Search Results (510)

Background/Objectives: This study aimed to evaluate the potential utility of plasma 4β-hydroxycholesterol (4β-OHC) as an endogenous biomarker of hepatic CYP3A activity for predicting ribociclib metabolism and systemic pharmacokinetics in patients with primary brain cancer. Methods: Plasma concentration data for ribociclib and its major metabolite, LEQ803, were obtained from 46 patients receiving oral ribociclib at daily doses of 400, 600, or 900 mg for 5 days. Plasma 4β-OHC concentrations and 4β-OHC/cholesterol ratios were determined by LC–MS/MS at baseline and at 4 and 24 h after ribociclib administration on day 5. A parent–metabolite population pharmacokinetic model was developed to simultaneously characterize ribociclib and LEQ803 disposition and identify clinical covariates influencing their pharmacokinetics. Results: The parent–metabolite model adequately described the multiple-dose plasma concentration–time profiles of ribociclib and LEQ803. On-treatment plasma 4β-OHC concentration was identified as a significant predictor for CYP3A-mediated ribociclib metabolism to LEQ803, accounting for ~36% of interindividual variability in the metabolic clearance. Plasma 4β-OHC showed limited ability to predict total apparent oral clearance or systemic exposure of ribociclib. Conclusions: These findings support the clinical utility of plasma 4β-OHC as an endogenous biomarker of hepatic CYP3A activity for predicting CYP3A-mediated metabolism of substrate drugs such as ribociclib, while highlighting its limitation for predicting total apparent oral clearance of drugs undergoing intestinal first-pass metabolism and elimination through multiple pathways. These results underscore the importance of considering the relative contribution of hepatic CYP3A metabolism to overall drug disposition when evaluating translational utility of endogenous CYP3A biomarkers in clinical pharmacology. Full article

Background/Objectives: Population pharmacokinetics (PopPK) has been used to aid valproic acid (VPA) dose individualization. However, this approach faces limitations owing to the complexity and high dimensionality of datasets. Machine learning (ML) can handle these challenges. However, the comparative evaluation of these ML algorithms and their practical application in optimizing VPA therapy have not yet been established. This review aims to summarize the current evidence, identify research gaps, and outline ML applications for VPA in clinical practice. Methods: PubMed, ScienceDirect, Scopus, the Association for Computing Machinery (ACM) Digital Library, and IEEE Xplore were searched from inception to October 2025. Eligible studies included original research articles using ML models to predict VPA pharmacokinetics or clinical outcomes (e.g., seizure control). Data on study design, population characteristics, features, predicted targets, ML algorithms, validation method, and model performance metrics were extracted. Results: Eleven studies were included. Most studies were retrospective, single-center designs. Ensemble tree-based models such as Random Forest, CatBoost, Gradient Boosted Regression Trees, and other ensemble methods, were consistently among the top-performing algorithms. Final models retained 3 to 19 input features, with daily dose, albumin, and body weight being the most common predictors. Only five studies performed external validation, limiting the generalizability of the models. Conclusions: Current VPA ML models demonstrated promising predictive performance. Nonetheless, most models are retrospective and single-center, with only limited external validation. Future VPA ML studies should use multicenter datasets and apply external evaluation to enhance model generalizability for clinical use. Full article

Background: Midazolam is widely used as a pediatric premedication, but evidence from direct comparisons of oral and sublingual administration in children with congenital heart disease remains limited, particularly that from pharmacokinetic and physiologic data analyzed together. Methods: We conducted a single-center prospective randomized study comparing oral midazolam 0.5 mg/kg with sublingual midazolam 0.3 mg/kg in children undergoing cardiac surgery or catheterization procedures under general anesthesia. Plasma midazolam and 1-hydroxymidazolam concentrations were measured approximately 30 min after administration. Log-transformed concentrations were compared using regression/ANCOVA models adjusted for dose and age. Changes in mean arterial pressure (MAP), heart rate (HR), and oxygen saturation (SpO₂) were analyzed from baseline to 15 and 30 min. Behavioral outcomes included the sedation score, separation from parents, and mask acceptance. Results: Sixty-eight children were randomized; 65 had evaluable pharmacokinetic samples and formed the complete-case pharmacokinetic cohort. Adjusted plasma midazolam concentrations did not differ significantly between the groups, with an adjusted geometric mean ratio for sublingual versus oral administration of 0.98 (95% CI 0.53–1.79; unadjusted p = 0.940; Holm-adjusted p = 1.000). The corresponding ratio for 1-hydroxymidazolam was 1.37 (95% CI 0.55–3.41; unadjusted p = 0.494; Holm-adjusted p = 1.000). HR and SpO₂ changes were non-significant between the groups. At 30 min, sublingual administration was associated with a lower adjusted change in MAP compared with oral administration (adjusted difference −12.08 mmHg, 95% CI −19.74 to −4.42; unadjusted p = 0.002, Holm-adjusted p = 0.012). Behavioral outcomes did not differ significantly between the groups. Conclusions: In this prospective randomized pediatric cardiac cohort, oral midazolam 0.5 mg/kg and sublingual midazolam 0.3 mg/kg produced comparable plasma concentrations and similar behavioral outcomes. Sublingual administration was not associated with worse HR or SpO₂ responses, although an isolated lower MAP change at 30 min warrants confirmation in larger studies. Sublingual midazolam may represent a feasible lower-dose alternative for premedication in this population. Full article

