Characterizing Exposure-Response Relationship for Therapeutic Monoclonal Antibodies in Immuno-Oncology and Beyond: Challenges, Perspectives, and Prospects

Interplay between pharmacokinetics and immunogenicity of therapeutic proteins: stepwise development of a bidirectional joint pharmacokinetics-anti-drug antibodies model

The aim of the analysis was to develop a phenomenological longitudinal population pharmacokinetics (PK)-anti-drug antibodies (ADA) model to enable an informed and quantitative framework for assessment of ADA influence. Data used were from seven clinical studies of avelumab across drug development phases in patients with several tumor types. ADA as covariate in a population PK model, and Markov models of ADA status (ADA+ or ADA-) were investigated. Finally, a joint PK-ADA model was developed. In the population PK models that evaluated ADA as a covariate, the clearance increase attributable to ADA+ status was 8.5% (time-varying ADA) to 19.9% (time-invariant ADA with inter-occasion variability in clearance). With a discrete-time Markov model (DTMM), tumor type was identified as a significant covariate on the probability of ADA- to ADA+ transition. When ADA time course predicted by the DTMM model was implemented as a covariate in the population PK model, an increase in avelumab clearance of 11-41% was estimated depending on tumor type. With a continuous-time Markov model (CTMM), in addition to tumor type, baseline ADA status was identified to significantly influence the ADA- to ADA+ transition rate constant. The joint PK-CTMM model estimated the maximal increase in CL due to ADA as 15% and a decrease in ADA- to ADA+ transition rate of up to 37% with increasing avelumab concentration, with 50% of the maximum decrease occurring at 349 µg/mL. The present work established a framework for the assessment of interactions between PK and immunogenicity for therapeutic proteins.

Population Pharmacokinetics and Exposure-Response of Subcutaneous Atezolizumab in Patients With Non-Small Cell Lung Cancer

IMscin001 is a two-part dose-finding (Phase Ib) and -confirmation (Phase III) study to evaluate atezolizumab pharmacokinetics of subcutaneous (SC) compared with intravenous (IV) administration in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). The objectives of the current analyses were to characterize the population pharmacokinetics (popPK) of atezolizumab and to determine the relationship between atezolizumab exposure and safety and efficacy endpoints in IMscin001. A previously validated IV popPK model was extended to add SC absorption parameters using SC and IV data from Phase Ib and Phase III of IMscin001 (N = 435), and covariate effects were investigated on the SC absorption parameters. The exposure-response (ER) investigation was performed using SC data following 1875 mg every three weeks (q3w) administration in the Phase III portion of IMscin001 (N = 246). The clinical endpoints were objective response rate, progression-free survival, or overall survival for efficacy, serious adverse events, special interest adverse events, Grades 3-5 adverse events, infusion-related reaction, or injection site reactions for safety. Atezolizumab SC absorption was characterized by a first-order absorption with a bioavailability of 71.8% and an absorption rate constant of 0.304 day-1. The extended popPK model was adequate to predict atezolizumab PK after IV and SC administrations and to predict individual exposure metrics. For all efficacy and safety endpoints, atezolizumab exposure was not statistically significant (p-value > 0.05) in the ER models. The non-inferior popPK exposure and flat ER results supported atezolizumab SC dose at 1875 mg q3w.

Population pharmacokinetics and exposure-response relationships of dostarlimab in primary advanced or recurrent endometrial cancer in part 1 of RUBY

AIMS

Dostarlimab-gxly is a humanized monoclonal antibody of the IgG4 isotype that binds to the programmed cell death protein-1 (PD-1) receptor and blocks its ligands. RUBY (NCT03981796) is a two-part multicentre study in patients with recurrent or primary advanced endometrial cancer. The overall aims were to characterise the population pharmacokinetics (PopPK) from Part 1 of this study, identify relevant covariates of interest, and assess exposure-efficacy/safety (ER) relationships.

METHODS

A PopPK model developed using GARNET (NCT02715284) study data for dostarlimab monotherapy was externally validated with RUBY Part 1 study data. Subsequently, the model was updated with data across the two studies. Exposure-safety analyses for adverse events related to dostarlimab alone or in combination with standard of care (SOC) were modelled using logistic regression. Exposure-efficacy analysis included Cox proportional hazards analysis of the primary efficacy endpoint of progression-free survival (PFS).

RESULTS

For the model update, 7957 pharmacokinetics observations from 868 patients pooled from both RUBY and GARNET studies were available. The model was consistent with the previous model. Dostarlimab clearance was estimated to be 7.79% lower when dostarlimab was given as SOC combination therapy. However, no significant covariates were clinically relevant. Hepatic or renal impairment did not affect pharmacokinetics. Among the safety endpoints, only rash showed a small yet statistically significant effect (P < .05) in all subjects; however, this was not not deemed clinically relevant. There were no other clinically significant exposure-safety or exposure-PFS relationships.

CONCLUSIONS

The addition of chemotherapy to dostarlimab had limited effect on dostarlimab PopPK, with no clinically significant covariates or clinically relevant exposure-safety or exposure-PFS relationships.

