Model-Based Analysis Reveals a Sustained and Dose-Dependent Acceleration of Wound Healing by VEGF-A mRNA (AZD8601).

Intradermal delivery of AZD8601, an mRNA designed to produce vascular endothelial growth factor A (VEGF-A), has previously been shown to accelerate cutaneous wound healing in a murine diabetic model. Here, we develop population pharmacokinetic and pharmacodynamic models aiming to quantify the effect of AZD8601 injections on the dynamics of wound healing. A dataset of 584 open wound area measurements from 131 mice was integrated from 3 independent studies encompassing different doses, dosing timepoints, and number of doses. Evaluation of several candidate models showed that wound healing acceleration is not likely driven directly by time-dependent VEGF-A concentration. Instead, we found that administration of AZD8601 induced a sustained acceleration of wound healing depending on the accumulated dose, with a dose producing 50% of the maximal effect of 92 µg. Simulations with this model showed that a single dose of 200 µg AZD8601 can reduce the time to reach 50% wound healing by up to 5 days.

Inhibition of the prostaglandin D2-GPR44/DP2 axis improves human islet survival and function.

AIMS/HYPOTHESIS: Inflammatory signals and increased prostaglandin synthesis play a role during the development of diabetes. The prostaglandin D2 (PGD2) receptor, GPR44/DP2, is highly expressed in human islets and activation of the pathway results in impaired insulin secretion. The role of GPR44 activation on islet function and survival rate during chronic hyperglycaemic conditions is not known. In this study, we investigate GPR44 inhibition by using a selective GPR44 antagonist (AZ8154) in human islets both in vitro and in vivo in diabetic mice transplanted with human islets. METHODS: Human islets were exposed to PGD2 or proinflammatory cytokines in vitro to investigate the effect of GPR44 inhibition on islet survival rate. In addition, the molecular mechanisms of GPR44 inhibition were investigated in human islets exposed to high concentrations of glucose (HG) and to IL-1ß. For the in vivo part of the study, human islets were transplanted under the kidney capsule of immunodeficient diabetic mice and treated with 6, 60 or 100 mg/kg per day of a GPR44 antagonist starting from the transplantation day until day 4 (short-term study) or day 17 (long-term study) post transplantation. IVGTT was performed on mice at day 10 and day 15 post transplantation. After termination of the study, metabolic variables, circulating human proinflammatory cytokines, and hepatocyte growth factor (HGF) were analysed in the grafted human islets. RESULTS: PGD2 or proinflammatory cytokines induced apoptosis in human islets whereas GPR44 inhibition reversed this effect. GPR44 inhibition antagonised the reduction in glucose-stimulated insulin secretion induced by HG and IL-1ß in human islets. This was accompanied by activation of the Akt-glycogen synthase kinase 3ß signalling pathway together with phosphorylation and inactivation of forkhead box O-1and upregulation of pancreatic and duodenal homeobox-1 and HGF. Administration of the GPR44 antagonist for up to 17 days to diabetic mice transplanted with a marginal number of human islets resulted in reduced fasting blood glucose and lower glucose excursions during IVGTT. Improved glucose regulation was supported by increased human C-peptide levels compared with the vehicle group at day 4 and throughout the treatment period. GPR44 inhibition reduced plasma levels of TNF-α and growth-regulated oncogene-α/chemokine (C-X-C motif) ligand 1 and increased the levels of HGF in human islets. CONCLUSIONS/INTERPRETATION: Inhibition of GPR44 in human islets has the potential to improve islet function and survival rate under inflammatory and hyperglycaemic stress. This may have implications for better survival rate of islets following transplantation.

Discovery of (3-(4-(2-Oxa-6-azaspiro[3.3]heptan-6-ylmethyl)phenoxy)azetidin-1-yl)(5-(4-methoxyphenyl)-1,3,4-oxadiazol-2-yl)methanone (AZD1979), a Melanin Concentrating Hormone Receptor 1 (MCHr1) Antagonist with Favorable Physicochemical Properties.

A novel series of melanin concentrating hormone receptor 1 (MCHr1) antagonists were the starting point for a drug discovery program that culminated in the discovery of 103 (AZD1979). The lead optimization program was conducted with a focus on reducing lipophilicity and understanding the physicochemical properties governing CNS exposure and undesired off-target pharmacology such as hERG interactions. An integrated approach was taken where the key assay was ex vivo receptor occupancy in mice. The candidate compound 103 displayed appropriate lipophilicity for a CNS indication and showed excellent permeability with no efflux. Preclinical GLP toxicology and safety pharmacology studies were without major findings and 103 was taken into clinical trials.

Structural and functional characterization of a specific antidote for ticagrelor.

Ticagrelor is a direct-acting reversibly binding P2Y12 antagonist and is widely used as an antiplatelet therapy for the prevention of cardiovascular events in acute coronary syndrome patients. However, antiplatelet therapy can be associated with an increased risk of bleeding. Here, we present data on the identification and the in vitro and in vivo pharmacology of an antigen-binding fragment (Fab) antidote for ticagrelor. The Fab has a 20 pM affinity for ticagrelor, which is 100 times stronger than ticagrelor's affinity for its target, P2Y12. Despite ticagrelor's structural similarities to adenosine, the Fab is highly specific and does not bind to adenosine, adenosine triphosphate, adenosine 5'-diphosphate, or structurally related drugs. The antidote concentration-dependently neutralized the free fraction of ticagrelor and reversed its antiplatelet activity both in vitro in human platelet-rich plasma and in vivo in mice. Lastly, the antidote proved effective in normalizing ticagrelor-dependent bleeding in a mouse model of acute surgery. This specific antidote for ticagrelor may prove valuable as an agent for patients who require emergency procedures.

Population pharmacokinetic modeling and deconvolution of enantioselective absorption of eflornithine in the rat.

Enantioselective pharmacokinetics and absorption of eflornithine in the rat was investigated using population pharmacokinetic modeling and a modified deconvolution method. Bidirectional permeability of L- and D-eflornithine was investigated in Caco-2 cells. The rat was administered racemic eflornithine hydrochloride as a single oral dose [40-3,000 mg/kg bodyweight (BW)] or intravenously (IV) (100-2,700 mg/kg BW infused over 60-400 min). Serial arterial blood samples were collected and L- and D-eflornithine were quantitated with a previously published chiral bioanalysis method. The D:L concentration ratio was determined in rat faeces. Intravenous L-and D-eflornithine plasma concentration-time data was analyzed using population pharmacokinetic modeling and described with a 3-compartment pharmacokinetic model with saturable binding to one of the peripheral compartments. Oral plasma concentration-time data was analyzed using a modified deconvolution method accounting for nonlinearities in the eflornithine pharmacokinetics. Clearance was similar for both enantiomers (3.36 and 3.09 mL/min). Oral bioavailability was estimated by deconvolution at 30 and 59% for L- and D-eflornithine. The D:L concentration ratio in feces was 0.49 and the Caco-2 cell permeability was similar for both enantiomers (6-10 × 10(-8) cm/s) with no evident involvement of active transport or efflux. The results presented here suggest that the difference in the bioavailability between eflornithine enantiomers is caused by a stereoselective difference in extent rather than rate of absorption. The presented modified deconvolution method made it possible to account for the non-linear component in the suggested three-compartment pharmacokinetic model thus rapidly estimating eflornithine oral bioavailability.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

Models from experiments: combinatorial drug perturbations of cancer cells.

We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.