Common Deficiencies of <Emphasis Type="Italic">in vitro</Emphasis> Binding Bioequivalence (BE) Studies Submitted in Abbreviated New Drug Applications (ANDAs)

There are several drug products that bind phosphate or bile acid in the gastrointestinal (GI) tract to exert their therapeutic efficacy. In vitro binding studies are used to assess bioequivalence (BE) of these products. The objective of this study is to identify the common deficiencies in Abbreviated New Drug Applications (ANDAs) for these products. Deficiencies were compiled from ANDAs containing in vitro binding BE studies. The deficiencies were classified into eight categories: Pre-Study Method Validation, During-Study Sample Analysis, Study Design, Study Procedure, Dissolution/Disintegration, Analytical Site Inspection, Data Submission, and Formulations. Within each category, additional subcategories were defined to characterize the deficiencies. A total of 712 deficiencies from 95 ANDAs for 11 drug products were identified and included in the analysis. The four categories with the most deficiencies were During-Study Sample Analysis (27.8%), Pre-Study Method Validation (17.3%), Data Submission (16.7%), and Study Design (15.7%). For the During-Study Sample Analysis category, failure to submit complete raw data or analytical runs ranked as the top deficiency (32.8%). For the Study Design category, using an unacceptable alternate study design (26.8%) was the most common deficiency. Within this category, other commonly occurring deficiencies included incorrect/insufficient number of absorbent concentrations, failure to pre-treat drug product with acid, insufficient number of replicates in study, incorrect calculation of k1 and k2 values, incorrect dosage form or pooled samples used in the study, and incorrect pH of study medium. The review and approval of these products may be accelerated if these common deficiencies are addressed in the original ANDA submissions.     

Nephrotoxicity and Kidney Transport Assessment on 3D Perfused Proximal Tubules

Proximal tubules in the kidney play a crucial role in reabsorbing and eliminating substrates from the body into the urine, leading to high local concentrations of xenobiotics. This makes the proximal tubule a major target for drug toxicity that needs to be evaluated during the drug development process. Here, we describe an advanced in vitro model consisting of fully polarized renal proximal tubular epithelial cells cultured in a microfluidic system. Up to 40 leak-tight tubules were cultured on this platform that provides access to the basolateral as well as the apical side of the epithelial cells. Exposure to the nephrotoxicant cisplatin caused a dose-dependent disruption of the epithelial barrier, a decrease in viability, an increase in effluent LDH activity, and changes in expression of tight-junction marker zona-occludence 1, actin, and DNA-damage marker H2A.X, as detected by immunostaining. Activity and inhibition of the efflux pumps P-glycoprotein (P-gp) and multidrug resistance protein (MRP) were demonstrated using fluorescence-based transporter assays. In addition, the transepithelial transport function from the basolateral to the apical side of the proximal tubule was studied. The apparent permeability of the fluorescent P-gp substrate rhodamine 123 was decreased by 35% by co-incubation with cyclosporin A. Furthermore, the activity of the glucose transporter SGLT2 was demonstrated using the fluorescent glucose analog 6-NBDG which was sensitive to inhibition by phlorizin. Our results demonstrate that we developed a functional 3D perfused proximal tubule model with advanced renal epithelial characteristics that can be used for drug screening studies.     

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.