Landmark and longitudinal exposure-response analyses in drug development

Exposure-Response Analyses of Polysomnography and Subjective Sleep Efficacy End Points From the Phase 3 Trials of Lemborexant, a Dual Orexin Receptor Antagonist for the Treatment of Insomnia

This report describes polysomnography and sleep diary exposure-response analyses from Study E2006-G000-304 (Study 304), a 1-month trial of 5- or 10-mg lemborexant, zolpidem, or placebo; and Study E2006-G000-303 (Study 303), a 6-month trial of 5- or 10-mg lemborexant or placebo. Studies 304 and 303 included 1006 (86%) and 956 (68%) (female) participants, respectively; >40% were ≥65 years, with individual lemborexant exposures derived from a previously described pharmacokinetic model. Linear mixed-effects analyses of polysomnography: latency to persistent sleep (LPS), sleep efficiency (SE), and wake after sleep onset (WASO) quantified the change from baseline given lemborexant exposure, time, and covariates, guided by consensus recommendations regarding clinical significance. A small impact of sex, body weight, and race was predicted for LPS and SE, irrespective of treatment. Effect of age on LPS was small; baseline SE was estimated to be 8% higher for a 50-year-old versus an 80-year-old, decreasing to 6% by 1 month. Baseline WASO was 13 minutes longer for Black versus White subjects, corresponding to a 5-minute lower change from baseline at the end of the study. For subjective end points, the statistically significant covariate effects for age, sex, and race were not deemed therapeutically relevant, likely reflecting physiologic sleep pattern changes across age and study subgroups. Both polysomnography and subjective analyses indicated clinically meaningful differences from baseline for both lemborexant treatments, with effects being greater for 10-mg versus 5-mg lemborexant, while indicating that covariate-specific lemborexant dose adjustments are not warranted.

Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis

Accurate characterization of longitudinal exposure-response of clinical trial endpoints is important in optimizing dose and dosing regimens in drug development. Clinical endpoints are often categorical, for which much progress has been made recently in latent variable indirect response (IDR) modeling with single drugs. However, such applications have not yet been used for trials employing multiple drugs administered concurrently. This study aims to demonstrate that the latent variable IDR approach provides a convenient longitudinal exposure-response modeling framework to assess potential interaction effects of combination therapies. This is illustrated by an application to the exposure-response modeling of guselkumab, a monoclonal antibody in clinical development that blocks the interleukin-23p19 subunit, and golimumab, a monoclonal antibody that binds with high affinity to tumor necrosis factor-alpha. A Phase 2a study was conducted in 214 patients with moderate-to severe active ulcerative colitis for which longitudinal assessments of disease severity based on patient-reported measures of rectal bleeding, stool frequency, and symptomatic remission were evaluated as categorical endpoints, and fecal calprotectin as a continuous endpoint. The modeling results suggested independent pharmacodynamic guselkumab and golimumab effects on fecal calprotectin as a continuous endpoint, as well as interaction effects on the categorical endpoints that may be explained by an additional pathway of competitive interaction.

Variability and uncertainty: interpretation and usage of pharmacometric simulations and intervals

Variability and estimation uncertainty are important sources of variation in pharmacometric simulations. Different combinations of uncertainty and the variability components lead to a variety types of simulation intervals, and many realized and unrealized confusions exist among pharmacometricians on their interpretation and usage. This commentary aims to clarify some of the important underlying concepts and provide a convenient guideline on pharmacometric simulation conduct and interpretation.

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.

Population pharmacokinetics and exposure-response modeling analyses of guselkumab in patients with psoriatic arthritis

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.

Pharmacokinetic Modeling and Predictive Performance: Practical Considerations for Therapeutic Monoclonal Antibodies

Population pharmacokinetic (PopPK) model parameter estimation and predictive performance depend on the data adequacy for model building. PopPK models of therapeutic monoclonal antibodies (mAbs) may not be well supported by commonly used sparse sampling in late-stage development because of the slow absorption (days) and long half-life (weeks) of mAbs, affecting accuracy of predicted exposure metrics which are often used to support drug development. A case study was presented for a representative mAb to compare the predictive performance of two established PopPK models from their respective data. Differences in datasets for model building (including sample size, sampling schedule and route of administration), model structure and parameters, and key derived exposure metrics were compared, and the resulting differences in model prediction were elaborated. With the majority of the data used for developing models being trough concentration (C_trough) data, both models projected similar C_trough and area under the concentration-time curve (AUC) but different peak concentrations (C_max) at steady state following the same subcutaneous dose regimen. Our case study supports the importance of appropriate sampling schemes for PopPK model development and exposure metric estimation. We recommend collecting proper random pharmacokinetic samples, in addition to troughs, to allow adequate characterization of PopPK models for mAbs. Selecting the informative model and relevant pharmacokinetic metrics could be critical in driving drug development decision-making, especially in simulation-based exposure matching to inform doses in special populations such as pediatrics.

