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.

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.

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.

High performance inkjet printed embedded electrochemical sensors for monitoring hypoxia in a gut bilayer microfluidic chip.

Sensing devices have shown tremendous potential for monitoring state-of-the-art organ chip devices. However, challenges like miniaturization while maintaining higher performance, longer operating times for continuous monitoring, and fabrication complexities limit their use. Herein simple, low-cost, and solution-processible inkjet dispenser printing of embedded electrochemical sensors for dissolved oxygen (DO) and reactive oxygen species (ROS) is proposed for monitoring developmental (initially normoxia) and induced hypoxia in a custom-developed gut bilayer microfluidic chip platform for 6 days. The DO sensors showed a high sensitivity of 31.1 nA L mg-1 with a limit of detection (LOD) of 0.67 mg L-1 within the 0-9 mg L-1 range, whereas the ROS sensor had a higher sensitivity of 1.44 nA µm-1 with a limit of detection of 1.7 µm within the 0-300 µm range. The dynamics of the barrier tight junctions are quantified with the help of an in-house developed trans-epithelial-endothelial electrical impedance (TEEI) sensor. Immunofluorescence staining was used to evaluate the expressions of HIF-1α and tight junction protein (TJP) ZO-1. This platform can also be used to enhance bioavailability assays, drug transport studies under an oxygen-controlled environment, and even other barrier organ models, as well as for various applications like toxicity testing, disease modeling and drug screening.

Wide-Range Humidity-Temperature Hybrid Flexible Sensor Based on Strontium Titanate and Poly 3,4 Ethylenedioxythiophene Polystyrene Sulfonate for Wearable 3D-Printed Mask Applications.

In this paper, we report a fast, linear wide-range hybrid flexible sensor based on a novel composite of strontium titanate (SrTiO3) and poly 3,4 ethylenedioxythiophene polystyrene sulfonate (PEDOT: PSS) as a sensing layer. Inter-digitate electrodes (IDEs) were printed for humidity monitoring (finger: 250 µm; spacing: 140 µm; length: 8 mm) whilst a meander-based pattern was printed for the temperature measurement (meander thickness: 180 µm; spacing: 400 µm) on each side of the PET substrate using silver ink. Moreover, active layers with different concentration ratios were coated on the electrodes using a spray coating technique. The as-developed sensor showed an excellent performance, with a humidity measurement range of (10-90% RH) and temperature measurement range of (25-90 °C) with a fast response (humidity: 5 s; temperature: 4.2 s) and recovery time (humidity: 8 s; temperature: 4.4 s). The reliability of the sensor during mechanical bending of up to 5.5 mm was validated with a reliable performance. The sensor was also used in real-world applications to measure human respiration. For this, a suggested sensor-based autonomous wireless node was included in a 3D-printed mask. The manufactured sensor was an excellent contender for wearable and environmental applications because of its exceptional performance, which allowed for the simultaneous measurement of both quantities by a single sensing device.

Extracellular Matrix Optimization for Enhanced Physiological Relevance in Hepatic Tissue-Chips.

The cellular microenvironment is influenced explicitly by the extracellular matrix (ECM), the main tissue support biomaterial, as a decisive factor for tissue growth patterns. The recent emergence of hepatic microphysiological systems (MPS) provide the basic physiological emulation of the human liver for drug screening. However, engineering microfluidic devices with standardized surface coatings of ECM may improve MPS-based organ-specific emulation for improved drug screening. The influence of surface coatings of different ECM types on tissue development needs to be optimized. Additionally, an intensity-based image processing tool and transepithelial electrical resistance (TEER) sensor may assist in the analysis of tissue formation capacity under the influence of different ECM types. The current study highlights the role of ECM coatings for improved tissue formation, implying the additional role of image processing and TEER sensors. We studied hepatic tissue formation under the influence of multiple concentrations of Matrigel, collagen, fibronectin, and poly-L-lysine. Based on experimental data, a mathematical model was developed, and ECM concentrations were validated for better tissue development. TEER sensor and image processing data were used to evaluate the development of a hepatic MPS for human liver physiology modeling. Image analysis data for tissue formation was further strengthened by metabolic quantification of albumin, urea, and cytochrome P450. Standardized ECM type for MPS may improve clinical relevance for modeling hepatic tissue microenvironment, and image processing possibly enhance the tissue analysis of the MPS.