Clinically actionable genotypes for anticancer prescribing among >1500 patients with pharmacogenomic testing.

BACKGROUND: In recent years, there has been increasing evidence supporting the role of germline pharmacogenomic factors predicting toxicity for anticancer therapies. Although somatic genomic data are used frequently in oncology care planning, germline pharmacogenomic testing is not. This study hypothesizes that comprehensive germline pharmacogenomic profiling could have high relevance for cancer care. METHODS: Between January 2011 and August 2020, patients at the University of Chicago Medical Center were genotyped across custom germline pharmacogenomic panels for reasons unrelated to cancer care. Actionable anticancer pharmacogenomic gene/drug interactions identified by the FDA were defined including: CYP2C9 (erdafitinib), CYP2D6 (gefitinib), DPYD (5-fluorouracil and capecitabine), TPMT (thioguanine and mercaptopurine), and UGT1A1 (belinostat, irinotecan, nilotinib, pazopanib, and sacituzumab-govitecan hziy). The primary objective was to determine the frequency of individuals with actionable or high-risk genotypes across these 5 key pharmacogenes, thus potentially impacting prescribing for at least 1 of these 11 commonly prescribed anticancer therapies. RESULTS: Data from a total of 1586 genotyped individuals were analyzed. The oncology pharmacogene with the highest prevalence of high-risk, actionable genotypes was UGT1A1, impacting 17% of genotyped individuals. Actionable TPMT and DPYD genotypes were found in 9% and 4% of patients, respectively. Overall, nearly one-third of patients genotyped across all 5 genes (161/525, 31%) had at least one actionable genotype. CONCLUSIONS: These data suggest that germline pharmacogenomic testing for 5 key pharmacogenes could identify a substantial proportion of patients at risk with standard dosing, an estimated impact similar to that of somatic genomic profiling. LAY SUMMARY: Differences in our genes may explain why some drugs work safely in certain individuals but can cause side effects in others. Pharmacogenomics is the study of how genetic variations affect an individual's response to medications. In this study, an evaluation was done for important genetic variations that can affect the tolerability of anticancer therapy. By analyzing the genetic results of >1500 patients, it was found that nearly one-third have genetic variations that could alter recommendations of what drug, or how much of, an anticancer therapy they should be given. Performing pharmacogenomic testing before prescribing could help to guide personalized oncology care.

Influence of N-acetyltransferase 2 gene polymorphisms on the in vitro metabolism of the epidermal growth factor receptor inhibitor rociletinib.

AIMS: Rociletinib showed activity in T790M-positive non-small cell lung cancer patients. It undergoes amide hydrolysis to form M502, followed by N-acetylation to M544 or amide hydrolysis to M460. We identified the enzymes responsible for rociletinib metabolism, and investigated the relationship between M544 formation and N-acetyltransferase 2 (NAT2) polymorphisms. METHODS: Rociletinib and metabolites were incubated with carboxylesterase (CES)1b, CES1c, CES2, NAT1, NAT2, arylacetamide deacetylase, inhibitors, pooled human liver microsomes (HLM) and cytosols (HLC). Cytosols (n = 107) were genotyped for NAT2 polymorphisms (rs1041983 and rs1801280) and incubated with M502. Human hepatocytes from intermediate (NAT2*6/*12A) and slow (NAT2*5B/*5B) acetylators were incubated with 10 µM rociletinib and metabolites for 24 hours. Metabolites were measured by high-performance liquid chromatography. RESULTS: M502 was formed from rociletinib and M544 by CES2 and HLM; M544 and N-acetyl-M460 were formed by NAT2 and HLC; M460 was not formed by CES or arylacetamide deacetylase. M502 formation by HLM was inhibited by bis-(4-nitrophenyl)phosphate and eserine (10 µM). M544 formation in HLC was inhibited by 100 µM quercetin and was associated with NAT2 genotype (P < .0001). M460 formation in HLM was inhibited by eserine, and M460 was N-acetylated in HLC. Hepatocytes formed M502, M544 and M460. The intermediate acetylator showed higher production (range: 3.4-5.1-fold) of N-acetylated metabolites than the slow acetylator. CONCLUSIONS: Results indicate that NAT2 and CES2 are involved in rociletinib metabolism, and polymorphic NAT2 could alter drug exposure in patients. Slow NAT2 acetylators would have higher exposure to M502 and M460 and consequently, be at increased risk of experiencing hyperglycaemia and QTc prolongation.

