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Infliximab is a chimeric monoclonal antibody against TNFα. The pharmacokinetic (PK) properties of infliximab have been studied in several adult patient populations, but a literature search identified no reported comparative population PK properties of this drug in pediatric patients. The current analysis applied population PK techniques to compare data on the PK properties of infliximab in pediatric and adult patients with moderately to severely active Crohn's disease (CD) from 2 Phase III studies. This analysis used serum infliximab concentration data from 692 patients (112 children, 580 adults; age range, 6–76 years) from 2 Phase III clinical studies (REACH [A Randomized, Multicenter, Open-Label Study to Evaluate the Safety and Efficacy of Anti-TNF-α Chimeric Monoclonal Antibody in Pediatric Subjects with Moderate-to-Severe Crohn's Disease] and ACCENT I [A Crohn's Disease Clinical Trial Evaluating Infliximab in a New, Long-term Treatment Regimen]). PK models were developed separately for children, adults, and a combination of both. The combined population was used for establishing important covariates of infliximab PK properties in the combined CD population. Exploratory simulations using combined PK and covariate data were performed to expand the interpretation of the results in children. Based on the findings, in a typical child (who, based on the median values in REACH, weighs 42 kg, has a baseline serum albumin concentration [SAC] 3.8 mg/dL, and has not developed antibodies to infliximab [ATIs]) who is receiving infliximab and an immunomodulator, PK estimates (typical value [SE]) were as follows: clearance (CL), 5.43 (0.15) mL/kg/d; V d in the central compartment (V 1), 54.2 (1.15) mL/kg; V d in the peripheral compartment (V 2), 29.2 (2.03) mL/kg; and intercompartmental clearance (Q), 3.52 (0.71) mL/kg/d. Corresponding properties in a typical adult (weight, 68 kg; SAC, 4.1 mg/dL) were CL, 5.39 (0.13) mL/kg/d; V 1, 52.7 (0.49) mL/kg; V 2, 19.0 (1.53) mL/kg; and Q, 2.15 (0.39) mL/kg/d. V 2 decreased as body weight increased, predicting a possible undercompensation for exposure with infliximab dosing per kg weight in lower-weight individuals. In pediatric and adult patients, CL was higher in those in whom ATIs developed or who had low baseline SAC. Concurrent immunomodulator use (purine antimetabolites or methotrexate) was associated with a 14% decrease in CL. In the pediatric and adult patients, observed trough serum infliximab concentrations, median infliximab t 1/2 (in children, 13.2 days; and in adults, 12.4 days), and exploratory PK simulations predicted infliximab PK properties to be comparable between children and adults. Infliximab PK properties appeared to be comparable between pediatric and adult patients with CD. Specifically, in this select population using nonlinear mixed effects modeling, infliximab CL increased as SAC decreased. CL also increased with ATI formation but decreased with immunomodulator coadministration. Although weight affects infliximab PK properties (total CL and total Vd increased with total body weight while per kg CL and Vd decrease with total body weight), age was not found to influence infliximab PK in the age range tested (6–76 years).

It is known that interleukin‐6 (IL‐6) can significantly modulate some key drug‐metabolizing enzymes, such as phase I cytochrome P450s (CYPs). In this study, a physiologically‐based pharmacokinetic (PBPK) model was developed to assess CYPs mediated therapeutic protein drug interactions (TP‐DIs) in patients with immune‐mediated inflammatory diseases (IMIDs) with elevated systemic IL‐6 levels when treated by anti‐IL‐6 therapies. Literature data of IL‐6 levels in various diseases were incorporated in SimCYP to construct respective virtual patient populations. The modulation effects of systemic IL‐6 level and local IL‐6 level in the gastrointestinal tract (GI) on CYPs activities were assessed. Upon blockade of the IL‐6 signaling pathway by an anti‐IL‐6 treatment, the area under plasma concentration versus time curves (AUCs) of S‐warfarin, omeprazole, and midazolam were predicted to decrease by up to 40%, 42%, and 46%, respectively. In patients with Crohn’s disease and ulcerative colitis treated with an anti‐IL‐6 therapy, the lowering of the elevated IL‐6 levels in the local GI tissue were predicted to result in further decreases in AUCs of those CYP substrates. The propensity of TP‐DIs under comorbidity conditions, such as in patients with cancer with IMID, were also explored. With further validation with relevant clinical data, this PBPK model may provide an in silico way to quantify the magnitude of potential TP‐DI in patients with elevated IL‐6 levels when an anti‐IL‐6 therapeutic is used with concomitant small‐molecule drugs. This model may be further adapted to evaluate the CYP modulation effect by other therapeutic modalities, which would significantly alter levels of proinflammatory cytokines during the treatment period.

