Population pharmacokinetics and exposure-response analyses of daratumumab plus pomalidomide/dexamethasone in relapsed or refractory multiple myeloma.

AIM: A population pharmacokinetic (PPK) model was developed to characterize pharmacokinetics (PK) of subcutaneous or intravenous daratumumab administration in a new indication (i.e., combination with pomalidomide and dexamethasone [D-Pd] in patients with relapsed or refractory multiple myeloma [RRMM]). Analyses were conducted to explore exposure-response (E-R) relationships for efficacy and select treatment-emergent adverse events (TEAEs). METHODS: The PPK analysis included pooled data from the D-Pd cohorts of the phase 3 APOLLO and phase 1b EQUULEUS studies. Covariates were evaluated in the PPK model. Model-predicted exposures to daratumumab were compared between covariate subgroups of interest and used to investigate relationships between daratumumab exposure and efficacy and safety in APOLLO. RESULTS: The PPK analysis included 1146 daratumumab PK samples from 239 patients (APOLLO, n = 140; EQUULEUS, n = 99). Observed concentration-time data of daratumumab were well described by a two-compartment PPK model with first-order absorption and parallel linear and nonlinear elimination pathways. Treatment with D-Pd provided similar daratumumab PK characteristics versus historical daratumumab monotherapy. The E-R dataset contained data from 290 APOLLO patients (D-Pd, n = 140; Pd, n = 150). The PK-efficacy relationship of daratumumab supported improved progression-free survival for patients in the D-Pd group vs. the Pd group. Additionally, TEAEs did not increase with increasing PK exposure in the D-Pd group. CONCLUSIONS: The PPK and E-R analyses support the daratumumab subcutaneous 1800 mg dosing regimen in combination with Pd for treatment of patients with RRMM. No dose adjustment is recommended in this indication for any of the investigated factors, none of which had clinically relevant effects on daratumumab PK.

Improving categorical endpoint longitudinal exposure-response modeling through the joint modeling with a related endpoint.

Exposure-response modeling is important to optimize dose and dosing regimens in clinical drug development. While primary clinical trial endpoints often have few categories and thus provide only limited information, sometimes there may be additional, more informative endpoints. Benefits of fully incorporating relevant information in longitudinal exposure-response modeling through joint modeling have recently been shown. This manuscript aims to further investigate the benefit of joint modeling of an ordered categorical primary endpoint with a related near-continuous endpoint, through the sharing of model parameters in the latent variable indirect response (IDR) modeling framework. This is illustrated by analyzing the data collected through up to 116 weeks from a phase 3b response-adaptive trial of ustekinumab in patients with psoriasis. The primary endpoint was based on the 6-point physician's global assessment (PGA) score. The Psoriasis area and severity Index (PASI) data, ranging from 0 to 72 with 0.1 increments, were also available. Separate and joint latent variable Type I IDR models of PGA and PASI scores were developed and compared. The results showed that the separate PGA model had a substantial structural bias, which was corrected by the joint modeling of PGA and PASI scores.

Combined exome sequencing and deep phenotyping in highly selected fetuses with skeletal dysplasia during the first and second trimesters improves diagnostic yield.

OBJECTIVE: To investigate the genetic etiology of skeletal dysplasia in highly selected fetuses during the first and second trimesters using deep phenotyping and exome sequencing (ES). METHOD: Fetuses with short femurs were identified using the established prenatal diagnostic approach. A multidisciplinary team reviewed fetal phenotypic information (prenatal ultrasound findings, fetal postmortem, and radiographs) in a cohort of highly selected fetuses with skeletal dysplasia during the first and second trimesters. The affected families underwent multiplatform genetic tests. RESULTS: Of the 27 affected fetuses, 21 (77.8%) had pathogenic or potential pathogenic variations in the following genes: COL1A1, FGFR3, COL2A1, COL1A2, FLNB, DYNC2LI1, and TRIP11. Two fetuses had compound heterozygous mutations in DYNC2LI1 and TRIP11, respectively, and the other 19 carried de novo autosomal dominant variants. Novel variants were identified in COL1A1, COL2A1, COL1A2, DYNC2LI1, and TRIP11 in 11 fetuses. We also included the first description of the phenotype of odontochondrodysplasia in a prenatal setting. CONCLUSIONS: ES or panel sequencing offers a high diagnostic yield for fetal skeletal dysplasia during the first and second trimesters. Comprehensive and complete phenotypic information is indispensable for genetic analysis and the expansion of genotype-phenotype correlations in fetal skeletal abnormalities.

