Therapeutic advances do not circumvent the devastating fact that the survival rate in glioblastoma multiforme (GBM) is less than 5%. Nanoparticles consisting of liposome-based therapeutics are provided against a variety of cancer types including GBM, but available liposomal formulations are provided without targeting moieties, which increases the dosing demands to reach therapeutic concentrations with risks of side effects. We prepared PEGylated immunoliposomes (ILs) conjugated with anti-human epidermal growth factor receptor (EGFR) antibodies Cetuximab (α-hEGFR-ILs). The affinity of the α-hEGFR-ILs for the EGF receptor was evaluated in vitro using U87 mg and U251 mg cells and in vivo using an intracranial U87 mg xenograft model. The xenograft model was additionally analyzed with respect to permeability to endogenous albumin, tumor size, and vascularization. The in vitro studies revealed significantly higher binding of α-hEGFR-ILs when compared with liposomes conjugated with isotypic nonimmune immunoglobulin. The uptake and internalization of the α-hEGFR-ILs by U87 mg cells were further confirmed by 3D deconvolution analyses. In vivo, the α-hEGFR-ILs accumulated to a higher extent inside the tumor when compared to nonimmune liposomes. The data show that α-hEGFR-ILs significantly enhance the uptake and accumulation of liposomes in this experimental model of GBM suggestive of improved specific nanoparticle-based delivery.

Despite the excellent prognosis for children and adolescents with acute lymphoblastic lymphoma (ALL), the involvement of the central nervous system (CNS) represents a major therapeutic challenge. Patients who develop CNS relapse have a very poor prognosis, and since current methods cannot reliably identify patients with CNS involvement or patients at high risk of CNS relapse, all children with ALL receive CNS-directed treatment. The current golden standard for detecting CNS involvement is the assessment of cytomorphology on cytospin slides of cerebrospinal fluid (CSF). This technique is inadequate due to low sensitivity and reproducibility. Flow cytometric analysis of CSF represent a novel, highly specific and sensitive technique for the detection of leukemic cells in the CNS. In prospective studies, CSF flow cytometry demonstrated two to three times higher rates of CNS involvement at diagnosis of childhood ALL than conventional cytospin, and especially demonstrated superior sensitivity in detecting low-level CNS disease. CNS involvement determined via flow cytometry has been linked to a higher risk of CNS relapse and poor outcomes in several studies. In this review, we discuss the central analytical concepts of CSF flow cytometry and summarize the current evidence supporting the use of flow cytometric detection of malignant blasts as a biomarker of CNS involvement in childhood ALL.

Background Population pharmacokinetic models can potentially provide suggestions for an initial dose and the magnitude of dose adjustment during therapeutic drug monitoring procedures of imatinib. Several population pharmacokinetic models for imatinib have been developed over the last two decades. However, their predictive performance is still unknown when extrapolated to different populations, especially children. Objective This study aimed to evaluate the predictive performance of these published models on an external real-world dataset containing data from both adults and children. Methods A real-world dataset was collected, containing observations from adult and pediatric patients with Philadelphia chromosome-positive/Philadelphia chromosome-like acute lymphoblastic leukemia and chronic myeloid leukemia (N = 39) treated with imatinib. A systematic review through PubMed was conducted to identify qualified population-pharmacokinetic models for external evaluation (i.e., prediction-based, simulation-based, and Bayesian forecasting diagnostics). Standard allometric scaling was used for models that were developed based on data from adults only. Results Fifteen published models were found for evaluation, of which only two were based on data from both children and adults. Prediction-based diagnostics showed that some models had an acceptable level of bias. The model by Shriyan et al. (with allometric scaling) performed best with a median prediction error of 1.24%. However, no models performed well on precision even when allometric scaling was used, where the lowest median absolute prediction error was 37.66% using the model by Schmidli et al. The models by Golabchifar et al. and Schmidli et al. (both with allometric scaling) performed the best of all tested models, with a median prediction error ≤ 15%, median absolute prediction error ≤ 40%, fraction of prediction error within ± 20% (F₂₀) ≥ 0.3, and within ± 30% (F₃₀) nearly 0.4. Simulation-based diagnostics showed that most of the observations outside the 90% prediction interval were from children. Bayesian forecasting showed that the model prediction could be improved using one prior sample, particularly in adults. Conclusions Current models fail to accurately predict imatinib plasma concentrations in our real-world dataset, especially for children. Future pharmacokinetic studies should focus on developing better models for pediatric populations.

Purpose: Adherence to 6-mercaptopurine (6-MP)/methotrexate maintenance treatment for acute lymphoblastic leukemia (ALL) is pivotal to preventing relapse, and the 6-MP metabolite DNA-incorporated thioguanine (DNA-TG) is associated with relapse risk. In the ALLTogether-1 (A2G1) Maintenance sub-study (EU CT nr 2022-501050-11-01), DNA-TG, thioguanine nucleotides (TGN), and methylated mercaptopurine metabolites (MeMP) are analyzed regularly. Upon levels below preset limits (TGN < 50, or MeMP < 200 or < 100 nmol/mmol hemoglobin for thiopurine S-methyltransferase (TPMT) wild type and heterozygous patients, respectively), the treating physician is informed of potential non-adherence. We investigated the feasibility of using DNA-TG as the primary flagging of potential non-adherence. Methods: We analyzed 6-MP metabolites in 3,074 blood samples from 368 children enrolled in the A2G1 Maintenance sub-study. Results: In 6% of samples, TGN (median 212, 95% range 40–642), MeMP (median 4,959, 95% range 135–23,880) or both were below the flagging potential non-adherence limits. DNA-TG was associated with TGN (estimate = 1.72, p < 0.0001), MeMP (estimate = 1.10, p < 0.0001), and prescribed 6-MP dose (estimate = 1.083 and 1.132, p < 0.0001, for TPMT wild type and heterozygous patients) in linear effects models, and the predicted probability of treatment interruption in logistic regression models. DNA-TG was below 200 fmol TG/µg DNA (13th percentile of all measurements, median 569, 95% range 73–1,823) in all samples with both TGN and MeMP below the flagging potential non-adherence limits. Conclusion: DNA-TG can provide a cost-effective guidance on when to measure TGN and MeMP to determine whether non-adherence should be suspected, which is an additional benefit to monitoring DNA-TG during maintenance therapy.