TECHNOLOGICAL DEMAND 01: Personalized Prescription System “Personalized Drug Prescription System” PPS

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: *JARA: Electronical Medical Record System. Extremadura Health Care Service (SES)

01.B). Current situation:

At this time, part of the information necessary for the “guided” prescription is available in the digital clinical record; another must be produced and integrated into it (e.g. pharmacogenetics). Until now, analyses of biomarkers involved in response to drugs have been interpreted manually, or with some application, isolated from the electronic medical record environment. Therefore, a technological tool is required, to be able to obtain the information contained in the electronic medical background, to evaluate it and to offer the most appropriate drug for a given situation at the moment of prescription. In short, personalize the prescription module of the Electronic Health Record, giving it the competence to capture the necessary information for choosing the right drug in a given situation.

01.C). Objective:

To develop an electronic prescription module that allows the personalized selection of the most appropriate drug in each situation. Integrated in the Electronic Clinical Record, specifically in JARA (electronical medical record system from Extremadura Health Service, SES).

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

Computer program, integrated into the Electronic Medical Record, specifically in the prescription module (JARA, SES), able to capture all information likely to increase the efficacy and safety of drugs and other elements used in drug therapeutics, at least the one included in the documents of the competent Drug Regulatory Agencies (European EMA and Spanish AEMPS). When appropriate, it should include relevant information regarding: 1) Clinically relevant interactions: drugs / drugs / food / medicinal herbs / environment, etc., 2) Clinical: Personal and Family Background, pathophysiological condition, other data from the individual’s life (habits, etc.), relevant electronic medical record regarding drug response, 3) Routine analytical data (hematology, biochemistry, urine, etc.), 4) Pharmacogenetic biomarkers, 5) Plasma levels of drugs and other relevant biomarkers (physiological, microbiome, etc.), intestinal perfusion, and any other element that may be determinant in the selection process’ optimization of a certain active principle, (6) Adverse reactions and therapeutic failures.

Collection of the available information in the Electronic Medical Record (Clinical, Analytical, etc.), at least the determinants in the decision making, and connection with the new ones created with pharmacogenetic biomarkers, pharmacokinetics, interactions, etc. Generation of data and an algorithm for decision making. It is expected to include at least (Table 1) one database of 1) clinically relevant interactions, 2) clinical data, 3) analytical data (biochemistry, hematology, urine, those included in the routine, etc.); and connection to the other databases able to be consulted when selecting an active principle and developed in other Subprojects: 4) Genetic Biomarkers, 5) Pharmacological and analytical (microbiome, etc.), 6) Adverse Reactions and Clinical Response.

In summary, to generate an algorithm integrating data that allows the selection of the most optimal drug in the context of pharmacological therapy, taking into account data of the prescription and the patient:

Algorithm: [Interactions + Clinic + Analytical + Genetic + Pharmacological] / Adverse Drug Reactions and Response

The generation of personal and secure access to data (personal card, etc.), integration in the digital medical record, possible adaptation to other prescription systems, especially in Europe and Latin America, will be valued. Additionally, the possibility of developing a professional training program for its use will be valued.

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

Group 1: Databases from the Clinical Record, Guidelines and Regulation

1a). Database of Interactions (regulatory recommendations, clinically relevant)
1b). Clinical Database:pathophysiology, antecedents, evaluation and clinical evolution.
1c). Database of Biomarkers of Biochemistry and Hematology, Urine, etc., routine

Group 2: Software and Algorithms

1d). [Connection] Databases of regulatory recommendations: genetic biomarkers (Subpr. 2)
1e). [Connection] Databases of pharmacological and analytical biomarkers (Subproject 3)
1f). [Connection] Database of Adverse Reaction and clinical response markers (Subproject 4)
1g). [Interconnection] between them in JARA environment
1h). Software and algorithms for drug selection based on queries in 1a-1b-1c-1d (+ data generated in Subpr 2) -1e (+ data generated in Subproject 3) to predict Adverse Reactions (data generated in Subpr.4)

Table 2. Summary of Databases of consultation for creation of the PPS in MEDEA


01. CLINICAL: Text of the clinical record (personal and family antecedents, evolution, physical examination). Pathophysiological data (pregnancy, lactation, illnesses, etc.). Coding data of the discharge report.
02. ANALYTICS: Analytical data: biochemistry, hematology, urine systematics.


03. GENETICAL: Creation of a Database with genetic biomarkers and another relevant one included in the Technical Data Sheet [BDFT]. Genetic database (from Challenge 2) [BDG]. Development of data interpretation algorithm of the [BDGa vs BDFTa] genes / drugs.
04. PHARMACOLOGICAL: Database of analytical and microbiological markers (from Challenge 3) [BDAyM]. Development of data interpretation algorithm of the [BDAa and BDMa with respect to BDFTa]
05. ADVERSE REACTIONS: Database of Adverse Reactions related to biomarkers (clinical, analytical, genetic, interactions or pharmacological)


06. INTERACTIONS: Database of interactions of drug / drug / food / medicinal plants / nutritional supplements etc. [BDI] Development of interaction data interpretation algorithm [BDGa vs BDIa].