<i>UGT1A1</i>
            Genotype-Guided Dosing of Irinotecan: Time to Prioritize Patient Safety

Peeters, Sofía; Deenen, Maarten J.; Thijs, Anna M.J.; Hulshof, Emma C.; Mathijssen, Ron H.J.; Gelderblom, Hans; Guchelaar, Henk‐Jan; Swen, Jesse J.

doi:10.2217/pgs-2023-0096

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…These reactions are catalyzed by carboxylesterase and UGT1A1, respectively. The genetic variant UGT1A1 *28 is associated with decreased glucuronidation activity, which in turn prolongs the mean half-life of the metabolite SN-38 and increases patient susceptibility to gastrointestinal and hematologic toxicity ( Iyer et al, 2002 ; Innocenti et al, 2004 ; Peeters et al, 2023 ). Similar toxicities to irinotecan have been noted in patients carrying polymorphisms in the ETS1 and ABCG2 genes, which encode carboxylesterase and a membrane transporter, respectively ( De With et al, 2023 ).…”

Section: Omics Data In Cancer Personalized Therapymentioning

confidence: 99%

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Mondello,

Dal Bo,

Toffoli

et al. 2024

Front. Pharmacol.

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Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

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Section: Omics Data In Cancer Personalized Therapymentioning

confidence: 99%

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Mondello,

Dal Bo,

Toffoli

et al. 2024

Front. Pharmacol.

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“… 6 , 7 Pre‐chemotherapy pharmacogenetic testing for DPYD and UGT1A1 has also been documented to significantly improve patient safety, for patients requiring fluoropyrimidine‐ and/or irinotecan‐based regimens. 8 , 9 , 10 , 11 …”

Section: Introductionmentioning

confidence: 99%

Real‐world implementation of DPYD and UGT1A1 pharmacogenetic testing in a community‐based cancer center

Muldoon,

Beck,

Sebree

et al. 2024

Clinical Translational Sci

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We set out to demonstrate the feasibility of pharmacogenetic testing of DPYD and UGT1A1 and explore the clinical benefits in a community‐based cancer center. We conducted a retrospective review of 280 patients who underwent pharmacogenetic testing between November 2020 and May 2023. The primary end points included the percentage of patients with results prior to chemotherapy initiation, percentage of DPYD‐ and UGT1A1‐guided interventions implemented before chemotherapy initiation, and the turnaround time (TAT) for the pharmacogenetics results. Exploratory end points included a comparison of unplanned hospitalizations and treatment interruptions among (1) DPD phenotypes in patients who received fluoropyrimidines and (2) UGT1A1 phenotypes in patients who received irinotecan ± fluoropyrimidines. We evaluated cancer progression among patients who received DPYD‐guided dose reductions in the curative setting. More than 75% of patients had results prior to initiation of chemotherapy and 61.5% of the actionable interventions were implemented before chemotherapy, with a median TAT of 7 calendar days (IQR 5–8 calendar days). A non‐significant higher percentage of unplanned hospitalizations and treatment interruptions occurred among patients with normal DPYD compared with patients with DPYD‐guided dose reductions. A non‐significant higher frequency of treatment interruptions and dose reductions were observed in UGT1A1 intermediate metabolizer (IM) compared with UGT1A1 normal metabolizer (NM). None of the patients who received DPYD‐guided dose reductions in the curative setting experienced progression after a median follow‐up of 342 days. The real‐world evidence generated from this study demonstrates the feasibility of pre‐chemotherapy pharmacogenetic testing of DPYD and UGT1A1 in a community‐based cancer center.

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Allele frequency of genetic variations related to the UGT1A1 gene-drug pair in a group of Iranian population

Sarhangi,

Fahimfar,

Rouhollah

et al. 2024

J Diabetes Metab Disord

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UGT1A1 Genotype-Guided Dosing of Irinotecan: Time to Prioritize Patient Safety

Cited by 5 publications

References 32 publications

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Real‐world implementation of DPYD and UGT1A1 pharmacogenetic testing in a community‐based cancer center

Allele frequency of genetic variations related to the UGT1A1 gene-drug pair in a group of Iranian population

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