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3. Discovering novel germline genetic variants linked to severe fluoropyrimidine-related toxicity in- and outside DPYD

Author

Knikman, Jonathan E., Zhai, Qinglian, Lunenburg, Carin A. T. C., Henricks, Linda M., Böhringer, Stefan, van der Lee, Maaike, de Man, Femke M., Offer, Steven M., Shrestha, Shikshya, Creemers, Geert-Jan, Baars, Arnold, Dezentjé, Vincent O., Imholz, Alexander L. T., Jeurissen, Frank J. F., Portielje, Johanna E. A., Jansen, Rob L. H., Hamberg, Paul, Droogendijk, Helga J., Koopman, Miriam, Nieboer, Peter, van de Poel, Marlène H. W., Mandigers, Caroline M. P. W., van Schaik, Ron H. N., Gelderblom, Hans, Mathijssen, Ron H. J., Schellens, Jan H. M., Cats, Annemieke, Guchelaar, Henk-Jan, and Swen, Jesse J.

Published
2024
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13. Correction: Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene–drug interaction between UGT1A1 and irinotecan

Author

Hulshof, Emma C., Deenen, Maarten J., Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J., Buunk, Anne-Marie, Houwink, Elisa J. F., Risselada, Arne, Rongen, Gerard A. P. J. M., van Schaik, Ron H. N., Touw, Daan J., van der Weide, Jan, van Westrhenen, Roos, Deneer, Vera H. M., Guchelaar, Henk-Jan, and Swen, Jesse J.

Published
2023
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15. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study

