TECHNOLOGICAL DEMAND 03: Pharmacological and Analytical Laboratory “Chemical-pharmaceutical and other relevant analysis”

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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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

(genetic biomarkers)

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)

03.B). Current situation:

In addition to genetic biomarkers, information from physical examination, and the family and personal background, there is information from analytical studies that may be decisive for the selection of a particular drug. Although the analysis of plasma levels of drugs and metabolites have been ordinary, these can be complemented with the study of other endogenous biomarkers that allow the knowledge of phenotypes that determine the response to drugs. These are not usually included in the analysis carried out to monitor the prescription. Additionally, of increasing relevance are other physiological and pathophysiological determinants which establish the absorption, for example, the intestinal perfusion and the microbiome.

03.C). Objective:

To develop a systematic analysis of plasma levels of drugs and metabolites, validated, which includes the most commonly prescribed drugs or those in which there is recommendation in the determination of levels or Serious Adverse Drug Reactions. A Database will be created (electronical medical record system, JARA) so that this information can be integrated in the prescription algorithm generated in the PPS (challenge 1, subproject 1). The addition of other parameters will be valued: endogenous biomarkers capable of mediating the determination of metabolic phenotypes and others, for example, microbiological analysis determining absorption.

Those relevant to the population of Extremadura, Spain and as far as possible European, will be determined. The possibility of extending to Latin American population will be valued. Application of the methodology developed, to the population of 3000 patients previously genotyped.

03.D). Functionality required by the suggested solutions:

Capability to measure plasma levels of drug and metabolites in a fast and accurate way. Additionally, to measure endogenous biomarkers that can be used to determine the metabolic phenotype, absorption, distribution or elimination.

The capability to perform intestinal perfusion studies and the ability to perform microbiological analysis, microbiome, will also be additionally valued.

All these chemical and microbiological analysis must be interpreted and added to the analytical databases of the PPS in order to be used in the prescription algorithm connected to JARA.

03.E). Summary of products and tasks (Table 1):

3a). Development of laboratory analysis methodology for its application in the care routine.

3b). Databases of pharmacokinetic and other analytical biomarkers (Connected to Subproject 1)

3c). Software for the functional interpretation of analysis (metabolic phenotypes, for example).

3d). Systematic report based only on pharmacological and analytical biomarkers.

3e). Corresponding analysis of the 3000 patients in pilot for its evaluation.

3f). Connection with the PPS in JARA environment (Connected to Subproject 1)