Evaluation of the role of the asialoglycoprotein receptor in the clearance of UK-279,276 (recombinant neutrophil inhibitory factor)

Sialylation-dependent pharmacokinetics and differential complement pathway inhibition are hallmarks of CR1 activity in vivo

Human Complement Receptor 1 (HuCR1) is a potent membrane-bound regulator of complement both in vitro and in vivo, acting via interaction with its ligands C3b and C4b. Soluble versions of HuCR1 have been described such as TP10, the recombinant full-length extracellular domain, and more recently CSL040, a truncated version lacking the C-terminal long homologous repeat domain D (LHR-D). However, the role of N-linked glycosylation in determining its pharmacokinetic (PK) and pharmacodynamic (PD) properties is only partly understood. We demonstrated a relationship between the asialo-N-glycan levels of CSL040 and its PK/PD properties in rats and non-human primates (NHPs), using recombinant CSL040 preparations with varying asialo-N-glycan levels. The clearance mechanism likely involves the asialoglycoprotein receptor (ASGR), as clearance of CSL040 with a high proportion of asialo-N-glycans was attenuated in vivo by co-administration of rats with asialofetuin, which saturates the ASGR. Biodistribution studies also showed CSL040 localisation to the liver following systemic administration. Our studies uncovered differential PD effects by CSL040 on complement pathways, with extended inhibition in both rats and NHPs of the alternative pathway compared to the classical and lectin pathways that were not correlated with its PK profile. Further studies showed that this effect was dose dependent and observed with both CSL040 and the full-length extracellular domain of HuCR1. Taken together, our data suggests that sialylation optimization is an important consideration for developing HuCR1-based therapeutic candidates such as CSL040 with improved PK properties and shows that CSL040 has superior PK/PD responses compared to full-length soluble HuCR1.

A single-species approach considering additional physiological information for prediction of hepatic clearance of glycoprotein derivate therapeutics

BACKGROUND AND OBJECTIVES

Existing methods for the prediction of human clearance of therapeutic proteins involve the use of allometry approaches. In general, these approaches have concentrated on the role of body weight, with only occasional attention given to more specific physiological parameters. The objective of this study was to develop a mechanism-based model of hepatic clearance (CL(H)), which combines a single-species scaling approach with liver physiology, for predicting CL(H) of selected glycoprotein derivate therapeutics, and to compare the outcome of this novel method with those of two empirical methods obtained from the literature - namely, the single-exponent theory and multiple-species allometry. Thus, this study was designed as an explanatory study to verify if the addition of physiological information is of benefit for extrapolating clearance of selected therapeutic proteins from one species to another.

METHODS

Five glycoprotein derivate therapeutics that are known to be principally eliminated by asialoglycoprotein receptors (ASGPRs) under in vivo conditions were selected. It was assumed that the interspecies differences in CL(H) reported for these compounds are reflected by the interspecies differences in the abundance of these receptors. Therefore, key scaling factors related to these differences were integrated into one model. Fourteen extrapolation (prediction) scenarios across species were used in this study while comparing the single-species model, based on physiology, with the single-exponent theory. In addition, the physiological model was compared with multiple-species allometry for three proteins.

RESULTS

In general, the novel physiological model is superior to the derived allometric methods. Overall, the physiological model produced a predicted CL(H) value with levels of accuracy of 100% within 3-fold, 100% within 2-fold and about 82% within 1.5-fold, compared with the observed values, whereas the levels of accuracy decreased to 93%, 77% and 53%, respectively, for allometry. The proposed physiological model is also superior to allometry on the basis of the root mean square error and absolute average fold error values.

CONCLUSIONS

It has been demonstrated that interspecies differences in the abundance of ASGPRs principally govern interspecies variations in CL(H) of compounds that are principally eliminated by ASGPRs. Overall, the proposed physiological model is an additional tool, which should facilitate investigation and prediction of human CL(H) of specific glycoproteins solely on the basis of clearance data determined in a single preclinical species.

Pharmacokinetics and clearance processes of UK-279,276 (rNIF) in rat and dog: comparison with human data

UK-279,276, or recombinant neutrophil inhibitory factor (rNIF), is a glycoprotein that binds to the human CD11b receptor (IC(50) = <0.5 nM) and inhibits neutrophil binding to vascular endothelial cells. In dogs, the pharmacokinetics of UK-279,276 are non-linear with clearance slowing as dose increases (apparent clearance 0.06 ml min(-1) kg(-1) at 0.l mg kg(-1) decreasing to 0.02 ml min(-1) kg(-1) at 2 mg kg(-1)). The observations are in keeping with human pharmacokinetic data. In rats, clearance is linear (0.07-0.13 ml min(-1) kg(-1)) across a similar dose range (0.006-2.25 mg kg(-1)) compared with dogs and humans. These data indicate that two clearance processes act on UK-279,276 in dogs and humans, but only one clearance process occurs in rats. A non-saturable, low-affinity/high-capacity clearance process is present in all animal species studied as well as in human, and it is believed to be hepatic uptake mediated by the asialoglycoprotein receptor. A second, saturable, clearance process is present in dogs and humans that is not apparent in the rat. This high-affinity/low-capacity process is thought to be mediated by interaction with the CD11b receptor, which is the pharmacological target for this molecule. This hypothesis is supported by the marked species differences seen for the affinity of CD11b for UK-279,276, with dogs and humans having high-affinity (IC(50) = <0.5 nM), whilst the rat affinity is low (IC(50) = 134 nM).