Author

Buunk, Annemarie, Goossens, Hanneke, Baas, Gert, Algera, Maartje, Schuil-Vlassak, Evelyn, Ambagts, Thijs, De Hoog-Schouten, Leonie, Musaafir, Sara, Bosch, Roelof, Tjong, Carol, Steeman, Sanne, Van der Plas, Martine, Baldew, Glenn, Den Hollander, Iris, De Waal, Zacharias, Heijn, Aurele, Nelemans, Leen, Kouwen-Lubbers, Kirsten, Van Leeuwen, Maartje, Hoogenboom, Sacha, Van Doremalen, Jacobine, Ton, Célin, Beetstra, Bastien, Meijs, Veronique, Dikken, Jan, Dubero, Dasha, Slager, Mark, Houben, Tom, Kanis, Thomas, Overmars, Wietske, Nijenhuis, Marga, Steffens, Michael, Bergs, Ingmar, Karamperis, Kariofyllis, Siamoglou, Stavroula, Ivantsik, Ouliana, Samiou, Georgia-Chryssa, Kordou, Zoe, Tsermpini, Evira, Ferentinos, Panagiotis, Karaivazoglou, Aikaterini, Rigas, George, Gerasimou, Harilaos, Voukelatou, Georgia, Georgila, Eleni, Tsermpini, Evangelia Eirini, Mendrinou, Efrossyni, Chalikiopoulou, Konstantina, Kolliopoulou, Alexandra, Mitropoulos, Konstantinos, Stratopoulos, Apostolos, Liopetas, Ioannis, Tsikrika, Athina, Barba, Evangelia, Emmanouil, Georgia, Stamopoulou, Theano, Stathoulias, Andreas, Giannopoulos, Panagiotis, Kanellakis, Filippos, Bartsakoulia, Marina, Katsila, Theodora, Douzenis, Athanassios, Gourzis, Filippos, Assimakopoulos, Konstantinos, Bignucolo, Alessia, Dal Cin, Lisa, Comello, Francesco, Mezzalira, Silvia, Puglisi, Fabio, Spina, Michele, Foltran, Luisa, Guardascione, Michela, Buonadonna, Angela, Bartoletti, Michele, Corsetti, Serena, Ongaro, Elena, Da Ros, Lucia, Bolzonello, Silvia, Spazzapan, Simon, Freschi, Andrea, Di Nardo, Paola, Palazzari, Elisa, Navarria, Federico, Innocente, Roberto, Berretta, Massimiliano, D'Andrea, Mario, Angelini, Francesco, Diraimo, Tania, Favaretto, Adolfo, Dávila-Fajardo, Cristina Lucía, Díaz-Villamarín, Xando, Martínez-González, Luis Javier, Antúnez-Rodríguez, Alba, Moreno-Escobar, Eduardo, Fernández-Gómez, Ana Estefanía, García-Navas, Paloma, Bautista-Pavés, Alicia Bautista Pavés, Burillo-Gómez, Francisco, Villegas-Rodríguez, Inmaculada, Sánchez-Ramos, Jesús Gabriel, Antolinos-Pérez, Mª José, Rivera, Ricardo, Martínez-Huertas, Susana, Thomas-Carazo, Jesús, Yañez-Sanchez, Jose Julio, Blancas-López-Barajas, Mª Isabel, García-Orta, Rocío, González-Astorga, Beatriz, Rodríguez-González, Carlos José, Ruiz-Carazo, Francisco Javier, Pérez-Campos, Manuel, Cano-Herrera, Irene, Herrera, Rosa, Gil-Jiménez, Teresa, Delgado-Ureña, Mª Teresa, Triviño-Juarez, Jose Matías, Campos-Velázquez, Salustiano, Alcántara- Espadafor, Silvia, Moreno Aguilar, Maria Rosario, Ontiveros- Ortega, Maria Carmen, Carnerero-Córdoba, Lidia, Guerrero-Jiménez, Margarita, Legeren- Álvarez, Marta, Yélamos-Vargas, Marisol, Castillo-Pérez, Isabel, Aomar-Millán, Ismael, Anguita-Romero, Manuel, Sánchez-García, María José, Sequero-Lopez, Silvia, Faro-Miguez, Naya, López-Fernández, Silvia, Leyva-Ferrer, Rosario Nieves, Herrera-Gómez, Norberto, Pertejo-Manzano, Laura, Pérez-Gutierrez, Eva Mª, Martín-de la Higuera, Antonio J., Plaza-Carrera, Jose, Baena-Garzón, Flor, Toledo-Frías, Pablo, Cruz-Valero, Inés, Chacón-McWeeny, Verónica, Gallardo- Sánchez, Isabel, Arrebola, Antonio, Guillén-Zafra, Lucía, Ceballos-Torres, Ángel, Guardia-Mancilla, Plácido, Guirao-Arrabal, Emilio, Canterero-Hinojosa, Jesús, Velasco-Fuentes, Sara, Sánchez- Cano, Daniel, Aguilar-Jaldo, Mª del Pilar, Caballero-Borrego, Juan, Praznik, Monika, Slapšak, Urška, Voncina, Blaz, Rajter, Branka, Škrinjar, Andrej, Marjetic Ulcakar, Angelika, Zidanšek, Anja, Stegne Ignjatvic, Tea, Mazej Poredoš, Barbara, Vivod Pecnik, Živka, Poplas Susic, Tonka, Juteršek, Milojka, Klen, Jasna, Skoporc, Janja, Kotar, Tjaša, Petek Šter, Marija, Zvezdana Dernovšk, Mojca, Mlinšek, Gregor, Miklavcic, Petra, Plemenitaš Ilješ, Anja, Grašic Kuhar, Cvetka, Oblak, Irena, Stražišar, Branka, Štrbac, Danijela, Matos, Erika, Mencinger, Marina, Vrbnjak, Marko, Saje, Marko, Radovanovic, Mirjana, Jeras, Katja, Bukovec, Lucija, Terzic, Tea, Minichmayr, Iris, Nanah, Abdulaziz, Nielsen, Elisabet, Zou, Yuanxi, Lauschke, Volker, Johansson, Inger, Zhou, Yitian, Nordling, Åsa, Aigner, Christof, Dames-Ludwig, Marlies, Monteforte, Rossella, Sunder-Plassmann, Raute, Steinhauser, Corinna, Sengoelge, Guerkan, Winnicki, Wolfgang, Schmidt, Alice, Vasileios, Fragoulakis, Fontana, Vanessa, Hanson, Anita, Little, Margaret, Hornby, Rachael, Dello Russo, Cinzia, French, Stephanie, Hampson, Jamie, Gumustekin, Mukaddes, Anyfantis, George, Hampson, Lucy, Lewis, David, Westhead, Ruth, Prince, Clare, Rajasingam, Arjunan, Swen, Jesse J, van der Wouden, Cathelijne H, Manson, Lisanne EN, Abdullah-Koolmees, Heshu, Blagec, Kathrin, Blagus, Tanja, Böhringer, Stefan, Cambon-Thomsen, Anne, Cecchin, Erika, Cheung, Ka-Chun, Deneer, Vera HM, Dupui, Mathilde, Ingelman-Sundberg, Magnus, Jonsson, Siv, Joefield-Roka, Candace, Just, Katja S, Karlsson, Mats O, Konta, Lidija, Koopmann, Rudolf, Kriek, Marjolein, Lehr, Thorsten, Mitropoulou, Christina, Rial-Sebbag, Emmanuelle, Rollinson, Victoria, Roncato, Rossana, Samwald, Matthias, Schaeffeler, Elke, Skokou, Maria, Schwab, Matthias, Steinberger, Daniela, Stingl, Julia C, Tremmel, Roman, Turner, Richard M, van Rhenen, Mandy H, Dávila Fajardo, Cristina L, Dolžan, Vita, Patrinos, George P, Pirmohamed, Munir, Sunder-Plassmann, Gere, Toffoli, Giuseppe, and Guchelaar, Henk-Jan

