Drug Transporters as Therapeutic Targets: Computational Models, Challenges, and Future Perspective

2018 ◽  
pp. 143-168
Author(s):  
Deepak Singla ◽  
Ritika Bishnoi ◽  
Sandeep Kumar Dhanda ◽  
Shailendra Asthana
Keyword(s):  
Computational Models ◽  
Drug Transporters ◽  
Therapeutic Targets ◽  
Future Perspective

scholarly journals Proteoglycans Are Attractive Biomarkers and Therapeutic Targets in Hepatocellular Carcinoma

2018 ◽  
Vol 19 (10) ◽  
pp. 3070 ◽  
Author(s):  
Yasuo Tanaka ◽  
Ryosuke Tateishi ◽  
Kazuhiko Koike
Keyword(s):  
Hepatocellular Carcinoma ◽  
Stromal Cells ◽  
Current Knowledge ◽  
Therapeutic Targets ◽  
Underlying Mechanism ◽  
Future Perspective ◽  
Tissue Microenvironment ◽  
Collagen Xviii ◽  
Cancer Cell Signaling ◽  
Cell Signaling Pathways

Proteoglycans, which consist of a protein core and glycosaminoglycan chains, are major components of the extracellular matrix and play physiological roles in maintaining tissue homeostasis. In the carcinogenic tissue microenvironment, proteoglycan expression changes dramatically. Altered proteoglycan expression on tumor and stromal cells affects cancer cell signaling pathways, which alters growth, migration, and angiogenesis and could facilitate tumorigenesis. This dysregulation of proteoglycans has been implicated in the pathogenesis of diseases such as hepatocellular carcinoma (HCC) and the underlying mechanism has been studied extensively. This review summarizes the current knowledge of the roles of proteoglycans in the genesis and progression of HCC. It focuses on well-investigated proteoglycans such as serglycin, syndecan-1, glypican 3, agrin, collagen XVIII/endostatin, versican, and decorin, with particular emphasis on the potential of these factors as biomarkers and therapeutic targets in HCC regarding the future perspective of precision medicine toward the “cure of HCC”.

scholarly journals ABC drug transporters and immunity: novel therapeutic targets in autoimmunity and cancer

2009 ◽  
Vol 86 (5) ◽  
pp. 1075-1087 ◽  
Author(s):  
Rieneke van de Ven ◽  
Ruud Oerlemans ◽  
Joost W. van der Heijden ◽  
George L. Scheffer ◽  
Tanja D. de Gruijl ◽  
...  
Keyword(s):  
Drug Transporters ◽  
Therapeutic Targets ◽  
Novel Therapeutic

scholarly journals A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Fernando Medeiros Filho ◽  
Ana Paula Barbosa do Nascimento ◽  
Maiana de Oliveira Cerqueira e Costa ◽  
Thiago Castanheira Merigueti ◽  
Marcio Argollo de Menezes ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Drug Targets ◽  
Computational Models ◽  
Therapeutic Targets ◽  
Growth Curves ◽  
World Health ◽  
Biological Behavior ◽  
Integrated Models ◽  
Dynamic Simulations ◽  
Starting Point

Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

Sequential Difficulty Effects During Strategy Execution

2012 ◽  
Vol 59 (5) ◽  
pp. 295-301 ◽  
Author(s):  
Kim Uittenhove ◽  
Patrick Lemaire
Keyword(s):  
Computational Models ◽  
Preceding Trial ◽  
Human Cognition ◽  
Strategy Execution ◽  
Strategy Performance ◽  
Empirical Implications

In two experiments, we tested the hypothesis that strategy performance on a given trial is influenced by the difficulty of the strategy executed on the immediately preceding trial, an effect that we call strategy sequential difficulty effect. Participants’ task was to provide approximate sums to two-digit addition problems by using cued rounding strategies. Results showed that performance was poorer after a difficult strategy than after an easy strategy. Our results have important theoretical and empirical implications for computational models of strategy choices and for furthering our understanding of strategic variations in arithmetic as well as in human cognition in general.

Does Viotin Activate Violin More Than Viocin?

2014 ◽  
Vol 61 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Manuel Perea ◽  
Victoria Panadero
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Developmental Dyslexia ◽  
Computational Models ◽  
Error Rates ◽  
Visual Word ◽  
Base Word ◽  
Similar Response ◽  
Skilled Readers ◽  
Young Readers

The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word’s overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children – this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word’s visual cues, presumably because of poor letter representations.

Sign in / Sign up

Export Citation Format

Share Document