A novel quantification of information for longitudinal data analyzed by mixed-effects modeling.

Nonlinear mixed-effects (NLME) modeling is one of the most powerful tools for analyzing longitudinal data especially under the sparse sampling design. The determinant of the Fisher information matrix is a commonly used global metric of the information that can be provided by the data under a given model. However, in clinical studies, it is also important to measure how much information the data provide for a certain parameter of interest under the assumed model, for example, the clearance in population pharmacokinetic models. This paper proposes a new, easy-to-interpret information metric, the "relative information" (RI), which is designed for specific parameters of a model and takes a value between 0% and 100%. We establish the relationship between interindividual variability for a specific parameter and the variance of the associated parameter estimator, demonstrating that, under a "perfect" experiment (eg, infinite samples or/and minimum experimental error), the RI and the variance of the model parameter estimator converge, respectively, to 100% and the ratio of the interindividual variability for that parameter and the number of subjects. Extensive simulation experiments and analyses of three real datasets show that our proposed RI metric can accurately characterize the information for parameters of interest for NLME models. The new information metric can be readily used to facilitate study designs and model diagnosis.

Development and Translational Application of a Minimal Physiologically Based Pharmacokinetic Model for a Monoclonal Antibody against Interleukin 23 (IL-23) in IL-23-Induced Psoriasis-Like Mice.

The interleukin (IL)-23/Th17/IL-17 immune pathway has been identified to play an important role in the pathogenesis of psoriasis. Many therapeutic proteins targeting IL-23 or IL-17 are currently under development for the treatment of psoriasis. In the present study, a mechanistic pharmacokinetics (PK)/pharmacodynamics (PD) study was conducted to assess the target-binding and disposition kinetics of a monoclonal antibody (mAb), CNTO 3723, and its soluble target, mouse IL-23, in an IL-23-induced psoriasis-like mouse model. A minimal physiologically based pharmacokinetic model with target-mediated drug disposition features was developed to quantitatively assess the kinetics and interrelationship between CNTO 3723 and exogenously administered, recombinant mouse IL-23 in both serum and lesional skin site. Furthermore, translational applications of the developed model were evaluated with incorporation of human PK for ustekinumab, an anti-human IL-23/IL-12 mAb developed for treatment of psoriasis, and human disease pathophysiology information in psoriatic patients. The results agreed well with the observed clinical data for ustekinumab. Our work provides an example on how mechanism-based PK/PD modeling can be applied during early drug discovery and how preclinical data can be used for human efficacious dose projection and guide decision making during early clinical development of therapeutic proteins.

Full covariate modelling approach in population pharmacokinetics: understanding the underlying hypothesis tests and implications of multiplicity.

AIMS: To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity. METHODS: Clinical trial simulations were performed using a simple one-compartment pharmacokinetic model. Different numbers of covariates, sample sizes, effect sizes of covariates, and correlations among covariates were explored. The false positive rate (FPR) and power were evaluated. RESULTS: The FPR for the FCM approach dramatically increases with number of covariates. The chance of incorrectly selecting ≥1 seemingly clinically relevant covariates can be increased from 5% to a 40-70% range for 10-20 covariates. The SCI approach may provide appropriate control of the family-wise FPR, allowing more appropriate decision making. As a result, the power detecting real effects without incorrectly identifying non-existing effects can be greatly improved by the SCI approach compared to the approach in current practice. The performance of the SCI approach is driven by the ratio of sample size to number of covariates. The FPR can be controlled at 5% and 10% using the SCI approach when the ratio was ≥20 and 10, respectively. CONCLUSION: The FCM approach still lies within the framework of statistical testing, and therefore multiplicity is an issue for this approach. It is imperative to consider multiplicity reporting and adjustments in FCM modelling practice to ensure more appropriate decision making.

Noninvasive prenatal test for FGFR3-related skeletal dysplasia based on next-generation sequencing and plasma cell-free DNA: Test performance analysis and feasibility exploration.

