Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model.

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.

Population Pharmacokinetics and Exposure-Response Analysis for the CTLA-4 Inhibitor Tremelimumab in Metastatic NSCLC Patients in the Phase III POSEIDON Study.

Blockade of CTLA-4 by tremelimumab combined with anti-PD-L1 durvalumab and chemotherapy provided increased antitumor activity and long-term survival benefits in first-line metastatic non-small cell lung cancer (mNSCLC) in the phase III POSEIDON study. We performed population pharmacokinetic modeling for tremelimumab using data from 1,605 patients across 6 studies (including POSEIDON) in multiple tumors (lung cancer, bladder cancer, malignant mesothelioma, and other solid tumors), and identified a 2-compartment model with linear and time-varying clearance for tremelimumab. Cox proportional hazard regression models were applied to 326 patients with mNSCLC from POSEIDON to evaluate the association between exposure metrics and efficacy end points, adjusting for baseline prognostic covariates. Improved progression-free survival (PFS) and overall survival (OS) in the tremelimumab arm (in combination with durvalumab and chemotherapy) was associated with higher tremelimumab exposure (e.g., minimum concentration at 5th dose (Cmin,dose5 ) and area under the curve at 5th dose (AUCdose5 )). However, further case-matching analyses yielded hazard ratios for the comparison of tremelimumab-treated patients in the Cmin,dose5 quartile 1 (Q1) subgroup with matched chemotherapy-treated patients of 1.04 (95% confidence interval (CI): 0.76-1.44) for OS and 0.99 (95% CI: 0.72-1.36) for PFS, suggesting that the observed apparent exposure-response relationship might be confounded. No relationship between tremelimumab exposure and safety (grade ≥3 treatment-emergent adverse events [AEs], AEs of special interest, or discontinuation due to AEs) was identified. These results support the consistent benefit observed with tremelimumab 75 mg every 3 weeks for up to 5 doses in combination with durvalumab and chemotherapy in POSEIDON as first-line therapy for mNSCLC.

Modeling of Proliferating CD4 and CD8 T-Cell Changes to Tremelimumab Exposure in Patients with Unresectable Hepatocellular Carcinoma.

The STRIDE (Single Tremelimumab Regular Interval Durvalumab) regimen of single-dose tremelimumab 300 mg, plus durvalumab 1,500 mg every 4 weeks demonstrated potential for long-term survival in studies of unresectable hepatocellular carcinoma (uHCC; Study 22 and HIMALAYA). The aim of this analysis was to investigate changes in proliferating CD4+ Ki67+ and CD8+ Ki67+ T cells and their relationship with tremelimumab exposure in patients with uHCC. Median cell count, change from baseline, and percent change from baseline in CD4+ and CD8+ T cells peaked around 14 days after STRIDE. A model of CD4+ and CD8+ T cell response to tremelimumab exposure was developed. Patients with lower baseline T cell counts had a greater percent change from baseline in T cell response to tremelimumab, and baseline T-cell count was included in the final model. With the full covariate model, the half-maximal effective concentration (EC50 ) of tremelimumab was 6.10 µg/mL (standard error = 1.07 µg/mL); > 98.0% of patients were predicted to have a minimum plasma concentration greater than EC50 with tremelimumab 300 or 750 mg. For EC75 (9.82 µg/mL), 69.5% and 98.2% of patients were predicted to exceed the EC75 with tremelimumab 300 and 750 mg, respectively. This analysis supports the clinical hypothesis that combination anti-cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) and anti-programmed cell death ligand-1 (anti-PD-L1) therapy primes an immune response that may then be sustained by anti-PD-L1 monotherapy and supports the clinical utility of the STRIDE regimen in patients with uHCC. These insights may also help inform dose selection of anti-CTLA-4 plus anti-PD-L1 combination strategies.

Population Pharmacokinetics and Exposure-Response Analysis of Tremelimumab 300 mg Single Dose Combined with Durvalumab 1500 mg Q4W (STRIDE) in Patients with Unresectable Hepatocellular Carcinoma.

