Silico Research

Terstappen, G. and Reggiani, A. 2001. In silico research in drug discovery. Trends in Pharmacological Sciences 22(1), 23-26. 

Terstappen, G. Reggiani, A. (2001). In silico research in drug discovery. Trends Pharmacol. Sci. 22(1), 23-26. Wamg, J. Hewick, R. (1999). Proteomics in drug discovery. Drug Discovery Today 4(3), 129-133. 

Panfilov, A. and Kerkhof, P. Quantifying ventricular fibrillation in silico research and clinical implications. IEEE Trans. Bio-med. Eng. 2004, 51 195-196. 

Bioinformatics is a new and exciting subject developing its own methodologies, approaches and nomenclature with an elan which has not suffered from lack of precision about defining exactly what the subject is. As might be expected of a product of the computer age, it depends heavily on internet and database access, which are the essential tools of what has become termed in silico research. A very readable introduction to the subject is provided by Attwood and Parry-Smith (1999). 

Chapter 17—BioIT Hardware Configuration Computing power and infrastructure will continue to be essential in drug-target discovery. Miller explores the various components of BioIT and how these support in silico research efforts. 

Simulation also has risks, of course. Black-box simulations cannot serve as vehicles for dialogue and inquiry for that they must be made into clear cube simulations. Simulations must be considered as a key technology in the discipline of in silico research and not accepted on blind trust. Simulations also have organizational risks. Top-down simulations have the potential to show that some research projects are likely to be ineffective in the clinic. Although this would be tremendously valuable to the organization, individual researchers will feel challenged. 

In the past 10 years, Entelos has addressed these issues through integrated development of methodologies for in silico research and the development of proprietary software technology. All of our researchers, engineers and life scientists, scientific advisors, and software engineers have contributed to the advancement of our top-down simulation. 

Figure 11.2 A decision tree, based on an associated inflnence diagram, can help organize and integrate information about risks and the way in which research work buys better information that allows choice of the options most likely to succeed. This example describes the relationship between in silico predictions and in vitro assay results for the same compound structures.

To date, many of the reported ADME/Tox models have been rule based. For example, some research groups have used relatively simple filters like the rule of 5 [93] and others [94] to limit the types of molecules evaluated with in silico methods and to focus libraries for HTS. However, being designed as rapid computational alert tools aimed at a single property of interest, they cannot offer a comprehensive picture when it comes to understanding ADME properties. 

This will extend, and partially replace, the traditional approach to biomedical research that is based on studying living cells or tissues in vitro, or on obtaining data from human volunteers in vivo, by introducing in silico experiments (a term, derived from the currently prevaihng sihcon-based computer chips). 

Thus, the virtual heart may be used to simulate cardiac pathologies, their effect on the ECG, and the consequences of drug administration. It can be seen that drug discovery and assessment will be among the first fields where in silico technologies could reform research and development in a whole industry. 

Virtual organs will increasingly determine bio-medical research. Advantages of in silico models include the following ... 

So much for the advantages. Virtual organs clearly have one major drawback they are models only. While this very nature of in silico technology is the core foundation for the benefits listed above, it also calls for a word of caution. It is imperative for in silico tools to be seen in the context of a whole range of scientific and research tools, and to never neglect that theoretical considerations will continue to need experimental validation. 

Thus, in silico models are by no means self-sufficient. They are irreplaceable for the future progress of bio-medicine. They do not aim, however, to substitute but to improve bio-medical research, which will remain indispensable, not the least for model development and validation. 

During the past five years, commencing with the publications of Lipinski and co-workers [1] and Palm and co-workers [2], a considerable amount of research has been performed in order to develop mathematical models for intestinal absorption in humans as well as other transport properties. The purpose of these investigations has been to develop computationally fast and accurate models for in silico electronic screening of large virtual compound libraries. 

In silico ADME profiling of compound libraries in early discovery has become a valuable addition to the research toolbox of computational and medicinal chemists. A computational alert was developed by Lipinski based on the physico-chemical... 

Palsson B. The challenges of in silico biology - moving from a reductionist paradigm to one that views cells as systems will necessitate changes in both the culture and the practice of research. Nature Biotechnol 2000 18 1147-1150. 

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