Impact of Pharmacogenetics on Intravenous Tacrolimus Exposure and Conversions to Oral Therapy

CYP3A5 and CYP3A4 are the predominant enzymes responsible for tacrolimus metabolism; however only a proportion of the population expresses CYP3A5 secondary to genetic variation. CYP3A5 is expressed in both the intestine and the liver and has been shown to impact both the bioavailability and metabolism of orally administered tacrolimus. Increasing the initial tacrolimus dose by 50% to 100% is recommended in patients who are known CYP3A5 expressers; however, whether this dose adjustment is appropriate for i.v. tacrolimus administration is unclear. The objective of this study was to evaluate the impact of CYP3A5 genotype as well as other pharmacogenes on i.v. tacrolimus exposure to determine whether the current genotype-guided dosing recommendations are appropriate for this formulation. In addition, this study aimed to investigate dose conversion requirements among CYP3A5 genotypes when converting from i.v. to p.o. tacrolimus. This study is a retrospective chart review of all patients who underwent allogeneic stem cell transplantation at Michigan Medicine between June 1, 2014, and March 1, 2018, who received i.v. tacrolimus at the time of their transplantation. Secondary use samples were obtained for genotyping CYP3A5, CYP3A4, and ABCB1. Patient demographic information, tacrolimus dosing and trough levels, and concomitant medications received at the time of tacrolimus trough were collected retrospectively from the patients' medical records. The i.v. dose-controlled concentration (C/D) and the i.v.:p.o. exposure ratio was calculated for all tacrolimus doses and patients, respectively. The impact of CYP3A5, CYP3A4, and ABCB1 genotypes on the i.v. C/D were evaluated with linear mixed modeling. The impact of CYP3A5 genotype on the i.v.:p.o. ratio was evaluated while controlling for age and concomitant use of an azole inhibitor. CYP3A5 and CYP3A4 genotypes were significantly associated with the i.v. C/D, with CYP3A5 expressers and CYP3A4 rapid metabolizers having 20% lower tacrolimus exposure. Neither genotype remained significant in the multivariable model, although age, hematocrit, and concomitant use of strong azole inhibitors were associated with increased i.v. C/D. When controlling for patient age and sex, CYP3A5 expressers had significantly higher i.v.:p.o. ratios than CYP3A5 nonexpressers (3.42 versus 2.78; P = .04). Post hoc analysis showed that the i.v.:p.o. ratio may differ among different CYP3A5 genotypes and azole inhibitor combinations. This study demonstrates that the current genotype-guided tacrolimus dose adjustment recommendations are inappropriate for CYP3A5 expressers receiving i.v. tacrolimus. Although CYP3A5 genotype is likely a minor contributor to i.v. tacrolimus exposure, genotype, in addition to capturing concomitant CYP3A inhibitors, would likely improve i.v.:p.o. dose conversion selection. © 2021 American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc.

Effect of Genetic Variability in 20 Pharmacogenes on Concentrations of Tamoxifen and Its Metabolites

BACKGROUND

Tamoxifen, as a treatment of estrogen receptor positive (ER+) breast cancer, is a weak anti-estrogen that requires metabolic activation to form metabolites with higher anti-estrogenic activity. Endoxifen is the most-studied active tamoxifen metabolite, and endoxifen concentrations are highly associated with CYP2D6 activity. Associations of tamoxifen efficacy with measured or CYP2D6-predicted endoxifen concentrations have been inconclusive. Another active metabolite, 4-OHtam, and other, less active metabolites, Z-4'-endoxifen and Z-4'-OHtam, have also been reported to be associated with tamoxifen efficacy.

METHOD

Genotype for 20 pharmacogenes was determined by VeriDose^® Core Panel and VeriDose^®CYP2D6 CNV Panel, followed by translation to metabolic activity phenotype following standard activity scoring. Concentrations of tamoxifen and seven metabolites were measured by UPLC-MS/MS in serum samples collected from patients receiving 20 mg tamoxifen per day. Metabolic activity was tested for association with tamoxifen and its metabolites using linear regression with adjustment for upstream metabolites to identify genes associated with each step in the tamoxifen metabolism pathway.

RESULTS

A total of 187 patients with genetic and tamoxifen concentration data were included in the analysis. CYP2D6 was the primary gene associated with the tamoxifen metabolism pathway, especially the conversion of tamoxifen to endoxifen. CYP3A4 and CYP2C9 were also responsible for the metabolism of tamoxifen. CYP2C9 especially impacted the hydroxylation to 4-OHtam, and this involved the OATP1B1 (SLCO1B1) transporter.

