Journal of Clinical Pharmacology最新文献

Anti-tuberculosis drug-induced liver injury (ATLI) is the most harmful to anti-tuberculosis (TB) treatment. This study aims to construct and validate a binary ATLI risk prediction model based on clinical data through seven machine learning algorithms (logistic regression, decision tree, support vector machine, random forest, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine [LightGBM]). A retrospective cohort of 2356 TB patients followed between January 2017 and December 2024 was used to develop and evaluate the prediction model. Random undersampling was performed to address class imbalance problem. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and retained 27 of 31 original features. Seven ML algorithms were trained and the LightGBM model demonstrated optimal performance in testing set based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity (AUC = 0.789, sensitivity = 0.734, and specificity = 0.706). The model exhibited optimal simplicity and stability when incorporating the 8-feature combination comprising baseline high-density lipoprotein cholesterol (HDLC), γ-glutamyl transpeptidase (GGT), triglycerides, total cholesterol (TCHOL), uric acid, total bilirubin (TBIL), globulin (GLB), and liver disease history (AUC = 0.764, sensitivity = 0.758, and specificity = 0.610), and Shapely additive explanations analysis also revealed that these variables were the most influential contributors. The optimal model maintained robust predictive ability in the external validation cohort (AUC = 0.721, sensitivity = 0.828, and specificity = 0.604). This study determined that the combination of baseline HDLC, GGT, triglycerides, total cholesterol (TCHOL), uric acid, TBIL, GLB, and liver disease history was an important predictor of ATLI through LightGBM model, which could help clinicians in the early identification of ATLI.

The poly(ADP-ribose) polymerase inhibitor talazoparib, combined with the androgen receptor inhibitor enzalutamide is approved for patients with homologous recombination repair (HRR) gene-mutated metastatic castration-resistant prostate cancer (mCRPC) in the US and with mCRPC in whom chemotherapy is not clinically indicated in Europe. We provide a population pharmacokinetic model for this combination in patients with mCRPC unselected for HRR deficiencies from TALAPRO-2 (NCT03395197). The pooled dataset included 811 patients treated with enzalutamide plus either talazoparib or placebo. The final enzalutamide model was a two-compartment model with first-order absorption and inter-individual variability (IIV) on apparent clearance (CL_e/F_e) and apparent central volume of distribution (Vc_e/F_e) and included effects of baseline body weight and age on CL_e/F_e and Vc_e/F_e. For the active metabolite N-desmethyl enzalutamide, a two-compartment model with IIV on CL_n and Vc_n adequately described the observed data and included the effect of body weight on CL_n and Vc_n. The final talazoparib model was well characterized by a two-compartment model with first-order absorption and IIV on talazoparib apparent base clearance (CL_t0/F_t) and Vc_t/F_t. The effect of enzalutamide and N-desmethyl enzalutamide on CL_t/F_t of talazoparib was modeled through a linear relationship. The single covariate effect of baseline creatinine clearance on CL_t0/F_t showed that relative to the reference value for normal renal function, CL_t0/F_t decreased by 8% for mild, 27% for moderate, and 46.7% for severe renal impairment. Simulations showed that a dose reduction of enzalutamide does not require talazoparib dose modification since the magnitude of exposure reduction for talazoparib was not considered clinically significant.

Despite the ability of radionuclide therapy to significantly prolong progression-free survival and overall survival in several different cancer types, most patients show only partial tumor responses or stable disease, while some patients show progressive disease in spite of therapy. Co-administration or sequential administration of selected drugs whose mechanism of action complement the properties of radionuclide therapy may provide a pharmacological basis to potentially overcome some of the limitations of radiopharmaceutical therapy and may enhance clinical efficacy by additive or synergistic cytotoxic effects. The rational design and evaluation of novel radiopharmaceutical-drug combinations and optimization of dosage and administration schedules, within the complex context of individualized multimodal cancer treatment, requires a multidisciplinary approach, principally including clinical pharmacology. Potential strategies include upregulation of target receptors, co-administration of cytotoxic chemotherapeutic drugs, co-administration of drugs that inhibit DNA damage repair, co-administration of drugs that inhibit the mammalian (or mechanistic) target of rapamycin signaling pathway, co-administration of drugs that activate immune responses, and co-administration aimed at modifying pharmacokinetics to prolong retention time of radiopharmaceuticals and increase the absorbed radiation dose. Several phase II trials are currently underway, highlighting a role for clinical pharmacology in the exploration of methods to further improve efficacy of radionuclide therapy.

Dordaviprone (ONC201) is a small molecule protease activator being developed for gliomas. The aim of this work was to evaluate the pharmacokinetics and safety of dordaviprone when administered to participants with moderate hepatic impairment compared to healthy matched participants. A non-randomized, open-label, single-dose study was conducted in eight participants with moderate hepatic impairment classified according to Child-Pugh criteria, and eight healthy participants matched based on age (+10 years), body mass index (BMI; +20%), and sex. Plasma concentrations of dordaviprone and the major inactive metabolite, ONC207, were determined by a validated liquid chromatography-tandem mass spectrometry method. Exposure following oral administration of 125 mg dordaviprone was increased in participants with moderate hepatic impairment relative to healthy matched participants, with the largest impact occurring on AUC. Ratios of geometric means and 90% confidence intervals (CIs) of dordaviprone exposure for C_max, AUC_last, and AUC_inf in the moderate hepatic impairment cohort compared to the healthy matched cohort were 1.21 (0.88, 1.67), 1.50 (1.02, 2.20), and 1.55 (1.05, 2.29), respectively. Treatment-emergent adverse events were mild in nature and considered not related to dordaviprone administration. While administration of dordaviprone in participants with moderate hepatic impairment led to increased dordaviprone exposures, the anticipated increase after the recommended 625 mg dose is within exposures assessed in the thorough QT study. Therefore, no dose adjustment in patients with mild or moderate hepatic impairment is recommended.

