J. Kevin Hicks

NALP3 inflammasome upregulation and CASP1 cleavage of the glucocorticoid receptor cause glucocorticoid resistance in leukemia cells

Glucocorticoids are universally used in the treatment of acute lymphoblastic leukemia (ALL), and resistance to glucocorticoids in leukemia cells confers poor prognosis. To elucidate mechanisms of glucocorticoid resistance, we determined the prednisolone sensitivity of primary leukemia cells from 444 patients newly diagnosed with ALL and found significantly higher expression of CASP1 (encoding caspase 1) and its activator NLRP3 in glucocorticoid-resistant leukemia cells, resulting from significantly lower somatic methylation of the CASP1 and NLRP3 promoters. Overexpression of CASP1 resulted in cleavage of the glucocorticoid receptor, diminished the glucocorticoid-induced transcriptional response and increased glucocorticoid resistance. Knockdown or inhibition of CASP1 significantly increased glucocorticoid receptor levels and mitigated glucocorticoid resistance in CASP1-overexpressing ALL. Our findings establish a new mechanism by which the NLRP3-CASP1 inflammasome modulates cellular levels of the glucocorticoid receptor and diminishes cell sensitivity to glucocorticoids. The broad impact on the glucocorticoid transcriptional response suggests that this mechanism could also modify glucocorticoid effects in other diseases.

Implementation of Clinical Pharmacogenomics within a Large Health System: From Electronic Health Record Decision Support to Consultation Services

The number of clinically relevant gene‐based guidelines and recommendations pertaining to drug prescribing continues to grow. Incorporating gene–drug interaction information into the drug‐prescribing process can help optimize pharmacotherapy outcomes and improve patient safety. However, pharmacogenomic implementation barriers exist such as integration of pharmacogenomic results into electronic health records (EHRs), development and deployment of pharmacogenomic decision support tools to EHRs, and feasible models for establishing ambulatory pharmacogenomic clinics. We describe the development of pharmacist‐managed pharmacogenomic services within a large health system. The Clinical Pharmacogenetics Implementation Consortium guidelines for HLA‐B*57:01‐abacavir, HLA‐B*15:02‐carbamazepine, and TPMT‐thiopurines (i.e., azathioprine, mercaptopurine, and thioguanine) were systematically integrated into patient care. Sixty‐three custom rules and alerts (20 for TPMT‐thiopurines, 8 for HLA‐B*57:01‐abacavir, and 35 for HLA‐B*15:02‐anticonvulsants) were developed and deployed to the EHR for the purpose of providing point‐of‐care pharmacogenomic decision support. In addition, a pharmacist and physician‐geneticist collaboration established a pharmacogenomics ambulatory clinic. This clinic provides genetic testing when warranted, result interpretation along with pharmacotherapy recommendations, and patient education. Our processes for developing these pharmacogenomic services and solutions for addressing implementation barriers are presented.

Developing knowledge resources to support precision medicine: Principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.

Voriconazole plasma concentrations in immunocompromised pediatric patients vary by CYP2C19 diplotypes.

AIM Our objective was to describe the association between voriconazole concentrations and CYP2C19 diplotypes in pediatric cancer patients, including children homozygous for the CYP2C19*17 gain-of-function allele. MATERIALS & METHODS A linear mixed effect model compared voriconazole dose-corrected trough concentrations (n = 142) among CYP2C19 diplotypes in 33 patients (aged 1-19 years). Voriconazole pharmacokinetics was described by a two-compartment model with Michaelis-Menten elimination. RESULTS Age (p = 0.05) and CYP2C19 diplotype (p = 0.002) were associated with voriconazole concentrations. CYP2C19*17 homozygotes never attained therapeutic concentrations, and had lower dose-corrected voriconazole concentrations (median 0.01 μg/ml/mg/kg; p = 0.02) than CYP2C19*1 homozygotes (median 0.07 μg/ml/mg/kg). Modeling indicates that higher doses may produce therapeutic concentrations in younger children and in those with a CYP2C19*17/*17 diplotype. CONCLUSION Younger age and the presence of CYP2C19 gain-of-function alleles were associated with subtherapeutic voriconazole concentrations. Starting doses based on age and CYP2C19 status could increase the number of patients achieving therapeutic voriconazole exposure.

