Simulation-based evaluation of personalized dosing approaches for anti-FGFR/KLB bispecific antibody fazpilodemab.

Personalized dosing approaches play important roles in clinical practices to improve benefit: risk profiles. Whereas this is also important for drug development, especially in the context of drugs with narrow therapeutic windows, such approaches have not been fully evaluated during clinical development. Fazpilodemab (BFKB8488A) is an agonistic bispecific antibody which was being developed for the treatment of nonalcoholic steatohepatitis. The objective of this study was to characterize the exposure-response relationships of fazpilodemab with the purpose of guiding dose selection for a phase II study, as well as to evaluate various personalized dosing strategies to optimize the treatment benefit. Fazpilodemab exhibited clear exposure-response relationships for a pharmacodynamic (PD) biomarker and gastrointestinal adverse events (GIAEs), such as nausea and vomiting. Static exposure-response analysis, as well as longitudinal adverse event (AE) analysis using discrete-time Markov model, were performed to characterize the observations. Clinical trial simulations were performed based on the developed exposure-response models to evaluate probability of achieving target PD response and the frequency of GIAEs to inform phase II dose selection. Dynamic simulation of personalized dosing strategies demonstrated that the AE-based personalized dosing is the most effective approach for optimizing the benefit-risk profiles. The approach presented here can be a useful framework for quantifying the benefit of personalized dosing for drugs with narrow therapeutic windows.

gPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve.

GPKPDviz is a Shiny application (app) dedicated to real-time simulation, visualization, and assessment of the pharmacokinetic/pharmacodynamic (PK/PD) models. Within the app, gPKPDviz is capable of generating virtual populations and complex dosing and sampling scenarios, which, together with the streamlined workflow, is designed to efficiently assess the impact of covariates and dosing regimens on PK/PD end points. The actual population data from clinical trials can be loaded into the app for simulation if desired. The app-generated dosing regimens include single or multiple dosing, and more complex regimens, such as loading doses or intermittent dosing. When necessary, the dosing regimens can be defined externally and loaded to the app for simulation. Using mrgsolve as the simulation engine, gPKPDviz is typically used for population simulation, however, with a slight modification of the mrgsolve model, gPKPDviz is capable of performing individual simulations with individual post hoc parameters, individual dosing logs, and individual sampling timepoints through an external dataset. A built-in text editor has a debugging feature for the mrgsolve model, providing the same error messages as model compilation in R. GPKPDviz has had stringent validation by comparing simulation results between the app and using mrgsolve in R. GPKPDviz is a member of the suite of Modeling and Simulation Shiny apps developed at Genentech to facilitate the typical modeling work in Clinical Pharmacology. For broader access to the Pharmacometric community, gPKPDviz has been published as an open-source application in GitHub under the terms of GNU General Public License.

A disease model predicting placebo response and remission status of patients with ulcerative colitis using modified Mayo score.

The Mayo Clinical Score is used in clinical trials to describe the clinical status of patients with ulcerative colitis (UC). It comprises four subscores: rectal bleeding (RB), stool frequency (SF), physician's global assessment, and endoscopy (ENDO). According to recent US Food and Drug Administration guidelines (Ulcerative colitis: developing drugs for treatment, Guidance Document, https://www.fda.gov/regulatory-information/s. 2022), clinical response and remission should be based on modified Mayo Score (mMS) relying on RB, SF, and ENDO. Typically, ENDO is performed at the beginning and end of each phase, whereas RB and SF are more frequently available. Item response theory (IRT) models allow the shared information to be used for prediction of all subscores at each observation time; therefore, it leverages information from RB and SF to predict ENDO. A UC disease IRT model was developed based on four etrolizumab phase III studies to describe the longitudinal mMS subscores, placebo response, and remission at the end of induction and maintenance. For each subscore, a bounded integer model was developed. The placebo response was characterized by a mono-exponential function acting on all mMS subscores similarly. The final model reliably predicted longitudinal mMS data. In addition, remission was well-predicted by the model, with only 5% overprediction at the end of induction and 3% underprediction at the end of maintenance. External evaluation of the final model using placebo arms from five different studies indicated adequate performance for both longitudinal mMS subscores and remission status. These results suggest utility of the current disease model for informed decision making in UC clinical development, such as assisting future clinical trial designs and evaluations.

