Morten Colding-Jørgensen

Absorption kinetics of insulin after subcutaneous administration

Many diabetic patients depend on regular and well-controlled administration of insulin to avoid unacceptable excursions in plasma glucose. A complicating factor is that the absorption of insulin shows a considerable variability, both between patients, and from administration to administration for the same patient. To understand the mechanisms that influence this variability we present a quantitative description of the absorption kinetics for both soluble insulin and insulin crystals. The concentration dependent distribution of insulin between different oligomers is first analysed and described. Next, the disappearance of soluble and crystalline insulin from subcutis is described and explained as a function of the administered dose, the insulin concentration and crystal specific parameters, but without diffusion. The effect of diffusion is then included, and the appearance of insulin in plasma following subcutaneous administration is simulated and discussed. Our results not only explain the observed variability, but they also explain how dose size, insulin concentration, insulin crystals etc. influence the absorption kinetics.

A Model of NEFA Dynamics with Focus on the Postprandial State

To improve the understanding of the mechanisms underlying the behavior of plasma non-esterified fatty acids (NEFA) in the postprandial state, we have developed a physiology-based mathematical model of plasma NEFA dynamics. Known physiological mechanisms are quantified and used to describe NEFA dynamics. Insulin is the major regulator of NEFA metabolism in the postprandial state. Plasma NEFA levels are thus highly dependent on the insulin concentration, the insulin sensitivity of adipose tissue, and the maximal lipolytic rate. In the postabsorptive state, e.g., at low insulin, adipose tissue lipolysis results in a net export of NEFA from adipose tissue to other tissues. Postprandially, the rise in insulin results in: Decreased lipolysis; a higher rate of lipoprotein lipase (LPL) activity; and decreased NEFA uptake and reesterification by adipose tissue stimulation of reesterification. The result is a drop in plasma NEFA after a carbohydrate containing meal. When insulin returns to postabsorptive levels, a rebound in plasma NEFA often occurs. This rebound is due to a restoration of lipolysis, a decrease in NEFA reesterification by adipose tissue and an increased LPL—as insulin activates LPL with a delay of several hours. In conclusion, movements of NEFA depend strongly on insulin—with postprandial plasma NEFA being almost inversely related to the insulin concentration in healthy humans. The model provides an integrative view of NEFA dynamics and a framework for quantitative and conceptual understanding of plasma NEFA fluxes.

Bioavailability and variability of biphasic insulin mixtures

Absorption of subcutaneously administered insulin is associated with considerable variability. Some of this variability was quantitatively explained for both soluble insulin and insulin suspensions in a recent contribution to this journal (Søeborg et al., 2009). In the present article, the absorption kinetics for mixtures of insulins is described. This requires that the bioavailability of the different insulins is considered. A short review of insulin bioavailability and a description of the subcutaneous depot thus precede the presentation of possible mechanisms associated with subcutaneous insulin degradation. Soluble insulins are assumed to be degraded enzymatically in the subcutaneous tissue. Suspended insulin crystals form condensed heaps that are assumed to be degraded from their surface by invading macrophages. It is demonstrated how the shape of the heaps affects the absorption kinetics. Variations in heap formation thus explain some of the additional variability associated with suspended insulins (e.g. NPH insulins) compared to soluble insulins. The heap model also describes how increasing concentrations of suspended insulins lead to decreasing bioavailability and lower values of Cmax. Together, the findings constitute a comprehensive, quantitative description of insulin absorption after subcutaneous administration. The model considers different concentrations and doses of soluble insulin, including rapid acting insulin analogues, insulin suspensions and biphasic insulin mixtures. The results can be used in both the development of novel insulin products and in the planning of the treatment of insulin dependent diabetic patients.

Insulin aspart pharmacokinetics: An assessment of its variability and underlying mechanisms

BACKGROUND Insulin aspart (IAsp) is used by many diabetics as a meal-time insulin to control post-prandial glucose levels. As is the case with many other insulin types, the pharmacokinetics (PK), and consequently the pharmacodynamics (PD), is associated with clinical variability, both between and within individuals. The present article identifies the main physiological mechanisms that govern the PK of IAsp following subcutaneous administration and quantifies them in terms of their contribution to the overall variability. MATERIAL AND METHODS CT scanning data from Thomsen et al. (2012) are used to investigate and quantify the properties of the subcutaneous depot. Data from Brange et al. (1990) are used to determine the effects of insulin chemistry in subcutis on the absorption rate. Intravenous (i.v.) bolus and infusion PK data for human insulin are used to understand and quantify the systemic distribution and elimination (Pørksen et al., 1997; Sjöstrand et al., 2002). PK and PD profiles for type 1 diabetics from Chen et al. (2005) are analyzed to demonstrate the effects of IAsp antibodies in terms of bound and unbound insulin. PK profiles from Thorisdottir et al. (2009) and Ma et al. (2012b) are analyzed in the nonlinear mixed effects software Monolix® to determine the presence and effects of the mechanisms described in this article. RESULTS The distribution of IAsp in the subcutaneous depot show an initial dilution of approximately a factor of two in a single experiment. Injected insulin hexamers exist in a chemical equilibrium with monomers and dimers, which depends strongly on the degree of dilution in subcutis, the presence of auxiliary substances, and a variety of other factors. Sensitivity to the initial dilution in subcutis can thus be a cause of some of the variability. Temporal variations in the PK are explained by variations in the subcutaneous blood flow. IAsp antibodies are found to be a large contributor to the variability of total insulin PK in a study by Chen et al. (2005), since only the free fraction is eliminated via the receptors. The contribution of these and other sources of variability to the total variability is quantified via a population PK analysis and two recent clinical studies (Thorisdottir et al., 2009; Ma et al., 2012b), which support the presence and significance of the identified mechanisms. CONCLUSIONS IAsp antibody binding, oligomeric transitions in subcutis, and blood flow dependent variations in absorption rate seem to dominate the PK variability of IAsp. It may be possible via e.g. formulation design to reduce some of these variability factors.

