A Model-Informed Drug Discovery and Development Strategy for the Novel Glucose-Responsive Insulin MK-2640 Enabled Rapid Decision Making

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.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts.

The future of insulin therapy

Although insulin therapy was already introduced one-hundred years ago, insulin formulations are still being refined to reduce the risk of hypoglycaemia and of other insulin side effects such as weight gain. This review summarises the available clinical data for some ongoing developments of new insulins and evaluates their potential for future insulin therapy. Once-weekly insulins will most likely be the next addition to the insulin armamentarium. First clinical studies indicate low peak-to-trough fluctuations with these insulins indicating the potential to achieve better glycaemic control or reduce hypoglycaemic events versus available basal insulins. Proof-of-concept has also been established for hepato-preferential and oral insulins; however, adverse effects and low bioavailability still need to be overcome. It will take much longer, before glucose-responsive "smart" insulins will be available. A first clinical study and numerous pre-clinical data show the potential, but also the challenges of designing an insulin that quickly reacts to blood glucose changes and prevents hypoglycaemia and pronounced hyperglycaemia. Nevertheless, it is reassuring that the search for better insulins has never stopped since its first use one-hundred years ago and is still ongoing. New developments have a high potential of further improving the safety and efficacy of insulin therapy in the future.

'Smart' insulin-delivery technologies and intrinsic glucose-responsive insulin analogues

Insulin replacement therapy for diabetes mellitus seeks to minimise excursions in blood glucose concentration above or below the therapeutic range (hyper- or hypoglycaemia). To mitigate acute and chronic risks of such excursions, glucose-responsive insulin-delivery technologies have long been sought for clinical application in type 1 and long-standing type 2 diabetes mellitus. Such 'smart' systems or insulin analogues seek to provide hormonal activity proportional to blood glucose levels without external monitoring. This review highlights three broad strategies to co-optimise mean glycaemic control and time in range: (1) coupling of continuous glucose monitoring (CGM) to delivery devices (algorithm-based 'closed-loop' systems); (2) glucose-responsive polymer encapsulation of insulin; and (3) mechanism-based hormone modifications. Innovations span control algorithms for CGM-based insulin-delivery systems, glucose-responsive polymer matrices, bio-inspired design based on insulin's conformational switch mechanism upon insulin receptor engagement, and glucose-responsive modifications of new insulin analogues. In each case, innovations in insulin chemistry and formulation may enhance clinical outcomes. Prospects are discussed for intrinsic glucose-responsive insulin analogues containing a reversible switch (regulating bioavailability or conformation) that can be activated by glucose at high concentrations.

Review: Glucose-sensitive insulin

BACKGROUND

Hypoglycemia, the condition of low blood sugar, is a common occurance in people with diabetes using insulin therapy. Protecting against hypoglycaemia by engineering an insulin preparation that can auto-adjust its biological activity to fluctuating blood glucose levels has been pursued since the 1970s, but despite numerous publications, no system that works well enough for practical use has reached clinical practise.

SCOPE OF REVIEW

This review will summarise and scrutinise known approaches for producing glucose-sensitive insulin therapies. Notably, systems described in patent applications will be extensively covered, which has not been the case for earlier reviews of this area.

MAJOR CONCLUSIONS

The vast majority of published systems are not suitable for product development, but a few glucose-sensitive insulin concepts have recently reached clinical trials, and there is hope that glucose-sensitive insulin will become available to people with diabetes in the near future.

The Chemical Synthesis of Insulin: An Enduring Challenge

The pancreatic peptide hormone insulin, first discovered exactly 100 years ago, is essential for glycemic control and is used as a therapeutic for the treatment of type 1 and, increasingly, type 2 diabetes. With a worsening global diabetes epidemic and its significant health budget imposition, there is a great demand for new analogues possessing improved physical and functional properties. However, the chemical synthesis of insulin's intricate 51-amino acid, two-chain, three-disulfide bond structure, together with the poor physicochemical properties of both the individual chains and the hormone itself, has long represented a major challenge to organic chemists. This review provides a timely overview of the past efforts to chemically assemble this fascinating hormone using an array of strategies to enable both correct folding of the two chains and selective formation of disulfide bonds. These methods not only have contributed to general peptide synthesis chemistry and enabled access to the greatly growing numbers of insulin-like and cystine-rich peptides but also, today, enable the production of insulin at the synthetic efficiency levels of recombinant DNA expression methods. They have led to the production of a myriad of novel analogues with optimized structural and functional features and of the feasibility for their industrial manufacture.