Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis

Abstract

BackgroundMechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion.ResultsVirtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon β (IFNβ) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFNβ that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFNβ on proliferation of fibroblast-like synoviocytes and interleukin-10 synthesis in macrophages each partially counteract reductions in synovial inflammation imparted by rituximab. A multianalyte biomarker panel predictive for virtual population therapeutic responses suggested population dependencies on B cell-dependent mediators as well as additional markers implicating fibroblast-like synoviocytes.ConclusionsThe results illustrate how the MAPEL algorithm can leverage knowledge of cellular and molecular function through biosimulation to propose clear mechanistic hypotheses for differences in clinical populations. Furthermore, MAPEL facilitates the development of multianalyte biomarkers prognostic of patient responses in silico.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

“…Therefore, it is important to evaluate the impacts of known variability and uncertainty, where QSP models often use ensembles of alternative parameterizations that appear to be plausibly in agreement with observed data (8,25,26). As one concrete example, it is not uncommon to find quantitatively different in vitro measures of the same process reported by two different labs.…”

“…QSP models enable testing the impact of mechanistic differences that would be inferred from interlab variability on endpoints of interest in silico, and therefore help triage impactful uncertainty that must be resolved or explained from uncertainty that is not impactful and is therefore not worth investing experimental or clinical resources to resolve. Alternate parameterizations are referred to as virtual subjects (8), or virtual patients (VPs) (26,27), and the ensembles are referred to as a virtual cohort or cohort of virtual patients (see Table I). Note that verification of plausibility generally may or may not be assumed in the description of a virtual patient depending on the workflow and algorithm (25), and here these are specifically referred to as Bplausible virtual patients^to avoid ambiguity.…”

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

“…Methods to statistically calibrate alternative QSP model parameterizations, also known as virtual patients or virtual subjects, to both preclinical and clinical data to develop virtual populations have been demonstrated and applied in several therapeutic areas. [106][107][108][109][110][111] In 1 study, a set of virtual patients in a model of rheumatoid arthritis was calibrated to multiple phase 3 trials for adalimumab, rituximab, and tocilizumab to develop a virtual population calibrated to methotrexate-inadequate responding and anti-TNF-inadequate responding patient trial data. 110 Subsequently, new clinical regimens of reduced dosing were implemented with the existing therapies in the calibrated virtual population.…”

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

“…Virtual populations have been used by others, particularly in the field of quantitative systems pharmacology (16,(22)(23)(24)(25)(26)(27)(28)(29)(30). Overwhelmingly (see, for instance, refs.…”

“…ple, virtual populations have been used to demonstrate the clinical importance of accounting for the correlated expression of different metabolic enzymes (26), and to provide mechanistic explanations for variations in response to a drug for rheumatoid arthritis (27).…”