Keith Danahey

The 1200 Patients Project: Creating a New Medical Model System for Clinical Implementation of Pharmacogenomics

The paradigm of individualized drug therapy based on genetics is an ideal that is now potentially possible. However, translation of pharmacogenomics into practice has encountered barriers such as limited availability and the high cost of genetic testing, the delays involved, disagreements about interpretation of results, and even lack of understanding about pharmacogenomics in general. We describe our institutional pharmacogenomics‐implementation project, “The 1200 Patients Project,” a model designed to overcome these barriers and facilitate the availability of pharmacogenomic information for personalized prescribing.

Adoption of a clinical pharmacogenomics implementation program during outpatient care–initial results of the University of Chicago “1,200 Patients Project”

Pharmacogenomic testing is viewed as an integral part of precision medicine. To achieve this, we originated The 1,200 Patients Project which offers broad, preemptive pharmacogenomic testing to patients at our institution. We analyzed enrollment, genotype, and encounter‐level data from the first year of implementation to assess utility of providing pharmacogenomic results. Results were delivered via a genomic prescribing system (GPS) in the form of traffic lights: green (favorable), yellow (caution), and red (high risk). Additional supporting information was provided as a virtual pharmacogenomic consult, including citation to relevant publications. Currently, 812 patients have participated, representing 90% of those approached; 608 have been successfully genotyped across a custom array. A total of 268 clinic encounters have occurred at which results were accessible via the GPS. At 86% of visits, physicians accessed the GPS, receiving 367 result signals for medications patients were taking: 57% green lights, 41% yellow lights, and 1.4% red lights. Physician click frequencies to obtain clinical details about alerts varied according to color severity (100% of red were clicked, 72% yellow, 20% green). For 85% of visits, clinical pharmacogenomic information was available for at least one drug the patient was taking, suggesting relevance of the delivered information. We successfully implemented an individualized health care model of preemptive pharmacogenomic testing, delivering results along with pharmacogenomic decision support. Patient interest was robust, physician adoption of information was high, and results were routinely utilized. Ongoing examination of a larger number of clinic encounters and inclusion of more physicians and patients is warranted.

Evidence for Clinical Implementation of Pharmacogenomics in Cardiac Drugs

OBJECTIVE To comprehensively assess the pharmacogenomic evidence of routinely used drugs for clinical utility. METHODS Between January 2, 2011, and May 31, 2013, we assessed 71 drugs by identifying all drug/genetic variant combinations with published clinical pharmacogenomic evidence. Literature supporting each drug/variant pair was assessed for study design and methods, outcomes, statistical significance, and clinical relevance. Proposed clinical summaries were formally scored using a modified AGREE (Appraisal of Guidelines for Research and Evaluation) II instrument, including recommendation for or against guideline implementation. RESULTS Positive pharmacogenomic findings were identified for 51 of 71 cardiovascular drugs (71.8%), representing 884 unique drug/variant pairs from 597 publications. After analysis for quality and clinical relevance, 92 drug/variant pairs were proposed for translation into clinical summaries, encompassing 23 drugs (32.4% of drugs reviewed). All were recommended for clinical implementation using AGREE II, with mean ± SD overall quality scores of 5.18±0.91 (of 7.0; range, 3.67-7.0). Drug guidelines had highest mean ± SD scores in AGREE II domain 1 (Scope) (91.9±6.1 of 100) and moderate but still robust mean ± SD scores in domain 3 (Rigor) (73.1±11.1), domain 4 (Clarity) (67.8±12.5), and domain 5 (Applicability) (65.8±10.0). Clopidogrel (CYP2C19), metoprolol (CYP2D6), simvastatin (rs4149056), dabigatran (rs2244613), hydralazine (rs1799983, rs1799998), and warfarin (CYP2C9/VKORC1) were distinguished by the highest scores. Seven of the 9 most commonly prescribed drugs warranted translation guidelines summarizing clinical pharmacogenomic information. CONCLUSION Considerable clinically actionable pharmacogenomic information for cardiovascular drugs exists, supporting the idea that consideration of such information when prescribing is warranted.