Published
2023
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23. Nonlinear Mixed‐Effects Model of Z‐Endoxifen Concentrations in Tamoxifen‐Treated Patients from the CEPAM Cohort.

Author

Mc Laughlin, Anna M., Helland, Thomas, Klima, Fenja, Koolen, Stijn L.W., van Schaik, Ron H.N., Mathijssen, Ron H.J., Neven, Patrick, Swen, Jesse J., Guchelaar, Henk‐Jan, Dalenc, Florence, White‐Koning, Melanie, Michelet, Robin, Mikus, Gerd, Schroth, Werner, Mürdter, Thomas, Brauch, Hiltrud, Schwab, Matthias, Søiland, Håvard, Mellgren, Gunnar, and Thomas, Fabienne

Subjects
CYTOCHROME P-450 CYP2D6, BIOTRANSFORMATION (Metabolism), TAMOXIFEN, BREAST cancer, BODY weight
Abstract

Tamoxifen is widely used in patients with hormone receptor‐positive breast cancer. The polymorphic enzyme CYP2D6 is primarily responsible for metabolic activation of tamoxifen, resulting in substantial interindividual variability of plasma concentrations of its most important metabolite, Z‐endoxifen. The Z‐endoxifen concentration thresholds below which tamoxifen treatment is less efficacious have been proposed but not validated, and prospective trials of individualized tamoxifen treatment to achieve Z‐endoxifen concentration thresholds are considered infeasible. Therefore, we aim to validate the association between Z‐endoxifen concentration and tamoxifen treatment outcomes, and identify a Z‐endoxifen concentration threshold of tamoxifen efficacy, using pharmacometric modeling and simulation. As a first step, the CYP2D6 Endoxifen Percentage Activity Model (CEPAM) cohort was created by pooling data from 28 clinical studies (> 7,000 patients) with measured endoxifen plasma concentrations. After cleaning, data from 6,083 patients were used to develop a nonlinear mixed‐effect (NLME) model for tamoxifen and Z‐endoxifen pharmacokinetics that includes a conversion factor to allow inclusion of studies that measured total endoxifen but not Z‐endoxifen. The final parent‐metabolite NLME model confirmed the primary role of CYP2D6, and contributions from body weight, CYP2C9 phenotype, and co‐medication with CYP2D6 inhibitors, on Z‐endoxifen pharmacokinetics. Future work will use the model to simulate Z‐endoxifen concentrations in patients receiving single agent tamoxifen treatment within large prospective clinical trials with long‐term survival to identify the Z‐endoxifen concentration threshold below which tamoxifen is less efficacious. Identification of this concentration threshold would allow personalized tamoxifen treatment to improve outcomes in patients with hormone receptor‐positive breast cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Quantitative modeling of tumor dynamics and development of drug resistance in non‐small cell lung cancer patients treated with erlotinib

Author

Yin, Anyue, primary, Veerman, G. D. Marijn, additional, van Hasselt, Johan G. C., additional, Steendam, Christi M. J., additional, Dubbink, Hendrikus Jan, additional, Guchelaar, Henk‐Jan, additional, Friberg, Lena E., additional, Dingemans, Anne‐Marie C., additional, Mathijssen, Ron H. J., additional, and Moes, Dirk Jan A. R., additional

