A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.

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.

Phase 2 randomized clinical trial of astegolimab in patients with moderate to severe atopic dermatitis.

BACKGROUND: The binding of IL-33 to its receptor ST2 (alias of IL1RL1) leads to the release of inflammatory mediators and may play a role in the pathogenesis of atopic dermatitis. Astegolimab is a fully human, IgG2 mAb that binds to ST2 and inhibits IL-33 signaling. OBJECTIVES: This study sought to assess the efficacy, safety, and pharmacokinetics of astegolimab in patients with atopic dermatitis. METHODS: This was a randomized, placebo-controlled, phase 2 study in which adults with chronic atopic dermatitis were randomized 1:1 to receive astegolimab 490 mg every 4 weeks or placebo, for 16 weeks. The primary outcome was the percentage of change from baseline to week 16 of the Eczema Area and Severity Index score. RESULTS: A total of 65 patients were enrolled in the study (placebo, n = 32; astegolimab, n = 33). The adjusted mean percentage of change from baseline to week 16 in the Eczema Area and Severity Index score was -51.47% for astegolimab compared with -58.24% for placebo, with a nonsignificant treatment difference of 6.77% (95% CI: -16.57-30.11; P = .5624). No differences were observed between treatment groups for secondary efficacy outcomes and in exploratory biomarkers (blood eosinophils, serum IL-5, serum CCL13). With the use of loading dose, pharmacokinetic exposure was sufficient from week 1. Astegolimab was well-tolerated, with a safety profile consistent with that observed in previous clinical trials. CONCLUSIONS: In patients with atopic dermatitis, astegolimab did not show a significant difference compared to placebo for the primary or secondary outcomes.

Astegolimab (anti-ST2) efficacy and safety in adults with severe asthma: A randomized clinical trial.

BACKGROUND: The IL-33/ST2 pathway is linked with asthma susceptibility. Inhaled allergens, pollutants, and respiratory viruses, which trigger asthma exacerbations, induce release of IL-33, an epithelial-derived "alarmin." Astegolimab, a human IgG2 mAb, selectively inhibits the IL-33 receptor, ST2. Approved biologic therapies for severe asthma mainly benefit patients with elevated blood eosinophils (type 2-high), but limited options are available for patients with low blood eosinophils (type 2-low). Inhibiting IL-33 signaling may target pathogenic pathways in a wider spectrum of asthmatics. OBJECTIVES: This study evaluated astegolimab efficacy and safety in patients with severe asthma. METHODS: This double-blind, placebo-controlled, dose-ranging study (ZENYATTA [A Study to Assess the Efficacy and Safety of MSTT1041A in Participants With Uncontrolled Severe Asthma]) randomized 502 adults with severe asthma to subcutaneous placebo or 70-mg, 210-mg, or 490-mg doses of astegolimab every 4 weeks. The primary endpoint was the annualized asthma exacerbation rate (AER) at week 54. Enrollment caps ensured ∼30 patients who were eosinophil-high (≥300 cells/µL) and ∼95 patients who were eosinophil-low (<300 cells/µL) per arm. RESULTS: Overall, adjusted AER reductions relative to placebo were 43% (P = .005), 22% (P = .18), and 37% (P = .01) for 490-mg, 210-mg, and 70-mg doses of astegolimab, respectively. Adjusted AER reductions for patients who were eosinophil-low were comparable to reductions in the overall population: 54% (P = .002), 14% (P = .48), and 35% (P = .05) for 490-mg, 210-mg, and 70-mg doses of astegolimab. Adverse events were similar in astegolimab- and placebo-treated groups. CONCLUSIONS: Astegolimab reduced AER in a broad population of patients, including those who were eosinophil-low, with inadequately controlled, severe asthma. Astegolimab was safe and well tolerated.

Population-based meta-analysis of roxithromycin pharmacokinetics: dosing implications of saturable absorption and protein binding.