OBJECTIVE: To explore the feasibility and accuracy of a noninvasive prenatal test for fibroblast growth factor receptor 3 (FGFR3)-related skeletal dysplasia based on next-generation sequencing (NGS) of plasma cell-free DNA. METHOD: Fragmented genome DNA (gDNA) of fetuses with achondroplasia (ACH) and thanatophoric dysplasia type I (TD I) was mixed with postdelivery maternal plasma cell-free DNA to generate spiked samples of different modeled fetal fractions. Multiplex polymerase chain reaction was used to amplify the 19 FGFR3 loci, and the amplification products were then sequenced by NGS to detect the fetal mutant alleles. Then, maternal plasma samples of pregnant women carrying ACH (n = 4) and TD I fetuses (n = 2), as well as healthy controls (n = 15), were tested by NGS, and the test performance was evaluated. RESULTS: Fetal FGFR3 mutations were detected in all artificial mixtures with fetal gDNA concentrations above 3%. In clinical validation, our method identified all fetal FGFR3 mutant alleles from maternal plasma, with no false positive results. The sensitivity and specificity of our method were 100% (95% CI, 54.1%-100%) and 100% (78.2%-100%), respectively. CONCLUSION: Our method had a favorable performance for noninvasively detecting fetal FGFR3 mutations in maternal plasma, highlighting its promising value in developing a noninvasive prenatal test for de novo and paternally inherited disorders.

Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis.

Accurate characterization of exposure-response relationship of clinical endpoints is important in drug development to identify optimal dose regimens. Endpoints with ≥ 10 ordered categories are typically analyzed as continuous. This manuscript aims to show circumstances where it is advantageous to analyze such data as ordered categorical. The results of continuous and categorical analyses are compared in a latent-variable based Indirect Response modeling framework for the longitudinal modeling of Mayo scores, ranging from 0 to 12, which is commonly used as a composite endpoint to measure the severity of ulcerative colitis (UC). Exposure response modeling of Mayo scores is complicated by the fact that studies typically include induction and maintenance phases with re-randomizations and other response-driven dose adjustments. The challenges are illustrated in this work by analyzing data collected from 3 phase II/III trials of golimumab in patients with moderate-to-severe UC. Visual predictive check was used for model evaluations. The ordered categorical approach is shown to be accurate and robust compared to the continuous approach. In addition, a disease progression model with an empirical bi-phasic rate of onset was found to be superior to the commonly used placebo model with one onset rate. An application of this modeling approach in guiding potential dose-adjustment was illustrated.

Joint longitudinal model development: application to exposure-response modeling of ACR and DAS scores in rheumatoid arthritis patients treated with sirukumab.

Exposure-response modeling is important to optimize dose and dosing regimen in clinical drug development. The joint modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript presents the results of joint modeling of continuous and ordered categorical endpoints in the latent variable IDR modeling framework through the sharing of model parameters, with an application to the exposure-response modeling of sirukumab. Sirukumab is a human anti- interleukin-6 (IL-6) monoclonal antibody that binds soluble human IL-6 thus blocking IL-6 signaling, which plays a major role in the pathophysiology of rheumatoid arthritis (RA). A phase 2 clinical trial was conducted in patients with active RA despite methotrexate therapy, who received subcutaneous (SC) administration of either placebo or sirukumab of 25, 50 or 100 mg every 4 weeks (q4w) or 100 mg every 2 weeks (q2w). Major efficacy endpoints were the 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria, and the 28-joint disease activity score using C-reactive protein (DAS28). The ACR endpoints were treated as ordered categorical and DAS28 as continuous. The results showed that, compared with the common approach of separately modeling the endpoints, the joint model could describe the observed data better with fewer parameters through the sharing of random effects, and thus more precisely characterize the dose-response relationship. The implications on future dose and dosing regimen optimization are discussed in contrast with those from landmark analysis.

A comprehensive evaluation of exposure-response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis.

Guselkumab, a human IgG1 monoclonal antibody that blocks interleukin-23, has been evaluated in one Phase 2 and two Phase 3 trials in patients with moderate-to-severe psoriasis, in which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Investigator's Global Assessment (IGA) scores. Through the application of landmark and longitudinal exposure-response (E-R) modeling analyses, we sought to predict the guselkumab dose-response (D-R) relationship using data from 1459 patients who participated in these trials. A recently developed novel latent-variable Type I Indirect Response joint model was applied to PASI75/90/100 and IGA response thresholds, with placebo effect empirically modeled. An effect of body weight on E-R, independent of pharmacokinetics, was identified. Thorough landmark analyses also were implemented using the same dataset. The E-R models were combined with a population pharmacokinetic model to generate D-R predictions. The relative merits of longitudinal and landmark analysis also are discussed. The results provide a comprehensive and robust evaluation of the D-R relationship.