A comprehensive approach to benefit-risk assessment in drug development.

Major regulatory agencies, for example, FDA and EMA, have started to request comprehensive benefit–risk analyses of pharmaceutical products prior to approval or labelling expansion. The purpose of this study is to develop a generally applicable and reliable data‐driven benefit–risk assessment method, where two or more drugs/doses can be compared. Our aim is to formulate an approach that is simple to apply, allows direct comparison of different types of risks and benefits, and is tailored for application in different disease areas both during clinical development and in the marketing approval phase. The proposed benefit–risk assessment method involves eight successive steps: (1) establishment of the decision context, (2) identification of benefit and risk criteria, (3) weighting, (4) scoring, (5) evaluation of uncertainty, (6) calculation of weighted scores, (7) visualization, and (8) discussion and formulation of an overall conclusion. To reduce the impact of subjective judgements, scores are assigned to each criterion on the basis of objective information (data) wherever possible. The proposed benefit–risk evaluation approach offers comprehensive, data‐driven assessments that can facilitate decision processes. It employs descriptive statistical methods to highlight the clinically significant differences between drugs in clinical trials. The approach can be used in single as well as in multiple trials and provides clear diagrams as the basis for presentation and discussion of the results.

Absorption Kinetics of Insulin Mixtures after Subcutaneous Administration

Day after day, millions of diabetic patients throughout the World must take injections of insulin in order to keep their blood glucose concentrations within an acceptable range [70]. To achieve the desired control it is important that the insulin concentration in the blood plasma, while coordinated with the supply of glucose, is kept within specific limits. Moreover, to ensure a consistent and predictable drug effect, the plasma insulin profiles (appearance curves) must be similar from injection to injection. Despite the fact that insulin has been used in the treatment of diabetes since the 1920s [2] and although the biological action of insulin is well described in the literature, the absorption process remains poorly understood. Large inter- and intra-patient variability is observed in most clinical data, and the processes that contribute to this variability have never been described in detail. Moreover, only a fraction of the injected dose becomes useful to the patient. Both the high variability and the reduced bioavailability are likely to be related to events that take place at the injection site during the absorption process.

Data‐Driven Assessment of the Association of Polymorphisms in 5‐Fluorouracil Metabolism Genes with Outcome in Adjuvant Treatment of Colorectal Cancer

A major challenge in the assessment of medicines, treatment options, etc., is to establish a framework for the comparison of risks and benefits of many different types and magnitudes, a framework that at the same time allows a clear distinction between the roles played by the statistical analyses of data and by judgements based on personal experience and expertise. The purpose of this study was to demonstrate how clinical data can be weighted, scored and presented by the use of an eight‐step data‐driven benefit–risk assessment method, where two genetic profiles are compared. Our aim was to present a comprehensive approach that is simple to apply, allows direct comparison of different types of risks and benefits, quantifies the clinical relevance of data and is tailored for the comparison of different options. We analysed a cohort of 302 patients with colorectal cancer treated with 5‐Fluorouracil (5‐FU). Endpoints were cure rate, survival rate, time‐to‐death (TTD), time‐to‐relapse (TTR) and main adverse drug reactions. Multifactor dimensionality reduction (MDR) was used to identify genetic interaction profiles associated with outcome. We have been able to demonstrate that a specific MDR‐derived combination (the MDR‐1 group) of dihydropyrimidine dehydrogenase and thymidylate synthase polymorphisms is associated with increased and clinically significant difference for cure and survival rates, TTD and probably also for TTR, which are seen as the most important endpoints. An inferior profile was observed for severe myocardial ischaemia. A probably inferior profile was seen for severe arthralgia/myalgia and severe infections. A clear superior profile was seen for severe mucositis/stomatitis. The proposed approach offers comprehensive, data‐driven assessment that can facilitate decision processes, for example, in a clinical setting. It employs descriptive statistical methods to highlight the clinically relevant differences between options.

Risk Perceptions in Diabetic Patients Who Have Experienced Adverse Events: Implications for Patient Involvement in Regulatory Decisions

BackgroundIncreasingly, patients are expected to influence decisions previously reserved for regulatory agencies, pharmaceutical companies, and healthcare professionals. Individual patients have previously represented their patient population when rare, serious adverse events (AEs) were weighed as part of a benefit-risk assessment. However, the degree of heterogeneity of the patient population is critical for how accurately they can be represented by individuals.ObjectivesThis study aims to explore patients’ risk perception of rare, serious adverse effects of medicines with regard to blood glucose-lowering antidiabetics used by the individual patient.MethodsSemi-structured interviews were conducted with 18 patients with diabetes with self-perceived serious, but not necessarily rare, AEs (e.g. stroke or valve or bypass surgery). The interviews explored the patients’ history of disease, perceptions of the terms rare and serious, and overall levels of risk aversion. A thematic analysis of the interviews, including a consensus discussion, was carried out.ResultsInterestingly, respondents rarely made a clear distinction between medicines-induced AEs and complications related to disease progression. Concerns regarding AEs were apparently diverse but were systematically related to the personal experiences of the respondents. Respondents routinely ignored information about possible rare, serious AEs, unless it could be related to personal experience. In the absence of experience, concerns were focused on common and less serious AEs, thus disregarding rare and more serious events.ConclusionThe study suggests that experience of AEs, related to either medicines or disease, constitutes an important factor of patient risk perception. We therefore propose that serious adverse experiences should be added to the traditional panel of socioeconomic factors that are accounted for when patients are invited to give input on regulatory decisions.