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Background Cytotoxic chemotherapeutics form the cornerstone of systemic treatment of many cancers. Patients are dosed at maximum tolerated dose (MTD), which is carefully determined in phase I studies. In contrast, in murine studies, dosages are often based on customary practice or small pilot studies, which often are not well documented. Consequently, research groups need to replicate experiments, resulting in an excess use of animals and highly variable dosages across the literature. In addition, while patients often receive supportive treatments in order to allow dose escalation, mice do not. These issues could affect experimental results and hence clinical translation. Methods To address this, we determined the single-dose MTD in BALB/c and C57BL/6 mice for a range of chemotherapeutics covering the canonical classes, with clinical score and weight as endpoints. Results We found that there was some variation in MTDs between strains and the tolerability of repeated cycles of chemotherapy at MTD was drug-dependent. We also demonstrate that dexamethasone reduces chemotherapy-induced weight loss in mice. Conclusion These data form a resource for future studies using chemotherapy in mice, increasing comparability between studies, reducing the number of mice needed for dose optimisation experiments and potentially improving translation to the clinic.

BackgroundTacrolimus (TAC) is a core immunosuppressant used to prevent transplant rejection after organ transplantation. However, its clinical use is limited by a narrow therapeutic window and substantial interindividual pharmacokinetic variability. Subtherapeutic TAC concentrations are closely associated with acute or chronic transplant rejection, whereas supratherapeutic concentrations often cause severe drug toxicity. As precision medicine advances, integrating multidimensional patient clinical characteristics with machine learning (ML) provides important support for individualized TAC dosing adjustments after liver and kidney transplantation.AimThis review summarizes the application of ML in determining the initial dose of TAC and in recommending early blood drug concentrations in liver and kidney transplant patients, analyzes the core challenges in model extrapolation and clinical translation in existing research, compares their characteristics with traditional models, and looks ahead to the construction direction of long-term immunosuppression monitoring and TAC dynamic adjustment models.MethodsThis narrative review summarizes the benefits of ML for tacrolimus dosing in liver and kidney transplantation. Its key strength is integrating patients’ multidimensional clinical data to enable personalized dosing. We focus on ML applications in initial tacrolimus dose selection, early post-transplant concentration range determination, and dosage form conversion. We also systematically discuss current model limitations and future directions.ConclusionML has been widely applied to develop precise models for TAC immunosuppressive drug administration in patients after liver and kidney transplantation, providing a reference for individualized treatment. However, such models have not yet been widely applied in clinical practice and generally lack the ability to dynamically regulate drug concentrations over the long term. How to promote the model’s true application in clinical practice remains to be explored in depth, but it holds significant prospects for application and research value in individualized treatment.

Interleukin-2 (IL-2) and its receptor (IL-2R) are essential in orchestrating immune responses. Their function and expression in the tumor microenvironment make them attractive targets for immunotherapy, leading to the development of IL-2/IL-2R-targeted therapeutic strategies. However, the dynamic interplay between IL-2/IL-2R and various immune cells and their dual roles in promoting immune activation and tolerance presents a complex landscape for clinical exploitation. This review discusses the pivotal roles of IL-2 and IL-2R in tumorigenesis, shedding light on their potential as diagnostic and prognostic markers and their therapeutic manipulation in cancer. It underlines the necessity to balance the anti-tumor activity with regulatory T-cell expansion and evaluates strategies such as dose optimization and selective targeting for enhanced therapeutic effectiveness. The article explores recent advancements in the field, including developing genetically engineered IL-2 variants, combining IL-2/IL-2R-targeted therapies with other cancer treatments, and the potential benefits of a multidimensional approach integrating molecular profiling, immunological analyses, and clinical data. The review concludes that a deeper understanding of IL-2/IL-2R interactions within the tumor microenvironment is crucial for realizing the full potential of IL-2-based therapies, heralding the promise of improved outcomes for cancer patients.

[This corrects the article DOI: 10.3389/fphar.2026.1741851.].

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long‐term tolerability beyond dose‐limiting toxicities, and the lack of reliable biomarkers for long‐term efficacy. Through the lens of Totality of Evidence and with the mindset of model‐informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Eftozanermin alfa (eftoza), a second-generation tumor necrosis factor-related apoptosis-inducing ligand receptor (TRAIL-R) agonist, induces apoptosis in tumor cells by activation of death receptors 4/5. This phase 1 dose-escalation/dose-optimization study evaluated the safety, pharmacokinetics, pharmacodynamics, and preliminary activity of eftoza in patients with advanced solid tumors. Patients received eftoza 2.5–15 mg/kg intravenously on day 1 or day 1/day 8 every 21 days in the dose-escalation phase, and 1.25–7.5 mg/kg once-weekly (QW) in the dose-optimization phase. Dose-limiting toxicities (DLTs) were evaluated during the first treatment cycle to determine the maximum tolerated dose (MTD) and recommended phase 2 dose (RP2D). Pharmacodynamic effects were evaluated in circulation and tumor tissue. A total of 105 patients were enrolled in the study (dose-escalation cohort, n = 57; dose-optimization cohort, n = 48 patients [n = 24, colorectal cancer (CRC); n = 24, pancreatic cancer (PaCA)]). In the dose-escalation cohort, seven patients experienced DLTs. MTD and RP2D were not determined. Most common treatment-related adverse events were increased alanine aminotransferase and aspartate aminotransferase levels, nausea, and fatigue. The one treatment-related death occurred due to respiratory failure. In the dose-optimization cohort, three patients (CRC, n = 2; PaCA, n = 1) had a partial response. Target engagement with regard to receptor saturation, and downstream apoptotic pathway activation in circulation and tumor were observed. Eftoza had acceptable safety, evidence of pharmacodynamic effects, and preliminary anticancer activity. The 7.5-mg/kg QW regimen was selected for future studies on the basis of safety findings, pharmacodynamic effects, and biomarker modulations. (Trial registration number: NCT03082209 (registered: March 17, 2017)).