Objectives: The macrolide antibiotic roxithromycin has seen widespread clinical use for several decades; however, no population pharmacokinetic analysis has been published. Early studies indicated saturation of protein binding and absorption at doses within the approved range, which may impact pharmacodynamic target attainment since regimens of 150 mg twice daily and 300 mg once daily are used interchangeably in clinical practice. This study aimed to develop a population-based meta-analysis of roxithromycin pharmacokinetics, and utilize this model to inform optimal dosing regimens. Methods: Following an extensive search, roxithromycin pharmacokinetic data were collected or digitized from literature publications. Population pharmacokinetic modelling was undertaken with ADAPT. Dosing simulations were performed to investigate differences in exposure and pharmacodynamic target attainment between dosing regimens. Results: A two-compartment model with saturable absorption described the data ( n = 63); changes in free drug exposure were simulated using a saturable protein binding model. Simulations indicated that a 300 mg daily regimen achieves a 37% and 53% lower total or free AUC ( f AUC), respectively, compared with 150 mg twice daily. These pharmacokinetic differences translated to significantly lower target attainment ( f AUC/MIC ratio >20) with a 300 mg daily regimen at MICs of 0.5 and 1 mg/L (51% and 7%) compared with patients receiving 150 mg twice daily (82% and 54%). Conclusions: Roxithromycin displays saturable absorption and protein binding leading to lower exposure and lower target attainment at MICs ≥0.5 mg/L with widely used once-daily dosing regimens, indicating that twice-daily regimens may be preferable for pathogens less susceptible to roxithromycin.

Comparative Evaluation of the Predictive Performances of Three Different Structural Population Pharmacokinetic Models To Predict Future Voriconazole Concentrations.

Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously, but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and nonlinear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into individually designed optimum dosing strategies (ID-ODS) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used were from two independent data sets, patients with proven or suspected invasive fungal infections (n = 57) and hematopoietic stem cell transplant recipients (n = 10). Observed voriconazole concentrations were predicted whereby for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95% CI) were calculated to measure absolute bias and precision, while ΔME and ΔMSE and their 95% CI were used to measure relative bias and precision, respectively. A total of 519 voriconazole concentrations were analyzed using three models. MEs (95% CI) were 0.09 (-0.02, 0.22), 0.23 (0.04, 0.42), and 0.35 (0.16 to 0.54) while the MSEs (95% CI) were 2.1 (1.03, 3.17), 4.98 (0.90, 9.06), and 4.97 (-0.54 to 10.48) for the linear, mixed, and nonlinear models, respectively. In conclusion, while simulations with the linear model were found to be slightly more accurate and similarly precise, the small difference in accuracy is likely negligible from the clinical point of view, making all three approaches appropriate for use in a voriconazole TDM program.

Terbinafine in combination with other antifungal agents for treatment of resistant or refractory mycoses: investigating optimal dosing regimens using a physiologically based pharmacokinetic model.

Terbinafine is increasingly used in combination with other antifungal agents to treat resistant or refractory mycoses due to synergistic in vitro antifungal activity; high doses are commonly used, but limited data are available on systemic exposure, and no assessment of pharmacodynamic target attainment has been made. Using a physiologically based pharmacokinetic (PBPK) model for terbinafine, this study aimed to predict total and unbound terbinafine concentrations in plasma with a range of high-dose regimens and also calculate predicted pharmacodynamic parameters for terbinafine. Predicted terbinafine concentrations accumulated significantly during the first 28 days of treatment; the area under the concentration-time curve (AUC)/MIC ratios and AUC for the free, unbound fraction (fAUC)/MIC ratios increased by 54 to 62% on day 7 of treatment and by 80 to 92% on day 28 compared to day 1, depending on the dose regimen. Of the high-dose regimens investigated, 500 mg of terbinafine taken every 12 h provided the highest systemic exposure; on day 7 of treatment, the predicted AUC, maximum concentration (Cmax), and minimum concentration (Cmin) were approximately 4-fold, 1.9-fold, and 4.4-fold higher than with a standard-dose regimen of 250 mg once daily. Close agreement was seen between the concentrations predicted by the PBPK model and the observed concentrations, indicating good predictive performance. This study provides the first report of predicted terbinafine exposure in plasma with a range of high-dose regimens.

Understanding variability in posaconazole exposure using an integrated population pharmacokinetic analysis.

