{"title":"A phase I drug–drug interaction study to assess the effect of futibatinib on P-gp and BCRP substrates and of P-gp inhibition on the pharmacokinetics of futibatinib","authors":"Amanda Long, Ikuo Yamamiya, Michelle Valentine, Ziv Machnes, Nanae Hangai, Bailey Anderson, Volker Wacheck, Ling Gao","doi":"10.1111/cts.70012","DOIUrl":"https://doi.org/10.1111/cts.70012","url":null,"abstract":"<p>Futibatinib, an inhibitor of fibroblast growth factor receptor 1–4, is approved for the treatment of patients with advanced cholangiocarcinoma with <i>FGFR2</i> fusions/rearrangements. In this phase I drug–drug interaction study, the effects of futibatinib on P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) substrates, and of P-gp inhibition on futibatinib pharmacokinetics (PK) were investigated in healthy adults aged 18–55 years. In part 1, 20 participants received digoxin (P-gp substrate) and rosuvastatin (BCRP substrate). Following a ≥10-day washout, futibatinib was administered for 7 days, with digoxin and rosuvastatin coadministered on the third day. In part 2, 24 participants received futibatinib. Following a ≥3-day washout, quinidine (P-gp inhibitor) was administered for 4 days, with futibatinib coadministered on day 4. Blood samples were collected predose and for 24 (futibatinib), 72 (rosuvastatin), and 120 h (digoxin) postdose. Urine samples (digoxin) were collected predose and for 120 h postdose. PK parameters were compared between treatments using analysis of variance. Coadministration with futibatinib had no effect on the PK of digoxin and rosuvastatin, and coadministration with quinidine had minimal effects on the PK of futibatinib. Differences in <i>C</i><sub>max</sub> and AUC with and without futibatinib and quinidine, respectively, were <20%. The most common treatment-emergent adverse events were diarrhea (80%) and increased blood phosphorous (75%) in part 1 and prolonged electrocardiogram QT interval (38%) in part 2. The data show that futibatinib has no clinically meaningful effects on the PK of P-gp or BCRP substrates and that the effect of P-gp inhibition on futibatinib PK is not clinically relevant.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yvonne Boyle, Hemme J. Hijma, Jamie Rees, Jagtar Nijjar, Eirini Panoilia, Yolanda Alvarez, Sarah Siederer, Emma Greening, Edward Emery, Kathy Abbott Banner, Geert Jan Groeneveld
{"title":"Randomized, placebo-controlled study on the effects of intravenous GSK3858279 (anti-CCL17) on a battery of evoked pain tests in healthy participants","authors":"Yvonne Boyle, Hemme J. Hijma, Jamie Rees, Jagtar Nijjar, Eirini Panoilia, Yolanda Alvarez, Sarah Siederer, Emma Greening, Edward Emery, Kathy Abbott Banner, Geert Jan Groeneveld","doi":"10.1111/cts.13873","DOIUrl":"https://doi.org/10.1111/cts.13873","url":null,"abstract":"<p>C–C Motif Chemokine Ligand 17 (CCL17) is a chemokine that binds and signals through the G-protein coupled CC-chemokine receptor 4 and has been implicated in the development of inflammatory and arthritic pain. GSK3858279 is a high-affinity, first-in-class, monoclonal antibody, binding specifically to CCL17 and inhibiting downstream signaling. In this phase I, randomized, single-center, double-blind, placebo-controlled, three-period, incomplete-block crossover study (NCT04114656), the analgesic effects and safety of intravenous GSK3858279 were assessed in a battery of evoked acute pain assessments on healthy, adult (aged ≥18 years), male participants. Participants were randomized 1:1 to receive either one placebo (0.9% w/v NaCl) dose followed by two GSK3858279 doses (PAA treatment sequence), or one GSK3858279 dose followed by two placebo doses (APP treatment sequence). The co-primary end points were ultraviolet B heat pain detection threshold (°C), cold pressor time to pain tolerance threshold (PTT, sec), and electrical PTT (mA, single stimulus). Twenty-one participants were enrolled (PAA = 11; APP = 10). Mean age (standard deviation) was 29.3 (7.9) years for PAA, 31.1 (7.7) years for APP. No significant differences were observed in the analgesic effect between GSK3858279 and placebo for any end point. Exposure to GSK3858279 was similar between Period 1 (APP sequence), and Periods 2 and 3 (PAA sequence), with some GSK3858279 carry-over. Changes in serum CCL17 levels were consistent with the expected GSK3858279 activity. All drug-related adverse events were mild in intensity and caused no discontinuations. The absence of an efficacy signal in this acute pain model does not preclude efficacy in chronic pain states.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.13873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hengbang Wang, Yun Yang, Zi Chen, Lei Fu, Min Yu, Lixin Jiang, Cunlin Wang, Lichuang Men, Ilisse Minto, Dajun Yang, Yifan Zhai
