National Institutes of Health Stroke Scale as an Outcome in Stroke Research: Value of ANCOVA Over Analyzing Change From Baseline

National Institutes of Health Stroke Scale (NIHSS), measured a few hours to days after stroke onset, is an attractive outcome measure for stroke research. NIHSS at the time of presentation (baseline NIHSS) strongly predicts the follow-up NIHSS. Because of the need to account for the baseline NIHSS in the analysis of follow-up NIHSS as an outcome measure, a common and intuitive approach is to define study outcome as the change in NIHSS from baseline to follow-up (ΔNIHSS). However, this approach has important limitations. Analyzing ΔNIHSS implies a very strong assumption about the relationship between baseline and follow-up NIHSS that is unlikely to be satisfied, drawing into question the validity of the resulting statistical analysis. This reduces the precision of the estimates of treatment effects and the power of clinical trials that use this approach to analysis. ANCOVA allows for the analysis of follow-up NIHSS as the dependent variable while adjusting for baseline NIHSS as a covariate in the model and addresses several challenges of using ΔNIHSS outcome using simple bivariate comparisons (eg, a t test, Wilcoxon rank-sum, linear regression without adjustment for baseline) for stroke research. In this article, we use clinical trial simulations to illustrate that variability in NIHSS outcome is less when follow-up NIHSS is adjusted for baseline compared to ΔNIHSS and how a reduction in this variability improves the power. We outline additional, important clinical and statistical arguments to support the superiority of ANCOVA using the final measurement of the NIHSS adjusted for baseline over, and caution against using, the simple bivariate comparison of absolute NIHSS change (ie, delta).

A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data

Vaccine efficacy is often assessed by counting disease cases in a clinical trial. A new quantitative framework proposed here (“PoDBAY,” Probability of Disease Bayesian Analysis), estimates vaccine efficacy (and confidence interval) using immune response biomarker data collected shortly after vaccination. Given a biomarker associated with protection, PoDBAY describes the relationship between biomarker and probability of disease as a sigmoid probability of disease (“PoD”) curve. The PoDBAY framework is illustrated using clinical trial simulations and with data for influenza, zoster, and dengue virus vaccines. The simulations demonstrate that PoDBAY efficacy estimation (which integrates the PoD and biomarker data), can be accurate and more precise than the standard (case-count) estimation, contributing to more sensitive and specific decisions than threshold-based correlate of protection or case-count-based methods. For all three vaccine examples, the PoD fit indicates a substantial association between the biomarkers and protection, and efficacy estimated by PoDBAY from relatively little immunogenicity data is predictive of the standard estimate of efficacy, demonstrating how PoDBAY can provide early assessments of vaccine efficacy. Methods like PoDBAY can help accelerate and economize vaccine development using an immunological predictor of protection. For example, in the current effort against the COVID-19 pandemic it might provide information to help prioritize (rank) candidates both earlier in a trial and earlier in development.

1013. Predicting RSV Efficacy for MK-1654 in Temperate and Tropical Climates using MBMA and Clinical Trial Simulation to Account for Seasonal Differences in RSV Force-of-Infection

Background MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody under development to prevent RSV infection in infants. A model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinically relevant endpoints (e.g. incidence rates) in humans, including lower respiratory tract infection (LRI) in infants, was presented previously. This model accounted for variable exposure to RSV over the course of the season through a force-of-infection (FOI) function modulating the overall risk of RSV infection over time. The objective of the current work was to determine whether variations in regional seasonality would impact the efficacy of a clinical trial evaluating MK-1654. Methods A FOI function to describe the degree of RSV exposure as a function of time was created by fitting epidemiological data to a Gaussian function added to a constant baseline value. Clinical trial simulations were conducted using the MBMA to predict seasonal incidence rates (IR) of RSV medically attended lower-respiratory tract infection (MALRI) and efficacies for a range of MK-1654 doses in both temperate and tropical regions. Results Epidemiological data was well captured by the FOI function. Clinical trial simulations indicated that seasonal IRs of RSV were sensitive to differences in the FOI represented by temperate and tropical regions; however, there was no substantial impact on efficacies across MK-1654 dose levels. Consistent with predictions for a temperate climate, MK-1654, when administered at the start of the RSV season in a region with a tropical climate, was also predicted to maintain high efficacy ( > 75%) for the prevention of RSV MALRI for 150 days. Conclusion Simulations indicated that while FOI is a substantial driver of overall RSV incidence rates, MK-1654 efficacy in a late-stage clinical trial is likely to be high, regardless of regional variations in RSV. Disclosures Nele Plock, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Jos Lommerse, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Brian M. Maas, PharmD, Merck & Co., Inc. (Employee, Shareholder) Jingxian Chen, PhD, Merck & Co., Inc. (Employee, Shareholder) Francesco Bellanti, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Li Qin, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Han Witjes, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Philippe Pierrillas, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Radha Railkar, PhD, Merck & Co., Inc. (Employee, Shareholder) Antonios O. Aliprantis, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Kalpit A. Vora, PhD, Merck & Co., Inc. (Employee, Shareholder) Wei Gao, PhD, Merck & Co., Inc. (Employee, Shareholder) Luzelena Caro, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Y. Amy Cheung, PhD, Certara (Employee, Shareholder) Jeffrey R. Sachs, PhD, Merck & Co., Inc. (Employee, Shareholder)

Blood parasite load as an early marker to predict treatment response in visceral leishmaniasis in Eastern Africa

