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A growing number of population pharmacokinetic analyses of therapeutic monoclonal antibodies (mAbs) have been published in the scientific literature. The aims of this article are to summarize the findings from these studies and to relate the findings to the general pharmacokinetic and structural characteristics of therapeutic mAbs. A two-compartment model was used in the majority of the population analyses to describe the disposition of the mAb. Population estimates of the volumes of distribution in the central (V 1 ) and peripheral (V 2 ) compartments were typically small, with median (range) values of 3.1 (2.4–5.5) L and 2.8 (1.3–6.8) L, respectively. The estimated between-subject variability in the V 1 was usually moderate, with a median (range) coefficient of variation (CV) of 26% (12–84%). Between-subject variability in other distribution-related parameters such as the V 2 and intercompartmental clearance were often not estimated. Although the pharmacokinetic models used most frequently in the population analyses were models with linear clearance, other models with nonlinear, or parallel linear and nonlinear clearance pathways were also applied, as many therapeutic mAbs are eliminated via saturable target-mediated mechanisms. Population estimates of the maximum elimination rate (V max ) and the mAb concentration at which elimination was at half maximum for Michaelis-Menten-type elimination pathways varied considerably among the different therapeutic mAbs. However, estimates of the total clearance (CL) of mAbs with linear clearance characteristics and of the clearance of mAbs via the linear clearance pathway (CL L ) with parallel linear and nonlinear clearance were quite similar for the different mAbs and typically ranged from 0.2 to 0.5 L/day, which is relatively close to the estimated clearance of endogenous IgG of 0.21 L/day. The between-subject variability in the V max , CL and CL L was moderate to high, with estimated CVs ranging from 15% to 65%. Measures of body size were the covariates most commonly identified as influencing the pharmacokinetics of therapeutic mAbs. In summary, many features of the population pharmacokinetics of currently used therapeutic mAbs are similar, despite differences in their pharmacological targets and studied patient populations.

Amlitelimab is a fully human, nondepleting, anti‐OX40 ligand monoclonal antibody being investigated for the treatment of moderate‐to‐severe atopic dermatitis (AD) in adults and adolescents. Population pharmacokinetic (PopPK) and pharmacokinetic/pharmacodynamic‐Eczema Area and Severity Index (PopPK/PD‐EASI) models were used to inform dosing regimen selection for amlitelimab phase 3 trials. The PopPK model was developed using phase 1 (healthy volunteers) and phase 2 (participants with AD) trial data, including individual exposure variables from the STREAM‐AD phase 2b trial following subcutaneous amlitelimab doses ranging from 62.5 to 250 mg given every 4 weeks (Q4W). The PopPK model was used to compute exposures for an extended dosing regimen of 250 mg Q12W (with 500 mg loading dose [+LD]). The PopPK/PD‐EASI model was developed from phase 2 trials to predict treatment responses (EASI values) with selected dosing scenarios. Finally, the dose for individuals with lower body weight (i.e., < 40 kg) was determined. Utilizing the PopPK model, the amlitelimab 250 mg Q12W + LD computed exposures were between the exposures of 62.5 mg Q4W and 250 mg Q4W + LD efficacious doses in the STREAM‐AD trial. Using the PopPK/PD‐EASI model, the simulated efficacy for dosing scenarios of 250 mg Q12W + LD regimen from initiation or 250 mg Q4W + LD for 24 weeks followed by Q12W to Week 60 was similar to continuous 250 mg Q4W. Simulations identified that a twofold dose reduction would allow participants < 40 kg to achieve amlitelimab exposures within the range observed in participants ≥ 40 kg on 250 mg Q4W or Q12W. These results support evaluation of a Q12W dosing regimen for adults and adolescents in phase 3 trials. Steps of the PopPK/PD‐EASI model to inform amlitelimab extended dosing regimen in phase 3 clinical trials.

