Genetic variability is one of the fundamental causes related to individual differences in response to drugs, some patients do not respond, and others have unexpected adverse reactions due to the presence of genetic polymorphisms, variability in their DNA that determines abnormal responses. Being able to predict unexpected responses by individually adjusting pharmacological treatment is the challenge of pharmacogenetics and therefore of Personalized Medicine. However, there are multiple challenges for its implementation in clinical practice, one of them is the rapid management of multiple genetic variables grouped into different phenotypes related to response prediction quantum algorithms.
QuantumPath® in the QHealth project
“QHealth: Quantum pharmacogenomics applied to aging,” is the first major research project on quantum computing applied to life sciences to receive funding from the CDTI (Centro para el Desarrollo Tecnológico Industrial). The project was awarded in the call of the CDTI 2020 Missions Program, specifically in the Mission focused on: “Providing a sustainable response to diseases and needs arising from aging“, within the framework of the Spanish Business Leadership in R&D program.
In the scientific field, QHealth is a project at the global forefront of basic research in quantum pharmacogenomics that:
· It addresses the hypothesis about the existence of a correlation among physiological and genetic limitations, consumption of prescribing drugs history, and the active principles an older adult has been exposed to, including the reactions to these and the possible side effects of new drugs in near future.
· It focuses on the most relevant factors to avoid side effects and errors in drug prescription and medical treatments very frequent in the case of older adults, for example, when doctors prescribe new drugs or introduce modification regarding the drugs the patient has been taking.
· Studies the creation of a methodological and technological environment that, according to the results obtained, will allow the development of future health software solution which use quantum computing for the optimization of the prescribing drugs administration.
The QHealth project is being carried out by a consortium of the companies aQuantum (alhambraIT), Gloin and Madrija and the collaboration of the Instituto Universitario Biosanitario de Investigación de Extremadura (INUBE) the Universidad de Extremadura (UEx) and the Universidad de Castilla La Mancha (UCLM). QHealth is a multi-year project (started in August 2020 and will end in December 2023), which has a total budget of 5,160,477.00 €, and received a grant from CDTI of 3,671,281.69 €. The project has been supported by the Spanish Ministerio de Ciencia e Innovación and by the FEDER (Fondo Europeo de Desarrollo Regional).
In this project aQuantum, with Grupo Alarcos of the UCLM and the Unidad de Farmacogenética y Medicina Personalizada del INUBE, is responsible for the work packages directly related to the research of quantum methods, technologies and solutions required for pharmacogenomics.
In 2020, the QHealth project consortium selected QuantumPath® (QPath®) as the quantum platform that provides technological support to the QHealth project.
The Consortium reached this conclusion after an extensive and detailed comparative study of the most relevant tools for quantum software development, which facilitate working with the proprietary technologies of the major quantum computing vendors. In this case, the potential of the application of QPath® in a critical business ecosystem such as the one that demands a health-oriented software solution was evaluated.
The fundamental objective of the study was to investigate the functionalities that could be provided by the state-of-the-art platforms and tools existing at that time, paying special attention to those that offered the greatest feasibility for the analysis of problems that make up pharmacogenomics applied to aging. From this point of view, the study found that:
· There were no de facto standards, so the product to be selected should be sufficiently modular and flexible to adapt to rapid technological changes that might occur.
· All manufacturers offered development environments and services highly oriented to the research and testing of specific environments. Each manufacturer brings its solutions, its business philosophy and has specific software dependencies. The chosen product should provide a level of abstraction that allows the solution to be independent of the underlying technologies.
· There were vendors of quantum computers based on gate technology that may become business realities in the next few years; but at that time, they still had to solve relevant issues such as qubit resolution and error control. The selected product should include these aspects so that it can be able to use and scale this functionality as advances in this field occur.
· QAOA optimization technologies, which rely on the principles of annealing, are mature and provide the confidence needed to rely on them in the research and design of a system such as the one proposed at QHealth. The chosen product had to be able to provide a set of interfaces and services that would allow these technologies to be used with confidence in an enterprise environment.
Since the priorities at that time were to have hardware products capable of serving the business, the importance of the software engineering associated with this type of product is being neglected. The selected product should ensure that both the generated assets and their life cycle are properly managed.
In this context, QPath® was chosen as the general-purpose quantum platform for the project, as the consortium concluded that it provides the necessary to give adequate support to a health research project of the profound depth of QHealth. In fact, it played a major role in overcoming the enormous challenges of the project that the platform faced:
· It is 100% agnostic, which makes it possible to run the experiments in as many providers as supported by the platform. Without having to redesign the work packages for it.
