Tissue biodistribution of intravenous iron-carbohydrate nanomedicines differs between preparations with varying physicochemical characteristics in an anemic rat model.

Intravenously administered iron-carbohydrate preparations are a structurally heterogenous class of nanomedicines. Iron biodistribution to target tissues is greatly affected by the physicochemical characteristics of these nanoparticles. Some regulatory agencies have recommended performing studies in animal models for biodistribution characterization and bioequivalence evaluation. In the present work, a systematic comparison of iron exposure, tissue biodistribution and pharmacodynamics of four intravenous iron-carbohydrates in anemic CD rats was conducted. A pilot study was performed to establish the anemic rat model, followed by a control study to evaluate the pharmacokinetics (serum iron, biodistribution) and pharmacodynamics (hematological parameters) in healthy and anemic controls and anemic rats receiving ferric carboxymaltose (FCM). The same parameters were then evaluated in a comparative study in anemic rats receiving FCM, iron sucrose (IS), iron isomaltoside 1000 (IIM), and iron dextran (ID). Despite similar serum iron profiles observed across the investigated nanomedicines, tissue iron biodistribution varied markedly between the individual intravenous iron-carbohydrate complexes. Tissue iron repletion differences were also confirmed by histopathology. These results suggest that employing serum iron profiles as a surrogate for tissue biodistribution may be erroneous. The variability observed in tissue biodistribution may indicate different pharmacodynamic profiles and warrants further study.

Identification of Chemokine Ligands by Biochemical Fragmentation and Simulated Peptide Evolution.

Short linear peptides can overcome certain limitations of small molecules for targeting protein-protein interactions (PPIs). Herein, the interaction between the human chemokine CCL19 with chemokine receptor CCR7 was investigated to obtain receptor-derived CCL19-binding peptides. After identifying a linear binding site of CCR7, five hexapeptides binding to CCL19 in the low micromolar to nanomolar range were designed, guided by pharmacophore and lipophilicity screening of computationally generated peptide libraries. The results corroborate the applicability of the computational approach and the chosen selection criteria to obtain short linear peptides mimicking a protein-protein interaction site.

Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning.

Lipophilicity prediction is routinely applied to small molecules and presents a working alternative to experimental log P or log D determination. For compounds outside the domain of classical medicinal chemistry these predictions lack accuracy, advocating the development of bespoke in silico approaches. Peptides and their derivatives and mimetics fill the structural gap between small synthetic drugs and genetically engineered macromolecules. Here, we present a data-driven machine learning method for peptide log D 7.4 prediction. A model for estimating the lipophilicity of short linear peptides consisting of natural amino acids was developed. In a prospective test, we obtained accurate predictions for a set of newly synthesized linear tri- to hexapeptides. Further model development focused on more complex peptide mimetics from the AstraZeneca compound collection. The results obtained demonstrate the applicability of the new prediction model to peptides and peptide derivatives in a log D 7.4 range of approximately -3 to 5, with superior accuracy to established lipophilicity models for small molecules.