Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology

Population pharmacokinetics and exposure-response analyses of polatuzumab vedotin in patients with previously untreated DLBCL from the POLARIX study

Polatuzumab vedotin is a CD79b-directed antibody-drug conjugate that targets B cells and delivers the cytotoxic payload monomethyl auristatin E (MMAE). The phase III POLARIX study (NCT03274492) evaluated polatuzumab vedotin in combination with rituximab, cyclophosphamide, doxorubicin, and prednisone (R-CHP) as first-line treatment of diffuse large B-cell lymphoma (DLBCL). To examine dosing decisions for this regimen, population pharmacokinetic (popPK) analysis, using a previously developed popPK model, and exposure-response (ER) analysis, were performed. The popPK analysis showed no clinically meaningful relationship between cycle 6 (C6) antibody-conjugated (acMMAE)/unconjugated MMAE area under the concentration-time curve (AUC) or maximum concentration, and weight, sex, ethnicity, region, mild or moderate renal impairment, mild hepatic impairment, or other patient and disease characteristics. In the ER analysis, C6 acMMAE AUC was significantly associated with longer progression-free and event-free survival (both p = 0.01). An increase of <50% in acMMAE/unconjugated MMAE exposure did not lead to a clinically meaningful increase in adverse events of special interest. ER data and the benefit-risk profile support the use of polatuzumab vedotin 1.8 mg/kg once every 3 weeks with R-CHP for six cycles in patients with previously untreated DLBCL.

Machine Learning-Based Quantification of Patient Factors Impacting Remission in Patients With Ulcerative Colitis: Insights from Etrolizumab Phase III Clinical Trials

Etrolizumab, an investigational anti-β7 integrin monoclonal antibody, has undergone evaluation for safety and efficacy in phase III clinical trials on patients with moderate to severe ulcerative colitis (UC). Etrolizumab was terminated because mixed efficacy results were shown in the induction and maintenance phase in patients with UC. In this post hoc analysis, we characterized the impact of explanatory variables on the probability of remission using XGBoost machine learning (ML) models alongside with the SHapley Additive exPlanations framework for explainability. We used patient-level data encompassing demographics, physiology, disease history, clinical questionnaires, histology, serum biomarkers, and etrolizumab drug exposure to develop ML models aimed at predicting remission. Baseline covariates and early etrolizumab exposure at week 4 in the induction phase were utilized to develop an induction ML model, whereas covariates from the end of the induction phase and early etrolizumab exposure at week 4 in the maintenance phase were used to develop a maintenance ML model. Both the induction and maintenance ML models exhibited good predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.74 ± 0.03 and 0.75 ± 0.06 (mean ± SD), respectively. Compared with placebo, the highest tertile of etrolizumab exposure contributed to 15.0% (95% confidence interval (CI): 9.7-19.9) and 17.0% (95% CI: 8.1-26.4) increases in remission probability in the induction and maintenance phases, respectively. Additionally, the key covariates that predicted remission were CRP, MAdCAM-1, and stool frequency for the induction phase and white blood cells, fecal calprotectin and age for the maintenance phase. These findings hold significant implications for establishing stratification factors in the design of future clinical trials.

Time-dependent clearance can confound exposure-response analysis of therapeutic antibodies: A comprehensive review of the current literature

Exposure-response (ER) analysis is used to optimize dose and dose regimens during clinical development. Characterization of relationships between drug exposure and efficacy or safety outcomes can be utilized to make dose adjustments that improve patient response. Therapeutic antibodies typically show predictable pharmacokinetics (PK) but can exhibit clearance that decreases over time due to treatment. Moreover, time-dependent changes in clearance are frequently associated with drug response, with larger decreases in clearance and increased exposure seen in patients who respond to treatment. This often confounds traditional ER analysis, as drug response influences exposure rather than the reverse. In this review, we survey published population PK analyses for reported time-dependent drug clearance effects across 158 therapeutic antibodies approved or in regulatory review. We describe the mechanisms by which time-dependent clearance can arise, and evaluate trends in frequency, magnitude, and time scale of changes in clearance with respect to indication, mechanistic interpretation of time-dependence, and PK modeling techniques employed. We discuss the modeling and simulation strategies commonly used to characterize time-dependent clearance, and examples where time-dependent clearance has impeded ER analysis. A case study using population model simulation was explored to interrogate the impact of time-dependent clearance on ER analysis and how it can lead to spurious conclusions. Overall, time-dependent clearance arises frequently among therapeutic antibodies and has spurred erroneous conclusions in ER analysis. Appropriate PK modeling techniques aid in identifying and characterizing temporal shifts in exposure that may impede accurate ER assessment and successful dose optimization.