· It has no limit in the scaling required for the execution of the experiments, beyond the own imposed limit of the providers that are selected for the execution. In addition, it provides an environment that allows executions in the most optimal way possible (both on simulators and quantum computers).
· Efficiently supports the two quantum technological approaches (gates and annealing), something that will be decisive for the most appropriate quantum solution to be used in the different challenges to be solved, taking into account the best possible algorithm to be applied without depending on the technological approach.
· It facilitates and provides real guarantees, in this case thanks to its qSOA® architecture, to make the practical projection of the research feasible, in which the feasibility of real-time integration of quantum services with classic healthcare systems is essential.
Let’s see below if QPath® has ratified in the execution of the QHealth project the reasons why it was selected as the valid platform to achieve the success of the project.
The pharmacogenomic need for a quantum solution: the Q-PGx model
The aging of the population, together with the dramatic increase in cardiovascular and neurological diseases in relation to age, has caused a large number of elderly patients to require chronic medical treatment for these disorders.
Age-related changes in physiology and body composition result in altered pharmacokinetics and pharmacodynamics that often require drug dose adjustments in older adults.
Polypharmacy and age-related comorbidities increase the risk of drug-drug interactions that can cause unexpected Adverse Reactions in elderly patients during pharmacological treatment. Therefore, the prevalence of polypharmacy among older adults, together with the association of age with physiological changes and comorbidities, provides important challenges in adherence and in the attempt to avoid drug-related adverse events.
To address this complex situation, the INUBE research team has chosen to define the Q-PGx Model (from Quantum Pharmacogenomics), which will make it possible to determine the factors related to pharmacogenetic variability and the interindividual difference in response to drugs of these sets of variables:
a. Genetic polymorphisms. As has been mentioned, agile tools are needed that quickly and efficiently relate the available genetic information, every day the availability of a large part of the information of the human genome is becoming closer. This fact will imply the use of multiple variants, from the grossest levels to the least evident: variations of a single nucleotide SNP.
SNPs are a type of genetic polymorphism that produce a variation in a single base pair. This genetic alteration can produce differences in any protein related to the pharmacokinetic (absorption, distribution, metabolism, or excretion) or pharmacodynamic (mechanism of action) process. If, therefore, the possible variations throughout the genome are combined, just in a single nucleotide, in one or more of the components that determine the concentration of the drug in the blood (pharmacokinetics), plus the variants that encode the mechanism of action, the number of possibilities is enormous, especially if one takes into account the existence of more than 3,000 drugs authorized in Spain, in more than 30,000 different pharmaceutical presentations.
b. Pharmacological polytherapy. The problem becomes even more complex when the existence of polytherapy is assumed, that is, the drugs are used alone rarely and less in the elderly. The conceptualization of several genes interacting in different phases and in several drugs at the same time is in itself an innovation of the QHealth project: Quantum pharmacogenetics applied to ageing.
c. Clinical conditions. As has been mentioned, the variability of the response to medications in the elderly is determined by the pre-existing genetic condition, the pharmacological combination, and finally by the change in the clinical condition. The modification in renal clearance, liver function, etc., determines the differences in the inter-individual variability in the response to drugs. Therefore, an older adult receiving the same pharmacological combination therapy, in an invariable genetic condition, may present differences in response due to changes in their own physiology due to disease and age.
The generation of genetically mediated drug-drug interaction prediction quantum algorithms is part of the research generated between the INUBE Pharmacogenetics Unit and aQuantum, the specific research on clinical variables related to drug response is part of the joint research with Madrija, that together with the typification of the databases by Gloin complete the interaction between the companies and INUBE.
Specific QPath® contributions to the quantum tasks of the QHealth project
To date, two annuities of the project have been successfully completed (2020 and 2021) and the quantum work teams are progressing at a good pace in the tasks of the 2022 annuity. Among the results related to the research and processing of the information to be processed with QuantumPath® we can highlight those detailed below:
After the first definitions and several logical iterations in this type of research the INUBE team, specialized in personalized pharmacogenomics and led by Dr. Adrián Llerena, has refined the specifications of variables, relationships and pharmacogenomic design, specifying the processing of the information that will serve as technological support for the experts in pharmacogenomics and also for the preparation of the data sets and the extraction of the relationships between the different variables.
In addition, progress has been made in defining the specifications required for processing in QPath®. aQuantum has conducted extensive research and designed the possible models of quantum algorithms to address the main problem of the project, so it has participated in the design of quantum algorithms for quantum simulation and, more specifically, in the research and implementation of quantum algorithms, making a first definition of feasible data exchange parameters for processing in the QPath® architecture.
