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Background and Objectives Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups. Methods A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived. Results The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL −1 (73.4 µmol L −1 ) has a V 1 , V 2 , CL and Q 2 of 42.9 L, 41.7 L, 4.10 L h −1 and 3.22 L h −1 . Clearance matures with age, reaching 50% of the maximal value (5.31 L h −1  70 kg −1 ) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups. Conclusions A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.

Pralsetinib, a potent and selective inhibitor of oncogenic RET fusion and RET mutant proteins, is a substrate of the drug metabolizing enzyme CYP3A4 and a substrate of the efflux transporter P‐gp based on in vitro data. Therefore, its pharmacokinetics (PKs) may be affected by co‐administration of potent CYP3A4 inhibitors and inducers, P‐gp inhibitors, and combined CYP3A4 and P‐gp inhibitors. With the frequent overlap between CYP3A4 and P‐gp substrates/inhibitors, pralsetinib is a challenging and representative example of the need to more quantitatively characterize transporter‐enzyme interplay. A physiologically‐based PK (PBPK) model for pralsetinib was developed to understand the victim drug–drug interaction (DDI) risk for pralsetinib. The key parameters driving the magnitude of pralsetinib DDIs, the P‐gp intrinsic clearance and the fraction metabolized by CYP3A4, were determined from PBPK simulations that best captured observed DDIs from three clinical studies. Sensitivity analyses and scenario simulations were also conducted to ensure these key parameters were determined with sound mechanistic rationale based on current knowledge, including the worst‐case scenarios. The verified pralsetinib PBPK model was then applied to predict the effect of other inhibitors and inducers on the PKs of pralsetinib. This work highlights the challenges in understanding DDIs when enzyme‐transporter interplay occurs, and demonstrates an important strategy for differentiating enzyme/transporter contributions to enable PBPK predictions for untested scenarios and to inform labeling.

There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed‐effect modeling approach to predict Clinical Dementia Rating Scale – Sum of Boxes (CDR‐SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR‐SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini‐Mental State Examination and CDR‐SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.

Despite considerable investment into potential therapeutic approaches for Alzheimer's disease (AD), currently approved treatment options are limited. Predictive modeling using quantitative systems pharmacology (QSP) can be used to guide the design of clinical trials in AD. This study developed a QSP model representing amyloid beta (Aβ) pathophysiology in AD. The model included mechanisms of Aβ monomer production and aggregation to form insoluble fibrils and plaques; the transport of soluble species between the compartments of brain, cerebrospinal fluid (CSF), and plasma; and the pharmacokinetics, transport, and binding of monoclonal antibodies to targets in the three compartments. Ordinary differential equations were used to describe these processes quantitatively. The model components were calibrated to data from the literature and internal studies, including quantitative data supporting the underlying AD biology and clinical data from clinical trials for anti‐Aβ monoclonal antibodies (mAbs) aducanumab, crenezumab, gantenerumab, and solanezumab. The model was developed for an apolipoprotein E (APOE) ɛ4 allele carrier and tested for an APOE ɛ4 noncarrier. Results indicate that the model is consistent with data on clinical Aβ accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aβ load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aβ load at different dose levels for mAbs with known targets and binding affinities. This model may facilitate the design of scientifically enriched and efficient clinical trials by enabling a priori prediction of biomarker dynamics in the brain and CSF.

Concentration‐QTc (C‐QTc) analysis has become a common approach for evaluating proarrhythmic risk and delayed cardiac repolarization of oncology drug candidates. Significant heart rate (HR) change has been associated with certain classes of oncology drugs and can result in over‐ or underestimation of the true QT prolongation risk. Because oncology early clinical trials typically lack a placebo control arm or time‐matched, treatment‐free baseline electrocardiogram collection, significant HR change brings additional challenges to C‐QTc analysis in the oncology setting. In this work, a spline‐based correction method (QTcSPL) was explored to mitigate the impact of HR changes in giredestrant C‐QTc analysis. Giredestrant is a selective estrogen receptor degrader being developed for the treatment of patients with estrogen receptor‐positive (ER+) breast cancer. A dose‐related HR decrease has been observed in patients under giredestrant treatment, with significant reductions (>10 bpm) observed at supratherapeutic doses. The QTcSPL method demonstrated superior functionality to reduce the correlation between QTc and HR as compared with the Fridericia correction (QTcF). The effect of giredestrant exposure on QTc was evaluated at the clinical dose of 30 mg and supratherapeutic dose of 100 mg based on a prespecified linear mixed effect model. The upper 90% confidence interval of ΔQTcSPL and ΔQTcF were below the 10 ms at both clinical and supratherapeutic exposures, suggesting giredestrant has a low risk of QT prolongation at clinically relevant concentrations. This work demonstrated the use case of QTcSPL to address HR confounding challenges in the context of oncology drug development for the first time.