Purpose Infliximab, a monoclonal antibody, is approved for the treatment of inflammatory diseases at doses that depend on the patient disease population. It was the aim of this study to evaluate its population pharmacokinetics in patients with moderately to severely active ulcerative colitis and characterize patient covariates that affect its disposition in this population. Methods Information collected from 482 patients in two randomized, double-blind, placebo-controlled international studies were analyzed using NONMEM. Results A two-compartment, population pharmacokinetic model described the serum infliximab concentration-time data. Population pharmacokinetic estimates (typical value ± standard error), based on the final covariate model, were clearance (CL: 0.407 ± 0.0103 L/day), apparent volumes of distribution in the central (V₁: 3.29 ± 0.0679 L) and peripheral (V₂: 4.13 ± 0.16 L) compartments, and intercompartment clearance (Q: 7.14 ± 0.489 L/day). Infliximab exhibited interindividual variability for CL and V₁ of 37.7% and 22.1%, respectively. Infliximab t₁/₂ is approximately 14 days. Covariate analysis showed that V₁ increased as body weight increased, and CL was higher in patients who developed antibodies to infliximab. An additional novel covariate, serum albumin concentration, was found to be inversely and strongly related to infliximab clearance in this population. Conclusions The disposition of infliximab in patients with moderately to severely active ulcerative colitis, unlike in rheumatoid arthritis, was not affected by coadministration of immunomodulators and corticosteroids but was related to formation of antibodies to infliximab and, notably, to serum albumin levels.

Thorough Overview Identifies and Addresses Critical Gaps in the Treatment of Several Chronic Diseases With increasing numbers of patients suffering from Immune-Mediated Inflammatory Diseases (IMIDs), and with the increasing reliance on biopharmaceuticals to treat them, it is imperative that researchers and medical practitioners have a thorough understanding of the absorption, distribution, metabolism and excretion (ADME) of therapeutic proteins as well as translational pharmacokinetic/pharmacodynamic (PK/PD) modeling for them. This comprehensive volume answers that need to be addressed. Featuring eighteen chapters from world-renowned experts and opinion leaders in pharmacology, translational medicine and immunology, editors Honghui Zhou and Diane Mould have curated a much-needed collection of research on the advanced applications of pharmacometrics and systems pharmacology to the development of biotherapeutics and individualized treatment strategies for the treatment of IMIDs. Authors discuss the pathophysiology of autoimmune diseases in addition to both theoretical and practical aspects of quantitative pharmacology for therapeutic proteins, current translational medicine research methodologies and novel thinking in treatment paradigm strategies for IMIDs. Other notable features include: • Contributions from well-known authors representing leading academic research centers, specialized contract research organizations and pharmaceutical industries whose pipelines include therapeutic proteins • Chapters on a wide range of topics (e.g., pathophysiology of autoimmune diseases, biomarkers in ulcerative colitis, model-based meta-analysis use in the development of therapeutic proteins) • Case studies of applying quantitative pharmacology approaches to guiding therapeutic protein drug development in IMIDs such as psoriasis, inflammatory bowel disease, multiple sclerosis and lupus Zhou and Mould's timely contribution to the critical study of biopharmaceuticals is a valuable resource for any academic and industry researcher working in pharmacokinetics, pharmacology, biochemistry, or biotechnology as well as the many clinicians seeking the safest and most effective treatments for patients dealing with chronic immune disorders.

