Implementation of a pharmacogenetic panel-based test for pharmacotherapy-based supportive care in an adult oncology clinic.

The University of Florida Health conducted a pragmatic implementation of a pharmacogenetics (PGx) panel-based test to guide medications used for supportive care prescribed to patients undergoing chemotherapy. The implementation was in the context of a pragmatic clinical trial for patients with non-hematologic cancers being treated with chemotherapy. Patients were randomized to either the intervention arm or control arm and received PGx testing immediately or at the end of the study, respectively. Patients completed the MD Anderson Symptom Inventory (MDASI) to assess quality of life (QoL). A total of 150 patients received PGx testing and enrolled in the study. Clinical decision support and implementation infrastructure were developed. While the study was originally planned for 500 patients, we were underpowered in our sample of 150 patients to test differences in the patient-reported MDASI scores. We did observed a high completion rate (92%) of the questionnaires; however, there were few medication changes (n = 6 in the intervention arm) based on PGx test results. Despite this, we learned several lessons through this pragmatic implementation of a PGx panel-based test in an outpatient oncology setting. Most notably, patients were less willing to undergo PGx testing if the cost of the test exceeded $100. In addition, to enhance PGx implementation success, reoccurring provider education is necessary, clinical decision support needs to appear in a more conducive way to fit in with oncologists' workflow, and PGx test results need to be available earlier in treatment planning.

Molecular Biomarker and Programmed Death-Ligand 1 Expression Testing in Patients With Advanced Stage Non-small Cell Lung Cancer Across North Carolina Community Hospitals.

BACKGROUND: Precision medicine in advanced non-small cell lung cancer (NSCLC) requires molecular biomarker testing in patients with nonsquamous and select patients with squamous histologies, and programmed death-ligand 1 (PD-L1) testing in both. RESEARCH QUESTION: What are rates of molecular and PD-L1 biomarker testing in patients with advanced NSCLC in community practices, and do rates vary by sociodemographic factors? What is the prevalence of molecular biomarker mutations and PD-L1 expression levels? STUDY DESIGN AND METHODS: From 389 stage IV NSCLC pathology reports obtained through the University of North Carolina Lineberger Comprehensive Cancer Center's Rapid Case Ascertainment Program from 38 community hospitals across North Carolina, we abstracted demographics, histology, molecular biomarker testing and results, and PD-L1 testing and expression. We geocoded patient and hospital addresses to determine travel time, distance to care, and census block level contextual variables. We compared molecular biomarker and PD-L1 testing rates, the prevalence of molecular biomarkers, and PD-L1 expression levels by race and sex, using χ2 tests. We determined predictors of testing, using multivariable logistic regression and report adjusted ORs and 95%CI. RESULTS: Among patients with nonsquamous NSCLC, 64.4% were tested for molecular biomarkers, and among all NSCLC patients 53.2% were tested for PD-L1 expression. Differences in biomarker testing rates by sociodemographic factors were not statistically significant in univariate or adjusted analyses. Adjusted analyses showed that patients living in areas with higher household internet access were more likely to undergo PD-L1 testing (adjusted OR = 1.66, 95% CI, 1.02-2.71). Sociodemographic differences in molecular biomarker prevalence and PD-L1 expression levels were not statistically significant, except for human epidermal growth factor receptor 2 (HER2) mutations, which occurred in 16.7% of males vs 0% in females, P = .05. INTERPRETATION: Biomarker testing remains underused in NSCLC. Future work should include larger populations and evaluate hospital-specific testing protocols to identify and address barriers to guideline-recommended testing.

Clinical validation of combinatorial pharmacogenomic testing and single-gene guidelines in predicting psychotropic medication blood levels and clinical outcomes in patients with depression.