Pharmacogenomics-Based Point-of-Care Clinical Decision Support Significantly Alters Drug Prescribing

Changes in behavior are necessary to apply genomic discoveries to practice. We prospectively studied medication changes made by providers representing eight different medicine specialty clinics whose patients had submitted to preemptive pharmacogenomic genotyping. An institutional clinical decision support (CDS) system provided pharmacogenomic results using traffic light alerts: green = genomically favorable, yellow = genomic caution, red = high risk. The influence of pharmacogenomic alerts on prescribing behaviors was the primary endpoint. In all, 2,279 outpatient encounters were analyzed. Independent of other potential prescribing mediators, medications with high pharmacogenomic risk were changed significantly more often than prescription drugs lacking pharmacogenomic information (odds ratio (OR) = 26.2 (9.0–75.3), P < 0.0001). Medications with cautionary pharmacogenomic information were also changed more frequently (OR = 2.4 (1.7–3.5), P < 0.0001). No pharmacogenomically high‐risk medications were prescribed during the entire study when physicians consulted the CDS tool. Pharmacogenomic information improved prescribing in patterns aimed at reducing patient risk, demonstrating that enhanced prescription decision‐making is achievable through clinical integration of genomic medicine.

Disease-drug database for pharmacogenomic-based prescribing.

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.

The Outlier in All of Us: Why Implementing Pharmacogenomics Could Matter for Everyone

The field of pharmacogenomics originally emerged in the 1950s from observations that a few rare individuals had unexpected, severe reactions to drugs. As recently as just 6 years ago, prominent views on the subject had largely remained unchanged, with authors from the US Food and Drug Administration (FDA) citing the purpose of pharmacogenetics as “tailoring treatment for the outliers.” It should not be surprising if this is the prevailing view—the best‐studied pharmacogenomic drug examples are indeed just that, genetic explanations of extreme responses or susceptibilities among usually a very small fraction of the human population. Thiopurine methyltransferase (TPMT) deficiency as a cause of severe myelosuppression upon treatment with azathioprine or mercaptopurine is found as a heterozygous trait in only ∼10% of patients, and homozygous (deficiency) carriers are even more rare—occurring in fewer than 1 in 300 patients. Malignant hyperthermia resulting from inhaled anesthetics and succinylcholine is believed to have a genetic incidence of only about 1 in 2000 people.

Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system

BACKGROUND A barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS). METHODS Actionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File - Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm. RESULTS As of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%. CONCLUSIONS Successful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

Patient Perceptions of Care as Influenced by a Large Institutional Pharmacogenomic Implementation Program

Despite growing clinical use of genomic information, patient perceptions of genomic‐based care are poorly understood. We prospectively studied patient‐physician pairs who participated in an institutional pharmacogenomic implementation program. Trust/privacy/empathy/medical decision‐making (MDM)/personalized care dimensions were assessed through patient surveys after clinic visits at which physicians had access to preemptive pharmacogenomic results (Likert scale, 1 = minimum/5 = maximum; mean [SD]). From 2012–2015, 1,261 surveys were issued to 507 patients, with 792 (62.8%) returned. Privacy, empathy, MDM, and personalized care scores were significantly higher after visits when physicians considered pharmacogenomic results. Importantly, personalized care scores were significantly higher after physicians used pharmacogenomic information to guide medication changes (4.0 [1.4] vs. 3.0 [1.6]; P < 0.001) compared with prescribing visits without genomic guidance. Multivariable modeling controlling for clinical factors confirmed personalized care scores were more favorable after visits with genomic‐influenced prescribing (odds ratio [OR] = 3.26; 95% confidence interval [CI] = (1.31–8.14); P < 0.05). Physicians seem to individualize care when utilizing pharmacogenomic results and this decision‐making augmentation is perceived positively by patients.

Analyzing the clinical actionability of germline pharmacogenomic findings in oncology: Germline Pharmacogenomics in Oncology

Germline and tumor pharmacogenomics impact drug responses, but germline markers less commonly guide oncology prescribing. The authors hypothesized that a critical number of clinically actionable germline pharmacogenomic associations exist, representing clinical implementation opportunities.