Clinical study design strategies to mitigate confounding effects of time-dependent clearance on dose optimization of therapeutic antibodies

Time-dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure-response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false-positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false-positive ER findings. Two previously published population PK models representative of slow- (pembrolizumab) and fast-onset (rituximab) TDPK were used to simulate virtual patient cohorts with time-dependent clearance and the frequency of false-positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false-positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false-positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false-positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.

Population pharmacokinetics and exposure-response analysis of durvalumab in combination with gemcitabine and cisplatin in patients with advanced biliary tract cancer

PURPOSE

Durvalumab in combination with gemcitabine/cisplatin has shown a favorable benefit-risk profile in the TOPAZ-1 study for advanced biliary tract cancers (BTC). This analysis evaluated the population pharmacokinetics (PopPK) of durvalumab, and exposure-response for efficacy and safety (ERES) of TOPAZ-1.

METHODS

The PopPK model for durvalumab was updated using data from 5 previously analysed studies and TOPAZ-1. Individual exposure metrics were derived from the individual empirical Bayes estimates as drivers for exposure-response (ER) analysis related to efficacy and safety.

RESULTS

Consistent with previous analyses, the durvalumab pharmacokinetics in BTC followed a 2-compartment model with time-dependent clearance. The final population parameters were: CL, 0.298 L/day; V1, 3.42 L; V2, 1.99 L; Q, 0.452 L/day; and the time dependent clearance suggests that the clearance could decrease up to 39% over the time course of treatment. There were 111 patients (3.53%) with treatment-emergent ADA positive in the pooled group of 6 studies, and the exposure was comparable for ADA positive and negative patients. Covariates had minimal clinical impact on PopPK parameters. No significant associations were found between exposure and overall survival (OS), progression-free survival (PFS), using Cox proportional analysis (CPH). Logistic regression analysis indicated no significant relationship between the exposure and relevant adverse events measures of Grade 3 and higher treatment-related AE, Grade 3 and higher treatment-related AESI (AEs of special interest), or AE leading to treatment discontinuation.

CONCLUSIONS

No dose adjustment for durvalumab is needed based on PopPK and ERES analyses. The analysis supports the TOPAZ-1 regimen for patients with advanced BTC.

Integrated modeling of biomarkers, survival and safety in clinical oncology drug development

Model-based approaches, including population pharmacokinetic-pharmacodynamic modeling, have become an essential component in the clinical phases of oncology drug development. Over the past two decades, models have evolved to describe the temporal dynamics of biomarkers and tumor size, treatment-related adverse events, and their links to survival. Integrated models, defined here as models that incorporate at least two pharmacodynamic/ outcome variables, are applied to answer drug development questions through simulations, e.g., to support the exploration of alternative dosing strategies and study designs in subgroups of patients or other tumor indications. It is expected that these pharmacometric approaches will be expanded as regulatory authorities place further emphasis on early and individualized dosage optimization and inclusive patient-focused development strategies. This review provides an overview of integrated models in the literature, examples of the considerations that need to be made when applying these advanced pharmacometric approaches, and an outlook on the expected further expansion of model-informed drug development of anticancer drugs.

Pharmacokinetics of trastuzumab and its efficacy and safety in HER2-positive cancer patients

Trastuzumab is a potent targeted therapy drug for HER2-positive cancer patients. A comprehensive understanding of trastuzumab's mechanism of action, pharmacokinetic (PK) parameters, and steady-state exposure in different treatment regimens and administration routes is essential for a thorough evaluation of the drug's safety and effectiveness. Due to the distinctive pharmacokinetics, indications, and administration methods of trastuzumab, this understanding becomes crucial. Drug exposure can be assessed by measuring trastuzumab's peak concentration, trough concentration, or area under the curve through assays like enzyme-linked immunosorbent assay (ELISA) or liquid chromatography-tandem mass spectrometry (LC-MS/MS). The dose-response (D-R) and exposure-response (E-R) relationships establish the correlation between drug dosage/exposure and the therapeutic effect and safety. Additionally, various covariates such as body weight, aspartate transaminase, and albumin levels can influence drug exposure. This review provides a comprehensive overview of trastuzumab's mechanism of action, data on steady-state concentration and PK parameters under multiple administration routes and indications, discussions on factors influencing PK parameters, and evaluations of the effectiveness and safety of E-R and D-R in diverse HER2-positive cancer patients.