Ibrutinib, a Bruton's tyrosine kinase (Btk) inhibitor, is a key therapy for chronic lymphocytic leukemia (CLL). In clinical practice, adverse events, such as hypertension, frequently necessitate dose reductions or treatment discontinuation. Emerging evidence suggests that reduced doses may retain clinical efficacy while mitigating toxicity. The synergistic ibrutinib–venetoclax combination remains understudied at low doses, particularly for ibrutinib. This study aimed to explore dose optimization strategies, with/without venetoclax, in treatment‐naïve (TN) and relapsed/refractory (R/R) CLL using mechanism‐based, model‐informed approaches to characterize the relationship between systemic ibrutinib exposure and efficacy and safety biomarkers. We leveraged data from phase 1b/2 and 3 studies, including plasma concentrations, leukocyte and lymphocyte counts, lymph node and spleen size measurements, and blood pressure. A previously developed semi‐mechanistic population pharmacokinetic‐pharmacodynamic (PKPD) framework was re‐evaluated, extended by integrating additional biomarkers and identifying differences between TN and R/R patients, and used to simulate alternative dosing strategies. The model successfully captured the temporal dynamics of all biomarkers simultaneously. We quantified a 76% longer phospho‐Btk half‐life and a 43% shorter peripheral CLL cell half‐life in TN versus R/R patients, with no evidence of ibrutinib resistance in TN patients. Dose reductions based on response depth or toxicity preserved comparable response rates and progression‐free survival to standard dosing. Ibrutinib de‐escalation schedules with venetoclax resulted in a ≤ 5% reduction in peripheral blood measurable residual disease compared to standard dosing at 2 years. This PKPD framework supports dose individualization to improve tolerability without sacrificing treatment outcomes, offering a path toward more personalized, effective CLL management.

Ropeginterferon alfa‐2b (ropeg) represents a new‐generation interferon‐based therapy approved for polycythaemia vera (PV) treatment. This study aimed to elucidate its population pharmacokinetics‐pharmacodynamics (PopPK‐PD) and exposure‐response (E‐R) relationships. A PopPK model was developed using pooled data from four clinical studies, including two Phase I studies in healthy volunteers (n = 48) and two Phase II studies in Chinese or Japanese patients with PV (n = 78). Sequential modeling was used to analyze pharmacokinetics‐pharmacodynamics (PK‐PD) regarding hematological parameters, including hematocrit, platelet, and white blood cell counts. Hematological changes were simulated using fast‐ and slow‐dose titration regimens. Individual exposure values were used to analyze the E‐R relationships regarding complete hematologic response (CHR), driver mutation, JAK2V617F allele burden, and safety. In this study, we developed a target‐mediated drug disposition model. Sigmoid indirect effects elucidated the PK‐PD in terms of hematological changes. Simulations showed that the fast‐titration regimen significantly accelerated hematocrit reduction. Logistic regression models showed that the probability of achieving CHR increased with exposure at Week 24 but not at Week 52. In contrast, JAK2V617F allele reductions correlated with exposure at both Weeks 24 and 52. Exposure‐safety analysis revealed a manageable risk of adverse events associated with transaminase increases. This study established a robust framework for ropeg PK‐PD, providing insights into its E‐R relationships and disease‐modifying action. Trial Registration: A17‐102, A19‐201, and A20‐202 are registered at ClinicalTrials.gov. The registration numbers are as follows: A17‐102, NCT03546465; A19‐201, NCT04182100; and A20‐202, NCT05485948. A17‐101 is registered at www.chinadrugtrials.org.cn. The registration number is CTR20190451

This study aimed to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam in patients under general anesthesia using a population analysis and to develop a web-based dashboard tool that directly displays the optimal dosing regimen for general anesthesia. A total of 20 patients received remimazolam for general anesthesia, during which intensive arterial blood samples and bispectral index (BIS) values were collected. A population PK/PD model was established, and goodness-of-fit and visual predictive check plots were utilized to evaluate the model's accuracy. Additionally, RxODE and Shiny in R were used to design a web-based dashboard tool to recommend optimal dosing regimens. The three-compartment model with first elimination best described the PK profiles of remimazolam. PK parameters were weight-adjusted via allometric scaling. The correlation between drug exposure and the BIS was optimally characterized through an effect compartment model employing an inhibitory sigmoid Emax model. In addition, a web-based dashboard tool was created to offer initial personalized dosing strategies for general anesthesia procedures, enhanced by graphical representations of the PK/PD profiles associated with the recommended dosing regimens. The developed population PK/PD model effectively captured the dose-exposure-response relationship for remimazolam, allowing for the optimization of personalized dosing strategies.

Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use.

Hereditary angioedema (HAE) is a rare disorder linked to kallikrein‐kinin system dysregulation, which leads to uncontrolled activation of plasma prekallikrein. Donidalorsen is an antisense oligonucleotide designed to selectively degrade prekallikrein messenger RNA and thereby reduce prekallikrein production. We aimed to develop population pharmacokinetic and pharmacokinetic/pharmacodynamic models of donidalorsen and evaluate the impact of potential intrinsic/extrinsic covariates on exposure and prekallikrein response. Plasma donidalorsen and prekallikrein data were obtained from phase 1 to 3 studies in healthy volunteers (NCT03263507, 721744‐CS9) and adult and adolescent patients with HAE (NCT04030598, NCT05139810). The evaluated doses were 20, 40, 60, and 80 mg every 4 weeks (Q4W) and 80 mg every 8 weeks (Q8W), administered subcutaneously over 13–21 weeks. Donidalorsen pharmacokinetics were well described by a linear 2‐compartment model with first‐order absorption. The population terminal elimination half‐life was 31.4 days. Prekallikrein was well described by an indirect response model with inhibition of prekallikrein production by donidalorsen. Covariate analysis identified body weight as the main factor affecting pharmacokinetic exposure; however, this effect was not considered clinically significant. The developed population pharmacokinetic/pharmacodynamic model well characterized the donidalorsen exposure–prekallikrein response relationship. Modeling analyses support that no dose adjustment is needed with respect to intrinsic/extrinsic factors in adults and adolescents with HAE. The nearly identical simulated pharmacokinetic or prekallikrein time courses for Q4W versus monthly dosing and for Q8W versus every‐2‐month dosing regimens support switching to more convenient regimens for patients. Study Highlights What is the current knowledge on the topic? ○Hereditary angioedema (HAE) is a rare, chronic disease characterized by unpredictable recurrent episodes of swelling that affect the extremities, face, abdomen, genitals, and larynx. Donidalorsen is a ligand‐conjugated antisense oligonucleotide designed to reduce production of prekallikrein and is indicated for prophylaxis to prevent attacks of HAE in adult and pediatric patients 12 years of age and older. What question did this study address? ○What intrinsic or extrinsic factors influence donidalorsen exposure and prekallikrein response, what is their magnitude of impact, and how do they influence dosing recommendations? What does this study add to our knowledge? ○Population pharmacokinetic and pharmacokinetic/pharmacodynamic models quantitatively describe the exposure‐response relationship between plasma donidalorsen concentrations and prekallikrein reduction. Body weight, injection site, and drug presentation were found to impact pharmacokinetic exposure in patients with HAE but did not translate into meaningful pharmacodynamic differences. How might this change drug discovery, development, and/or therapeutics? ○Analyses support donidalorsen 80 mg administered monthly or every 2 months to achieve clinically significant and sustained prekallikrein reductions in adult and adolescent patients with HAE and that no dose adjustment is necessary with respect to intrinsic or extrinsic factors.

