Rajesh Narwal

Safety and antitumour activity of durvalumab plus tremelimumab in non-small cell lung cancer: a multicentre, phase 1b study

BACKGROUND PD-L1 and CTLA-4 immune checkpoints inhibit antitumour T-cell activity. Combination treatment with the anti-PD-L1 antibody durvalumab and the anti-CTLA-4 antibody tremelimumab might provide greater antitumour activity than either drug alone. We aimed to assess durvalumab plus tremelimumab in patients with advanced squamous or non-squamous non-small cell lung cancer (NSCLC). METHODS We did a multicentre, non-randomised, open-label, phase 1b study at five cancer centres in the USA. We enrolled immunotherapy-naive patients aged 18 years or older with confirmed locally advanced or metastatic NSCLC. We gave patients durvalumab in doses of 3 mg/kg, 10 mg/kg, 15 mg/kg, or 20 mg/kg every 4 weeks, or 10 mg/kg every 2 weeks, and tremelimumab in doses of 1 mg/kg, 3 mg/kg, or 10 mg/kg every 4 weeks for six doses then every 12 weeks for three doses. The primary endpoint of the dose-escalation phase was safety. Safety analyses were based on the as-treated population. The dose-expansion phase of the study is ongoing. This study is registered with ClinicalTrials.gov, number NCT02000947. FINDINGS Between Oct 28, 2013, and April 1, 2015, 102 patients were enrolled into the dose-escalation phase and received treatment. At the time of this analysis (June 1, 2015), median follow-up was 18·8 weeks (IQR 11-33). The maximum tolerated dose was exceeded in the cohort receiving durvalumab 20 mg/kg every 4 weeks plus tremelimumab 3 mg/kg, with two (30%) of six patients having a dose-limiting toxicity (one grade 3 increased aspartate aminotransferase and alanine aminotransferase and one grade 4 increased lipase). The most frequent treatment-related grade 3 and 4 adverse events were diarrhoea (11 [11%]), colitis (nine [9%]), and increased lipase (eight [8%]). Discontinuations attributable to treatment-related adverse events occurred in 29 (28%) of 102 patients. Treatment-related serious adverse events occurred in 37 (36%) of 102 patients. 22 patients died during the study, and three deaths were related to treatment. The treatment-related deaths were due to complications arising from myasthenia gravis (durvalumab 10 mg/kg every 4 weeks plus tremelimumab 1 mg/kg), pericardial effusion (durvalumab 20 mg/kg every 4 weeks plus tremelimumab 1 mg/kg), and neuromuscular disorder (durvalumab 20 mg/kg every 4 weeks plus tremelimumab 3 mg/kg). Evidence of clinical activity was noted both in patients with PD-L1-positive tumours and in those with PD-L1-negative tumours. Investigator-reported confirmed objective responses were achieved by six (23%, 95% CI 9-44) of 26 patients in the combined tremelimumab 1 mg/kg cohort, comprising two (22%, 95% CI 3-60) of nine patients with PD-L1-positive tumours and four (29%, 95% CI 8-58) of 14 patients with PD-L1-negative tumours, including those with no PD-L1 staining (four [40%, 95% CI 12-74] of ten patients). INTERPRETATION Durvalumab 20 mg/kg every 4 weeks plus tremelimumab 1 mg/kg showed a manageable tolerability profile, with antitumour activity irrespective of PD-L1 status, and was selected as the dose for phase 3 studies, which are ongoing. FUNDING MedImmune.

Sustained peripheral depletion of amyloid-β with a novel form of neprilysin does not affect central levels of amyloid-β

Lowering levels of peripheral amyloid-β has been proposed as a strategy to reduce plaques in patients with Alzheimer’s disease. Henderson et al. test a modified version of the amyloid-degrading enzyme neprilysin in rats, monkeys and Tg2576 mice. Levels of amyloid-β were reduced in the bloodstream, but not in the CNS.

Population Pharmacokinetics of Durvalumab in Cancer Patients and Association With Longitudinal Biomarkers of Disease Status

The objectives of this analysis were to develop a population pharmacokinetics (PK) model of durvalumab, an anti‐PD‐L1 antibody, and quantify the impact of baseline and time‐varying patient/disease characteristics on PK. Pooled data from two studies (1,409 patients providing 7,407 PK samples) were analyzed with nonlinear mixed effects modeling. Durvalumab PK was best described by a two‐compartment model with both linear and nonlinear clearances. Three candidate models were evaluated: a time‐invariant clearance (CL) model, an empirical time‐varying CL model, and a semimechanistic time‐varying CL model incorporating longitudinal covariates related to disease status (tumor shrinkage and albumin). The data supported a slight decrease in durvalumab clearance with time and suggested that it may be associated with a decrease in nonspecific protein catabolic rate among cancer patients who benefit from therapy. No covariates were clinically relevant, indicating no need for dose adjustment. Simulations indicated similar overall PK exposures following weight‐based and flat‐dosing regimens.

