Modeling of Proliferating CD4 and CD8 T-Cell Changes to Tremelimumab Exposure in Patients with Unresectable Hepatocellular Carcinoma.

The STRIDE (Single Tremelimumab Regular Interval Durvalumab) regimen of single-dose tremelimumab 300 mg, plus durvalumab 1,500 mg every 4 weeks demonstrated potential for long-term survival in studies of unresectable hepatocellular carcinoma (uHCC; Study 22 and HIMALAYA). The aim of this analysis was to investigate changes in proliferating CD4+ Ki67+ and CD8+ Ki67+ T cells and their relationship with tremelimumab exposure in patients with uHCC. Median cell count, change from baseline, and percent change from baseline in CD4+ and CD8+ T cells peaked around 14 days after STRIDE. A model of CD4+ and CD8+ T cell response to tremelimumab exposure was developed. Patients with lower baseline T cell counts had a greater percent change from baseline in T cell response to tremelimumab, and baseline T-cell count was included in the final model. With the full covariate model, the half-maximal effective concentration (EC50 ) of tremelimumab was 6.10 µg/mL (standard error = 1.07 µg/mL); > 98.0% of patients were predicted to have a minimum plasma concentration greater than EC50 with tremelimumab 300 or 750 mg. For EC75 (9.82 µg/mL), 69.5% and 98.2% of patients were predicted to exceed the EC75 with tremelimumab 300 and 750 mg, respectively. This analysis supports the clinical hypothesis that combination anti-cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) and anti-programmed cell death ligand-1 (anti-PD-L1) therapy primes an immune response that may then be sustained by anti-PD-L1 monotherapy and supports the clinical utility of the STRIDE regimen in patients with uHCC. These insights may also help inform dose selection of anti-CTLA-4 plus anti-PD-L1 combination strategies.

The SARS-CoV-2 monoclonal antibody combination, AZD7442, is protective in nonhuman primates and has an extended half-life in humans.

Despite the success of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines, there remains a need for more prevention and treatment options for individuals remaining at risk of coronavirus disease 2019 (COVID-19). Monoclonal antibodies (mAbs) against the viral spike protein have potential to both prevent and treat COVID-19 and reduce the risk of severe disease and death. Here, we describe AZD7442, a combination of two mAbs, AZD8895 (tixagevimab) and AZD1061 (cilgavimab), that simultaneously bind to distinct, nonoverlapping epitopes on the spike protein receptor binding domain to neutralize SARS-CoV-2. Initially isolated from individuals with prior SARS-CoV-2 infection, the two mAbs were designed to extend their half-lives and reduce effector functions. The AZD7442 mAbs individually prevent the spike protein from binding to angiotensin-converting enzyme 2 receptor, blocking virus cell entry, and neutralize all tested SARS-CoV-2 variants of concern. In a nonhuman primate model of SARS-CoV-2 infection, prophylactic AZD7442 administration prevented infection, whereas therapeutic administration accelerated virus clearance from the lung. In an ongoing phase 1 study in healthy participants (NCT04507256), a 300-mg intramuscular injection of AZD7442 provided SARS-CoV-2 serum geometric mean neutralizing titers greater than 10-fold above those of convalescent serum for at least 3 months, which remained threefold above those of convalescent serum at 9 months after AZD7442 administration. About 1 to 2% of serum AZD7442 was detected in nasal mucosa, a site of SARS-CoV-2 infection. Extrapolation of the time course of serum AZD7442 concentration suggests AZD7442 may provide up to 12 months of protection and benefit individuals at high-risk of COVID-19.

Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Treated With Durvalumab.

Therapy optimization remains an important challenge in the treatment of advanced non-small cell lung cancer (NSCLC). We investigated tumor size (sum of the longest diameters (SLD) of target lesions) and neutrophil-to-lymphocyte ratio (NLR) as longitudinal biomarkers for survival prediction. Data sets from 335 patients with NSCLC from study NCT02087423 and 202 patients with NSCLC from study NCT01693562 of durvalumab were used for model qualification and validation, respectively. Nonlinear Bayesian joint models were designed to assess the impact of longitudinal measurements of SLD and NLR on patient subgrouping (by Response Evaluation Criteria in Solid Tumors 1.1 criteria at 3 months after therapy start), long-term survival, and precision of survival predictions. Various validation scenarios were investigated. We determined a more distinct patient subgrouping and a substantial increase in the precision of survival estimates after the incorporation of longitudinal measurements. The highest performance was achieved using a multivariate SLD and NLR model, which enabled predictions of NSCLC clinical outcomes.

Population pharmacokinetic analysis of esomeprazole in Japanese subjects with various CYP2C19 phenotypes.