Population Pharmacokinetics of Ranibizumab Delivered via the Port Delivery System Implanted in the Eye in Patients with Neovascular Age-Related Macular Degeneration.

The port delivery system with ranibizumab (PDS) is designed to continuously deliver ranibizumab to maintain therapeutic drug concentrations in the vitreous of the eye for an extended duration. The PDS has been evaluated for the treatment of neovascular age-related macular degeneration in the Ladder (PDS 10, 40, and 100 mg/mL, with refill exchanges as needed, versus monthly intravitreal ranibizumab 0.5 mg), Archway (PDS 100 mg/mL with 24-week refill exchanges, versus monthly intravitreal ranibizumab 0.5 mg), and ongoing Portal (PDS 100 mg/mL with 24-week refill exchanges) clinical trials. Data from Ladder, Archway, and Portal were used to develop a population pharmacokinetics (PK) model to estimate the ranibizumab release rate from the PDS implant, describe ranibizumab PK in serum and aqueous humor, and predict the concentration in vitreous humor. A model was developed to adequately describe the serum and aqueous humor PK data, as suggested by goodness-of-fit plots as well as visual predictive checks. In the final model, the first-order implant release rate was estimated to be 0.00654 (1/day), corresponding to a half-life of 106 days, consistent with the implant release rate determined in vitro. The model-predicted vitreous concentrations achieved with PDS 100 mg/mL given every 24 weeks were below the intravitreal peak concentration and above the intravitreal trough concentration of ranibizumab over the entire 24-week refill interval. The results demonstrate a durable release of ranibizumab from the PDS with a half-life of 106 days, providing vitreous exposure to ranibizumab for at least 24 weeks that is within the range of exposure for monthly intravitreal treatment.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

Pharmacokinetics of the Port Delivery System with Ranibizumab in the Ladder Phase 2 Trial for Neovascular Age-Related Macular Degeneration.

INTRODUCTION: Ladder was a phase 2 trial that evaluated the Port Delivery System with ranibizumab (PDS) for neovascular age-related macular degeneration. Serum and aqueous humor samples were collected to characterize the pharmacokinetics (PK) of ranibizumab delivered through the PDS. METHODS: Ladder was a multicenter, randomized, active treatment-controlled, phase 2 clinical trial. Patients with neovascular age-related macular degeneration (n = 220) were randomized (3:3:3:2) to PDS 10 mg/ml, PDS 40 mg/ml, PDS 100 mg/ml, or monthly intravitreal ranibizumab 0.5 mg. Serum PK samples were collected in all arms and analyzed for ranibizumab concentration using an enzyme-linked immunosorbent assay. The main PK analyses were conducted in the PK-evaluable population (n = 68), which excluded patients who received fellow eye intravitreal treatment, supplemental ranibizumab treatment, or had previous treatment with bevacizumab in either eye within 9 months of randomization. RESULTS: In the PDS 10 mg/ml arm, median serum ranibizumab concentrations were below the serum trough concentration (Ctrough; 130 pg/ml) expected with monthly intravitreal ranibizumab 0.5 mg at all time points. In the PDS 40 mg/ml and 100 mg/ml arms, median serum ranibizumab concentrations were above the Ctrough expected with monthly intravitreal ranibizumab 0.5 mg (130 pg/ml) through month 3 and month 12 after implantation, respectively, and remained above the lower limit of quantification through month 15 and month 16 after implantation, respectively. CONCLUSIONS: These PK data indicate that the implant in the PDS 100 mg/ml arm maintained ranibizumab concentrations within the range of monthly intravitreal ranibizumab 0.5 mg injections (130-2220 pg/ml) through month 12 after implantation. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT02510794.

Assessment of pharmacokinetic linearity after repeated drug administration.