A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition

When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.

Abstract 4531: Systems pharmacology to predict cellular biomarkers and optimize mono- and combination-therapy regimens: Focusing on immune checkpoint targets PD-1, PD-L1 and CTLA-4

Cancer immunotherapy focuses on stimulating and promoting the immune system to recognize and eliminate cancer cells, with several FDA approvals in recent years. However, identifying patients best suited for specific immune therapies, and determining optimal treatment regimens continue to be a clinical challenge. Using a molecular-detailed computational systems pharmacology model to identify cellular biomarkers and optimize regimens, we may be able to predict the efficacy of regimens in specific patient populations, and expedite drug development for cancer treatment. We developed a cell/receptor-based multi-compartment systems pharmacology model focusing on the immune response against a growing tumor, with the intent to test the effects of immune checkpoint inhibitors against PD-1, PD-L1 and CTLA-4 administered as mono- and combination therapies. Additionally, the model also allows for testing of other immuno-therapies, such as adoptive cell therapies, which can be combined with the checkpoint inhibitors. The model was designed and developed using the SimBiology plug-in in MATLAB. Simulations were performed with parameters that define the immune response at particular tumor stages of melanoma and NSCLC. All results were qualitatively and quantitatively compared to experimental pre-clinical and clinical data for model validation, or used for the generation of predictions suitable for further experimental testing. In silico, we have identified that administrations of the prescribed doses of 1-10 mg/kg of anti-CLTA-4 (based on binding kinetics) effectively saturates the receptors on the T cells, and promotes both an extended life span of the antigen presenting cells (APCs), and the maximum attainable activation levels of the effector T cells. The model further predicts that the effectiveness of anti-CTLA-4 therapy is limited by the immunogenicity of the system (i.e., the antigen intensity level and number of APCs presenting the antigens) in a monotonic fashion. Furthermore, injecting activated APCs without therapy would show a temporary tumor response and a subsequent recovery by the tumor to its original growth trajectory, while raising the antigen intensity had a sustained effect on tumor response. Other simulations indicate that, despite the lack of apparent tumor response, a sustained immune attack may be ongoing in the body; however, the immune activity is proportionally limited by the tumor and regulatory cells. Lastly, several dose-responses and clinical trials were simulated for both combination and monotherapies, and correlated with published clinical trial data. Future work will focus on uncovering the cellular biomarkers responsible for such results, experimentally validating them, as well as simulating optimal combination treatment regimens for future evaluation. Citation Format: Oleg Milberg, Chang Gong, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander Popel. Systems pharmacology to predict cellular biomarkers and optimize mono- and combination-therapy regimens: Focusing on immune checkpoint targets PD-1, PD-L1 and CTLA-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4531. doi:10.1158/1538-7445.AM2017-4531

Development of Immunoassays for the Quantitative Assessment of Amyloid-β in the Presence of Therapeutic Antibody: Application to Pre-Clinical Studies

Abstract Utilizing decision making biomarkers in drug development requires thorough assay validation. Special considerations need to be taken into account when monitoring biomarkers using immunoassays in the presence of therapeutic antibodies. We have developed robust and sensitive assays to assess target engagement and proof of mechanism to support the clinical progression of a human monoclonal antibody against the neurotoxic amyloid-β (Aβ)42 peptide. Here we present the introduction of novel pre-treatment steps to ensure drug-tolerant immunoassays and describe the validation of the complete experimental procedures to measure total Aβ42 concentration (bound and unbound) in cerebrospinal fluid (CSF) and plasma, free Aβ42 concentration (unbound) in CSF, and Aβ40 concentration in CSF. The difference in composition of the matrices (CSF and plasma) and antigen levels therein, in combination with the hydrophobic properties of Aβ protein, adds to the complexity of validation. Monitoring pharmacodynamics of an Aβ42 specific monoclonal antibody in a non-human primate toxicology study using these assays, we demonstrated a 1500-fold and a 3000-fold increase in total Aβ42 in plasma, a 4-fold and 8-fold increase in total Aβ42 in CSF together with a 95% and 96% reduction of free Aβ42 in CSF following weekly intravenous injections of 10 mg/kg and 100 mg/kg, respectively. Levels of Aβ40 were unchanged. The accuracy of these data is supported by previous pre-clinical studies as well as predictive pharmacokinetic/pharmacodynamics modeling. In contrast, when analyzing the same non-human primate samples excluding the pre-treatment steps, we were not able to distinguish between free and total Aβ42. Our data clearly demonstrate the importance of thorough evaluation of antibody interference and appropriate validation to monitor different types of biomarkers in the presence of a therapeutic antibody.