We evaluated the clinical validity of a combinatorial pharmacogenomic test and single-gene Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines against patient outcomes and medication blood levels to assess their ability to inform prescribing in major depressive disorder (MDD). This is a secondary analysis of the Genomics Used to Improve DEpression Decisions (GUIDED) randomized-controlled trial, which included patients with a diagnosis of MDD, and ≥1 prior medication failure. The ability to predict increased/decreased medication metabolism was validated against blood levels at screening (adjusted for age, sex, smoking status). The ability of predicted gene-drug interactions (pharmacogenomic test) or therapeutic recommendations (single-gene guidelines) to predict patient outcomes was validated against week 8 outcomes (17-item Hamilton Depression Rating Scale; symptom improvement, response, remission). Analyses were performed for patients taking any eligible medication (outcomes N=1,022, blood levels N=1,034) and the subset taking medications with single-gene guidelines (outcomes N=584, blood levels N=372). The combinatorial pharmacogenomic test was the only significant predictor of patient outcomes. Both the combinatorial pharmacogenomic test and single-gene guidelines were significant predictors of blood levels for all medications when evaluated separately; however, only the combinatorial pharmacogenomic test remained significant when both were included in the multivariate model. There were no substantial differences when all medications were evaluated or for the subset with single-gene guidelines. Overall, this evaluation of clinical validity demonstrates that the combinatorial pharmacogenomic test was a superior predictor of patient outcomes and medication blood levels when compared with guidelines based on individual genes.

Effect of SLCO1B1 Polymorphisms on High-Dose Methotrexate Clearance in Children and Young Adults With Leukemia and Lymphoblastic Lymphoma.

High-dose (HD) methotrexate (MTX) is a critical component of treatment for hematologic malignancies in children and young adults. Therapeutic drug monitoring is necessary due to substantial interindividual variation in MTX clearance. Common function-altering polymorphisms in SLCO1B1 (encodes OATP1B1, which transports MTX) may contribute to clearance variability. We performed pharmacokinetic modeling using data for 106 children and young adults treated with HD MTX for hematologic malignancies; of 396 total courses of HD MTX, 360 consisted of 5 g/m2 over 24 hours. We evaluated the contribution of clinical covariates and SLCO1B1 genotype (388A>G and 521T>C) to MTX clearance variability. Of the clinical covariates studied, patient weight improved the pharmacokinetic model most significantly (P < 0.001). The addition of the SLCO1B1 variants individually further improved the model (P < 0.05 for each). An interaction between these variants was suggested when both were included (P = 0.017). SLCO1B1 genotype should be considered in efforts to personalize HD MTX dosing.

The Influences of Adherence to Tamoxifen and CYP2D6 Pharmacogenetics on Plasma Concentrations of the Active Metabolite (Z)-Endoxifen in Breast Cancer.

Tamoxifen efficacy in breast cancer is suspected to depend on adherence and intact drug metabolism. We evaluated the role of adherence behavior and pharmacogenetics on the formation rate of (Z)-endoxifen. In 192 Brazilian patients, we assessed plasma levels of tamoxifen and its metabolites at 3, 6, and 12 months of treatment (liquid-chromatography tandem mass spectrometry), adherence behavior (Morisky, Green, and Levine medication adherence scale), and cytochrome P450 2D6 (CYP2D6) and other pharmacogene polymorphisms (matrix-assisted laser-desorption-ionization time of flight) mass spectrometry, real-time polymerase chain reaction). Adherence explained 47% of the variability of tamoxifen plasma concentrations (P < 0.001). Although CYP2D6 alone explained 26.4%, the combination with adherence explained 40% of (Z)-endoxifen variability at 12 months (P < 0.001). The influence of low adherence to not achieving relevant (Z)-endoxifen levels was highest in patients with noncompromised CYP2D6 function (relative risk 3.65; 95% confidence interval 1.48-8.99). As a proof-of-concept, we demonstrated that (Z)-endoxifen levels are influenced both by patient adherence to tamoxifen and CYP2D6, which is particularly relevant for patients with full CYP2D6 function.

Demand for Precision Medicine: A Discrete-Choice Experiment and External Validation Study.

