Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.

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Clinical research in infectious respiratory diseases has been profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19. On top of trial delays or even discontinuation which have been observed in all disease areas, NPIs altered transmission pattern of many seasonal respiratory viruses which followed regular patterns for decades before the pandemic. Clinical trial design based on pre-pandemic historical data therefore needs to be put in question. In this article, we show how knowledge-based mathematical modeling can be used to address this issue. We set up an epidemiological model of respiratory tract infection (RTI) sensitive to a time dependent between-host transmission rate and coupled this model to a mechanistic description of viral RTI episodes in an individual patient. By reducing the transmission rate when the lockdown was introduced in the United Kingdom in March 2020, we were able to reproduce the perturbed 2020 RTI disease burden data. Using this setup, we simulated several NPIs scenarios of various strength (none, mild, medium, strong) and conducted placebo-controlled in silico clinical trials in pediatric patients with recurrent RTIs (RRTI) quantifying annual RTI rate distributions. In interventional arms, virtual patients aged 1-5 years received the bacterial lysate OM-85 (approved in several countries for the prevention of pediatric RRTIs) through a pro-type I immunomodulation mechanism of action described by a physiologically based pharmacokinetics and pharmacodynamics approach (PBPK/PD). Our predictions showed that sample size estimates based on the ratio of RTI rates (or the post-hoc power of fixed sample size trials) are not majorly impacted under NPIs which are less severe (none, mild and medium NPIs) than a strict lockdown (strong NPI). However, NPIs show a stronger impact on metrics more relevant for assessing the clinical relevance of the effect such as absolute benefit. This dichotomy shows the risk that successful trials (even with their primary endpoints being met) still get challenged in risk benefit assessment during the review of market authorization. Furthermore, we found that a mild NPI scenario already affected the time to recruit significantly when sticking to eligibility criteria complying with historical data. In summary, our model predictions can help rationalize and forecast post-COVID-19 trial feasibility. They advocate for gauging absolute and relative benefit metrics as well as clinical relevance for assessing efficacy hypotheses in trial design and they question eligibility criteria misaligned with the actual disease burden.

Introduction Epidermal growth factor receptor (EGFR) mutations occur in about 40% of Asian and 13-25% of Western patients with lung adenocarcinoma (LUAD) [1,2]. EGFR tyrosine kinase inhibitors (TKIs) have been developed to target tumors with an EGFR driver mutation. However such tumors also harbor additional mutations and genotypic alterations, which contribute to the variability in treatment response. Overall, intratumor heterogeneity is a dynamic source of therapeutic resistance. Here, we assessed the impact of tumor mutational profile on inter-patient treatment response variability by using a mechanistic model of late stage EGFR-mutant LUAD [3]. Methods We developed in the jinko platform a knowledge-based model of late stage EGFR-mutant LUAD, whose parameters each hold a pathophysiology-related meaning. Indeed, causality between disease-related biological phenomena is inherent to the mechanistic knowledge-based model, easing the biological interpretation of the impact of parameter values on model outcomes, which is highly valuable in the context of uncommon populations. To explore the impact of tumor heterogeneity, tumors are implemented with distinct subpopulations of cancer cells, called subclones. Each subclone is defined by a set of mutations and a corresponding proliferating phenotype. A virtual population representative of real world EGFR-mutant LUAD patients was developed to reproduce the variability observed between and within patients’ tumors for the modeled mutations. A physiologically-based pharmacokinetics model of a TKI drug, integrating a mechanistic modeling of the drug’s mechanism of action, is connected to the disease model. Results After simulating a clinical trial on a virtual population in the jinko platform using the developed model, we were able to follow the proliferating phenotype of each cancer subclone over time, as well as the overall evolution of the tumor size of each patient. The model reproduced the emergence of treatment-resistant subclones on EGFR TKI therapy. Modeling tumor size evolution throughout the clinical trial enabled computing the patients’ time to progression as clinical outcome, based on the RECIST 1.1 criteria, and generating corresponding survival curves. Stratification of virtual patients according to their tumor mutations allowed us to pinpoint key mutations that negatively impacted treatment response. Conclusion Modeling and simulation can help understanding how intratumor heterogeneity affects cancer evolution and drives resistance to treatment. Clinical trial simulations using knowledge-based models of disease and treatment provide relevant additional tools to help clinicians in exploring new hypotheses, providing treatment guidance and supporting therapeutic innovation. References [1] Dearden et al., AnnOnc, 2013 [2] Zhao et al., Sci Rep, 2017 [3] Palgen et al., ActaBiotheor., 2022 Citation Format: Perrine Masson, Claire Couty, Arnaud Nativel, Evgueni Jacob, Raphaël Toueg, Michaël Duruisseaux, Adèle L'Hostis, Jean-Louis Palgen, Claudio Monteiro. Simulations of tumor heterogeneity impact on treatment response using a mechanistic model of EGFR-mutant lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 859.

e21190 Background: 16,4% of lung adenocarcinomas (LUAD) are presenting a mutation in the epidermal growth factor receptor (EGFR), resulting in its constitutive activation and leading to uncontrolled cell proliferation [1]. Tyrosine kinase inhibitors (TKI) have been developed to inhibit EGFR activity but the presence of metastases and resistance mutations explain the lack of durable response to the treatment [2]. Knowledge-based mechanistic models reproducing existing clinical trials, based on population characteristics, can be used to help the design of future clinical trials. In particular, they can inform on the best responders to given treatments. Methods: We developed a physiologically based pharmacokinetic model of osimertinib, a 3rd generation TKI, to account for the distribution of the drug in the primary tumor and metastases after oral administration. This model was then combined to a pathophysiological mechanistic model of EGFR-mutant LUAD to represent the impact of osimertinib on the signals arising from EGFR activation. The combined model outputs the evolution of the primary tumor and each metastasis to allow the evaluation of the patient progression according to the RECIST criteria. Furthermore, each tumor in the model is composed of several subclones each possessing their own set of mutations and therefore responding differently to the treatment. Data from the clinical trials FLAURA and AURA3, in which osimertinib was given respectively as first and second line, were used to calibrate the model. Visual predictive checks as well as statistical tests were performed to ensure the proper behavior of the model. Results: The model successfully reproduced the time to progression in an EGFR mutant LUAD population treated with osimertinib as first line or as second line. In addition, the model reproduced the causes of progression according to the RECIST criteria and the sites of apparition of new metastases (in lung, brain, liver and bone). Conclusions: Reproduction of real world data brings credibility to the model. This is essential to use the model as an investigational tool to provide relevant insights, potentially on the best responders to osimertinib. After validation with additional clinical patient level data, the model could be used to create synthetic control arms in upcoming clinical trials. This would grant an improved analysis of covariate relationships with the comparison of an investigational treatment to the standard of care osimertinib administered as first or second line. It would also reduce the number of patients needed in the trial. References: [1] DOI: 10.1007/s11523-021-00848-9 [2] DOI: 10.3389/fonc.2020.602762 Acknowledgments: The authors would like to thank the novadiscovery team associated with this project and Janssen-Cilag France for their support.