A novel single-dose regimen of 300 mg tremelimumab in combination with durvalumab (STRIDE) has demonstrated a favorable benefit-risk profile in the phase 1/2 Study 22 trial (in patients with unresectable hepatocellular carcinoma, uHCC) and in the phase 3 HIMALAYA study. The current analysis evaluated the population pharmacokinetics (PopPK) of tremelimumab and durvalumab, and the exposure-response (ER) relationship for efficacy and safety of STRIDE in patients with uHCC. Previous PopPK models for tremelimumab and durvalumab were updated using data from previous studies in various cancers combined with data from Study 22 and HIMALAYA. Typical population mean parameters and associated inter- and intra-individual variability were assessed, as was the influence of covariates. Individual exposure metrics were derived from the individual empirical Bayes estimates as drivers for ER analysis related to efficacy and safety from HIMALAYA. The observed pharmacokinetics of tremelimumab in uHCC were well described by a 2-compartment model with both linear and time-dependent clearance. All identified covariates changed tremelimumab PK parameters by <25%, and thus had minimal clinical relevance; similar results were obtained from durvalumab PopPK analysis. None of tremelimumab or durvalumab exposure metrics were significantly associated with overall survival (OS), progression-free survival (PFS), or adverse events. Baseline aspartate aminotransferase and neutrophil-to-lymphocyte ratio (NLR) were associated with OS (P < .001) by the Cox proportional hazards model. No covariate was identified as a significant factor for PFS. No dose adjustment for tremelimumab or durvalumab is needed based on PopPK covariate analyses or ER analyses. Our findings support the novel STRIDE dosing regimen in patients with uHCC.

Synthetic Model Combination: A new machine-learning method for pharmacometric model ensembling.

When aiming to make predictions over targets in the pharmacological setting, a data-focused approach aims to learn models based on a collection of labeled examples. Unfortunately, data sharing is not always possible, and this can result in many different models trained on disparate populations, leading to the natural question of how best to use and combine them when making a new prediction. Previous work has focused on global model selection or ensembling, with the result of a single final model across the feature space. Machine-learning models perform notoriously poorly on data outside their training domain, however, due to a problem known as covariate shift, and so we argue that when ensembling models the weightings for individual instances must reflect their respective domains-in other words, models that are more likely to have seen information on that instance should have more attention paid to them. We introduce a method for such an instance-wise ensembling of models called Synthetic Model Combination (SMC), including a novel representation learning step for handling sparse high-dimensional domains. We demonstrate the use of SMC on an example with dosing predictions for vancomycin, although emphasize the applicability of the method to any scenario involving the use of multiple models.

Population Pharmacokinetics of Monalizumab in Patients With Advanced Solid Tumors.

Monalizumab is a novel, first-in-class humanized immunoglobulin G4 monoclonal antibody immune checkpoint inhibitor that targets the inhibitory CD94/NKG2A receptors. The objectives of this analysis were to develop a population pharmacokinetic (PK) model of monalizumab, evaluate the impact of clinically relevant covariates on monalizumab PK, and provide dose justification for clinical trials. We developed a monalizumab population PK model to characterize the PK properties of monalizumab in patients with advanced solid tumors or head and neck squamous cell carcinoma. Data from clinical studies D419NC00001 (NCT02671435) and IPH2201-203 (NCT02643550) were pooled for the analysis, resulting in a data set of 3066 PK samples derived from 507 subjects. The PK of monalizumab were reasonably described by a 2-compartment model with first-order elimination. Monalizumab generally exhibited linear PK over a dose range of 22.5-750 mg or 10 mg/kg every 2 weeks. The estimate of clearance was ≈0.255 L/day, and apparent volume of distribution was 6.36 L for a typical individual, consistent with previous findings for endogenous immunoglobulin Gs and other therapeutic monoclonal antibodies. Baseline albumin and body weight were identified as significant covariates of clearance; body weight, sex, and smoking status had a significant impact on volume of distribution; and none of these covariates had impact on peripheral volume of distribution. Although these covariates were identified as statistically significant, they are considered to be not clinically meaningful, as changes in monalizumab exposure were <30%. Therefore, no dose adjustments of monalizumab based on patient or disease characteristics are recommended.

Exposure-Response Analyses of Tremelimumab Monotherapy or in Combination with Durvalumab in Patients with Unresectable Hepatocellular Carcinoma.