BACKGROUND: A limited evidence base and lack of clear clinical guidelines challenge healthcare systems' adoption of precision medicine. The effect of these conditions on demand is not understood. OBJECTIVE: This research estimated the public's preferences and demand for precision medicine outcomes. METHODS: A discrete-choice experiment survey was conducted with an online sample of the US public who had recent healthcare experience. Statistical analysis was undertaken using an error components mixed logit model. The responsiveness of demand in the context of a changing evidence base was estimated through the price elasticity of demand. External validation was examined using real-world demand for the 21-gene recurrence score assay for breast cancer. RESULTS: In total, 1124 (of 1849) individuals completed the web-based survey. The most important outcomes were survival gains with statistical uncertainty, cost of testing, and medical expert agreement on changing care based on test results. The value ($US, year 2017 values) for a test where most (vs. few) experts agreed to changing treatment based on test results was $US1100 (95% confidence interval [CI] 916-1286). Respondents were willing to pay $US265 (95% CI 46-486) for a test that could result in greater certainty around life-expectancy gains. The predicted demand of the assay was 9% in 2005 and 66% in 2014, compared with real-world uptake of 7% and 71% (root-mean-square prediction error 0.11). Demand was sensitive to price (1% increase in price resulted in > 1% change in demand) when first introduced and insensitive to price (1% increase in price resulted in < 0.1% change in demand) as the evidence base became established. CONCLUSIONS: Evidence of external validity was found. Demand was weak and responsive to price in the near term because of uncertainty and an immature evidence base. Clear communication of precision medicine outcomes and uncertainty is crucial in allowing healthcare to align with individual preferences.

Precision Medicine Approaches and the Health of Populations: Study Design Concerns and Considerations.

Biomedical advances in the past decade have aimed to capitalize on two movements that have dominated the research conversation: precision medicine and the ascent of big data. These emerging shifts have resulted in growing confidence that we can better characterize health, predict who will get ill and with what, develop new treatments which exploit genetic, metabolic, and other vulnerabilities in cancers and infectious agents, and tailor some of these treatments to match characteristics of the individual patient and their specific disease. However, we suggest that there are important cautions. Weaknesses in the data and the methods used to study them raise three potential concerns. First, any data collected, and analysis attempted, will have limited utility absent internal validity, unless fundamental issues of accurate and consistent measurement can be addressed. Second, lack of attention to external validity limits generalizability beyond the narrow (even if large) samples in hand, so that the utility of inference that can emerge from these approaches remains limited. Third, the proposed approaches seldom include consideration of ubiquitous forces that can determine whether observed associations are truly attributable to the innovation or to other, unmeasured forces. This essay discusses these limitations and explores how they can influence inference drawn from big data precision medicine science.

Clinical Implementation of Pharmacogenetic Testing in a Hospital of the Spanish National Health System: Strategy and Experience Over 3 Years.

In 2014, we established a pharmacogenetics unit with the intention of facilitating the integration of pharmacogenetic testing into clinical practice. This unit was centered around two main ideas: i) individualization of clinical recommendations, and ii) preemptive genotyping in risk populations. Our unit is based on the design and validation of a single nucleotide polymorphism (SNP) microarray, which has allowed testing of 180 SNPs associated with drug response (PharmArray), and clinical consultation regarding the results. Herein, we report our experience in integrating pharmacogenetic testing into our hospital and we present the results of the 2,539 pharmacogenetic consultation requests received over the past 3 years in our unit. The results demonstrate the feasibility of implementing pharmacogenetic testing in clinical practice within a national health system.

Multilevel models improve precision and speed of IC50 estimates.

AIM: Experimental variation in dose-response data of drugs tested on cell lines result in inaccuracies in the estimate of a key drug sensitivity characteristic: the IC50. We aim to improve the precision of the half-limiting dose (IC50) estimates by simultaneously employing all dose-responses across all cell lines and drugs, rather than using a single drug-cell line response. MATERIALS & METHODS: We propose a multilevel mixed effects model that takes advantage of all available dose-response data. RESULTS: The new estimates are highly concordant with the currently used Bayesian model when the data are well behaved. Otherwise, the multilevel model is clearly superior. CONCLUSION: The multilevel model yields a significant reduction of extreme IC50 estimates, an increase in precision and it runs orders of magnitude faster.