The prediction of drug concentration time courses after different dosing scenarios is greatly facilitated if the pharmacokinetics (PK) can be assumed linear. The assumption of linear PK thus needs careful evaluation for any new drug in development. Under linear PK, exposure is proportional to dose (linear PK across doses) and exposure at steady state can be predicted from a single dose based on the superposition principle (linear PK over time). While investigation of dose-proportionality is common practice, evaluation of time dependent PK has received less attention in the literature. In particular, the superposition principle can be used to assess whether the observed extent of accumulation after repeated administration is expected under the premise of linear PK. This work emphasizes the importance of the time related aspect of linear PK by introducing the predictability ratio (PR). Linear PK over time can be concluded if PR = 1. Accumulation is higher than expected if PR >1, and lower if PR <1. If PK data from multiple dose cohorts are available, the PR is assessed for each dose cohort and a supportive hypothesis test can be applied to test for potential differences between doses in PR.

A longitudinal model for the Mayo Clinical Score and its sub-components in patients with ulcerative colitis.

Clinical trials in patients with ulcerative colitis (UC) face the challenge of high and variable placebo response rates. The Mayo Clinical Score (MCS) is used widely as the primary endpoint in clinical trials to describe the clinical status of patients with UC. The MCS is comprised of four subscores, each scored 0, 1, 2 and 3: rectal bleeding (RB), stool frequency (SF), physician's global assessment (PGA), and endoscopy (ENDO) subscore. Excluding the PGA subscore gives the modified MCS. Quantitative insight on the placebo response, and its impact on the components of the MCS over time, can better inform clinical trial design and interpretation. Longitudinal modeling of the MCS, and the modified MCS, can be challenging due to complex clinical trial design, population heterogeneity, and limited assessments for the ENDO subscore. The current study pooled patient-level placebo/standard of care (SoC) arm data from five clinical trials in the TransCelerate database to develop a longitudinal placebo response model that describes the MCS over time in patients with UC. MCS subscores were modeled using proportional odds models, and the removal of patients from the placebo/SoC arm, or "dropout", was modeled using logistic regression models. The subscore and dropout models were linked to allow for the prediction of the MCS and the modified MCS. Stepwise covariate modeling identified prior exposure to TNF-α antagonists as a statistically significant predictor on the RB + SF subscore. Patients with prior exposure to TNF-α antagonists had higher post-baseline RB + SF subscores than naive patients.

Population pharmacokinetic and exploratory exposure-response analysis of the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with HER2-positive early breast cancer in the FeDeriCa study.

PURPOSE: To 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. METHODS: Population 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. RESULTS: SC 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. CONCLUSION: The 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.

Model-Informed Therapeutic Dose Optimization Strategies for Antibody-Drug Conjugates in Oncology: What Can We Learn From US Food and Drug Administration-Approved Antibody-Drug Conjugates?

Antibody-drug conjugates (ADCs) combine the specificity of an antibody with the cytotoxicity of a chemical agent. They represent a rapidly evolving area of oncology drug development and hold significant promise. There are currently nine ADCs on the market, more than half of which gained US Food and Drug Administration approval more recently, since 2019. Despite their enormous promise, the therapeutic window for these ADCs remains relatively narrow, especially when compared with other oncology drugs, such as targeted therapies or checkpoint inhibitors. In this review, we provide a detailed overview of the five dosing regimen optimization strategies that have been leveraged to broaden the therapeutic window by mitigating the safety risks while maintaining efficacy. These include body weight cap dosing; treatment duration capping; dose schedule (e.g., dosing frequency and dose fractionation); response-guided dosing recommendations; and randomized dose-finding. We then discuss how the lessons learned from these studies can inform ADC development going forward. Informed application of these dosing strategies should allow researchers to maximize the safety and efficacy for next-generation ADCs.

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

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.

A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics.

Progression-free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum-resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.

Intrinsic and Extrinsic Pharmacokinetic Variability of Small Molecule Targeted Cancer Therapy.