Application of Beta-Distribution and Combined Uniform and Binomial Methods in Longitudinal Modeling of Bounded Outcome Score Data

Disease status is often measured with bounded outcome scores (BOS) which takes a discrete set of values on a finite range. The distribution of such data is often skewed, rendering the standard analysis methods assuming normal distribution inappropriate. Among the methods used for BOS analyses, two of them have the ability to predict the data within its natural range and accommodate data skewness: (1) a recently proposed beta-distribution based approach and (2) a mixture model known as CUB (combined uniform and binomial). This manuscript compares the two approaches, using an established mechanism-based longitudinal exposure-response model to analyze data from a phase 2 clinical trial in psoriatic patients. The beta-distribution-based approach was confirmed to perform well, and CUB also showed potential. A separate issue of modeling clinical trial data is that the collected baseline disease score range may be more limited than that of post-treatment disease score due to clinical trial inclusion criteria, a fact that is typically ignored in longitudinal modeling. The effect of baseline disease status restriction should in principle be adjusted for in longitudinal modeling.

Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents

Longitudinal exposure-response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects' achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design.

Pharmacokinetics, Pharmacodynamics, and Exposure-Response of Lanadelumab for Hereditary Angioedema

Hereditary angioedema (HAE) with C1 inhibitor deficiency is a rare disorder characterized by unpredictable, potentially life‐threatening recurrent angioedema attacks. Lanadelumab is a fully human monoclonal antibody with selective binding to active plasma kallikrein, and prevents the formation of cleaved high molecular weight kininogen (cHMWK) and bradykinin, thereby preventing HAE attacks. The clinical pharmacology of lanadelumab was characterized following subcutaneous administration in 257 subjects (24 healthy subjects and 233 patients with HAE). The pharmacokinetics of lanadelumab were described using a one‐compartment model with first‐order rate of absorption and linear clearance, showing slow absorption and a long half‐life (14.8 days). A covariate analysis retained body weight and health status on apparent clearance (CL/F) and body weight on volume of distribution (V/F). Population estimates of CL/F and V/F were 0.0249 L/hour (0.586 L/day) and 12.8 L, respectively. An indirect‐response Imax model showed 53.7% maximum suppression in cHMWK formation with a low potential for interactions with concomitant medications (analgesic, anti‐inflammatory, and antirheumatic medications). A 300 mg dose administered Q2W was associated with a mean steady‐state minimum concentration (C_min,ss; 25.4 μg/mL) that was ~ 4.5‐fold higher than the half‐maximal inhibitory concentration for cHMWK reduction (5.71 μg/mL). Exposure‐response analyses suggest that 300 mg Q2W dosing was associated with a significantly reduced HAE attack rate, prolonged time to first attack after treatment initiation, and lower need for concomitant medications. The response was comparable across patient body weight groups. Findings from this analysis support the dosing rationale for lanadelumab to prevent attacks in patients with HAE.

Population Pharmacokinetics and Exposure-Response Modeling of Golimumab in Adults With Moderately to Severely Active Ulcerative Colitis

PURPOSE

Golimumab is a fully human monoclonal antibody to tumor necrosis factor-α and is indicated for the treatment of moderately to severely active ulcerative colitis (UC). This study analyzed the population pharmacokinetic (PK) properties of golimumab and exposure-response for efficacy and safety, using data from combined Phase II/III UC studies.

METHODS

Data on serum golimumab concentration following IV and subcutaneous (SC) administration were fitted simultaneously using nonlinear mixed-effects modeling for the development of a population PK model. Logistic regression models were used for assessing relationships between serum golimumab concentrations and clinical efficacy outcomes in SC induction and maintenance studies. The percentages of patients developing infections, serious infections, and serious adverse events were assessed by golimumab exposure metric quartiles.

FINDINGS

The PK properties of golimumab are well described by a 2-compartment model with first-order absorption and elimination. Typical values of PK parameters in a 70-kg patient were clearance, 0.544 L/d; central and peripheral compartment V_d, 3.43 and 2.27 L, respectively; and intercompartmental clearance, 0.291 L/d. Golimumab t_1/2 was 10.5 days; bioavailability following SC administration was 52.2%. Body weight, anti-golimumab antibodies, serum albumin, C-reactive protein, and alkaline phosphatase affected golimumab disposition. A positive exposure-response relationship was established between golimumab concentration and efficacy outcomes. No apparent correlation between golimumab exposure and rate of infections, serious infections, or serious adverse events was observed in patients receiving golimumab 50 or 100 mg SC every 4 weeks through 1 year.

IMPLICATIONS

Body weight, serum albumin, and anti-golimumab antibodies explain some of the variability observed in the PK properties of golimumab, and exposure-response findings support the recommended posology of golimumab in UC. ClinicalTrials.gov identifiers: NCT00488774, NCT00487539, and NCT00488631.

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.

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.