Role of mechanistically-based pharmacokinetic/pharmacodynamic models in drug development : a case study of a therapeutic protein

BACKGROUND AND OBJECTIVE

This case study describes the pharmacokinetic and pharmacodynamic modelling undertaken during the development programme for UK-279,276 (neutrophil inhibitory factor), focusing on the transition from early empirical-based models to a final mechanistic-based model. UK-279,276 binds to the CD11b/CD18 (MAC-1) on neutrophils and was under development for the treatment of ischaemic stroke.

METHODS

The aims, data, models, results and value-to-drug development process across four stages of model development are described: (i) the validation of the pharmacokinetic assay; (ii) the development and application of an empirical patient pharmacokinetic/pharmacodynamic model; (iii) the development of a mechanistic-based model to bridge between patients and healthy volunteers; and (iv) propagation of the stage III model to a large efficacy study. The analyses utilised available concentration measurements (stages I-IV), CD11b receptor occupancy data (stages I-III) and neutrophil count data (stages III-IV) from three healthy volunteers (study 1, n=51; study 2, n=31; study 4, n=15) and two patient studies (study 3, n=169; study 5, n=992). In studies 1-4, subjects received placebo or between three and six doses of UK-279,276 covering a range of 0.006 and 1.5 mg/kg as a single 15-minute intravenous infusion. In study 5, subjects received placebo or one of 15 possible doses of UK-279,276 (10--20mg) assigned through adaptive design and administered as a single 15-minute intravenous infusion. All model building was conducted using NONMEM version VI (beta). The empirical pharmacokinetic/pharmacodynamic model developed during stage I was used to demonstrate that the pharmacokinetic assay was measuring biologically active drug. Simulations from the stage II model, developed from study 3, were used in the design of study 5. The model supported the switch to a fixed-dose regimen and the selection of the maximum dose and dosage increments. The common mechanistic-based model developed during stage III was used to support the 'comparability strategy' for UK-279,276 and provided insight into the underlying clearance mechanisms. At stage 4, the prior functionality available with NONMEM was used to successfully propagate the model from stage III in order to analyse the pharmacokinetic data from study 5. The analysis indicated that the exposure in study 5 was consistent with prior data. The role of empirical-based models in providing the learning for future mechanistic model development was highlighted. Similarly, the qualitative and quantitative aspects to knowledge propagation and the ultimate benefits from the development of the mechanistic-based model were demonstrated. While the empirical-based models were used to guide some early drug development decisions for UK-279,276, the development of the mechanistic-based model was valuable in linking the complex pharmacokinetics/pharmacodynamics of UK-279,276 across the phases of drug development.

Bridging the Pharmacokinetics and Pharmacodynamics of UK-279,276 Across Healthy Volunteers and Stroke Patients Using a Mechanistically Based Model for Target-Mediated Disposition

PURPOSE

UK-279,276 is a recombinant glycoprotein and is a selective antagonist of CD11b, which in preclinical models of acute stroke blocks the infiltration of activated neutrophils into the site of infarction. Binding of UK-279,276 to the CD11b receptors is hypothesized to facilitate its elimination. The event of an acute stroke leads to proliferation of neutrophils and an up-regulation of CD11b, which results in different pharmacokinetics/pharmacodynamics (PK/PD) in patients than in healthy volunteers. The aim of this current analysis was to develop a mechanistically based model to bridge the differences between healthy volunteers and patients.

METHODS

PK samples, neutrophil counts, and total number and number of free CD11b receptors per neutrophils from three healthy volunteer studies (n=98) and one patient study (n=169) were modeled using the mixed effects modeling software NONMEM version VI (beta). Three mechanistic submodels were developed based on underlying physiology and pharmacology: (1) neutrophil maturation and proliferation, (2) CD11b up-regulation, and (3) three clearance pathways for UK-279-276 including CD11b-mediated elimination.

RESULTS

The model accurately described the time course of CD11b expression, CD11b binding, and the measured PK of UK-279,276 and accounted for the PK/PD differences between healthy volunteers and patients.

CONCLUSIONS

A complex mechanistic model that closely resembled the "true" underlying system provided an effective bridge between healthy volunteers and patients by appropriately accounting for the underlying disease-dependent target mediated disposition.