Posaconazole oral suspension is widely used for antifungal prophylaxis and treatment in immunocompromised patients, with highly variable pharmacokinetics reported in patients due to inconsistent oral absorption. This study aimed to characterize the pharmacokinetics of posaconazole in adults and investigate factors that influence posaconazole pharmacokinetics byusing a population pharmacokinetic approach. Nonlinear mixed-effects modeling was undertaken for two posaconazole studies in patients and healthy volunteers. The influences of demographic and clinical characteristics, such as mucositis, diarrhea, and drug-drug interactions, on posaconazole pharmacokinetics were investigated using a stepwise forward inclusion/backwards deletion procedure. A total of 905 posaconazole concentration measurements from 102 participants were analyzed. A one-compartment pharmacokinetic model with first-order oral absorption with lag time and first-order elimination best described posaconazole pharmacokinetics. Posaconazole relative bioavailability was 55% lower in patients who received posaconazole than in healthy volunteers. Coadministration of proton pump inhibitors (PPIs) or metoclopramide, as well as the occurrence of mucositis or diarrhea, reduced posaconazole relative bioavailability by 45%, 35%, 58%, and 45%, respectively, whereas concomitant ingestion of a nutritional supplement significantly increased bioavailability (129% relative increase). Coadministration of rifampin or phenytoin increased apparent posaconazole clearance by more than 600%, with a smaller increase observed with fosamprenavir (34%). Participant age, weight, or sex did not significantly affect posaconazole pharmacokinetics. Posaconazole absorption was reduced by a range of commonly coadministered medicines and clinical complications, such as mucositis and diarrhea. Avoidance of PPIs and metoclopramide and administration with food or a nutritional supplement are effective strategies to increase posaconazole absorption.

Optimizing azole antifungal therapy in the prophylaxis and treatment of fungal infections.

PURPOSE OF REVIEW: Azole antifungals are widely used in the prophylaxis and treatment of fungal infections, but are associated with a range of pharmacokinetic challenges and safety issues that necessitate individualized therapy to achieve optimal clinical outcomes. Recent advances in our knowledge of azole exposure-response relationships, therapeutic drug monitoring and individualized dosing strategies are reviewed as follows. RECENT FINDINGS: Recent studies have significantly improved the understanding of exposure-response relationships for efficacy and toxicity, increasing confidence in target exposure ranges for azole antifungal agents. Population pharmacokinetic modelling of voriconazole has led to studies demonstrating the feasibility of model-guided dose individualization strategies with the drug, which holds significant promise for optimizing therapy. The recent approval of a solid oral tablet formulation of posaconazole with improved bioavailability and once-daily dosing has significantly improved the clinical utility of this agent. Further clinical experience with the investigational azole isavuconazole is needed to determine the role of individualized therapy. SUMMARY: The coordination of CYP2C19 pharmacogenomic testing with model-guided dose individualization holds significant promise for optimizing therapy with voriconazole. Pharmacokinetic challenges with itraconazole, voriconazole and posaconazole oral suspension continue to require therapeutic drug monitoring to individualize therapy and optimize treatment outcomes.

Understanding variability with voriconazole using a population pharmacokinetic approach: implications for optimal dosing.

OBJECTIVES: Voriconazole exhibits highly variable, non-linear pharmacokinetics and is associated with a narrow therapeutic range. This study aimed to investigate the population pharmacokinetics of voriconazole in adults, including the effect of CYP2C19 genotype and drug-drug interactions. METHODS: Non-linear mixed effects modelling (NONMEM) was undertaken of six voriconazole studies in healthy volunteers and patients. Dosing simulations to examine influential covariate effects and voriconazole target attainment (2-5 mg/L) stratified by CYP2C19 phenotype were performed. RESULTS: We analysed 3352 voriconazole concentration measurements from 240 participants. A two-compartment pharmacokinetic model with first-order oral absorption with lag time and Michaelis-Menten elimination best described voriconazole pharmacokinetics. Participants with one or more CYP2C19 loss-of-function (LoF) alleles had a 41.2% lower Vmax for voriconazole. Co-administration of phenytoin or rifampicin, St John's wort or glucocorticoids significantly increased voriconazole elimination. Among patients receiving 200 mg of voriconazole twice daily, predicted trough concentrations on day 7 were <2 mg/L for oral and intravenous regimens for 72% and 63% of patients without CYP2C19 LoF alleles, respectively, with 49% and 35% below this threshold with 300 mg twice daily dosing. Conversely, these regimens resulted in 29%, 39%, 57% and 77% of patients with CYP2C19 LoF alleles with voriconazole trough concentrations ≥5 mg/L. CONCLUSIONS: Current dosing regimens for voriconazole result in subtherapeutic exposure in many patients without CYP2C19 LoF alleles, suggesting the need for higher doses, whereas these regimens result in supratherapeutic exposure in a high proportion of patients with reduced CYP2C19 activity. These findings support the essential role of therapeutic drug monitoring in ensuring efficacious and safe voriconazole exposure.

