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There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection.

Population pharmacokinetics (popPK) modeling for osimertinib, a third‐generation, irreversible, oral epidermal growth factor receptor (EGFR)‐tyrosine kinase inhibitor (TKI) that potently and selectively inhibits both EGFR‐TKI sensitizing mutations and EGFR T790M, was previously reported utilizing AURA and AURA2 data (advanced non‐small cell lung cancer [NSCLC]). We report updated popPK modeling incorporating AURA3 and FLAURA data (advanced NSCLC); model validation used ADAURA data (resected stage IB–IIIA NSCLC). Updated popPK analyses were based on patients from AURA (n = 599), AURA2 (n = 210), AURA3 (n = 277), and FLAURA (n = 278) using a linear one‐compartmental disposition model for osimertinib and its metabolite, AZ5104, with first‐order oral absorption. A full covariate model, using Monte Carlo simulations, was developed to assess the effects of covariates on osimertinib and AZ5104 clearance. External validation was conducted using ADAURA study data (n = 325). In the final popPK model, the apparent clearance and volume of distribution of osimertinib (14.3 L/h; 918 L) and AZ5104 (31.3 L/h; 143 L) were comparable to previous analyses. Albumin levels and body weight influenced osimertinib PK, but the effects were not considered clinically meaningful; other covariates had no impact on PK. Goodness‐of‐fit plots indicated that the model adequately described all data. Visual predictive checks showed that the final model validated osimertinib steady‐state PK for adjuvant treatment. PopPK modeling indicated that osimertinib dose adjustment is not required for patients' age, sex, body weight, race, smoking status, or line of therapy, confirming that a fixed 80 mg once‐daily dose is optimal for osimertinib. Osimertinib has proven efficacy in EGFR mutation‐positive NSCLC; a popPK model was previously developed using data from 2nd‐/later‐line settings. We updated this model with additional data, confirming that a fixed 80 mg once‐daily dose is optimal for osimertinib across all lines of treatment.

Background and Objectives Recently a number of antibody–drug conjugate (ADC) pharmacometric models have been reported in the literature, describing one or two ADC-related analytes. The objective of this analysis was to build a population pharmacokinetic (popPK) three-analyte ADC model to describe efficacy and safety of zilovertamab vedotin, an ROR1-targeting ADC conjugated to monomethyl auristatin E (MMAE). Methods Data from a phase 1 study of zilovertamab vedotin in subjects with hematologic malignancies was used in a stepwise ADC modeling strategy based on the simplified ADC popPK model proposed by Gibiansky. This choice provided opportunity to model three analytes: conjugated monomethyl auristatin E (acMMAE), total monoclonal antibody (total mAb), and free MMAE. The model was extrapolated to the pediatric population using a clearance maturation function and accounting for weight dependent pharmacokinetic (PK) changes. Results The simplified model provided a good structure to fit the adult acMMAE, total mAb, and free MMAE data. Analysis showed that MMAE was released through deconjugation of the payload and full proteolytic degradation of the acMMAE. Deconjugation was associated with an immediate release of MMAE, proteolytic clearance introduced a delay in the release of MMAE. Simulation of the model extrapolated to the pediatric population was the basis for pediatric dosing strategies for zilovertamab vedotin that were approved in the United States and European Union. Conclusions The total mAb, acMMAE, and free MMAE model showed a good fit to the data. The pediatric population can match the acMMAE adult exposure at the same weight-based dose regimen without concerns that the toxic MMAE concentration will reach higher levels than found in adults.

INTRODUCTION Effect on amyloid plaque as measured by positron emission tomography imaging with Centiloid standardization of two therapeutic approaches targeting amyloid beta (Aβ) was investigated using exposure‐response modeling. METHODS Individual‐level verubecestat data from the APECS trial were pooled with summary‐level data from the literature for amyloid monoclonal antibodies (mAbs) and fitted in a joint non‐linear mixed‐effects model. RESULTS An indirect‐response (turnover) model with verubecestat inhibiting plaque formation and mAbs stimulating plaque removal well represented the data. The estimated plaque elimination half‐life was 6.4 years. Daily verubecestat 40 mg was estimated to reduce formation by 91.8%. Aducanumab 10 mg/kg every 4 weeks (Q4W), donanemab 1400 mg Q4W, gantenerumab 1200 mg Q4W, and lecanemab 10 mg/kg Q2W were estimated to increase the removal rate by 9.3‐, 18.6‐, 5.3‐, and 13.8‐fold, respectively. DISCUSSION The model provides a fundamental measure of drug effects on plaque, independent of disease stage and study‐design factors, improving cross‐study comparisons and enabling predictions. Highlights The plaque turnover model describes natural progression and BACE and mAb intervention. The model estimation of the underlying plaque elimination half‐life is 6.4 years. Approach improves cross‐study comparison independently of population and study design. Predictions of alternative regimens/therapeutic approaches will aid future study design.