Glucuronidation of OTS167 in Humans Is Catalyzed by UDP-Glucuronosyltransferases UGT1A1, UGT1A3, UGT1A8, and UGT1A10.

OTS167 is a potent maternal embryonic leucine zipper kinase inhibitor undergoing clinical testing as antineoplastic agent. We aimed to identify the UDP-glucuronosyltransferases (UGTs) involved in OTS167 metabolism, study the relationship between UGT genetic polymorphisms and hepatic OTS167 glucuronidation, and investigate the inhibitory potential of OTS167 on UGTs. Formation of a single OTS167-glucuronide (OTS167-G) was observed in pooled human liver (HLM) (Km = 3.4 ± 0.2 µM), intestinal microsomes (HIM) (Km = 1.7 ± 0.1 µM), and UGTs. UGT1A1 (64 µl/min/mg) and UGT1A8 (72 µl/min/mg) exhibited the highest intrinsic clearances (CLint) for OTS167, followed by UGT1A3 (51 µl/min/mg) and UGT1A10 (47 µl/min/mg); UGT1A9 was a minor contributor. OTS167 glucuronidation in HLM was highly correlated with thyroxine glucuronidation (r = 0.91, P < 0.0001), SN-38 glucuronidation (r = 0.79, P < 0.0001), and UGT1A1 mRNA (r = 0.72, P < 0.0001). Nilotinib (UGT1A1 inhibitor) and emodin (UGT1A8 and UGT1A10 inhibitor) exhibited the highest inhibitory effects on OTS167-G formation in HLM (68%) and HIM (47%). We hypothesize that OTS167-G is an N-glucuronide according to mass spectrometry. A significant association was found between rs6706232 and reduced OTS167-G formation (P = 0.03). No or weak UGT inhibition (range: 0-21%) was observed using clinically relevant OTS167 concentrations (0.4-2 µM). We conclude that UGT1A1 and UGT1A3 are the main UGTs responsible for hepatic formation of OTS167-G. Intestinal UGT1A1, UGT1A8, and UGT1A10 may contribute to first-pass OTS167 metabolism after oral administration.

Prolonged Pharmacokinetic Interaction Between Capecitabine and a CYP2C9 Substrate, Celecoxib.

This study investigated the time course and magnitude of the pharmacokinetic interaction between capecitabine and the cytochrome P450 (CYP) 2C9 substrate celecoxib, with implications for coadministration of fluoropyrimidines with CYP2C9 substrates such as warfarin. Patients received celecoxib 200 mg orally twice daily continuously, with capecitabine (1000 mg/m2 orally twice daily for 14 days every 21 days) starting 7 days later. Assessment of the drug-drug interaction (DDI) potential was performed using equivalence testing, which assumes that there is no clinically relevant DDI when the calculated 90% confidence intervals (CIs) of the drug exposure ratios fall within the range of 0.80 to 1.25. Comparison of steady-state pharmacokinetic parameters of celecoxib between day 7 (cycle 0, celecoxib only) and day 14 (cycle 1, celecoxib + capecitabine) showed geometric mean ratios of 1.24 (90%CI, 1.04-1.49), 1.30 (1.11-1.53) and 1.28 (1.11-1.47) for maximum plasma concentration, minimum plasma concentration, and area under the concentration-time curve from time zero to 8 hours, respectively. Comparison of day 7 vs day 21 (cycle 1, after 1 week washout of capecitabine) showed a further increase in the geometric mean ratio of maximum plasma concentration (1.39; 90%CI, 1.16-1.66), minimum plasma concentration (1.53; 1.10-2.12) and area under the concentration-time curve from time zero to 8 hours (1.41; 1.19-1.68). Because the 90%CIs fell outside the prespecified equivalence margin, we conclude that coadministration results in a DDI (increased celecoxib exposure) that persists for at least 7 days after capecitabine discontinuation. Close monitoring should be undertaken when administering fluoropyrimidines with CYP2C9 substrates with narrow therapeutic indexes while also weighing the benefits and risks for individual patients.

Dose-finding and pharmacokinetic study to optimize the dosing of irinotecan according to the UGT1A1 genotype of patients with cancer.