Influence of carboplatin on the proliferation and apoptosis of ovarian cancer cells through mTOR/p70s6k signaling pathway.

PURPOSE: To investigate the influence of carboplatin on the proliferation and apoptosis of ovarian cancer cells through mTOR/P70S6K signaling pathway. METHODS: The mRNA and protein expressions were detected via Western blotting and RT-PCR to study whether the mTOR/p70S6K signaling pathway was activated in OVCAR-3 and Caov-3 ovarian cancer cell lines. After cells were treated with different concentrations of carboplatin, the mRNA and protein expressions of mTOR, p70S6K and 4E-BP1 were detected via RT-PCR and Western blotting. OVCAR-3 cells were treated with 20 and 50 µM carboplatin for 4 hrs, and then apoptosis was analyzed and assessed. OVCAR-3 cells were treated with different concentrations of carboplatin (20, 50, 100, 150 and 200 µM) for 24 and 48 hrs, respectively. RESULTS: The mTOR signaling pathway was activated in OVCAR-3 and Caov-3 ovarian cancer cell lines. The mRNA level of mTOR in Caov-3 cells was higher, but that of p70S6K was lower. Carboplatin significantly reduced the mRNA expression of mTOR (p<0.01), whereas the mRNA expressions of p70S6K and 4E-BP1 in carboplatin-treated cells were increased in a dose-dependent manner (p<0.01). Carboplatin inhibited the mTOR protein expression in a dose-dependent manner (p<0.01). The proliferation of OVCAR-3 cells exposed to carboplatin was reduced compared with that of untreated cells (p<0.01), and the inhibitory effect of carboplatin on the proliferation of OVCAR-3 cells was time- and dose-dependent. CONCLUSION: The mTOR/p70S6K pathway was activated in ovarian cancer. Carboplatin could rapidly inhibit the expression of mTOR, and the phosphorylation of its major downstream effectors p70S6K and 4E-binding protein 1 (4E-BP1) arrested cells in G0/G1 phase and induced ovarian cancer cell apoptosis.

Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients.

Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.

Challenges in longitudinal exposure-response modeling of data from complex study designs: a case study of modeling CDAI score for ustekinumab in patients with Crohn's disease.

Informative exposure-response modeling of clinical endpoints is important in drug development to identify optimum dose and dosing regimens. Despite much recent progress in mechanism-based longitudinal modeling of clinical data, challenges remain in clinical trials of diseases such as Crohn's disease, where a commonly used composite endpoint Crohn's Disease Activity Index (CDAI) has considerable variation in its administration and scoring between different assessors and complex study designs typically include maintenance phases with randomized withdrawal re-randomizations and other response driven dose adjustments. This manuscript illustrates the complexities of exposure-response modeling of such composite endpoint data through a latent-variable based Indirect Response model framework for CDAI scores using data from three phase III trials of ustekinumab in patients with moderate-to-severe Crohn's Disease. Visual predictive check was used to evaluate model performance. Potential impacts of the study design on model development and evaluation of the E-R relationship in the induction and maintenance phases of treatment are discussed. Certain biases appeared difficult to overcome, and an autocorrelated residual error model was found to provide improvement.

Deciphering the In Vivo Performance of a Monoclonal Antibody to Neutralize Its Soluble Target at the Site of Action in a Mouse Collagen-Induced Arthritis Model.

