Using Computers to ESKAPE the Antibiotic Resistance Crisis

Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement

Increasing the effectiveness of SRI corporate engagement on climate change through a responsive regulation framework

Purpose: Socially responsible investment (SRI) engagement currently performs a variety of supportive regulatory functions such as reframing norms, establishing dialogue and providing resources to improve performance, however corporate responses are voluntary. This chapter will examine the potential gains in effectiveness for SRI engagement in a responsive regulatory regime.\ud \ud Approach: Global warming is a pressing environmental, social and governance (ESG) issue. By using the example of climate change the effectiveness of SRI engagement actors and the regulatory context can be considered. This chapter builds the conceptual framework for responsive regulation of climate change.\ud \ud Findings: SRI engagement may face resistance from corporations due to its voluntary nature and conflict with other goals. Legitimacy and accountability limit the effectiveness of SRI engagement functioning as a voluntary regulatory mechanism. This chapter argues that the effectiveness of SRI engagement on climate change could be enhanced if it served as part of a responsive regulation regime.\ud \ud Practical implications: Engagement is used by SRIs for ESG issues. A comprehensive regulatory regime could enhance corporate adaptation to climate change through increasing compliance with SRI engagement. The implication for SRI practitioners is that lobbying for a supportive regulatory regime has a large potential benefit.\ud \ud Social implications: Responsive regulatory policy involves both support and sanctions to improve compliance, enhancing policy efficiency and effectiveness. There are potentially large net social benefits from utilising SRI engagement in a regulatory regime.\ud \ud Originality of chapter: In seeking to re-articulate voluntary and legal approaches this research addresses a gap in the literature on climate change regulation

Genome-wide In Vivo CNS Screening Identifies Genes that Modify CNS Neuronal Survival and mHTT Toxicity

Unbiased in vivo genome-wide genetic screening is a powerful approach to elucidate new molecular mechanisms, but such screening has not been possible to perform in the mammalian central nervous system (CNS). Here, we report the results of the first genome-wide genetic screens in the CNS using both short hairpin RNA (shRNA) and CRISPR libraries. Our screens identify many classes of CNS neuronal essential genes and demonstrate that CNS neurons are particularly sensitive not only to perturbations to synaptic processes but also autophagy, proteostasis, mRNA processing, and mitochondrial function. These results reveal a molecular logic for the common implication of these pathways across multiple neurodegenerative diseases. To further identify disease-relevant genetic modifiers, we applied our screening approach to two mouse models of Huntington’s disease (HD). Top mutant huntingtin toxicity modifier genes included several Nme genes and several genes involved in methylation-dependent chromatin silencing and dopamine signaling, results that reveal new HD therapeutic target pathways