Integrated Two-Analyte Population Pharmacokinetic Model for Antibody-Drug Conjugates in Patients: Implications for Reducing Pharmacokinetic Sampling

Clinical pharmacology strategies to accelerate the development of polatuzumab vedotin and summary of key findings

The favorable benefit-risk profile of polatuzumab vedotin, as demonstrated in a pivotal Phase Ib/II randomized study (GO29365; NCT02257567), coupled with the need for effective therapies in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), prompted the need to accelerate polatuzumab vedotin development. An integrated, fit-for-purpose clinical pharmacology package was designed to support regulatory approval. To address key clinical pharmacology questions without dedicated clinical pharmacology studies, we leveraged non-clinical and clinical data for polatuzumab vedotin, published clinical data for brentuximab vedotin, a similar antibody-drug conjugate, and physiologically based pharmacokinetic and population pharmacokinetic modeling approaches. We review strategies and model-informed outcomes that contributed to regulatory approval of polatuzumab vedotin plus bendamustine and rituximab in R/R DLBCL. These strategies made polatuzumab vedotin available to patients earlier than previously possible; depending on the strength of available data and the regulatory/competitive environment, they may also prove useful in accelerating the development of other agents.

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.

Population pharmacokinetics of belantamab mafodotin, a BCMA-targeting agent in patients with relapsed/refractory multiple myeloma

Belantamab mafodotin (belamaf) is an antibody-drug conjugate (ADC) targeting B-cell maturation antigen (BCMA). Nonlinear mixed-effects models were developed to characterize the population pharmacokinetics (PopPK) of ADC, total monoclonal antibody (mAb), and cysteine-maleimidocaproyl-MMAF (cys-mcMMAF) after 0.03-4.6 mg/kg dosing every 3 weeks in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM; DREAMM-1, n = 73; DREAMM-2, n = 218). Sequential modeling methodology was used. Individual post hoc parameter estimates from the final ADC model were used to develop total mAb and cys-mcMMAF models. Formal covariate selection used a modified stepwise forward inclusion method with backward elimination. A linear, two-compartment PopPK model with a time-varying clearance (CL) described ADC PK. Initial ADC typical value for CL for a DREAMM-2 patient was 0.936 L/day with a half-life of 11.5 days, over time CL was reduced by 28% resulting in a half-life of 14.3 days. Time to 50% maximal CL change was ~ 50 days. Baseline soluble BCMA (sBCMA), immunoglobulin (IgG), albumin, and bodyweight impacted ADC CL. Cys-mcMMAF concentrations were described with a linear two-compartment model linked to ADC; input rate was governed by deconjugation/intracellular proteolytic degradation of ADC represented by an exponentially decreasing MMAF:mAb (drug antibody ratio [DAR]) after each dose. Time to 50% DAR reduction was 10.3 days. Baseline sBCMA and IgG impacted cys-mcMMAF central volume of distribution. In conclusion, ADC, total mAb, and cys-mcMMAF concentration-time profiles in RRMM were well-described by PopPK models, and exposure was most strongly impacted by disease-related characteristics.

Application of a Two-Analyte Integrated Population Pharmacokinetic Model to Evaluate the Impact of Intrinsic and Extrinsic Factors on the Pharmacokinetics of Polatuzumab Vedotin in Patients with Non-Hodgkin Lymphoma

PURPOSE

The established two-analyte integrated population pharmacokinetic model was applied to assess the impact of intrinsic/extrinsic factors on the pharmacokinetics (PK) of polatuzumab vedotin (pola) in patients with non-Hodgkin lymphoma (NHL) following bodyweight-based dosing.

METHODS

Model simulations based on individual empirical Bayes estimates were used to evaluate the impact of intrinsic/extrinsic factors as patient subgroups on Cycle 6 exposures. Intrinsic factors included bodyweight, age, sex, hepatic and renal functions. Extrinsic factors included rituximab/obinutuzumab or bendamustine combination with pola and manufacturing process. The predicted impact on exposures along with the established exposure-response relationships were used to assess clinical relevance.

RESULTS

No clinically meaningful differences in Cycle 6 pola exposures were found for the following subgroups: bodyweight 100-146 kg versus 38-<100 kg, age ≥ 65 years versus <65 years, female versus male, mild hepatic impairment versus normal, mild-to-moderate renal impairment versus normal. Co-administration of rituximab/obinutuzumab or bendamustine, and change in the pola manufacturing process, also had no meaningful impact on PK.

CONCLUSIONS

In patients with NHL, bodyweight-based dosing is adequate, and no further dose adjustment is recommended for the heavier subgroup (100-146 kg). In addition, no dose adjustments are recommended for other subgroups based on intrinsic/extrinsic factors evaluated.

Polatuzumab vedotin, an anti-CD79b antibody-drug conjugate for the treatment of relapsed/refractory diffuse large B-cell lymphoma

Refractory/relapsed diffuse large B-cell lymphoma remains a major unmet medical need with poor outcome, especially for patients considered ineligible for stem cell transplant. Polatuzumab vedotin (PV) is a first-in-class anti-CD79b antibody-drug conjugate that contains the microtubule inhibitor monomethyl auristatin E. The development of PV is currently very active. This drug was US FDA approved in 2019 in combination with bendamustine and rituximab for the treatment of refractory/relapsed diffuse large B-cell lymphoma in third line and more, after demonstrating relevant efficacy and acceptable safety in a pivotal randomized Phase II trial. This review summarizes the features of this new drug with the primary focus on the clinical work supporting efficacy, relevance and tolerability of PV.

Capturing the Magic Bullet: Pharmacokinetic Principles and Modeling of Antibody-Drug Conjugates

Over the past two decades, antibody-drug conjugates (ADCs) have emerged as a promising class of drugs for cancer therapy and have expanded to nononcology fields such as inflammatory diseases, atherosclerosis, and bacteremia. Eight ADCs are currently approved by FDA for clinical applications, with more novel ADCs under clinical development. Compared with traditional chemotherapy, ADCs combine the target specificity of antibodies with chemotherapeutic capabilities of cytotoxic drugs. The benefits include reduced systemic toxicity and enhanced therapeutic index for patients. However, the heterogeneous structures of ADCs and their dynamic changes following administration create challenges in their development. The understanding of ADC pharmacokinetics (PK) is crucial for the optimization of clinical dosing regimens when translating from animal to human. In addition, it contributes to the optimization of dose selection and clinical monitoring with regard to safety and efficacy. This manuscript reviews the PK characteristics of ADCs and summarizes the diverse approaches for PK modeling that can be used to evaluate an ADC at the preclinical and clinical stages to support their successful development. Despite the numerous available options, fit-for-purpose modeling approaches for the PK and PD of ADCs should be critically planned and well-thought-out to adequately support the development of an ADC.

Integrated Two-Analyte Population Pharmacokinetic Model of Polatuzumab Vedotin in Patients With Non-Hodgkin Lymphoma

A two‐analyte integrated population pharmacokinetic (PK) model that simultaneously describes concentrations of antibody‐conjugated monomethyl auristatin E (acMMAE) and unconjugated MMAE following repeated administrations of polatuzumab vedotin (pola) was developed based on data from four clinical studies of pola in patients with non‐Hodgkin lymphoma. A two‐compartment model with a nonspecific, time‐dependent linear clearance, a linear time‐dependent exponentially declining clearance, and a Michaelis–Menten clearance provided a good fit of the acMMAE plasma PK profiles. All three acMMAE elimination pathways contributed to the input to the central compartment of unconjugated MMAE, which was also described by a two‐compartment model. Population PK parameters, covariate effects, and interindividual variability of model parameters were estimated. The impact of clinically relevant covariates on PK exposures of each analyte were quantified and reported to support key label claims.

Preclinical and translational pharmacokinetics of a novel THIOMAB™ antibody-antibiotic conjugate against Staphylococcus aureus

DSTA4637S, a novel THIOMAB™ antibody-antibiotic conjugate (TAC) against Staphylococcus aureus (S. aureus), is currently being investigated as a potential therapy for complicated S. aureus bloodstream infections. DSTA4637S is composed of a monoclonal THIOMAB^TM IgG1 recognizing S. aureus linked to a rifamycin-class antibiotic (dmDNA31) via a protease-cleavable linker. The pharmacokinetics (PK) of DSTA4637A (a liquid formulation of DSTA4637S) and its unconjugated antibody MSTA3852A were characterized in rats and monkeys. Systemic concentrations of three analytes, total antibody (TAb), antibody-conjugated dmDNA31 (ac-dmDNA31), and unconjugated dmDNA31, were measured to describe complex TAC PK in nonclinical studies. In rats and monkeys, following intravenous administration of a single dose of DSTA4637A, systemic concentration-time profiles of both TAb and ac-dmDNA31 were bi-exponential, characterized by a short distribution phase and a long elimination phase as expected for a monoclonal antibody-based therapeutic. Systemic exposures of both TAb and ac-dmDNA31 were dose proportional over the dose range tested, and ac-dmDNA31 cleared 2-3 times faster than TAb. Unconjugated dmDNA31 plasma concentrations were low (<4 ng/mL) in every study regardless of dose. In this report, an integrated semi-mechanistic PK model for two analytes (TAb and ac-dmDNA31) was successfully developed and was able to well describe the complicated DSTA4637A PK in mice, rats and monkeys. DSTA4637S human PK was predicted reasonably well using this model with allometric scaling of PK parameters from monkey data. This work provides insights into PK behaviors of DSTA4637A in preclinical species and informs clinical translatability of these observed results and further clinical development. Abbreviations: ADC: Antibody-drug conjugate; AUC_inf: time curve extrapolated to infinity; ac-dmDNA31: antibody-conjugated dmDNA31; C_max: maximum concentration observed; DAR: drug-to-antibody ratio; CL: clearance; CL_D: distribution clearance; CL₁: systemic clearance of all DAR species; k_DC: deconjugation rate constant; PK: Pharmacokinetics; IV: Intravenous; IgG: Immunoglobulin G; mAb: monoclonal antibody; S. aureus: Staphylococcus aureus; TAC: THIOMAB^TM antibody-antibiotic conjugate; TDC: THIOMAB^TM antibody-drug conjugate; TAb: total antibody; t_{1/2, λz}: terminal half-life; vc linker: valine-citrulline linker; V_ss: volume of distribution at steady state; V_c: volume of distribution for the central compartment; V_p: the volume of distribution for the peripheral compartment.