Granulocyte colony‐stimulating factor (G‐CSF) mobilizes peripheral blood (PB) progenitor cells from bone marrow (BM) into circulation for PB stem cell transplantation (PBSCT). This study aimed to develop a population pharmacokinetic–pharmacodynamic (PK‐PD) model of filgrastim in healthy subjects to optimize PB CD34+ cell collection. Plasma filgrastim concentrations and CD34+ cell count data were obtained from a clinical study involving healthy Korean subjects. A total of 1378 plasma concentration measurements and 982 CD34+ cell count data collected from 53 subjects were used in the PK‐PD model. Filgrastim PKs were adequately described by a one‐compartment linear disposition model with an additional transit compartment for absorption. Log‐transformed body weight was the only significant covariate affecting the volume of distribution and clearance. CD34+ cell mobilization was best captured by a modified Friberg model, assuming continual entry of proliferating BM stem cells into PB via a single transit compartment. Simulation results suggested that the 5 μg/kg twice‐daily dosing regimen may yield higher CD34+ cell counts compared to the 10 μg/kg once‐daily regimen for achieving target CD34+ cell counts of 20/μL and 50/μL. We successfully developed a robust PK‐PD model of G‐CSF that optimizes the yield of CD34+ cells during allogeneic PBSCT. This model can guide the efficient determination of optimal G‐CSF dosing regimens and CD34+ cell harvesting strategies.

Linerixibat, an ileal bile acid transporter (IBAT) inhibitor, is being evaluated for the treatment of pruritus in primary biliary cholangitis (PBC). Diarrhea is commonly reported with this drug class as IBAT inhibition redirects bile acids (BA) to the colon. Serum 7‐alpha‐hydroxy‐4‐cholesten‐3‐one (C4) measurement is a validated method to identify BA diarrhea. To inform dose selection, we characterized the relationship between linerixibat dose, C4 levels, and patient‐reported bother on the gastrointestinal symptom rating scale (GSRS) diarrhea question. A kinetic‐pharmacodynamic model was developed using data from five Phase 1/2 trials, to describe the effect of linerixibat dose (1–180 mg) and regimen (once/twice daily) on C4 concentrations over time. GSRS data from patients with PBC and pruritus in the Phase 2b GLIMMER study (NCT02966834) were used to develop a proportional odds model to predict the probability of a score of 1–7 (no–very severe discomfort) to the question “Have you been bothered by diarrhea during the past week?” in relation to linerixibat dose. The two models were linked to describe the linerixibat dose‐C4‐diarrhea bother relationship. Models were validated using graphical and numerical assessment and visual predictive checks. Linerixibat caused dose‐dependent increases in C4 until saturation (~180 mg total daily dose). Increased C4 concentrations trended with increased GSRS diarrhea scores. Simulations demonstrated increases in moderate‐to‐very severe (≥ 4) diarrhea scores with increasing linerixibat dose. Increases in patient‐reported diarrhea scores were linerixibat dose‐dependent. Selecting an optimal dose that maximizes linerixibat's ability to improve pruritus while minimizing patient‐reported diarrhea bother is important to support treatment adherence.

Non‐small cell lung cancer (NSCLC) is often intrinsically resistant to several first‐ and second‐line therapeutics and can rapidly acquire further resistance after a patient begins treatment. Treatment outcomes are, therefore, significantly impacted by the optimization of scheduling. Previous preclinical research has suggested that scheduling bevacizumab sequentially with combination antiproliferatives could improve clinical outcomes. Mathematical modeling is a well‐suited tool for investigating this proposed modification. To address this critical need, individual patient tumor data from 11 clinical trials in NSCLC have been collated and used to develop a semi‐mechanistic model of NSCLC growth and response to the therapeutics represented in those trials. Precise estimates of clinical parameters fundamental to cancer modeling have been produced, such as the rate of acquired resistance to various pharmaceuticals, the relationship between drug concentration and cancer cell death, as well as the fine dynamics of vascular remodeling in response to bevacizumab. In a reserved portion of the dataset, this model predicted the efficacy of individual treatment time courses with an average difference between final prediction and observation of 59.7% after a single tumor measurement and 11.7% after three successive tumor measurements. A delay of 9.6 h between pemetrexed‐cisplatin and bevacizumab administration is predicted to optimize the benefit of sequential administration. At this gap, approximately 93.5% of simulated patients benefited from a gap in administration compared with concomitant administration. Of those simulated patients, the mean improvement in tumor reduction was 20.7%. This suggests that scheduling a modest gap between the administration of bevacizumab and partner antiproliferatives could meaningfully improve patient outcomes in NSCLC.