Background: Identifying influential individuals is a critical step in nonlinear mixed-effects modeling, yet commonly used diagnostics primarily reflect local model fit or parameter perturbation and may fail to capture the full impact of individual data on model estimation. Methods: We conducted a simulation study based on a one-compartment pharmacokinetic model with first-order absorption. Four scenarios were investigated: a reference scenario without induced influence, a residual outlier scenario, a structurally influential individual with enriched sampling, and a latent subpopulation scenario. For each scenario, 25 datasets of 100 individuals were simulated, and individual influence was assessed using a leave-one-out jackknife approach. Influence metrics included the change in objective function value (ΔOFV) and parameter perturbation measures. Detection performance was evaluated at both subject and dataset levels. Results: All jackknife runs were successfully completed and analyzable. Residual outliers were consistently identified by both ΔOFV and parameter-based metrics. In contrast, structurally influential individuals were reliably detected by ΔOFV (median rank = 1) but not by parameter-based metrics (median rank ≈ 25). Across scenarios, the association between ΔOFV and parameter perturbation was weak, and nearly absent in the structurally influential scenario. In the latent subpopulation scenario, individual-level detection was limited, but dataset-level detection remained effective, with at least one subpopulation member frequently identified among top-ranked individuals. Conclusions: Individual influence in nonlinear mixed-effects models is strongly mechanism-dependent. Jackknife-based ΔOFV provides a direct and general measure of individual influence, capable of detecting both residual outliers and structurally influential individuals. In contrast, parameter-based metrics quantify parameter sensitivity rather than individual influence and may therefore overlook influential subjects in specific contexts. These findings support the use of jackknife deletion as a reference approach for influence assessment in pharmacometric workflows. Full article

Background: Isoniazid (INH) remains a cornerstone of tuberculosis (TB) prevention and treatment, administered either as monotherapy or in combination with rifamycin-containing regimens. In this study, the preventive regimens analyzed included 6 months of daily INH monotherapy (6H), 3 months of daily INH plus rifampicin (3HR), and 3 months of twice-weekly INH plus rifapentine (3H2P2). Despite the adoption of shorter-course regimens, substantial inter-individual variability (IIV) in INH exposure persists, potentially impacting both therapeutic efficacy and toxicity. A quantitative, regimen-specific characterization of INH pharmacokinetics is therefore critical to support model-informed dosing strategies. Methods: Population pharmacokinetic models were developed separately for INH administered as 6H, 3HR, and 3H2P2. The models characterized absorption, clearance, and IIV of INH, while accounting for co-administered rifamycin. The effects of N-acetyltransferase 2 (NAT2) acetylator phenotype and relevant clinical covariates were systematically evaluated. Model performance was assessed using goodness-of-fit diagnostics, posterior predictive checks, and visual predictive checks. Population pharmacokinetic models were developed using a Bayesian nonlinear mixed-effects framework implemented in Stan through CmdStanR version 0.9.0, with additional data processing, statistical summaries, and visualization performed using R version 4.2.3. Results: INH pharmacokinetics were adequately described by regimen-specific models, revealing distinct differences in absorption and variability across regimens. Typical INH apparent oral clearance (CL/F) estimates were 21.83 L/h for 6H, 26.32 L/h for 3HR, and 25.98 L/h for 3H2P2. NAT2 phenotype was a major determinant of INH clearance across regimens: compared with intermediate acetylators, slow acetylators showed 35.4% lower CL/F, whereas fast acetylators showed 48.4% higher CL/F, indicating higher INH exposure in slow acetylators and lower exposure in fast acetylators. Body weight also influenced INH pharmacokinetics. Co-administration with rifampicin or rifapentine influenced INH pharmacokinetics in a manner consistent with reduced exposure in rifamycin-containing regimens, particularly among fast acetylators. Conclusions: Regimen-specific population pharmacokinetic modeling elucidated clinically relevant differences in INH exposure across commonly used preventive and treatment regimens. These findings highlight the importance of accounting for regimen- and genotype-specific effects when optimizing INH dosing and provide a quantitative framework for future model-informed precision dosing approaches in TB care. Full article

