TECHNOLOGICAL DEMAND 4:
Analysis in clinical units and Clinical Tests "Evaluation and Implementation in Clinical and Healthcare Tests Units"
Summary table of the technological challenge 3
|Subproject/Technological Challenge||Innovation Sector||Products and tasks|
|4. Clinical implementation and research units analysis Evaluation and implementation||Companies for the evaluation of health technologies and their impact on health outcomes||4a). Study and evaluation (clinical, cost-effectiveness) to develop a implementation strategy in clinical assistance and clinical research units (potential development of a data base)|
4b). Development Methodology to clinical implementation in clinical research units: Generation of metabolic phenotype evaluation based on genotyping (relationship with 3b))
For more information: https://www.boe.es/diario_boe/txt.php?id=BOE-A-2018-8151 *JARA Historia Clinica Electrónica, Servicio Extremeño de Salud (SES)
The rise of pharmacogenetics, pharmacogenomics in recent decades has led to a large number of research projects with controlled variables, which together with other case-only publications have generated a large amount of information, which has been reflected in the increase of pharmacogenetic biomarkers presence at regulatory level. However, there is no information available that allows evaluation under real conditions of use, determining the clinical impact in the prevention of adverse reactions, therapeutic failures, as well as evaluation in health outcomes, mainly of utilization of health services and economic analysis (cost-benefit). In summary, it would be convenient to carry out a study of Health Technology Assessment of this tool, which could be classified as a “Decision Supporting Tool”, that would allow decision-making regarding the implementation strategy.
As mentioned elsewhere in this document, in the clinical research process and assistance it will be essential the use of genetic biomarkers, this will allow to generate drugs for groups of patients (stratification) or in some cases individualization. For the current Clinical Test concept, knowledge of the genetic profile can help to avoid interactions with other drugs. From the assistance point of view it is essential to generate an assistance model that stratifies / personalizes the prescription. Among the genetic markers, those that determine the metabolic capacity are the most studied and those most included in the regulation framework. However, one of the great problems is the extrapolation of the phenotype data from the genotype, therefore, it is necessary to generate a new algorithm that allows qualitative information to be transformed into quantitative information, something that can be done from the existing data in the literature and that generated within the project. The possibility of extrapolating the metabolic phenotype could be useful in the selection of healthy volunteers or patients to be included in Clinical Tests. It is therefore necessary to consider an analysis of the economic impact that would mean the implementation of this type of strategies in Clinical Test Units.
Carry out an evaluation of the clinical impact but mostly economic, also of the use of health systems in the healthcare and clinical research context, mainly patients involved in Clinical Tests in order to design implementation strategies.
Secondarily generate an algorithm of the geno / phenotype relationship for clinical implementation based on experimental or review data that serves as a basis to optimize these implementation strategies in assistance, but especially in clinical research.
Functionality required by the proposed solutions:
Design able to determine the decision to implement or not a personalized system of prescription or participant’s selection in Clinical Tests, with analysis of economic impact and use of health systems.
Additionally, ability to predict the metabolic phenotype from a certain genotype (in relation to Subproject 3) in patients during treatment, in order to select healthy patients or volunteers more suitable for a given study.
Summary of products and tasks (Table):
4a). Study and evaluation (use of health services, economic -cost-effectiveness-) that could generate a useful database to establish a general methodology of implementation in healthcare and research (Clinical Tests).
4b). Methodology for its application in Clinical Tests: Generation of algorithms for metabolic phenotypes based on the gene / phenotype relationship (potentially related to 3b).