Optimal sampling of antipsychotic medicines: a pharmacometric approach for clinical practice.

AIM: To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach. METHODS: This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. d-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state. RESULTS: Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively. CONCLUSION: This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.

Understanding CYP3A4 and P-gp mediated drug-drug interactions through PBPK modeling - Case example of pralsetinib.

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.

Integrating Dynamic in vitro Systems and Mechanistic Absorption Modeling: Case Study of Pralsetinib.

Dynamic in vitro absorption systems and mechanistic absorption modeling via PBPK have both shown promise in predicting human oral absorption, although these efforts have been largely separate; this work aimed to integrate knowledge from these approaches to investigate the oral absorption of a RET inhibitor, pralsetinib, with BCS Class II properties. Tiny-TIM (TIM B.V., Weteringbrug​, The Netherlands) is a dynamic in vitro model with close simulation of the successive physiological conditions of the human stomach and small intestine. Tiny-TIM runs with pralsetinib were performed at doses of 200 mg and 400 mg under fasting conditions. Mechanistic modeling of absorption was performed in Simcyp V21 (Certara, Manchester, UK). Pralsetinib fasted bioaccessibility in the Tiny-TIM system was 63% at 200 mg and 53% at 400 mg; a 16% reduction at 400 mg was observed under elevated gastric pH. Maximum pralsetinib solubility from the small intestinal compartment in Tiny-TIM directly informed the supersaturation/precipitation model parameters. The PBPK model predicted a similar fraction absorbed at 200 mg and 400 mg, consistent with the dose proportional increases in observed pralsetinib exposure. Integrating dynamic in vitro systems with mechanistic absorption modeling provides a promising approach for understanding and predicting human absorption with challenging low solubility compounds.

Exploring the Use of a Kinetic pH Calculation to Correct the ACAT Model with a Single Stomach Compartment Setting: Impact of Stomach Setting on Food Effect Prediction for Basic Compounds.

Advanced compartmental absorption and transit (ACAT) based computational models have become increasingly popular in the industry for predicting oral drug product performance. However, due to its complexity, some compromises have been made in practice, and the stomach is often assigned as a single compartment. Although this assignment worked generally, it may not be sufficient to reflect the complexity of the gastric environment under certain conditions. For example, this setting was found to be less accurate in estimating stomach pH and solubilization of certain drugs under food intake, which leads to a misprediction of the food effect. To overcome the above, we explored the use of a kinetic pH calculation (KpH) for the single-compartment stomach setting. Several drugs have been tested with the KpH approach and compared with the default setting of Gastroplus. In general, the Gastroplus prediction of food effect is greatly improved, suggesting this approach is effective in improving the estimation of physicochemical properties related to food effect for several basic drugs by Gastroplus.

Factors contributing to the nonlinear pharmacokinetics of astegolimab: a close examination of potential causes.

Aim: This paper describes a case study of an antibody therapeutic targeting a membrane-bound receptor, also present in systemic circulation, as a soluble receptor. During phase I studies of astegolimab, nonlinear pharmacokinetics (PKs) were observed. We investigated the potential contribution of antidrug antibodies, target-mediated drug disposition and assay format. Materials & methods: A more target-tolerant assay was developed, and a subset of phase I samples were evaluated in both free and total PK assay formats. Results & conclusion: Our results demonstrate that there were two main contributors to PK nonlinearity: soluble target interference in the free PK assay, in addition to target-mediated drug disposition. Antidrug antibody status did not significantly impact PK.

Belvarafenib penetrates the BBB and shows potent antitumor activity in a murine melanoma brain metastasis model.

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.

Modeling Alzheimer's disease progression utilizing clinical trial and ADNI data to predict longitudinal trajectory of CDR-SB.

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.

Concentration QTc analysis of giredestrant: Overcoming QT/heart rate confounding in the presence of drug-induced heart rate changes.

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.

Randomized Phase II Study of the Safety and Efficacy of Semorinemab in Participants With Mild-to-Moderate Alzheimer Disease: Lauriet.