Aim: This study aimed to develop a population pharmacokinetic (PK) model of intravenous fluconazole and, using model-based simulations, propose initial dosing recommendations that optimize efficacy while limiting toxicity. Methods: Therapeutic drug monitoring data from 116 adult patients (183 serum concentrations) treated with intravenous fluconazole between 2022 and 2025 at a single center were analyzed using nonlinear mixed-effects modeling. Monte Carlo simulations were applied to identify dosing strategies that maximize the probability of achieving the PK/PD efficacy target. Results: Estimated glomerular filtration rate (eGFR) was the strongest predictor of fluconazole clearance, and adjusted body weight (ABW) influenced the volume of distribution. For a patient with ABW of 75 kg and eGFR of 90 mL/min, the estimated volume of distribution and clearance were 41.6 L and 0.81 L/h, respectively. An eGFR-based dosing nomogram was developed, and simulations demonstrated that this approach outperformed standard dosing regimens in achieving the PK/PD target. Conclusions: These findings suggest an initial loading dose (2.8 times the maintenance dose) followed by individualized, eGFR-guided maintenance dosing to enhance the safety and efficacy of intravenous fluconazole therapy; however, prospective clinical validation is required before routine clinical implementation. Full article

Background: Assessment of antimicrobial exposure at the site of infection is essential for pharmacokinetic/pharmacodynamic (PK/PD)-guided antimicrobial therapy, yet plasma concentrations may not adequately reflect extracellular target-site exposure. This study characterized the pharmacokinetics of enrofloxacin and its active metabolite ciprofloxacin in plasma and interstitial fluid (ISF) following high-dose oral enrofloxacin administration in dogs and evaluated PK/PD target attainment using nonlinear mixed-effects (NLME) modeling and Monte Carlo simulation. Methods: Nine healthy Beagle dogs were enrolled and received a single oral dose of enrofloxacin (target dose 20 mg/kg). Following exclusion of one dog after vomiting, pharmacokinetic analyses included plasma data from eight dogs and interstitial fluid (ISF) data from six dogs. Serial plasma samples and protein-free ISF samples collected over 24 h by in vivo ultrafiltration were analyzed using LC–MS/MS. Non-compartmental and population pharmacokinetic analyses were performed separately for plasma and ISF data. Final population models were used to simulate 5000 virtual individuals for probability of target attainment (PTA) analyses across a range of minimum inhibitory concentrations (MICs) using established AUC_0–24/MIC and C_max/MIC targets. Results: Both enrofloxacin and ciprofloxacin exhibited delayed peak concentrations and greater overall exposure in ISF than in plasma. For enrofloxacin, mean AUC_0–24 was higher in ISF than in plasma (44.9 vs. 21.6 µg × h/mL). Exploratory population pharmacokinetic models adequately described the plasma and ISF concentration-time data. Monte Carlo simulations showed consistently higher PTA in ISF than in plasma. For the more conservative AUC_0–24/MIC targets (≥100 and ≥125), the MIC associated with ≥90% PTA for enrofloxacin was four-fold higher in ISF than in plasma (0.125 vs. 0.03 µg/mL). Conclusions: High-dose oral enrofloxacin produced sustained extracellular exposure, resulting in more favorable PK/PD target attainment in ISF than in plasma. These findings indicate that plasma pharmacokinetics alone may not accurately reflect target-site antimicrobial exposure and support further evaluation of ISF-based pharmacokinetic data for PK/PD-guided optimization of fluoroquinolone therapy in dogs. Full article

