TECHNOLOGICAL DEMAND 04: Analysis in clinical and research units “Evaluation and Implementation in Clinical and Research Units”
The selection of a specific drug for pharmacological prescription in the context of medical therapeutics has traditionally been carried out on the basis of trial and error. However, the information available to increase the effectiveness and prevent adverse drug reactions (hereinafter ADR) is increasing every day. The sources of this information can be found in the product’s technical data sheet (summary of product characteristics (SmPC) authorized by the Spanish Agency for Medicines and Health Products (hereinafter AEMPS) or the European Medicines Agency (hereinafter the EMA). Additionally, there is scientific information available as well as the recommendations of various consortiums and scientific societies. There is clinical information such as the pathophysiological conditions that determine the prescription, but also biomarkers, each time in a more important way. At present, more than one third of EMA SmPCs contain a genetic biomarker, with different relevance levels in their recommendation of clinical implementation.
Among the useful information to increase the efficiency of pharmacological prescription are several factors: Clinical (personal, family background of drug response and adverse drug reactions and the pathophysiology of the disease), Clinically relevant pharmacological interactions (drug / food / medicinal plants interactions, etc.); plasma levels of drugs and metabolites, genetic and other biochemical biomarkers (physiological and pathophysiological), clinical routine analytical data (biochemistry/hematology/microbiology, etc.), and genetic biomarkers; in addition to others to be potentially taken into account (eg the microbiome).
Although some of this information is available in the electronic medical record, its management is done manually, therefore a guided drug prescription tool that allows a rapid increase in the choice of drug in a context of polytherapy and multiple pathology is required, as recommended by the National Strategy proposed by the Spanish Senate. On the one hand, it is necessary to generate software that integrates it, on the other hand to produce the necessary information (e.g. pharmacogenetic biomarkers to position it in the digital clinical medical record). This system must be evaluated to establish a cost/effectiveness analysis that allows decision-making regarding its implementation in the National Health Service.
01.A) General Introduction of MEDEA project strategic line
- Personalized medicine and variability in the response to drugs. Although the personalization of medicine and therefore of pharmacological therapy exists from the very beginning of therapeutics, being its essence, at present the enormous information generated by the development of pharmacogenetics and pharmacogenomics, and the accessibility of genetic information, together with the daily development of computer tools capable of handling a greater amount of information make it a reality to try to objectify the numerous variables that determine the variability in the response to drugs. Therefore, a scenario has been generated in which the objectification of the empirical variables that have enabled the personalization of pharmacological treatment is now feasible. On the other hand, objectification supports personalization of the therapeutic guideline, which is of special relevance from the drug regulatory point of view.
- Contribution to the Sustainability of health services. The objectification of empirical observation and its potential use in electronic clinical background environments is one of the strategies for the reduction of variability in the response to drugs, specifically adverse reactions and therapeutic failures. This strategy can contribute in a decisive way to the sustainability of health systems, by reducing the indirect costs due to the failure of pharmacological therapy (i.e. ADR), mainly from the development of preventive strategies. This would be the basis for the development of a Personalized Medicine Program for the individualization of pharmacological therapy.
- Barriers to overcome for its implementation. Although the necessity of the generalized application of Personalized or Precision Medicine has been proclaimed, for example, in the recent general document of the Senate on National Strategy of Genomic, Personalized and Precision Medicine for the National Health System, there are still barriers to extend its implementation, specifically to public health services. These include, on the one hand, the exclusive use of genetics, without including other relevant physiological or environmental variables (or other type of biomarkers) of relevance in the variability in the response to drugs and, on the other hand, the lack of computer applications that simplify the prescriber’s decision-making in healthcare. There is an additional problem most of the applications are focused on a single drug, although the problem lies in selecting the prescription during polymedication in multi-pathology.
In summary, the main barrier detected for the implementation of personalized drug prescription based on the available objective information is the lack of a system that allows the selection of individual treatment in a pharmacological polytherapy situation (interactions are a crucial factor) objectifying the genetic variability together with other factors of relevance for drug response and ADR.
- The technological challenge: Development of a Personalized Drug Prescription System. The individualization of the prescription based on the objectification of multiple determinants of the variability in the response to drugs, depends on information partially existing in the Electronic Medical Record (a, b, c) and other new generation (c, d, e). Namely: a) clinical information: (personal and family background, codification at discharge, background of previous prescriptions-failures or successes, pathophysiological status: pregnancy, lactation, renal failure, liver disease, etc.), information on life habits- consumption of tobacco, alcohol, etc. b) routine analytical data – hematology, biochemistry, urine-. c) pharmacogenetic factors; d) other relevant data: plasma levels of drugs, etc. e) Interactions clinically relevant. Once the system is built, there is a second challenge that is its implementation in terms of public health services for both assistance and clinical research.
In summary, it is intended to have a drug prescription system that allows real-time consultation in the electronic clinical background of pharmacogenetic variables and other determinants in the choice of a drug, mainly contained in the Clinical Guidelines or at least in the drug regulatory level.