PURPOSE: The risk of severe neutropenia from treatment with irinotecan is related in part to UGT1A1*28, a variant that reduces the elimination of SN-38, the active metabolite of irinotecan. We aimed to identify the maximum-tolerated dose (MTD) and dose-limiting toxicity (DLT) of irinotecan in patients with advanced solid tumors stratified by the *1/*1, *1/*28, and *28/*28 genotypes. PATIENTS AND METHODS: Sixty-eight patients received an intravenous flat dose of irinotecan every 3 weeks. Forty-six percent of the patients had the *1/*1 genotype, 41% had the *1/*28 genotype, and 13% had the *28/*28 genotype. The starting dose of irinotecan was 700 mg in patients with the *1/*1 and *1/*28 genotypes and 500 mg in patients with the *28/*28 genotype. Pharmacokinetic evaluation was performed at cycle 1. RESULTS: In patients with the *1/*1 genotype, the MTD was 850 mg (four DLTs per 16 patients), and 1,000 mg was not tolerated (two DLTs per six patients). In patients with the *1/*28 genotype, the MTD was 700 mg (five DLTs per 22 patients), and 850 mg was not tolerated (four DLTs per six patients). In patients with the *28/*28 genotype, the MTD was 400 mg (one DLT per six patients), and 500 mg was not tolerated (three DLTs per three patients). The DLTs were mainly myelosuppression and diarrhea. Irinotecan clearance followed linear kinetics. At the MTD for each genotype, dosing by genotype resulted in similar SN-38 areas under the curve (AUCs; r(2) = 0.0003; P = .97), but the irinotecan AUC was correlated with the actual dose (r(2) = 0.39; P < .001). Four of 48 patients with disease known to be responsive to irinotecan achieved partial response. CONCLUSION: The UGT1A1*28 genotype can be used to individualize dosing of irinotecan. Additional studies should evaluate the effect of genotype-guided dosing on efficacy in patients receiving irinotecan.

The effect of thalidomide on the pharmacokinetics of irinotecan and metabolites in advanced solid tumor patients.

PURPOSE: Irinotecan and thalidomide are commonly administered antineoplastic drugs. Combination treatment may potentiate their antitumor effect and protect against irinotecan's intestinal toxicity. We investigated whether thalidomide can modulate the pharmacokinetics of irinotecan and metabolites. METHODS: The study employed a crossover design in which advanced solid tumor patients were randomized to two arms and treated with irinotecan 350 mg/m(2) intravenously (IV) every 3 weeks and thalidomide orally (p.o.) 400 mg daily. Pharmacokinetic data when irinotecan was administered as a single agent in each arm were compared to data when the two study agents were co-administered using paired t tests. Eighty percent and 90% confidence intervals for the true difference were also calculated. RESULTS: The differences in pharmacokinetic parameters and metabolic markers after thalidomide administration were small and unlikely to be clinically significant. With the exception of APC T (1/2), none of the upper confidence limits exceeds a 50% increase. CONCLUSIONS: This study did not find any clinically meaningful effects of thalidomide on the pharmacokinetics of irinotecan or its metabolites.

Two drug interaction studies of sirolimus in combination with sorafenib or sunitinib in patients with advanced malignancies.

PURPOSE: Sirolimus is the prototypical mTOR inhibitor. Sorafenib and sunitinib are small molecule inhibitors of multiple kinases including VEGF receptor (VEGFR) kinases. These agents have different mechanisms of action, providing a strong rationale for combination. EXPERIMENTAL DESIGN: Patients with advanced cancer were assigned to receive either sirolimus or the VEGFR inhibitor alone for a 2-week lead-in period, followed by combination therapy. The primary end point of each trial was to determine whether a drug interaction exists between sirolimus and either sorafenib or sunitinib, as defined by a difference in C(max) for each drug alone compared with its C(max) during combination therapy. RESULTS: The sorafenib and sunitinib trials enrolled 34 and 23 patients, respectively. There were no clinically significant differences in C(max) for any of the drugs alone compared with the C(max) during combination therapy. Toxicity profiles were similar to those expected for each drug alone. One patient with adrenal cortical cancer had a partial response to sirolimus and sunitnib. CONCLUSIONS: Sirolimus can be safely combined with sorafenib or sunitinib. Our trial design is feasible and informative in screening for potential drug-drug interactions, using a relatively small number of patients and limited pharmacokinetic sampling.