Preclinical and clinical studies conducted in the mid-1990s reported strong association and causality between the T-cell helper (T H ) 1 inductor cytokine interleukin (IL)-12 and numerous immune-mediated disorders, which spurred the development of therapeutic agents targeting IL-12 function. One of the first to enter the clinic, ustekinumab, is a human monoclonal antibody (mAb) that binds to the p40 subunit of IL-12. Subsequent to the generation of ustekinumab, it was discovered that IL-23 also contains the p40 subunit. Thus, although ustekinumab was designed to target IL-12, it also modulates IL-23, a cytokine important to the development and/or maintenance of T H 17 cells. Clinical observations established that IL-12/23p40 is integral to the pathologies of psoriasis, psoriatic arthritis and Crohn's disease. The molecular and cellular evaluations conducted in ustekinumab clinical programs have provided numerous insights into the pathologic processes of these disorders, illustrating how a novel molecular entity can contribute to our understanding of disease. The individual contributions of these cytokines to specific pathologies require investigation and clinical evaluation of the role of IL-12– and IL-23–specific inhibitors.

The pharmacokinetics of golimumab, a human monoclonal antibody that inhibits the activity of tumor necrosis factor α, after a single subcutaneous (SC) or intravenous (IV) administration have been previously studied. The purpose of this study was to assess the pharmacokinetics of golimumab after multiple SC or IV administrations in patients with active rheumatoid arthritis (RA). The effect of concomitant methotrexate (MTX) use on golimumab pharmacokinetics was evaluated. In this open-label, randomized, Phase I study, 49 adult patients with RA received SC golimumab 100 mg (n = 33) every 4 weeks through week 20 or IV golimumab 2 mg/kg (n = 16) at weeks 0 and 12. Serial blood samples were collected, and serum golimumab concentration was measured with an electrochemiluminescent immunoassay. Golimumab pharmacokinetic parameters were derived with the use of a noncompartmental analysis. Adverse events were monitored at every visit. The population was predominantly Caucasian (84%) and female (76%), and the median age was 57 years. After SC golimumab administration, the serum golimumab concentration achieved steady state by ∼12 weeks with mean trough serum concentrations ranging from 1.15 to 1.24 μg/mL. After the final 30-minute IV infusion of golimumab 2 mg/kg, the mean (SD) clearance (CL) was 7.5 (2.6) mL/d/kg. The mean terminal half-life after SC and IV administrations was ∼13 days. The mean absolute bioavailability for SC golimumab was estimated to be 53%. The geometric mean of golimumab CL/F in patients with and without concomitant MTX use was 13.9 and 21.2 mL/d/kg, respectively, and the geometric mean ratio of CL/F was 65.5% (90% CI: 45.2%–94.9%, P = 0.06). Golimumab was generally well tolerated. No malignancies or deaths occurred during the study. Pharmacokinetics of golimumab were consistent after SC or IV administration in this population of patients with RA. Golimumab was well tolerated and no unexpected adverse events were observed in this trial. ClinialTrials.gov identifier: NCT01362153.

The aims of this work were to develop a population pharmacokinetic (PK) model for chimeric antigen receptor (CAR) transgene after single intravenous infusion administration of ciltacabtagene autoleucel in adult patients with relapsed or refractory multiple myeloma. CAR transgene level in blood were measured by quantitative polymerase chain reaction (qPCR) from 97 subjects in a phase Ib/II CARTITUDE‐1 study (NCT03548207), with a targeted cilta‐cel dose of 0.75 × 106 (range 0.5–1.0 × 106) CAR positive viable T‐cells per kg body weight. The population PK model development was primarily guided by the current mechanistic understanding of CAR‐T kinetics and the principles of building a parsimonious model. Cilta‐cel PK was adequately described by a two‐compartment model (with a fast and a slow apparent decline rate from each compartment, respectively) and a chain of four transit compartments with a lag time empirically representing the process from infused CAR‐T cell to measurable CAR transgene. No apparent relationship was observed between cilta‐cel dose (i.e., the actual number of CAR positive viable T‐cells infused), given the narrow dose range, and the observed transgene level. Based on covariate search and subgroup analysis of maximum systemic CAR transgene level (Cmax) and area under curve from the first dose to day 28 (AUC0–28d), none of the investigated subjects' demographics, baseline characteristics, and manufactured product characteristics had significant effects on cilta‐cel PK. The developed model is deemed robust and adequate for enabling subsequent exposure‐safety and exposure‐efficacy analyses.