- Needs to be solved: the choice of drug at adequate doses, based on pharmacogenetic and other analytical biomarkers, according to the particular conditions of each patient (consumption of other drugs, clinical and pathophysiological situation), in order to prevent ADR and therapeutic failures, decreasing costs of health services as a last resort. Additionally, to provide a tool for the personalized intelligent selection of individuals in clinical studies with drugs.
- Subprojects. Next, the different components of the system to be built into different Technological Challenges are exposed, in such a way that, once integrated, they generate a prescription system that allows the necessary consultations and supports in the decision making, offering the possible alternatives in a concrete situation. The Innovative Public Procurement (CPI) to be executed in the MEDEA project is broken down into 5 Subprojects (technological demand, one Technological Challenge for each of them) that are summarized in Table 1:
1.- Personalized Prescription System -TIC System
2.- Molecular Laboratory (Genetic Biomarkers)
3.- Pharmacological and Analytical Laboratory (Chemical Biomarkers and others)
4.- Analysis in clinical units and of Clinical Trials
5.- Tools for clinical evaluation and adverse reactions
Table 1. Summary of the Subprojects and Technological Challenges
|Subproject/Technological Challenge||Innovation Sector||Products and tasks|
1. Personalized Prescription System (PPS)
(joint management of variables involved in the response to drugs: 1a-1b-1c-1d-1e-1f)
|TICS. e-Health||1a). Interactions Database (regulatory recommendations, clinically relevant).
1b). Clinical Data Base: pathophysiology, antecedents, evaluation and clinical evolution.
1c). Analytical Biomarkers Database: Biochemistry, Hematology, Urine, etc., routine checkup.
1d). [Connection] Genetic Biomarkers Database (2b)
1e). [Connection] Pharmacological and other biomarker database (3b)
1f). [Connection] Database of clinical response markers and Adverse Drug Reactions (5b)
1g). [Interconnection] between them in the environment of JARA*
1h). Software and drug selection algorithms
2. Molecular analysis
|Molecular analysis companies: pharmacogenetic biomarkers (RT-PCR, sequencing)||2a). Development of laboratory methodology for its application in the clinical care routine.
2b). Drug Regulatory Recommendations Database: genetic biomarkers (1d)
2c). Functional interpretation software of genetic analysis (4b).
2d). Systematic report based, only, on genetic biomarkers.
2e). Analysis in pilot of, at least, 3000 patients for its evaluation.
2f). Connection with the PPS in the JARA environment (1d)
3. Pharmacological and analytical analyses
other chemical pharmacological and others (microbiological) biomarkers
|Pharmacokinetics and analytical chemistry companies (drugs, metabolites, endogenous biomarkers)
Possible studies of intestinal absorption (microbiome).
|3a). Development of laboratory methodology for its application in the clinical care routine
3b). Databases of pharmacokinetic and other analytical biomarkers (1e)
3c). Software for the functional interpretation of analyzes (metabolic phenotypes, for example) (4b).
3d). Systematic report based only on pharmacological and analytical biomarkers
3e). Corresponding analyzes in a pilot of 3000 patients for their evaluation
3f). Connection with the PPS in the JARA environment (1e)
4. Clinical implementation and research unit 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)
5. Clinical effect and adverse drug reactions tools
Evaluation of clinical effect and ADR
|Biosensors companies; e-Health||5a). Application to a group of patients among those studied in the pilot 5a). Development of tools and/or devices (i.e. biosensors) for the evaluation of clinical response and adverse reactions in cardiovascular and metabolic, mental and oncological diseases, pain
5b) Database of markers in the clinical response and Adverse Drug Reactions (potential algorithm) (1f)
For further information: https://www.boe.es/diario_boe/txt.php?id=BOE-A-2018-8151 *JARA: Electronical Medical Record System. Extremadura Health Care Service (SES)
04.B). Current status:
The rise of pharmacogenetics and 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 present at drug 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 clinical implementation strategy.
As mentioned elsewhere in this document, the use of genetic biomarkers will be essential in the clinical research process and assistance. This will allow to identify drugs for groups of patients (stratification) or in some cases individualization. For the current Clinical Trials concept, knowledge of the genetic profile can help to avoid interactions with other drugs. From the clinical routine point of view, it is essential to generate a model that stratifies / personalizes the prescription. Among the genetic markers, those that determine the metabolic capacity are the most studied and, indeed, most included in the regulation framework. However, one of the great problems is the extrapolation of the metabolic 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 Trials. It is, therefore, necessary to consider an analysis of the economic impact of the implementation of this type of strategies in Clinical Trial 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 clinical routine care assistance, but especially in clinical research.
04.D). Functionality required by the proposed solutions:
Design able to help the decision to implement or not a personalized system of prescription or participant’s selection in Clinical Trials, with analysis of economic impact and use of health care services.
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.
04.E). Summary of products and tasks (Table 1):
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 Trials).
4b). Methodology for its potential application in Clinical Trials: Generation of algorithms for metabolic phenotypes based on the gene / phenotype relationship (potentially related to 3b).