Systems Pharmacology


ent human sub-populations (see section above). In addition the difference in molecular, pathway/net- work, cellular, tissue and organism reaction time- frames and proximal distances is dramatic (see Table 1) and attempts to integrate all such process- es appears exceedingly difficult. However, in spite of these daunting obstacles there has been a great deal of activity in the application of systems biology to the DDD process, and a number of books have been written on the subject25. But, to date, the effect of systems biology on DDD has been some- what underwhelming because of interpretation and utilisation difficulties with the data and informa- tion obtained on specific biological sub-system or system perturbations. Due to the aforementioned limitations, systems


biology has only provided notable insights into: i) drug-target networks; ii) predictions of drug-tar- get interactions; iii) adverse drug effects of drugs; iv) drug repositioning; and v) predictions of drug combination25,26. There are continued efforts to broaden the applicability of systems biology to the DDD process. For example, recently we and others have suggested a systems biology approach to pro- vide an understanding of causal onset, progression and effective treatment of any disease, including complex disease states such as Type II diabetes27 and Alzheimer’s Disease19,28. We have proposed the following broad-based systems biology approach to drug discovery19:


i) Network biology discovery: Multi-omic analysis at the gene, protein and metabolite level.


ii) Identification of potential targets: The network biology analysis should provide a prioritised list of target genes and/or proteins.


iii) Functional validation: Utilisation of RNAi screens to either overexpress or knock down each of the selected genes in the system under investigation.


iv) Drug candidate screen: Selected, prioritised molecular targets that are expressed in the tissue or organ under investigation.


v) Target selection evaluation: Any target must be expressed in the pathobiological tissue or organ and causal onset, progression and dynamic (tem- poral) elements must be demonstrated.


Clearly, there is much to do before systems biol-


ogy can adequately demonstrate its routine and practical usefulness in DDD, but the trends dis- cussed here, albeit briefly, provide encouragement


Drug Discovery World Winter 2018/19


for the near future. In comparison, systems biology has had a much greater impact on the evolution of precision medicine over the past decade17-19,27.


Precision medicine We described recently, in some detail, the advent of precision medicine drugs2. The development of such therapeutic agents is a continuing and realistic attempt to improve the efficacy of therapeutic drugs by treating targeted patient sub-populations. The term ‘precision medicine’ was first coined by Clayton Christensen in his book the Innovator’s Prescription published in 200929. However, the descriptor ‘precision medicine’ did not gain wide acceptance and usage until a report entitled ‘Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease’ was published by the US National Research Council (NRC) in 201130. The report stated: “Precision medicine is the tailor- ing of medical treatment to the individual charac- teristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases they may devel- op, or in their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side-effects for those who will not”30. This approach utilises individuals and defined (sub)-population-based cohorts that have a com- mon network of disease taxonomy. In addition, it requires an integrated molecular and clinical pro- file of both the individual as well as the sub-popu- lation-based cohort. We have argued that precision medicine uses a ‘1-in-N’ model (in contrast to the ‘N-of-1’ personalised medicine model)18. This is predicated on widely-used biostatistical data anal- ysis and ‘big data’ analytical tools, and forms the basis of precision medicine drug development2.


Precision medicine drugs Precision medicine drugs are defined as “those therapeutic products for which the label includes reference to specific biological marker(s), identi- fied by diagnostic tools, that help guide decisions and/or procedures for their use in individual patients”2. It is important to note that the physi- cian utilises the biological biomarker(s) listed on the drug label in prescribing the precision medicine drug. Last year the Center for Drug Evaluation and Research (CDER) at the FDA approved a record high 59 new drugs. However,


References 1a Naylor, S and Schonfeld JM. Therapeutic Drug Repurposing, Repositioning, and Rescue: Part I- Overview. Drug Discov. World. 2014; Winter Edition, 49-62 (2014). 1b Kauppi, DM and Naylor, S. Therapeutic Drug Repurposing, Repositioning and Rescue: Part IV- Financial Model and Analysis. Drug Discov. World. Winter Edition, 54-63 (2016). 2 Kiernan, UK and Naylor, S. Precision Medicine Drugs: Pleonasm or Reality? Drug Discov. World. Summer Edition, 9-16 (2018). 3 Kiernan, UK and Naylor, S. Emerging Paradigm of Integrated Platform-Drug Discovery and Development Companies. Drug Discov. World. Fall Edition, 33-43, (2018). 4 Paul, SM et al. How to Improve R&D Productivity: the Pharmaceutical Industry’s Grand Challenge. Nature Reviews: Drug Discovery, 9, 203-214 (2010). 5 Deloitte Centre for Health solutions. Measuring the Return from Pharmaceutical Innovation Turning a Corner? (2014). http://www2.deloitte. com/content/dam/Deloitte/uk/ Documents/life-sciences- health-care/measuring-the- return-from-pharmaceutical- innovation-2014.pdf. 6 Cook, D et al. Lessons Learned from the Fate of Astra-Zeneca’s Drug Pipeline: A Five Dimensional Network. Nature Reviews: Drug Discovery. 13, 419-431 (2014). 7 DiMasi, JA, Grabowski, HG and Hansen, RW. Innovations in the Pharmaceutical Industry: New Estimates of R&D Costs. J. Health Econ. 47, 20-33, (2016). 8 Gaffney, A. How Often Does FDA Withdraw Drugs Using Discontinuation Petitions? Very Rarely. Regulatory Affairs Professional Society. June 15th, (2015).


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