Model-informed Evidence for Clinical Non-inferiority of Every-2-Weeks Versus Standard Weekly Dosing Schedule of Cetuximab in Metastatic Colorectal Cancer

Cetuximab was initially developed and approved as a first-line treatment in patients with unresectable metastatic colorectal cancer (mCRC) for weekly administration (250 mg/m2 Q1W with 400 mg/m2 loading dose). An every-2-weeks schedule (500 mg/m2 Q2W) was approved recently by several health authorities. Being synchronized with chemotherapy, Q2W administration should improve patients' convenience and healthcare resource utilization. Herein, we present evidence of non-inferiority of Q2W cetuximab, compared with Q1W dosing using pharmacometrics modeling and clinical trial simulation (CTS). Pooled data from five phase I-III clinical trials in 852 patients with KRAS wild-type mCRC treated with Q1W or Q2W cetuximab were modeled using a population exposure-tumor size (TS) model linked to overall survival (OS); exposure was derived from a previously established population pharmacokinetic model. A semi-mechanistic TS model adapted from the Claret model incorporated killing rate proportional to cetuximab area under the concentration-time curve over 2 weeks (AUC) with Eastern Cooperative Oncology Group (ECOG) status as covariate on baseline TS. The OS was modeled with Weibull hazard using ECOG, baseline TS, primary tumor location, and predicted percent change in TS at 8 weeks as covariates. Model-based simulations revealed indistinguishable early tumor shrinkage and survival between Q2W vs. Q1W cetuximab. CTS evaluated OS non-inferiority (predefined margin of 1.25) in 1,000 trials, each with 2,000 virtual patients receiving Q2W or Q1W cetuximab (1:1), and demonstrated non-inferiority in 94% of cases. Taken together, these analyses provide model-based evidence for clinical non-inferiority of Q2W vs. Q1W cetuximab in mCRC with potential benefits to patients and healthcare providers.

Mirikizumab Exposure-Response Relationships in Patients with Moderately-to-Severely Active Ulcerative Colitis in Randomized Phase II and III Studies

Mirikizumab is a humanized anti-interleukin-23p19 monoclonal antibody being developed for ulcerative colitis (UC) and Crohn's disease. We characterized the relationship of mirikizumab systemic exposure with efficacy and safety end points in patients with UC using phase II (NCT02589665) and III (NCT03518086, NCT03524092) trial data. Exposure-response models were developed for clinical remission, clinical response, endoscopic remission, and change in modified Mayo score following induction (50-1,000 mg i.v. every 4 weeks) and maintenance (200 mg s.c. every 4 or 12 weeks) treatment. These models evaluated observed and pharmacokinetic model-predicted mirikizumab exposures as the exposure measure. Key safety event rates were compared across mirikizumab exposure quartiles in the phase III trial. Mirikizumab efficacy in patients with UC showed an apparent positive association with systemic exposure following both induction and maintenance. However, further analysis found this relationship to be overstated by the presence of confounding factors that were not among the tested patient covariates. While prior biologic experience and baseline disease severity showed statistically significant influences on estimated placebo effect, no patient factors affected the mirikizumab effect parameters in any of the phase III exposure-response models. There was no apparent mirikizumab concentration relationship with any adverse event of special interest. When the phase II and III data and confounding are considered together, efficacy was unlikely to be strongly affected by variation in exposures across individual patients at the phase III dose. Together with the previously demonstrated mirikizumab exposure insensitivity to patient factors, these findings indicate that mirikizumab dose adjustment to patient characteristics is not required.

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.

Longitudinal modeling of efficacy response in patients with lupus nephritis receiving belimumab

Belimumab was approved for active lupus nephritis (LN) in adults in the European Union and patients ≥ 5 years of age in the USA based on a Phase 3, double-blind, placebo-controlled, 104-week study. The study evaluated the efficacy of belimumab plus background standard therapy in adults with active LN using an intravenous (IV) dose of 10 mg/kg. A longitudinal analysis of Primary Efficacy Renal Response (PERR) and Complete Renal Response (CRR) was performed to assess whether patients with high proteinuria at the start of belimumab treatment would benefit from a higher dose. Responder probability was modeled as a logistic regression with probability a function of time and treatment (belimumab or placebo). Dropout risk at each visit was incorporated into a joint model of efficacy response; only efficacy data prior to dropout events (belimumab discontinuation, treatment failure, or withdrawal) were included. Average belimumab concentration over the first 4 and 12 weeks and baseline proteinuria were considered as continuous covariates. In general, renal response (PERR and CRR) over time was higher in patients receiving belimumab than in those receiving placebo. Baseline proteinuria was considered the most relevant predictor of renal response, with reduced efficacy in patients with increased proteinuria for both belimumab or placebo treatment. For belimumab-treated patients, belimumab exposure was not found to be an important predictor of renal response. In conclusion, the 10 mg/kg IV dose was considered appropriate in all patients and there was no evidence to suggest a higher response would be achieved by increasing the dose.

