Membrane Structural Parameters Research Articles

Forward osmosis for concentrating lithium-enriched brine: From membrane performance to system design

Using concentrated salt-lake brine as the draw solution (DS), forward osmosis (FO) has been proposed as the low carbon footprint concentration technology for extracting water from lithium-enriched brine. From membrane modules to a process, there exist tremendous risks when considering total cost in time and investment for a trial. To address this issue, a bridge between the FO membrane/ module characteristics and a FO system design is highly desirable. This study for the first time developed a system design model using hollow fiber (HF) FO membrane modules for FO concentration of lithium-enriched brine. The concentration polarization along the module, the membrane structural parameters (S), crossflow velocity, and DS concentration were the main factors considered in the model. The effect of all factors on module water flux and total membrane area was systematically investigated for a typical industrial output of 10 k ton of Li2CO3. A master curve of the water flux verse feed concentration for the tailor-made HF modules was utilized as the basis. On a module scale, the structural parameter S is of first importance and indicates requirement of FO membranes with low S. The crossflow velocity has relatively small impact to the membrane area, but not negligible. On a system scale, the empirical equation of the structural parameters and crossflow velocity on the theoretical minimum system membrane area was summarized. The results show that increasing the stages can reduce the number of modules and required DS flow. Increasing DS dilution rate in system design efficiently utilizes the osmotic pressure of DS. The present model serves as a practical solution for process optimization and FO membrane selection. The methodology is valuable for other FO applications using HF FO membranes.

Breaking the limits of ambient air conditions in evaporative cooling with vacuum-assisted hollow fiber membrane technology

Conventional direct evaporative cooling systems suffer from persistent issues like air–water cross-contamination, mold growth, and scaling of paddings. Moreover, their widespread application in humid climates is hindered by ambient air conditions. This research presents a promising solution in the form of a vacuum-assisted hollow fiber membrane-based evaporative water cooler (VMEWC). It shifts from relying on air state to vacuum pressure for cold water production. A numerical model was established to reflect the coupled heat and mass transfer characteristics, employing Knudsen diffusion to describe water vapor migration within membrane pores. Experimental tests were performed on the constructed VMEWC system, revealing strong alignment between simulation and measured data, with a 2.2 % error in outlet water temperature predictions. Numerical studies explored the effects of six parameters (inlet water temperature, water velocity, shell-side pressure, fiber length, fiber inner diameter, and overall membrane structure parameter) on VMEWC’s outlet water temperature (Tw2), cooling capacity per unit volume (Qv), and coefficient of performance (COP). The results emphasized that elevated inlet water temperature, reduced shell-side pressure, and a large overall membrane structure parameter effectively enhanced the overall performance of VMEWC. Under specific conditions, Qv and COP reached impressive values of 3753.1 kW/m3 and 13.6, respectively. This work provides a novel and practical perspective, offering references for advancing efficient and sustainable refrigeration technologies.

Machine learning to predict the intrinsic membrane parameters in pressure retarded osmosis for an economic salinity gradient power plant

Pressure retarded osmosis (PRO) is highly investigated in the literature as one of the blue energy techniques. The PRO membrane plays a key role in harvesting the osmotic energy from a salinity gradient resource and process optimization. Therefore, a well-selected membrane will improve the power density generated in the PRO process to meet a designed power density threshold required for an economic salinity gradient power plant. In order to select a proper membrane for the PRO process, it is crucial to know its intrinsic properties, such as the membrane water permeability, salt permeability, and structural parameters, that impact the process performance. Determining the membrane's exact intrinsic parameters in a full-scale PRO module is challenging and time-consuming, and assuming constant parameters will compromise the accuracy of the results and power generation in the PRO process. This study employs artificial neural networks and Boosting-based tree models to predict the intrinsic parameters of the PRO membrane based on the minimum theoretical power density that could be predetermined and was assumed to be 5 W/m2 in this study. The Random Forest and XGBoost algorithms demonstrate superior predictive power (R2 = 0.97) compared to the other examined machine learning algorithms. The results reveal that machine learning algorithms can provide significant predictive power for the membrane's intrinsic parameters and power density based on the input parameters. Additionally, the algorithms were used to evaluate the feature importance of each input parameter affecting the power density of the pressure retarded osmosis membrane.