Published
2024
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29. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction of CYP2C9, HLA-Aand HLA-Bwith anti-epileptic drugs

Author

Manson, Lisanne E. N., Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J., Buunk, Anne-Marie, Houwink, Elisa J. F., Risselada, Arne, Rongen, Gerard A. P. J. M., van Schaik, Ron H. N., Swen, Jesse J., Touw, Daan J., van Westrhenen, Roos, Deneer, Vera H. M., and Guchelaar, Henk-Jan

Abstract

By developing evidence-based pharmacogenetics guidelines to optimize pharmacotherapy, the Dutch Pharmacogenetics Working Group (DPWG) aims to advance the implementation of pharmacogenetics (PGx). This guideline outlines the gene-drug interaction of CYP2C9and HLA-Bwith phenytoin, HLA-Aand HLA-Bwith carbamazepine and HLA-Bwith oxcarbazepine and lamotrigine. A systematic review was performed and pharmacotherapeutic recommendations were developed. For CYP2C9intermediate and poor metabolisers, the DPWG recommends lowering the daily dose of phenytoin and adjust based on effect and serum concentration after 7–10 days. For HLA-B*15:02carriers, the risk of severe cutaneous adverse events associated with phenytoin, carbamazepine, oxcarbazepine, and lamotrigine is strongly increased. For carbamazepine, this risk is also increased in HLA-B*15:11and HLA-A*31:01carriers. For HLA-B*15:02, HLA-B*15:11and HLA-A*31:01positive patients, the DPWG recommends choosing an alternative anti-epileptic drug. If not possible, it is recommended to advise the patient to report any rash while using carbamazepine, lamotrigine, oxcarbazepine or phenytoin immediately. Carbamazepine should not be used in an HLA-B*15:02positive patient. DPWG considers CYP2C9genotyping before the start of phenytoin “essential” for toxicity prevention. For patients with an ancestry in which the abovementioned HLA-alleles are prevalent, the DPWG considers HLA-B*15:02genotyping before the start of carbamazepine, phenytoin, oxcarbazepine, and lamotrigine “beneficial”, as well as genotyping for HLA-B*15:11and HLA-A*31:01before initiating carbamazepine.

Published
2024
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30. Dutch pharmacogenetics working group guideline for the gene-drug interaction of ABCG2, HLA-B and Allopurinol, and MTHFR, folic acid and methotrexate

Author

Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, van der Pol, Karel H, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne Marie, Guchelaar, Henk-Jan, Risselada, Arne, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van der Weide, Jan, van Westrhenen, Roos, Deneer, Vera H M, Houwink, Elisa J F, Rongen, Gerard A, Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, van der Pol, Karel H, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne Marie, Guchelaar, Henk-Jan, Risselada, Arne, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van der Weide, Jan, van Westrhenen, Roos, Deneer, Vera H M, Houwink, Elisa J F, and Rongen, Gerard A

Published
2024

31. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2D6, CYP3A4 and CYP1A2 and antipsychotics

Author

Apotheek Klinische Farmacie, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, van der Weide, Jan, Apotheek Klinische Farmacie, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, and van der Weide, Jan

Published
2024

32. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2D6, CYP3A4 and CYP1A2 and antipsychotics

Author

Afd Pharmacoepi & Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, Pharmacoepidemiology and Clinical Pharmacology, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, van der Weide, Jan, Afd Pharmacoepi & Clinical Pharmacology, PECP – Centre for Clinical Therapeutics, Pharmacoepidemiology and Clinical Pharmacology, Beunk, Lianne, Nijenhuis, Marga, Soree, Bianca, de Boer-Veger, Nienke J, Buunk, Anne-Marie, Guchelaar, Henk Jan, Houwink, Elisa J F, Risselada, Arne, Rongen, Gerard A P J M, van Schaik, Ron H N, Swen, Jesse J, Touw, Daan, van Westrhenen, Roos, Deneer, Vera H M, and van der Weide, Jan

Published
2024

33. Quantitative modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib

Author

Yin, Anyue, Veerman, G. D.Marijn, van Hasselt, Johan G.C., Steendam, Christi M.J., Dubbink, Hendrikus Jan, Guchelaar, Henk Jan, Friberg, Lena E., Dingemans, Anne Marie C., Mathijssen, Ron H.J., Moes, Dirk Jan A.R., Yin, Anyue, Veerman, G. D.Marijn, van Hasselt, Johan G.C., Steendam, Christi M.J., Dubbink, Hendrikus Jan, Guchelaar, Henk Jan, Friberg, Lena E., Dingemans, Anne Marie C., Mathijssen, Ron H.J., and Moes, Dirk Jan A.R.

