Biosystems modelling for in silico target validation: challenges to implementation

Computer simulation of biological systems for in silico target validation has the potential for increasing the efficiency of pharmaceutical R&D by expanding the number of parameters tested ‘virtually’. Only the most interesting subset of these has to then be probed in vivo. By avoiding less informative experiments and focusing on variables with the greatest influence on clinical end points, valuable drug targets can be advanced more quickly and those with little or no leverage bypassed. With many compounds failing today due to insufficient efficacy, use of in silico methods should lead to a greater percentage of compounds making the transition to clinical drug. Though biosystems modelling has great advantages, the actual adoption and implementation within each organisation will be a variable process because it has a disruptive approach to current pharma R&D practices. Some companies may have scientific personnel, technical infrastructure and a management culture that provide fertile ground for such a change; other companies will likely struggle more than once to adopt such methods. Innovators need to understand the challenges to implementation they will face and what actions are likely to improve their chances of success. Organisations which successfully implement biosystems modelling for in silico target validation should be able to design experiments and trials more tailored to each drug target and thus achieve higher yields of approved drugs from compounds tested.

[1]  Reinhart Heinrich,et al.  A Linear Steady-State Treatment of Enzymatic Chains Critique of the Crossover Theorem and a General Procedure to Identify Interaction Sites with an Effector , 1974 .

[2]  R. Lifton Molecular Genetics of Human Blood Pressure Variation , 1996, Science.

[3]  G. Zaltman,et al.  Innovations and organizations , 2020, Organizational Innovation.

[4]  T. Kuhn,et al.  The Structure of Scientific Revolutions , 1963 .

[5]  Denis Noble,et al.  A return to rational drug discovery: computer-based models of cells, organs and systems in drug target identification , 2000 .

[6]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[7]  H. Kacser,et al.  The control of flux. , 1995, Biochemical Society transactions.

[8]  J. Edwards,et al.  Systems Properties of the Haemophilus influenzaeRd Metabolic Genotype* , 1999, The Journal of Biological Chemistry.

[9]  D. Mosier,et al.  Macrophage-tropic HIV: critical for AIDS pathogenesis? , 1994, Immunology today.

[10]  Clayton M. Christensen,et al.  Meeting the Challenge of Disruptive Change , 2000 .

[11]  L. Leon,et al.  Lack of Obesity and Normal Response to Fasting and Thyroid Hormone in Mice Lacking Uncoupling Protein-3* , 2000, The Journal of Biological Chemistry.

[12]  J E Bailey,et al.  MPS: An artificially intelligent software system for the analysis and synthesis of metabolic pathways , 1988, Biotechnology and bioengineering.

[13]  R. M. Alexander,et al.  Sources of Power , 1982 .

[14]  P W Kuchel,et al.  Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: computer simulation and metabolic control analysis. , 1999, The Biochemical journal.

[15]  John Archibald Wheeler,et al.  At Home in the Universe , 1994 .

[16]  T. Kennedy Managing the drug discovery/development interface , 1997 .

[17]  Reinhart Heinrich,et al.  A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. , 1974, European journal of biochemistry.

[18]  Clayton M. Christensen The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail , 2013 .

[19]  A. Bowden Metabolic control analysis in biotechnology and medicine , 1999, Nature Biotechnology.

[20]  D. Hartl,et al.  What did Gregor Mendel think he discovered? , 1992, Genetics.

[21]  G. Brown,et al.  Will disruptive innovations cure health care? , 2001, Harvard business review.

[22]  T. Kuhn The Structure of Scientific Revolutions 2nd edition , 1970 .

[23]  B. Palsson,et al.  The underlying pathway structure of biochemical reaction networks. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[24]  B. Palsson,et al.  Toward Metabolic Phenomics: Analysis of Genomic Data Using Flux Balances , 1999, Biotechnology progress.

[25]  Jain Transforming innovation and commercialization in drug discovery. , 2000, Drug discovery today.

[26]  J. Drews In Quest of Tomorrow's Medicines , 1999 .

[27]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[28]  P. Schroeder The Goal: A Process of Ongoing Improvement , 1994 .

[29]  G. Caldwell Compound optimization in early- and late-phase drug discovery: acceptable pharmacokinetic properties utilizing combined physicochemical, in vitro and in vivo screens. , 2000, Current opinion in drug discovery & development.

[30]  A. Roses Pharmacogenetics and the practice of medicine , 2000, Nature.

[31]  U Dave,et al.  Critical Chain , 1998, J. Oper. Res. Soc..

[32]  M. Egerton,et al.  Expression databases--resources for pharmacogenomic R&D. , 2000, Pharmacogenomics.