Covariate modeling in pharmacometrics: General points for consideration

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

Immunogenicity of avelumab in patients with metastatic Merkel cell carcinoma or advanced urothelial carcinoma

Like other monoclonal antibodies, immune checkpoint inhibitors may be immunogenic in some patients, potentially affecting pharmacokinetics (PKs) and clinical outcomes. In post hoc analyses, we characterized antidrug antibody (ADA) development with avelumab monotherapy in patients with metastatic Merkel cell carcinoma (mMCC) from the JAVELIN Merkel 200 trial (first-line [1L; N = 116] and second-line or later [≥2L; N = 88] cohorts) or with advanced urothelial carcinoma (aUC) from the JAVELIN Bladder 100 (1L maintenance [N = 350]) and JAVELIN Solid Tumor (≥2L [N = 249]) trials. Treatment-emergent ADAs developed in a numerically higher proportion of patients with aUC (1L maintenance, 19.1%; ≥2L, 18.1%) versus mMCC (1L, 8.2%; ≥2L, 8.9%); incidences within tumor types were similar by line of therapy. In PK analyses, numerically lower avelumab trough concentration and higher baseline clearance were observed in treatment-emergent ADA+ versus ADA- subgroups; however, differences were not clinically relevant. Numerical differences in overall survival, progression-free survival, or objective response rate by ADA status were observed; however, no clinically meaningful trends were identified. Proportions of patients with treatment-emergent adverse events (TEAEs; any grade or grade 3/4), serious TEAEs, TEAEs leading to treatment discontinuation, or infusion-related reactions were similar, with overlapping 80% confidence intervals between ADA subgroups. Efficacy and safety observations were similar in subgroups defined by early development of ADA+ status during treatment. In conclusion, no meaningful differences in PKs, efficacy, and safety were observed between subgroups of avelumab-treated patients with different ADA status. Overall, these data suggest that ADAs are not relevant for treatment decisions with avelumab.

Clinical dose rationale of tislelizumab in patients with solid or hematological advanced tumors

Tislelizumab, an anti-programmed cell death protein 1 monoclonal antibody, has demonstrated improved survival benefits over standard of care for multiple cancer indications. We present the clinical rationale and data supporting tislelizumab dose recommendation in patients with advanced tumors. The phase I, first-in-human, dose-finding BGB-A317-001 study (data cutoff [DCO]: August 2017) examined the following tislelizumab dosing regimens: 0.5-10 mg/kg every 2 weeks (q2w), 2-5 mg/kg q2w or q3w, and 200 mg q3w. Similar objective response rates (ORRs) were reported in the 2 and 5 mg/kg q2w or q3w cohorts. Safety outcomes (grade ≥3 adverse events [AEs], AEs leading to dose modification/discontinuation, immune-mediated AEs, and infusion-related reactions) were generally comparable across the dosing range examined. These results, alongside the convenience of a fixed q3w dose, formed the basis of choosing 200 mg q3w as the recommended dosing regimen for further clinical use. Pooled exposure-response (E-R) analyses by logistic regression using data from study BGB-A317-001 (DCO: August 2020) and three additional phase I/II studies (DCOs: 2018-2020) showed no statistically significant correlation between tislelizumab pharmacokinetic exposure and ORR across multiple solid tumor types or classical Hodgkin's lymphoma, nor was exposure associated with any of the safety end points evaluated over the dose range tested. Hence, tislelizumab showed a relatively flat E-R relationship. Overall, the totality of data, including efficacy, safety, and E-R analyses, together with the relative convenience of a fixed q3w dose, provided clinical rationale for the recommended dosing regimen of tislelizumab 200 mg q3w for multiple cancer indications.

Antibody Drug Clearance: An Underexplored Marker of Outcomes with Checkpoint Inhibitors

Immune-checkpoint inhibitor (ICI) therapy has dramatically changed the clinical landscape for several cancers, and ICI use continues to expand across many cancer types. Low baseline clearance (CL) and/or a large reduction of CL during treatment correlates with better clinical response and longer survival. Similar phenomena have also been reported with other monoclonal antibodies (mAb) in cancer and other diseases, highlighting a characteristic of mAb clinical pharmacology that is potentially shared among various mAbs and diseases. Though tempting to attribute poor outcomes to low drug exposure and arguably low target engagement due to high CL, such speculation is not supported by the relatively flat exposure-response relationship of most ICIs, where a higher dose or exposure is not likely to provide additional benefit. Instead, an elevated and/or increasing CL could be a surrogate marker of the inherent resistant phenotype that cannot be reversed by maximizing drug exposure. The mechanisms connecting ICI clearance, therapeutic efficacy, and resistance are unclear and likely to be multifactorial. Therefore, to explore the potential of ICI CL as an early marker for efficacy, this review highlights the similarities and differences of CL characteristics and CL-response relationships for all FDA-approved ICIs, and we compare and contrast these to selected non-ICI mAbs. We also discuss underlying mechanisms that potentially link mAb CL with efficacy and highlight existing knowledge gaps and future directions where more clinical and preclinical investigations are warranted to clearly understand the value of baseline and/or time-varying CL in predicting response to ICI-based therapeutics.

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 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.

