Modeling energy intake by adding homeostatic feedback and drug intervention

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.

Modeling energy intake and body weight effects of a long-acting amylin analogue

The inhibitory effect of anti-obesity drugs on energy intake (EI) is counter-acted by feedback regulation of the appetite control circuit leading to drug tolerance. This complicates the design and interpretation of EI studies in rodents that are used for anti-obesity drug development. Here, we investigated a synthetic long-acting analogue of the appetite-suppressing peptide hormone amylin (LAMY) in lean and diet-induced obese (DIO) rats. EI and body weight (BW) were measured daily and LAMY concentrations in plasma were assessed using defined time points following subcutaneous administration of the LAMY at different dosing regimens. Overall, 6 pharmacodynamic (PD) studies including a total of 173 rats were considered in this evaluation. Treatment caused a dose-dependent reduction in EI and BW, although multiple dosing indicated the development of tolerance over time. This behavior could be adequately described by a population model including homeostatic feedback of EI and a turnover model describing the relationship between EI and BW. The model was evaluated by testing its ability to predict BW loss in a toxicology study and was utilized to improve the understanding of dosing regimens for obesity therapy. As such, the model proved to be a valuable tool for the design and interpretation of rodent studies used in anti-obesity drug development.

Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin-concentrating Hormone Receptor 1 Antagonist

In this study, we present the translational modeling used in the discovery of AZD1979, a melanin‐concentrating hormone receptor 1 (MCHr1) antagonist aimed for treatment of obesity. The model quantitatively connects the relevant biomarkers and thereby closes the scaling path from rodent to man, as well as from dose to effect level. The complexity of individual modeling steps depends on the quality and quantity of data as well as the prior information; from semimechanistic body‐composition models to standard linear regression. Key predictions are obtained by standard forward simulation (e.g., predicting effect from exposure), as well as non‐parametric input estimation (e.g., predicting energy intake from longitudinal body‐weight data), across species. The work illustrates how modeling integrates data from several species, fills critical gaps between biomarkers, and supports experimental design and human dose‐prediction. We believe this approach can be of general interest for translation in the obesity field, and might inspire translational reasoning more broadly.

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Unlock the Thermogenic Potential of Adipose Tissue: Pharmacological Modulation and Implications for Treatment of Diabetes and Obesity

Brown adipose tissue (BAT) is considered an interesting target organ for the treatment of metabolic disease due to its high metabolic capacity. Non-shivering thermogenesis, once activated, can lead to enhanced partitioning and oxidation of fuels in adipose tissues, and reduce the burden of glucose and lipids on other metabolic organs such as liver, pancreas, and skeletal muscle. Sustained long-term activation of BAT may also lead to meaningful bodyweight loss. In this review, we discuss three different drug classes [the thiazolidinedione (TZD) class of PPARγ agonists, β3-adrenergic receptor agonists, and fibroblast growth factor 21 (FGF21) analogs] that have been proposed to regulate BAT and beige recruitment or activation, or both, and which have been tested in both rodent and human. The learnings from these classes suggest that restoration of functional BAT and beige mass as well as improved activation might be required to fully realize the metabolic potential of these tissues. Whether this can be achieved without the undesired cardiovascular side effects exhibited by the TZD PPARγ agonists and β3-adrenergic receptor agonists remains to be resolved.