Population Modeling of Tumor Kinetics and Overall Survival to Identify Prognostic and Predictive Biomarkers of Efficacy for Durvalumab in Patients With Urothelial Carcinoma

Durvalumab is an anti‐PD‐L1 monoclonal antibody approved for patients with locally advanced or metastatic urothelial carcinoma (UC) that has progressed after platinum‐containing chemotherapy. A population tumor kinetic model, coupled with dropout and survival models, was developed to describe longitudinal tumor size data and predict overall survival in UC patients treated with durvalumab (NCT01693562) and to identify prognostic and predictive biomarkers of clinical outcomes. Model‐based covariate analysis identified liver metastasis as the most influential factor for tumor growth and immune‐cell PD‐L1 expression and baseline tumor burden as predictive factors for tumor killing. Tumor or immune‐cell PD‐L1 expression, liver metastasis, baseline hemoglobin, and albumin levels were identified as significant covariates for overall survival. These model simulations provided further insights into the impact of PD‐L1 cutoff values on treatment outcomes. The modeling framework can be a useful tool to guide patient selection and enrichment strategies for immunotherapies across various cancer indications.

PRECLINICAL DISCOVERY AND DEVELOPMENT OF MEDI1814, A MONOCLONAL ANTIBODY SELECTIVELY TARGETING BETA-AMYLOID 42 (Aβ42)

analysis of cognition collaboration’ (OA-Cog) aims to identify the most efficient cognitive measurement and analysis technique for cognition data and dementia in randomised controlled trials including patients with or at risk of vascular dementia or Alzheimer’s disease. Methods: Chief investigators of randomised controlled trials with cognitive outcome assessments are asked to share individual patient data from their trials. Variables requested include baseline prognostic factors, treatment group, cognitive measures (e.g. Mini Mental State Examination (MMSE), Alzheimer’s Disease Assessment Scale cognitive sub-score (ADAS-cog)) and other outcome measures (e.g. death, dementia). Shared trial data are merged into a single dataset and analysed using various endpoints (e.g. mean MMSE score at end of trial, MMSE score as a gradient over time) and statistical methods (e.g. Wilcoxon rank-sum test, repeated measures ANOVA) in order to identify which is the most efficient approach. Methods for dealing with missing data and, in particular, the case of missing data due to death will be addressed; currently, such patients are often ignored from analyses. Results: As of 23 December 2016, data from 32 clinical trials have been shared with the collaboration. Some of these trials have more than two treatment arms, so 50 datasets are available with a total of 120,576 participants. Conclusions:Optimising the design and analysis of cognition trials will allow future trials to detect smaller but still clinically important effects, and/or have smaller sample sizes than current trials.

Abstract CT113: Safety and activity of second-line durvalumab + tremelimumab in non-squamous advanced NSCLC

