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Precision Medicine


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10 Persidis, A. The Benefits of Drug Repositioning. Drug Discov. World Spring Edition: 9-12 (2011). 11 Kaitin, KI. Deconstructing the Drug Development Process: The New Face of Innovation. Clin. Pharmacol. Ther. 87, 356-361 (2010). 12 Naylor, S. NostraPharmus Revisited: Splendid Isolation or Multifactorial Participation for the Pharmaceutical Industry? Drug Discov. World. Summer Supplement. 10-12 (2010). 13 Naylor, S. Technology: Bane or Bonanza for the Pharmaceutical Industry. Drug Discov. World. Fall Edition. 51- 57 (2007). 14 Davidov, EJ, Holland, J, Marple, E and Naylor, S. Advancing Drug Discovery through Systems Biology. Drug Discovery Today, 8, 175-181 (2003). 15 Naylor, S. Systems Biology, Information, Disease and Drug Discovery. Drug Discov. World. Winter Edition. 23-31 (2004/2005). 16 Culbertson, AW, Valentine, SJ and Naylor, S. Personalized Medicine: Technological Innovation and Patient Empowerment or Exuberant Hyperbole? Drug Discov. World. Summer Edition 18-28 (2007). 17 Naylor, S and Chen, SY. Unraveling Human Complexity and Disease with Systems Biology and Personalized Medicine. Personal. Med. 7. 275-287 (2010). 18Waring, SC and Naylor, S. The Silent Epidemic of Alzheimer’s Disease: Can Precision Medicine Provide Effective Drug Therapies? J. Precision Med. 4. 38-49 (2016). 19 Naylor, S. What’s in a Name? The Evolution of ‘P- Medicine’. J. Precision Med. 2, 15-29 (2015). 20 Personalized Medicine Coalition (PMC). The Case for Personalized Medicine. (2015). http://www.personalizedmedici necoalition.org.


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predicated on the individual patient/subpopulation model, as “one-step-up” from the individual patient focus of Personalised Medicine27. Implicit in his statement is that Personalised Medicine is based on a single individual ‘N-of-1’ model where- as Precision Medicine uses a ‘1-in-N’ model predi- cated on widely-used biostatistical data analysis and “big data” analytical tools. Precision Medicine can best be described as an amalgam of Personalised Medicine and modern conventional medicine. It is clear that Precision Medicine has attracted


huge attention and is in the ascendancy compared with Personalised Medicine. The attributes of the 1-in-N model of Precision Medicine have been more fully accepted and rendered into practice compared to the challenging N-of-1 model of Personalised Medicine. A number of disease area specialties have started to query or implement ele- ments of Precision Medicine into everyday practice and treatment of patients and they include such diverse areas as diabetes28 and Alzheimer’s Disease18. In particular, the oncology community has been quick to embrace and reduce Precision Medicine to practise in the diagnosis and treatment of a wide variety of cancers29,30, and has pio- neered the development and use of Targeted or Precision Medicine Drugs.


Precision Medicine Drugs We have discussed above the limited efficacy of currently available therapeutic drugs. These effica- cy limitations also apply to blockbuster drugs. For example, the effectiveness of Cymbalta (duloxe- tine- treatment for depression) only applies for 1- in-9 patients, Copaxane (glatiramer acetate – mul- tiple sclerosis) is 1-in-16 patients and for Nexium (esomeprazole – heartburn) it is 1-in-25 patients31. Even more stunning is the report that the widely prescribed class of blockbuster statin drugs, used in the management and treatment of elevated choles- terol levels, is only effective at a 21% response ratio32. Such poor efficacy has led to a reassess- ment of the clinical trial process31. Imagine if a manufacturing and QA/QC process resulted in your smart phone only working 10-20% of the time in an emergency situation! The classical form of clinical trials requires the


compilation of a number of specific measurements from thousands of selected patients. This is a cost- ly, time-consuming, risky and inefficient process. In an attempt to enhance clinical trial design and potentially account for patient variability, a num- ber of other approaches have been utilised. A ‘bas- ket’ clinical trial utilises a specific biomarker, often


a genetic marker in oncology trials, and a mode of action for the candidate drug against a number of related disease indications. In contrast, an ‘umbrel- la’ trial tests the effectiveness of a myriad of drug candidates against a single disease indication31. More recently, Schork has suggested an N-of-1 clinical trial, in which a systems-level analysis of data is collected on an individual patient who is being treated with a therapeutic agent31. In all cases the intertwining of appropriate biomarkers, companion diagnostics and mechanistic under- standing of the drug mode of action are driving such efforts.


Definition of a Precision Medicine Drug The oncology research and clinical communities have pioneered the development of ‘Targeted Therapies’. It was long recognised that in a patient population with the same clinical disease state, het- erogeneity of the molecular etiology and develop- ment of the tumour led to different therapeutic responses by individual patients. However, a tar- geted therapy may be effective in a sub-population of patients who have different underlying molecu- lar similarities. The PMC has extended this con- cept beyond just the oncology sector. When evalu- ating New Medical Entities (NMEs) approved by the FDA, PMC categorised personalised medicines as “those therapeutic products for which the label includes reference to specific biological markers, identified by diagnostic tools, that help guide deci- sions and/or procedures for their use in individual patients”33,34. However, we would propose, based on our discussions above concerning the differ- ences between personalised (N-of-1 model) versus precision (1-in-N model) medicine that it is more appropriate to refer to them as ‘Precision Medicine Drugs’. It is important to note that the physician utilises


the biological marker(s) listed on the drug label in prescribing the Precision Medicine Drug. This should not be confused with Companion Diagnostic biological markers. There appears to be widespread agreement


that a Companion


Diagnostic is a biomarker(s) used in a specific con- text that provides biological and/or clinical infor- mation that enables better decision-making about the development and use of a potential drug thera- py35. In recent years the use of Companion Diagnostics has found broad applicability in clini- cal trials and are used in the optimised selection of clinical trial patient populations. In particular they have found use in selection or exclusion of patient groups for treatment with that particular drug in determining responders and non-responders to the


Drug Discovery World Summer 2018


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