Guselkumab is an anti‐interleukin‐23 human monoclonal antibody effective in treating psoriatic arthritis (PsA). To characterize the pharmacokinetics (PKs) and exposure‐response relationship of guselkumab in PsA, population PKs, and exposure‐response modeling, analyses were conducted using data from pivotal phase III studies of subcutaneous guselkumab in patients with PsA. The observed serum concentration‐time data of guselkumab were adequately described by a one‐compartment linear PK model with first‐order absorption and elimination. Covariates identified as contributing to the observed guselkumab PK variability were body weight and diabetes comorbidity; however, the magnitude of the effects of these covariates was not considered clinically relevant, and dose adjustment was not warranted for the patient population investigated. Positive exposure‐response relationships were demonstrated with landmark and longitudinal exposure‐response analyses between guselkumab exposure and clinical efficacy end points (American College of Rheumatology [ACR] 20%, 50%, and 70% improvement criteria and Investigator’s Global Assessment [IGA] of psoriasis) at weeks 20 and/or 24, with no clinically relevant differences observed in improvement of PsA signs and symptoms between the two guselkumab treatment regimens evaluated (100 mg every 4 weeks or 100 mg at weeks 0 and 4, then every 8 weeks). Baseline Disease Activity Score in 28 joints (DAS28), Psoriasis Area and Severity Index (PASI) score, and/or C‐reactive protein level were identified as influencing covariates on guselkumab exposure‐response model parameters. These results provide a comprehensive evaluation of subcutaneous guselkumab PKs and exposure‐response relationship that supports the dose regimen of 100 mg at weeks 0 and 4, then every 8 weeks in patients with PsA.

Golimumab is approved to treat moderate-to-severe active rheumatoid arthritis when given intravenously at weeks 0 and 4, then every 8 weeks (Q8W) with concomitant methotrexate. These analyses assessed whether a shorter dosing interval could ameliorate diminished efficacy experienced by a small proportion of patients toward the end of the dosing interval. Population pharmacokinetic and exposure–response modeling simulations were performed for intravenous golimumab 2 mg/kg at weeks 0 and 4, then Q8W or every 6 weeks (Q6W) through 1 year. A 2-compartment pharmacokinetic model with linear clearance developed based on GO-FURTHER (A Multicenter, Randomized, Double-blind, Placebo-controlled Trial of Golimumab, an Anti-TNFα Monoclonal Antibody, Administered Intravenously, in Patients With Active Rheumatoid Arthritis Despite Methotrexate Therapy) study data was used for pharmacokinetic simulations. A latent-variable indirect exposure–response model developed based on GO-FURTHER American College of Rheumatology (ACR) 20%/50%/70% improvement (ACR20, ACR50, and ACR70, respectively) data was used to predict clinical endpoints of ACR20/ACR50/ACR70 response rates. For Q6W and Q8W dosing, respectively, predicted median golimumab steady-state trough (Ctrough,ss) concentrations were 0.57 and 0.24 µg/mL, and Cmax at steady state values were 33.1 and 32.9 µg/mL. Predicted peak median ACR20 steady-state response rates were 76.7% (Q6W) and 75.6% (Q8W). Predicted median ACR20 response rates at Ctrough,ss increased by 4.7 percentage points with Q6W (73.7%) versus Q8W (69.0%) dosing. Greater improvement in ACR20 response rates at trough time points was predicted in patients with lower golimumab trough serum concentrations. Consistent findings were observed for ACR50/ACR70 response rates. These simulations suggest that intravenous golimumab Q6W dosing increases golimumab Ctrough,ss, which may improve clinical response in the small proportion of patients with rheumatoid arthritis with waning efficacy at the end of the standard dosing interval. ClinicalTrials.gov identifier: NCT00973479. Clinicaltrialsregister.eu: EudraCT 2008-006064-11.

The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target‐mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well‐established, physiologically based model by Li et al. in a step‐wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self‐guided exploration and hands‐on analysis.