Institutional Profile: Cleveland Clinic’s Center for Personalized Healthcare: setting the stage for value-based care

Cleveland Clinic (OH, USA) launched the Center for Personalized Healthcare in 2011 to establish an evidence-based system for individualizing care by incorporating unique patient characteristics, including but not limited to genetic and family health history information, into the standard medical decision-making process. Using MyFamily, a web-based tool integrated into our electronic health record, a patients family health history is used as a surrogate for genetic, environmental and behavioral risks to identify those with an elevated probability of developing disease. Complementing MyFamily, the Personalized Medication Program was created for the purpose of identifying gene-drug pairs for integration into clinical practice and developing the implementation tools needed to incorporate pharmacogenomics into the clinical workflow. We have successfully implemented the gene-drug pairs HLA-B*57:01-abacavir and TPMT-thiopurines into patient care. Our efforts to establish personalized medical care at Cleveland Clinic may serve as a model for large-scale integration of personalized healthcare.

Pharmacogenomics Implementation: Considerations for Selecting a Reference Laboratory

One of the initial steps for implementing pharmacogenomics into routine patient care is selecting an appropriate clinical laboratory to perform the testing. With the rapid advances in genotyping technologies, many clinical laboratories are now performing pharmacogenomic testing. Selection of a reference laboratory depends on whether a particular genotype assay is already performed by an internal health care organization laboratory or only available externally. Other factors for consideration are coverage of genomic variants important for the patient population, technical support, and cost. In some instances, the decision to select a particular reference laboratory may be the responsibility of the clinician who is recommending genomic interrogation. Only limited guidance is available that describes the laboratory characteristics to consider when selecting a reference laboratory. We provide practical considerations for selecting a clinical laboratory for pharmacogenomic testing broadly categorized into four domains: pharmacogene and variant selection; logistics; reporting of results; and test costs along with reimbursement.

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring

Background Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy. Methods and findings We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure. Conclusions This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

Patient Decisions to Receive Secondary Pharmacogenomic Findings and Development of a Multidisciplinary Practice Model to Integrate Results Into Patient Care

Whole exome sequencing (WES) has the potential of identifying secondary findings that are predictive of poor pharmacotherapy outcomes. The purpose of this study was to investigate patients’ wishes regarding the reporting of secondary pharmacogenomic findings. WES results (n = 106 patients) were retrospectively reviewed to determine the number of patients electing to receive secondary pharmacogenomic results. Phenotypes were assigned based on Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. The percent of patients with a predicted phenotype associated with a gene‐based CPIC dosing recommendation was determined. Ninety‐nine patients (93.4%) elected to receive secondary pharmacogenomic findings. For each gene–drug pair analyzed, the number of patients with an actionable phenotype ranged from two (2%) to 43 patients (43.4%). Combining all gene–drug pairs, 84 unique patients (84.8%) had an actionable phenotype. A prospective multidisciplinary practice model was developed for integrating secondary pharmacogenomic findings into clinical practice. Our model highlights a unique collaboration between physician‐geneticists, pharmacists, and genetic counselors.

Chapter 6 – Pharmacogenetics and Pharmacogenomics

Pharmacogenomics is a growing field of research that links genetic variation with drug effects to determine impact on pharmacology and clinical application. Relationships between genetic polymorphisms and drug effect have been observed for a growing number of commonly used drugs, and validation studies are defining clinical use for prediction of severe adverse drug reactions, alternative dosage needs, and aberrant efficacy. For certain gene–drug associations, the evidence is sufficiently strong to warrant clinical implementation. This has led to the widespread availability of initial pharmacogenomic tests for use in clinical practice. However, solutions to practical limitations such as clinical implementation and reimbursement, along with studies addressing clinical impact, are crucial to moving the field forward.

Genetic Contributions and Personalized Medicine

Chronic diseases can be attributed to lifestyle choices, environmental exposures, and genetics. Genomic alterations can increase the risk of developing a chronic condition, and genetic susceptibility can be exacerbated by lifestyle or environment. Numerous medications are available to treat chronic disorders, and even when adhering to best practices, multiple treatment strategies may exist. Polymorphisms in genes encoding drug-metabolizing enzymes, transporters, and targets can influence drug response; therefore gene-based drug-prescribing strategies may identify medications that are more likely to result in a good response. Pharmacogenomics is the study of how variations in genes encoding pharmacokinetic and pharmacodynamic proteins affect pharmacotherapy outcomes. There is a growing body of evidence demonstrating a correlation between genetic polymorphisms and aberrant efficacy, adverse reactions, and dosage requirements. For certain gene-drug interactions, the evidence is sufficiently strong to warrant clinical implementation. Models are being developed exploring how to integrate genomic medicine into routine clinical practice. Methods are needed to discretely curate genomic alterations in electronic medical records, with dissemination of clinical decision support to remind clinicians of important results. Future studies will need to investigate the impact and cost-effectiveness of implementing personalized medicine into patient care.