Background In order to expedite the development of new oral treatment regimens for visceral leishmaniasis (VL), there is a need for early markers to evaluate treatment response and predict long-term outcomes. Methods Data from three clinical trials were combined in this study, where Eastern African VL patients received various antileishmanial therapies. Leishmania kinetoplast DNA was quantified in whole blood with real-time quantitative PCR (qPCR) before, during and up to six months after treatment. The predictive performance of pharmacodynamic parameters for clinical relapse was evaluated using receiver-operating characteristic curves. Clinical trial simulations were performed to determine the power associated with the use of blood parasite load as a surrogate endpoint to predict clinical outcome at six months. Results The absolute parasite density on day 56 after start of treatment was found to be a highly sensitive predictor of relapse within six months of follow-up at a cut-off of 20 parasites/mL (AUC 0.92, specificity 0.91, sensitivity 0.89). Blood parasite loads correlated well with tissue parasite loads (ρ= 0.80) and with microscopy gradings of bone marrow and spleen aspirate smears. Clinical trial simulations indicated a >80% power to detect a difference in cure rate between treatment regimens if this difference was high (>50%) and when minimally 30 patients were included per regimen. Conclusion Blood Leishmania parasite load determined by qPCR is a promising early biomarker to predict relapse in VL patients. Once optimized, it might be useful in dose finding studies of new chemical entities.

Novel Semi-Mechanistic Model Leveraging Preclinical and Clinical Data to Inform the Recommended Phase 2 Dose (RP2D) Selection for Epcoritamab (DuoBody CD3xCD20)

Introduction: Epcoritamab (DuoBody-CD3×CD20) is a subcutaneously administered bispecific antibody (bsAb) that simultaneously binds to CD3 on T cells and CD20 on malignant B cells, resulting in T-cell activation and expansion and selective T-cell-mediated killing of CD20+ cells. Target engagement (TE) and crosslinking of CD3 and CD20 (trimer formation) lead to activation and expansion of T cells, which in turn leads to tumor cell killing and is the first step driving the pharmacology of epcoritamab. Hence, the optimal clinical dose of epcoritamab can be informed by TE and trimer formation. Unlike typical monoclonal antibodies, bsAbs exhibit a hook effect in which trimer formation is impaired at high drug concentrations. Therefore, targeting complete receptor occupancy can lead to suboptimal trimer formation and clinical efficacy; instead, the aim should be to identify epcoritamab concentrations that result in maximal trimer formation. Epcoritamab is currently being investigated in an ongoing, open-label, multi-center, first-in-human trial in patients with relapsed/refractory B-cell non-Hodgkin lymphoma (NCT03625037). Endpoints include safety and identification of RP2D and pharmacokinetic/pharmacodynamic (PK/PD) analyses. Here, we present our approach for recommending the RP2D for epcoritamab based on a novel PK/PD model that predicts trimer formation. Methods: A semi-mechanistic PK/PD model was developed to quantitatively describe biodistribution of epcoritamab, trimer formation, and tumor response (Figure 1). This model makes use of preclinical data from cynomolgus monkeys, clinical PK/PD data, patient biomarker data, patient tumor characteristics, and response data. The model incorporates a minimal physiological-based PK model to predict epcoritamab concentration in tumors. The model also considers T- and B-cell dynamics, expression of CD3 and CD20 on these cells, and dynamic binding of epcoritamab to CD3 and CD20, as well as levels of trimer formation. Clinical trial simulations were performed using the PK/PD model, incorporating individual variability in key parameters, to predict the extent of trimer formation and tumor response in patients with diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). Variability in model parameters was based on either interindividual variability observed in the first-in-human trial or literature values. Results: The population PK/PD model was able to describe epcoritamab concentration-time profiles across doses (range: 0.0128-60 mg). Epcoritamab exhibited slow absorption, with Tmax of 2.8 days, a terminal half-life of 8.67 days, and target-mediated disposition (TMD). The model predicted the saturation of TMD to occur at dose levels ≥48 mg, indicating engagement and saturation of CD3 and CD20 in blood. In addition, the model was able to describe the exposure-response relationship observed in the clinic. Clinical trial simulations using the PK/PD model demonstrated that a 48-mg dose can achieve optimal trimer formation and clinical response in both FL and DLBCL. An exposure-adverse event analysis showed a flat relationship between epcoritamab exposure and risk of cytokine release syndrome (CRS) in the dose range evaluated. Based on these findings, 48 mg was identified as the potential RP2D for epcoritamab. Conclusions: For bsAbs such as epcoritamab, the optimal dose is one that leads to maximum trimer formation; hence, traditional PK modeling methodologies and exposure-response analyses are not adequate to guide RP2D selection. To overcome these limitations, we have developed a novel, semi-mechanistic PK/PD model incorporating preclinical, clinical, and patient biomarker data that identified the optimal dose for epcoritamab. This PK/PD model represents a novel approach and provides a general framework that can be applied to other CD3 bsAbs. Disclosures Li: Genmab: Current Employment. Hiemstra:Genmab: Current Employment, Current equity holder in publicly-traded company. Chiu:Genmab: Current Employment. Oliveri:Genmab: Current Employment, Current equity holder in publicly-traded company. DeMarco:Genmab: Current Employment, Current equity holder in publicly-traded company. Salcedo:Genmab: Current Employment. Lihme Egerod:Genmab: Current Employment. Gupta:Genmab: Current Employment. OffLabel Disclosure: Epcoritamab is an investigational agent undergoing evaluation in patients with relapsed/refractory B-cell non-Hodgkin lymphoma.