Pediatric sarcopenic obesity (PSO) is an emerging conceptual framework characterized by the coexistence of excess visceral adiposity and impaired skeletal muscle accretion. Evidence suggests that this pathology is driven by systemic meta-inflammation rooted in the gut microbiota–immunometabolism axis. Dysbiosis, particularly the depletion of infant-type Bifidobacterium, compromises the intestinal barrier, potentially causing metabolic endotoxemia. In preclinical models, this triggers a pro-inflammatory, “Warburg-like” glycolytic shift in innate immune cells, releasing cytokines (IL-6, TNF-$\mathsf{\alpha }$) that heavily upregulate the ubiquitin–proteasome system in developing muscle. To address this cascade, we hypothesize that a targeted synbiotic approach utilizing dietary anthocyanins (e.g., cyanidin-3-O-galactoside) and prebiotic galacto-oligosaccharides (GOS) may offer metabolic benefits. This review clarifies the pharmacokinetic distinction between the systemic toxicity of high-dose injected galactose and the safety of dietary galactosides. Preclinical data suggest that ingested galactosides resist upper gastrointestinal digestion and undergo colonic cleavage by commensal $\mathsf{\beta }$-galactosidase, yielding short-chain fatty acids (SCFAs) that support intestinal permeability while releasing bioactive phenolic aglycones. Systemically, these aglycones may attenuate skeletal muscle catabolism by supporting PI3K/Akt signaling. Synthesizing current preclinical and adult-derived evidence, this review highlights the theoretical therapeutic potential of early-life synbiotic interventions as adjunctive therapies to support healthy muscle developmental trajectories in pediatric populations. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic tool used to predict how a drug moves through the body by incorporating real human physiology, including organ sizes, blood flows, tissue compositions, and enzyme activities. It has been widely employed to estimate drug exposure in different populations with organ impairment, genotype variabilities, and physiological variations. Metoclopramide is an antiemetic and prokinetic agent that is subject to CYP2D6 polymorphism. The study aims to develop PBPK models for several CYP2D6 variants to predict changes in the pharmacokinetic (PK) behavior of metoclopramide. Methods: To conduct this study, a literature review was conducted, and the retrieved physicochemical, biochemical, and PK data were integrated into PK-Sim to develop a PBPK model. Initially, a non-genotype-specific model was developed and extrapolated to genotype-based models. The models were verified using a Visual Predicted Check (VPC), mean predicted-to-observed ratio (R_pre/obs) values, and mean relative deviation (MRD). Results: The simulated profiles were aligned with the reported data, and all the predicted and observed PK parameters were comparable, as the R_pre/obs values were within the 0.5–2 range and MRD values were <2. Moreover, an increasing trend in AUC_0–∞ was observed across CYP2D6*wt/*wt, CYP2D6*wt/*10, CYP2D6*10/*10, and CYP2D6*5/*10, with approximately 1.63-, 2.64-, and 2.88-fold increases compared with the CYP2D6*wt/*wt genotype. Conclusions: The models have adequately estimated the PK behavior of metoclopramide across different CYP2D6 variants. These models might be helpful for populations with diverse CYP2D6 genotypes in dose optimization. Full article

Background/Objectives: Selecting appropriate doses for pediatric patients remains one of the most complex challenges in drug development because developmental changes in physiology, metabolism, organ function, and pharmacodynamics substantially influence drug exposure and response. This review summarizes current evidence-based approaches to pediatric dose selection across the developmental continuum and evaluates contemporary model-informed strategies for individualized dosing. Methods: A narrative review of the literature was conducted focusing on pediatric dose-scaling methodologies, developmental pharmacology, physiologically based pharmacokinetic (PBPK) modeling, population pharmacokinetic (PopPK) approaches, exposure–response analysis, therapeutic drug monitoring, and regulatory extrapolation frameworks. Special populations and clinical scenarios relevant to pediatric dose optimization were also evaluated. Results: Simple body weight-based scaling from adult doses inadequately accounts for developmental changes in drug disposition and response. Allometric scaling combined with maturation functions provides improved dose prediction in neonates and infants, while PBPK and PopPK modeling support mechanistic and data-driven dose optimization across pediatric age groups. Fat-free-mass (FFM)-based scaling is preferred over total body weight for many drugs in children with obesity. Additional considerations including obesity, biologics, formulation and excipient safety, pharmacogenomics, critical illness, therapeutic hypothermia, extracorporeal support, therapeutic drug monitoring, and drug–drug interactions substantially influence pediatric dosing strategies. Regulatory frameworks including ICH E11A increasingly support model-informed pediatric extrapolation and precision dosing approaches. Conclusions: Pediatric dose selection has evolved from empirical weight-based dosing toward integrated model-informed strategies incorporating developmental physiology, pharmacometrics, and regulatory science. Allometry, maturation functions, FFM-based scaling, PBPK, PopPK, and therapeutic drug monitoring provide complementary tools for rational pediatric dose optimization, although drug- and pathway-specific validation remains essential, particularly in neonates and critically ill children. Full article

