Genetics and genomics in neuropsychopharmacology: the impact on drug discovery and development

2001 ◽  
Vol 11 (6) ◽  
pp. 491-499 ◽  
Author(s):  
Orest Hurko
2006 ◽  
Vol 34 (2) ◽  
pp. 313-316 ◽  
Author(s):  
G.P. Belfield ◽  
S.J. Delaney

The discipline of molecular biology has become increasingly important in recent times for the process of drug discovery. We describe the impact of molecular biology across the whole process of drug discovery and development, including (i) the identification and validation of new drug targets, (ii) the development of molecular screens to find new candidate drugs, and (iii) the generation of safety data and competences leading to enhanced clinical efficacy. We also speculate on emerging developments in drug discovery where it seems likely that molecular biology will play an even more vital role in the generation of future therapies.


2021 ◽  
pp. 247255522110006
Author(s):  
Florian David ◽  
Andrew M. Davis ◽  
Michael Gossing ◽  
Martin A. Hayes ◽  
Elvira Romero ◽  
...  

The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline’s application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline’s contribution can further enhance the opportunities for the drug discovery and development value chain.


2003 ◽  
Vol 01 (01) ◽  
pp. 169-177 ◽  
Author(s):  
SETH MICHELSON

Systems biology is creating a context for interpreting the vast amounts of genomic and proteomic data being produced by pharmaceutical companies in support of drug development. While major data collection efforts capitalize on technical advances in miniaturization and automation and represent an industrialization of existing laboratory research, the transition from mental models to predictive computer simulations is setting the pace for advances in this field. This article addresses current approaches to the mathematical modeling of biological systems and assesses the potential impact of predictive biosimulation on drug discovery and development.


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