PURPOSE: A novel single-dose regimen of 300 mg tremelimumab in combination with durvalumab [Single Tremelimumab Regular Interval Durvalumab (STRIDE)] has demonstrated a favorable benefit-risk profile in the phase I/II Study 22 (NCT02519348) and phase III HIMALAYA study (NCT03298451). This study evaluated the pharmacokinetics, exposure-response, and exposure-pharmacodynamics relationships of tremelimumab in patients with unresectable hepatocellular carcinoma (uHCC). PATIENTS AND METHODS: A previous tremelimumab population pharmacokinetic model was validated using data from parts 2 and 3 of Study 22. Exposure-response analyses explored relationships of tremelimumab exposure with efficacy and safety. Pharmacokinetics and pharmacodynamics relationships were evaluated using linear and nonlinear regression models. RESULTS: The observed pharmacokinetics of tremelimumab in uHCC were consistent with predictions; no significant covariates were identified. Tremelimumab exposure was not significantly associated with adverse events, objective response rate, or progression-free survival. Overall survival (OS) was longer for patients with tremelimumab exposure, minimum serum drug concentration (Cmin1) ≥ median versus Cmin1 < median (18.99 months vs. 10.97 months), but this exposure-survival analysis might be confounded with baseline characteristics of albumin level and neutrophil to lymphocyte ratio, which had a significant impact on OS (P = 0.0004 and 0.0001, respectively). The predicted Cmin1 of tremelimumab in STRIDE regimen (12.9 µg/mL) was greater than the estimated concentration of tremelimumab eliciting half-maximal increases (EC50 = 5.24 µg/mL) in CD8+Ki67+ T-cell counts. CONCLUSIONS: Our findings support novel insights into tremelimumab pharmacokinetics and exposure-response relationships in HCC and support the clinical utility of the STRIDE regimen in patients with uHCC.

Pharmacokinetics and pharmacodynamics of MEDI0680, a fully human anti-PD-1 monoclonal antibody, in patients with advanced malignancies.

MEDI0680 is a humanized immunoglobulin monoclonal antibody that targets human programmed cell death protein 1 (PD-1) for the treatment of cancer. A population two-compartmental pharmacokinetic (PK) model and a sequential direct maximal effective drug concentration receptor occupancy (RO) model with baseline parameters were developed to quantify PK variability, identify significant covariates, and characterize the relationship between the PK and the RO of MEDI0680. A total of 58 patients with advanced malignancies received MEDI0680 by intravenous infusion at a dose of 0.1-20 mg/kg in a phase 1 study. The clearance was 0.27 L per day and the central volume of distribution (V1) was 3.14 L, with a modest between-subject variability of 30 and 19%, respectively. None of the evaluated covariates showed any impact on PK parameters except for a nonclinically meaningful relevant impact of body weight on V1. The estimated half-maximal effective concentration for MEDI0680 binding to the PD-1 antigen was approximately 1.88 µg/mL. Visual predictive check results demonstrated good predictability of the final population PK-RO model. PK-RO simulations demonstrated that > 90% RO could be maintained in all subjects after a 20-mg/kg dose every 2 weeks (Q2W). Therefore, 20 mg/kg Q2W and an equivalently fixed dose of 1500 mg was recommended for phase 2 studies.

Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples.

Quantitative Systems Toxicology (QST) models, recapitulating pharmacokinetics and mechanism of action together with the organic response at multiple levels of biological organization, can provide predictions on the magnitude of injury and recovery dynamics to support study design and decision-making during drug development. Here, we highlight the application of QST models to predict toxicities of cancer treatments, such as cytopenia(s) and gastrointestinal adverse effects, where narrow therapeutic indexes need to be actively managed. The importance of bifurcation analysis is demonstrated in QST models of hematologic toxicity to understand how different regions of the parameter space generate different behaviors following cancer treatment, which results in asymptotically stable predictions, yet highly irregular for specific schedules, or oscillating predictions of blood cell levels. In addition, an agent-based model of the intestinal crypt was used to simulate how the spatial location of the injury within the crypt affects the villus disruption severity. We discuss the value of QST modeling approaches to support drug development and how they align with technological advances impacting trial design including patient selection, dose/regimen selection, and ultimately patient safety.

Clinical Pharmacology of Elagolix: An Oral Gonadotropin-Releasing Hormone Receptor Antagonist for Endometriosis.