Objectives: AC02, a novel synthetic peptide derived from ACTH, is being developed as a potential therapeutic alternative to porcine ACTH_1-39 for use in infantile spasms. Methods: The study comprised single-ascending dose cohorts (0.02, 0.04, 0.08, 0.16 mg/kg AC02) and multiple-ascending dose cohorts (0.04, 0.08 mg/kg AC02 daily for 5 days). A separate positive-control arm received porcine ACTH_1-39 (25 U/day for 5 days). PD effects (free and total cortisol) were compared head-to-head with the positive control. Plasma concentrations of AC02, porcine ACTH_1-39, free and total cortisol were quantified by validated LC-MS/MS methods, and anti-drug antibody responses were measured using a validated electrochemiluminescence bridging immunoassay. Population PK/PD modeling characterized the concentration–response relationships for free cortisol. Results: Across the 0.02–0.16 mg/kg dose range, AC02 exhibited approximately dose-proportional pharmacokinetics and no accumulation after multiple doses. At 0.04 and 0.08 mg/kg, the baseline-corrected effects on free cortisol were generally consistent with those of porcine ACTH_1-39. Dynamic monitoring of the free cortisol fraction revealed a biphasic pattern across all groups: an early peak followed by a later rise. Total cortisol, by contrast, showed only a monophasic decline, indicating the limitations of total cortisol as a sole PD biomarker. No unexpected safety signals were observed. At 0.04 mg/kg, AC02 demonstrated pharmacodynamic responses comparable to those of marketed ACTH product based on the biologically active component of free cortisol, with favorable safety and pharmacokinetic profiles. Conclusions: This first-in-human study demonstrates that AC02 has favorable pharmacokinetic properties, and an acceptable safety profile. Full article

Background: Hyperuricemia is an adverse effect frequently observed during favipiravir treatment. The time course, from uric acid elevation to recovery, and quantitative relationship between drug exposure and changes in serum uric acid levels remain insufficiently characterized. We investigated the pharmacodynamic mechanism of uric acid elevation and described its time course by population pharmacokinetic and pharmacodynamic modelling. Methods: Patients who received favipiravir for coronavirus disease 2019 or severe fever with thrombocytopenia syndrome were retrospectively evaluated. The pharmacokinetics of favipiravir were described by a one-compartment model with first-order absorption and elimination. Metabolite concentrations were predicted based on previously reported values. Changes in serum uric acid levels were described by a turnover model with zero-order production and first-order elimination. The drug effect was implemented as inhibition of the uric acid elimination process. Simulations based on the final model were performed for 10 consecutive days after the clinical regimen, with a 21-day follow-up. Results: The final model supported the inhibition of uric acid elimination by favipiravir and its metabolite. Regarding simulations, serum uric acid levels reached a median peak of 6.93 mg/dL at 6.7 days after treatment initiation and returned to pre-treatment levels within 4.0 days after treatment discontinuation. Conclusions: This combined population pharmacokinetic and pharmacodynamic turnover model quantified favipiravir-associated increases in serum uric acid levels and showed a transient profile with rapid recovery after drug discontinuation. These findings underscore the need for monitoring serum uric acid levels during favipiravir treatment, particularly in patients at a higher risk of gout. Full article

