Measurement of binding of basic drugs to acidic phospholipids using surface plasmon resonance and incorporation of the data into mechanistic tissue composition equations to predict steady-state volume of distribution

Sensitive surface plasmon resonance biosensor by optimized carboxylate functionalized carbon nanotubes/chitosan for amlodipine detecting

The adoption of biophotonic sensing technologies holds significant promise for application in health care and biomedical industries in all aspects of human life. Then, this piece of writing employs the powerful effective medium theory and FDTD simulation to anticipate the most favorable state and plasmonic attributes of a magnificent nanocomposite, comprising carboxylate functionalized carbon nanotubes and chitosan (CS). Furthermore, it thoroughly explores the exhibited surface plasmon resonance behaviors of this composite versus the quantity of CS variation. Subsequently, enlightening simulations are conducted on the nanocomposite with a delicate layer and a modified golden structure integrating as a composite. The intricate simulations eventually unveil an optimal combination to pave the way for crafting an exceptional specific biosensor that far surpasses its counterpart as a mere Au thin layer in terms of excellence. The proposed biosensor demonstrated linear behavior across a wide range from 0.01 μM to 150 μM and achieved a detection limit of 10 nM, with a sensitivity of 134◦RIU-1.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fu_mic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r²=0.86) or mouse (r²=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r² of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fu_mic using four different in silico models for a set of 53-60 compounds. The correlation (r²) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r²=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r²<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Postmortem fatal and non-fatal concentrations of amlodipine

Amlodipine is a dihydropyridine calcium channel blocker widely used in the treatment of high blood pressure and coronary heart disease. Intoxication can lead to reflex tachycardia following massive hypotension and death. The objective of this work was to study the post-mortem concentrations of amlodipine in 62 patients in order to determine whether the use of the reference concentrations from the living patients was applicable in postmortem setting, and to define more precisely the fatal and non-fatal postmortem concentrations of amlodipine. The amlodipine concentrations were measured in femoral whole blood by LC-MS/MS validated method. When sufficient information was available, the data were classified into 2 different groups, based on the conclusions of the autopsy and toxicological results: G1: non-toxic death and G2: fatal poisoning involving amlodipine alone or as part of a multidrug poisoning. The median concentration of amlodipine [1st quartile - 3rd quartile] of the whole population (n = 62) was 81 [42-134] ng/mL. Twenty-two cases were classified as G1 and thirteen as G2. The observed median [1st quartile - 3rd quartile] concentration of amlodipine was 66 [40.5-79.5] ng/mL in G1 and 240 [170-404] ng/mL in G2. The median concentrations observed in "non-toxic" deaths (66 ng/mL) were three times higher than those usually observed in living patients. Amlodipine distribution ratio between plasma and whole blood concentrations seems insufficient to explain this difference and postmortem redistribution from organs should be considered, and could suggest the same redistribution pattern for other drugs belonging to the same family.

An introduction to physiologically-based pharmacokinetic models

Physiologically-based pharmacokinetic (PBPK) models represent drug kinetics in one or more 'real' organs (and hence require submodels of organs/tissues) and they describe 'whole-body' kinetics by joining together submodels with drug transport by blood flow as dictated by anatomy. They attempt to reproduce 'measureable' physiological and/or pharmacokinetic processes rather than more abstract rate constants and volumes. PBPK models may be built using a 'bottom-up' approach, where parameters are chosen from first principles, literature, or in vitro data as opposed to a 'top-down' approach, where all parameters are estimated from data. The basic principles of PBPK models are described, focusing on the equations for three individual organs: a single flow-limited compartment describing distribution only, a membrane-limited compartment describing distribution, and a single flow-limited compartment with elimination. These organ models are linked to make a basic three-compartment physiological model of the whole body. PBPK models are particularly suited to scaling kinetics across body size (e.g., adult to neonate) and species (e.g., animal to first-in-man) as physiology and pharmacology can be represented by independent parameters. Maturation models can be incorporated as for compartmental models. PBPK models are now available in commercial software packages, and are perhaps now more accessible than ever. Alternatively, even complex PBPK models can be represented in generic differential equation-solving software using the simple principles described here. The relative ease of constructing the code for PBPK models belies the most difficult aspect of their implementation-collecting, collating, and justifying the data used to parameterize the model.

Correction of MFG-E8 Resolves Inflammation and Promotes Cutaneous Wound Healing in Diabetes

Milk fat globule epidermal growth factor-factor 8 (MFG-E8) is a peripheral glycoprotein that acts as a bridging molecule between the macrophage and apoptotic cells, thus executing a pivotal role in the scavenging of apoptotic cells from affected tissue. We have previously reported that apoptotic cell clearance activity or efferocytosis is compromised in diabetic wound macrophages. In this work, we test the hypothesis that MFG-E8 helps resolve inflammation, supports angiogenesis, and accelerates wound closure. MFG-E8(-/-) mice displayed impaired efferocytosis associated with exaggerated inflammatory response, poor angiogenesis, and wound closure. Wound macrophage-derived MFG-E8 was recognized as a critical driver of wound angiogenesis. Transplantation of MFG-E8(-/-) bone marrow to MFG-E8(+/+) mice resulted in impaired wound closure and compromised wound vascularization. In contrast, MFG-E8(-/-) mice that received wild-type bone marrow showed improved wound closure and improved wound vascularization. Hyperglycemia and exposure to advanced glycated end products inactivated MFG-E8, recognizing a key mechanism that complicates diabetic wound healing. Diabetic db/db mice suffered from impaired efferocytosis accompanied with persistent inflammation and slow wound closure. Topical recombinant MFG-E8 induced resolution of wound inflammation, improvements in angiogenesis, and acceleration of closure, upholding the potential of MFG-E8-directed therapeutics in diabetic wound care.

Consideration of the unbound drug concentration in enzyme kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system under investigation. As a consequence, the apparent kinetic parameters that are derived, such as K m or K i, can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus, as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components that can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.