The amyloid beta (A beta) peptide of Alzheimer's disease binds copper(II), and the peptide-bound metal may be a source of reactive oxygen species and neurotoxicity. To circumvent peptide aggregation and reduce redox activity, there is growing interest in using metal chelates as drug therapeutics for AD, whose design requires accurate data on the affinity of A beta peptides for copper(II). Reports on Cu2+ binding to A beta range from approximately 10(5) to approximately 10(9); these values' being obtained for different peptide lengths (1-16, 1-28, 1-40, 1-42) at varying pH. Herein, we report that Cu2+'s binding to A beta(1-40) at 37 degrees C occurs in a 1:1 stoichiometry with a pH-dependent binding constant: 1.1 (+/-0.2) x 10 (9) M (-1) and 2.4 (+/-0.2) x 10 (9) M(-1) at pH 7.2 and 7.4, respectively. Under identical conditions, A beta(1-16) reveals a comparable binding constant, confirming that this portion of the peptide is the binding region. Several previously reported values can be reconciled with the current measurement by careful consideration of thermodynamics associated with the presence of competing ligands used to solubilize copper.

SUMMARY There is no consensus on the coordinating ligands for Cu2+ by A β. Yet the differences in peptide sequence between human and rat have been hypothesized to alter metal ion binding in a manner that alters Cu2+-induced aggregation of A β. Herein, we employ isothermal titration calorimetry (ITC), circular dichroism (CD) and electron paramagnetic resonance (EPR) spectroscopy to examine the Cu2+ coordination spheres to human and rat A β and an extensive set of A β(16) mutants. EPR of the mutant peptides is consistent with a 3N1O binding geometry, like the native human peptide at pH 7.4. The thermodynamic data reveal an equilibrium between three coordination spheres, {NH2, O-, NIm His6, amide N−}, {NH2, O-, NIm His6, NIm His13} and {NH2, O-, NIm His6, NIm His14} for human A β(16) but only one for rat A β(16), { NH2, O-, NIm His6, N−}, at pH 7.4 -6.5. ITC and CD data establish that the mutation R5G is sufficient for reproducing this difference in Cu2+ binding properties at pH 7.4. The substitution of bulky and positively charged Arg by Gly is proposed to stabilize the coordination {NH2, O-, NIm His6, amide N−} that then results in one dominating coordination sphere for the case of the rat peptide. The differences in the coordination geometries for Cu2+ by the human and rat A β are proposed to contribute to the variation in the ability of Cu2+ to induce aggregation of A β peptides.

This work examines the temperature dependence of electron transfer (ET) kinetics in solid-state films of mixed-valent states of monodisperse, small (<2 nm) Au monolayer protected clusters (MPCs). The mixed valent MPC films, coated on interdigitated array electrodes, are Au25(SR)18(0/1-), Au25(SR)18(1+/0), and Au144(SR)60(1+/0), where SR = hexanethiolate for Au144 and phenylethanethiolate for Au25. Near room temperature and for ca. 1:1 mol:mol mixed valencies, the bimolecular ET rate constants (assuming a cubic lattice model) are ~2 × 10(6) M(-1) s(-1) for Au25(SR)18(0/1-), ~3 × 10(5) M(-1) s(-1) for Au25(SR)18(1+/0), and ~1 × 10(8) M(-1) s(-1) for Au144(SR)60(1+/0). Their activation energy ET barriers are 0.38, 0.34, and 0.17 eV, respectively. At lowered temperatures (down to ca. 77 K), the thermally activated (Arrhenius) ET process dissipates revealing a tunneling mechanism in which the ET rates are independent of temperature but, among the different MPCs, fall in the same order of ET rate: Au144(+1/0) > Au25(0/1-) > Au25(1+/0).

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Reported here are second-order rate constants of associative ligand exchanges of Au25L18 nanoparticles (L = phenylethanethiolate) of various charge states, measured by proton nuclear magnetic resonance at room temperature and below. Differences in second-order rate constants (M(-1) s(-1)) of ligand exchange (positive clusters ∼1.9 × 10(-5) versus negative ones ∼1.2 × 10(-4)) show that electron depletion retards ligand exchange. The ordering of rate constants between the ligands benzeneselenol > 4-bromobenzene thiol > benzenethiol reveals that exchange is accelerated by higher acidity and/or electron donation capability of the incoming ligand. Together, these observations indicate that partial charge transfer occurs between the nanoparticle and ligand during the exchange and that this is a rate-determining effect in the process.

The electronic conductivity of films of iridium oxide (IrO(x)) composed of ca. 2 nm nanoparticles (NPs) is strongly dependent on the film oxidation state. The Ir(IV)O(x) NPs can be electrochemically converted to several oxidation states, ranging from Ir(III) to Ir(V) oxides. The NP films exhibit a very high apparent conductivity, e.g., 10(-2) S cm(-1), when the NPs are in the oxidized +4/+5 state. When the film is fully reduced to its Ir(III) state, the apparent conductivity falls to 10(-6) S cm(-1).

Electron transfers (ETs) in mixed-valent ferrocene/ferrocenium materials are ordinarily facile. In contrast, the presence of ~1:1 mixed-valent ferrocenated thiolates in the organothiolate ligand shells of <2 nm diameter Au225, Au144, and Au25 monolayer-protected clusters (MPCs) exerts a retarding effect on ET between them at and below room temperature. Near room temperature, in dry samples, bimolecular rate constants for ET between organothiolate-ligated MPCs are diminished by the addition of ferrocenated ligands to their ligand shells. At lower temperatures (down to ~77 K), the thermally activated (Arrhenius) ET process dissipates, and the ET rates become temperature-independent. Among the Au225, Au144, and Au25 MPCs, the temperature-independent ET rates fall in the same order as at ambient temperatures: Au225 > Au144 > Au25. The MPC ET activation energy barriers are little changed by the presence of ferrocenated ligands and are primarily determined by the Au nanoparticle core size.

To date, few examples of dissolution models for real-time release testing (RTRT) have been approved for commercial drug products or published in literature. Thus, a structured approach has not been established by which a novice to the field could design, develop, validate, and implement an RTRT dissolution model. Moreover, with scant examples available, there has not been a body of work by which to learn of general regulatory expectations for such models.To address these gaps and to encourage conversation between regulatory and industrial experts on these topics, a virtual (web-based) workshop entitled "Predictive Dissolution Models for Real-Time Release Testing: Development and Implementation" was held November 11-12, 2021. This article summarizes key points from the podium presentations, panel discussions, and breakout sessions focusing on (1) the current best practices to establish predictive model specifications; (2) designing models to predict the "safe space" of a release test and creating models utilizing process analytical technology (PAT); and (3) exploring the strategy of compliant regulatory submissions, including model validation and post-approval lifecycle management. Industrial case studies were presented showcasing attempted approaches to and successful implementations of RTRT of dissolution for drug product manufacturing.