Munir Pirmohamed discusses the potential impact of medicines optimisation in terms of ensuring the right patients get the right choice if medicine at the right time. He presents a case history of over prescription and introduces three examples of medicines optimisation through use of genetics, big data, and pharmacogenetics profiling.

1. Medicines Optimisation
Through Precision
Munir Pirmohamed
David Weatherall Chair of Medicine
Department of Molecular and Clinical Pharmacology
Institute of Translational Medicine
University of Liverpool

2.  ensuring that the right
patients get the right choice of
medicine, at the right time.
 the goal is to help patients to:
 improve their outcomes;
 take their medicines
correctly;
 avoid taking unnecessary
medicines;
 reduce wastage of
medicines; and
 improve medicines safety.
Medicines optimisation

3. Current Paradigm
Efficacy • One drug fits all
Safety • One dose fits all

4. Case History
 70 year old man
 Attended for hypertension – was on 7 antihypertensives
 In total was on 17 drugs
 Assured me he was fully adherent
 Was on beta-blocker but HR 92/min
 Assured me he was fully adherent
Urinary drug
Screen by LCMS
CAFFEINE

6. GWAS Warfarin Mean Weekly Dose
(UK Prospective Cohort; n=714)
CYP2C9
VKORC1
Total = 57.9%
Age: 11.2%
Height 3.56%
Weight: 5.98%
Interacting meds: 0.98%
Sum of interacting meds: 2.2%
VKORC1: 25.61%
CYP2C9: 16.65%
CYP4F2: 0.49%

7. Time in Therapeutic Range
7%

8. VK
*3
*2
ParaDNA: Point of Care Device
1. Take sample
2. Use device to transfer and
seal sample into four wells
pre-loaded with single or
multiplex test chemicals
3. Four separate
independent
analyses possible
4. Analysis is complete in
45 minutes.
8
Courtesy of LGC, Commercial in Confidence
Sample Dispense
Three genotyping tests
(VKORC1, CYP2C9*2, *3)

9. Paul Downie, 56, of Grappenhall in Warrington was referred to the anticoagulation clinic following
treatment for an irregular heart rate.
He said: “The old way of prescribing warfarin is more hit and miss; this is bespoke medication, calculated
on my gene type.
“My mum went on warfarin eight months ago and she was back and forward to the clinic at least four
times on a weekly basis before they got the dose right. I went back once, which meant I could go back to
work quicker, feeling well enough to go back to normal life. I think this a win-win, for me and for the
health service.”
NIHR Collaboration for
Leadership in
Applied Health Research and Care
North West Coast
University of Liverpool
Clinics in Chester, Warrington
and Liverpool

10.  6.5% of all admissions to hospital are due to ADRs
 2nd commonest cause was due to diuretic-induced acute renal
impairment
 No clear guidance for renal function monitoring in the literature
 NICE guidance suggests once every 6 months – but not based on
robust data
 Practice likely to vary throughout UK
BMJ, 2004

11. Big Data Approach to Personalised
Renal Function Monitoring: Phase I
Big Data
Anonymised
electronic health
records
Cardiac failure
Diuretic doses
Machine learning
Development of
robust algorithms
Patient acceptability
GP acceptability
Aims
Personalised renal
function monitoring
guidance
Assess clinical
effectiveness and
cost effectiveness

12.  Point of care renal function monitoring
 Use of sensor for non-invasive monitoring
 Results sent via mobile phone technology to secure cloud based
server
 Personalised renal function monitoring algorithm – to be continually
refined Personalised messaging to GP about review of patient to
assess diuretic dosage
 Personalised renal function monitoring would optimise diuretic
dosing, prevent renal impairment, and prevent hospital admission in
keeping with 5-year forward view
 Cost effectiveness would need to be assessed
Big Data Approach to Personalised
Renal Function Monitoring: Phase II

13. Changing Demographics
• Multiple diseases
• Multiple organ systems affected
• Deterioration in:
• Renal function
• Liver function
• Respiratory function
• Cognitive function
• Mobility
• Polypharmacy

14. • Over 50 years
• Polypharmacy patients
• Small trial n=57 vs 53
• Pharmacogenetic profiling
• Clinical outcome measures
• We already have kidney and
liver tests before we
prescribe.
• What if we also had genetic
profiling?

15. • €15 million, H2020, 10 EU countries
• Implement pre-emptive PGx testing in a real world clinical
setting across 7 EU sites
• Evaluate patient outcome and cost effectiveness using
solid scientific methodology
• Start 1-1-2016, 5 years
• Consortium members:
• H-J Guchelaar (Coordinator),
• JJ Swen, M Kriek, LUMC
• M Pirmohamed, R Turner, UOL
• J Stingl, FDMD
• M Ingelman-Sundberg, KI
• M Karlsson, S Jönsson, PBUU
• M Schwab, E Schaeffeler, IKP
• VHM Deneer STZHM
• M Samwald, G Sunder-Plassmann, MUWV
• M van Rhenen, KC Cheung, KNMP
• C Mitropoulou, GHXF
• D Steinberger, BIOL
• CL Davila Fajardo, SAS
• G Patrinos, UPAT
• V Dolžan, ULMF
• A Cambon-Thomsen, UPS
• G Toffoli, E Cecchin, CROA

16. N=8,000
Project Outline

17. The Only Thing That Is Constant Is
Change
Heraclitus (535BC - 475BC)