BACKGROUND AND OBJECTIVES: Accumulation of tau pathology in Alzheimer disease (AD) correlates with cognitive decline. Anti-tau immunotherapies were proposed as potential interventions in AD. While antibodies targeting N-terminal tau failed to demonstrate clinical efficacy in prodromal-to-mild AD, their utility at other disease stages was not evaluated in prior studies. Lauriet is a phase 2 study of an anti-tau monoclonal antibody, semorinemab, in patients with mild-to-moderate AD. METHODS: The phase 2 Lauriet study included a randomized, placebo-controlled, double-blind period, during which participants with mild-to-moderate AD received 4,500 mg of IV semorinemab or placebo every 4 weeks for 48 or 60 weeks. Participants who chose to continue in the subsequent optional open-label extension received 4,500 mg of semorinemab every 4 weeks for up to 96 weeks. Coprimary efficacy endpoints were change from baseline to week 49 or 61 on the 11-item version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog11) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) scale. Secondary efficacy endpoints included change from baseline on the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating-Sum of Boxes (CDR-SB). Safety, pharmacokinetics, and pharmacodynamic effects were also evaluated. RESULTS: Between December 3, 2018, and February 27, 2020, 624 individuals were screened, 272 participants were randomized, and 238 were included in the modified intent-to-treat population (received ≥1 dose(s) of study medication and underwent baseline and ≥1 postbaseline assessment(s)). Baseline characteristics were well balanced. At week 49, the semorinemab arm demonstrated a 42.2% reduction (-2.89 points, 95% CI -4.56 to -1.21, p = 0.0008) in decline on the ADAS-Cog11 (coprimary endpoint) relative to the placebo arm. However, no treatment effects were observed on the ADCS-ADL scale (coprimary endpoint; absolute difference between the 2 treatment arms in the ADCS-ADL score change from baseline of -0.83 points, 95% CI -3.39 to 1.72, p = 0.52) or on the MMSE or CDR-SB (secondary endpoints). Semorinemab was safe and well tolerated. DISCUSSION: Based on the results of the prespecified coprimary endpoints, this study was negative. While semorinemab had a significant effect on cognition measured by the ADAS-Cog11, this effect did not extend to improved functional or global outcomes. These results may warrant further exploration of semorinemab or other anti-tau therapies in mild-to-moderate AD. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that semorinemab does not slow functional decline in patients with mild-to-moderate AD. TRIAL REGISTRATION INFORMATION: The Lauriet study is registered on ClinicalTrials.gov, NCT03828747, and EudraCT 2018-003398-87.

Quantitative systems pharmacology model of the amyloid pathway in Alzheimer's disease: Insights into the therapeutic mechanisms of clinical candidates.

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 (Aß) pathophysiology in AD. The model included mechanisms of Aß 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-Aß 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 Aß accumulation in untreated individuals and those treated with monoclonal antibodies, capturing increases in Aß load accurately. This model may be used to investigate additional AD mechanisms and their impact on biomarkers, as well as predict Aß 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.

Streamlining Food Effect Assessment - Are Repeated Food Effect Studies Needed? An IQ Analysis.

Current regulatory guidelines on drug-food interactions recommend an early assessment of food effect to inform clinical dosing instructions, as well as a pivotal food effect study on the to-be-marketed formulation if different from that used in earlier trials. Study waivers are currently only granted for BCS class 1 drugs. Thus, repeated food effect studies are prevalent in clinical development, with the initial evaluation conducted as early as the first-in-human studies. Information on repeated food effect studies is not common in the public domain. The goal of the work presented in this manuscript from the Food Effect PBPK IQ Working Group was to compile a dataset on these studies across pharmaceutical companies and provide recommendations on their conduct. Based on 54 studies collected, we report that most of the repeat food effect studies do not result in meaningful differences in the assessment of the food effect. Seldom changes observed were more than twofold. There was no clear relationship between the change in food effect and the formulation change, indicating that in most cases, once a compound is formulated appropriately within a specific formulation technology, the food effect is primarily driven by inherent compound properties. Representative examples of PBPK models demonstrate that following appropriate validation of the model with the initial food effect study, the models can be applied to future formulations. We recommend that repeat food effect studies should be approached on a case-by-case basis taking into account the totality of the evidence including the use of PBPK modeling.