Project description:Recent estimates suggest close to one million people per year die globally owing to HIV-related illnesses. Therefore, there is still a need to identify new targets to develop future treatments. Many of the more recently identified targets are host-related and these might be more difficult for the virus to develop drug resistance to. In addition, there are virus-related targets (capsid and RNAse H) that have yet to be exploited clinically. Several of the newer targets also address virulence factors, virus latency or target persistence. The targets highlighted in this review could represent the next generation of viable candidates for drug discovery projects as well as continue the search for a cure for this disease.
Project description:Phenotypic screening methods have placed numerous preclinical candidates into the antimalarial drug-discovery pipeline. As more chemically validated targets become available, efforts are shifting to target-based drug discovery. Here, we briefly review some of the most attractive targets that have been identified in recent years.
Project description:High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.
Project description:Alzheimer's disease (AD) is the most common cause of dementia and represents one of the highest unmet needs in medicine today. Drug development efforts for AD have been encumbered by largely unsuccessful clinical trials in the last decade. Drug repositioning, a process of discovering a new therapeutic use for existing drugs or drug candidates, is an attractive and timely drug development strategy especially for AD. Compared with traditional de novo drug development, time and cost are reduced as the safety and pharmacokinetic properties of most repositioning candidates have already been determined. A majority of drug repositioning efforts for AD have been based on positive clinical or epidemiological observations or in vivo efficacy found in mouse models of AD. More systematic, multidisciplinary approaches will further facilitate drug repositioning for AD. Some experimental approaches include unbiased phenotypic screening using the library of available drug collections in physiologically relevant model systems (e.g. stem cell-derived neurons or glial cells), computational prediction and selection approaches that leverage the accumulating data resulting from RNA expression profiles, and genome-wide association studies. This review will summarize several notable strategies and representative examples of drug repositioning for AD.
Project description:A mathematical model which predicts the intraerythrocytic stages of Plasmodium falciparum infection was developed using data from malaria-infected mice. Variables selected accounted for levels of healthy red blood cells, merozoite (Plasmodium asexual phase) infected red blood cells, gametocyte (Plasmodium sexual phase) infected red blood cells and a phenomenological variable which accounts for the mean activity of the immune system of the host. The model built was able to reproduce the behavior of three different scenarios of malaria. It predicts the later dynamics of malaria-infected humans well after the first peak of parasitemia, the qualitative response of malaria-infected monkeys to vaccination and the changes observed in malaria-infected mice when they are treated with antimalarial drugs. The mathematical model was used to identify new targets to be focused on drug design. Optimization methodologies were applied to identify five targets for minimizing the parasite load; four of the targets thus identified have never before been taken into account in drug design. The potential targets include: 1) increasing the death rate of the gametocytes, 2) decreasing the invasion rate of the red blood cells by the merozoites, 3) increasing the transformation of merozoites into gametocytes, 4) decreasing the activation of the immune system by the gametocytes, and finally 5) a combination of the previous target with decreasing the recycling rate of the red blood cells. The first target is already used in current therapies, whereas the remainders are proposals for potential new targets. Furthermore, the combined target (the simultaneous decrease of the activation of IS by gRBC and the decrease of the influence of IS on the recycling of hRBC) is interesting, since this combination does not affect the parasite directly. Thus, it is not expected to generate selective pressure on the parasites, which means that it would not produce resistance in Plasmodium.
Project description:Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.
Project description:In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.
Project description:Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.
Project description:Celiac disease is a lifelong, immunological disorder induced by dietary protein-gluten, in a genetically susceptible populations, resulting in different clinical manifestations, the release of antibodies, and damage to the intestinal mucosa. The only recommended therapy for the disease is to strictly follow a gluten-free diet (GFD), which is difficult to comply with. A GFD is found to be ineffective in some active Celiac disease cases. Therefore, there is an unmet need for an alternative nondietary therapeutic approach. The review focuses on the novel drug targets for Celiac disease.
Project description:Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious infection that may break the healthcare system of several countries. Here, we aimed at presenting a critical view of ongoing drug repurposing efforts for COVID-19 as well as discussing opportunities for development of new treatments based on current knowledge of the mechanism of infection and potential targets within. Finally, we also discuss patent protection issues, cost effectiveness and scalability of synthetic routes for some of the most studied repurposing candidates since these are key aspects to meet global demand for COVID-19 treatment.