The clinical pharmacology of elagolix was extensively evaluated in clinical studies in healthy subjects and in women with endometriosis. Elagolix pharmacokinetics (PK) show significant population variability, however they are minimally affected by patients' baseline characteristics and demographics, except for clinically relevant extrinsic and intrinsic factors such as coadministrated strong organic anion transporting polypeptide (OATP) 1B1 inhibitors and severe hepatic impairment, which are contraindications for the use of elagolix. These studies enabled a comprehensive understanding of elagolix mechanism of action and the downstream pharmacodynamic (PD) effects on gonadotropin and ovarian hormones, as well as full characterization of the PK/PD (PKPD) relationships of elagolix at various dosages, including the approved 150 mg once daily and 200 mg twice daily dosing regimens for the management of moderate to severe pain associated with endometriosis. Several model-based analyses have contributed to understanding of the benefit-risk profile of elagolix in patients with endometriosis, through characterization of the exposure relationship with responder rates, with changes in bone mineral density over time, as well as the interaction with coadministered drugs. Collectively, these studies and analyses served as supportive evidence for the effectiveness of the approved dosages and provided general dosing instructions of the first approved oral gonadotropin-releasing hormone receptor antagonist.

Clinical Pharmacokinetics and Pharmacodynamics of Evolocumab, a PCSK9 Inhibitor.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) increases plasma low-density lipoprotein cholesterol (LDL-C) by decreasing expression of the LDL receptor on hepatic cells. Evolocumab is a human monoclonal immunoglobulin G2 that binds specifically to human PCSK9 to reduce LDL-C. Evolocumab exhibits nonlinear kinetics as a result of binding to PCSK9. Elimination is predominantly through saturable binding to PCSK9 at lower concentrations and a nonsaturable proteolytic pathway at higher concentrations. The effective half-life of evolocumab is 11-17 days. The pharmacodynamic effects of evolocumab on PCSK9 are rapid, with maximum suppression within 4 h. At steady state, peak reduction of LDL-C occurs approximately 1 week after a subcutaneous dose of 140 mg every 2 weeks (Q2W) and 2 weeks after a subcutaneous dose 420 mg once monthly (QM), and returns towards baseline over the dosing interval. In several clinical studies, these doses of evolocumab reduced LDL-C by approximately 55-75% compared with placebo. Evolocumab also reduced lipoprotein(a) [Lp(a)] levels and improved those of other lipids in clinical studies. No clinically meaningful differences in pharmacodynamic effects on LDL-C were observed in adult subjects regardless of mild/moderate hepatic impairment, renal impairment or renal failure, body weight, race, sex, or age. No clinically meaningful differences were observed for the pharmacodynamic effects of evolocumab on LDL-C between patients who received evolocumab alone or in combination with a statin, resulting in additional lowering of LDL-C when evolocumab was combined with a statin. No dose adjustment is necessary based on patient-specific factors or concomitant medication use.

Pharmacokinetics, Immunogenicity, and Efficacy of Etanercept in Pediatric Patients With Moderate to Severe Plaque Psoriasis.

Etanercept has been recently approved in the United States for the treatment of moderate to severe plaque psoriasis in patients aged 4-17 years. The objective of this study was to characterize etanercept pharmacokinetics, immunogenicity, and efficacy in pediatric patients. Data from a phase 3 study and open-label extension study were analyzed. Etanercept serum concentrations in pediatric patients receiving etanercept 0.8 mg/kg (maximum, 50 mg) weekly were compared with adult psoriasis patients and pediatric patients with juvenile idiopathic arthritis (JIA) who received etanercept 0.4 mg/kg twice weekly. The developments of antietanercept antibodies and efficacy based on the Psoriasis Area and Severity Index were evaluated. Steady-state trough etanercept concentrations were similar across visits from weeks 12-48, between patients aged 4-11 and 12-17 years, between pediatric and adult psoriasis patients, and between pediatric patients with psoriasis or JIA. Etanercept serum concentrations and safety profiles were similar in patients with (15.9%) and without antietanercept antibodies. Dosing used in the study provided similar exposures and efficacy across ranges of weight and body mass index. Pharmacokinetic, immunogenicity, and efficacy data support 0.8 mg/kg (maximum, 50 mg) weekly dosing of etanercept in patients aged 4-17 years.