Background/Objectives: Special populations, including pediatric, geriatric, and organ-impaired patients, are consistently underrepresented in randomized controlled trials (RCTs), resulting in limited evidence for safe and effective dosing. Off-label use is common, and variability in drug exposure and response increases the risk of adverse drug reactions (ADRs). This review aims to examine how integrating pharmacometrics (PMX) with real-world data (RWD) can address evidence gaps by supporting dose optimization, population expansion, and safety evaluation in these vulnerable groups. Methods: A narrative literature review was conducted using PubMed, Embase, and Web of Science (January 2000–November 2025). Using Boolean combinations of PMX and RWD-related search terms, approximately 200–300 records were identified across the three databases; approximately 30 full-text articles were reviewed, and representative case studies were selected based on population diversity, methodological variation, and regulatory or clinical impact. Results: RWD–PMX integration has been applied across three domains: (i) dosing optimization through therapeutic drug monitoring (TDM)-informed PopPK modeling and model external validation in pediatric and neonatal populations; (ii) population expansion supporting dose extrapolation and regulatory decision-making for unapproved groups; and (iii) safety evaluation enabling identification of exposure–toxicity risk factors in vulnerable cohorts. Conclusions: Integrating PMX with RWD provides a practical and mechanistically grounded framework for evaluating dosing, treatment eligibility, and safety in populations insufficiently represented in clinical trials. Accumulating evidence indicates that RWD–PMX methodologies can complement traditional clinical research and inform regulatory decision-making. Continued refinement of data quality standards, validation practices, and guidance frameworks will be essential for broader adoption. Full article

Background: Prostate cancer remains one of the most prevalent malignancies worldwide, with high mortality in advanced and metastatic stages. Drug repurposing offers a cost-effective and time-efficient strategy to identify new therapeutic options. Objectives: This study aimed to apply physiologically based pharmacokinetic (PBPK) modeling to evaluate repurposed antiretroviral drugs efavirenz (EFV), etravirine (ETV), and saquinavir (SAQ) in prostate cancer, and to assess potential drug–drug interactions (DDIs) between EFV and ETV. Methods: PBPK models for EFV and SAQ were obtained and an ETV was developed and validated using literature and ADMET Predictor^® data. Prostate tissue models were modified to simulate malignant conditions, and population-based simulations examined the influence of age and obesity. The GastroPlus^® DDI module was applied to explore mechanistic interactions between EFV and ETV under different physiological scenarios. Results: Tumor-specific prostate tissue alterations produced minimal systemic pharmacokinetic changes but increased total drug accumulated in simulated tissue, with differences in unbound concentrations, while demographic variables such as age and weight significantly affected drug exposure, which are comorbidities in prostate cancer. Lighter individuals exhibited higher plasma concentrations across all drugs, consistent with known previously reported pharmacokinetic trends in obese individuals. DDI simulations indicated only minor changes in ETV pharmacokinetics when combined with EFV, with no clinically significant interaction detected. Conclusions: The integration of PBPK modeling, population variability, and DDI analysis highlights the potential of SAQ, EFV, and ETV as viable drugs for prostate cancer repurposing, but with a heavy focus on dosing personalization. In silico approaches provide a useful framework for early preclinical evaluation and the optimization of repurposed drugs, supporting the early evaluation of repurposed drug candidates in oncology. Full article

Background/Objectives: Adalimumab (ADM) is a recombinant, fully human monoclonal antibody that exhibits pronounced inter- and intra-individual pharmacokinetic variability attributed to several factors. This study aims to externally evaluate the published ADM population pharmacokinetic models and their potential use in clinical practice, as well as to develop novel population pharmacokinetic model. Methods: Literature search was conducted using PubMed to identify ADM population pharmacokinetic models. Data from 195 patients with Crohn’s disease treated at the University Medical Center “Zvezdara”, Serbia, were used for the external evaluation of previously published models. In addition, the development of the new population pharmacokinetic model incorporated informative priors derived from the best-performing published model. Nonlinear mixed-effects modeling was performed in NONMEM^® (versions 7.6) for both prediction- and simulation-based diagnostics of existing models, as well as for the development of a new model. Results: Eight published pharmacokinetic models of ADM were included in the external evaluation. Although none of the models satisfied both population-level and normalized prediction distribution error (NPDE) diagnostic criteria, individual-level performance was acceptable: median prediction errors (MDPEs) were within ±20% across all studies, and median absolute prediction errors (MDAPEs) were below 30% in most cases (7 of 8 studies). The best-performing model was identified and implemented as a priori information in subsequent model development. A one-compartment model using with first-order absorption and elimination best described the data. The apparent clearance (CL/F) was estimated at 0.334 L/day, while informative priors were used for V/F and the effect of anti-drug antibodies (ADAs) on CL/F. Covariate analysis on CL/F identified C-reactive protein (CRP) and dosing regimen as statistically significant predictors (p < 0.01). Conclusions: The previous pharmacokinetic models of ADM exhibited suboptimal performance in population-level metrics and simulation-based diagnostics, while individual-level metrics showed substantial improvement. The newly developed model of ADM highlights associations among immunogenicity, drug pharmacokinetics, and inflammatory burden. Full article