Next-Generation Model-Based Drug Discovery and Development: Quantitative and Systems Pharmacology

2010 ◽  
Vol 88 (1) ◽  
pp. 135-137 ◽  
Author(s):  
S R B Allerheiligen
2011 ◽  
Vol 16 (11-12) ◽  
pp. 512-519 ◽  
Author(s):  
Peter M. Woollard ◽  
Nalini A.L. Mehta ◽  
Jessica J. Vamathevan ◽  
Stephanie Van Horn ◽  
Bhushan K. Bonde ◽  
...  

2017 ◽  
Vol 12 (12) ◽  
pp. 1207-1218 ◽  
Author(s):  
Elizabeth C. M. de Lange ◽  
Willem van den Brink ◽  
Yumi Yamamoto ◽  
Wilhelmus E. A. de Witte ◽  
Yin Cheong Wong

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Srinivasa Reddy Bonam ◽  
Mahendran Sekar ◽  
Girija S Guntuku ◽  
Sridhar G Nerella ◽  
Krishna M Pawar A ◽  
...  

The recent emergence of COVID-19 influenced the layman’s knowledge of drugs. Although several drugs have been discovered serendipitously, research has moved to the next-generation era of drug discovery. The use of drugs is inevitable and they have become lifesavers in the present era. Although research from different scientific backgrounds has supported the translational research of drug discovery, the prime role of pharmacy has to be remembered. Here we have summarized the role of some important subjects in pharmacy education, which have paved different ways in drug discovery and development.


2016 ◽  
Vol 21 (6) ◽  
pp. 521-534 ◽  
Author(s):  
Andrew M. Stern ◽  
Mark E. Schurdak ◽  
Ivet Bahar ◽  
Jeremy M. Berg ◽  
D. Lansing Taylor

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.


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