Modeling approaches to type 2 diabetes

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists

ABSTRACT

Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk

OBJECTIVE

To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the analysis of comorbidities.

MATERIALS AND METHODS

In this cross-sectional study, we have used data from 11,028 anonymized records of patients admitted to a high-complexity hospital in Bogota, Colombia between 2017-2019 with a primary diagnosis of Diabetes. These cases were classified according to Charlson's comorbidity index in several risk categories. The main variables analyzed in this study are hospitalization costs (which include pharmaceutical and non-pharmaceutical expenditures), age, gender, length of stay, medicines and services consumed, and comorbidities assessed by the Charlson's index. The model's dependent variable is expenditure (composed of pharmaceutical and non-pharmaceutical expenditures). Based on these variables, different machine learning models (Multivariate linear regression, Lasso model, and Neural Networks) were used to estimate the pharmaceutical and non-pharmaceutical expenditures associated with the clinical risk classification. To evaluate the performance of these models, different metrics were used: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2).

RESULTS

The results indicate that the Neural Networks model performed better in terms of accuracy in predicting pharmaceutical and non-pharmaceutical expenditures considering the clinical risk based on Charlson's comorbidity index. A deeper understanding and experimentation with Neural Networks can improve these preliminary results, therefore we can also conclude that the main variables used and those that were proposed can be used as predictors for the medical expenditures of patients with diabetes type-II.

CONCLUSIONS

With the increase of technology elements and tools, it is possible to build models that allow decision-makers in hospitals to improve the resource planning process given the accuracy obtained with the different models tested.

A systematic approach to orient the human protein-protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Modeling of Diabetes and Its Clinical Impact

Understanding all aspects of diabetes treatment is hindered by the complexity of this chronic disease and its multifaceted complications and comorbidities, including social and financial impacts. In vivo studies as well as clinical trials provided invaluable information for unraveling not only metabolic processes but also risk estimations of, for example, complications. These approaches are often time- and cost-consuming and have frequently been supported by simulation models. Simulation models provide the opportunity to investigate diabetes treatment from additional viewpoints and with alternative objectives. This review presents selected models focusing either on metabolic processes or risk estimations and financial outcomes to provide a basic insight into this complex subject. It also discusses opportunities and challenges of modeling diabetes.

Third Exposure to a Reduced Carbohydrate Meal Lowers Evening Postprandial Insulin and GIP Responses and HOMA-IR Estimate of Insulin Resistance

Background

Postprandial hyperinsulinemia, hyperglycemia, and insulin resistance increase the risk of type 2 diabetes (T2D) and cardiovascular disease mortality. Postprandial hyperinsulinemia and hyperglycemia also occur in metabolically healthy subjects consuming high-carbohydrate diets particularly after evening meals and when carbohydrate loads follow acute exercise. We hypothesized the involvement of dietary carbohydrate load, especially when timed after exercise, and mediation by the glucose-dependent insulinotropic peptide (GIP) in this phenomenon, as this incretin promotes insulin secretion after carbohydrate intake in insulin-sensitive, but not in insulin-resistant states.

Methods

Four groups of eight metabolically healthy weight-matched postmenopausal women were provided with three isocaloric meals (a pre-trial meal and two meals during the trial day) containing either 30% or 60% carbohydrate, with and without two-hours of moderate-intensity exercise before the last two meals. Plasma glucose, insulin, glucagon, GIP, glucagon-like peptide 1 (GLP-1), free fatty acids (FFAs), and D-3-hydroxybutyrate concentrations were measured during 4-h postprandial periods and 3-h exercise periods, and their areas under the curve (AUCs) were analyzed by mixed-model ANOVA, and insulin resistance during fasting and meal tolerance tests within each diet was estimated using homeostasis-model assessment (HOMA-IR).

Results

The third low-carbohydrate meal, but not the high-carbohydrate meal, reduced: (1) evening insulin AUC by 39% without exercise and by 31% after exercise; (2) GIP AUC by 48% without exercise and by 45% after exercise, and (3) evening insulin resistance by 37% without exercise and by 24% after exercise. Pre-meal exercise did not alter insulin-, GIP- and HOMA-IR- lowering effects of low-carbohydrate diet, but exacerbated evening hyperglycemia.

Conclusions

Evening postprandial insulin and GIP responses and insulin resistance declined by over 30% after three meals that limited daily carbohydrate intake to 30% compared to no such changes after three 60%-carbohydrate meals, an effect that was independent of pre-meal exercise. The parallel timing and magnitude of postprandial insulin and GIP changes suggest their dependence on a delayed intestinal adaptation to a low-carbohydrate diet. Pre-meal exercise exacerbated glucose intolerance with both diets most likely due to impairment of insulin signaling by pre-meal elevation of FFAs.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

The impact of mathematical modeling on the understanding of diabetes and related complications

Diabetes is a chronic and complex multifactorial disease caused by persistent hyperglycemia and for which underlying pathogenesis is still not completely understood. The mathematical modeling of glucose homeostasis, diabetic condition, and its associated complications is rapidly growing and provides new insights into the underlying mechanisms involved. Here, we discuss contributions to the diabetes modeling field over the past five decades, highlighting the areas where more focused research is required.

