Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting (original) (raw)
Related papers
Pharmacogenomic Approaches for Automated Medication Risk Assessment in People with Polypharmacy
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018
Medication regimen may be optimized based on individual drug efficacy identified by pharmacogenomic testing. However, majority of current pharmacogenomic decision support tools provide assessment only of single drug-gene interactions without taking into account complex drug-drug and drug-drug-gene interactions which are prevalent in people with polypharmacy and can result in adverse drug events or insufficient drug efficacy. The main objective of this project was to develop comprehensive pharmacogenomic decision support for medication risk assessment in people with polypharmacy that simultaneously accounts for multiple drug and gene effects. To achieve this goal, the project addressed two aims: (1) development of comprehensive knowledge repository of actionable pharmacogenes; (2) introduction of scoring approaches reflecting potential adverse effect risk levels of complex medication regimens accounting for pharmacogenomic polymorphisms and multiple drug metabolizing pathways. After ...
Pharmacogenomics Clinical Decision Support Systems
OMICS: A Journal of Integrative Biology, 2024
Pharmacogenomics is the study of genome-by-drug interactions. Pharmacogenomics challenges the traditional one-size-fits-all treatment paradigm, and aims to understand the mechanisms of person-to-person and between-population differences in drug efficacy and safety so as to deliver individually-tailored therapeutics. Pharmacogenomics emerged from its predecessor field of pharmacogenetics. That genetics contributes not only to disease phenotypes but also drug response variability was the path-breaking idea that paved the way for pharmacogenetics more than six decades ago (Motulsky, 1957; Kalow, 1962). For the past three decades, the question “why is clinical uptake of pharmacogenomics slow?” has been on the public agenda ever since the high-throughput omics technologies became available at scale. The uptake of pharmacogenomics around the world is a complex task with multifactorial determinants. Moreover, individually-tailored precision medicines require customization within the local context of each country. Clinical Decision Support Systems (CDSSs) are health information technologies deployed to facilitate decision-making in the above complex socio-technical terrain of pharmacogenomics implementation science. Pharmacogenetics has left its impressions in the sands of time since the mid-20th century while pharmacogenomics continues to harbinger the changing times in precision/personalized medicine today with the availability of CDSSs and digital health. https://www.liebertpub.com/doi/10.1089/omi.2024.0170
Pharmacogenomics, 2020
Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.
Disease-drug database for pharmacogenomic-based prescribing
Clinical Pharmacology & Therapeutics, 2016
Providers have expressed a strong desire to have additional clinical decision-support tools to help with interpretation of pharmacogenomic results. We developed and tested a novel disease-drug association tool that enables pharmacogenomic-based prescribing to treat common diseases. First, 324 drugs were mapped to 484 distinct diseases (mean number of drugs treating each disease was 4.9; range 1-37).Then the disease-drug association tool was pharmacogenomically annotated, with an average of 1.8 pharmacogenomically annotated drugs associated/disease. Applying this tool to a prospectively enrolled >1,000 patient cohort from a tertiary medical center showed that 90% of the top ∼20 diseases in this population and ≥93% of patients could appropriately be treated with ≥1 medication with actionable pharmacogenomic information. When combined with clinical patient genotypes, this tool permits delivery of patient-specific pharmacogenomically informed disease treatment recommendations to inform the treatment of many medical conditions of the US population, a key initial step towards implementation of precision medicine. Adverse drug reactions (ADRs) account for >100,000 deaths in the United States annually, including situations when drugs are appropriately prescribed and administered. 1 A systematic review reported that among the 27 drugs frequently cited in ADRs, 60% were
Multidisciplinary model to implement pharmacogenomics at the point of care
Genetics in Medicine, 2016
Despite potential clinical benefits, implementation of pharmacogenomics (PGx) faces many technical and clinical challenges. These challenges can be overcome with a comprehensive and systematic implementation model. Methods: The development and implementation of PGx were organized into eight interdependent components addressing resources, governance, clinical practice, education, testing, knowledge translation, clinical decision support (CDS), and maintenance. Several aspects of implementation were assessed, including adherence to the model, production of PGx-CDS interventions, and access to educational resources. Results: Between August 2012 and June 2015, 21 specific druggene interactions were reviewed and 18 of them were implemented in the electronic medical record as PGx-CDS interventions. There was complete adherence to the model with variable production time (98-392 days) and delay time (0-148 days). The implementation impacted approximately 1,247 unique providers and 3,788 unique patients. A total of 11 educational resources complementary to the drug-gene interactions and 5 modules specific for pharmacists were developed and implemented. Conclusion: A comprehensive operational model can support PGx implementation in routine prescribing. Institutions can use this model as a roadmap to support similar efforts. However, we also identified challenges that will require major multidisciplinary and multi-institutional efforts to make PGx a universal reality.
Integrating Genomic Data with AI Algorithms to Optimize Personalized Drug Therapy: A Pilot Study
Library Progress International, 2024
Personalized medicine has become more prominent in the course of the last few years to improve treatment methods by taking into account patients’ genetic makeup. Combining the genomic information into powerful new AI platforms in drug therapies opens up the way of reducing drug toxicity while enhancing the prospects for drug efficacy. This pilot study aims to determine the possibilities of using AI to analyze genomics data to help improve the approachability and effectiveness of drug therapies, which has been a major challenge given the lacunae in precision in the treatment strategies used. This pilot study is intended to enroll 50 patients with diverse chronic diseases. Targeted gene-specific sequencing was performed to obtain polymorphic loci on drug metabolism and treatment efficacy. AI tools such as machine learning models are used to help find patterns and relationships between genomic data and treatment results and risks. These were then compared to clinical outcomes in order to determine the viability of the AI-integrated method for recommending drug regimens. This study shows that the incorporation of genomic data in conjunction with AI greatly improves the accuracy of individualized pharmacotherapy. The AI-generated suggestions matched well with the enhanced patient experience to show the potential of this concept in the real world. It employs a broader clinically ascertained population and is warranted to replicate these findings, supporting the benefits of using genomic-informed AI applications for drug therapy to drive further development of personalized medicine.
Exploring polypharmacy with artificial intelligence: data analysis protocol
2021
Background Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada. Methods We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health...
Medicina, 2021
Pharmacogenomic (PGx) information can guide drug and dose selection, optimize therapy outcomes, and/or decrease the risk of adverse drug events (ADEs). This report demonstrates the impact of a pharmacist-led medication evaluation, with PGx assisted by a clinical decision support system (CDSS), of a patient with multiple comorbidities. Following several sub-optimal pharmacotherapy attempts, PGx testing was recommended. The results were integrated into the CDSS, which supported the identification of clinically significant drug–drug, drug–gene, and drug–drug–gene interactions that led to the phenoconversion of cytochrome P450. The pharmacist evaluated PGx results, concomitant medications, and patient-specific factors to address medication-related problems. The results identified the patient as a CYP2D6 intermediate metabolizer (IM). Duloxetine-mediated competitive inhibition of CYP2D6 resulted in phenoconversion, whereby the patient’s CYP2D6 phenotype was converted from IM to poor meta...
Clinical pharmacology and therapeutics, 2018
Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here, we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them. This article is protected by copyright. All rights reserved.