Background : The anti-PD-L1 antibody durvalumab (D) has shown manageable safety and encouraging clinical activity in patients with advanced NSCLC. Combination of D with anti-CTLA-4 antibody tremelimumab (T) may amplify T-cell responses against tumors through non-redundant immune checkpoint blockade and provide synergistic antitumor activity. We previously reported that D+T showed antitumor activity and manageable tolerability in the dose-escalation part of a phase 1b study (NCT02000947) of patients with advanced NSCLC. Here we present safety, clinical activity, and long-term follow-up for one of 3 expansion cohorts. Methods : Immunotherapy-naive patients with non-squamous NSCLC who progressed after 1 prior platinum-based therapy, ECOG PS 0-1, received D IV 20 mg/kg every 4 weeks (Q4W) for up to 12 months and T IV 1 mg/kg Q4W with the first 4 cycles of D. Tumor PD-L1 expression (fresh biopsy or archival sample within 3 mo) was assessed with the Ventana PD-L1 (SP263) assay, PD-L1 cutoff: ≥25% of tumor cells with membrane staining. Results : As of 20 Oct 2017, 213 patients (71% ECOG PS 1) received therapy and were followed for a median of 13.3 (0.5-21.0) mo. Treatment-related adverse events (AEs) were reported in 76% of patients; the most common were fatigue (19%), pruritus (17%), diarrhea (15%), decreased appetite (14%), and rash (14%). 14 patients (7%) had a treatment-related AE leading to discontinuation, with colitis (1%), diarrhea (1%), and pneumonitis (1%) reported in more than 1 patient. Grade 3/4 treatment-related AEs occurred in 23% of patients; the most common were increased lipase (5%), colitis (3%), and increased amylase (3%). There was 1 treatment-related death (multifactorial hypoxia). The 12-month OS rate was 53.8% (95% CI, 46.4-60.6). The Table shows antitumor activity and survival by PD-L1 status. Conclusions: Second-line D+T had a manageable safety profile in patients with non-squamous NSCLC. Clinical activity was seen in both PD-L1 ≥25% and Citation Format: Jamie Chaft, Byoung Chul Cho, Myung-Ju Ahn, Sylvestre Le Moulec, Eun Kyung Cho, Vassiliki Papadimitrakopoulou, Edward Garon, Sylvia Lee, Santiago Ponce Aix, Patrick C. Ma, Gregory Otterson, Rajesh Narwal, Guozhi Gao, Jennifer McDevitt, Judson Englert, Scott Antonia. Safety and activity of second-line durvalumab + tremelimumab in non-squamous advanced NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr CT113.

Abstract 5045: Pharmacokinetics and pharmacodynamics of MEDI0680, a fully human anti-PD1 monoclonal antibody, in patients with advanced malignancies

Background: MEDI0680 (AMP-514) is a humanized immunoglobulin gamma 4, kappa (IgG4κ) monoclonal antibody (mAb) specific for human programmed cell death-1 (PD-1), developed for the treatment of cancer. The primary objectives of this analysis were to (a) describe the pharmacokinetics (PK) of MEDI0680 and quantitate the impact of patient/disease characteristics on PK variability (b) to compare body weight (WT)-based and fixed dosing regimens of MEDI0680 and (c) to characterize PK-pharmacodynamic (receptor occupancy) relationship. Methods: A total of 905 serum concentration records from 58 patients in Phase 1 study (D6020C00002) designed to evaluate safety, tolerability and PK following 0.1, 0.5, 2.5, 10, and 20 mg/kg every 3 weeks (Q3W), every 2 weeks (Q2W) or weekly doses (QW) as intravenous (IV) infusion of MEDI0680 were included in this analysis. The population PK analysis was performed using a non-linear mixed effects modeling approach in NONMEM (version 7.2) software. Impact of patient demographics, clinical indices and biomarkers on PK parameters were explored. The appropriateness of the final model was tested using visual predictive check (VPC). A sequential PK-PD analysis was performed using receptor occupancy (RO) data from 35 subjects. Results: MEDI0680 PK profiles were best described using a 2-compartment model with linear clearance. The clearance (CL), volume of distribution (Vc) were 0.27 L/day, 5.07 L with a modest between-subject variability of 30% and 19%, respectively. None of the evaluated covariates showed any impact on PK parameters except a minor (not clinically relevant) impact of body weight on volume of distribution. VPC results demonstrated good predictability of the final population PK model. A direct Emax model described the PK-PD relationship of MEDI0680. The estimate of EC50 was approximately 9.3 µg/mL. PK/PD simulations indicate that following 20 mg/kg Q2W dose, >90% receptor occupancy can be maintained in all subjects. Based on preclinical/clinical PK, PD, and safety data, a dose of 20 mg/kg Q2W was selected for phase 2 studies. Conclusions: A population PK model of MEDI0680 was developed and validated. Modeling results indicate no need for dose adjustment based on patient/disease characteristics. Similar PK is expected following both WT-based and fixed dosing regimens. PK/PD findings support the dose of 20 mg/kg Q2W. Clinical studies are ongoing in various tumor types. Citation Format: Xuyang Song, Xizhe Gao, Bo Zheng, Chelsea Black, Matthew Gribbin, Joyson Karakunnel, Lorin Roskos, Rajesh Narwal. Pharmacokinetics and pharmacodynamics of MEDI0680, a fully human anti-PD1 monoclonal antibody, in patients with advanced malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5045. doi:10.1158/1538-7445.AM2017-5045