A Novel Approach to Drug Development in Heart Failure: Towards Personalized Medicine

2014 ◽  
Vol 30 (3) ◽  
pp. 288-295 ◽  
Author(s):  
Licette C.Y. Liu ◽  
Adriaan A. Voors ◽  
Mattia A.E. Valente ◽  
Peter van der Meer
Keyword(s):  
Heart Failure ◽  
Personalized Medicine ◽  
Drug Development ◽  
Novel Approach

A novel approach to investigate SIRS derived heart failure using a murine liver transplantation model

2007 ◽  
Vol 55 (S 1) ◽  
Author(s):  
LO Conzelmann ◽  
N Kayhan ◽  
NA Stumpf ◽  
U Gaffga ◽  
AA Peivandi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Liver Transplantation ◽  
Novel Approach ◽  
Murine Liver ◽  
Transplantation Model

scholarly journals The Role of Network Science in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa
Keyword(s):  
Personalized Medicine ◽  
Drug Development ◽  
Information Flow ◽  
Clinical Studies ◽  
Network Science ◽  
Cancer Genomics ◽  
High Dimensional ◽  
Network Discovery ◽  
Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

scholarly journals Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder

2020 ◽  
Vol 4 ◽  
pp. 247054702098472
Author(s):  
Siyan Fan ◽  
Samaneh Nemati ◽  
Teddy J. Akiki ◽  
Jeremy Roscoe ◽  
Christopher L. Averill ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Personalized Medicine ◽  
Drug Development ◽  
Depressive Disorder ◽  
Treatment Modality ◽  
Symptom Improvement ◽  
Specific Response ◽  
Major Depressive ◽  
Brain Connectome ◽  
Percent Improvement

Background Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in providing more personalized medicine. Using a network restricted strength predictive modeling (NRS-PM) approach, the goal of the current study was to identify pretreatment functional connectome fingerprints (CFPs) that (1) predict symptom improvement regardless of treatment modality and (2) predict treatment specific improvement. Methods Functional magnetic resonance imaging and behavioral data from unmedicated patients with MDD (n = 200) were investigated. Participants were randomized to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000 iterations of 10 cross-validation were implemented to identify brain connectivity signatures that predict percent improvement in depression severity at week-8. Results The study identified a pretreatment CFP that significantly predicts symptom improvement independent of treatment modality but failed to identify a treatment specific CFP. Regardless of treatment modality, improved antidepressant response was predicted by high pretreatment connectivity between modules in the default mode network and the rest of the brain, but low external connectivity in the executive network. Moreover, high pretreatment internal nodal connectivity in the bilateral caudate predicted better response. Conclusions The identified CFP may contribute to drug development and ultimately to enhanced prognostic predictions. However, the results do not assist with providing personalized medicine, as pretreatment functional connectivity failed to predict treatment specific response.

Acute Effects of Left Ventricular Support With Impella 5.5 on Biventricular Hemodynamics

2021 ◽  
Author(s):  
Kay D. Everett ◽  
Pankaj Jain ◽  
Richard Botto ◽  
Michael Salama ◽  
Satoshi Miyashita ◽  
...  
Keyword(s):  
Heart Failure ◽  
Right Ventricle ◽  
Cardiogenic Shock ◽  
Left Ventricular ◽  
Acute Effects ◽  
Hemodynamic Evaluation ◽  
Novel Approach ◽  
Ventricular Support

Identification of patients with cardiogenic shock and right ventricle (RV) dysfunction who may require biventricular rather than isolated left ventricular (LV) support remains challenging. In this setting, rigorous hemodynamic evaluation of biventricular contractility and load during initiation of LV support guides therapy. We now report a novel approach to assess biventricular pressure-volume loops in a patient receiving Impella 5.5 support for heart failure and shock.

scholarly journals The Challenge of Drug Development in Acute Heart Failure

2013 ◽  
Vol 1 (5) ◽  
pp. 442-444 ◽  
Author(s):  
Peter S. Pang ◽  
Michael M. Givertz
Keyword(s):  
Heart Failure ◽  
Drug Development ◽  
Acute Heart Failure

Targeting microRNAs involved in human diseases: A novel approach for modification of gene expression and drug development

2011 ◽  
Vol 82 (10) ◽  
pp. 1416-1429 ◽  
Author(s):  
Roberto Gambari ◽  
Enrica Fabbri ◽  
Monica Borgatti ◽  
Ilaria Lampronti ◽  
Alessia Finotti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Development ◽  
Human Diseases ◽  
Novel Approach
Sign in / Sign up

Export Citation Format

Share Document