Chuanpu Hu

Confirmatory population pharmacokinetic analysis for bapineuzumab phase 3 studies in patients with mild to moderate Alzheimer's disease

The population pharmacokinetics of bapineuzumab, a humanized monoclonal IgG1 antibody that was generated from a murine monoclonal antibody and binds specifically to amino acids 1 to 5 of the free N‐terminus of human amyloid‐beta peptide, were characterized in patients with mild‐to‐moderate alzheimers disease in two Phase 3 studies (ELN115727‐301 and ELN115727‐302). A total of 8,040 serum concentration measurements were analyzed from 1,458 patients who received 6 doses of bapineuzumab intravenously once every 13 weeks. A confirmatory analysis was conducted using a prespecified two‐compartment model with first‐order elimination. After the primary covariate effect assessment, a reduced model was obtained. Based on the reduced model, the typical population values for clearance (CL) and volume (Vc) from the central compartment in a Caucasian subject with a standardized body weight of 70 kg were 0.17 L/day and 3.13 L, respectively. Bapineuzumab CL and Vc increased with body weight. Furthermore, CL was 15% higher in non‐Caucasian subjects; however, this was not considered clinically relevant. None of the other evaluated covariates had a meaningful impact on CL. The median terminal elimination half‐life was estimated to be approximately 29 days. Sensitivity analyses and bootstrapping results supported model stability.

Population Pharmacokinetic Modeling of Guselkumab, a Human IgG1λ Monoclonal Antibody Targeting IL‐23, in Patients with Moderate to Severe Plaque Psoriasis

Psoriasis is a common inflammatory skin disorder that requires chronic treatment and is associated with multiple comorbidities. Guselkumab, a human immunoglobulin‐G1‐lambda monoclonal antibody, binds to interleukin‐23 with high specificity and affinity and is effective in treating moderate to severe plaque psoriasis. As part of the guselkumab psoriasis clinical trial program, using a confirmatory approach, a population pharmacokinetics (PopPK) model was established using 13 014 PK samples from 1454 guselkumab‐treated patients across 3 phase 2/3 trials. Observed serum guselkumab concentrations were adequately described by a 1‐compartment linear PK model with first‐order absorption and elimination. The final PK model was robust and stable, with apparent clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (ka) estimates of 0.516 L/day, 13.5 L, and 1.11 day‐1, respectively. A model‐derived elimination half‐life of 18.1 days indicated achievement of steady‐state serum guselkumab concentrations within 12–14 weeks. The primary covariate contributing to the observed PK variability was body weight, which accounted for only 28% (CL/F) and 32% (V/F) of the interindividual proportion of variance. Diabetes was identified to marginally reduce guselkumab exposure, owing to 12% higher CL/F in diabetic versus nondiabetic patients, but its contribution was not clinically relevant. None of the other covariates tested (eg, age, sex, ethnicity, immune response to guselkumab, or concomitant medications) had a clinically relevant effect on guselkumab exposure.

Landmark and longitudinal exposure–response analyses in drug development

Exposure–response (E–R) analysis plays an important role in optimizing dose and treatment regimens during clinical drug development. Two modeling approaches are commonly used for the E–R analysis, namely landmark analysis and longitudinal modeling. The landmark analysis approach aims to link the clinical response (e.g., efficacy or safety endpoints) to the pharmacokinetic (PK) parameters [e.g., trough drug concentration (Cmin), maximum drug concentration (Cmax), or area under the drug concentration– time curve (AUC)] at a certain fixed time point. Since some clinical responses depend on the PK profile shape, the most influential PK parameters responsible for a given clinical response should be identified before conducting landmark analysis. In contrast, longitudinal modeling uses all PK and clinical response data over the entire time course of treatment, along with the drug’s mechanism of action, to characterize the PK and clinical response time course in all subjects. This requires nonlinear mixed-effect modeling and is computationally much more intensive. Landmark analysis has been widely accepted as a sufficient E–R analysis method [1]. What, then, can longitudinal analysis additionally contribute to drug development? More generally, how do the results of landmark analysis compare with those of longitudinal modeling in theoretical and practical aspects? Is a graphical comparison of both approaches possible? This commentary aims to examine the pros and cons of both modeling approaches, address the related issues, and consider the potential utility of these approaches in clinical drug development. It is noted that a careful study design is required to fully understand the dose–exposure–response (D–E–R) relationship, especially in progressive diseases with complex etiology [2]. Thus, this commentary will not discuss the optimization of clinical study designs for E–R modeling of complex progressive diseases.

