule-on-chip system shows improved in vivo-like function and indicates the potential utility for renal fibrosis drug screening. Additionally, repurposed Lubiprostone shows an effective potency to treat TIF via inhibiting 3 major profibrotic signaling pathways such as TGFß/Smad, JAK/STAT, and epithelial-mesenchymal transition (EMT), and restores kidney function.

Functionalized graphene oxide-based lamellar membranes for organic solvent nanofiltration applications.

In this study, two-dimensional graphene oxide-based novel membranes were fabricated by modifying the surface of graphene oxide nanosheets with six-armed poly(ethylene glycol) (PEG) at room conditions. The as-modified PEGylated graphene oxide (PGO) membranes with unique layered structures and large interlayer spacing (∼1.12 nm) were utilized for organic solvent nanofiltration applications. The as-prepared 350 nm-thick PGO membrane offers a superior separation (>99%) against evans blue, methylene blue and rhodamine B dyes along with high methanol permeance ∼ 155 ± 10 L m-2 h-1, which is 10-100 times high compared to pristine GO membranes. Additionally, these membranes are stable for up to 20 days in organic solvent. Hence the results suggested that the as-synthesized PGO membranes with superior separation efficiency for dye molecules in organic solvent can be used in future for organic solvent nanofiltration application.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing.

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

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.

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles.

Psoriasis is a skin disease which results in scales on the skin caused by flaky patches. Psoriasis is triggered by various conditions such as drug reactions, trauma, and skin infection etc. Globally, there are 125 million people affected by psoriasis and yet there is no effective treatment available, and it emphasizes the need for discovery of efficacious treatments. De-novo drug development takes 10-17 years and $2-$3 billion of investment with <10 % success rate to bring drug from concept to a market ready product. A possible alternative is drug repurposing, which aims at finding other indications of already approved drugs. In this study, a computational drug repurposing framework is developed and applied to differential gene expressions of Psoriasis targets obtained from the publicly available database (GEO). This strategy uses the gene expression signatures of the Psoriasis and compares it with perturbagen available in the CMap. Based on the connected signature drugs are ranked which could possibly reverse the signatures to stop the psoriasis. The drugs with most negative connectivity scores are ranked efficient and vice versa. The top hit drugs are verified using the literature survey of the peer reviewed journal, electronic health records, patents, and hospital database. As a result, 50/150 and 37/150 drugs are confirmed to have anti-psoriasis efficacy in two datasets. Top 10 drugs are suggested as potential repurposable drugs for psoriasis. This study offers, a powerful yet simple approach for rapid identification of potential drug repurposing candidates in Psoriasis and any disease of interest.

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.

Renal Hypoxic Reperfusion Injury-on-Chip Model for Studying Combinational Vitamin Therapy.

Renal ischemic-reperfusion injury decreases the chances of long-term kidney graft survival and may lead to the loss of a transplanted kidney. During organ excision, the cycle of warm ischemia from the donor and cold ischemia is due to storage in a cold medium after revascularization following organ transplantation. The reperfusion of the kidney graft activates several pathways that generate reactive oxygen species, forming a hypoxic-reperfusion injury. Animal models are generally used to model and investigate renal hypoxic-reperfusion injury. However, these models face ethical concerns and present a lack of robustness and intraspecies genetic variations, among other limitations. We introduce a microfluidics-based renal hypoxic-reperfusion (RHR) injury-on-chip model to overcome current limitations. Primary human renal proximal tubular epithelial cells and primary human endothelial cells were cultured on the apical and basal sides of a porous membrane. Hypoxic and normoxic cell culture media were used to create the RHR injury-on-chip model. The disease model was validated by estimating various specific hypoxic biomarkers of RHR. Furthermore, retinol, ascorbic acid, and combinational doses were tested to devise a therapeutic solution for RHR. We found that combinational vitamin therapy can decrease the chances of RHR injury. The proposed RHR injury-on-chip model can serve as an alternative to animal testing for injury investigation and the identification of new therapies.

Silicone Elastomer Composites Fabricated with MgO and MgO-Multi-Wall Carbon Nanotubes with Improved Thermal Conductivity.

The effect of multiwall carbon nanotubes (MWCNTs) and magnesium oxide (MgO) on the thermal conductivity of MWCNTs and MgO-reinforced silicone rubber was studied. The increment of thermal conductivity was found to be linear with respect to increased loading of MgO. In order to improve the thermal transportation of phonons 0.3 wt % and 0.5 wt % of MWCNTs were added as filler to MgO-reinforced silicone rubber. The MWCNTs were functionalized by hydrogen peroxide (H2O2) to activate organic groups onto the surface of MWCNTs. These functional groups improved the compatibility and adhesion and act as bridging agents between MWCNTs and silicone elastomer, resulting in the formation of active conductive pathways between MgO and MWCNTs in the silicone elastomer. The surface functionalization was confirmed with XRD and FTIR spectroscopy. Raman spectroscopy confirms the pristine structure of MWCNTs after oxidation with H2O2. The thermal conductivity is improved to 1 W/m·K with the addition of 20 vol% with 0.5 wt % of MWCNTs, which is an ~8-fold increment in comparison to neat elastomer. Improved thermal conductive properties of MgO-MWCNTs elastomer composite will be a potential replacement for conventional thermal interface materials.