The current status and future prospects for therapeutic targeting of KEAP1-NRF2 and ß-TrCP-NRF2 interactions in cancer chemoresistance.

Drug resistance is one of the biggest challenges in cancer treatment and limits the potential to cure patients. In many tumors, sustained activation of the protein NRF2 makes tumor cells resistant to chemo- and radiotherapy. Thus, blocking inappropriate NRF2 activity in cancers has been shown to reduce resistance in models of the disease. There is a growing scientific interest in NRF2 inhibitors. However, the compounds developed so far are not target-specific and are associated with a high degree of toxicity, hampering clinical applications. Compounds that can enhance the binding of NRF2 to its ubiquitination-facilitating regulator proteins, either KEAP1 or ß-TrCP, have the potential to increase NRF2 degradation and may be of value as potential chemosensitising agents in cancer treatment. Approaches based on molecular glue-type mechanisms, in which ligands stabilise a ternary complex between a protein and its binding partner have shown to enhance ß-catenin degradation by stabilising its interaction with ß-TrCP. This strategy could be applied to rationally discover degradative ß-TrCP-NRF2 and KEAP1-NRF2 protein-protein interaction enhancers. We are proposing a novel approach to selectively suppress NRF2 activity in tumors. It is based on recent methodology and has the potential to be a promising new addition to the arsenal of anticancer agents.

Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on ß2 adrenoceptor.

G protein-coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over-represented. We computationally docked ~2700 known ß2AR ligands to multiple ß2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K972.68×67 , F194ECL2 , S2035.42×43 , S2045.43×44 , S2075.46×641 , H2966.58×58 , and K3057.32×31 . Meanwhile, the antagonist ligands made interactions with W2866.48×48 and Y3167.43×42 , both residues considered to be important in GPCR activation. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure-activity relationship that identifies small changes to the ligands that invert their pharmacological activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.

An FPGA-based design for power efficient low delay rate adaptive pacemaker using accelerometer and heart rate sensor.

This paper presents a power efficient, low delay and rate adaptive dual chamber pacemaker (PLRDPM) using heart rate and accelerometer sensor. In recent years, number of modifications have been done in the pacemaker design. However, design of an implantable device on an open source is still challenging. Through this paper, we are proposing a "proof of concept" for the design of PLRDPM on FPGA for improving the vital parameters: delay and power consumption. The proposed PLRDPM comprises of accelerometer and heart rate sensors to measure physical activity's effect on heart rate of the bradycardia patients. A rate adaptive pacing algorithm has been designed using two sensor's data, to reduce the delay and power consumption. However, delay in the responses of various components in the circuitry produces an accumulative delay effect in any practical circuit. The delay and the power consumption for the proposed PLRDPM are found to be 2.82 ns and 9 mW, respectively.

Low delay rate adaptive pacemaker using FPGA embedded piezoelectric sensor.

This paper presents the hardware implementation of low delay, power-efficient, rate-adaptive dual-chamber pacemaker (RDPM) using a piezoelectric sensor. Rate adaptive pacemaker has the ability to sense the patient's activity by means of some special sensors and it controls the pacing rate according to the patient's activity. Ideally, there should be no delay between sensing and the subsequent pacing operation performed by the pacemaker. However, delay in the responses of various components in the circuitry produces an accumulative delay effect in any practical circuit. Physical activity and the physiological needs of the patient can be easily adapted by the rate-responsive pacemakers using a wide range of sensor information. The piezo-electric sensor recognises the pressure on human muscles because of physical activity and converts it to an electrical signal, which is received by the pulse generator of the pacemaker. When the patient is in the rest mode, the heart rate is the only parameter that is to be detected by the pacemaker. Thus, the heart rate and the physical activity both are the inevitable parameters for the design of RDPM. Performance analysis of the proposed RDPM shows a significant reduction in the delay between sensing and pacing. Device utility analysis shows that the proposed design not only requires lesser memory but also reduces the number of components on the chip. Therefore, it becomes very clear that the proposed pacemaker design will consume much lesser power.