In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance

Infections due to parasitic nematodes are common causes of morbidity and fatality around the world especially in developing nations. At present however, there are only three major classes of drugs for treating human nematode infections. Additionally the scientific knowledge on the mechanism of action and the reason for the resistance to these drugs is poorly understood. Commercial incentives to design drugs that are endemic to developing countries are limited therefore, virtual screening in academic settings can play a vital role is discovering novel drugs useful against neglected diseases. In this study we propose to build robust machine learning model to classify and screen compounds active against parasitic nematodes.A set of compounds active against parasitic nematodes were collated from various literature sources including PubChem while the inactive set was derived from DrugBank database. The support vector machine (SVM) algorithm was used for model development, and stratified ten-fold cross validation was used to evaluate the performance of each classifier. The best results were obtained using the radial basis function kernel. The SVM method achieved an accuracy of 81.79% on an independent test set. Using the model developed above, we were able to indentify novel compounds with potential anthelmintic activity.In this study, we successfully present the SVM approach for predicting compounds active against parasitic nematodes which suggests the effectiveness of computational approaches for antiparasitic drug discovery. Although, the accuracy obtained is lower than the previously reported in a similar study but we believe that our model is more robust because we intentionally employed stringent criteria to select inactive dataset thus making it difficult for the model to classify compounds. The method presents an alternative approach to the existing traditional methods and may be useful for predicting hitherto novel anthelmintic compounds.12 page(s

Plugging the leak in Dengue shock

Recent structural and functional advances provide fresh insight into the biology of the dengue virus non-structural protein, NS1 and suggest new avenues of research. The work of our lab and others have shown that the secreted, hexameric form of NS1 has a systemic toxic effect, inducing inflammatory cytokines and acting directly on endothelial cells to produce the hallmark of dengue disease, vascular leak. We also demonstrated that NS1 exerts its toxic activity through recognition by the innate immune receptor TLR4, mimicking the bacterial endotoxin LPS. This monograph covers the background underpinning these new findings and discusses new avenues for antiviral and vaccine intervention

Dengue viruses – an overview

Dengue viruses (DENVs) cause the most common arthropod-borne viral disease in man with 50–100 million infections per year. Because of the lack of a vaccine and antiviral drugs, the sole measure of control is limiting the Aedes mosquito vectors. DENV infection can be asymptomatic or a self-limited, acute febrile disease ranging in severity. The classical form of dengue fever (DF) is characterized by high fever, headache, stomach ache, rash, myalgia, and arthralgia. Severe dengue, dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) are accompanied by thrombocytopenia, vascular leakage, and hypotension. DSS, which can be fatal, is characterized by systemic shock. Despite intensive research, the underlying mechanisms causing severe dengue is still not well understood partly due to the lack of appropriate animal models of infection and disease. However, even though it is clear that both viral and host factors play important roles in the course of infection, a fundamental knowledge gap still remains to be filled regarding host cell tropism, crucial host immune response mechanisms, and viral markers for virulence