Pharmacokinetic (PK) variability in cancer clinical trials may be due to heterogeneous populations and identifying sources of variability is important. Use of healthy subjects in clinical pharmacology studies together with detailed knowledge of the characteristics of patients with cancer can allow for quick identification and quantification of factors affecting PK variability. PK data and sources of variability of 40 marketed molecularly targeted oncology therapeutics were compiled from regulatory approval documents covering an 18-year period (1999-2017). Variability in PK parameters was compared and contributors to variability were identified. The results show that PK variability was ~ 16% higher for peak plasma concentration (Cmax ) and area under the concentration time curve (AUC) in patients with cancer compared with healthy subjects. Several factors were identified as major contributors to variability including hepatic/renal impairment and cytochrome P450 inhibition/induction. Lower PK variability in healthy subjects may represent an opportunity to perform rapid and robust pharmacological and PK assessments to inform subsequent studies in the development of new cancer therapies.

Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models.

Longitudinal-ordered categorical data, common in clinical trials, can be effectively analyzed with nonlinear mixed effect models. In this article, we systematically evaluated the performance of three different models in longitudinal muscle spasm adverse event (AE) data obtained from a clinical trial for vismodegib: a proportional odds (PO) model, a discrete-time Markov model, and a continuous-time Markov model. All models developed based on weekly spaced data can reasonably capture the proportion of AE grade over time; however, the PO model overpredicted the transition frequency between grades and the cumulative probability of AEs. The influence of data frequency (daily, weekly, or unevenly spaced) was also investigated. The PO model performance reduced with increased data frequency, and the discrete-time Markov model failed to describe unevenly spaced data, but the continuous-time Markov model performed consistently well. Clinical trial simulations were conducted to illustrate the muscle spasm resolution time profile during the 8-week dose interruption period after 12 weeks of continuous treatment.

Time-to-Event Modeling of Peripheral Neuropathy: Platform Analysis of Eight Valine-Citrulline-Monomethylauristatin E Antibody-Drug Conjugates.

Peripheral neuropathy (PN) is a common long-term debilitating toxicity of antimicrotubule agents. PN was the most frequent adverse event resulting in dose modifications and/or discontinuation of treatment for valine-citrulline-monomethylauristatin E antibody-drug conjugates (ADCs) developed at Genentech. A pooled time-to-event analysis across eight ADCs (~700 patients) was performed to evaluate the relationship between the ADC exposure and the risk for developing a clinically significant (grade ≥ 2) PN. In addition, the impact of demographic and pathophysiological risk factors on the risk for PN was explored. The time-to-event analysis suggested that the development of PN risk increased with ADC exposure, treatment duration, body weight, and previously reported PN. This model can be used to inform clinical strategies such as adaptations to dosing regimen and/or treatment duration as well as inform clinical eligibility to reduce the incidence of grade ≥ 2 PN.

Pharmacokinetic and exploratory exposure-response analysis of pertuzumab in patients with operable HER2-positive early breast cancer in the APHINITY study.

PURPOSE: To characterize the pharmacokinetics (PK) of, and perform an exploratory exposure-response (E-R) analysis for, pertuzumab in patients with HER2-positive early breast cancer (EBC) within the APHINITY study (NCT01358877, BIG 4-11/BO25126/TOC4939G). METHODS: A previously developed pertuzumab two-compartment linear population pharmacokinetic (popPK) model was subjected to external validation to examine appropriateness for describing pertuzumab concentrations from the APHINITY study. Pharmacokinetic drug-drug interactions (DDIs) between pertuzumab, trastuzumab, and chemotherapy were assessed by comparing observed serum or plasma Cmax, Cmin, and AUClast geometric mean ratios with 90% CIs. Predictions of pertuzumab Cmax,ss, Cmin,ss, and AUCss were derived from individual parameter estimates and used in an exploratory E-R analysis. RESULTS: Using data from 72 patients, based on goodness-of-fit, the popPK model was deemed appropriate for predictions of individual exposures for subsequent comparisons to historical data, assessment of DDIs, and E-R analyses. No evidence of DDIs for pertuzumab on trastuzumab, trastuzumab on pertuzumab, or pertuzumab on chemotherapy PK was observed. Analyses of differences in exposure between patients with and without invasive disease-free survival events did not indicate improved efficacy with increased exposure. Overall Grade ≥ 3 diarrhea prevalence was higher with pertuzumab versus placebo, but was not greater with increasing pertuzumab exposure. No apparent E-R relationship was suggested with respect to other grade ≥ 3 AEs. CONCLUSION: Overall, the limited available data from this exploratory study suggest that no dose adjustments are needed for pertuzumab when administered in combination with trastuzumab and an EBC chemotherapy regimen.

