A new population pharmacokinetic model for recombinant factor IX‐Fc fusion concentrate including young children with haemophilia B

Aims: Recombinant factor IX Fc fusion protein (rFIX‐Fc) is an extended half‐life factor concentrate administered to haemophilia B patients. So far, a population pharmacokinetic (PK) model has only been published for patients aged ≥12 years. The aim was to externally evaluate the predictive performance of the published rFIX‐Fc population PK model for patients of all ages and develop a model that describes rFIX‐Fc PK using real‐world data. Methods: We collected prospective and retrospective data from patients with haemophilia B treated with rFIX‐Fc and included in the OPTI‐CLOT TARGET study (NTR7523) or United Kindom (UK)‐EHL Outcomes Registry (NCT02938156). Predictive performance was assessed by comparing predicted with observed FIX activity levels. A new population PK model was constructed using nonlinear mixed‐effects modelling. Results: Real‐world data were obtained from 37 patients (median age: 16 years, range 2–71) of whom 14 were aged <12 years. Observed FIX activity levels were significantly higher than levels predicted using the published model, with a median prediction error of −48.8%. The new model showed a lower median prediction error (3.4%) and better described rFIX‐Fc PK, especially for children aged <12 years. In the new model, an increase in age was correlated with a decrease in clearance (P < .01). Conclusions: The published population PK model significantly underpredicted FIX activity levels. The new model better describes rFIX‐Fc PK, especially for children aged <12 years. This study underlines the necessity to strive for representative population PK models, thereby avoiding extrapolation outside the studied population

Long-Term Neurodevelopmental Outcome of Monochorionic and Matched Dichorionic Twins

Contains fulltext : 79941.pdf (publisher's version ) (Open Access)BACKGROUND: Monochorionic (MC) twins are at increased risk for perinatal mortality and serious morbidity due to the presence of placental vascular anastomoses. Cerebral injury can be secondary to haemodynamic and hematological disorders during pregnancy (especially twin-to-twin transfusion syndrome (TTTS) or intrauterine co-twin death) or from postnatal injury associated with prematurity and low birth weight, common complications in twin pregnancies. We investigated neurodevelopmental outcome in MC and dichorionic (DC) twins at the age of two years. METHODS: This was a prospective cohort study. Cerebral palsy (CP) was studied in 182 MC infants and 189 DC infants matched for weight and age at delivery, gender, ethnicity of the mother and study center. After losses to follow-up, 282 of the 366 infants without CP were available to be tested with the Griffiths Mental Developmental Scales at 22 months corrected age, all born between January 2005 and January 2006 in nine perinatal centers in The Netherlands. Due to phenotypic (un)alikeness in mono-or dizygosity, the principal investigator was not blinded to chorionic status; perinatal outcome, with exception of co-twin death, was not known to the examiner. FINDINGS: Four out of 182 MC infants had CP (2.2%) - two of the four CP-cases were due to complications specific to MC twin pregnancies (TTTS and co-twin death) and the other two cases of CP were the result of cystic PVL after preterm birth - compared to one sibling of a DC twin (0.5%; OR 4.2, 95% CI 0.5-38.2) of unknown origin. Follow-up rate of neurodevelopmental outcome by Griffith's test was 76%. The majority of 2-year-old twins had normal developmental status. There were no significant differences between MC and DC twins. One MC infant (0.7%) had a developmental delay compared to 6 DC infants (4.2%; OR 0.2, 95% 0.0-1.4). Birth weight discordancy did not influence long-term outcome, though the smaller twin had slightly lower developmental scores than its larger co-twin. CONCLUSIONS: There were no significant differences in occurrence of cerebral palsy as well as neurodevelopmental outcome between MC and DC twins. Outcome of MC twins seems favourable in the absence of TTTS or co-twin death

ChatGPT in pharmacometrics? Potential opportunities and limitations

The potential of using ChatGPT in pharmacometrics was explored in this study, with a focus on developing a population pharmacokinetic (PK) model for standard half-life factor VIII. Our results demonstrated that ChatGPT can be utilized to accurately obtain typical PK parameters from literature, generate a population PK model in R and develop an interactive Shiny application to visualize the results. ChatGPT's language generation capabilities enabled the development of R codes with minimal programming knowledge and helped to identify as well fix errors in the code. While ChatGPT presents several advantages, such as its ability to streamline the development process, its use in pharmacometrics also has limitations and challenges, including the accuracy and reliability of AI-generated data, the lack of transparency and reproducibility regarding codes generated by ChatGPT. Overall, our study demonstrates the potential of using ChatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs.</p

Insights into the Dose-Response Relationship of Radioembolization with Resin 90Y-Microspheres : A Prospective Cohort Study in Patients with Colorectal Cancer Liver Metastases

Randomized controlled trials are investigating the benefit of hepatic radioembolization added to systemic therapy in the first- and second-line treatment of patients with colorectal liver metastases (CRLM). Remarkably, administered activity may still be suboptimal, because a dose-response relationship has not been defined. The purpose of this study was to characterize the relationship between tumor-absorbed dose and response after (90)Y radioembolization treatment for CRLM. METHODS: Thirty patients with unresectable chemorefractory CRLM were treated with resin (90)Y-microspheres in a prospective phase II clinical trial. Tumor-absorbed dose was quantified on (90)Y PET. Metabolic tumor activity, defined as tumor lesion glycolysis (TLG*) on (18)F-FDG PET, was measured at baseline and 1 mo after treatment. The relationship between tumor-absorbed dose and posttreatment metabolic activity was assessed per metastasis with a linear mixed-effects regression model. RESULTS: Treated metastases (n = 133) were identified. The mean tumor-absorbed dose was 51 ± 28 Gy (range, 7-174 Gy). A 50% reduction in TLG* was achieved in 46% of metastases and in 11 of 30 (37%) patients for the sum of metastases. The latter was associated with a prolonged median overall survival (11.6 vs. 6.6 mo, P = 0.02). A strong and statistically significant dose-response relationship was found (P < 0.001). The dose effect depended on baseline TLG* (P < 0.01). The effective tumor-absorbed dose was conservatively estimated at a minimum of 40-60 Gy. CONCLUSION: A strong dose-response relationship exists for the treatment of CRLM with resin microsphere (90)Y radioembolization. Treatment efficacy is, however, still limited, because the currently used pretreatment activity calculation methods curb potentially achievable tumor-absorbed dose values. A more personalized approach to radioembolization is required before concluding on its clinical potential