Abstract

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics’ parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance.

Published
2024

36. Inflammation altered correlation between CYP2C19 genotype and CYP2C19 activity in patients receiving voriconazole.

Author

Klomp, Sylvia D., Veringa, Anette, Alffenaar, Jan‐Willem C., de Boer, Mark G. J., Span, Lambert F. R., Guchelaar, Henk‐Jan, and Swen, Jesse J.

Subjects
CYTOCHROME P-450 CYP2C19, VORICONAZOLE, GENOTYPES, INFLAMMATION, MYCOSES
Abstract

Voriconazole is the cornerstone of the treatment and prevention of fungal infections. While there is a good correlation between CYP2C19 genotype and voriconazole exposure during prophylactic treatment, no correlation was found in patients with invasive aspergillosis. Proinflammatory cytokines result in inhibition of CYP2C19 enzyme activity (and may result in phenoconversion). Here we investigated the relationship between inflammation, CYP2C19 genotype‐predicted‐phenotype, and CYP2C19 activity in patients receiving voriconazole. Data were obtained from two prospective studies investigating voriconazole treatment (NCT02074462 and NCT00893555). Dose‐corrected voriconazole plasma concentration and C‐reactive protein (CRP) were used as proxies for CYP2C19 activity and inflammation, respectively. After data extraction and synthesis, data from 39 patients with paired voriconazole and CRP measurements were available. The distribution of CYP2C19 genotype‐predicted metabolizer phenotypes was 31% intermediate (IM), 41% normal (NM), and 28% rapid metabolizer (RM). During inflammation, dose‐corrected voriconazole levels were increased by 245%, 278%, and 486% for CYP2C19 NMs IMs and RMs, respectively. Patients with moderate or high CRP levels (>50 mg/L) were phenoconverted to a lower metabolizer phenotype irrespective of their CYP2C19 genotype. In a subgroup analysis of eight patients with longitudinal data available with and without inflammation, the pattern of the dose‐corrected voriconazole and CRP measurements were similar, with CYP2C19 activity following decreasing or increasing CRP levels. In conclusion, voriconazole plasma concentrations increase during inflammation due to downregulation of CYP2C19 activity. While this effect appears largest for CYP2C19 RMs, no clinically relevant differences were observed between the CYP2C19 genotypes. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Personalized Chronomodulated 5‐Fluorouracil Treatment: A Physiologically‐Based Pharmacokinetic Precision Dosing Approach for Optimizing Cancer Therapy.

Author

Marok, Fatima Zahra, Wojtyniak, Jan‐Georg, Selzer, Dominik, Dallmann, Robert, Swen, Jesse J., Guchelaar, Henk‐Jan, Schwab, Matthias, and Lehr, Thorsten

Subjects
CIRCADIAN rhythms, CANCER treatment, DIHYDROPYRIMIDINE dehydrogenase, CLOCK genes, PHARMACOKINETICS, ENZYME metabolism
Abstract

The discovery of circadian clock genes greatly amplified the study of diurnal variations impacting cancer therapy, transforming it into a rapidly growing field of research. Especially, use of chronomodulated treatment with 5‐fluorouracil (5‐FU) has gained significance. Studies indicate high interindividual variability (IIV) in diurnal variations in dihydropyrimidine dehydrogenase (DPD) activity – a key enzyme for 5‐FU metabolism. However, the influence of individual DPD chronotypes on chronomodulated therapy remains unclear and warrants further investigation. To optimize precision dosing of chronomodulated 5‐FU, this study aims to: (i) build physiologically‐based pharmacokinetic (PBPK) models for 5‐FU, uracil, and their metabolites, (ii) assess the impact of diurnal variation on DPD activity, (iii) estimate individual DPD chronotypes, and (iv) personalize chronomodulated 5‐FU infusion rates based on a patient's DPD chronotype. Whole‐body PBPK models were developed with PK‐Sim(R) and MoBi(R). Sinusoidal functions were used to incorporate variations in enzyme activity and chronomodulated infusion rates as well as to estimate individual DPD chronotypes from DPYD mRNA expression or DPD enzymatic activity. Four whole‐body PBPK models for 5‐FU, uracil, and their metabolites were established utilizing data from 41 5‐FU and 10 publicly available uracil studies. IIV in DPD chronotypes was assessed and personalized chronomodulated administrations were developed to achieve (i) comparable 5‐FU peak plasma concentrations, (ii) comparable 5‐FU exposure, and (iii) constant 5‐FU plasma levels via "noise cancellation" chronomodulated infusion. The developed PBPK models capture the extent of diurnal variations in DPD activity and can help investigate individualized chronomodulated 5‐FU therapy through testing alternative personalized dosing strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Self‐management support with the Respiratory Adherence Care Enhancer instrument in asthma and chronic obstructive pulmonary disease: An implementation trial.

