Prof. Milan Macek. Professor of Medical and Molecular Genetics Chairman of Department of Biology and Medical Genetics Division of Clinical Molecular Genetics and the National Cystic Fibrosis Centre- University Hospital Motol and 2nd School of Medicine -Charles University Prague- Czech Republic. ----- There is an increasing need to manage cost-effectiveness issues of novel or relatively expensive technologies that are currently in use or being proposed for the treatment of rare diseases. Cystic fibrosis (CF), where so called „CFTR modulating therapies“ rendered by several novel orphan medicinal products (e.g. ivacaftor, lumacaftor) are rapidly being introduced into clinical practice, will be used as a model. Health-economic evaluations of rising pharmacotherapeutic costs, as the major driver of overall cost, have to be part of the cost analysis of chronic and progressive (rare) diseases like CF that may require lifelong therapy. Total costs include not only direct healthcare costs but also the cost of lost productivity by both patients and family caregivers. When considering the results of cost-effectiveness analysis of new technologies associated with the management of CF, it is unreasonable to expect that the incremental cost-effectiveness ratio to be less than the generally applied thresholds (willingness to pay) for other common diseases. This issue is further compounded by mutation specific therapies for a subset of the overal cohort of CF patients. Therefore, when assessing CF and other rare diseases, such analyses should include complex health technology assessment approaches, which evaluate comparative treatment effectiveness (novel and established), as well as wider social benefits and ethical aspects. We will present the experience of the Prague CF center in terms of costs of illness studies and pharmacoeconomical approaches to studying children and adolescents with this disease.

1. Cost of illness studies in rare diseases:
cystic fibrosis as an example
Prof. Milan Macek, M.D, D.Sc.
Department of Biology and Medical Genetics
Charles University Prague – 2nd Faculty of Medicine
Barcelona, Val d´ Hebron VHIR– November 18, 2014

2. 25 years of CF
research
How to apply this success in
clinical practice
in the time of austerity ?

3. Technology
development
&
„Interpretive
Gap“
FORGE
Canada
2014
Phenotype
PMID: 24906018 PMID: 24387988

4. The challenge
• Over 4700 Mendelian phenotypes with known
causative gene (OMIM.org, NGS panels)
• Allelic heterogeneity is the rule
– Many genes have >100 mutations
• Disease implications
– Known (usually) for common mutations
– Variably known for low frequency mutations (<5%)
– Unknown (usually) for rare mutations (<1%)
• Clinical diagnostic DNA sequencing identifies all 3
types of mutation

5. Need for accurate assessment of
disease-liability of mutations
• Diagnosis of clinical cases
• Newborn screening
• Carrier screening
– Pregnancy decisions
• Mutation-specific therapy
(CFTR modulating therapies)

6. • Molecular genetics – Mutation Specific
Therapies – CFTR modulating therapies

7. CFTR mutations
• Over 2000 mutations identified
– One mutation is common (p.Phe508del; 70% of CF
alleles)
– Twenty mutations are low frequency (15% of CF
alleles)
– Remainder (>1800) are rare (15% of CF alleles)
• Disease implications of most rare mutations
is unknown

8. Clinical variant annotation the next
challenge in medical genomics:
Mutation versus Variant
PMID: 24387988

9. Databases

10. CFTR modulating therapy: mutation classes
S549R

11. Gating defect mutations: S549R (T>G) and G551D
„Potentiators versus Correctors“
J Cyst Fibros. 2012 May;11(3):237-45

12. Collection of mutation data
Clinic
Laboratory
Repository

13. Collection of mutation data
Clinic
Laboratory
> 2000 mutations 39,689 patients

14. Contributors to CFTR2

16. Summary of data collected
Clinical data analysis
35,319 patients
Lung function
(FEV1% predicted)
24,946 patientsb
Sweat chloride
(mmol/L)
24,913 patientsa
Pancreatic status
(PS or PI)
30,236 patientsc
CFTR2 database
39,689 patients; 25 clinics/registries
159 mutations
with allele
frequency ≥0.01%
4,377 patients
(5 registries/clinics) excluded*
Genotype
70,777
chromosomes

17. How did we determine which mutations cause CF
and which ones don’t?
A 3-pronged approach for assigning disease liability
Clinically consistent mutation
Average sweat [Cl-] ≥ 60 mEq/L
Genetically consistent mutation
Mutation not seen in non-transmitted
‘healthy’ CFTR gene in father of CF patient
NCFo-nc-aduissienags em cuatuastiionng


Functionally consistent mutation 
< 10% of WT CFTR function
Genetically inconsistent mutation
Mutation found on non-transmitted ‘healthy’
CFTR gene in father of CF patient

18. Improved clinical care: CFTR2.org

20. 99
90% 87%
54%
97
53%
18 18
Sweat Chloride
Lung function
Pancreatic insufficiency
Average Age

