An SPR-based method for Hill coefficient measurements: the case of insulin-degrading enzyme

Detection of insulin oligomeric forms by a novel surface plasmon resonance-diffusion coefficient based approach

Insulin is commonly used to treat diabetes and undergoes aggregation at the site of repeated injections in diabetic patients. Moreover, aggregation is also observed during its industrial production and transport and should be avoided to preserve its bioavailability to correctly adjust glucose levels in diabetic patients. However, monitoring the effect of various parameters (pH, protein concentration, metal ions, etc.) on the insulin aggregation and oligomerization state is very challenging. In this work, we have applied a novel Surface Plasmon Resonance (SPR)-based experimental approach to insulin solutions at various experimental conditions, monitoring how its diffusion coefficient is affected by pH and the presence of metal ions (copper and zinc) with unprecedented sensitivity, precision, and reproducibility. The reported SPR method, hereby applied to a protein for the first time, besides giving insight into the insulin oligomerization and aggregation phenomena, proved to be very robust for determining the diffusion coefficient of any biomolecule. A theoretical background is given together with the software description, specially designed to fit the experimental data. This new way of applying SPR represents an innovation in the bio-sensing field and expanding the potentiality of commonly used SPR instruments well over the canonical investigation of biomolecular interactions.

In Silico Investigation of the Clinical Translatability of Competitive Clearance Glucose-Responsive Insulins

The glucose-responsive insulin (GRI) MK-2640 from Merck was a pioneer in its class to enter the clinical stage, having demonstrated promising responsiveness in in vitro and preclinical studies via a novel competitive clearance mechanism (CCM). The smaller pharmacokinetic response in humans motivates the development of new predictive, computational tools that can improve the design of therapeutics such as GRIs. Herein, we develop and use a new computational model, IM3PACT, based on the intersection of human and animal model glucoregulatory systems, to investigate the clinical translatability of CCM GRIs based on existing preclinical and clinical data of MK-2640 and regular human insulin (RHI). Simulated multi-glycemic clamps not only validated the earlier hypothesis of insufficient glucose-responsive clearance capacity in humans but also uncovered an equally important mismatch between the in vivo competitiveness profile and the physiological glycemic range, which was not observed in animals. Removing the inter-species gap increases the glucose-dependent GRI clearance from 13.0% to beyond 20% for humans and up to 33.3% when both factors were corrected. The intrinsic clearance rate, potency, and distribution volume did not apparently compromise the translation. The analysis also confirms a responsive pharmacokinetics local to the liver. By scanning a large design space for CCM GRIs, we found that the mannose receptor physiology in humans remains limiting even for the most optimally designed candidate. Overall, we show that this computational approach is able to extract quantitative and mechanistic information of value from a posteriori analysis of preclinical and clinical data to assist future therapeutic discovery and development.