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High‐dose methotrexate (>0.5 g/m2) is among the first‐line chemotherapeutic agents used in treating acute lymphoblastic leukemia (ALL) and osteosarcoma in children. Despite rapid hydration, leucovorin rescue, and routine therapeutic drug monitoring, severe toxicity is not uncommon. This study aimed at developing population pharmacokinetic (popPK) models of high‐dose methotrexate for ALL and osteosarcoma and demonstrating the possibility and convenience of popPK model–based individual dose optimization using R and shiny, which is more accessible, efficient, and clinician‐friendly than NONMEM. The final data set consists of 36 ALL (354 observations) and 16 osteosarcoma (585 observations) patients. Covariate model building and parameter estimations were done using NONMEM and Perl‐speaks‐NONMEM. Diagnostic Plots and bootstrapping validated the models' performance and stability. The dose optimizer developed based on the validated models can obtain identical individual parameter estimates as NONMEM. Compared to calling a NONMEM execution and reading its output, estimating individual parameters within R reduces the execution time from 8.7‐12.8 seconds to 0.4‐1.0 second. For each subject, the dose optimizer can recommend (1) an individualized optimal dose and (2) an individualized range of doses. For osteosarcoma, recommended optimal doses by the optimizer resemble the final doses at which the subjects were eventually stabilized. The dose optimizers developed demonstrated the potential to inform dose adjustments using a model‐based, convenient, and efficient tool for high‐dose methotrexate. Although the dose optimizer is not meant to replace clinical judgment, it provides the clinician with the individual pharmacokinetics perspective by recommending the (range of) optimal dose. [ABSTRACT FROM AUTHOR]

Introduction: In DBS for patients with PD, STN is the most common DBS target with the sweet point located dorsal ipsilaterally adjacent to the pyramidal tract. During awake DBS lead implantation, macrostimulation is performed to test the clinical effects and side effects especially the pyramidal tract side effect (PTSE) threshold. A too low PTSE threshold will compromise the therapeutic stimulation window. When DBS lead implantation is performed under general anaesthesia (GA), there is a lack of real time feedback regarding the PTSE. In this study, we evaluated the macrostimulation-induced PTSE by electromyography (EMG) during DBS surgery under GA. Our aim is to investigate the prediction of post-operative programming PTSE threshold using EMG-based PTSE threshold, and its potential application to guide intra-operative lead implantation., Methods: 44 patients with advanced PD received STN DBS under GA were studied. Intra-operative macrostimulation via EMG was assessed from the contralateral upper limb. EMG signal activation was defined as the amplitude doubling or greater than the base line. In the first programming session at one month post-operation, the PTSE threshold was documented. All patients were followed up for one year to assess clinical outcome., Results: All 44 cases (88 sides) demonstrated activations of limb EMG via increasing amplitude of macrostimulation the contralateral STN under GA. Revision tracts were explored in 7 patients due to a low EMG activation threshold (<= 2.5 mA). The mean intraoperative EMG-based PTSE threshold was 4.3 mA (SD 1.2 mA, Range 2.0-8.0 mA), programming PTSE threshold was 3.7 mA (SD 0.8 mA, Range 2.0-6.5 mA). Linear regression showed that EMG-based PTSE threshold was a statistically significant predictor variable for the programming PTSE threshold ( p value <0.001). At one year, the mean improvement of UPDRS Part III score at medication-off/DBS-on was 54.0% (SD 12.7%) and the levodopa equivalent dose (LED) reduction was 59.5% (SD 23.5%)., Conclusion: During STN DBS lead implantation under GA, PTSE threshold can be tested by EMG through macrostimulation. It can provide real-time information on the laterality of the trajectory and serves as reference to guide intra-operative DBS lead placement., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (© 2024 Leung, Lau, Kan, Ng, Chan, Ng, Cheung, Hui, Chan, Zhu, Chan and Poon.)