CONCLUSION

Multiple genes are involved in tamoxifen metabolism and multi-gene panels could be useful to predict active metabolite concentrations and guide tamoxifen dosing.

Drug-drug interactions in subjects enrolled in SWOG trials of oral chemotherapy

BACKGROUND

Patients with cancer are at increased risk of drug-drug interactions (DDI), which can increase treatment toxicity or decrease efficacy. It is especially important to thoroughly screen DDI in oncology clinical trial subjects to ensure trial subject safety and data accuracy. This study determined the prevalence of potential DDI involving oral anti-cancer trial agents in subjects enrolled in two SWOG clinical trials.

METHODS

Completed SWOG clinical trials of commercially available agents with possible DDI that had complete concomitant medication information available at enrollment were included. Screening for DDI was conducted through three methods: protocol-guided screening, Lexicomp® screening, and pharmacist determination of clinical relevance. Descriptive statistics were calculated.

RESULTS

SWOG trials S0711 (dasatinib, n = 83) and S0528 (everolimus/lapatinib, n = 84) were included. Subjects received an average of 6.6 medications (standard deviation = 4.9, range 0-29) at enrollment. Based on the clinical trial protocols, at enrollment 18.6% (31/167) of subjects had a DDI and 12.0% (20/167) had a DDI that violated a protocol exclusion criterion. According to Lexicomp®, 28.7% of subjects (48/167) had a DDI classified as moderate or worse, whereas pharmacist review indicated that 7.2% of subjects (12/167) had a clinically relevant interaction. The majority of clinically relevant DDI identified were due to the coadministration of acid suppression therapies with dasatinib (83.3%, 10/12).

CONCLUSIONS

The high DDI prevalence in subjects enrolled on SWOG clinical trials, including a high prevalence that violate trial exclusion criteria, support the need for improved processes for DDI screening to ensure trial subject safety and trial data accuracy.

Improvement Initiative to Develop and Implement a Tool for Detecting Drug-Drug Interactions During Oncology Clinical Trial Enrollment Eligibility Screening

OBJECTIVES

Screening subjects for drug-drug interactions (DDIs) before enrollment in oncology clinical trials is integral to ensuring safety, but standard procedures or tools are not readily available to screen DDI in this setting. Our objectives were to develop a DDI screening tool for use during oncology clinical trial enrollment and to test usability in single-center and multicenter pilot studies.

METHODS

A multistage approach was used for this quality improvement intervention. Semistructured interviews with individuals responsible for DDI screening were conducted to develop a prototype tool. The tool was used for screening DDI in subjects enrolling in National Clinical Trials Network trials of commercially available agents during a single-center 3-month pilot. Improvements were made, and a 3-month multicenter pilot was conducted at volunteer SWOG Cancer Research Network sites. Participants were surveyed to determine tool usability and efficiency.

RESULTS

A tool was developed from semistructured interviews. A critical feature was reporting which medications had specific pharmacokinetic and pharmacodynamic characteristics including transporter and cytochrome P450 substrates, inhibitors, or inducers and QT prolongation. In the 12-site study, average (SD) DDI screening time for each patient decreased by 15.7 (10.2) minutes (range, 3-35 minutes; P < 0.001). Users reported the tool highly usable, with >90% agreeing with all positive usability characterizations and disagreeing with all negative complexity characterizations.

CONCLUSIONS

A DDI screening tool for oncology clinical trial enrollment was created and its usability confirmed. Further testing with more diverse investigator sites and study drugs during eligibility screening is warranted to improve safety and data accuracy within clinical trials.

Genetic variation in Charcot-Marie-Tooth genes contributes to sensitivity to paclitaxel-induced peripheral neuropathy

Aim: This study explored whether inherited variants in genes causing the hereditary neuropathy condition Charcot-Marie-Tooth disease are associated with sensitivity to paclitaxel-induced peripheral neuropathy (PN). Patients & methods: Hereditary neuropathy genes previously associated with risk of paclitaxel-induced PN were sequenced in paclitaxel-treated patients. Eight putative genetic predictors in five hereditary neuropathy genes (ARHGEF10, SBF2, FGD4, FZD3 and NXN) were tested for association with PN sensitivity after accounting for systemic exposure and clinical variables. Results: FZD3 rs7833751, a proxy for rs7001034, decreased PN sensitivity (additive model, β = -0.41; 95% CI: -0.66 to -0.17; p = 0.0011). None of the other genetic predictors were associated with PN sensitivity. Conclusion: Our results support prior evidence that FZD3 rs7001034 is protective of PN and may be useful for individualizing paclitaxel treatment to prevent PN.

Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

INTRODUCTION

Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb-drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug-drug interactions, but their performance to detect herb-drug interactions is not known. This study compared the performance of eight drug-drug interaction screening tools to detect herb-drug interaction with anti-cancer agents.

METHODS

The herb-drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug-drug interaction tools was assessed. Clinical relevance of each herb-drug interaction was determined using Natural Medicine and each drug-drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools.

RESULTS

All tools had poor sensitivity (<0.20) for detecting herb-drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb-drug interaction for every 10 herb-drug interactions screened by a free tool.

CONCLUSION

Lexicomp is the best available tool for screening herb-drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb-drug interaction was far lower than for drug-drug interactions, and overall quite poor. Further research is needed to improve herb-drug interaction screening performance.

Genetic variation in EPHA contributes to sensitivity to paclitaxel-induced peripheral neuropathy

AIMS

Chemotherapy-induced peripheral neuropathy (PN) is a treatment limiting toxicity of paclitaxel. We evaluated if EPHA genetic variation (EPHA4, EPHA5, EPHA6, and EPHA8) is associated with PN sensitivity by accounting for variability in systemic paclitaxel exposure (time above threshold).

METHODS

Germline DNA from 60 patients with breast cancer was sequenced. PN was measured using the 8-item sensory subscale (CIPN8) of the patient-reported CIPN20. Associations for 3 genetic models were tested by incorporating genetics into previously published PN prediction models integrating measured paclitaxel exposure and cumulative treatment. Significant associations were then tested for association with PN-related treatment disruption.

RESULTS

EPHA5 rs7349683 (minor allele frequency = 0.32) was associated with increased PN sensitivity (β-coefficient = 0.39, 95% confidence interval 0.11-0.67, p = 0.007). Setting a maximum tolerable threshold of CIPN8 = 30, optimal paclitaxel exposure target is shorter for rs7349683 homozygous (11.6 h) than heterozygous (12.6 h) or wild-type (13.6 h) patients. Total number of missense variants (median = 0, range 0-2) was associated with decreased PN sensitivity (β-coefficient: -0.42, 95% confidence interval -0.72 to -0.12, P = .006). No association with treatment disruption was detected for the total number of missense variants or rs7349683.

CONCLUSION

Isolating toxicity sensitivity by accounting for exposure is a novel approach, and rs7349683 represents a promising marker for PN sensitivity that may be used to individualize paclitaxel treatment.

EPHA genetics for prediction of paclitaxel-induced peripheral neuropathy sensitivity

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Background: Chemotherapy induced peripheral neuropathy (CIPN) is a common, debilitating paclitaxel side effect that has been primarily attributed to cumulative systemic paclitaxel exposure. Paclitaxel dose-adjustment to achieve target systemic exposure decreases but does not eliminate severe CIPN, suggesting some patients are inherently CIPN-sensitive. Ephrin ( EPHA) polymorphisms have been reported to increase CIPN occurrence (Baldwin 2012, Leandro-Garcia 2013, Boora 2016) but replication has been challenging, perhaps due to the inability to isolate CIPN-sensitivity by accounting for systemic exposure differences. The study purpose was to determine if EPHA genetic variants previously associated with CIPN occurrence are associated with CIPN-sensitivity. Methods: PN was assessed at baseline and weekly using the 8-item sensory subscale (CIPN8) of the patient-reported EORTC CIPN20 in patients receiving paclitaxel 80 mg/m2 weekly x 12. EPHA4, EPHA5, EPHA6, and EPHA8 were sequenced in germline DNA. Associations with higher PN-sensitivity were tested for three genetic models (total variants, coding variants, and rs7349683) by incorporating genetics into previously published CIPN8 predictive models that included measured paclitaxel exposure. Significant associations were tested for association with higher risk of PN-related treatment disruption (i.e. dose decrease, delay, or discontinuation). Results: In the 59 included patients, carrying EPHA5 rs7349683 was associated with greater CIPN sensitivity (beta coefficient: 0.40, standard error: 0.14, p = 0.005), indicating these patients had a greater increase in CIPN8 during treatment than would be predicted based on cumulative paclitaxel exposure. Rs7349683 was not associated with increased PN-induced treatment disruption, perhaps due to the low number of events (n = 19). Conclusions: Using a novel approach that isolates CIPN-sensitivity by accounting for measured paclitaxel exposure, our results provide further evidence that EPHA5 rs7349683 may be a promising marker for CIPN. If additional validation studies confirm this association, genetic testing could enable personalized treatment strategies to prevent CIPN in patients with breast cancer.