A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non-small cell lung cancer

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-β and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Skipping a pillar does not make for strong foundations: Pharmacokinetic-pharmacodynamic reasoning behind the shape of dose-response relationships in oncology

Dose-response analysis is often applied to the quantification of drug-effect especially for slowly responding disease end points where a comparison is made across dose levels after a particular period of treatment. It has long been recognized that exposure - response is more appropriate than dose-response. However, trials necessarily are designed as dose-response experiments. Second, a wide range of functional forms are used to express relationships between dose and response. These considerations are also important for clinical development because pharmacokinetic (PK; and variability) plus pharmacokinetic-pharmacodynamic modeling may allow one to anticipate the shape of the dose-response curve and so the trial design. Here, we describe how the location and steepness of the dose response is determined by the PKs of the compound being tested and its exposure-response relationship in terms of potency (location), efficacy (maximum effect) and Hill coefficient (steepness). Thus, the location (50% effective dose [ED50 ]) is dependent not only on the potency (half-maximal effective concentration) but also the compound's PKs. Similarly, the steepness of the dose response is shown to be a function of the half-life of the drug. It is also shown that the shape of relationship varies dependent on the assumed time course of the disease. This is important in the context of drug-discovery where the in vivo potencies of compounds are compared as well as when considering an analysis of summary data (for example, model-based meta-analysis) for clinical decision making.

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.

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.

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.

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.

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.

An introduction to causal inference for pharmacometricians

As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference concepts. This tutorial seeks to orient pharmacometricians to three such fundamental concepts: potential outcomes, g-formula, and directed acyclic graphs (DAGs).

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.

Population pharmacokinetic modelling of tremelimumab in patients with advanced solid tumours and the impact of disease status on time-varying clearance

AIMS

Tremelimumab, a cytotoxic T-lymphocyte-associated protein 4 human monoclonal antibody of the immunoglobulin G2 κ isotype, has been studied in oncology clinical trials as both monotherapy and in combination with durvalumab. This study characterized the pharmacokinetics of tremelimumab as monotherapy and in combination with durvalumab and evaluated the impact of patient covariates on pharmacokinetics.

METHODS

A pooled-analysis population pharmacokinetics model was built using NONMEM methodology. Pharmacokinetic data from 5 studies spanning different tumour types and therapy regimens were pooled for model development (956 patients). A dataset pooled from 4 additional studies was used for external validation (554 patients). Demographic and relevant clinical covariates were explored during model development.

RESULTS

Tremelimumab exhibited linear pharmacokinetics, well described by a 2-compartment model, with time-varying clearance (0.276 L/day at baseline) associated primarily with therapy regimen and linked with changes in disease status. As monotherapy and combination therapy, tremelimumab clearance over 1 year increased by ~16% and decreased by ~17%, respectively. Pharmacokinetic behaviour was consistent across patient demographics and cancer subtypes. Patients with higher bodyweight and lower albumin levels at baseline had significantly higher clearance; however, no dosage adjustments are warranted. A flat dose (75 mg) was projected to provide comparable exposure to weight-based dosing (1 mg/kg) in adults.

CONCLUSION

Tremelimumab exhibited linear pharmacokinetics but consistently opposite trends of time-varying clearance as monotherapy and in combination with durvalumab. Baseline bodyweight and albumin were significant covariates, but conversion from weight-based dosing at 1 mg/kg to flat dosing at 75 mg had no clinically relevant impact.

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.

Population pharmacokinetics modeling and exposure-response analyses of cemiplimab in patients with recurrent or metastatic cervical cancer

A population pharmacokinetic (PopPK) model was previously developed for cemiplimab in patients with solid tumors, including advanced cutaneous squamous cell carcinoma (CSCC). Here, we update the existing PopPK model and characterize exposure-response relationships using efficacy and safety data obtained in patients with recurrent or metastatic cervical cancer (R/M CC). To improve model stability and robustness of the existing PopPK model in 1062 patients, the random-effect error model was revised, and structural covariates were removed from the base model to be tested in the covariate analysis. The updated model was used for external validation of cemiplimab pharmacokinetics (PK) in patients with R/M CC on cemiplimab monotherapy (350 mg every 3 weeks intravenously) from a phase III study (NCT03257267). Exposure-response relationships for cemiplimab efficacy (overall survival [OS], progression-free survival [PFS], duration of response [DOR], objective response rate [ORR]), and safety (immune-related adverse events [irAEs]) were analyzed in 295 patients with R/M CC from the aforementioned study. The updated PopPK model showed improved stability with 94.8% successful bootstrap runs vs. 47.6% in the prior model. Cemiplimab exposure was similar across tumor types, including basal cell carcinoma, CSCC, and non-small cell lung cancer. External validation showed the updated model adequately described cemiplimab PK in patients with R/M CC. In exposure-response efficacy analyses, Cox proportional hazard modeling (CPHM) showed no trend between exposure and OS, Kaplan-Meier plots showed no trend between exposure and PFS or DOR, and logistic regression analyses conducted on ORR showed no exposure-response relationship. In exposure-response safety analyses, CPHM showed no trend between exposure and irAEs.

Model-informed approach for risk management of bleeding toxicities for bintrafusp alfa, a bifunctional fusion protein targeting TGF-β and PD-L1

PURPOSE

Bintrafusp alfa (BA) is a bifunctional fusion protein composed of the extracellular domain of the transforming growth factor-β (TGF-β) receptor II fused to a human immunoglobulin G1 antibody blocking programmed death ligand 1 (PD-L1). The recommended phase 2 dose (RP2D) was selected based on phase 1 efficacy, safety, and pharmacokinetic (PK)-pharmacodynamic data, assuming continuous inhibition of PD-L1 and TGF-β is required. Here, we describe a model-informed dose modification approach for risk management of BA-associated bleeding adverse events (AEs).