Correlation analysis between camrelizumab trough concentration levels and efficacy or safety in East Asian patients with advanced lung cancer

BACKGROUND

Camrelizumab combined with chemotherapy is approved across tumor types. However, only a fraction of patients benefits from immunotherapy, and biomarkers such as the expression of PD-L1, tumor mutational burden, and CXCL11 are expensive and suboptimal specificity for cancer patients. An exposure-response (E-R) relationship has been reported in many immune checkpoint inhibitors (ICIs), and the trough concentrations and other drug exposure metrics are broadly used to guide dosing decisions, assess exposure-outcomes relationships, and ultimately predict outcomes based on those relationships. However, the potential use of trough concentration levels for camrelizumab is still not clear.

METHODS

Blood samples were obtained at trough levels after doses 3 and 4 from 77 patients with advanced lung cancer who received camrelizumab (200 mg Q3 W) monotherapy or combined with chemotherapy. We optimized a competitive ELISA method to measure the trough concentration.

RESULTS

We found that the trough concentration was steady after 3 dose cycles, and the trough concentration level of camrelizumab was higher in patients who developed immune-related adverse effects (irAEs) than in those who did not (P < 0.05) but was not observed in disease progression and PFS (P > 0.05). Age (< 65 years old), no smoking history, and efficacy evaluation after 4-dose treatment were associated with PFS (P < 0.05), but no significance was observed in other clinical characteristics. Total bilirubin and albumin had an influence on trough concentration, and monocytes and albumin were independent risk factors for PFS (P < 0.05).

CONCLUSIONS

Our results suggest that the trough concentration level of camrelizumab might be a risk factor for the occurrence of irAEs in advanced lung cancer, and using the immunotherapy as early as possible may bring better clinical outcomes.

Confounding mitigation for the exposure-response relationship of bevacizumab in colorectal cancer patients

AIMS

The exposure-response relationship of bevacizumab may be confounded by various factors, including baseline characteristics, time-dependent target engagement and recursive relationships between exposure and response, requiring effective mitigation. This study aimed to investigate the exposure-response relationships of bevacizumab in metastatic colorectal cancer (mCRC) patients while mitigating potential biases.

METHODS

Bevacizumab pharmacokinetics was described using target-mediated drug disposition modelling. Relationships between target kinetics, progression-free (PFS) and overall (OS) survivals were assessed using joint pharmacokinetic and parametric hazard function models. Both prognostic-driven and response-driven potential biases were mitigated. These models evaluated the impact of increased antigen target levels, clearance and intensified dosing regimen on survival.

RESULTS

Estimated target-mediated pharmacokinetic parameters in 130 assessed patients were baseline target levels (R0  = 8.4 nM), steady-state dissociation constant (KSS  = 10 nM) and antibody-target complexes elimination constant (kint  = 0.52 day-1 ). The distribution of R0 was significantly associated with increased baseline concentrations of carcinoembryonic antigen, circulating vascular endothelial growth factor and the presence of extrahepatic metastases. Unbound target levels (R) significantly influenced both progression and death hazard functions. Increasing baseline target levels and/or clearance values led to decreased bevacizumab unbound concentrations, increased R levels and shortened PFS and OS, while increasing bevacizumab dose led to decreased R and longer survival.

CONCLUSION

This study is the first to demonstrate the relationship between bevacizumab concentrations, target involvement and clinical efficacy by effectively mitigating potential sources of bias. Most of the target amount may be tumoural in mCRC. Future studies should provide a more in-depth description of this relationship.

Case-control matching-guided exposure-efficacy relationship for avelumab in patients with urothelial carcinoma