Predicting water flux and reverse solute flux in forward osmosis processes using artificial neural networks (ANN) modelling with structural parameters

Artificial neural network (ANN) has gained increasing attention in modelling wastewater treatment processes because it harnesses historical datasets to uncover relationships between input and output data, enabling accurate predictions without the need for complex equations. However, there are few ANN studies that simultaneously predict both water flux (Jw) and reverse solute flux (Js) in the forward osmosis (FO) process. In this work, we present an ANN model designed with only eight input variables, introducing the rarely explored membrane structural parameter (S) as an input variable for the first time. Through the optimisation of hidden layers, neuron counts, and the combination of activation functions, the ANN model achieved high R-squared values of 94.6 % and 95.2 % for Jw and Js prediction, respectively. To assess the model’s versatility across different scenarios, the application scope of the model was further evaluated using three distinct membrane types (namely, substrate-modified, active layer-modified and interlayer-modified). Results demonstrated the ANN model’s capacity to accurately predict the water flux of the substrate-modified membranes. Remarkably, even though the interlayer-modified and active layer-modified membranes were not part of the model’s training, the ANN model still exhibited relative accuracy, effectively capturing the modifications made through the structural parameter input. Compared with the solution diffusion (SD) model, the ANN model showed promising potential as a simplified yet powerful tool to predict the water flux in FO processes. Its capability to deliver accurate results with fewer complexities makes it an attractive choice for the modelling of FO processes.

Feasibility and challenges of high-pressure pressure retarded osmosis applications utilizing seawater and hypersaline water sources

Pressure retarded osmosis (PRO) harnesses salinity gradient energy through the mixing of freshwater and saltwater, addressing the demand for sustainable energy sources. PRO typically utilizes river water or secondary wastewater as the feed solution, paired with seawater reverse osmosis (SWRO) brine as the draw solution. However, the limited availability of low-saline water presents a significant obstacle to energy generation. Therefore, the feasibility of a high-pressure PRO process utilizing seawater as the feed solution and hypersaline water as the draw solution was assessed to generate sustainable blue energy. Seawater has the potential to achieve the maximum extractable Gibbs-free energy through high-pressure PRO by maximizing the feed/draw ratio. The performance of the high-pressure PRO was theoretically evaluated, resulting in a tenfold increase in specific energy production compared to conventional SWRO-PRO due to the higher feed/draw ratio. However, high hydraulic pressure increased the membrane structural parameter and further reduced water flux and power density due to significant internal concentration polarization. Nevertheless, the high-pressure PRO process can achieve a power density exceeding 84 W/m2 when the structural parameter remains below 100 μm. The implications of the high-pressure PRO were further discussed to advance the concept of a blue circular economy, enhance environmental resilience, and promote sustainability.

Multimillion Atom Simulations of Di-8-ANEPPS Chromophores Embedded in a Model Plasma Membrane: Toward the Investigation of Realistic Dyed Cell Membranes.

A multistep computational approach has been employed to study a multimillion all-atom dyed plasma membrane, with no less than 42 different lipid species spanning the major head groups and a variety of fatty acids, as well as cholesterol, with the objective of investigating its structure and dynamics, as well as its impact on the embedded di-8-ANEPPS dyes. The latter are commonly used as bioimaging probes and serve as local microscopes. So, they provide information on membrane morphology via their second harmonic nonlinear optical (NLO) responses, which have the advantage of being specific to interface regions and sensitive to the chromophore environment. In previous studies, this chromophore has only been studied in simpler membrane models, far from the complexity of real lipid bilayers, while, owing to the ever-increasing computational resources, multimillion lipid bilayers have been studied, giving access to the effects of its heterogeneity. First, using molecular dynamics (MD) simulations, it is found that the combination of lipids produces a more ordered and denser membrane compared to its homogeneous model counterparts, while the local environment of the embedded dyes becomes enriched in phosphatidylcholine. Subsequently, the second harmonic first hyperpolarizability of the probes was calculated at the TDDFT level on selected frames of MD, highlighting the influence of the lipid environment. Due to the complexity of the system, machine learning (ML) tools have been employed to establish relationships between the membrane structural parameters, the orientation of the probes, and their NLO responses. These ML approaches have revealed influential features, including the presence of diacylglycerol lipids close to the dye. On the whole, this work provides a first step toward understanding the cooperation, synergy, and interactions that occur in such complex guest-host environments, which have emerged as new targets for drug design and membrane lipid therapy.