METHODS

The PK and AE data from studies NCT02517398, NCT02699515, NCT03840915, and NCT04246489 (n = 936) were used. Logistic regression analyses were conducted to evaluate potential relationships between bleeding AEs and BA time-averaged concentration (C_avg), derived using a population PK model. The percentage of patients with trough concentrations associated with PD-L1 or TGF-β inhibition across various dosing regimens was derived.

RESULTS

The probability of bleeding AEs increased with increasing C_avg; 50% dose reduction was chosen based on the integration of modeling and clinical considerations. The resulting AE management guidance to investigators regarding temporary or permanent treatment discontinuation was further refined with recommendations on restarting at RP2D or at 50% dose, depending on the grade and type of bleeding (tumoral versus nontumoral) and investigator assessment of risk of additional bleeding.

CONCLUSION

A pragmatic model-informed approach for management of bleeding AEs was implemented in ongoing clinical trials of BA. This approach is expected to improve benefit-risk profile; however, its effectiveness will need to be evaluated based on safety data generated after implementation.

Population pharmacokinetics and exposure-response of anti-programmed cell death protein-1 monoclonal antibody dostarlimab in advanced solid tumours

AIM

Develop a population pharmacokinetic (PopPK) model to characterise the pharmacokinetics (PK) of anti-programmed cell death protein-1 (PD-1) antibody dostarlimab, identify covariates of clinical relevance, and investigate efficacy/safety exposure-response (ER) relationships.

METHODS

A PopPK model was developed using Phase 1 GARNET (NCT02715284) trial data for dostarlimab (1, 3 or 10 mg kg-1 every 2 wk; 500 mg every 3 wk or 1000 mg every 6 wk; 500 mg every 3 wk × 4 then 1000 mg every 6 wk [recommended regimen]) serum concentrations over time. Concentration-time data were analysed using nonlinear mixed effects modelling with standard stepwise covariate modelling. ER was explored for treatment-related adverse events and overall response rate (ORR) using logistic regression.

RESULTS

PopPK model/adverse event ER analyses included 546 patients (ORR ER analysis n = 362). Dostarlimab PK was well described by a 2-compartment model with time-dependent linear elimination. Time-dependent clearance decreased over time to a maximum of 14.9%. At steady state, estimated dostarlimab geometric mean coefficient of variation % clearance was 0.179 (30.2%) L d-1 ; volume of distribution was 5.3 (14.2%) L; terminal elimination half-life was 23.5 (22.4%) days. Statistically significant covariates were age, body weight, sex, time-varying albumin and alanine aminotransferase for clearance; body weight, albumin and sex for volume of distribution of the central compartment. Hepatic or renal impairment did not affect PK. There were no clinically significant ER relationships.

CONCLUSION

Dostarlimab PK parameters are similar to other anti-programmed cell death protein-1 antibodies. The clinical impact of covariates on exposure was limited-to-moderate, supporting recommended dostarlimab monotherapy therapeutic dosing.

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.

Not your usual drug-drug interactions: Monoclonal antibody-based therapeutics may interact with antiseizure medications

Therapeutic monoclonal antibodies (mAbs) have emerged as the fastest growing drug class. As such, mAbs are increasingly being co-prescribed with other drugs, including antiseizure medications (ASMs). Although mAbs do not share direct targets or mechanisms of disposition with small-molecule drugs (SMDs), combining therapeutics of both types can increase the risk of adverse effects and treatment failure. The primary goal of this literature review was identifying mAb-ASM combinations requiring the attention of professionals who are treating patients with epilepsy. Systematic PubMed and Embase searches (1980-2021) were performed for terms relating to mAbs, ASMs, drug interactions, and their combinations. Additional information was obtained from documents from the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Evidence was critically appraised - key issues calling for clinicians' consideration and important knowledge gaps were identified, and practice recommendations were developed by a group of pharmacists and epileptologists. The majority of interactions were attributed to the indirect effects of cytokine-modulating antibodies on drug metabolism. Conversely, strong inhibitors or inducers of drug-metabolizing enzymes or drug transporters could potentially interact with the cytotoxic payload of antibody-drug conjugates, and ASMs could alter mAb biodistribution. In addition, mAbs could potentiate adverse ASM effects. Unfortunately, few studies involved ASMs, requiring the formulation of class-based recommendations. Based on the current literature, most mAb-ASM interactions do not warrant special precautions. However, specific combinations should preferably be avoided, whereas others require monitoring and potentially adjustment of the ASM doses. Reduced drug efficacy or adverse effects could manifest days to weeks after mAb treatment onset or discontinuation, complicating the implication of drug interactions in potentially deleterious outcomes. Prescribers who treat patients with epilepsy should be familiar with mAb pharmacology to better anticipate potential mAb-ASM interactions and avoid toxicity, loss of seizure control, or impaired efficacy of mAb treatment.