Patients carrying CYP2C8*3 have shorter systemic paclitaxel exposure

AIM

First, evaluate if patients carrying putatively diminished activity CYP2C8 genotype have longer paclitaxel exposure (e.g., time above threshold concentration of 0.05 μM [T_{c >0.05}]). Second, screen additional pharmacogenes for associations with T_{c >0.05}. Methods: Pharmacogene panel genotypes were translated into genetic phenotypes for associations with T_{c >0.05} (n = 58).

RESULTS

Patients with predicted low-activity CYP2C8 had shorter T_{c >0.05} after adjustment for age, body surface area and race (9.65 vs 11.03 hrs, β = 5.47, p = 0.02). This association was attributed to CYP2C8*3 (p = 0.006), not CYP2C8*4 (p = 0.58). Patients with predicted low-activity SLCO1B1 had longer T_{c >0.05} (12.12 vs 10.15 hrs, β = 0.85, p = 0.012).

CONCLUSION

Contrary to previous publications, CYP2C8*3 may confer increased paclitaxel metabolic activity. SLCO1B1 and CYP2C8 genotype may explain some paclitaxel pharmacokinetic variability.

Prevalence of drug-drug interactions in oncology patients enrolled on National Clinical Trials Network oncology clinical trials

Background

Drug-drug interactions (DDIs) in subjects enrolling in clinical trials can impact not only safety of the patient but also study drug outcomes and data validity. This makes it critical to adequately screen and manage DDIs. The study objective was to determine the prevalence of DDIs involving study medications in subjects enrolling in National Clinical Trials Network (NCTN) clinical trials at a single institution. DDIs were evaluated based on study protocol recommendations for concomitant medication use (i.e. exclude, avoid or use caution), screening via DDI tool, and pharmacist review.

Methods

Subjects enrolled in NCTN trials of commercially available agents between January 2013 and August 2017 were included if a complete medication list was available. Complete medication lists were collected from the date of enrollment or the next available date then screened utilizing protocol guidance and the DDI screening tool, Lexicomp® Drug Interactions (Wolters Kluwer, Hudson, OH). Interactions were reviewed for clinical relevance: defined as a DDI that would require a medication change to ensure study agent safety and efficacy at enrollment.

Results

One hundred and twenty-eight subjects enrolled in 35 clinical trials were included. Protocol guidance detected 15 unique DDI pairs that should be avoided or used with caution in 10.2% (13/128) of subjects. The majority of these subjects did not have a clinically relevant DDI (69.2%, 9/13) based on pharmacist review. Lexicomp® detected moderate to major DDIs in 24.2% (31/128) of subjects, with 9.4% (12/128) having a clinically relevant DDI.

Conclusions

This study confirms a high prevalence of DDIs present in subjects enrolling in oncology clinical trials. Further efforts should be made to improve methods to detect and manage DDIs in patients enrolling on clinical trials to ensure patient safety and trial data validity.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-5076-0) contains supplementary material, which is available to authorized users.

Development of a drug-drug interaction screening tool for oncology clinical trial enrollment