21. Improved education via publically
available apps: CFGeneE

22. Mutations cluster by ‘theratype’
D1152H
F1052V
WT
R117C R117H*
D579G
S549R*
R1070W*
R347P
S945L
P67L
I336K L206W
A455E
D614G
P205S
R1066H
G85E
100
80
60
40
20
0
1 10 100
CFTR folding (as % of WT)
Log CFTR chloride current (as % of WT)
T338I
L927P
G1244E
S341P
G551D
R334W*
S549N
G970R
R352Q
G178R
R347H*
S1251N*
S997F*
D110H
Channel defect
Folding and
channel defect
#
Folding defect
Mutations in red are
potentiated by
Ivacaftor/Kalydeco
Mutation in blue
responds to corrector
(VX-809)

24. VX-770, Ivacaftor – in vitro studies

25. VX-770, Ivacaftor – sweat chloride

26. VX-770, Ivacaftor – FEV1

27. KalydecoTm – mutation specific therapy in CF
„CFTR modulating therapy“
Ultra-Orphan drug
for ~ 4% of CF patients

28. 2012 2013
http://www.medpagetoday.com/Pulmonology/CysticFibrosis/42018
http://www.forbes.com/sites/matthewherper/2012/12/27/the-most-important-new-drug-of-2012/

33. Courtesy Dr. Wills Hughes-Wilson

36. Increase costs in oncology using „biological therapy“

37. „End of Life“ versus „Life Saving Therapies“
http://www.commonwealthfund.org/~/media/Files/Publications/Issue%20Brief/2012/Jan/1576_Chalkidou_
end_of_life_drugs_Intl_brief.pdf
Health care
Versus
Social care
Savings?
Different bugets?
Redistribution of
insurance

40. „Generics dividend“…. Will it shitt towards orphan medicinal products?

42. Background
• Analysis of the economic burden in CF is important for disease management
• Assessment of baseline (direct) costs prior to the introduction of CFTR
modulating therapies for health insurance companies
• Assessment of cost effectivness and implemenation of CF treatment schemes
Klimeš et al. ERS Monogr 2014; 64: 304–319

43. Objectives
• To assess the direct costs of CF within the CZ medical care
• The prevalence-based cost of illness analysis was performed in
relation previously identified „major cost drivers“:
– severity of CF lung disease (measured by FEV1 % predicted)
– Age / gender
– BMI (reflecting underweight = general nutritional status)
– Presence of chronic sino-bronchial infections (P. aeruginosa)

44. Patients and Methods
• Clinical and laboratory data from the national CF registry
(www.cfregistr.cz)
• Cost data from the health insurance (www.vzp.cz, www.szpcr.cz)
• Overall, 245 CF randomly selected patients were stratified by their
age, gender, BMI and BMI z-score, P. aeruginosa and FEV1%
(“mild” >70; „moderate 40< and <70 and severe CF lung disease
<40; % predicted)
• Healthcare costs were considered within: a) inpatient care, b)
medicinal products and devices (MPD) and c) Procedures
(laboratory examinations, diagnostics and outpatient care)
• All costs were in year 2010 prices
• Descriptive statistics, Multivariate regression analysis (generalized
linear model – GLM)

45. Results - 1
• The average (median) patient age was 16.46 (15.0) and 34.7% were
adults (older than 18 years)
• The average (median) FEV1% was 86.8% (94.0%): 76.7% patients
had mild-, 14.3% moderate- and 9% severe- CF lung disease
• A total of 23.3% cases were chronically colonized with P.
aeruginosa.
• The mean (median) costs of mild, moderate and severe lung
disease were €3,804 (€1,069), €5,825 (€1,271), and €13,929
(€6,197)
• Patients with P. aeruginosa had substantially higher costs than
those without proven infection (€3,455 vs. €10,105; Mann-Whitney
p=0.0001), using ECFS registry classification.
• Costs are mostly clustered around lower monetary values, but have
been increasing markedly with decreasing FEV1%

46. Results - 2
• In regression analysis we used only variables which are
related to the direct costs:
– In the case of total costs, disease severity (measured by FEV1%)
had the most significant impact on costs, while, for instance, its
decrease by approximately 10 percentage points (pp) means
an increase in costs by approximately 10%.
– The presence of P. aeruginosa increased the costs by 1.82 x
– Other studied variables (gender, nutritional status and age) did
not significantly influence total costs.
• Chronic infection of P. aeruginosa has substantial impact
on each cost category (~increased hospitalisation costs)
– inpatient costs were increased by 2.7 times
– MPD cost were increased by 5.8 times

47. DISCUSSION and Conclusions
• Health care costs and their subsections (for inpatients, MPD,
procedures) are predominantly influenced by the overall CF
severity reflected by FEV1, and P. aeruginosa.
• Deteriorated nutritional status significantly influences procedure
costs (p=0.014).
• the costs presented in our analysis are below those reported in
other European countries studies. Even if accounted for different
price level (by purchasing power parity (PPP) € exchange rate,
average total costs equal to PPP €6,913 (vs. €5,002)
• These data are used for negotiations with health-insurance
companies
– Needs further breakdown by e.g. individual drugs utilized,
adolescents x adults
– Relatively younger and generally less severe (or better treated)
cohort….

48. Figure 1: Total mean (median) annual
costs related to the overall disease
severity (cost in € 2010)
Figure 2: Mean annual costs of each cost
component based on the disease
severity (cost in € 2010)

56. THANK YOU FOR YOUR ATTENTION!
milan.macek.jr@lfmotol.cuni.cz