Identification of covariates, including biomarkers, spirometry, and diaries/questionnaires, that predict asthma exacerbations would allow better clinical predictions, shorter phase II trials and inform decisions on phase III design, and/or initiation (go/no‐go). The objective of this work was to characterize asthma‐exacerbation hazard as a function of baseline and time‐varying covariates. A repeated time‐to‐event (RTTE) model for exacerbations was developed using data from a 52‐week phase IIb trial, including 502 patients with asthma randomized to placebo or 70 mg, 210 mg, or 490 mg astegolimab every 4 weeks. Covariate analysis was performed for 20 baseline covariates using the full random effects modeling approach, followed by time‐varying covariate analysis of nine covariates using the stepwise covariate model (SCM) building procedure. Following the SCM, an astegolimab treatment effect was explored. Diary‐based symptom score (difference in objective function value [dOFV] of −83.7) and rescue medication use (dOFV = −33.5), and forced expiratory volume in 1 s (dOFV = −14.9) were identified as significant time‐varying covariates. Of note, time‐varying covariates become more useful with more frequent measurements, which should favor the daily diary scores over others. The most influential baseline covariates were exacerbation history and diary‐based symptom score (i.e., symptom score was important as both time‐varying and baseline covariate). A (nonsignificant) astegolimab treatment effect was included in the final model because the limited data set did not allow concluding the remaining effect size as irrelevant. Without time‐varying covariates, the treatment effect was statistically significant (p < 0.01). This work demonstrated the utility of a population RTTE approach to characterize exacerbation hazard in patients with severe asthma.

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.

Brain metastasis is a common complication in melanoma patients with BRAF and NRAS mutations and has a poor prognosis. Although BRAF inhibitors are clinically approved, their poor brain penetration limits their efficacy in brain metastasis. Thus, melanoma brain metastasis still requires better treatment. Belvarafenib, a pan-RAF inhibitor, has reported antitumor activity in melanoma with RAF and RAS mutations in animal models and patients. However, brain permeability and antitumor efficacy on brain metastasis have not been determined. This study confirmed the brain penetration of belvarafenib, the antitumor activity on BRAF and NRAS mutant melanoma, and the efficacy on melanoma within the brain. Belvarafenib strongly suppressed melanoma in BRAF V600E mutant A375SM tumor-bearing mice. It also significantly inhibited tumor growth in NRAS mutant SK-MEL-30 and K1735 tumor-bearing mice and synergized to enhance the antitumor activity combined with cobimetinib or atezolizumab. Belvarafenib was penetrated at considerable levels into the brains of mice and rats following oral administration. The exposure of belvarafenib in the brain was similar to or higher than that in plasma, and this high brain penetration differed significantly from that of other BRAF inhibitors with low brain penetration. Most importantly, belvarafenib strongly reduced tumor burden and markedly improved survival benefits in mice intracranially implanted with A375SM melanoma. These results demonstrated that belvarafenib, which has favorable BBB permeability, and potent antitumor activity on the tumors with BRAF/NRAS mutations, may be a promising therapeutic option for patients with BRAF/NRAS mutant melanoma brain metastasis.

INTRODUCTION Semorinemab, an anti‐tau monoclonal antibody, was assessed in two Phase II trials for Alzheimer's disease (AD). Plasma and cerebrospinal fluid (CSF) biomarkers provided insights into the drug's potential mechanism of action. METHODS Qualified assays were used to measure biomarkers of tau, amyloidosis, glial activity, neuroinflammation, synaptic function, and neurodegeneration from participant samples in Tauriel (NCT03289143) and Lauriet (NCT03828747) Phase II trials. RESULTS Plasma phosphorylated Tau 181 (pTau181) and CSF chitinase‐3‐like protein 1 (YKL‐40) increased following semorinemab treatment in both studies. In Lauriet, increasing plasma glial fibrillary protein (GFAP) concentrations stabilized with semorinemab, while this was not observed in Tauriel. Other AD pathophysiology biomarkers showed no consistent response to semorinemab. DISCUSSION Increases in CSF YKL‐40 suggest that semorinemab may stimulate microglia activation in the presence of AD‐associated Tau pathology, but not in healthy controls. Stabilization of plasma GFAP in Lauriet indicates a possible impact on reactive gliosis in mild‐to‐moderate AD. Trial Registration: Tauriel ClinicalTrials.gov Identifier: NCT03289143. Lauriet ClinicalTrials.gov Identifier: NCT03828747. Phase 1 ClinicalTrials.gov Identifier: NCT02820896. Highlights AD pathophysiology biomarkers were measured to assess the mechanism of action. Semorinemab increased CSF YKL‐40 in participants with AD but not in healthy controls. Semorinemab possibly stabilized plasma GFAP in the Lauriet trial. Semorinemab treatment may activate microglia and moderate reactive gliosis.