Linezolid is a widely used antimicrobial agent in clinical practice; however, its usage is often limited by linezolid-induced thrombocytopenia (LIT). While prior studies have assessed linezolid plasma concentrations, the clinical relevance of its metabolites, PNU-142300 and PNU-142586, remains unclear. This study aimed to investigate the association between these metabolite concentrations and LIT, and to identify predictive thresholds. This study retrospectively analyzed patients treated with linezolid at Osaka Metropolitan University Hospital between January 2017 and December 2024. Patient characteristics, trough concentrations (C_trough), AUC₂₄, and pharmacokinetic parameters of linezolid and its metabolites were compared among patients with and without thrombocytopenia. Receiver operating characteristic (ROC) analysis identified thresholds for LIT prediction. Risk factors were assessed using multivariate logistic regression analysis. Forty-eight patients were included, with thrombocytopenia developing in 26 patients (54.2%). C_trough and AUC₂₄ of PNU-142300 and PNU-142586 were significantly higher in the thrombocytopenia group; meanwhile, linezolid C_trough and AUC₂₄ did not differ significantly. ROC analysis identified C_trough ≥1.43 µg/mL and AUC₂₄ ≥37.8 mg h/L for PNU-142586 as predictive thresholds. Multivariate analysis revealed that linezolid therapy ≥14 days (OR = 9.53, P =  .044) and PNU-142586 C_trough ≥1.43 µg/mL (OR = 37.60, P =  .002) as independent risk factors. Elevated PNU-142586 concentrations were associated with a higher cumulative incidence of LIT. Accumulation of linezolid metabolites, especially PNU-142586, is closely associated with LIT. Monitoring of PNU-142586 may improve the safety of linezolid therapy and support individualized dosing strategies.

Developing therapeutics for multiple immune-mediated indications has become a prominent strategy, driven by advances in disease biology, pharmacology, clinical and regulatory science. We analyzed 34 FDA-approved small molecular and antibody-based drug products with more than one adult immune-mediated inflamamatory indication by December 2024. A clear trend toward pathway-driven development was seen, with shared targets (e.g., TNFα, IL-17, IL-23, and JAKs) enabling indication expansion. Approved indications per product ranged from 2 to 8 (median: 3). Over time, the median gap between subsequent indication approvals decreased from 3.2 years (range 1.1-12.8) for products first approved in 1998-2008 to 1.7 years (range 0.67-2.75) for those approved after 2018, reflecting improved efficiency likely due to broader adoption of pathway-based strategies. Route of administration also evolved for antibody-based therapies, with median delays of 5.8-6.0 years for intravenous-to-subcutaneous (IV-to-SC) and 4.4-8.5 years for subcutaneous-to-intravenous (SC-to-IV) transitions. Fewer than 25% of therapies kept the same dosing regimen across indications, with small molecules more likely to retain dosing (62.5%, 5/8) than antibodies (11.5%, 3/26), indicating the need for tailored approaches based on disease context, tissue involvement, and patient characteristics. Key clinical pharmacology considerations include rational dose selection, surrogate and extrapolation strategies, and model-informed bridging, leveraging existing data to inform subsequent indications. Early regulatory engagement, strategic target selection, robust trial design, harmonized safety database, and cross-functional coordination are critical. Our analysis provides insights to guide multi-indication development to improve patient access to therapies for diverse immune-mediated disorders.

Malnutrition occurs at higher rates in children with complex medical conditions and can independently influence drug disposition and action. Yet FDA-approved product labels rarely address dosing in malnutrition. This study explores the extent to which malnourished children are expressly excluded from clinical trials. Industry-sponsored, pediatric, phase I-III studies deposited in ClinicalTrials.Gov through December 2024 with a full study protocol were reviewed. Protocols were evaluated for inclusion and exclusion (I/E) criteria related to anthropometric and clinical indicators of malnutrition. I/E criteria were fully characterized along with the study phase, intervention type, and treatment indication. 9882 studies were identified, 1759 with an uploaded protocol. 616 studies (35%) contained 777 distinct I/E criteria related to malnutrition (1-6 per study). Across all protocols, 71% exclusively restricted participation of children with evidence of undernutrition, 9% with overnutrition, and 20% with both. There were no statistical differences observed based on intervention type, though differences by study phase were observed. Restrictions were seen most frequently for respiratory, mental/behavioral, obstetric/perinatal, and emergency use indications and least frequently for dermatologic, oncologic, and eyes, ears, nose, and throat disorders. Non-specific I/E criteria suggest that these findings likely underestimate the extent of malnutrition-based exclusions. Despite growing attention paid to obesity, pediatric clinical trials are far more likely to restrict the participation of undernourished children. Though unrealistic to relax malnutrition related I/E criteria for all studies, consideration should be given for conditions where high rates of malnutrition are expected to avoid trial populations that do not reflect clinical practice.