Retrospective analysis of model-based predictivity of human pharmacokinetics for anti-IL-36R monoclonal antibody MAB92 using a rat anti-mouse IL-36R monoclonal antibody and RNA expression data (FANTOM5)

Review of the Existing Translational Pharmacokinetics Modeling Approaches Specific to Monoclonal Antibodies (mAbs) to Support the First-In-Human (FIH) Dose Selection

The interest in therapeutic monoclonal antibodies (mAbs) has continuously growing in several diseases. However, their pharmacokinetics (PK) is complex due to their target-mediated drug disposition (TMDD) profiles which can induce a non-linear PK. This point is particularly challenging during the pre-clinical and translational development of a new mAb. This article reviews and describes the existing PK modeling approaches used to translate the mAbs PK from animal to human for intravenous (IV) and subcutaneous (SC) administration routes. Several approaches are presented, from the most empirical models to full physiologically based pharmacokinetic (PBPK) models, with a focus on the population PK methods (compartmental and minimal PBPK models). They include the translational approaches for the linear part of the PK and the TMDD mechanism of mAbs. The objective of this article is to provide an up-to-date overview and future perspectives of the translational PK approaches for mAbs during a model-informed drug development (MIDD), since the field of PK modeling has gained recently significant interest for guiding mAbs drug development.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

A Novel Immune-Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.

Methods

First, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.

Results

A nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.

Conclusions

This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.

Preclinical characterization of the ADME properties of a surrogate anti-IL-36R monoclonal antibody antagonist in mouse serum and tissues

The decision to pursue a monoclonal antibody (mAb) as a therapeutic for disease intervention requires the assessment of many factors, such as target-biology, including the total target burden and its accessibility at the intended site of action, as well as mAb-specific properties like binding affinity and the pharmacokinetics in serum and tissue. Interleukin-36 receptor (IL-36 R) is a member of the IL-1 family cytokine receptors and an attractive target to treat numerous epithelial-mediated inflammatory conditions, including psoriatic and rheumatoid arthritis, asthma, and chronic obstructive pulmonary disease. However, information concerning the expression profile of IL-36 R at the protein level is minimal, so the feasibility of developing a therapeutic mAb against this target is uncertain. Here, we present a characterization of the properties associated with absorption, distribution, metabolism, and excretion of a high-affinity IL-36 R-targeted surrogate rat (IgG2a) mAb antagonist in preclinical mouse models. The presence of IL-36 R in the periphery was confirmed unequivocally as the driver of non-linear pharmacokinetics in blood/serum, although a predominant site of tissue accumulation was not observed based upon the kinetics of radiotracer. Additionally, the contribution of IL-36 R-mediated catabolism of mAb in kidney was tested in a 5/6 nephrectomized mouse model where minimal effects on serum pharmacokinetics were observed, although analysis of functional mAb in urine suggests that target can influence the amount of mAb excreted. Our data highlight an interesting case of target-mediated drug disposition (TMDD) where low, yet broadly expressed levels of membrane-bound target result in a cumulative effect to drive TMDD behavior typical of a large, saturable target sink. The potential differences between our mouse model and IL-36 R target profile in humans are also presented.

Quantification of surrogate monoclonal antibodies in mouse serum using LC-MS/MS

Background: Surrogate monoclonal antibodies (mAbs) used in preclinical in vivo studies can be challenging to quantify due to lack of suitable immunoaffinity reagents or unavailability of the mAb protein sequence. Generic immunoaffinity reagents were evaluated to develop sensitive LC-MS/MS assays. Peptides of unknown sequence can be used for selective LC-MS quantification. Results: anti-mouse IgG1 was found to be an effective immunoaffinity reagent, enabling quantification of mouse IgG1 mAbs in mouse serum. Selective peptides of unknown sequence were applied for multiplex LC-MS quantification of two rat mAbs co-dosed in mouse. Conclusion: Generic anti-mouse IgG subtype-specific antibodies can be used to improve assay sensitivity and peptides of unknown sequence can be used to quantify surrogate mAbs when the mAb protein sequence in unavailable.

Identification and validation of the prognostic value of immune-related genes in non-small cell lung cancer

Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The aim of this research study was to identify differentially expressed immune-related genes (DEIRGs) and establish a Cox prediction model for the evaluation of prognosis in patients with non-small cell lung cancer (NSCLC). Transcription expression data, immune gene data, and tumor transcription factor data from The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and the Cistrome Cancer database were analyzed to detect differentially expressed genes (DEGs), DEIRGs, and differentially expressed transcription factors (DETFs). Multivariate Cox regression analysis was used to obtain potential DEIRGs as independent prognostic factors. Oncomine, The Human Protein Atlas (HPA), TIMER databases were performed to validate the mRNA and protein expression level of DEIRGs. TIMER database was performed to explore the immunocytes infiltration of DEIRGs. In total, 7448 DEGs, 536 DEIRGs, 87 DETFs were identified from 1,037 NSCLC tissues and 108 normal tissues in TCGA database. Fifteen-DEIRG signatures (THBS1, S100P, S100A16, DLL4, CD70, DKK1, IL33, NRTN, PDGFB, STC2, VGF, GCGR, HTR3A, LGR4, SHC3) could be perceived as independent prognostic factors for predicting the overall survival of patients with NSCLC (P = 4.89e--09). Immune cell correlation analysis showed that neutrophils and b cells were positively and negatively correlated with the riskscore of the prediction model, respectively. Our study identified a Cox prediction model based on DEIRGs to predict the overall survival of patients with NSCLC. The immunocyte infiltration analysis provided a novel horizon for monitoring the status of the NSCLC immune microenvironment.