Membrane properties overview in integrated forward osmosis/osmotically assisted reverse osmosis systems

Integration of forward osmosis (FO) and osmotically assisted reverse osmosis (OARO) provides low energy consumption and controlled fouling for brine treatment. This study explains the membrane properties required for cost–optimal OARO-based systems. A detailed model accounting for the relationship between burst pressure, permeate spacer gap, and structural parameters of OARO membranes was implemented with woven and non-woven spacers. Results indicate that the tradeoff between internal concentration polarization (ICP) and permeate–side pressure drop in OARO stages is a crucial consideration for optimal membrane element design. Due to the lower ICP of no-woven spacers, a system using non-woven spacers yielded lower levelized cost of water (LCOW) and specific energy consumption (5.83 $/m3 and 11.65 kWh/m3, respectively) as compared to the system with woven spacers (7.31 $/m3 and 9.90 kWh/m3, respectively) for dewatering a stream of 75 g/L salinity with 70 % water recovery. Sensitivity analysis shows that increasing the maximum pressure in OARO membranes requires a higher membrane structural parameter, aggravating ICP; however, the elevated driving force mitigates adverse effects of ICP. Moreover, a 15 % reduction in LCOW can be achieved if the membrane replacement factor of RO and OARO is reduced from 15 % to 5 %, which accounts for mitigated fouling.

Modeling and optimization of Graphene Oxide (GO) membranes for nanofiltration with artificial neural networks

It is important to understand the correlation between the nanostructure and membrane performances (water flux and rejection) in membrane separation technology, which is helpful to develop novel membranes. In this study, a back propagation artificial neural network model optimized with genetic algorithms was proposed to predict the nanofiltration performances (water flux and rejection) of GO membranes. The aim is to explore the feature importance of the membrane structure parameters (such as interlayer spacing, Zeta potential, water contact angle, roughness and thickness) and the operating condition (operation pressure) on the membrane properties, so as to provide clues for improving the membrane performances. The obtained results showed that genetic algorithm-back propagation artificial neural network (GABPANN) exhibited more accurate performance to predict the correlation between the parameters (including membrane structure and operation pressure) and the membrane performances according to the published experimental results. Moreover, the GABPANN results indicated that the water contact angle is the most powerful parameter determining the water flux, while the surface charge is the most powerful one determining the rejection of GO membranes. This work provided a novel strategy to efficiently optimize the nanofiltration performance of GO membranes, beneficial for better understanding and controlling the structural design of GO membranes.

Viscosity-Sensitive Membrane Dyes as Tools To Estimate the Crystalline Structure of Lipid Bilayers.

Lipid membranes are crucial for cellular integrity and regulation, and tight control of their structural and mechanical properties is vital to ensure that they function properly. Fluorescent probes sensitive to the membrane's microenvironment are useful for investigating lipid membrane properties; however, there is currently a lack of quantitative correlation between the exact parameters of lipid organization and a readout from these dyes. Here, we investigate this relationship for "molecular rotors", or microviscosity sensors, by simultaneously measuring their fluorescence lifetime to determine the membrane viscosity, while using X-ray diffraction to determine the membrane's structural properties. Our results reveal a phase-dependent correlation between the membrane's structural parameters and mechanical properties measured by a BODIPY-based molecular rotor, giving excellent predictive power for the structural descriptors of the lipid bilayer. We also demonstrate that differences in membrane thickness between different lipid phases are not a prerequisite for the formation of lipid microdomains and that this requirement can be disrupted by the presence of line-active molecules. Our results underpin the use of membrane-sensitive dyes as reporters of the structure of lipid membranes.