Impact of Target-Mediated Elimination on the Dose and Regimen of Evolocumab, a Human Monoclonal Antibody Against Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9).

Understanding the pharmacokinetic (PK) and pharmacodynamic (PD) relationship of a therapeutic monoclonal antibody against proprotein convertase subtilisin/kexin type 9 (PCSK9) exhibiting target-mediated drug disposition (TMDD) is critical for selecting optimal dosing regimens. We describe the PK/PD relationship of evolocumab using a mathematical model that captures evolocumab binding and removal of unbound PCSK9 as well as reduction in circulating low-density lipoprotein cholesterol (LDL-C). Data were pooled from 2 clinical studies: a single-dose escalation study in healthy subjects (7-420 mg SC; n = 44) and a multiple-dose escalation study in statin-treated hypercholesterolemic patients (14 mg weekly to 420 mg monthly [QM] SC; n = 57). A TMDD model described the time course of unbound evolocumab concentrations and removal of unbound PCSK9. The estimated linear clearance and volume of evolocumab were 0.256 L/day and 2.66 L, respectively, consistent with other monoclonal antibodies. The time course of LDL-C reduction was described by an indirect response model with the elimination rate of LDL-C being modulated by unbound PCSK9. The concentration of unbound PCSK9 associated with half-maximal inhibition (IC50 ) of LDL-C elimination was 1.46 nM. Based on simulations, 140 mg every 2 weeks (Q2W) and 420 mg QM were predicted to achieve a similar time-averaged effect of 69% reduction in LDL-C in patients on statin therapy, suggesting that an approximate 3-fold dose increase is required for a 2-fold extension in the dosing interval. Evolocumab dosing regimens of 140 mg Q2W or 420 mg QM were predicted to result in comparable reductions in LDL-C over a monthly period, consistent with results from recently completed phase 3 studies.

A model-based meta-analysis of monoclonal antibody pharmacokinetics to guide optimal first-in-human study design.

The objectives of this retrospective analysis were (1) to characterize the population pharmacokinetics (popPK) of four different monoclonal antibodies (mAbs) in a combined analysis of individual data collected during first-in-human (FIH) studies and (2) to provide a scientific rationale for prospective design of FIH studies with mAbs. The data set was composed of 171 subjects contributing a total of 2716 mAb serum concentrations, following intravenous (IV) and subcutaneous (SC) doses. mAb PK was described by an open 2-compartment model with first-order elimination from the central compartment and a depot compartment with first-order absorption. Parameter values obtained from the popPK model were further used to generate optimal sampling times for a single dose study. A robust fit to the combined data from four mAbs was obtained using the 2-compartment model. Population parameter estimates for systemic clearance and central volume of distribution were 0.20 L/day and 3.6 L with intersubject variability of 31% and 34%, respectively. The random residual error was 14%. Differences (> 2-fold) in PK parameters were not apparent across mAbs. Rich designs (22 samples/subject), minimal designs for popPK (5 samples/subject), and optimal designs for non-compartmental analysis (NCA) and popPK (10 samples/subject) were examined by stochastic simulation and estimation. Single-dose PK studies for linear mAbs executed using the optimal designs are expected to yield high-quality model estimates, and accurate capture of NCA estimations. This model-based meta-analysis has determined typical popPK values for four mAbs with linear elimination and enabled prospective optimization of FIH study designs, potentially improving the efficiency of FIH studies for this class of therapeutics.

Population pharmacokinetics of brodalumab in healthy adults and adults with psoriasis from single and multiple dose studies.

Brodalumab, a human monoclonal IgG2-antibody, acts as a potent antagonist at the interleukin-17 receptor A, which is important in the pathogenesis of psoriasis. To characterize the pharmacokinetics of brodalumab and assess the effects of covariates, brodalumab concentrations from Phase 1a and Phase 2 clinical studies in healthy adults and subjects with psoriasis were used to construct a population PK model. The final two-compartment model with parallel linear and non-linear elimination pathways fit the data well. The population typical values for PK parameters CL, V, and V(max) were 0.223 L/day, 4.62 L, and 5.40 mg/day with between-subject-variability of 69.2, 69.6, and 25.9%CV, respectively. Body weight (BW) was an important covariate on CL (and Q), V (and V(2)) and V(max), with estimated effect exponents of 0.598, 0.849, and 1.12, respectively. Based on simulations from the final model, for doses between 140 and 210 mg, AUC was predicted to be greater than two fold higher in subjects weighing less than 75 kg compared to reference subjects. Age and diagnosis had smaller influence on exposure and was not clinically significant. These data suggest that BW is an important covariate explaining some of the variability in population PK observed in human clinical trials with brodalumab.