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Background: Screening drug-drug interactions (DDI) for subjects enrolling in oncology clinical trials is critical to ensuring patient safety and the validity of clinical trial data. We previously reported that DDI screening is not uniformly conducted when screening patients for enrollment into SWOG clinical trials and found that at the University of Michigan Rogel Cancer Center up to 24.2% of subjects enrolled in National Clinical Trial Network (NCTN) trials had a DDI. Screening tools aid in DDI reduction in clinical practice, but none have been created for clinical trial enrollment. Our objective was to develop a clinical trial specific DDI screening tool based on features requested by the end-users of the tool at U-M. Methods: Semi-structured and informal interviews were conducted with all data managers who enroll patients into NCTN clinical trials at the U-M cancer center. Data managers were asked about their current workflow and desired features of a DDI screening tool. Responses were combined and reviewed for feasibility. Desired features were conveyed to PEPID, LLC (Phoenix, AZ) for tool development. Results: Four data managers were interviewed. Protocol-guided screening was a key workflow feature, which was completed by gathering DDI information primarily from the exclusion criteria and drug information sections of each respective protocol, Google, CredibleMeds, and the Indiana University P450 Drug Interaction Table. Consequently, a critical feature was the display of drug characteristics with wording that aligned with that in the protocol including transporter and CYP450 substrates, inhibitors, or inducers and QT prolongation potential. Additional desirable features included separate entry of study and concomitant drugs, filtering to display only DDI with study drugs, and PDF export of results. PEPID developed a prototype tool including these desired attributes for a prospective implementation pilot study. Conclusions: A first generation clinical trial specific DDI screening tool was developed based on end-user feedback. We are designing a prospective study to determine whether implementation of this tool can reduce DDI, enhance patient safety, and ensure validity of clinical trial data.

Comparison of Nine Tools for Screening Drug-Drug Interactions of Oral Oncolytics

PURPOSE

Patients with cancer are an especially vulnerable population to potential drug-drug interactions (DDIs). This makes it important to adequately screen them for DDIs. The objective of this study was to compare the abilities of nine DDI screening tools to detect clinically relevant interactions with oral oncolytics.

METHODS

Subscription-based tools (ie, PEPID, Micromedex, Lexicomp, Facts & Comparisons) and free tools (ie, Epocrates Free, Medscape, Drugs.com, RxList, WebMD) were compared for their abilities to detect clinically relevant DDIs for 145 drug pairs including an oral oncology agent. Clinical relevance was determined by a pharmacist using Stockley's Drug Interactions. Descriptive statistics were calculated for each tool, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and then compared grouped by free or subscription-based tools for the secondary analysis and analyzed via generalized estimating equations.

RESULTS

For individual metrics, PPV had overall higher values (0.88 to 0.97) relative to the low values included for sensitivity (0.65 to 0.96), specificity (0.53 to 0.93) and NPV (0.38 to 0.83). The top-performing subscription and free tools, Lexicomp and Drugs.com, had no statistically significant differences in performance. Overall, subscription tools had a significantly higher sensitivity (0.85 ± 0.017 v 0.78 ± 0.017; P = .0082) and NPV (0.57 ± 0.039 v 0.48 ± 0.032; P = .031) than free tools. No differences were observed between the specificity and PPV.

CONCLUSION

Due to the low performance of some tools for sensitivity, specificity, and NPV, individual performance should be examined and prioritized on the basis of the intended use when selecting a DDI tool. If a strong-performing subscription-based tool is unavailable, a strong-performing free option, like Drugs.com, is available.

Comprehensive assessment of cytochromes P450 and transporter genetics with endoxifen concentration during tamoxifen treatment

OBJECTIVES

Tamoxifen bioactivation to endoxifen is mediated primarily by CYP2D6; however, considerable variability remains unexplained. Our aim was to perform a comprehensive assessment of the effect of genetic variation in tamoxifen-relevant enzymes and transporters on steady-state endoxifen concentrations.

PATIENTS AND METHODS

Comprehensive genotyping of CYP enzymes and transporters was performed using the iPLEX ADME PGx Pro Panel in 302 tamoxifen-treated breast cancer patients. Predicted activity phenotype for 19 enzymes and transporters were analyzed for univariate association with endoxifen concentration, and then adjusted for CYP2D6 and clinical covariates.

RESULTS

In univariate analysis, higher activity of CYP2C8 (regression β=0.22, P=0.020) and CYP2C9 (β=0.20, P=0.04), lower body weight (β=-0.014, P<0.0001), and endoxifen measurement during winter (each β<-0.39, P=0.002) were associated with higher endoxifen concentrations. After adjustment for the CYP2D6 diplotype, weight, and season, CYP2C9 remained significantly associated with higher concentrations (P=0.02), but only increased the overall model R by 1.3%.

CONCLUSION

Our results further support a minor contribution of CYP2C9 genetic variability toward steady-state endoxifen concentrations. Integration of clinician and genetic variables into individualized tamoxifen dosing algorithms would marginally improve their accuracy and potentially enhance tamoxifen treatment outcomes.