Another major task to be solved in the project is to define the methods and solutions to perform the Quantum Software Modernization. The feasibility of this task has been solved using the capacity of QPath® to integrate its own- and third-party applications and solutions to the platform. Within this activity multiple tasks have been addressed:
· It has been investigated how to provide quantum extensions for existing UML diagrams, with the objective of representing all the information of quantum systems to be processed in QPath® in combination with that related to classical information systems.
· The mapping (high-level transformation) between KDM and UML models has been defined.
· Extensions in both metamodels for quantum computing/information and the feasibility of their incorporation into QPath® have been investigated.
Progress has continued in the task of Quantum Software Testing with the tools offered for this purpose by QPath®. This activity has addressed tasks such as:
· Research so that testing generation engine to take the original circuit to be tested and the most appropriate operators to apply them to QHealth.
· Research on how the QPath® generation engine should run the test cases against the original circuit and against each case, attending to the rigor of testing for a healthcare system.
· Research on the most appropriate methods to perform the testing of annealing formulations for healthcare, taking advantage of the functionalities offered by QPath®.
Another of the tasks in which there are important advances is related to Quantum Software Quality, in which the results of the work have allowed:
· Propose a hybrid life cycle, which is the most suitable for applications such as those of the QHealth project, in which the quantum part must be integrated with classical IT, which can be executed with the tools offered by QPath® for life cycle management.
· Investigate the main quantum services frameworks, to customize them to the needs posed by the QHealth project.
Significant results have been obtained in the research and adaptation (where necessary) of QPath® support modules for healthcare software:
· It has been investigated how to make a metamodel that allows the storage of quantum algorithm knowledge and its resources, as well as how to provision the dictionary to share and publish the components of the quantum pharmacogenomic application to the outside.
· The scientific foundations have been laid at the analytical level to allow the mathematization of the problem to be solved in QPath® for the case of drug-drug/gene interactions.
· Mathematical approaches have been explored to allow, suggest, or inspire the development of a specific algorithm, totally tailored to the problems to be addressed.
· The mathematical/technical problems presented have been described and a solution proposed.
· It has been defined what the algorithm is going to solve using quantum technologies through QPath®.
· Different algorithms and their possible implementation have been investigated since the relationships between the variables included in the pharmacogenomic study will be carried in the form of quantum algorithms in QPath®, in charge of carrying out the quantum simulation process.
· The feasibility of transferring the design of such algorithms to QPath® has been investigated, and that both the design of the algorithm and its coding and implementation are done under the principles stipulated by the best practices of Quantum Software Engineering, so that it is a reality to achieve the quantum platform that provides guarantees on the support of the quality parameters.
· One of the first vertical service models incorporating quantum technologies has been defined to offer such services to healthcare systems. QHealth provides the complete design of a use case, in this project oriented to health in one of its multiple branches, pharmacogenomics. This use case can be refactored in other similar ones or, at least, serve as a basis for other new models.
Another of the tasks on which we have been working hard together with the rest of the Consortium members is the research and adaptation of QPath® support interfaces for healthcare software:
· Research has been done on the possibility of integrating new abilities into QPath® that allow exploiting the features of a given technology that is identified as useful for the healthcare system for aging.
· Research has been done on the integration of health indicators with the quantum software created in QPath® for the treatment of aging, more specifically the modelling of the variables considered in the clinical analysis.
· Research has been carried out on the health indicators of cardiac and neuronal nature, contained in the clinical records of patients, susceptible to be exploited by the solution implemented in QPath® to obtain the personalized treatment of patients.
· Based on the selected data set and the relationships between them, a storage method has been defined according to the volume of data to be processed, considering the use of Big Data technologies.
In response to the growing offer of quantum providers, during the implementation of the QHealth project, we decided to work with the quantum infrastructure services of AWS. The use of Amazon Braket resources in the project makes it possible to access more and better quantum resources from QPath®, strengthening the list of potential QPUs that will be responsible for processing data that will substantially improve the lives of many people.
In addition, the variety of quantum computers offered by Amazon Braket, the existing differences between them, as well as their support of quantum gate and annealing technology approaches, facilitate the proof of concept of the project with QPath® to meet the established objective of independence of the design of QHealth’s algorithms and solutions with respect to the quantum technologies to be used.