Exposure-response (E-R) analyses are an integral component of understanding the benefit/risk profile of novel oncology therapeutics. These analyses are typically conducted using data from the treatment arm to characterize the relationship between drug exposure (low vs. high) and efficacy or safety outcomes. For example, outcomes of patients with lower exposure in the treatment arm (e.g., Q1) might be compared to outcomes of those with higher drug exposure (Q2, Q3, and Q4). Outcomes from the lowest exposure quartile may be also compared to the control arm to evaluate whether the Q1 subgroup derived clinical benefit. However, the sample size and the distribution of patient baseline characteristics and disease risk factors are not balanced in such a comparison (Q1 vs. control), which may bias the analysis and causal interpretation of clinical benefit in the Q1 subgroup. Herein, we report the use of case-control matching to account for this bias and better understand the E-R relationship for avelumab in urothelial carcinoma, a PD-L1 inhibitor approved for the treatment of several cancers. Data from JAVELIN-100 was utilized which is a phase III study of avelumab in first-line maintenance treatment in patients with urothelial carcinoma; this clinical study demonstrated superiority of avelumab versus best-supportive care leading to approval in the United States, Europe, and other countries. A post hoc case-control matching method was implemented to compare the efficacy outcome between Q1 avelumab subgroup and matched patients extracted from the control arm with similar baseline characteristics, which showed a clinically relevant difference in overall survival in favor of the Q1 avelumab subgroup. This analysis demonstrates the importance of accounting for imbalance in important baseline covariates when comparing efficacy outcomes between subgroups within the treatment arm versus the control arm.

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.

Relationship Between Cetuximab Target-Mediated Pharmacokinetics and Progression-Free Survival in Metastatic Colorectal Cancer Patients

BACKGROUND AND OBJECTIVE

Cetuximab, an anti-epidermal growth factor receptor (EGFR) monoclonal immunoglobulin (Ig)G1 antibody, has been approved for the treatment of metastatic colorectal cancer (mCRC). The influence of target-antigen on cetuximab pharmacokinetics has never been investigated using target-mediated drug disposition (TMDD) modelling. This study aimed to investigate the relationship between cetuximab concentrations, target kinetics and progression-free survival (PFS).

METHODS

In this ancillary study (NCT00559741), 91 patients with mCRC treated with cetuximab were assessed. Influence of target levels on cetuximab pharmacokinetics was described using TMDD modelling. The relationship between cetuximab concentrations, target kinetics and time-to-progression (TTP) was described using a joint pharmacokinetic-TTP model, where unbound target levels were assumed to influence hazard of progression by an Emax model. Mitigation strategies of concentration-response relationship, i.e., time-varying endogenous clearance and mutual influences of clearance and time-to-progression were investigated.

RESULTS

Cetuximab concentration-time data were satisfactorily described using the TMDD model with quasi-steady-state approximation and time-varying endogenous clearance. Estimated target parameters were baseline target levels (R0 = 43 nM), and complex elimination rate constant (kint = 0.95 day-1). Estimated time-varying clearance parameters were time-invariant component of CL (CL0= 0.38 L/day-1), time-variant component of CL (CL1= 0.058 L/day-1) and first-order rate of CL1 decreasing over time (kdes = 0.049 day-1). Part of concentration-TTP was TTP-driven, where clearance and TTP were inversely correlated. In addition, increased target occupancy was associated with increased TTP.

CONCLUSION

This is the first study describing the complex relationship between cetuximab target-mediated pharmacokinetics and PFS in mCRC patients using a joint PK-time-to-progression model. Further studies are needed to provide a more in-depth description of this relationship.

Population Pharmacokinetics and Exposure-Response with Teclistamab in Patients With Relapsed/Refractory Multiple Myeloma: Results From MajesTEC-1

BACKGROUND

Teclistamab, a B-cell maturation antigen × CD3 bispecific antibody, is approved in patients with relapsed/refractory multiple myeloma (RRMM) who have previously received an immunomodulatory agent, a proteasome inhibitor, and an anti-CD38 antibody.

OBJECTIVE

We report the population pharmacokinetics of teclistamab administered intravenously and subcutaneously (SC) and exposure-response relationships from the phase I/II, first-in-human, open-label, multicenter MajesTEC-1 study.

METHODS

Phase I of MajesTEC-1 consisted of dose escalation and expansion at the recommended phase II dose (RP2D; 1.5 mg/kg SC weekly, preceded by step-up doses of 0.06 and 0.3 mg/kg); phase II investigated the efficacy of teclistamab RP2D in patients with RRMM. Population pharmacokinetics and the impact of covariates on teclistamab systemic exposure were assessed using a 2-compartment model with first-order absorption for SC and parallel time-independent and time-dependent elimination pathways. Exposure-response analyses were conducted, including overall response rate (ORR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and the incidence of grade ≥ 3 anemia, neutropenia, lymphopenia, leukopenia, thrombocytopenia, and infection.