Alzheimer's disease assessment scale-cognitive 11-item progression model in mild-to-moderate Alzheimer's disease trials of bapineuzumab

The objective of this study was to estimate longitudinal changes in disease progression (measured by Alzheimers disease assessment scale‐cognitive 11‐item [ADAS‐cog/11] scale) after bapineuzumab treatment and to identify covariates (demographics or baseline characteristics) contributing to the variability in disease progression rate and baseline disease status.

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.

Alzheimer's disease progression model using disability assessment for dementia scores from bapineuzumab trials

Disability assessment for dementia (DAD) measurements from two phase‐3 studies of bapineuzumab in APOE ε4 noncarrier and carrier Alzheimers disease (AD) patients were integrated to develop a disease progression model.

Exposure‐Response Modeling Analyses for Sirukumab, a Human Monoclonal Antibody Targeting Interleukin 6, in Patients With Moderately to Severely Active Rheumatoid Arthritis

To characterize the dose‐exposure–response relationship of sirukumab, an anti–interleukin 6 human monoclonal antibody, in the treatment of moderately to severely active rheumatoid arthritis (RA), we conducted exposure‐response (E‐R) modeling analyses based on data from two pivotal phase 3 placebo‐controlled trials of sirukumab in patients with RA who were inadequate responders to nonbiologic disease‐modifying antirheumatic drugs or anti‐tumor necrosis factor α agents. A total of 2176 patients were included for the analyses and received subcutaneous administration of either placebo or sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. The clinical endpoints were 20%, 50%, and 70% improvement in the American College of Rheumatology response criteria (ie, ACR20, ACR50, and ACR70), and 28‐joint Disease Activity Index Score (DAS28) using C‐reactive protein. To provide a thorough assessment of the sirukumab E‐R relationship, 2 pharmacokinetic/pharmacodynamic modeling approaches were implemented, including joint longitudinal modeling (ie, indirect response modeling of the time course of the 2 clinical endpoints) and landmark analyses (ie, direct linking of selected pharmacokinetic parameters to response at week 16 or 24). Results from both modeling analyses were generally consistent, and collectively suggested that the sirukumab subcutaneous dose of 50 mg every 4 weeks would produce near‐maximal efficacy. No covariates identified in the E‐R modeling analyses would have a significant impact on dose‐response. Despite body weight and comorbid diabetes having significant effect on sirukumab exposure, simulations suggested that their effect on efficacy was small. Our work provides a comprehensive evaluation of sirukumab E‐R to support dose recommendations in patients with RA.

Confirmatory Population Pharmacokinetic Analysis for Sirukumab, a Human Monoclonal Antibody Targeting Interleukin‐6, in Patients With Moderately to Severely Active Rheumatoid Arthritis

The population pharmacokinetics of sirukumab, a human immunoglobulin G1κ monoclonal antibody against interleukin‐6, were characterized in patients with moderately to severely active rheumatoid arthritis in 4 phase 3 studies (SIRROUND‐D, ‐T, ‐H, and ‐M). A total of 17 034 serum concentrations were analyzed from 1991 rheumatoid arthritis patients who received subcutaneous administration of sirukumab 50 mg every 4 weeks or 100 mg every 2 weeks. A stepwise confirmatory population PK analysis was conducted to accommodate the staged data release and the sparse sampling nature of phase 3 studies and to assess the potential covariate influences in an unbiased and timely manner. The base model, that is, a 1‐compartment linear model with first‐order absorption and first‐order elimination, was prespecified based on prior information from a phase 2 study along with information about phase 3 study design. The covariate model was also prespecified based on pharmacological/physiological relevance and sample size. After the primary covariate analysis, a simplified model was produced by removing covariates with effect sizes <10%. The estimated apparent clearance (CL/F) and volume of distribution were 0.641 L/day and 16.1 L, respectively, at standard body weights of 70 kg. The terminal elimination half‐life was approximately 17.4 days. Sirukumab CL/F and volume of distribution increased with body weight, and CL/F was higher in patients with diabetic comorbidity. Simulations suggest that the effects of diabetic comorbidity and weight on sirukumab exposure were additive. To fully understand the clinical relevance including potential dose adjustment, current covariate findings need to be evaluated concurrently with the efficacy and safety data.

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.