PURPOSE: Study the disposition and target-neutralization capability of an anti-interleukin-6 (IL-6) monoclonal antibody (mAb) at the joint in a mouse collagen-induced arthritis (CIA) model. METHODS: A mechanistic pharmacokinetic/pharmacodynamic study was conducted in a mouse CIA model using CNTO 345, a rat anti-mouse IL-6 mAb, as model compound. The drug, total/free IL-6 concentrations in both serum and joint lavage fluid were quantitatively assessed and compared to those in the normal control mice. RESULTS: CNTO 345 exhibited higher clearance and significantly higher joint lavage/serum ratio in the CIA mice than in the normal control mice. The mAb concentrations in the joint lavage are approximately proportional to the serum concentrations at all the time points being examined. Dosing of CNTO 345 led to sustained free IL-6 suppression in both serum and joint lavage in a dose-dependent manner. A dose-dependent increase in total IL-6 was observed in serum, but not in the joint lavage fluid. Though no change in disease activity was observed following a single dose of anti-IL-6 mAb at peak of the disease, a dose-dependent decrease in serum amyloid A, a downstream biomarker of IL-6, was observed. CONCLUSIONS: This study provided quantitative assessments of the distribution and target-neutralization capability of an anti-IL-6 mAb at the site of action in an animal disease model.

First-in-human study to assess guselkumab (anti-IL-23 mAb) pharmacokinetics/safety in healthy subjects and patients with moderate-to-severe psoriasis.

PURPOSE: The purpose of this study is to evaluate the pharmacokinetics, immunogenicity, safety, and tolerability of guselkumab, a human monoclonal antibody with high affinity and specificity for binding to interleukin-23. METHODS: In this first-in-human, phase 1, randomized study, a single intravenous (IV; 0.03-10 mg/kg) or subcutaneous (SC; 10-300 mg) dose of guselkumab was administered to 47 healthy subjects, and a single SC dose (placebo, 10, 30, 100, 300 mg) was administered to 24 patients with moderate-to-severe psoriasis. RESULTS: Mean maximum observed serum concentration and area under the zero-to-infinity serum concentration-time curve of guselkumab increased in an approximately dose-proportional manner over the dose range of 0.03-10 mg/kg following a single IV administration or 10-300 mg following a single SC administration. Mean clearance and volume of distribution ranged from 3.62-6.03 mL/day/kg and 99.38-123.22 mL/kg, respectively. Mean half-life ranged from 12 to 19 days in healthy subjects and patients with psoriasis. Among guselkumab-treated subjects/patients, 1/30 (3.3 %) healthy subjects in the IV group, 0/6 healthy subjects in the SC group, and 1/20 (5.0 %) patients with psoriasis tested positive for antibodies to guselkumab. No clinically significant adverse events were identified in this study. CONCLUSION: Guselkumab pharmacokinetic profiles were generally comparable between healthy subjects and patients with psoriasis. Guselkumab, administered as an IV infusion or SC injection, was well tolerated in healthy subjects and patients with psoriasis.

Pharmacological considerations for predicting PK/PD at the site of action for therapeutic proteins.

For therapeutic proteins whose sites of action are distal to the systemic circulation, both drug and target concentrations at the tissue sites are not necessarily proportional to those in systemic circulation, highlighting the importance of understanding pharmacokinetic/pharmacodynamic (PK/PD) relationship at the sites of action. This review summarizes the pharmacological considerations for predicting local PK/PD and the importance of measuring PK and PD at site of action. Three case examples are presented to show how mechanistic and physiologically based PK/PD (PBPK/PD) models which incorporated the PK and PD at the tissue site can be used to facilitate understanding the exposure-response relationship for therapeutic proteins.

Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis.

Improving the quality of exposure-response modeling is important in clinical drug development. The general joint modeling of multiple endpoints is made possible in part by recent progress on the latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate, when modeling a continuous and a categorical clinical endpoint, the level of improvement achievable by joint modeling in the latent variable IDR modeling framework through the sharing of model parameters for the individual endpoints, guided by the appropriate representation of drug and placebo mechanism. This was illustrated with data from two phase III clinical trials of intravenously administered mAb X for the treatment of rheumatoid arthritis, with the 28-joint disease activity score (DAS28) and 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria were used as efficacy endpoints. The joint modeling framework led to a parsimonious final model with reasonable performance, evaluated by visual predictive check. The results showed that, compared with the more common approach of separately modeling the endpoints, it is possible for the joint model to be more parsimonious and yet better describe the individual endpoints. In particular, the joint model may better describe one endpoint through subject-specific random effects that would not have been estimable from data of this endpoint alone.

Minimal physiologically-based pharmacokinetic (mPBPK) model for a monoclonal antibody against interleukin-6 in mice with collagen-induced arthritis.