Optimized Dosing: The Next Step in Precision Medicine in Non-Small-Cell Lung Cancer

In oncology, and especially in the treatment of non-small-cell lung cancer (NSCLC), dose optimization is often a neglected part of precision medicine. Many drugs are still being administered in "one dose fits all" regimens or based on parameters that are often only minor determinants for systemic exposure. These dosing approaches often introduce additional pharmacokinetic variability and do not add to treatment outcomes. Fortunately, pharmacological knowledge is increasing, providing valuable information regarding the potential of, for example, therapeutic drug monitoring. This article focuses on the evidence for the most promising and easily implemented optimized dosing approaches for the small-molecule inhibitors, chemotherapeutic agents, and monoclonal antibodies as treatment options currently approved for NSCLC. Despite limitations such as investigations having been conducted in oncological diseases other than NSCLC or the retrospective origin of many analyses, an alternative dosing regimen could be beneficial for treatment outcomes, prescriber convenience, or financial burden on healthcare systems. This review of the literature provides recommendations on the implementation of dose optimization and advice regarding promising strategies that deserve further research in NSCLC.

The Influence of Underlying Disease on Rituximab Pharmacokinetics May be Explained by Target-Mediated Drug Disposition

BACKGROUND AND OBJECTIVES

Rituximab is an anti-CD20 monoclonal antibody approved in several diseases, including chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), rheumatoid arthritis (RA), and anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV). The influence of underlying disease on rituximab pharmacokinetics has never been investigated for several cancer and non-cancer diseases simultaneously. This study aimed at assessing this influence using an integrated semi-mechanistic model accounting for target-mediated elimination of rituximab.

METHODS

Rituximab concentration-time data from five studies previously published in patients with CLL, DLBCL, FL, RA, and AAV were described using a two-compartment model with irreversible binding of rituximab to its target antigen. Both underlying disease and target antigen measurements were assessed as covariates.

RESULTS

Central volume of distribution was [95% confidence interval] 1.7-fold [1.6-1.9] higher in DLBCL than in RA, FL, and CLL, and it was 1.8-fold [1.6-2.1] higher in RA, FL, and CLL than in AAV. First-order elimination rate constants were 1.8-fold [1.7-2.0] and 1.3-fold [1.2-1.5] higher in RA, DLBCL, and FL than in CLL and AAV, respectively. Baseline latent antigen level (L₀) was 54-fold [30-94], 20-fold [11-36], and 29-fold [14-64] higher in CLL, DLBCL, and FL, respectively, than in RA and AAV. In lymphoma, L₀ increased with baseline total metabolic tumor volume (p = 6.10^-7). In CLL, the second-order target-mediated elimination rate constant (k_deg) increased with baseline CD20 count on circulating B cells (CD20_cir, p = 0.0081).

CONCLUSIONS

Our results show for the first time that rituximab pharmacokinetics is strongly influenced by underlying disease and disease activity. Notably, neoplasms are associated with higher antigen amounts that result in decreased exposure to rituximab compared to inflammatory diseases. Our model might be used to estimate unbound target amounts in upcoming studies.

Risk-Based Pharmacokinetic and Drug-Drug Interaction Characterization of Antibody-Drug Conjugates in Oncology Clinical Development: An International Consortium for Innovation and Quality in Pharmaceutical Development Perspective

Antibody-drug conjugates (ADCs) represent a rapidly evolving area of drug development and hold significant promise. To date, nine ADCs have been approved by the US Food and Drug Administration (FDA). These conjugates combine the target specificity of monoclonal antibodies with the anticancer activity of small-molecule therapeutics (also referred to as payload). Due to the complex structure, three analytes, namely ADC conjugate, total antibody, and unconjugated payload, are typically quantified during drug development; however, the benefits of measuring all three analytes at later stages of clinical development are not clear. The cytotoxic payloads, upon release from the ADC, are considered to behave like small molecules. Given the relatively high potency and low systemic exposure of cytotoxic payloads, drug-drug interaction (DDI) considerations for ADCs might be different from traditional small molecule therapeutics. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ Consortium) convened an ADC working group to create an IQ ADC database that includes 26 ADCs with six unique payloads. The analysis of the ADC data in the IQ database, as well as nine approved ADCs, supports the strategy of pharmacokinetic characterization of all three analytes in early-phase development and progressively minimizing the number of analytes to be measured in the late-phase studies. The systemic concentrations of unconjugated payload are usually too low to serve as a DDI perpetrator; however, the potential for unconjugated payloads as a victim still exists. A data-driven and risk-based decision tree was developed to guide the assessment of a circulating payload as a victim of DDI.

Trough levels of ipilimumab in serum as a potential biomarker of clinical outcomes for patients with advanced melanoma after treatment with ipilimumab

Background

Immune checkpoint blockade (ICB) using anti-CTLA-4 and anti-PD-1/PD-L1 has revolutionized the treatment of advanced cancer. However, ICB is effective for only a small fraction of patients, and biomarkers such as expression of PD-L1 in tumor or serum levels of CXCL11 have suboptimal sensitivity and specificity. Exposure–response (E-R) relationships have been observed with other therapeutic monoclonal antibodies. There are many factors influencing E-R relationships, yet several studies have shown that trough levels of anti-PD-1/PD-L1 correlated with clinical outcomes. However, the potential utility of anti-CTLA-4 levels as a biomarker remains unknown.

Methods

Serum was obtained at trough levels at weeks 7 and 12 (after doses 2 and 4) from patients with advanced melanoma who received ipilimumab alone (3 mg/kg every 3 weeks for four treatments) via an expanded access program (NCT00495066). We have successfully established a proteomics assay to measure the concentration of ipilimumab in serum using an liquid chromatography with tandem mass spectrometry-based nanosurface and molecular-orientation limited proteolysis (nSMOL) approach. Serum samples from 38 patients were assessed for trough levels of ipilimumab by the nSMOL assay.

Results

We found that trough levels of ipilimumab were higher in patients who developed immune-related adverse events but did not differ based on the presence or absence of disease progression. We found that patients with higher trough levels of ipilimumab had better overall survival when grouped based on ipilimumab trough levels. Trough levels of ipilimumab were inversely associated with pretreatment serum levels of CXCL11, a predictive biomarker we previously identified, and soluble CD25 (sCD25), a prognostic biomarker for advanced melanoma, as well as C reactive protein (CRP) and interleukin (IL)-6 levels at week 7.

Conclusions

Our results suggest that trough levels of ipilimumab may be a useful biomarker for the long-term survival of patients with advanced melanoma treated with ipilimumab. The association of ipilimumab trough levels with pretreatment serum levels of CXCL11 and sCD25 is suggestive of a baseline-driven E-R relationship, and the association of ipilimumab trough levels with on-treatment levels of CRP and IL-6 is suggestive of response-driven E-R relationship. Our findings highlight the potential utility of trough levels of ipilimumab as a biomarker.

Trial registration number

NCT00495066.

Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody-Drug Conjugates

Antibody‐drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody‐drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early‐ and late‐stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small‐molecule therapeutics. The complex structure of antibody‐drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody‐drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure‐response modeling, platform modeling by pooling data across multiple antibody‐drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

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.

Overall survival in metastatic melanoma correlates with pembrolizumab exposure and T cell exhaustion markers

Trial data support an absence of an exposure-survival relationship for pembrolizumab. As these relationships remain unexamined in a real-world setting, we determined them in metastatic melanoma prospectively in an observational study. Translational objectives included identifying biomarkers of progressive disease (PD). Checkpoint blockade naïve patients receiving 2 mg/kg Q3W pembrolizumab had pharmacokinetic and clinical outcome data collected. Trough, a valid surrogate for drug exposure, was assessed using ELISA. T-cell exhaustion and chemokine markers were determined using flow cytometry. Geometric means of exposures and biomarkers were tested against objective response groups using one-way ANOVA. The cohort was split by the median into high versus low pembrolizumab exposure groups. Kaplan-Meier progression-free survival (PFS) and overall survival (OS) curves were estimated for high versus low exposure, compared using the log rank test. The high pembrolizumab exposure group (n = 14) experienced substantially longer median OS (not reached vs. 48 months, p = .014), than the low exposure group (n = 14). A similar positive exposure PFS relationship was found (median not reached vs. 48 months, p = .045). The frequency of TIM-3 expression on CD4+ T cells was significantly higher in PD (mean 27.8%) than complete response (CR) (13.38%, p = .01) and partial response (12.4%, p = .05). There was a near doubling of CXCR6 and TIM-3 co-expression on CD4+ T cells in PD (mean 23.3%) versus CR (mean 11.4, p = .003) and partial response (9.8%, p = .0001). We describe positive exposure-PFS and exposure-OS relationships for pembrolizumab in metastatic melanoma. TIM-3, alongside co-expression of CXCR6 and TIM-3 on circulating CD4+ T cells are potential bio markers of treatment failure.

Population pharmacokinetic and exposure-response analyses of intravenous and subcutaneous rituximab in patients with chronic lymphocytic leukemia

A subcutaneous formulation of the anti-CD20 antibody rituximab has been developed. Fixed-dose subcutaneous rituximab delivers noninferior serum trough concentrations (C_trough ), ensuring similar target saturation and comparable efficacy/safety, to intravenous rituximab, but with simplified and shortened preparation and administration. We aimed to characterize the pharmacokinetic (PK) and exposure-response properties of subcutaneous rituximab. Data from two clinical trials were analyzed to describe PKs and pharmacodynamics in patients with chronic lymphocytic leukemia following intravenous and subcutaneous rituximab administration. Intravenous and subcutaneous rituximab were described by a linear two-compartment population PK model with time-dependent and time-independent clearances, and first-order subcutaneous absorption. Main covariates influencing exposure were body size and baseline white blood cell count. Occurrence of adverse events was not correlated with rituximab exposure. Although greater and more sustainable B-cell depletion was observed with higher exposure, inherent limitations to the data (use of one dose level, and time-dependent and target-impacted PKs) prevented reliable assessment of exposure-response relationships.