{"title":"Pharmacokinetics of olverembatinib (HQP1351) in the presence of a strong CYP3A4 inhibitor (itraconazole) or inducer (rifampin) in healthy volunteers","authors":"Hengbang Wang, Yun Yang, Zi Chen, Lei Fu, Min Yu, Lixin Jiang, Cunlin Wang, Lichuang Men, Ilisse Minto, Dajun Yang, Yifan Zhai","doi":"10.1111/cts.70021","DOIUrl":"10.1111/cts.70021","url":null,"abstract":"<p>Olverembatinib (HQP1351) is a BCR-ABL1 tyrosine kinase inhibitor with promising clinical activity. It is approved in China for the treatment of patients with chronic myeloid leukemia harboring drug-resistant mutations, such as <i>T315I</i>. In vitro studies suggested that metabolism of olverembatinib is primarily mediated by cytochrome P450 (CYP3A4). The effects of CYP3A4 inhibition and induction on the pharmacokinetics of olverembatinib were evaluated in an open-label, 2-part, fixed-sequence study in healthy volunteers. In Part 1 of this study, 16 participants received a single oral dose of olverembatinib (20 mg) and the oral CYP3A4 inhibitor itraconazole (200 mg). In Part 2, 16 participants received a single oral dose of olverembatinib (40 mg) and the oral CYP3A4 inducer rifampin (600 mg). To measure pharmacokinetic parameters, serial blood samples were collected after administration of olverembatinib alone and combined with itraconazole or rifampin. Coadministration of olverembatinib with itraconazole increased the peak plasma concentration of olverembatinib, its area under the time-concentration curve (AUC)<sub>0-last</sub>, and AUC<sub>0-inf</sub> by 75.63%, 147.06%, and 158.66%, respectively. Coadministration with rifampin decreased these same variables by 61.27%, 74.21%, and 75.19%, respectively. These results confirm that olverembatinib is primarily metabolized by CYP3A4 in humans, suggesting that caution should be exercised with concurrent use of olverembatinib and strong CYP3A4 inhibitors or inducers.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Federico Amato, Rainer Strotmann, Roberto Castello, Rolf Bruns, Vishal Ghori, Andreas Johne, Karin Berghoff, Karthik Venkatakrishnan, Nadia Terranova
{"title":"Explainable machine learning prediction of edema adverse events in patients treated with tepotinib","authors":"Federico Amato, Rainer Strotmann, Roberto Castello, Rolf Bruns, Vishal Ghori, Andreas Johne, Karin Berghoff, Karthik Venkatakrishnan, Nadia Terranova","doi":"10.1111/cts.70010","DOIUrl":"10.1111/cts.70010","url":null,"abstract":"<p>Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring <i>MET</i> exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is still limited understanding about the factors contributing to its occurrence. Herein, we apply machine learning (ML)-based approaches to predict the likelihood of occurrence of edema in patients undergoing tepotinib treatment, and to identify factors influencing its development over time. Data from 612 patients receiving tepotinib in five Phase I/II studies were modeled with two ML algorithms, Random Forest, and Gradient Boosting Trees, to predict edema AE incidence and severity. Probability calibration was applied to give a realistic estimation of the likelihood of edema AE. Best model was tested on follow-up data and on data from clinical studies unused while training. Results showed high performances across all the tested settings, with F1 scores up to 0.961 when retraining the model with the most relevant covariates. The use of ML explainability methods identified serum albumin as the most informative longitudinal covariate, and higher age as associated with higher probabilities of more severe edema. The developed methodological framework enables the use of ML algorithms for analyzing clinical safety data and exploiting longitudinal information through various covariate engineering approaches. Probability calibration ensures the accurate estimation of the likelihood of the AE occurrence, while explainability tools can identify factors contributing to model predictions, hence supporting population and individual patient-level interpretation.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Caroline M. Junker Mentzel, Yan Hui, Tanja Maria Stentoft Hammerich, Malene Klug-Dambmann, Yi Liu, Line Fisker Zachariassen, Lars Hestbjerg Hansen, Antonios Aslampaloglou, Maria Kiersgaard, Dennis Sandris Nielsen, Axel Kornerup Hansen, Lukasz Krych
