Parameter Identification for a Model of Neonatal Fc Receptor-Mediated Recycling of Endogenous Immunoglobulin G in Humans

Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC

Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.

Local Identifiability Analysis, Parameter Subset Selection and Verification for a Minimal Brain PBPK Model

Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.

Modelling acute antibody-mediated rejection of human kidney transplants using ex-vivo warm machine perfusion

BACKGROUND

Transplant rejection is a major cause of graft loss and morbidity. Currently, no human models of antibody-mediated rejection (AMR) exist, limiting mechanistic investigation and organ-specific targeted therapy. Here, using 12 human kidneys and ex-vivo normothermic machine perfusion, we demonstrate phenotypes of AMR after addition of antibodies against either human HLA class I or blood group antigens (A, B), thus modelling clinical AMR that can follow HLA incompatible (HLAi) or blood group incompatible (ABOi) transplantation.

METHODS

Discarded human kidneys with wide ranging demographics and cold ischaemia times (11-54 h) were perfused with red blood cells and fresh frozen plasma (FFP) as a source of complement/coagulation factors. For the HLAi model, 600 μg of W6/32 anti-class 1 HLA antibody was added to the circuit (time '0'). For the ABOi model, high titre FFP of the relevant blood group antibody was added. Renal blood flow index (RBFi, mL/min/100 g), C3 desArg, prothrombin fragments 1 + 2 and histology were determined. Our endpoints included haemodynamic changes, thrombosis, and biopsy proven complement deposition.

FINDINGS

Compared to control kidneys perfused without anti-donor antibodies, both models demonstrated haemodynamic collapse after antibody perfusion with only the HLAi model showing glomerular C4d deposition.

INTERPRETATION

We show that a clinically relevant human kidney model of AMR is feasible, and anticipate that these models, with refinements, could provide a basis to test different strategies to prevent AMR.

FUNDING

The Rosetrees and Stonygate Trust, The Royal College of Surgeons of England Fellowship Grant, NIHR Biomedical Research Centre/KCL Early Career Grant, Kidney Research U.K.