We present a meta-analytics approach to quantify NSCLC disease burden by integrative survival models. Aggregated survival data from public sources were used to parameterize the models for early as well as advanced NSCLC stages incorporating chemotherapies, targeted therapies, and immunotherapies. Overall survival (OS) was predicted in a heterogeneous patient cohort based on various stratifications and initial conditions. Pharmacoeconomic metrics (life years gained (LYG) and quality-adjusted life years (QALY) gained), were evaluated to quantify the benefits of specialized treatments and improved early detection of NSCLC. Simulations showed that the introduction of novel therapies for the advanced NSCLC sub-group increased median survival by 8.1 months (95 % CI: 5.9, 10.0), with corresponding gains of 2.9 months (95 % CI: 2.2, 3.6) in LYG and 1.65 months (95 % CI: 1.2, 2.0) in QALY. Scenarios representing improved detection of early cancer in the whole patient cohort, revealed up to 17.6 (95 % CI: 16.5, 19.0) and 15.7 months (95 % CI: 14.8, 16.6) increase in median survival, with respective gains of 6.2 months (95 % CI: 5.9, 6.4) and 5.2 months (95 % CI: 4.9, 5.4) in LYG and 6.6 months (95 % CI: 6.4, 6.7) and 6.0 months (95 % CI: 5.9, 6.2) in QALY for conventional and optimal treatment. This integrative modeling platform, aimed at characterizing cancer burden, allows to precisely quantify the cumulative benefits of introducing specialized therapies into the treatment schemes and survival prolongation upon early detection of the disease.

Early assessment of efficacy and dose optimization remain critical challenges in the development of anticancer therapies. Empirical models of solid tumor size dynamics—a key prognostic biomarker—have played a central role in addressing these challenges. However, a systematic comparison of commonly used tumor size models, in terms of descriptive and predictive performance as well as generalizability within a population framework, has not been conducted to date. The present research sought to develop a methodological framework for the optimization of tumor models, offering a basis for more accurate predictions of tumor dynamics. The corresponding modeling workflow was practically tested against clinical data of erlotinib, a treatment administered to patients with advanced NSCLC. Five widely used tumor size models were evaluated, of which only three—the Bi‐Exponential (BiExp), the Linear‐Exponential (LExp), and Claret's Tumor Growth Inhibition (TGI) model—demonstrated reproducibility of the base model during a repeated cross‐validation approach. Among these, the TGI model exhibited superior descriptive and predictive performance. However, a thorough literature search showed that erlotinib clinical data in NSCLC have been analyzed using only the BiExp and LExp models. Furthermore, extrapolation from 3 to 16 months revealed outlier predictions for the BiExp and TGI models, while the LExp model showed higher consistency, suggesting that models utilizing an exponential growth function may have a more limited extrapolation range than those assuming linear growth. Despite a clear ranking of models based on descriptive and predictive performance, no hierarchy emerged with respect to discriminatory ability. All three models showed high accuracy in distinguishing RECIST‐based objective responders, while accuracy in predicting the emergence of acquired resistance remained uniformly low. Trial Registration: Clinical trial number: NCT00364351

Background/Objectives: Olokizumab (OKZ) is a humanized monoclonal antibody targeting interleukin-6 (IL-6). OKZ demonstrated efficacy in subjects with active rheumatoid arthritis (RA) in randomized controlled trials versus adalimumab (ADLM) and placebo over 24 weeks, with sustained efficacy confirmed through 106 weeks in an open-label single-arm extension study (OLE). This analysis aimed to predict 1-year efficacy of OKZ 64 mg administered every two weeks (q2w) and every four weeks (q4w) compared with ADLM and placebo through virtual clinical trial simulations based on population pharmacokinetic/pharmacodynamic (PK/PD) models. Methods: Nonlinear mixed-effects models were developed using pooled data from phase III studies. Turnover models with a maximum inhibition I_max-type relationship between OKZ concentrations and clinical scores were applied. Simulated clinical trials were performed to estimate 52-week response rates, defined as proportions of patients achieving low disease activity (DAS28-CRP ≤ 3.2) and remission (CDAI ≤ 2.8). The reliability of the simulated extrapolations was validated against published data. Results: Log-transformed C-reactive protein (CRP) baseline levels and region were identified as significant covariates, influencing the dynamics of RA activity scores. OKZ and ADLM showed comparable predicted efficacy profiles, with overlapping 95% prediction intervals, and both were superior to placebo. Model predictions showed strong agreement with published long-term data for ADLM and placebo and closely reproduced the actual 1-year response profiles from the OKZ OLE. Conclusions: The developed PK/PD models showed good agreement with observed long-term efficacy outcomes for OKZ, ADLM, and placebo in MTX-IR RA patients. Both OKZ dosing regimens sustained low disease activity and remission rates comparable with those for ADLM over 52 weeks, supporting their use as effective treatment options.