{"title":"Low-gainer diet-induced obese microbiota transplanted mice exhibit increased fighting","authors":"Caroline M. Junker Mentzel, Yan Hui, Tanja Maria Stentoft Hammerich, Malene Klug-Dambmann, Yi Liu, Line Fisker Zachariassen, Lars Hestbjerg Hansen, Antonios Aslampaloglou, Maria Kiersgaard, Dennis Sandris Nielsen, Axel Kornerup Hansen, Lukasz Krych","doi":"10.1111/cts.13906","DOIUrl":"https://doi.org/10.1111/cts.13906","url":null,"abstract":"<p>Weight gain variation is a great challenge in diet-induced obesity studies since low-gainer animals are of limited experimental value. The inbred C57BL/6 (B6) mice are frequently used models due to their genetic homogeneity and susceptibility to diet-induced obesity (DIO). The aim of this study is to investigate if the gut microbiota (GM) influences the fraction of low weight gainers in DIO studies. A total of 100 male B6 mice (donor population) were fed a high-fat diet for 14 weeks and divided into the study groups high gainer (HG) and low gainer (LG) based on their weight gain. Subsequently, fecal matter transplantation (FMT) was done on germ-free B6 mice with GM from HG and LG donors (FMT population). LG (13.35 ± 2.5 g) and HG (25.52 ± 2.0 g) animals were identified by the weight gain from week 1 to week 12. Interestingly, the start weight of the LG (20.36 ± 1.4 g) and HG (21.59 ± 0.7 g) groups differed significantly. Transplanting LG or HG fecal matter to germ-free mice resulted in significant differences in weight gain between HG and LG, as well as differences in serum leptin levels and epididymal fat pad weight. A clear LG-specific GM composition could not be distinguished by 16S rRNA gene amplicon sequencing. Surprisingly, significantly more fighting was recorded in LG groups of both donor populations and when transplanted to germ-free mice. The HG and LG phenotypes could be transferred to germ-free mice. The increased fighting in the LG group in both studies suggests not only that the tendency to fight can be transferred by FMT in these mice, but also that fighting should be prevented in DIO studies to minimize the number of LG animals.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.13906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"From organs to algorithms: Redefining cancer classification in the age of artificial intelligence","authors":"Sean Khozin","doi":"10.1111/cts.70001","DOIUrl":"https://doi.org/10.1111/cts.70001","url":null,"abstract":"<p>Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI-driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sophia Hernandez, Lucia A. Hindorff, Joannella Morales, Erin M. Ramos, Teri A. Manolio
{"title":"Patterns of pharmacogenetic variation in nine biogeographic groups","authors":"Sophia Hernandez, Lucia A. Hindorff, Joannella Morales, Erin M. Ramos, Teri A. Manolio","doi":"10.1111/cts.70017","DOIUrl":"https://doi.org/10.1111/cts.70017","url":null,"abstract":"<p>Frequencies of pharmacogenetic (PGx) variants are known to differ substantially across populations but much of the available PGx literature focuses on one or a few population groups, often defined in nonstandardized ways, or on a specific gene or variant. Guidelines produced by the Clinical Pharmacogenetic Implementation Consortium (CPIC) provide consistent methods of literature extraction, curation, and reporting, including comprehensive curation of allele frequency data across nine defined “biogeographic groups” from the PGx literature. We extracted data from 23 CPIC guidelines encompassing 19 genes to compare the sizes of the populations from each group and allele frequencies of altered function alleles