Dataset Information

The role of nanomaterials in translational medicine.

ABSTRACT: There are a range of definitions for nanomaterials and a range of length scales that are considered nano, but one thing is consistent among fields: nanomaterials are small and special. Nanomaterials have the potential to have tremendous impact on medical treatments. In one example, nanomaterials are permitting the tracking of cells via magnetic resonance imaging (MRI) in clinical trials to assess the efficacy and safety of cellular therapies. In a second example, nanomaterials are acting as drug delivery vehicles for the targeted delivery of therapies to increase efficacy and to reduce side effects. However, there are distinct challenges that must be considered in the development and application of these materials, including careful analysis of the distribution and clearance of nanomaterials and their potential off-target effects. By carefully assessing materials early in their development at the bench, one may be able to move successful approaches through to the clinic more rapidly, which is indeed the goal of the field. For far too many conditions and diseases, the tools we have are less than adequate, and nanomaterials have the potential to fill that void. To realize this potential, investigators must be willing to invest time and resources to develop and to translate these technologies to the point where the risk is low enough that they have real commercial possibilities. Working collaboratively and leveraging resources and experience play important roles in moving technologies through preclinical and clinical testing. It requires incredible dedication of teams of researchers, but the result is new treatments and therapies.

SUBMITTER: Lavik E

PROVIDER: S-EPMC3110679 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The role of nanomaterials in translational medicine.

Lavik Erin E von Recum Horst H

ACS nano 20110501 5

There are a range of definitions for nanomaterials and a range of length scales that are considered nano, but one thing is consistent among fields: nanomaterials are small and special. Nanomaterials have the potential to have tremendous impact on medical treatments. In one example, nanomaterials are permitting the tracking of cells via magnetic resonance imaging (MRI) in clinical trials to assess the efficacy and safety of cellular therapies. In a second example, nanomaterials are acting as drug ...[more]

PMID: 21604811

Similar Datasets

Project description:BackgroundTranslational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.MethodsWe developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.ResultsWe applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.ConclusionsWe developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Dataset Information

The role of nanomaterials in translational medicine.

Publications

The role of nanomaterials in translational medicine.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure