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Antibody–drug conjugates (ADCs) are important molecular entities in the treatment of cancer. These conjugates combine the target specificity of monoclonal antibodies with the potent anti-cancer activity of small-molecule therapeutics. The complex structure of ADCs poses unique challenges to characterize the drug’s pharmacokinetics (PKs) and pharmacodynamics (PDs) since it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (e.g., ADC conjugate, total antibody and unconjugated cytotoxic drug). As a result, clinical pharmacology strategy of an ADC is rather unique and dependent on the linker/cytotoxic drug technology, heterogeneity of the ADC, PK and safety/efficacy profile of the specific ADC in clinical development. In this review, we summarize the clinical pharmacology strategies in supporting development and approval of ADCs using the approved ADCs as specific examples to illustrate the customized approach to clinical pharmacology assessments in their clinical development.

The modern oncology drug development landscape has shifted away from traditional cytotoxic chemotherapies. Following their initial approvals, many oncology drugs have been approved in subsequent indications either as monotherapy or in combination to benefit a broader patient population. To date, dose selection strategies for subsequent indications have not been systematically reviewed. This review examines how approved dosing regimens were selected in subsequent indications for FDA-approved oncology drugs. The Drugs@FDA database was used to identify FDA-approved new molecular entities (NMEs) between 2010 and 2023. NMEs with more than 1 approved indication were included in the analysis. In total, the dosing regimens for 67 novel oncology drugs that obtained FDA approvals for multiple indications were evaluated. Overall, in subsequent indications, 72% of NMEs used the same or clinically equivalent alternative dosing regimens to those approved in the initial indications. Amongst the 28% of NMEs that used different dosing regimens, safety/tolerability was the leading cause of a dosing regimen changes in both monotherapy and combination therapy settings. Other factors leading to changes in dosing regimens include differences in tumor biology, disease burden, pharmacokinetics, and overall benefit-risk profiles obtained from dose-finding studies. Our analysis highlighted the importance of selecting a safe, tolerable, and yet efficacious dosing regimen for the initial indication as a suboptimal initially approved regimen could lead to dosing regimen changes in later indications. Preclinical and clinical data could be leveraged to understand the pharmacology, pharmacokinetic, and pharmacodynamic differences between indications and thus support dose selection in subsequent indications.

This study establishes quantitative relationships between neighborhood layouts, as evaluated by key neighborhood morphological parameters and pedestrian wind environments across China’s five major climate zones. We analyzed 3204 residential neighborhoods using satellite imaging and simulated 281 scenarios by CFD simulations, identifying six typical neighborhood layouts and quantifying their performance in terms of climate specific wind comfort criteria. This work takes an approach that takes into account mechanical wind effects and region-specific criteria for evaluating pedestrian-level wind environment performance, going beyond previous studies that utilize universal evaluation standards. The most influential parameter is building enclosure ratio with sensitivity indices of 0.844 for winter wind proofing. Closed perimeter layout confers 15–20% better winter wind proofing in cold climates and semi-open design enhances summer ventilation by 12–18% in hot climates according to our cross-climate analysis. Quantitative optimization adopting regression technique (R2 = 0.727–0.810) points to an optimal enclosure ratio of 0.25–0.28 or 0.52–0.61 with aspect ratio of 1.75–2.75. The results can provide evidence-based design guidelines for high-rise residential neighborhood planning and pedestrian wind environment, aiming to improve urban livability and support climate adaptation strategies across a broad range of climate zones.

Mosunetuzumab is a CD3/CD20 bispecific antibody. As an on‐target effect, transient elevation of interleukin‐6 (IL‐6) occurs in early treatment cycles. A physiologically‐based pharmacokinetic (PBPK) model was developed to assess potential drug interaction caused by IL‐6 enzyme suppression on cytochrome P450 3A (CYP3A) during mosunetuzumab treatment. The model's performance in predicting IL‐6 CYP3A suppression and subsequent drug–drug interactions (DDIs) was verified using existing clinical data of DDIs caused by chronic and transient IL‐6 elevation. Sensitivity analyses were performed for a complete DDI risk assessment. The IL‐6 concentration‐ and time‐dependent CYP3A suppression during mosunetuzumab treatment was simulated using PBPK model with incorporation of in vitro IL‐6 inhibition data. At clinically approved doses/regimens, the DDI at maximum CYP3A suppression was predicted to be a midazolam maximum drug concentration in plasma (Cmax) and area under the plasma drug concentration–time curve (AUC) ratio of 1.17 and 1.37, respectively. At the 95th percentile of IL‐6 concentration level or when gut CYP3A suppression was considered, the predicted DDI risk for mosunetuzumab remained low (<2‐fold). The PBPK‐based DDI predictions informed the mosunetuzumab product label to monitor, in early cycles, the concentrations and toxicities for sensitive CYP3A substrates with narrow therapeutic windows.

PurposeThe phase Ib/II open-label study (NCT01992653) evaluated the antibody-drug conjugate polatuzumab vedotin (pola) plus rituximab/obinutuzumab, cyclophosphamide, doxorubicin, and prednisone (R/G-CHP) as first-line therapy for B-cell non-Hodgkin lymphoma (B-NHL). We report the pharmacokinetics (PK) and drug–drug interaction (DDI) for pola.MethodsSix or eight cycles of pola 1.0–1.8 mg/kg were administered intravenously every 3 weeks (q3w) with R/G-CHP. Exposures of pola [including antibody-conjugated monomethyl auristatin E (acMMAE) and unconjugated MMAE] and R/G-CHP were assessed by non-compartmental analysis and/or descriptive statistics with cross-cycle comparisons to cycle 1 and/or after multiple cycles. Pola was evaluated as a potential victim and perpetrator of a PK drug–drug interaction with R/G-CHP. Population PK (popPK) analysis assessed the impact of prior treatment status (naïve vs. relapsed/refractory) on pola PK.ResultsPola PK was similar between treatment arms and independent of line of therapy. Pola PK was dose proportional from 1.0 to 1.8 mg/kg with R/G-CHP. Geometric mean volume of distribution and clearance of acMMAE ranged from 57.3 to 95.6 mL/kg and 12.7 to 18.2 mL/kg/day, respectively. acMMAE exhibited multi-exponential decay (elimination half-life ~ 1 week). Unconjugated MMAE exhibited formation rate-limited kinetics. Exposures of pola with R/G-CHP were similar to those in the absence of CHP; exposures of R/G-CHP in the presence of pola were comparable to those in the absence of pola.ConclusionsPola PK was well characterized with no clinically meaningful DDIs with R/G-CHP. Findings are consistent with previous studies of pola + R/G, and support pola + R/G-CHP use in previously untreated diffuse large B-cell lymphoma.

PurposeThe 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.MethodsModel 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.ResultsNo 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.ConclusionsIn 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.

Mosunetuzumab (Mosun) is a CD20xCD3 T‐cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. The approved step‐up dose regimen of 1/2/60/30 mg IV is designed to mitigate cytokine release syndrome (CRS) and maximize efficacy in early cycles. A population pharmacokinetic (popPK) model was developed from 439 patients with relapsed/refractory B‐Cell Non‐Hodgkin lymphoma receiving Mosun IV monotherapy, including fixed dosing (0.05–2.8 mg IV every 3 weeks (q3w)) and Cycle 1 step‐up dosing groups (0.4/1/2.8–1/2/60/30 mg IV q3w). Prior to Mosun treatment, ~50% of patients had residual levels of anti‐CD20 drugs (e.g., rituximab or obinutuzumab) from prior treatment. CD20 receptor binding dynamics and rituximab/obinutuzumab PK were incorporated into the model to calculate the Mosun CD20 receptor occupancy percentage (RO%) over time. A two‐compartment model with time‐dependent clearance (CL) best described the data. The typical patient had an initial CL of 1.08 L/day, transitioning to a steady‐state CL of 0.584 L/day. Statistically relevant covariates on PK parameters included body weight, albumin, sex, tumor burden, and baseline anti‐CD20 drug concentration; no covariate was found to have a clinically relevant impact on exposure at the approved dose. Mosun CD20 RO% was highly variable, attributed to the large variability in residual baseline anti‐CD20 drug concentration (median = 10 μg/mL). The 60 mg loading doses increased Mosun CD20 RO% in Cycle 1, providing efficacious exposures in the presence of the competing anti‐CD20 drugs. PopPK model simulations, investigating Mosun dose delays, informed treatment resumption protocols to ensure CRS mitigation.

PurposeTo characterize pertuzumab pharmacokinetics (PK) in FeDeriCa (NCT03493854: fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] versus intravenous pertuzumab plus trastuzumab); derive individual pertuzumab exposures in the PH FDC SC arm for subsequent pertuzumab exposure–response (ER) analyses; compare observed trastuzumab PK with predicted exposures from a previous SC trastuzumab model; assess whether pertuzumab affects trastuzumab PK; evaluate pertuzumab exposure–efficacy and –safety relationships and support the approved SC dosing regimen.MethodsPopulation pharmacokinetic modeling and simulations were used to describe the data. Standard goodness-of-fit diagnostics and prediction-corrected visual predictive checks were used for model performance assessment. Covariates were included from previously reported models. ER analysis was conducted using logistic regression.ResultsSC pertuzumab PK was described adequately by a two-compartment model with first-order absorption; significant covariates included in the final model were albumin, lean body weight, and Asian region; however, these appeared not to be clinically relevant. Trastuzumab concentrations were described adequately by the previous model; there was no evidence of a pertuzumab effect on trastuzumab PK as part of PH FDC SC and higher model-predicted pertuzumab exposure was not associated with differences in pathologic complete response rate or an increased probability of selected grade ≥ 3 adverse events of interest.ConclusionThe approved PH FDC SC dose [loading: 1200/600 mg pertuzumab/trastuzumab (15 mL); maintenance: 600 mg pertuzumab/trastuzumab (10 mL) and 2000 U/mL recombinant human hyaluronidase every 3 weeks] provides a positive benefit–risk profile with comparable efficacy and safety to intravenous pertuzumab plus trastuzumab.

Tumor dynamic models are vital for evaluating oncology treatments and guiding clinical drug development decisions. However, few studies rigorously assess their predictive capabilities, especially when forecasting tumor trajectories from clinical trials with short or inconsistent follow‐up across treatment arms. Poor predictive performance or biases related to follow‐up time could potentially limit the general utility of tumor growth inhibition (TGI) models. This study quantitatively evaluates prediction bias across several established tumor dynamic models, comparing five classical pharmacometric TGI models with the deep learning‐based Tumor Dynamic Neural‐ODE (TDNODE) framework. Using time‐truncated clinical trial data from 3106 patients with non‐small cell lung cancer (NSCLC) across four completed atezolizumab phase III studies, we consistently observed moderate‐to‐high positive bias in the predictions from pharmacometric models, particularly with more limited follow‐up. By examining the structures of these models and comparing them to observed data, we highlight how the assumed kinetic patterns potentially lead to biased parameter estimation and systemic overestimation of tumor size when applied to immature datasets. In contrast, the TDNODE framework, using deep learning, demonstrated promising early results, exhibiting improved predictive performance in the same evaluations. These findings underscore the critical need to address prediction bias in tumor dynamic modeling with immature data and to consider alternative approaches to established paradigms for certain drug development applications. This study also generally demonstrates the potential of novel methods, such as deep learning, to potentially enhance the reliability of tumor dynamics modeling, especially in challenging early‐phase clinical decision‐making scenarios. What is the current knowledge on the topic? Tumor dynamic models (TGI) are vital for evaluating oncology treatments and guiding drug development decisions. However, few studies rigorously assess their predictive capabilities when forecasting tumor trajectories from clinical trials with short or inconsistent follow‐up, which can lead to biases. Addressing follow‐up bias and thoroughly assessing model performance are recognized needs in the field. What question did this study address? This study addressed how prediction bias varies across five classical TGI models and the deep learning‐based Tumor Dynamic Neural‐ODE (TDNODE) framework when used to predict future tumor trajectories from time‐truncated clinical trial data in patients with NSCLC. What does this study add to our knowledge? The study demonstrates that classical TGI pharmacometric models consistently exhibit moderate‐to‐high positive prediction bias, significantly overestimating tumor size when applied to immature datasets. This bias is linked to model structural assumptions and increasing errors in key parameter estimation (e.g., the growth parameter KG). In contrast, the TDNODE framework consistently demonstrated improved predictive performance and substantially lower prediction bias across all scenarios evaluated. How might this change drug discovery, development, and/or therapeutics? These findings underscore the critical need to address prediction bias in tumor dynamic modeling, especially when using immature data prevalent in early‐phase clinical trials. Using alternative, more flexible methods like TDNODE could potentially enhance the reliability of tumor dynamics modeling, thereby supporting early clinical decision‐making scenarios and dose optimization efforts.