This analysis was conducted to assess the risk of anemia in schizophrenia patients during long-term treatment with the glycine transporter-1 (GlyT1) inhibitor iclepertin. A population pharmacokinetic-pharmacodynamic (popPKPD) analysis to characterize the impact of iclepertin exposure on hemoglobin levels was performed using a sequential nonlinear mixed effects modeling approach. The effects of patient characteristics were investigated in a covariate analysis to identify vulnerable patient subgroups, and population simulations were conducted to evaluate different treatment scenarios. Simulations predicted a new, decreased hemoglobin steady state under chronic iclepertin treatment, reached after approximately 120 days. For a typical patient, the intended therapeutic dose of 10 mg iclepertin daily led to a 2% decrease of hemoglobin levels. In a potential extreme scenario of iclepertin exposure fivefold higher than the average exposure following a 10 mg dose (e.g., due to co-administration of a strong CYP3A4 inhibitor), a 7.6% decrease of hemoglobin levels was found. In both scenarios more than 97.5% of the virtual patients stayed above the drug discontinuation safety threshold of 100 g/L hemoglobin (Phase III trial drug discontinuation threshold defined by the patient safety team). Sex, race, age, body mass index and alanine transaminase levels were found to correlate with changes in hemoglobin levels. No correlation with kidney function could be identified. None of the investigated covariate effects were strong enough to raise any safety concerns during chronic treatment with 10 mg iclepertin daily. This work provides a generalizable modeling and simulation framework to assess the anemia risk in patients and vulnerable patient subgroups during chronic iclepertin treatment. The results of this analysis suggest that iclepertin drug effects on patient hemoglobin levels are small, reversible, and of limited significance, even under long-term treatment. This is the first model of the relationship between iclepertin exposure and hemoglobin levels, and, to our best knowledge, the first model to characterize a drug effect on hemoglobin levels over time that has been validated with clinical data that extend beyond the erythrocyte life span of ∼126 days.

Abstract Background Hyperthyroidism is the most common endocrinopathy in cats and is frequently managed using anti-thyroid medication. Hypothesis/Objectives To develop and validate an algorithm to predict the optimal starting daily dose of thiamazole required to control hyperthyroidism in cats. Animals One hundred eighty-eight client-owned cats with hyperthyroidism for algorithm development (2011-2021) and 45 hyperthyroid cats to validate the algorithm (2022-2024). Methods Retrospective case-control study. Cats with hyperthyroidism controlled medically using thiamazole within a year since diagnosis were enrolled. Controlled dose of thiamazole was categorized into “≤5 mg” or “>5 mg.” Binary logistic regression was performed to explore predictors associated with thiamazole dose. The performance of the final multivariable model in prediction was assessed by receiver operating characteristic (ROC) curve analysis. A cohort of cats subsequently diagnosed with hyperthyroidism and managed chronically with thiamazole were used to test algorithm performance. Results At hyperthyroidism diagnosis, baseline plasma total thyroxine (TT4); (odds ratio [OR] 1.29 [95% CI, 1.19-1.42] per 10 nmol/L; P < .001) and creatinine concentrations (OR 0.83 [95% CI, 0.7-0.96] per 0.1 mg/dL; P = .02) were independent predictors for higher thiamazole dose (>5 mg). The area under the ROC curve was 0.92 (95% CI, 0.88-0.96). In the test cohort, 26 cats controlled on ≤ 5 mg and 19 required >5 mg thiamazole. The predictive model had overall accuracy of 91.1%, sensitivity of 84.2%, and specificity of 96.2%. Conclusions and clinical importance Hyperthyroid cats with higher plasma TT4 and lower creatinine concentrations at diagnosis are likely to require >5 mg total daily dose of thiamazole to achieve euthyroidism.

Drugs’ safety and effectiveness are evaluated in randomized, dose-ranging trials in most therapeutic areas. However, this is only sometimes feasible in oncology, and dose-ranging studies are mainly limited to Phase 1 clinical trials. Moreover, although new treatment modalities (e.g., small molecule targeted therapies, biologics, and antibody-drug conjugates) present different characteristics compared to cytotoxic agents (e.g., target saturation limits, wider therapeutic index, fewer off-target side effects), in most cases, the design of Phase 1 studies and the dose selection is still based on the Maximum Tolerated Dose (MTD) approach used for the development of cytotoxic agents. Therefore, the dose was not optimized in some cases and was modified post-marketing (e.g., ceritinib, dasatinib, niraparib, ponatinib, cabazitaxel, and gemtuzumab-ozogamicin). The FDA recognized the drawbacks of this approach and, in 2021, launched Project Optimus, which provides the framework and guidance for dose optimization during the clinical development stages of anticancer agents. Since dose optimization is crucial in clinical development, especially of targeted therapies, it is necessary to identify the role of pharmacological tools such as pharmacogenomics, therapeutic drug monitoring, and pharmacodynamics, which could be integrated into all phases of drug development and support dose optimization, as well as the chances of positive clinical outcomes.