Role of Thrombin in Soluble Thrombomodulin-Induced Suppression of Peripheral HMGB1-Mediated Allodynia in Mice

High mobility group box 1 (HMGB1), a nuclear protein, once released into the extracellular space under pathological conditions, plays a pronociceptive role in redox-dependent distinct active forms, all-thiol HMGB1 (at-HMGB1) and disulfide HMGB1 (ds-HMGB1), that accelerate nociception through the receptor for advanced glycation endproducts (RAGE) and Toll-like receptor 4 (TLR4), respectively. Thrombomodulin (TM), an endothelial membrane protein, and soluble TM, known as TMα, promote thrombin-mediated activation of protein C and also sequester HMGB1, which might facilitate thrombin degradation of HMGB1. The present study aimed at clarifying the role of thrombin in TMα-induced suppression of peripheral HMGB1-dependent allodynia in mice. Thrombin-induced degradation of at-HMGB1 and ds-HMGB1 was accelerated by TMα in vitro. Intraplantar (i.pl.) injection of bovine thymus-derived HMGB1 in an unknown redox state, at-HMGB1, ds-HMGB1 or lipopolysaccharide (LPS), known to cause HMGB1 secretion, produced long-lasting mechanical allodynia in mice, as assessed by von Frey test. TMα, when preadministered i.pl., prevented the allodynia caused by bovine thymus-derived HMGB1, at-HMGB1, ds-HMGB1 or LPS, in a dose-dependent manner. The TMα-induced suppression of the allodynia following i.pl. at-HMGB1, ds-HMGB1 or LPS was abolished by systemic preadministration of argatroban, a thrombin-inhibiting agent, and accelerated by i.pl. co-administered thrombin. Our data clearly indicate that TMα is capable of promoting the thrombin-induced degradation of both at-HMGB1 and ds-HMGB1, and suppresses the allodynia caused by either HMGB1 in a thrombin-dependent manner. Considering the emerging role of HMGB1 in distinct pathological pain models, the present study suggests the therapeutic usefulness of TMα for treatment of intractable and/or persistent pain.     

How to mathematically optimize drug regimens using optimal control

This article gives an overview of a technique called optimal control, which is used to optimize real-world quantities represented by mathematical models. I include background information about the historical development of the technique and applications in a variety of fields. The main focus here is the application to diseases and therapies, particularly the optimization of combination therapies, and I highlight several such examples. I also describe the basic theory of optimal control, and illustrate each of the steps with an example that optimizes the doses in a combination regimen for leukemia. References are provided for more complex cases. The article is aimed at modelers working in drug development, who have not used optimal control previously. My goal is to make this technique more accessible in the biopharma community.     

Drug–physiology interaction and its influence on the QT prolongation-mechanistic modeling study

The current study is an example of drug–disease interaction modeling where a drug induces a condition which can affect the pharmacodynamics of other concomitantly taken drugs. The electrophysiological effects of hypokaliemia and heart rate changes induced by the antiasthmatic drugs were simulated with the use of the cardiac safety simulator. Biophysically detailed model of the human cardiac physiology—ten Tusscher ventricular cardiomyocyte cell model—was employed to generate pseudo-ECG signals and QTc intervals for 44 patients from four clinical studies. Simulated and observed mean QTc values with standard deviation (SD) for each reported study point were compared and differences were analyzed with Student’s t test (α?=?0.05). The simulated results reflected the QTc interval changes measured in patients, as well as their clinically observed interindividual variability. The QTc interval changes were highly correlated with the change in plasma potassium both in clinical studies and in the simulations (Pearson’s correlation coefficient?>?0.55). The results suggest that the modeling and simulation approach could provide valuable quantitative insight into the cardiological effect of the potassium and heart rate changes caused by electrophysiologically inactive, non-cardiological drugs. This allows to simulate and predict the joint effect of several risk factors for QT prolongation, e.g., drug-dependent QT prolongation due to the ion channels inhibition and the current patient physiological conditions.     

Cost evolution of biological agents for the treatment of spondyloarthritis in a tertiary hospital: influential factors in price

Background Spending on biological agents has risen dramatically due to the high cost of the drugs and the increased prevalence of spondyloarthritis. Objective To evaluate the annual cost per patient and cost for each biological drug for treating patients with spondyloarthritis from 2009 to 2016, and to calculate factors that affect treatment cost, such as optimizing therapies by monitoring drug serum levels, the use of biosimilar-TNF inhibitors, and official discounts or negotiated rebates in biologicals acquired by the pharmacy department. Method Retrospective, observational study in a Spanish tertiary hospital. Main outcome Annual cost per patient and per drug. Factors that influenced the costs and socio-demographic parameters and disease activity. Results A total of 129, 215, and 224 patients were treated in 2009, 2013, and 2016, respectively. The annual cost per patient decreased: EUR11,604 in 2009, EUR8513 in 2013, and EUR7464 in 2016. The introduction of new drugs drives economic competition, leading to total savings per drug, with discounts reaching 5.8, 12.4, 16.7, 17.7, 13.7, and 24.8% for original infliximab, etanercept, adalimumab, ertolizumab, golimumab, and secukinumab, respectively, while rebates for biosimilar infliximab reached 31.90% in 2016. The number of patients with optimized therapies reached 47.5% in 2016, which led to cost savings of EUR798,614, in addition to savings from official discounts and rebates of EUR252,706 and savings from optimized therapies of EUR545,908 in 2016. Conclusion The cost of biological treatments declined after official discounts, negotiated rebates, and optimized therapies, leading to a significant decrease in the annual cost per patient. The greatest contribution to economic savings in biological therapy according to our study was biological therapy optimization.