across groups. The European group was the largest in the curated literature for 16 of the 19 genes, while the American and Oceanian groups were the smallest. Nearly 200 alleles were detected in nonreference groups that were not reported in the largest (reference) group. The genes <i>CYP2B6</i> and <i>CYP2C9</i> were <i>more</i> likely to have higher frequencies of altered function alleles in nonreference groups compared to the reference group, while the genes <i>CYP4F2</i>, <i>DPYD</i>, <i>SLCO1B1</i>, and <i>UGT1A1</i> were <i>less</i> likely to have higher frequencies in nonreference groups. PGx allele frequencies and function differ substantially across nine biogeographic groups, all but two of which are underrepresented in available PGx data. Awareness of these differences and increased efforts to characterize the breadth of global PGx variation are needed to ensure that implementation of PGx-guided drug selection does not further widen existing health disparities among populations currently underrepresented in PGx data.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Monika Tomaszewska-Kiecana, Elisabete Carapuça, Amalia Florez-Igual, Javier Queiruga-Parada
{"title":"MB09, a denosumab biosimilar candidate: Biosimilarity demonstration in a phase I study in healthy subjects","authors":"Monika Tomaszewska-Kiecana, Elisabete Carapuça, Amalia Florez-Igual, Javier Queiruga-Parada","doi":"10.1111/cts.70013","DOIUrl":"https://doi.org/10.1111/cts.70013","url":null,"abstract":"<p>This was a Phase I, randomized, double-blinded, three-arm, single-dose, parallel study aimed to demonstrate pharmacokinetic (PK) similarity between MB09 (a denosumab biosimilar candidate) and reference denosumab (XGEVA® from European Union [EU-reference] and United States [US-reference]) in a healthy male population. The primary PK endpoints included: Area under the serum concentration versus time curve from time 0 to the last quantifiable concentration timepoint (AUC<sub>0–last</sub>); and maximum observed serum concentration (<i>C</i><sub>max</sub>). Secondary endpoints included: AUC from time 0 extrapolated to infinity (AUC<sub>0–∞</sub>), time to reach maximum observed concentration, clearance, terminal phase half-life, pharmacodynamic, safety, and immunogenicity assessments. A total of 255 subjects were randomized (1:1:1) to receive a subcutaneous 35 mg dose of MB09 or reference denosumab. <i>C</i><sub>max</sub> was reached after denosumab administration, followed by a decline in the concentration with similar terminal phase half-live across treatment arms. Systemic exposure of MB09 (AUC<sub>0–last</sub> and <i>C</i><sub>max</sub>) was equivalent to the reference denosumab, as the 90% confidence intervals around the geometric least square mean ratios laid within the predefined acceptance limits (80.00%, 125.00%) across all comparisons. Pharmacodynamic parameters, based on the percent of change from baseline in serum C-terminal telopeptide of Type 1 collagen levels, were similar across the three arms. The treatments were considered safe and generally well tolerated, with 92 treatment-emergent adverse events reported (most Grade 2 and 3) and similarly distributed. Immunogenicity was low and similarly distributed. These results provide strong evidence that supports the biosimilarity between MB09 and denosumab reference products.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christina A. Clarke, Breeana L. Mitchell, Girish Putcha, Emma Alme, Peter Bach, Jonathan P. Beer, Tomasz M. Beer, Michelle A. Beidelschies, Jody Hoyos, Eric Klein, Peter Kuhn, Nancy Krunic, Kathryn Lang, Jerry S. H. Lee, Dorys Lopez Ramos, David Morgenstern, Elissa Quinn, Victoria M. Raymond, Wendy S. Rubinstein, Stephanie A. Sanchez, Ryan Serra, Mark Stewart, Lauren C. Leiman
