Personalized Medicine: Changing the Paradigm of Drug Development

Author(s):  
Robin D. Couch ◽  
Bryan T. Mott
Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa

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.


2020 ◽  
Vol 4 ◽  
pp. 247054702098472
Author(s):  
Siyan Fan ◽  
Samaneh Nemati ◽  
Teddy J. Akiki ◽  
Jeremy Roscoe ◽  
Christopher L. Averill ◽  
...  

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.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Plummer ◽  
Stephanie Wallace ◽  
Graeme Ball ◽  
Roslyn Lloyd ◽  
Paula Schiapparelli ◽  
...  

2010 ◽  
Vol 8 (6) ◽  
pp. 677-686 ◽  
Author(s):  
David M. Thomas ◽  
Andrew J. Wagner

Connective tissue tumors comprise a rich array of subtypes, many of which possess strong pathognomonic phenotypes and genotypes of therapeutic significance. This article describes recent applications of targeted and nontargeted therapeutic agents in connective tissue tumors that illustrate important themes in drug development. Targeted therapy has exploited the paradigms of oncogene and lineage addiction. In other cases, potential targets are more difficult to classify, such as the role of the insulin-like growth factor 1 pathway in Ewing's sarcoma. Understanding why these pathways seem critical in some cancers, and in some individuals but not others, is important in identifying novel therapeutic opportunities in an age of personalized medicine.


2015 ◽  
Vol 34 (1) ◽  
pp. 39-43
Author(s):  
Robert C. Millonig ◽  
Marsha Rose Gillentine ◽  
Rebecca Hammond

2015 ◽  
Vol 1 (7) ◽  
pp. e1500439 ◽  
Author(s):  
Dean Ho ◽  
Chung-Huei Katherine Wang ◽  
Edward Kai-Hua Chow

The implementation of nanomedicine in cellular, preclinical, and clinical studies has led to exciting advances ranging from fundamental to translational, particularly in the field of cancer. Many of the current barriers in cancer treatment are being successfully addressed using nanotechnology-modified compounds. These barriers include drug resistance leading to suboptimal intratumoral retention, poor circulation times resulting in decreased efficacy, and off-target toxicity, among others. The first clinical nanomedicine advances to overcome these issues were based on monotherapy, where small-molecule and nucleic acid delivery demonstrated substantial improvements over unmodified drug administration. Recent preclinical studies have shown that combination nanotherapies, composed of either multiple classes of nanomaterials or a single nanoplatform functionalized with several therapeutic agents, can image and treat tumors with improved efficacy over single-compound delivery. Among the many promising nanomaterials that are being developed, nanodiamonds have received increasing attention because of the unique chemical-mechanical properties on their faceted surfaces. More recently, nanodiamond-based drug delivery has been included in the rational and systematic design of optimal therapeutic combinations using an implicitly de-risked drug development platform technology, termed Phenotypic Personalized Medicine–Drug Development (PPM-DD). The application of PPM-DD to rapidly identify globally optimized drug combinations successfully addressed a pervasive challenge confronting all aspects of drug development, both nano and non-nano. This review will examine various nanomaterials and the use of PPM-DD to optimize the efficacy and safety of current and future cancer treatment. How this platform can accelerate combinatorial nanomedicine and the broader pharmaceutical industry toward unprecedented clinical impact will also be discussed.


2019 ◽  
Vol 21 (2) ◽  
pp. 115-116

Drug development in psychiatry is gradually moving from serendipity to personalized medicine. Some promising paths will be reviewed in this issue.


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