Structure-activity relationships on cynnamoyl derivatives as inhibitors of p300 Histone acetyltransferase

Human p300 is a polyhedric transcriptional coactivator, playing a crucial role by acetylating histones on specific lysine residues. A great deal of evidences shows that p300 is involved in several diseases as leukemia, tumors and viral infection. Its involvement in pleiotropic biological roles and connections to diseases provide the rationale as to how its modulation could represent an amenable drug target. Several p300 inhibitors (HATi) have been described so far, but all suffer from low potency, lack of specificity or low cell-permeability, highlighting the need to find more effective inhibitors. Our cinnamoyl derivative, RC 56, was identified as active and selective p300 inhibitor, proving to be a good hit candidate to investigate the structure-activity relationship towards p300. Herein we describe the design, synthesis and biological evaluation of new HATi structurally related to our hit, investigating, moreover, the interactions between p300 and the best-emerged hits by means of induced fit docking and molecular dynamics simulations, gaining insight on the peculiar chemical features that influenced their activity toward the targeted enzyme

Optimal Control of Multiple Transmission of Water-Borne Diseases

A controlled SIWR model was considered which was an extension of the simple SIR model by adjoining a compartment () that tracks the pathogen concentration in the water. New infections arise both through exposure to contaminated water as well as by the classical SIR person-person transmission pathway. The controls represent an immune boosting and pathogen suppressing drugs. The objective function is based on a combination of minimizing the number of infected individuals and the cost of the drugs dose. The optimal control is obtained by solving the optimality system which was composed of four nonlinear ODEs with initial conditions and four nonlinear adjoint ODEs with transversality conditions. The results were analysed and interpreted numerically using MATLAB

ENHANCING SECURITY IN IoT PLATFORM USING SECURE AUTHENTICATION PROTOCOL

In recent years IoT is becoming the trending technology which is playing a major role inbusiness, health care, military applications. Wireless communications are highly vulnerableto security threats as anything connected to internet are prone to cyberattacks and place forhackers. Various challenges in IoT are causing security threats and not ensuring End-to-Endencryption during transmission of information. Currently most IoT devices use default logincredentials and not secured with better configurations and protocols which paves way forcyberattacks. Advanced security standards cannot be employed for all IoT devices. Thispaper proposed a secure authentication protocol for the IoT platform for ensuring securityamong IoT devices which keeps track of security threats. An evaluation of the proposedprotocol is presented which proves that the protocol is able to address various securitythreats

Study of adverse drug reactions associated with antiepileptic drugs: a pharmacovigilance study using spontaneous reporting system

Background: More than 25 antiepileptic drugs (AEDs) are available in the Indian market to treat epilepsy of which many have similar efficacy but differ in their tolerability and are associated with many adverse drug reactions (ADRs). ADRs are one of the most common causes of death and clinical trials are not sufficient to uncover all the ADRs, hence post-marketing surveillance or pharmacovigilance is necessary. The aim of the study was to analyze the ADRs of AEDs by spontaneous reporting system under Pharmacovigilance Program of India (PvPI).Methods: Suspected ADR reporting forms provided by PvPI were used to collect the data from healthcare professionals of Madras Medical College and Rajiv Gandhi Government General Hospital, Chennai.Results: A total of 77 ADRs from 61 reports were analysed of which 34 were male and 27 were female patients and maximum were in the middle-aged adult group (N=44). Majority of the ADRs were related to skin and subcutaneous disorders (N=55) and most implicated ADR was found to be maculopapular rash (N=12) associated with phenytoin. Most of the ADRs were non-serious (N=42) and were probable category (N=45) as per WHO-UMC scale.Conclusions: Monitoring ADRs in patients using antiepileptic drugs is a matter of importance; hence a robust pharmacovigilance practice is essential

Contribution of Internet of Things: A Survey

Internet of Things plays an important role in our day to day life activities by engaging ourselves in implementing various technology Enabled embedded smart devices for our daily use. In this paper, we have discussed about the application uses of Iot along with other subject areas namely, Iot with cloud computing, big data, Internet of computer, data mining, embedded security, challenges in IOT-MD and Social impact of IOT. IOT together with subject area provides good enhancement by making things easier

Phase Transfer Catalyzed Synthesis of BIS (4-Chlorobenzyl) Sulfide Using Hydrogen Sulfide

The present research work is oriented towards a green technology that utilizes the environmentally hazardous chemicals like hydrogen sulfide (H2S) and to synthesize commercially important chemicals to overcome the disposal problems as well as to improve the economy of the process. This proposed work involves two stages: firstly, selective absorption of H2S in aqueous alkanolamine solution likes Methyl-diethanolamine (MDEA) and then the reaction of this H2S-rich MDEA with the organic compound. The overall objectives are to synthesize the aromatic thioether like bis(4-chlorobenzyl) sulfide using hydrogen sulfide rich aqueous MDEA solution and 4-chlorobenzyl chloride (CBC). 4-chlorobenzyl mercaptan (CBM) was identified from the reaction mixture as a secondary product. The biphasic reactions were performed in a batch reactor using a phosphonium based phase transfer catalyst, Ethyltriphenyl phosphonium bromide because of its thermal stability. In this system, we developed the alternative to the expensive Claus process for utilization of hydrogen sulfide to produce commercially significant value added chemicals. The role of various phases such as agitation speed, catalyst concentration, temperature variation, sulfide concentration, MDEA concentration and reactant concentration in enhancing the selectivity towards bis(4-chlorobenzyl) sulfide has been investigated. The apparent activation energy was found to be 11.28 kJ/mol that emphases the reaction to be a kinetically controlled reaction. The experiments show encouraging results with 87.57% conversion of reactant 4-chlorobenzyl chloride and 89.48% selectivity of the desired product bis(4-chlorobenzyl) sulfide

Improved half-maximal inhibitory concentration regression model using amyotrophic lateral sclerosis data

The current research addresses the critical need for precise half-maximal inhibitory concentration regression in the neurodegenerative condition amyotrophic lateral sclerosis (ALS). Unavailable drug-induced gene expressions and irrelevant molecular descriptors have yielded regression models with less accuracy using traditional machine learning (ML). Drugs can be converted to graph format and integrated with gene expressions to learn drug-gene interactions better thereby producing precise half-maximal inhibitory concentration regression models. To accomplish this, three variants of graph neural networks (GNN) namely graph attention networks (GAT), message passing neural networks, and graph isomorphism networks are utilized in the proposed work. The gene expression profiles of ALS drugrelated genes were retrieved from the DepMap PRISM drug repurposing hub, and the drug graphs with their accompanying half-maximal inhibitory concentration values were obtained from the ChEMBL databases. The graph is constructed for ninety approved drugs connected to 32 key protein targets of ALS and its related conditions. The half-maximal inhibitory concentration regression model trained with optimized hyperparameters in GAT performs well with an R2 score of 0.92, a mean absolute error (MAE) of 0.20, and a root mean square error (RMSE) of 0.17. This model produced better results than other ML and deep learning models