RESULTS

In total, 4840 measurable serum concentration samples from 338 pharmacokinetics-evaluable patients who received teclistamab were analyzed. The typical population value of time-independent and time-dependent clearance were 0.449 L/day and 0.547 L/day, respectively. The time-dependent clearance decreased rapidly to < 10% after 8 weeks of teclistamab treatment. Patients who discontinue teclistamab after the 13th dose are expected to have a 50% reduction from Cmax in teclistamab concentration at a median (5th to 95th percentile) time of 15 days (7-33 days) after Tmax and a 97% reduction from Cmax in teclistamab concentration at a median time of 69 days (32-163 days) after Tmax. Body weight, multiple myeloma type (immunoglobulin G vs non-immunoglobulin G), and International Staging System (ISS) stage (II vs I and III vs I) were statistically significant covariates on teclistamab pharmacokinetics; however, these covariates had no clinically relevant effect on the efficacy of teclistamab at the RP2D. Across all doses, ORR approached a plateau at the concentration range associated with RP2D, and in patients who received the RP2D, a flat exposure-response curve was observed. No apparent relationship was observed between DoR, PFS, OS, and the incidence of grade ≥3 adverse events across the predicted exposure quartiles.

CONCLUSION

Body weight, myeloma type, and ISS stage impacted systemic teclistamab exposure without any clinically relevant effect on efficacy. The exposure-response analyses for ORR showed a positive trend with increasing teclistamab systemic exposure, with a plateau at the RP2D, and there was no apparent exposure-response trend for safety or other efficacy endpoints. These analyses support the RP2D of teclistamab in patients with RRMM.

CLINICAL TRIAL REGISTRATION

NCT03145181 (phase I, 09 May 2017); NCT04557098 (phase II, 21 September 2020).

Radiosensitivity Index is Not Fit to be Used for Dose Adjustments: A Pan-Cancer Analysis

AIMS

To explore the preclinical and latest clinical evidence of the radiation sensitivity signature termed 'radiosensitivity index' (RSI), to assess its suitability as an input into dose-adjustment algorithms.

MATERIALS AND METHODS

The original preclinical test-set data from the publication where RSI was derived were collected and reanalysed by comparing the observed versus predicted survival fraction at 2 Gy (SF2). In addition, the predictive capability of RSI was also compared to random guessing. Clinical data were collected from a recently published dataset that included RSI values, overall survival outcomes, radiotherapy dose and tumour site for six cancers (glioma, triple-negative breast, endometrial, melanoma, pancreatic and lung cancer). Cox proportional hazards models were used to assess: (i) does adjusting for RSI elucidate a dose response and (ii) does an interaction between RSI and dose exist with good precision.

RESULTS

Preclinically, RSI showed a negative correlation (Spearman's rho = -0.61) between observed and predicted SF2, which remained negative after removing leukaemia cell lines. Furthermore, random guesses showed better correlation to SF2 than RSI, 98% of the time on the full dataset and 80% after removing leukaemia cell lines. The preclinical data show that RSI does not explain the variance in SF2 better than random guessing. Clinically, a dose response was not seen after adjusting for RSI (hazard ratio = 1.00, 95% confidence interval 0.97-1.04; P = 0.876) and no evidence of an interaction between RSI and dose was found (P = 0.844).

CONCLUSIONS

These results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.

Advances in pharmacokinetic-pharmacodynamic modelling for pediatric drug development: extrapolations and exposure-response analyses

INTRODUCTION

Pharmacokinetic (PK)-Pharmacodynamic (PD) and exposure-response (E-R) modeling are critical parts of pediatric drug development. By integrating available knowledge and supportive data to support the design of future studies and pediatric dose selection, these techniques increase the efficiency of pediatric drug development and lowers the risk of exposing pediatric study participants to suboptimal or unsafe dose regimens.

AREAS COVERED

The role of PK, PK-PD and E-R modeling within pediatric drug development and pediatric dose selection is discussed. These models allow investigation of the impact of age and bodyweight on PK and PD in children, despite the often sparse data on the pediatric population. Also discussed is how E-R analyses strengthen the evidence basis to support (full or partial) extrapolation of drug efficacy from adults to children, and between different pediatric age groups.

EXPERT OPINION

Accelerated pediatric drug development and optimized pediatric dosing guidelines are expected from three future developments: (1) Increased focus on E-R modeling of currently approved drugs in children resulting in (novel) E-R modeling techniques and best practices, (2) increased use of real-world data for E-R (3) increased implementation of available population PK and E-R information in pediatric drug dosing guidelines.

Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance via Computational Experimentations

Exposure-response (E-R) is a key aspect of pharmacometrics analysis that supports drug dose selection. Currently, there is a lack of understanding of the technical considerations necessary for drawing unbiased estimates from data. Due to recent advances in machine learning (ML) explainability methods, ML has garnered significant interest for causal inference. To this end, we used simulated datasets with known E-R "ground truth" to generate a set of good practices for the development of ML models required to avoid introducing biases when performing causal inference. These practices include the use of causal diagrams to enable the careful consideration of model variables by which to obtain desired E-R relationship insights, keeping a strict separation of data for model-training and for inference generation to avoid biases, hyperparameter tuning to improve the reliability of models, and estimating proper confidence intervals around inferences using a bootstrap sampling with replacement strategy. We computationally confirm the benefits of the proposed ML workflow by using a simulated dataset with nonlinear and non-monotonic exposure-response relationships.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Optimizing benefit/risk in oncology: Review of post-marketing dose optimization and reflections on the road ahead

Oncology therapies shifted from chemotherapy to molecularly targeted agents and finally to the era of immune-oncology agents. In contrast to cytotoxic agents, molecularly targeted agents are more selective, exhibit a wider therapeutic window, and may maximally modulate tumor growth at doses lower than the maximum tolerated dose (MTD). However, first-in-patient oncology studies for molecularly targeted agents continued to evaluate escalating doses using limited number of patients per dose cohort assessing dose-limiting toxicities to identify the MTD which is commonly selected for further development adopting a 'more is better' approach that led to several post-marketing requirement (PMR) studies to evaluate alternative, typically lower, doses or dosing frequencies to optimize the benefit-risk profile. In this review, post-marketing dose optimization efforts were reviewed including those required by a regulatory pathway or voluntarily conducted by the sponsor to improve efficacy, safety, or method of administration. Lessons learned and future implications from this deep dive review are discussed considering the evolving regulatory landscape on dose optimization for oncology compounds.

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.

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs

The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E-R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree-based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E-R relationship using clinical trial datasets. The E-R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E-R relationships for impacting key dosing decisions in drug development.

Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?

Project Optimus is a US Food and Drug Administration (FDA) initiative to reform dose selection in oncology drug development. Here, we focus on tumor evolution, a broadly observed phenomenon that invariably leads to therapeutic failure and disease relapse, and its effect on the exposure-response (E-R) relationships of oncology drugs. We propose a greater emphasis on tumor evolution during clinical development to facilitate the selection of optimal doses for molecularly targeted therapies and immunotherapies in oncology.

Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure-response relationships for large-molecule oncology drugs

A Cox proportional hazard (CoxPH) model is conventionally used to assess exposure-response (E-R), but its performance to uncover the ground truth when only one dose level of data is available has not been systematically evaluated. We established a simulation workflow to generate realistic E-R datasets to assess the performance of the CoxPH model in recovering the E-R ground truth in various scenarios, considering two potential reasons for the confounded E-R relationship. We found that at high doses, when the pharmacological effects are largely saturated, missing important confounders is the major reason for inferring false-positive E-R relationships. At low doses, when a positive E-R slope is the ground truth, either missing important confounders or mis-specifying the interactions can lead to inaccurate estimates of the E-R slope. This work constructed a simulation workflow generally applicable to clinical datasets to generate clinically relevant simulations and provide an in-depth interpretation on the E-R relationships with confounders inferred by the conventional CoxPH model.

Exposure-response relationship of ramucirumab in RANGE, a randomized phase III trial in advanced urothelial carcinoma refractory to platinum therapy

AIMS

Patients with advanced urothelial carcinoma (UC) who progress after platinum-based chemotherapy have a poor prognosis, and there is a medical need to improve current treatment options. Ramucirumab plus docetaxel significantly improved progression-free survival but not overall survival (OS) in platinum-refractory advanced UC (RANGE trial; NCT02426125). Here, we report the exposure-response (ER) of ramucirumab plus docetaxel using data from the RANGE trial.

METHODS

Pharmacokinetic (PK) samples were collected (cycle 1-3, 5, 9 [day 1] and 30 days from treatment discontinuation), and PK data were analysed using population PK (popPK) analysis. The minimum ramucirumab concentration after first dose administration (Cmin,1 , or trough concentration immediately prior to the second dose) was derived by popPK analysis and used as the exposure parameter for ER analysis. Cox proportional hazards regression models and matched case-control analyses were used to evaluate the relationship between Cmin,1 and OS. The Cmin,1 relationship with safety was assessed descriptively.

