The reductionist paradox: are the laws of chemistry and physics sufficient for the discovery of new drugs?

Reductionism is alive and well in drug-discovery research. In that tradition, we continually improve experimental and computational methods for studying smaller and smaller aspects of biological systems. Although significant improvements continue to be made, are our efforts too narrowly focused? Suppose all error could be removed from these methods, would we then understand biological systems sufficiently well to design effective drugs? Currently, almost all drug research focuses on single targets. Should the process be expanded to include multiple targets? Recent efforts in this direction have lead to the emerging field of polypharmacology. This appears to be a move in the right direction, but how much polypharmacology is enough? As the complexity of the processes underlying polypharmacology increase will we be able to understand them and their inter-relationships? Is “new” mathematics unfamiliar in much of physics and chemistry research needed to accomplish this task? A number of these questions will be addressed in this paper, which focuses on issues and questions not answers to the drug-discovery conundrum.

[1]  Life Transcending Physics and Chemistry , 1967 .

[2]  James M. Keller,et al.  Applications of Fuzzy Logic in Bioinformatics , 2008, Series on Advances in Bioinformatics and Computational Biology.

[3]  M. Tewari,et al.  The Clinical Applications of a Systems Approach , 2006, PLoS medicine.

[4]  J. Trimmer,et al.  APPLICATION OF PREDICTIVE BIOSIMULATION TO THE STUDY OF ATHEROSCLEROSIS: DEVELOPMENT OF THE CARDIOVASCULAR PHYSIOLAB ® PLATFORM AND EVALUATION OF CETP INHIBITOR THERAPY , 2007 .

[5]  Russell G. Almond Graphical belief modeling , 1995 .

[6]  John L. Casti,et al.  Topological Methods for Social and Behavioral Systems , 1982 .

[7]  A. Smith RESEARCH AND DEVELOPMENT IN THE PHARMACEUTICAL INDUSTRY. , 1965, Dental student.

[8]  M. Tewari,et al.  The Limits of Reductionism in Medicine: Could Systems Biology Offer an Alternative? , 2006, PLoS medicine.

[9]  M. Wehling,et al.  The translatability of animal models for clinical development: biomarkers and disease models. , 2010, Current opinion in pharmacology.

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  David L. Wild and Mansoor A. S. Saqi Structural Proteomics: Inferring Function from Protein Structure , 2004 .

[12]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[13]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[14]  J. Weinstein 'Omic' and hypothesis-driven research in the molecular pharmacology of cancer. , 2002, Current opinion in pharmacology.

[15]  Luca Cardelli,et al.  Efficient, Correct Simulation of Biological Processes in the Stochastic Pi-calculus , 2007, CMSB.

[16]  David Haussler,et al.  Occam's Razor , 1987, Inf. Process. Lett..

[17]  A. Rouhi CHIRAL BUSINESS: Fine chemicals companies are jockeying for position to deliver the increasingly complicated chiral small molecules of the future , 2003 .

[18]  Joanna Owens Screening: Dirty drugs' secrets uncovered , 2006, Nature Reviews Drug Discovery.

[19]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[20]  Sanghamitra Bandyopadhyay,et al.  Analysis of Biological Data: A Soft Computing Approach , 2007, Science, Engineering, and Biology Informatics.

[21]  S. Thorgeirsson,et al.  Application of comparative functional genomics to identify best-fit mouse models to study human cancer , 2004, Nature Genetics.

[22]  Sui Huang,et al.  The practical problems of post-genomic biology , 2000, Nature Biotechnology.

[23]  S. Brenner,et al.  Theoretical biology in the third millennium. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[24]  James C. Bezdek,et al.  Fuzzy models—What are they, and why? [Editorial] , 1993, IEEE Transactions on Fuzzy Systems.

[25]  H. Kitano,et al.  Computational systems biology , 2002, Nature.

[26]  P. Aloy,et al.  Unveiling the role of network and systems biology in drug discovery. , 2010, Trends in pharmacological sciences.

[27]  D. Lepage,et al.  Animal models for disease: knockout, knock-in, and conditional mutant mice. , 2006, Methods in molecular medicine.

[28]  W. Campbell,et al.  What's new in ... clinical pharmacology and therapeutics. , 2006, Wisconsin medical journal.

[29]  Lotfi A. Zadeh,et al.  Applied Soft Computing - Foreword , 2001, Appl. Soft Comput..

[30]  Vladik Kreinovich,et al.  Handbook of Granular Computing , 2008 .

[31]  David B. Fogel,et al.  Evolution-ary Computation 1: Basic Algorithms and Operators , 2000 .

[32]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[33]  E. Petricoin,et al.  Proteins, drug targets and the mechanisms they control: the simple truth about complex networks , 2007, Nature Reviews Drug Discovery.

[34]  G. Edelman,et al.  Degeneracy and complexity in biological systems , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Ian Stark,et al.  The Continuous pi-Calculus: A Process Algebra for Biochemical Modelling , 2008, CMSB.

[36]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[37]  M. V. Regenmortel,et al.  Reductionism and complexity in molecular biology , 2004, HIV/AIDS: Immunochemistry, Reductionism and Vaccine Design.

[38]  C. Allen,et al.  Stanford Encyclopedia of Philosophy , 2011 .

[39]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[40]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.

[41]  Glen A. Evans,et al.  Designer science and the “omic” revolution , 2000, Nature Biotechnology.

[42]  Sarath Chandra Janga,et al.  Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. , 2009, Molecular bioSystems.