Systems biology of infectious diseases: a focus on fungal infections

The study of infectious disease concerns the interaction between the host species and a pathogen organism. The analysis of such complex systems is improving with the evolution of high-throughput technologies and advanced computational resources. This article reviews integrative, systems-oriented approaches to understanding mechanisms underlying infection, immune response and inflammation to find biomarkers of disease and design new drugs. We focus on the systems biology process, especially the data gathering and analysis techniques rather than the experimental technologies or latest computational resources.

Validating a dimensionless number for glucose homeostasis in humans

Understanding type 2 diabetes is challenged by the diversity of patient phenotypes. Translating data across species and among individuals is a barrier for understanding the genetic loci that underpin this multifactorial disease. Dynamic scaling, based upon dimensional analysis, is a common technique in engineering used to translate data among different systems. The objective of this study was to gain insight using dimensional analysis into the relative changes in insulin production capacity vs. insulin-dependent glucose metabolism in patient groups that represent distinct stages of disease progression. A dimensionless number was derived using variables involved in the production of insulin and in the sensitivity of glucose metabolism to insulin. The resulting dynamic scaling relationship was validated against patient data obtained for over 2000 individuals that range in phenotype from normal to severe type 2 diabetes. Individuals were identified in the third National Health and Nutrition Evaluation Survey. Patient groups clustered in different regions based upon the severity of clinical symptoms. The cross-sectional comparison among patient groups shows that progression from normal to clinical onset of type 2 diabetes exhibits a non-linear change in the ratio of insulin production to insulin-dependent glucose metabolism: normals are balanced, pre-diabetic individuals exhibit an increase, and individuals with clinical type 2 diabetes exhibit a decrease in this ratio. This dimensionless number provides a method for discriminating between patient groups from first principles. By analogy with other dimensionless numbers, this number may be used to monitor basic physiological variables responsible for glucose homeostasis. In addition, a similar dynamic trajectory to the clinical populations could provide a criterion for selecting relevant animal models for diabetes.

Robust signaling networks of the adipose secretome

Type 2 diabetes is a prototypical complex systems disease that has a strong hereditary component and etiologic links with a sedentary lifestyle, overeating and obesity. Adipose tissue has been shown to be a central driver of type 2 diabetes progression, establishing and maintaining a chronic state of low-level inflammation. The number and diversity of identified endocrine factors from adipose tissue (adipokines) is growing rapidly. Here, I argue that a systems biology approach to understanding the robust multi-level signaling networks established by the adipose secretome will be crucial for developing efficient type 2 diabetes treatment. Recent advances in whole-genome association studies, global molecular profiling and quantitative modeling are currently fueling the emergence of this novel research strategy.

The challenges for molecular nutrition research 4: the "nutritional systems biology level"

Nutritional systems biology may be defined as the ultimate goal of molecular nutrition research, where all relevant aspects of regulation of metabolism in health and disease states at all levels of its complexity are taken into account to describe the molecular physiology of nutritional processes. The complexity spans from intracellular to inter-organ dynamics, and involves iterations between mathematical modelling and analysis employing all profiling methods and other biological read-outs. On the basis of such dynamic models we should be enabled to better understand how the nutritional status and nutritional challenges affect human metabolism and health. Although the achievement of this proposition may currently sound unrealistic, many initiatives in theoretical biology and biomedical sciences work on parts of the solution. This review provides examples and some recommendations for the molecular nutrition research arena to move onto the systems level.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Applying computational modeling to drug discovery and development

Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can play a key role in the pharmaceutical industry. We discuss pharmaceutically-relevant computational modeling approaches currently used as predictive tools. Specific examples demonstrate how companies can employ these computational models to improve the efficiency of transforming targets into therapies.

Reengineering the pharmaceutical industry by crash-testing molecules

The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.

Systems modeling: a pathway to drug discovery

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.

Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.

Modeling insulin action for development of a closed-loop artificial pancreas

Three models of glucose homeostasis are compared in terms of their steady-state dose-response characteristics, how they characterize glucose distribution kinetics, and how they characterize the dynamics of insulin action. The three models [minimal model, AIDA (Automated Insulin Dosage Advisor), and a model by Sorensen] are used to discuss a wider variety of questions related to metabolic modeling. Simulations are performed comparing each model's response to an intravenous glucose tolerance test, with and without incremental insulin responses, to existing data in individuals with type 1 diabetes mellitus. Predicted changes in blood glucose following a subcutaneous bolus of insulin or an incremental increase in basal insulin delivery are simulated. From these results, the models are evaluated as potential candidates for simulating changes in treatment and developing a closed-loop insulin delivery algorithm. While no consensus model is proposed, relevant issues needing to be addressed are highlighted.

Robustness of cellular functions

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is a long-recognized key property of living systems. Owing to intimate links to cellular complexity, however, its molecular and cellular basis has only recently begun to be understood. Theoretical approaches to complex engineered systems can provide guidelines for investigating cellular robustness because biology and engineering employ a common set of basic mechanisms in different combinations. Robustness may be a key to understanding cellular complexity, elucidating design principles, and fostering closer interactions between experimentation and theory.