Clinicopathological factors influencing outcome in metastatic colorectal cancer patients treated with fluoropyrimidine and bevacizumab maintenance treatment vs observation : an individual patient data meta-analysis of two phase 3 trials

BACKGROUND: The CAIRO3 and AIO 0207 trials demonstrated the efficacy of fluoropyrimidine plus bevacizumab (FP+Bev) maintenance treatment in metastatic colorectal cancer (mCRC) patients. In this individual patient data meta-analysis with updated follow-up, we aim to provide more precise estimates of treatment effects and to identify subgroups that benefit most from maintenance treatment or observation. METHODS: In 871 patients, randomised to FP+Bev maintenance treatment or observation, we investigated whether treatment effect was modified by sex, age, performance status, response to induction treatment, primary tumour location, number of metastatic sites, disease stage and primary tumour resection, serum LDH, platelet count, CEA, and RAS/BRAF mutation status. Primary end point was time to second progression after reintroduction of the induction regimen (PFS2). Secondary end points were first progression-free survival (PFS1) and overall survival (OS). RESULTS: At a median follow-up of 68.5 months (IQR 54.6-87.0 months), maintenance treatment was more effective compared with observation in PFS1 (HR 0.40(95% CI 0.34-0.47)) and PFS2 (HR 0.70(0.60-0.81)). No subgroups were identified that did not benefit from maintenance treatment in PFS1 and PFS2; no clinically relevant subgroup effects were observed. Regarding OS, pooled results were not significant (HR 0.91(0.78-1.05)), and the trials showed marked heterogeneity in overall treatment effect and subgroup effects. CONCLUSIONS: FP+Bev maintenance treatment is effective in all patients, regardless of the investigated subgroups

Organoids as a biomarker for personalized treatment in metastatic colorectal cancer: drug screen optimization and correlation with patient response

Abstract Background The inability to predict treatment response of colorectal cancer patients results in unnecessary toxicity, decreased efficacy and survival. Response testing on patient-derived organoids (PDOs) is a promising biomarker for treatment efficacy. The aim of this study is to optimize PDO drug screening methods for correlation with patient response and explore the potential to predict responses to standard chemotherapies. Methods We optimized drug screen methods on 5–11 PDOs per condition of the complete set of 23 PDOs from patients treated for metastatic colorectal cancer (mCRC). PDOs were exposed to 5-fluorouracil (5-FU), irinotecan- and oxaliplatin-based chemotherapy. We compared medium with and without N-acetylcysteine (NAC), different readouts and different combination treatment set-ups to capture the strongest association with patient response. We expanded the screens using the optimized methods for all PDOs. Organoid sensitivity was correlated to the patient’s response, determined by % change in the size of target lesions. We assessed organoid sensitivity in relation to prior exposure to chemotherapy, mutational status and sidedness. Results Drug screen optimization involved excluding N-acetylcysteine from the medium and biphasic curve fitting for 5-FU & oxaliplatin combination screens. CellTiter-Glo measurements were comparable with CyQUANT and did not affect the correlation with patient response. Furthermore, the correlation improved with application of growth rate metrics, when 5-FU & oxaliplatin was screened in a ratio, and 5-FU & SN-38 using a fixed dose of SN-38. Area under the curve was the most robust drug response curve metric. After optimization, organoid and patient response showed a correlation coefficient of 0.58 for 5-FU (n = 6, 95% CI -0.44,0.95), 0.61 for irinotecan- (n = 10, 95% CI -0.03,0.90) and 0.60 for oxaliplatin-based chemotherapy (n = 11, 95% CI -0.01,0.88). Median progression-free survival of patients with resistant PDOs to oxaliplatin-based chemotherapy was significantly shorter than sensitive PDOs (3.3 vs 10.9 months, p = 0.007). Increased resistance to 5-FU in patients with prior exposure to 5-FU/capecitabine was adequately reflected in PDOs (p = 0.003). Conclusions Our study emphasizes the critical impact of the screening methods for determining correlation between PDO drug screens and mCRC patient outcomes. Our 5-step optimization strategy provides a basis for future research on the clinical utility of PDO screens

Additional file 1 of Organoids as a biomarker for personalized treatment in metastatic colorectal cancer: drug screen optimization and correlation with patient response

Additional file 1: Supplementary Table 1. Composition of organoid culture medium. Supplementary Table 2. Chemotherapies and targeted treatments used in drug screens. Supplementary Table 3. Baseline characteristics of the cohort of patients. Supplementary Table 4. Quality control analysis of the drug screens showing the Z’-factor. Supplementary Fig. 1. Quality control analysis of the drug screens illustrating the difference between duplicate assays. Supplementary Fig. 2. Individual drug response curves for each PDO per treatment. Supplementary Fig. 3. Comparing different drug screening methods. Supplementary Fig. 4. The impact of different drug screening methods on organoid sensitivity and correlation with patient response

Vaccines for established cancer: overcoming the challenges posed by immune evasion

Tumorimmunolog