{"title":"Lexicon for blood-based early detection and screening: BLOODPAC consensus document","authors":"Christina A. Clarke, Breeana L. Mitchell, Girish Putcha, Emma Alme, Peter Bach, Jonathan P. Beer, Tomasz M. Beer, Michelle A. Beidelschies, Jody Hoyos, Eric Klein, Peter Kuhn, Nancy Krunic, Kathryn Lang, Jerry S. H. Lee, Dorys Lopez Ramos, David Morgenstern, Elissa Quinn, Victoria M. Raymond, Wendy S. Rubinstein, Stephanie A. Sanchez, Ryan Serra, Mark Stewart, Lauren C. Leiman","doi":"10.1111/cts.70016","DOIUrl":"https://doi.org/10.1111/cts.70016","url":null,"abstract":"<p>In the United States, 2.0 million new cancer cases and around 600,000 cancer deaths are estimated to occur in 2024. Early detection gives cancer patients the best chance for treatment success. Currently, cancer screening in the general population is recommended for a limited set of cancers; as a result, most cancer types are not regularly screened. Thus, in recent years, we have seen a wave of novel, non-invasive, single- and multi-cancer detection tests (SCD and MCD), promising detection of cancer signals prior to the onset of symptoms and/or clinical diagnosis. To accelerate the development, access, and adoption of these tests, the Blood Profiling Atlas in Cancer (BLOODPAC) Consortium, a collaborative infrastructure for developing standards and best practices, established the Early Detection & Screening (ED&S) Working Group. The early detection space is in need of consensus around definitions for SCD and MCD tests that harmonize terminology across diverse stakeholders, thereby reducing communication barriers and ultimately advancing the discipline. To this end, the ED&S Working Group compiled a lexicon of terms, chosen based on perceived importance, frequency of use, lack of clarity, and unique challenges in the context of SCD and MCD tests. This lexicon was submitted to the FDA for their feedback, which was incorporated. In this work, we present the first installment of the lexicon, consisting of 14 primary terms, that will be part of an online dictionary and provide a foundation for future projects of BLOODPAC's ED&S Working Group.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 9","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142100319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgios Vlasakakis, Michael T. McCabe, Yu Liu Ho, Geraldine Ferron-Brady, Paul Martin, Darren Bentley, Catherine Ellis, Mary Antonysamy, Sandra A. G. Visser
{"title":"Momelotinib: Mechanism of action, clinical, and translational science","authors":"Georgios Vlasakakis, Michael T. McCabe, Yu Liu Ho, Geraldine Ferron-Brady, Paul Martin, Darren Bentley, Catherine Ellis, Mary Antonysamy, Sandra A. G. Visser","doi":"10.1111/cts.70018","DOIUrl":"10.1111/cts.70018","url":null,"abstract":"<p>Myelofibrosis is a chronic myeloproliferative disorder characterized by bone marrow fibrosis, splenomegaly, anemia, and constitutional symptoms, with a median survival of ≈6 years from diagnosis. While currently approved Janus kinase (JAK) inhibitors (ruxolitinib, fedratinib) improve splenomegaly and symptoms, most can exacerbate myelofibrosis-related anemia, a negative prognostic factor for survival. Momelotinib is a novel JAK1/JAK2/activin A receptor type 1 (ACVR1) inhibitor approved in the US, European Union, and the UK and is the first JAK inhibitor indicated specifically for patients with myelofibrosis with anemia. Momelotinib not only addresses the splenomegaly and symptoms associated with myelofibrosis by suppressing the hyperactive JAK–STAT (signal transducer and activator of transcription) pathway but also improves anemia and reduces transfusion dependency through ACVR1 inhibition. The recommended dose of momelotinib is 200 mg orally once daily, which was established after review of safety, efficacy, pharmacokinetic, and pharmacodynamic data. Momelotinib is metabolized primarily by CYP3A4 and excreted as metabolites in feces and urine. Steady-state maximum concentration is 479 ng/mL (CV%, 61%), with a mean AUC<sub>tau</sub> of 3288 ng.h/mL (CV%, 60%); its major metabolite, M21, is active (≈40% of pharmacological activity of parent), with a metabolite-to-parent AUC ratio of 1.4–2.1. This review describes momelotinib's mechanism of action, detailing how the JAK–STAT pathway is involved in myelofibrosis pathogenesis and ACVR1 inhibition decreases hepcidin, leading to improved erythropoiesis. Additionally, it summarizes the pivotal studies and data that informed the recommended dosage and risk/benefit assessment.</p>","PeriodicalId":50610,"journal":{"name":"Cts-Clinical and Translational Science","volume":"17 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cts.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}