Summary of results
During the investigation, a Q-PGx model has been generated that includes the three groups of variables, namely, genetic, pharmacological, or clinical. The interrelation between the three would generate a unique and different situation for each patient, and for each patient over time. In other words, the aim is to address interindividual and intraindividual (transtemporal) variability, unimaginable and unfathomable scenarios in current pharmacotherapy. Although the prediction of the response based on genetic information is the challenge in which there are some proposals, the approach in polytherapy and polygenic influence is already a novelty. It should be noted that the inclusion of temporary clinical variables was, before this project, unimaginable and inconceivable with the tools available.
To these variables have been added the set of socio-health and clinical variables that are generated in the medical records in a systematized way: the MBDS, the minimum basic data set.
the model considers the interrelation of three groups of variables (with
different temporal characteristics), genetic information (invariant with the
current information in use, but that will vary when including epigenomics,
etc., modifiable by the environment). A second group of variables modifiable by
pulses over time (discontinuous variable): pharmacological polytherapy, the
drugs are administered in different combinations several times a day. Finally,
the clinic and physiological and pathophysiological conditions that will generate
continuous temporary changes. The joint management of all the variables will
allow the generation of prediction scenarios of inter-individual and
intra-individual variability according to temporal variables, something
unimaginable without the quantum solutions proposed in QHealth project.
It has also confirmed the need for research on methods and techniques to ensure quality quantum software and the advantage of using the tools offered by QPath® by default. This research is essential in the QHealth project since the aim is for quantum software to provide a reliable and sustainable response to the diseases and needs arising from aging and personalized medicine.
On the other hand, it has also been shown that it is essential to have services of the type supported by qSOA®, a technology that enables appropriate integration with existing healthcare information systems. In the research carried out, the usefulness of the dynamic definition of agnostic circuits, which make possible the ideal scaling to make feasible the executions of the experiments in the optimal possible way (both in simulators and in QPUs from different vendors according to different criteria in a transparent way), is clear.
Working with Amazon Braket quantum computers has allowed us to perform the different proofs of concept in a real environment similar to the one in which, later on, quantum services for personalized pharmacogenomics could be deployed. The results achieved validate the feasibility of the infrastructures required for QHealth.
During the course of the QHealth project, a set of high-level adjustments to the platform has also been defined to adapt it, in detail, to the specific resolutions of QHealth, which will allow optimization with respect to the state of the art of quantum technology applied to pharmacogenomics at present and in the future.
The review of the state of the art of pharmacogenomic variables that may be involved in the adverse effects of drugs in the elderly carried out by the INUBE team has provided the technical team with the necessary knowledge to design a data model that will serve as the basis for the following tasks, as well as to model the algorithms that will solve the complex treatments of the information to be processed in QPath®.
Another area of research in which significant progress has been made in the QHealth project is the study of the definition of the large number of variables and their storage, as well as the creation of tools adapted to support the enormous needs of the project in this regard. As we estimated from the beginning of the project, the volume of data and variables to be processed is enormous, and with a clear tendency to increase constantly. This is quite a challenge for the development of the hybrid classical/quantum system required by the QHealth project and, as it is being designed, it supports qSOA® with solvency together with the agnostic capacity of the QPU of QPath®. This makes it possible for the system to adapt in a transparent, clear, parameterized, and controlled way to new and improved quantum technologies and as fast as these can change without affecting the whole system.
Ongoing research in the project on the ideal scaling to make experiment runs as feasible as possible (both on simulators and quantum computers) has confirmed that the QPath® architecture robustly supports the requirements of the QHealth project in the dynamic management of data and variables. In this sense, it has been confirmed that qSOA® is a key piece for the success of this hybrid classical/quantum project, and a piece that is difficult to replace for the real, effective and reliable integration of classical health services with quantum services required for the success of the QHealth project.
The solid progress of the project in general, and in particular those directly related to quantum computing tasks in the QHealth project, has allowed us to verify the high capacity of QuantumPath® to meet the needs of a project of enormous demand in the practical application of the disruptive quantum technology, both for the high capacity of its native services and its independence from quantum providers, and for the ease with which we have been able to adapt the platform to the specificities of the project.
The advances registered in the creation of a methodological and technological environment with QuantumPath® leave no doubt about the feasibility of using quantum computing for the optimization of the prescribing drugs administration.
It has been an exciting journey for us during these two years of the QHealth project, and it has been no less satisfying to see that the project consortium made the right decision in selecting QuantumPath®.
Finally, we are proud that QPath® can contribute directly to the success of a research project as important from a human and social point of view as QHealth, focused on improving the quality of life of the elderly through the practical application of personalized medicine.