A semi-mechanistic model to characterize the pharmacokinetics and pharmacodynamics of brodalumab in healthy volunteers and subjects with psoriasis in a first-in-human single ascending dose study.

Pharmacokinetic-pharmacodynamic (PK-PD) modeling can provide a framework for quantitative "learning and confirming" from studies in all phases of drug development. Brodalumab is a human monoclonal antibody (IgG2 ) targeting the IL-17 receptor A that blocks signaling by cytokines thought to play a central role in the pathogenesis of psoriasis (IL-17A, IL-17F, and IL-17A/F). We used semi-mechanistic modeling of single dose, first-in-human data to characterize the exposure-response relationship between brodalumab and the Psoriasis Area and Severity Index (PASI) in a Phase 1 clinical trial. Fifty-seven healthy volunteers and 25 subjects with moderate to severe psoriasis received single intravenous or subcutaneous administration of placebo or brodalumab (7-700 mg). A two-compartment model with parallel linear and nonlinear (Michaelis-Menten) elimination pathways described brodalumab PK. The PK-PASI relationship was characterized by linking a signaling compartment with an indirect response model of psoriatic plaques, where signaling suppressed plaque formation. The concentration of half-maximal inhibition IC50 was 2.86 µg/mL (SE: 50%). The endogenous psoriatic plaque formation rate of 0.862 (SE: 40%) PASI units/day was comparable with literature precedent. Despite the small sample size and single administration data, this semi-mechanistic modeling approach provided a quantitative framework to inform design of dose-ranging Phase 2 studies of brodalumab in psoriasis.

The use of pharmacometrics to optimize biosimilar development.

Pharmacometric approaches can assist in biosimilar development by leveraging quantitative knowledge of the originator product characteristics such as dose-exposure and exposure-response information to support a targeted approach to clinical studies. The degree to which these approaches can be applied relies on the level of information known about the originator and information that supports application of the originator model to the biosimilar. A model-based approach testing the hypothesis that the biosimilar PK and/or PK/PD profile is similar to the originator in the target patient population is aligned with the central comparability exercise required for the biosimilar approval. This Commentary details the key opportunities in study design and study analysis where pharmacometrics approaches can aid biosimilar development.

Quantitative model of the relationship between dipeptidyl peptidase-4 (DPP-4) inhibition and response: meta-analysis of alogliptin, saxagliptin, sitagliptin, and vildagliptin efficacy results.

Dipeptidyl peptidase-4 (DPP-4) inhibition is a well- characterized treatment for type 2 diabetes mellitus (T2DM). The objective of this model-based meta-analysis was to describe the time course of HbA1c response after dosing with alogliptin (ALOG), saxagliptin (SAXA), sitagliptin (SITA), or vildagliptin (VILD). Publicly available data involving late-stage or marketed DPP-4 inhibitors were leveraged for the analysis. Nonlinear mixed-effects modeling was performed to describe the relationship between DPP-4 inhibition and mean response over time. Plots of the relationship between metrics of DPP-4 inhibition (ie, weighted average inhibition [WAI], time above 80% inhibition, and trough inhibition) and response after 12 weeks of daily dosing were evaluated. The WAI was most closely related to outcome, although other metrics performed well. A model was constructed that included fixed effects for placebo and drug and random effects for intertrial variability and residual error. The relationship between WAI and outcome was nonlinear, with an increasing response up to 98% WAI. Response to DPP-4 inhibitors could be described with a single drug effect. The WAI appears to be a useful index of DPP-4 inhibition related to HbA1c. Biomarker to response relationships informed by model-based meta-analysis can be leveraged to support study designs including optimization of dose, duration of therapy, and patient population.