Safety and Efficacy Modelling in Anti-Diabetic Drug Development

A central aim in drug development is to ensure that the new drug is efficacious and safe in the intended patient population. Mathematical models describing the pharmacokinetic-pharmacodynamic (PK-PD) properties of a drug are valuable to increase the knowledge about drug effects and disease and can be used to inform decisions. The aim of this thesis was to develop mechanism-based PK-PD-disease models for important safety and efficacy biomarkers used in anti-diabetic drug development. Population PK, PK-PD and disease models were developed, based on data from clinical studies in subjects with varying degrees of renal function, non-diabetic subjects with insulin resistance and patients with type 2 diabetes mellitus (T2DM), receiving a peroxisome proliferator-activated receptor (PPAR) α/γ agonist, tesaglitazar. The PK model showed that a decreased renal elimination of the metabolite in renally impaired subjects leads to increased levels of metabolite undergoing interconversion and subsequent accumulation of tesaglitazar. Tesaglitazar negatively affects the glomerular filtration rate (GFR), and since renal function affects tesaglitazar exposure, a PK-PD model was developed to simultaneously describe this interrelationship. The model and data showed that all patients had decreases in GFR, which were reversible when discontinuing treatment. The PK-PD model described the interplay between fasting plasma glucose (FPG), glycosylated haemoglobin (HbA1c) and haemoglobin in T2DM patients. It provided a mechanistically plausible description of the release and aging of red blood cells (RBC), and the glucose dependent glycosylation of RBC to HbA1c. The PK-PD model for FPG and fasting insulin, incorporating components for β-cell mass, insulin sensitivity and impact of disease and drug treatment, realistically described the complex glucose homeostasis in the heterogeneous patient population. The mechanism-based PK, PK-PD and disease models increase the understanding about T2DM and important biomarkers, and can be used to improve decision making in the development of future anti-diabetic drugs

Exposure- response for biomarkers of anticoagulant effects by the oral direct thrombin inhibitor AZD0837 in patients with atrial fibrillation.

AZD0837 is a novel oral anticoagulant investigated in clinical studies for stroke prevention in patients with atrial fibrillation (AF). It is bioconverted to its active form, AR-H067637, a potent, specific and reversible thrombin inhibitor

A case‐study of model‐informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Abstract Here, we show model‐informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first‐in‐human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de‐risk a phase IIb study. The focus of the case‐study is on the cross‐functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD

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.Funding agencies: Swedish Research Council; Swedish Diabetes Foundation; Linkoping Initiative within Life Science Technologies; CENIIT; Ostergotland County Council; EU [FP7-HEALTH-305707]; AstraZeneca</p