Maud Hennion (1), Elisabeth Rouits (1), Valérie Pireaux (2), Hans Warrinier (2)
(1) Cencora Pharmalex, (2) Bioxodes SA
Introduction:
Non-traumatic intracerebral haemorrhage (ICH) accounts for about 10-15% of all stroke cases. It is responsible for about 40% of all stroke deaths and represents the most severe subtype of stroke. A major contributor to poor outcomes after ICH is the secondary brain injury (SBI). No pharmacological treatments have demonstrated efficacy for SBIs caused by haemorrhage. In addition, due to the risk of bleeding, classical anticoagulation is not indicated within the first hours (< 24-48h) of haemorrhage onset. Ixodes ricinus-Contact Phase Inhibitor (Ir-CPI) is a novel investigational product for the prevention of acute thromboinflammatory events, especially under clinical circumstances where the use of classical anticoagulation is not adequate. Ir-CPI is a protein able to inhibit the intrinsic coagulation pathway (inhibition of FXII and FXI activation).
The objective of the following development is to characterize the pharmacokinetics (PK) of plasma Ir-CPI concentrations for its clinical development in patients with Spontaneous Intracerebral Haemorrhage.
Methods
A First-in-Human (FIH) Phase I study, double-blind, placebo-controlled, single dose escalation of Ir-CPI in healthy male provided an initial assessment of the safety, tolerability, PK, and pharmacodynamics (PD) of Ir-CPI after IV administration as 6 hour-infusion to 32 healthy adult male participants. The study included five groups of eight participants randomized to Ir-CPI doses of 1.5, 3, 6 and 9 mg/kg or placebo.
A Population PK analysis was conducted via nonlinear mixed effects modeling using NONMEM, V7.5. The First-Order Conditional Estimation with Interaction (FOCEI) method was used during the estimation process. Assessment of model adequacy and decisions about increasing model complexity were driven by the data and guided by goodness-of-fit criteria, including visual inspection of diagnostic plots, plausibility of parameter estimates, precision of parameter estimates, objective function value, and inspection of visual predictive checks. After graphical exploration and preliminary analyses, a two-compartment PK model with a first-order elimination from the central compartment was assumed for the initial structural model.
Results
407 quantifiable Ir-CPI concentrations from a total of 24 healthy male volunteers were used in the popPK analysis. Ir-CPI PK was adequately described by a 2-compartment model with linear elimination. The estimated PK parameters (RSE %) were apparent central clearance (CL/F) of 7.65 L/h (6.41%), apparent central volume of distribution (Vc/F) of 39.3 L (12.4%), apparent peripheral clearance (Q/F) of 12.8 L/h (6.27%), apparent peripheral volume of distribution (Vp/F) of 178 L (6.57%). Inter-individual variability was estimated on all parameters. Body weight was included on all clearances and volumes parameters using allometric scaling with fixed exponents.
Conclusion
Peptides cannot cross biomembranes easily and, therefore, are mostly confined in the extracellular space. Volume of distribution of peptides is typically small and not greater than the volume of the extracellular body fluid (Vss<15 L or 0.2 L/Kg). [1] Owing to the ubiquitous availability of proteases and peptidases throughout the body, proteolytic degradation is not limited to classic elimination organs. Since peptides are generally freely filtered by the kidneys, glomerular filtration and subsequent renal metabolism by proteolysis contribute to the elimination of many therapeutic peptides.[2]
The popPK model built on Phase I data provided good results in terms of goodness-of-fit plots and PK parameters with accurate estimates. Concentration-time course of Ir-CPI were reasonably well described. Although fitting reasonably well the observed PK data, the selected popPK model could not perfectly characterize the elimination process at higher exposure. Output parameters slightly differ from what might be expected for a peptide. Model development will be further investigated at higher exposures with a larger sample size when Phase II data will be available.
References:
[1] Di L. Strategic approaches to optimizing peptide ADME properties. AAPS J. 2015 Jan;17(1):134-43. doi: 10.1208/s12248-014-9687-3. Epub 2014 Nov 4. PMID: 25366889; PMCID: PMC4287298.
[2] Diao L, Meibohm B. Pharmacokinetics and pharmacokinetic-pharmacodynamic correlations of therapeutic peptides. Clin Pharmacokinet. 2013 Oct;52(10):855-68. doi: 10.1007/s40262-013-0079-0. PMID: 23719681.
[3] Urday, S., et al., Targeting secondary injury in intracerebral haemorrhage-perihaematomal oedema. Nat Rev Neurol, 2015. 11(2): p. 111-22.
[4] Qureshi, A.I., et al., Spontaneous intracerebral hemorrhage. N Engl J Med, 2001. 344(19): p. 1450-60.
[5] Shao, Z., S. Tu, and A. Shao, Pathophysiological Mechanisms and Potential Therapeutic Targets in Intracerebral Hemorrhage. Front Pharmacol, 2019. 10: p. 1079.
Reference: PAGE 32 (2024) Abstr 10800 [www.page-meeting.org/?abstract=10800]
Poster: Drug/Disease Modelling - Other Topics