Therapeutic monoclonal antibodies (mAb) targeting soluble inflammatory cytokines exert their pharmacological effects in rheumatoid arthritis through binding and neutralizing free cytokines in target tissue sites. Therefore suppression of free cytokines in such sites directly relates to the magnitude of therapeutic response. Although the interrelationships between mAb and cytokines have been examined in the systemic circulation, less is known about the interaction of mAb and cytokines in inflamed joints. In the present study, the interplay between the mAb, CNTO 345, and its target IL-6 in serum as well as ankle joint synovial fluid were characterized in collagen-induced arthritic mice. A minimal physiologically-based pharmacokinetic model with target-mediated drug disposition (TMDD) features in serum and ankle joint synovial fluid was developed for the assessment of the TMDD dynamics of CNTO 345 and IL-6. Our model indicates that TMDD kinetics in ankle joints differ greatly from that in serum. The differences can be attributed to the limited tissue distribution of CNTO 345 in ankle joint synovial fluid, the significant rise of the IL-6 baseline in ankle joint synovial fluid in comparison with serum, and the relative time-scales of elimination rates between CNTO 345, free IL-6 and CNTO 345-IL-6 complex in serum and ankle joint synovial fluid.

Association between serum concentration of infliximab and efficacy in adult patients with ulcerative colitis.

BACKGROUND & AIMS: We analyzed data collected during the Active Ulcerative Colitis Trials (ACT-1 and ACT-2) to assess relationships between serum concentrations of infliximab and outcomes of adults with moderate-to-severe ulcerative colitis. METHODS: We compared serum concentrations of infliximab with outcomes of 728 patients with moderately-to-severely active ulcerative colitis who participated in ACT-1 or ACT-2; efficacy data were collected at weeks 8, 30, and 54 (for ACT-1 only). Relationships between serum concentration of infliximab and efficacy outcomes were assessed using trend, logistic regression, and receiver operating characteristic curve analyses. We also evaluated factors that affected the relationship between exposure and response. RESULTS: Median serum concentrations of infliximab at weeks 8, 30, and/or 54 were significantly higher in patients with clinical response, mucosal healing, and/or clinical remission than in patients who did not meet these response criteria. There were statistically significant relationships between quartile of infliximab serum concentration and efficacy at these time points (P < .01). Infliximab therapy was effective for a smaller proportion of patients in the lowest quartile, and these patients had lower serum levels of albumin and a higher incidence of antibodies to infliximab than patients in other quartiles. Although the relationship between exposure to infliximab and response varied among patients, approximate serum concentrations of 41 µg/mL infliximab at week 8 of induction therapy and 3.7 µg/mL at steady-state during maintenance therapy produced optimal outcomes in patients. CONCLUSIONS: Serum concentrations of infliximab are associated with efficacy in patients with moderate-to-severe ulcerative colitis; however, complex factors determine the relationship between exposure to this drug and response. A prospective evaluation of the value of measuring serum concentrations of infliximab should be performed before these data can be included in patient management strategies. Clinicaltrials.gov numbers: NCT00036439 and NCT00096655.

Mechanisms of monoclonal antibody-drug interactions.

The concept of monoclonal antibodies (mAbs) as pharmacotherapeutics was validated in the mid-1980s with the successful clinical development of the fully murine mAb muromonab-CD3 for prevention of acute organ rejection. However, clinical applications of fully murine mAbs are restricted, owing to the high incidence of serious immune-mediated side effects, particularly upon repeated exposure. The rate and severity of immune-mediated toxicities of these agents were significantly attenuated by the development of mouse/human chimeric, fully human, and humanized mAbs. These refinements in molecular structure allowed repeated, long-term administration, where therapeutically warranted, which, in turn, broadened the scope of indications for this class of therapy. Presently, numerous mAbs are approved or are undergoing clinical evaluation for treatment of oncologic and chronic inflammatory diseases. The current experimental development landscape spans respiratory, metabolic, and central nervous system disorders as well as infectious disease. A consequence of the expanding numbers of mAb indications is concomitant administration of these agents with established small molecule pharmacotherapies, which necessitates a comprehensive understanding of mAb-small molecule drug interactions as well as mAb-mAb interactions. Current knowledge indicates that mAbs do not elicit a direct effect on the metabolic/clearance pathways for small molecular therapeutics. However, the immunomodulatory properties of mAbs can indirectly alter clearance of certain small molecule entities through the attenuation of noncatabolic enzymatic pathways.