Dataset Information

Sizing up the Next Generation of Nanomedicines.

ABSTRACT: During the past two decades the nanomedicine field has experienced significant progress. To date, over sixty nanoparticle (NP) formulations have been approved in the US and EU while many others are in clinical or preclinical development, indicating a concerted effort to translate promising bench research to commercially viable pharmaceutical products. The use of NPs as novel drug delivery systems, for example, can improve drug safety and efficacy profiles and enable access to intracellular domains of diseased cells, thus paving the way to previously intractable biological targets. However, the measurement of their physicochemical properties presents substantial challenges relative to conventional injectable formulations. In this perspective, we focus exclusively on particle size, a core property and critical quality attribute of nanomedicines. We present an overview of relevant state-of-the-art technologies for particle sizing, highlighting the main parameters that can influence the selection of techniques suitable for a specific size range or material. We consider the increasing need, and associated challenge, to measure size in physiologically relevant media. We detail the importance of standards, key to validate any measurement, and the need for suitable reference materials for processes used to characterize novel and complex NPs. This perspective highlights issues critical to achieve compliance with regulatory guidelines and to support research and manufacturing quality control.

SUBMITTER: Clogston JD

PROVIDER: S-EPMC7274461 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sizing up the Next Generation of Nanomedicines.

Clogston Jeffrey D JD Hackley Vincent A VA Prina-Mello Adriele A Puri Sanyogitta S Sonzini Silvia S Soo Patrick Lim PL

Pharmaceutical research 20191211 1

During the past two decades the nanomedicine field has experienced significant progress. To date, over sixty nanoparticle (NP) formulations have been approved in the US and EU while many others are in clinical or preclinical development, indicating a concerted effort to translate promising bench research to commercially viable pharmaceutical products. The use of NPs as novel drug delivery systems, for example, can improve drug safety and efficacy profiles and enable access to intracellular domai ...[more]

PMID: 31828540

Similar Datasets

Project description:BACKGROUND: High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat's serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently. RESULTS: We analysed the SRA dataset (SRX026839 and SRX026838) consisting of single end reads and SRA data SRR1027730 consisting of paired-end reads. We used TopHat v2.0.8 to analyse these datasets and noted the CPU usage, memory footprint and execution time during spliced alignment. With this basic information, we designed PVT, a pipelined version of TopHat that removes the redundant computational steps during 'spliced alignment' and breaks the job into a pipeline of multiple stages (each comprising of different step(s)) to improve its resource utilization, thus reducing the execution time. CONCLUSIONS: PVT provides an improvement over TopHat for spliced alignment of NGS data analysis. PVT thus resulted in the reduction of the execution time to ~23% for the single end read dataset. Further, PVT designed for paired end reads showed an improved performance of ~41% over TopHat (for the chosen data) with respect to execution time. Moreover we propose PVT-Cloud which implements PVT pipeline in cloud computing system.

Dataset Information

Sizing up the Next Generation of Nanomedicines.

Publications

Sizing up the Next Generation of Nanomedicines.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets