The history and future of population pharmacokinetic analysis in drug development

1. The analysis of pharmacokinetic data has been in a constant state of evolution since the introduction of the term pharmacokinetics. Early work focused on mechanistic understanding of the absorption, distribution, metabolism and excretion of drug products.2. The introduction of non-linear mixed effects models to perform population pharmacokinetic analysis initiated a paradigm shift. The application of these models represented a major shift in evaluating variability in pharmacokinetic parameters across a population of subjects.3. While technological advancements in computing power have feuled the growth of population pharmacokinetics in drug development efforts, there remain many challenges in reducing the time required to incorporate these learnings into a model-informed development process. These challenges exist because of expanding datasets, increased number of diagnostics, and more complex mathematical models.4. New machine learning tools may be potential solutions for these challenges. These new methodologies include genetic algorithms for model selection, machine learning algorithms for covariate selection, and deep learning models for pharmacokinetic and pharmacodynamic data. These new methods promise the potential for less bias, faster analysis times, and the ability to integrate more data.5. While questions remain regarding the ability of these models to extrapolate accurately, continued research in this area is expected to address these questions.

Sutimlimab Pharmacokinetics and Pharmacodynamics in Patients with Cold Agglutinin Disease

Sutimlimab, a humanized monoclonal antibody targeting the classical complement pathway, is approved in the US, Japan, and EU for the treatment of hemolytic anemia in adults with cold agglutinin disease. The objectives of this study were to support dose selection for Phase 3 studies, assess dose recommendations, and establish the relationship between sutimlimab exposure and clinical outcome (hemoglobin [Hb] levels). Clinically meaningful biomarkers were graphically analyzed and the exposure-response relationship was proposed. The pharmacokinetic (PK) characteristics of sutimlimab were best described by a two-compartment model with parallel linear and non-linear clearance terms. Body weight was a significant covariate for the volume of distribution in the central compartment (Vc) and total body clearance of sutimlimab. Ethnicity (Japanese, non-Japanese) was a covariate on Vc and maximal non-linear clearance. There were no PK differences between healthy participants and patients. Following graphical exposure-response analysis for biomarkers, a pharmacokinetic-pharmacodynamic model was developed by integrating an indirect response/turnover model for Hb with a maximum effect (Emax) model, relating the Hb-elevating effect of sutimlimab to plasma exposure. Renal function and occurrence of blood transfusion were identified as covariates on Hb change from baseline. Simulations showed that Emax was attained with the approved dosing (6.5 g in patients <75 kg, and 7.5 g in patients {greater than or equal to}75 kg), independent of covariate characteristics, and provided adequate sutimlimab exposure to maximize effects on Hb, bilirubin, and total complement component C4 levels. A change in Hb from baseline at steady state of 2.2 g/dL was projected, consistent with Phase 3 study observations. Significance Statement The final validated population PK and PK/PD models confirm that the approved dosing regimen for sutimlimab (6.5 g in patients <75 kg, and 7.5 g in patients ≥75 kg) is sufficient, without the need for further dose adjustments in populations of patients with cold agglutinin disease.

Evaluation of covariate effects using forest plots and introduction to the coveffectsplot R package

The current tutorial describes why forest plots are needed for an effective communication of covariates effects, how they are constructed, and how they should be presented. Simulation-based methodologies allowing the user to evaluate the marginal impact of changing one covariate at a time or by considering the joint effects of correlated covariates are introduced along with graphical tools for an optimal assessment of the covariate effects. The R package coveffectsplot and an associated R Shiny application are provided to facilitate the design and construction of forest plots for the visualization of covariate effects. All codes and materials are available on a public Github repository.

Fabrication and evaluation of SixNy coatings for total joint replacements

Wear particles from the bearing surfaces of joint implants are one of the main limiting factors for total implant longevity. Si(3)N(4) is a potential wear resistant alternative for total joint replacements. In this study, Si(x)N(y)-coatings were deposited on cobalt chromium-discs and Si-wafers by a physical vapour deposition process. The tribological properties, as well as surface appearance, chemical composition, phase composition, structure and hardness of these coatings were analysed. The coatings were found to be amorphous or nanocrystalline, with a hardness and coefficient of friction against Si(3)N(4) similar to that found for bulk Si(3)N(4). The low wear rate of the coatings indicates that they have a potential as bearing surfaces of joint replacements. The adhesion to the substrates remains to be improved.

Cultivation of hepatoma cell line HepG2 on nanoporous aluminum oxide membranes

Nanoporous aluminum oxide membranes were prepared by anodic oxidation of aluminum for application as novel cell culture substrates. Self-supporting as well as mechanically stabilized nanoporous membranes were produced from aluminum plates and micro-imprinted aluminum foils, respectively. Membranes of two different pore sizes (70 and 260 nm) were selected to investigate cellular interactions with such nanoporous substrates using cells of hepatoma cell line HepG2. The membranes express excellent cell-growth conditions. As shown by scanning electron microscopy investigations, the cells could easily adhere to the membranes and proliferate during a 4 day cell culture period. The cells exhibit normal morphology and were able to penetrate into pores with a diameter of 260 nm by small extensions (filopodia). On mechanically stabilized aluminum oxide membranes it was observed that the cells even adhere to the walls of the small cavities. It was demonstrated experimentally that the nanoporous aluminum oxide membranes are well suited as substrates in cell culture model systems for metabolic, pharmacological/toxicological research, tissue engineering and studies on pathogens as well as bioartificial liver systems.

Nanoporous aluminum oxide as a novel support material for enzyme biosensors

To construct novel amperometric sensors for the detection of hydrogen peroxide and pyruvate, peroxidase and pyruvate oxidase were immobilized in self-supporting nanoporous alumina membranes those made by anodic oxidation. Pyruvate oxidase and other enzymes were enclosed in poly(carbamoylsulfonate) hydrogel and sucked into the nanoporous alumina structure before polymerization. The alumina membranes were investigated by scanning electron microscopy before and after the enzyme immobilization. In an amperometric flow detector cell, pyruvate and hydrogen peroxide were detected under flow injection analysis conditions in concentration ranges from 1 microM to 100 microM and 5 microM to 500 microM, respectively. The achieved operational stability showed that alumina membranes can be used to construct enzyme-modified electrodes.