Development of a Subcutaneous Fixed-Dose Combination of Pertuzumab and Trastuzumab: Results From the Phase Ib Dose-Finding Study.

Adding pertuzumab to trastuzumab (both monoclonal antibodies targeting human epidermal growth factor receptor 2 [HER2]) has proven survival benefits when combined with chemotherapy for patients with HER2-positive breast cancer. The combination of pertuzumab and trastuzumab together in 1 vial for subcutaneous (SC) administration is being developed as a ready-to-use formulation to reduce the treatment burden on patients while improving healthcare efficiency. An open-label, 2-part, phase Ib dose-finding study (NCT02738970) was undertaken in healthy male volunteers (part 1) and female patients with HER2-postive early breast cancer who had completed standard (neo)adjuvant treatment (part 2). This study aimed to identify an SC pertuzumab dose given with recombinant human hyaluronidase that results in comparable exposure to that of the intravenous (IV) pertuzumab dose, based on pertuzumab serum trough concentration and area under the serum concentration-time curve. Pharmacokinetics (PK), safety, and tolerability of a single dose of SC pertuzumab given alone or in a fixed-dose combination (comixed or coformulated) with trastuzumab were also assessed. A maintenance dose of 600 mg for SC pertuzumab resulted in an equivalent exposure to that of IV pertuzumab, and no new safety signals were identified for SC pertuzumab or trastuzumab. A loading dose of 1200 mg for SC pertuzumab was selected based on approximate dose proportionality. The PK and safety results support further development of a fixed-dose coformulation combination of pertuzumab and trastuzumab for SC administration, which will be investigated in an upcoming phase III trial in patients with HER2-positive early breast cancer.

Correction to: Population pharmacokinetics of trastuzumab emtansine in previously treated patients with HER2-positive advanced gastric cancer (AGC).

The online version of the original article can be.

Population pharmacokinetics of trastuzumab emtansine in previously treated patients with HER2-positive advanced gastric cancer (AGC).

PURPOSE: Ado-trastuzumab emtansine (T-DM1) is an antibody-drug conjugate comprising trastuzumab conjugated via a stable thioether linker to DM1, a highly potent cytotoxic agent. A population pharmacokinetics (PK) analysis was performed to characterize T-DM1 PK and evaluate the impact of patient characteristics on T-DM1 PK in previously treated patients with HER2-positive advanced gastric cancer (AGC). METHODS: Following T-DM1 weekly or every three weeks dosing, T-DM1 concentration measurements (n = 780) were collected from 136 patients in the GATSBY (NCT01641939) study and analyzed using nonlinear mixed effects modeling. The influence of demographic, baseline laboratory, and disease characteristics on T-DM1 PK was examined. RESULTS: T-DM1 PK was best described by a two-compartment model with parallel linear and nonlinear (Michaelis-Menten) elimination from the central compartment. The final population model estimated linear clearance (CL) of 0.79 L/day, volume of distribution in the central compartment (V c) of 4.48 L, distribution clearance (Q) of 0.62 L/day, volume of distribution in the peripheral compartment (V p) of 1.49 L, nonlinear CL of 2.06 L/day, and KM of 1.63 µg/mL. Parameter uncertainty was low to moderate for fixed effects, except KM (estimated with poor precision). Patients with high body weight and low baseline trastuzumab concentrations had significantly faster linear CL; those with higher body weight had significantly larger V c. CONCLUSIONS: In a HER2-positive AGC population, T-DM1 PK was best described by a two-compartment model with parallel linear and nonlinear elimination. Baseline body weight and trastuzumab concentration were identified as significant covariates for T-DM1 PK in a HER2-positive AGC population.