RESULTS

Several poor prognostic factors (ECOG 1, haemoglobin concentration <100 g/L, presence of liver metastases) appeared more frequently in the lower exposure quartiles, suggesting a possible disease-PK interaction. A significant association was identified between Cmin,1 and OS (P = .0108). Higher exposure quartiles were associated with longer survival and smaller hazard ratios compared to placebo. No new exposure-safety trends were observed within the exposure range (ramucirumab 10 mg/kg once every 3 weeks).

CONCLUSIONS

This prespecified ER analyses suggests a positive relationship between efficacy and ramucirumab exposure, with an imbalance associated with disease prognostic factors. Further investigation may elucidate a possible disease-PK relationship.

Evolution of Preclinical Characterization and Insights into Clinical Pharmacology of Checkpoint Inhibitors Approved for Cancer Immunotherapy

Cancer immunotherapy has significantly advanced the treatment paradigm in oncology, with approvals of immuno‐oncology agents for over 16 indications, many of them first line. Checkpoint inhibitors (CPIs) are recognized as an essential backbone for a successful anticancer therapy regimen. This review focuses on the US Food and Drug Administration (FDA) regulatory approvals of major CPIs and the evolution of translational advances since their first approval close to a decade ago. In addition, critical preclinical and clinical pharmacology considerations, an overview of the pharmacokinetic and dose/regimen aspects, and a discussion of the future of CPI translational and clinical pharmacology as combination therapy becomes a mainstay of industrial immunotherapy development and in clinical practice are also discussed.

Tumor Growth Inhibition-Overall Survival (TGI-OS) Model for Subgroup Analysis Based on Post-Randomization Factors: Application for Anti-drug Antibody (ADA) Subgroup Analysis of Atezolizumab in the IMpower150 Study

Longitudinal changes of tumor size or tumor-associated biomarkers have been receiving growing attention as early markers of treatment benefits. Tumor growth inhibition-overall survival (TGI-OS) models represent mathematical frameworks used to establish a link from tumor size trajectory to survival outcome with the aim of predicting survival benefit with tumor data from a small number of subjects with a short follow-up time. In the present study, we applied the TGI-OS model to assess treatment benefit in the IMpower150 study for patients who exhibited development of anti-drug antibodies (ADA). Direct comparison between subgroups of the active arm [ADA positive (ADA +) and negative (ADA -) groups] to the entire control group is not appropriate, due to potential imbalances of baseline prognostic factors between ADA + and ADA - patients. Thus, the TGI-OS modeling framework was employed to adjust for differences in prognostic factors between the ADA subgroups to more accurately estimate the treatment benefits. After adjustment, the TGI-OS model predicted comparable hazard ratios (HRs) of OS between ADA + and ADA - subgroups, suggesting that the development of ADA does not have a clinically significant impact on the treatment benefit of atezolizumab.

Evaluation of atezolizumab immunogenicity: Clinical pharmacology (part 1)

Baseline patient characteristics and prognostic factors are important considerations in oncology when evaluating the impact of immunogenicity on pharmacokinetics (PK) and efficacy. Here, we assessed the impact of anti-drug antibodies (ADA) on the PK of the immune checkpoint inhibitor atezolizumab (an anti-PD-L1 monoclonal antibody). We evaluated data from ≈ 4500 patients from 12 clinical trials across different tumor types, treatment settings, and dosing regimens. In our dataset, ~ 30% of patients (range, 13-54%) developed treatment-emergent ADA, and in vitro neutralizing antibodies (NAb) were seen in ~ 50% of ADA-positive (+) patients. Pooled time course data showed a trend toward lower atezolizumab exposure in ADA+ patients, which was more pronounced in ADA+/NAb+ patients. However, the atezolizumab concentration distributions overlapped, and drug concentrations exceeded 6 µg/ml, the target concentration required for receptor saturation, in greater than 95% of patients. Patients had sufficient exposure regardless of ADA status. The dose selected to allow for dosing over effects from ADA resulted in a flat exposure-response relationship. Analysis of study results by ADA titer showed that exposure and overall survival were not affected in a clinically meaningful way. High tumor burden, low albumin, and high CRP at baseline showed the greatest association with ADA development but not with subsequent NAb development. These imbalanced factors at baseline can confound analysis of ADA impact. ADA increases atezolizumab clearance minimally (9%), and its impact on exposure based on the totality of the clinical pharmacology assessment does not appear to be clinically meaningful.