The European Society of Cardiology represents countries with a population of >900 million, and there are at least 15 million patients with HF in those 51 countries. In Europe, the prevalence of heart failure is estimated at 1-2%, and the incidence approaches 5-10 per 100 persons per year. In addition, in Europe every year an estimated 1.7 million patients are hospitalised for acute heart failure. The overall prevalence of heart failure is increasing because of the ageing of the population, the success in prolonging survival in patients suffering coronary events, and the success in postponing coronary events by effective prevention in those at high risk or those who have already survived a first event.
HF is the cause of 5% of acute hospital admissions, is present in 10% of patients in hospital beds, and accounts for 2% of national expenditure on health, mostly due to the cost of hospital admissions.
Impact for healthcare & patient
The results of BIOSTAT-CHF will identify patients with an elevated risk of death or heart failure hospitalization, despite current “optimal” heart failure treatment. In addition, this will result in identification of a group of patients that respond well to the currently recommended treatment. This is important for both groups. For the group of “bad-responders”, alternative treatments can and should be developed. For the group of “good-responders” no additional drugs are needed. Therefore, BIOSTAT-CHF is a first and major step towards personalised treatment of heart failure patients.
Economic Impact
The economic impact of BIOSTAT-CHF is large for two major reasons:
- Personalised treatment will result in less medication, leading to significant reduction of health care costs;
- The majority of the costs of heart failure patients are related to heart failure hospitalizations. Personalised treatment will in particular result in better treatment of the “bad-responders” leading to a reduced risk of heart failure hospitalizations.
Impact on science & future research
For several reasons, BIOSTAT-CHF will have major impact on science
- BIOSTAT-CHF is the very first attempt to provide a model that predicts response to therapy, incorporating demographics, biomarkers, genome-wide analysis and proteomics. This will enable novel treatment strategies to be developed. This will not only influence heart failure therapy, but other therapies in medicine as well.
- Knowledge of new genes will open a whole new field of research to study the mechanisms of the association with drug response and will result in the development of safer and more efficient drugs.
- Studying proteomics in response to treatment may identify pleiotropic effects of drugs and this may also open up a new line of research, aimed at identifying new functions of the drugs.
- Knowledge about biomarkers predicting a response therapy and changes of biomarkers during therapy will further identify unrevealed effects of current therapies, potentially leading to newer indications.
- BIOSTAT-CHF will also establish the relation between clinical improvement and biomarkers after initiation of recommended heart failure therapy and hard clinical endpoints. This will provide clear insights in the use of clinical response and/or biomarkers as surrogate outcome parameters in studying new drugs in heart failure.
Impact for Europe
The starting point of this proposal is the current heart failure guidelines of the European Society of Cardiology. BIOSTAT-CHF will be performed in 8 different European countries, i.e. France, Germany, Greece, Italy, Norway, Poland, the Netherlands, and United Kingdom. The participating centres are all linked to a HF management program network, which is an official instrument of the Heart failure Society of the European Society of Cardiology. Therefore, the results can be translated to the population of Europe.
Impact for Industry
The prediction model that predicts the response to current optimal heart failure therapy, the primary results of this project, may be patented.
In addition, a set of genetic markers in response to therapy may also have direct impact on the industry, since we will make the knowledge publicly available and it can therefore be used directly in developing new heart failure drugs.
Dissemination of knowledge
The knowledge that will be developed will be transferred to the scientific community via publications in scientific journals and presentations at scientific meetings. (Cardiology, Genetics, Pharmacology, Health economy)
Personalised therapy with SNP’s
99.9% of our genes are identical to anyone else's. The other 0.1% that makes us unique is made up of 3 million single nucleotide polymorphisms (SNPs) that occur one in every 1000 bases. A SNP is a variation of one nucleotide between the DNA sequences of individuals.
Pharmacogenetics is the study of the genetic basis for the difference between individuals in response to drugs in order to tailor drug prescriptions to individual genotypes. SNPs can be used to distinguish between patients who will benefit from a particular drug against those who will not. This ability to divide the population into drug responders and non responders makes it possible to target a specific population that would benefit from a new drug more effectively. The result will probably be a significant increase in the chance of getting a new drug through to market. This new approach defines a new discovery paradigm that moves beyond genomics to personalised drug treatment.
The information obtained from these polymorphic studies could be used in target validation. If a target is determined to be highly polymorphic, it could be abandoned. Drugs that were abandoned because they caused severe side effects in a minority of people, could be revived.
It is believed that in the future doctors will use "SNP-chips", tiny microarrays studded with the DNA sequences that bind to different SNPs. A patient's DNA would be washed over the chip and fragments that matched the sequence would bind to the chip and light up. With computer analysis, doctors would know which gene variations each person carried. Given this head start, they could intervene, long before the disease began to manifest or they could determine which medication would best suit that inpidual’s genetic makeup.
Factors contributing to Interinpidual variability in drug disposition and action
- Age
- Race/Ethnicity
- Weight
- Gender
- Concomitant Diseases - personalised medicine
- Concomitant Drugs
- Social factors
- Proteomics
- Genomics