Nanotechnology has the potential to circumvent several drawbacks of conventional therapeutic formulations. In fact, significant strides have been made towards the application of engineered nanomaterials for the treatment of cancer with high specificity, sensitivity and efficacy. Tailor-made nanomaterials functionalized with specific ligands can target cancer cells in a predictable manner and deliver encapsulated payloads effectively. Moreover, nanomaterials can also be designed for increased drug loading, improved half-life in the body, controlled release, and selective distribution by modifying their composition, size, morphology, and surface chemistry. To date, polymeric nanomaterials, metallic nanoparticles, carbon-based materials, liposomes, and dendrimers have been developed as smart drug delivery systems for cancer treatment, demonstrating enhanced pharmacokinetic and pharmacodynamic profiles over conventional formulations due to their nanoscale size and unique physicochemical characteristics. The data present in the literature suggest that nanotechnology will provide next-generation platforms for cancer management and anticancer therapy. Therefore, in this critical review, we summarize a range of nanomaterials which are currently being employed for anticancer therapies and discuss the fundamental role of their physicochemical properties in cancer management. We further elaborate on the topical progress made to date toward nanomaterial engineering for cancer therapy, including current strategies for drug targeting and release for efficient cancer administration. We also discuss issues of nanotoxicity, which is an often-neglected feature of nanotechnology. Finally, we attempt to summarize the current challenges in nanotherapeutics and provide an outlook on the future of this important field.

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Complex‐trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome‐wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP‐heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low‐dimensional model for the heritability of each SNP. We use state‐of‐art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ

Current combination therapy of PEG-INF and ribavirin against the Hepatitis C Virus (HCV) genotype-1 infections is ineffective in maintaining sustained viral response in 50% of the infection cases. New compounds in the form of protease inhibitors can complement the combination therapy. Asunaprevir is new to the drug regiment as the NS3-4A protease inhibitor, but it is susceptible to two mutations, namely, R155K and D168A in the protein. Thus, in our study, we sought to evaluate Andrographolide, a labdane-diterpenoid from the Andrographis paniculata plant as an effective compound for inhibiting the NS3-4A protease as well as its concomitant drug-resistant mutants by using molecular docking and dynamic simulations. Our study shows that Andrographolide has best docking scores of −15.0862, −15.2322, and −13.9072 compared to those of Asunaprevir −3.7159, −2.6431, and −5.4149 with wild-type R155K and D168A mutants, respectively. Also, as shown in the MD simulations, the compound was good in binding the target proteins and maintains strong bonds causing very less to negligible perturbation in the protein backbone structures. Our results validate the susceptibility of Asunaprevir to protein variants as seen from our docking studies and trajectory period analysis. Therefore, from our study, we hope to add one more option in the drug regiment to tackle drug resistance in HCV infections.

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The aim of our study is to identify probable deleterious genetic variations that can alter the expression and the function of the CHRNA3 gene using in silico methods. Of the 2305 SNPs identified in the CHRNA3 gene, 115 were found to be non-synonymous and 12 and 15 nsSNPs were found to be in the 5' and 3' UTRs, respectively. Further, out of the 115 nsSNPs investigated, eight were predicted to be deleterious by both SIFT and PredictSNP servers. The major mutations predicted to affect the structure of the protein are phenylalanine to valine (Y43V) and lysine to asparagine (K216N) as shown by the trajectory run in molecular dynamics studies. The random transition of the protein structures over the simulation period caused by these mutations hints at how the native state is distorted which could lead to the loss of structural stability and functionality of the nicotinic acetylcholine receptors subunit α-3 protein. Based on this work, we propose that the nsSNP with SNP id of rs75495285 and rs76821682 will have comparatively more deleterious effects than the other predicted mutations in destabilizing the protein structure.