Author

Visser, Claire D., Antonisse, Lisanne L. M., Alleda, Floor M., Bos, Colin, Saini, Privender, Kuipers, Esther, Guchelaar, Henk‐Jan, and Teichert, Martina

Subjects
CHRONIC obstructive pulmonary disease, DRUGSTORES, PATIENT satisfaction, ASTHMA, RESPIRATORY therapy, ASTHMATICS
Abstract

Aim: Suboptimal self‐management with controller inhalation therapy in asthma and COPD is frequently observed with poor treatment outcomes. The developed 'Respiratory Adherence Care Enhancer' (RACE) instrument identifies and addresses individual barriers to self‐management with a theoretical underpinning. This study investigates the feasibility of pharmaceutical support with this instrument. Methods: An implementation trial was conducted with asthma and COPD patients in 5 community pharmacies in the Netherlands. Patients were allocated to standard care or add‐on support with the RACE instrument. Patients were invited to complete the RACE questionnaire at baseline, 5‐week and 10‐week follow‐up. Barrier profiles were accessible for the intervention group with subsequent consultations at baseline and 5‐weeks. Experiences were collected from patients and consultants with a questionnaire and reported findings. Primary endpoints focused on the acceptability, practicality and implementation process. Secondary endpoints included between‐group differences in barrier and disease control outcomes from baseline at 10‐weeks follow‐up. Results: In total, 84 patients were included; 48 were assigned to intervention and 36 to standard care. Patient satisfaction of support with the RACE instrument was high (71%). Patients felt motivated, reassured and more confident about their disease management. Consultants reported an increase in awareness of patient barriers. Patient recognition of barrier profiles was 83.9% (±12.9%). The barrier inhaler techniques decreased significantly for the intervention group at follow‐up with odds ratio 0.30 (95% confidence interval, 0.10–0.91). No significant differences were observed for changes in number of barriers and disease control. Conclusion: Self‐management support with the RACE instrument is feasible and appreciated, facilitating behaviour change with patient‐centred pharmaceutical care in asthma and COPD. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Population Pharmacokinetic and Pharmacogenetic Analysis of Mitotane in Patients with Adrenocortical Carcinoma: Towards Individualized Dosing

Author

Yin, Anyue, Ettaieb, Madeleine H. T., Swen, Jesse J., van Deun, Liselotte, Kerkhofs, Thomas M. A., van der Straaten, Robert J. H. M., Corssmit, Eleonora P. M., Gelderblom, Hans, Kerstens, Michiel N., Feelders, Richard A., Eekhoff, Marelise, Timmers, Henri J. L. M., D’Avolio, Antonio, Cusato, Jessica, Guchelaar, Henk-Jan, Haak, Harm R., and Moes, Dirk Jan A. R.

Published
2021
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42. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction of CYP2C9, HLA-A and HLA-B with anti-epileptic drugs

Author

Nijenhuis, Marga, primary, Manson, Lisanne, additional, Soree, Bianca, additional, de Boer-Veger, Nienke, additional, Buunk, Anne Marie, additional, Houwink, Elisa, additional, Risselada, Arne, additional, Rongen, Gerard, additional, Schaik, Ron van, additional, Swen, Jesse, additional, Touw, Daniel, additional, Westrhenen, Roos van, additional, Deneer, Vera, additional, and Guchelaar, Henk-Jan, additional

Published
2023
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