Numerical simulation of waste heat recovery process from the hot stripped gas in carbon capture process by using ceramic membrane

CO2 chemical absorption process suffers from the high CO2 regeneration heat requirement, so a rich solvent-split mechanism was put forward to recover the waste heat from the hot stripped gas for reducing the regeneration heat consumption. Ceramic membrane-based transport membrane condenser (TMC) can provide a higher heat transfer efficiency, therefore better meet the requirements for waste heat recovery from the hot stripped gas. The ceramic membrane structure is a key factor affecting the heat recovery performance, which has been neglected by previous studies. For investigating the effect of membrane structure, in this study, a condensation and mass transfer model was firstly constructed using the computational fluid dynamics approach to describe the process of waste heat recovery from the hot stripped gas. The errors between the simulated outlet temperature of stripped gas or CO2-rich solvent and the experimental ones are about 1.74 %, indicating the satisfactory accuracy of the model. Based on the model, the effects of ceramic membrane structure and physical parameters on the waste heat recovery performance were comprehensively investigated. The results showed that reducing the wall thickness or inner diameter of ceramic membrane contributes to improving the waste heat recovery efficiency of TMC. Notably, there may be an optimal membrane area beyond which the improvement of waste heat recovery is difficult. Additionally, when the thermal conductivity of ceramic membrane exceeds a critical value (∼10 W/(m K) in this study), the improvement of waste heat recovery performance may be difficult only by increasing the thermal conductivity. This study provides an important guidance for the selection of ceramic membranes for waste heat recovery process from the stripped gas in the future.

Fabrication of high-performance forward osmosis membrane based on asymmetric integrated nanofiber porous support induced by a new controlled photothermal induction method

The high surface roughness and large pore size of electrospun nanofibers (ESNF) make it impossible to form good polyamide (PA) layers through interfacial polymerization. Therefore, ESNF cannot be directly used as the supporting layer of nanofiber-supported thin film composite forward osmosis (NTFC-FO) membrane. However, its advantages of high porosity and large pore size are required for forward osmosis (FO) process. In this work, for the first time, an asymmetric integrated ESNF is constructed by introducing photothermal materials PANI into the supporting layer and using in situ photothermal annealing to controllably reduce the surface roughness and pore size of the support membrane, which not only meets the requirements of interface polymerization to form a good PA layer, but also maintains the high porosity, large pore size and structural stability of the ESNF. With this strategy, the FO membrane with highly selective PA layer and low membrane structural parameter (S) were obtained. The results shows that NTFC-FO membrane shows high water flux of 40.2 L·m−2·h−1 and low reverse salt flux of 4.9 g·m−2·h−1, and specific salt flux of Js/Jw is only 0.12 with deionized water and 1.0 M NaCl solution as the feed solution and draw solution, respectively, and the FO membrane also exhibit excellent rejection to dye wastewater. It can be considered that the asymmetric integrated ESNF supporting layer constructed by the photothermal annealing strategy provides a simple and feasible method for the preparation of high-performance NTFC-FO membranes.

Ensemble machine learning reveals key structural and operational features governing ion selectivity of polyamide nanofiltration membranes

Diversified ion-selective separation applications have dramatically incentivized the exploitation and performance modulation of highly ion-selective nanofiltration (NF) membranes. However, the ion selectivity of NF membranes is synergistically governed by multi-scale factors of membrane structural parameters and operational parameters, with the intrinsic ion-selective mechanism still ambiguous. Herein, we proposed an ensemble machine learning (ML) method to decouple key factors affecting the ionic selectivity of polyamide NF membranes. Membrane structural parameters and operational parameters were typically extracted as input variables and linked to mono−/divalent ion selectivity by model training based on Random Forest and XGBoost algorithms. The feature importance assessment indicated the critical role of membrane structure parameters on ion selectivity, wherein pore radius dominated the mono−/divalent anionic selectivity while zeta potential for mono−/divalent cationic selectivity. Partial dependence analyses further depicted intensive insights regarding the influence of membrane structural parameters on ion selectivity. Moreover, stochastic dataset splitting measurements demonstrated the accurate predictive capability of the model simultaneously possessing excellent stability and reliability. We anticipated that the implementation of ensemble ML in explicating the intricate ion-selective mechanism created platforms for understanding the structure-membrane performance correlation and orientally manufacturing highly ion-selective NF membranes.

Performance evaluation and model of spacesuit cooling by hydrophobic hollow fiber-membrane based water evaporation through pores

A comprehensive mathematical model is presented that accurately estimates and predicts failure modes through the computations of heat rejection, temperature drop and lumen side pressure drop of the hollow fiber (HF) membrane-based NASA Spacesuit Water Membrane Evaporator (SWME). The model is based on mass and energy balances in terms of the physical properties of water and membrane transport properties. The mass flux of water vapor through the pores is calculated based on Knudsen diffusion with a membrane structure parameter that accounts for effective mean pore diameter, porosity, thickness, and tortuosity. Lumen-side convective heat transfer coefficients are calculated from laminar flow boundary layer theory using the Nusselt correlation. Lumen side pressure drop is estimated using the Hagen-Poiseuille equation. The coupled ordinary differential equations for mass flow rate, water temperature and lumen side pressure are solved simultaneously with the equations for mass flux and convective heat transfer to determine overall heat rejection, water temperature and lumen side pressure drop. A sensitivity analysis is performed to quantify the effect of input variability on SWME response and identify critical failure modes. The analysis includes the potential effect of organic and/or inorganic contaminants and foulants, partial pore entry due to hydrophilization, and other unexpected operational failures such as bursting or fiber damage. The model can be applied to other hollow fiber membrane-based applications such as low temperature separation and concentration of valuable biomolecules from solution.

Revealing key structural and operating features on water/salts selectivity of polyamide nanofiltration membranes by ensemble machine learning

Nanofiltration (NF) plays an increasingly central role in water/salts separation, which puts forward tailored requirements on NF membranes in a variety of application scenarios. However, the ambiguous separation mechanisms of NF including membrane structure parameters and operating conditions hinder the rational design of versatile NF membranes. Herein, machine learning was used to explore the correlation between membrane structure parameters and operating conditions with water/salts selectivity, and reveal the importance of the diverse features based on literature data. Two structural features of polyamide NF membrane (pore radius and zeta potential) and two operating parameters (pressure and feed concentration) were typically extracted and associated with water/salts selectivity. Random Forest and XGBoost models were employed to learn from relevant variables and assess their importance. The results showed that membrane structure parameters attached more importance to water/salts selectivity than that of operating conditions accompanying the variable influence for different typed salts, where symmetrical salts were mainly governed by size sieving while Donnan exclusion for asymmetrical salts. Structure-performance relationships between pore radius, zeta potential and diverse water/salts selectivity were established using partial dependence plot analysis. It is anticipated that the constructed comprehensive insights can be further leveraged to tackle performance modulation and oriented design of multi-scenarios NF membranes.

SAXS Investigation of the Effect of Freeze/Thaw Cycles on the Nanostructure of Nafion® Membranes.

In this study, we performed small-angle X-ray scattering (SAXS) to investigate the structure of Nafion® membranes. The effect of freeze/thaw (F/T) cycles (from ambient temperature down to −40 °C) on the membrane nanostructure was considered for the first time. The SAXS measurements were taken for different samples: a commercial Nafion® 212 membrane swollen in water and methanol solution, and a water-swollen silica-modified membrane. The membrane structure parameters were obtained from the measured SAXS profiles using a model-dependent approach. It is shown that the average radius of water channels (Rwc) decreases during F/T cycles due to changes in the membrane structure as a result of ice formation in the pore volume after freezing. The use of water-methanol solution (methanol content of 20 vol.%) for the membrane soaking prevents changes in the membrane structure during F/T cycles compared to the water-swollen membrane. Modification of the membrane surface with silica (SiO2 content of 20 wt.%) led to a redistribution of water in the membrane volume and resulted in a decrease in Rwc. However, Rwc for the modified membrane did not decrease with the increasing number of F/T cycles due to the involvement of SiO2 in the sorption of membrane water and, therefore, the prevention of ice formation.

Metabolomics analysis of buck semen cryopreserved with trehalose.

Trehalose is commonly used as an impermeable cryoprotectant for cryopreservation of cells, but its cryoprotective mechanism has now not but been determined. This study investigated the cryopreservation impact of trehalose on buck semen cryopreservation and finished metabolic profiling of freeze-thawed media by way of the GC–MS-based metabolomics for the first time. Metabolic pattern recognition and metabolite identification by means of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and metabolic pathway topology analysis revealed the results of trehalose on buck sperm metabolism at some point of cryopreservation. The results confirmed that trehalose drastically progressed sperm motility parameters and structural integrity after thawing. PCA and PLS-DA analysis discovered that the metabolic patterns of the freezing-thawing media of buck semen cryopreserved with trehalose (T group) or without trehalose (G group, Control) were certainly separated. Using screening conditions of VIP >1.5 and p vaule <0.05, a total of 48 differential metabolites have been recognized, whithin l-isoleucine, L-leucine, L-threonine, and dihydroxyacetone were notably enriched in valine, leucine and isoleucine biosynthesis, glycerolipid metabolism, and aminoacyl-tRNA biosynthesis pathways. In brief, trehalose can efficiently improve membrane structural integrity and motion parameters in buck sperm after thawing, and it exerts a cryoprotective impact with the aid of changing sperm amino acid synthesis and the glycerol metabolism pathway.

Temperature effects on salinity gradient energy harvesting and utilized membrane properties – Experimental and numerical investigation

Salinity gradient energy (SGE) is a source of renewable energy which utilizes the osmotic pressure difference between two water streams of different salinities. The present paper investigates the effect of environmental water temperature variations on the performance of the pressure retarded osmosis (PRO) process employed to harvest SGE. Using experimental and numerical approaches, the effect of water temperature on water and salt permeabilities as well as membrane structure parameter have been measured using a bench scale system and a commercially available FO membrane. The water and salt permeability coefficients have been found to increase with the water temperature while the membrane structure parameter decreases. This variation in the membrane properties has enhanced the maximum power density of the PRO process by about 90% when the water temperature increases from 10 to 35 °C. New correlations to predict the membrane properties at different temperatures have been developed and validated against the tested membrane as well as other membrane properties data available in the literature. This study highlights the importance of understanding the effect of water stream temperature to accurately predict the performance of PRO process that is utilized for harvesting the salinity gradient energy.

Novel Thin Film Nanocomposite Forward Osmosis Membranes Prepared by Organic Phase Controlled Interfacial Polymerization with Functional Multi-Walled Carbon Nanotubes.

Novel high-quality thin film nanocomposite (TFN) membranes for enhanced forward osmosis (FO) were first synthesized through organic phase controlled interfacial polymerization by utilizing functional multi-walled carbon nanotubes (MWCNTs). As 3-aminopropyltriethoxysilane (APTES) grafted MWCNTs via an amidation reaction significantly promoted the dispersion in organic solution, MWCNTs-APTES with better compatibility effectively restricted the penetration of trimesoyl chloride (TMC), thus adjusting the morphology and characters of TFN membranes. Various techniques such as Fourier transform infrared spectra (FTIR), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), sessile droplet analysis and FO experiments and reverse osmosis (RO) operation were taken to characterize and evaluate the performance of nanocomposites and membranes. The prepared TFN FO membranes exhibited good hydrophilicity and separation efficiency, in which water flux was about twice those of thin film composite (TFC) membranes without MWCNTs-APTES in both AL-DS and AL-FS modes. Compared with the original TFC membrane, the membrane structural parameter of the novel TFN FO membrane sharply was cut down to 60.7%. Based on the large number of low mass-transfer resistance channels provided by functional nanocomposites, the progresses may provide a facile approach to fabricate novel TFN FO membranes with advanced selectivity and permeability.