To have maximal benefit, Alzheimer's disease-modifying treatments might need to be started before the onset of clinical symptoms. Mutations of the PSEN1 gene are inherited as fully penetrant, autosomal-dominant traits, which almost always result in the clinical onset of Alzheimer's disease before the age of 65 years. We aimed to evaluate the efficacy, including possible delayed emergence of cognitive impairment, and safety of crenezumab, an anti-amyloid monoclonal antibody, in cognitively unimpaired carriers of the PSEN1 mutation at high imminent risk of developing symptoms due to Alzheimer's disease. This 5-8-year common-close, double-blind, placebo-controlled, single-centre trial screened kindred members aged 30-60 years from the main health-care site in Medellín, Colombia. Participants who were cognitively unimpaired and carried the PSEN1 autosomal-dominant mutation were randomly assigned 1:1 to receive placebo or subcutaneous crenezumab (investigators and participants were masked to treatment allocation), with an initial 300 mg dose every 2 weeks that increased to 720 mg every 2 weeks, and a later optional increase to 60 mg/kg intravenously every 4 weeks. Randomisation was stratified by age, education, APOE ɛ4 carrier status, and baseline Clinical Dementia Rating. Mutation non-carriers received placebo and were included in a 1:2 ratio of non-carriers to carriers to maintain genotype masking and include a genetic kindred control. Dual primary outcomes were the annualised rates of change in the Alzheimer's Prevention Initiative (API) preclinical autosomal-dominant Alzheimer's disease (ADAD) composite test total score and Free and Cued Selective Reminding Test-Cueing Index (FCSRT-CI) assessed in randomised participants who received at least one dose of the study drug, according to treatment assignment. Primary endpoints were assessed with a random coefficient regression model with a missing-at-random assumption adjusting for randomisation factors. Safety endpoints for mutation carriers were assessed in randomised participants who received at least one dose of the study drug. This trial is registered with ClinicalTrials.gov (NCT01998841) and is completed. 619 Colombian API registrants were prescreened, 315 were assessed for eligibility, and 252 were enrolled (crenezumab-carrier, n=85; placebo-carrier, n=84; placebo-non-carrier, n=83; 160 [63%] women and 92 [37%] men) between Dec 20, 2013, and Feb 27, 2017. 237 (94%) completed the trial, with final data collection on March 22, 2022. The annualised rate of change in the API ADAD composite was -1·10 (SE 0·29) in the crenezumab group and -1·43 (0·29) in the placebo group (between-group difference 0·33 [95% CI -0·48 to 1·13]; p=0·43). The annualised rate of change in FCSRT-CI was -0·03 (0·00) in the crenezumab group and -0·04 (0·00) in the placebo group (between-group difference 0·01 [0·00 to 0·02]; p=0·16). All participants had at least one adverse event; serious adverse events occurred in 23 (27%) of 84 in the crenezumab group and 21 (25%) of 84 in the placebo group. No fatalities occurred. Crenezumab therapy administered for 5-8 years did not result in significant benefits on our primary clinical outcomes in cognitively unimpaired participants predisposed to developing ADAD dementia; secondary and exploratory outcomes also showed no significant effect on removal of amyloid plaques or other clinical or biomarker outcomes. Together with the results of other anti-amyloid β trials, robust fibrillar amyloid removal appears necessary for clinical efficacy in people with elevated brain amyloid. This study will further inform the biomarker, cognitive, and clinical trajectory of preclinical ADAD, the risk of clinical progression in amyloid-positive and amyloid-negative mutation carriers, and the size and design of future secondary and primary prevention trials. US National Institute on Aging (NIA), Banner Alzheimer's Institute, Genentech, F Hoffmann-La Roche.