WHAT IS KNOWN AND OBJECTIVE: Esomeprazole, the S-isomer of omeprazole, is a proton pump inhibitor which has been approved by over 125 countries, also known as NEXIUM® . Esomeprazole was developed to provide further improvement on efficacy for acid-related diseases with higher systemic bioavailability due to the less first-pass metabolism and lower plasma clearance. Esomeprazole is primarily metabolized by CYP2C19. Approximately <1% of Caucasians and 5%-10% of Asians have absent CYP2C19 enzyme activity. Although the influence of various CYP2C19 phenotypes on esomeprazole pharmacokinetics has been studied, this is the first report in the Japanese population where 27 low CYP2C19 metabolizers were included. METHODS: In this study, a population PK model describing the PK of esomeprazole was developed to understand the difference of CYP2C19 phenotypes on clearance in the Japanese population. The model quantitatively assessed the influence of CYP2C19 phenotype on esomeprazole PK in healthy Japanese male subjects after receiving repeated oral dosing. The inhibition mechanism of esomeprazole on CYP2C19 activity was also included in the model. RESULTS AND DISCUSSION: CYP2C19 phenotype and dose were found as statistically significant covariates on esomeprazole clearance. The apparent clearance at 10-mg dose was 17.32, 9.77 and 7.37 (L/h) for homozygous extensive metabolizer, heterozygous extensive metabolizer and poor metabolizer subjects, respectively. And the apparent clearance decreased as dose increased. WHAT IS NEW AND CONCLUSION: The established population PK model well described the esomeprazole PK and model-predicted esomeprazole PK was in good agreement with external clinical data, suggesting the robustness and applicability of the current model for predicting esomeprazole PK.

Modeling Tumor Growth and Treatment Resistance Dynamics Characterizes Different Response to Gefitinib or Chemotherapy in Non-Small Cell Lung Cancer.

Differences in the effect of gefitinib and chemotherapy on tumor burden in non-small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan-Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR-positive, chemotherapy in EGFR-positive, chemotherapy in EGFR-negative, and gefitinib in EGFR-negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR-positive than in EGFR-negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

Optimal designs for regional bridging studies using the Bayesian power prior method.

As described in the ICH E5 guidelines, a bridging study is an additional study executed in a new geographical region or subpopulation to link or "build a bridge" from global clinical trial outcomes to the new region. The regulatory and scientific goals of a bridging study is to evaluate potential subpopulation differences while minimizing duplication of studies and meeting unmet medical needs expeditiously. Use of historical data (borrowing) from global studies is an attractive approach to meet these conflicting goals. Here, we propose a practical and relevant approach to guide the optimal borrowing rate (percent of subjects in earlier studies) and the number of subjects in the new regional bridging study. We address the limitations in global/regional exchangeability through use of a Bayesian power prior method and then optimize bridging study design with a return on investment viewpoint. The method is demonstrated using clinical data from global and Japanese trials in dapagliflozin for type 2 diabetes.

Joint longitudinal and time-to-event models for multilevel hierarchical data.

Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Development and clinical validation of an in situ biopsy-based multimarker assay for risk stratification in prostate cancer.

PURPOSE: Prostate cancer aggressiveness and appropriate therapy are routinely determined following biopsy sampling. Current clinical and pathologic parameters are insufficient for accurate risk prediction leading primarily to overtreatment and also missed opportunities for curative therapy. EXPERIMENTAL DESIGN: An 8-biomarker proteomic assay for intact tissue biopsies predictive of prostate pathology was defined in a study of 381 patient biopsies with matched prostatectomy specimens. A second blinded study of 276 cases validated this assay's ability to distinguish "favorable" versus "nonfavorable" pathology independently and relative to current risk classification systems National Comprehensive Cancer Network (NCCN and D'Amico). RESULTS: A favorable biomarker risk score of ≤0.33, and a nonfavorable risk score of >0.80 (possible range between 0 and 1) were defined on "false-negative" and "false-positive" rates of 10% and 5%, respectively. At a risk score ≤0.33, predictive values for favorable pathology in very low-risk and low-risk NCCN and low-risk D'Amico groups were 95%, 81.5%, and 87.2%, respectively, higher than for these current risk classification groups themselves (80.3%, 63.8%, and 70.6%, respectively). The predictive value for nonfavorable pathology was 76.9% at biomarker risk scores >0.8 across all risk groups. Increased biomarker risk scores correlated with decreased frequency of favorable cases across all risk groups. The validation study met its two coprimary endpoints, separating favorable from nonfavorable pathology (AUC, 0.68; P < 0.0001; OR, 20.9) and GS-6 versus non-GS-6 pathology (AUC, 0.65; P < 0.0001; OR, 12.95). CONCLUSIONS: The 8-biomarker assay provided individualized, independent prognostic information relative to current risk stratification systems, and may improve the precision of clinical decision making following prostate biopsy.

A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza.

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system.