Novel Plasma Functionalized Graphene Nanoplatelets (GNPs) incorporated in forward osmosis substrate with improved performance and tensile strength

In this study, a novel thin-film nanocomposite forward osmosis (FO) membrane incorporated with plasma functionalized graphene nanoplatelets (GNPs) was developed. The well distributed GNPs in the polyethersulfone (PES) support increased the substrate porosity by 8%, and significantly improved the substrate tensile modulus even at a higher loading of GNPs. Unlike graphene oxide (GO), the linear increase in tensile strength with increasing the GNPs loading is due to the good dispersion properties of GNPs. The results indicated that in terms of water flux improvement, the optimal amount of GNPs incorporation is 0.3 wt%, while in terms of tensile strength, adding higher amounts of GNPs does not decrease the membrane stiffness even at 1 wt% of loading. The GNPs addition also significantly reduced the membrane structural parameter from 1323 µm with 0 wt% to 449 µm with 0.5 wt% loadings. Under the FO mode, water flux of 28.1 LMH was obtained for the membrane with the 0.5 wt% GNPs loading using 2 M NaCl as draw solution and DI water as feed. Overall, this study recommends that applying GNPs for the FO membrane development may be promising considering all types of FO applications where different membranes with different properties are required.

Case Study 8: Status of the Structural Mass Action Kinetic Model of P-gp-Mediated Transport Through Confluent Cell Monolayers.

In the first edition of this book, we presented the basics of explicitly incorporating the lipid biochemistry into a confluent cell monolayer transport model and the novel findings of this model up to 2013, including the use of global optimization to fit the elementary rate constants and the efflux active P-glycoprotein (P-gp) membrane concentrations for the transport of four P-gp substrates across MDCKII-hMDR1-NKI confluent cell monolayers. This chapter is an update on that model, which has been focused primarily on discovering how microvilli morphology regulates the efflux active P-gp and the existence of, as yet, unidentified uptake transporters of P-gp substrates in all of the commonly used P-gp expressing cell lines used in the pharmaceutical industry, thereby adding new players to DDI predictions and IVIVE. The structural mass action kinetic model uses the general mass action reactions for P-gp binding and efflux, with the membrane structural parameters for the confluent cell monolayer to predict drug transport over time. Binding of drug to P-gp occurs within the cytosolic monolayer of the apical membrane, according to (a) the molar partition coefficient of the drug to the cytosolic monolayer and (b) the association rate constant, k1 (M-1s-1), of the drug from the basolateral or apical outer monolayers into the P-gp binding site. Release of substrate from P-gp back into the cytosolic monolayer occurs with a dissociation rate constant kr (s-1) or, much less frequently, into the apical aqueous chamber with an efflux rate constant k2 (s-1). The model fits the efflux active P-gp concentration, T(0), i.e., the P-gp whose effluxed drug actuallyreaches the apical aqueous chamber, as opposed to the majority of P-gp whose effluxed drug is reabsorbed back into the same or neighboring microvilli prior to reaching the apical aqueous chamber. Efflux active P-gp largely resides near the tips of the microvilli. We have shown using kinetics and structured illumination microscopy that: (a) efflux active P-gp is controlled by microvilli morphology; (b) there are apical (AT) and basolateral (BT) uptake transporters for P-gp substrates in most, if not all, P-gp expressing cell lines used in the pharmaceutical industry, whichexist, but which remain unidentified; (c) the lab-to-lab variability in P-gp IC50 values observed in the P-gp IC50 initiative was due to the conflated inhibition of P-gp and the basolateral digoxin uptake transporters by all 15 P-gp substrates tested in that study; (d)even the IC50 values for P-gp inhibition alone do not obey the Cheng-Prusoff relationship; (e) the fitted elementary rate constants and the molecular dissociation constant Ki for this kinetic model are system independent; and (f) the time dependence of product formation for these confluent cell monolayers is correlated withthe P-gp Vmax/Km, whendefined by its fitted elementary rate constants and uptake transporter clearances, without any steady-state assumptions.