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1
Xie, Jiang. "Improved permeability prediction using multivariate analysis methods." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3223.
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Stenberg, Patric. "Computational models for the prediction of intestinal membrane permeability." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2001. http://publications.uu.se/theses/91-554-4934-4/.
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Florence, Francois-Andre. "Validation/enhancement of the "Jones-Owens" technique for the prediction of permeability in low permeability gas sands." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5846.
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This work presents the validation and enhancement of existing correlations for estimating and predicting
the permeability in low permeability gas sands. The "original" problem of predicting the corrected or
"liquid equivalent" permeability has been under investigation since the early 1940s â in particular, using
the application of "gas slippage" theory to petrophysics by Klinkenberg.
In the first part of this work, the viability of the Jones-Owens and Sampath-Keighin correlations for
estimating the Klinkenberg-corrected (absolute) permeability from single-point, steady-state measurements
were investigated. We also provide an update to these correlations using modern petrophysical
data.
In the second part of this work we proposed and validated a new "microflow" model for the evaluation of
an equivalent liquid permeability from gas flow measurements. This work was based on a more detailed
application of similar concepts employed by Klinkenberg. In fact, we obtained the Klinkenberg result as
an approximate form of this result. A theoretical "microflow" result was given as a rational polynomial
(i.e., a polynomial divided by a polynomial) in terms of the Knudsen number (ratio of the mean free path
of the gas molecules to the characteristic flow length (typically the radius of the capillary)), and this result
can be applied as an explicit correlation device, or as an implicit prediction model (presuming the model is
tuned to a particular data set).
The following contributions are derived from this work:
â Validation and extension of the correlations proposed by Jones-Owens and Sampath-Keighin for low
permeability samples.
â Development and validation of a new "microflow" model which correctly represents the flow of gases
in low permeability core samples. This model is also applied as a correlation for prediction of the
equivalent liquid permeability in much the same fashion as the Klinkenberg model, although the new
model is substantially more theoretical (and robust) as compared to the Klinkenberg correction model.
4
Wu, Tao. "Permeability prediction and drainage capillary pressure simulation in sandstone reservoirs." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1496.
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Knowledge of reservoir porosity, permeability, and capillary pressure is essential to
exploration and production of hydrocarbons. Although porosity can be interpreted fairly
accurately from well logs, permeability and capillary pressure must be measured from
core. Estimating permeability and capillary pressure from well logs would be valuable
where cores are unavailable.
This study is to correlate permeability with porosity to predict permeability and
capillary pressures. Relationships between permeability to porosity can be complicated
by diagenetic processes like compaction, cementation, dissolution, and occurrence of clay
minerals. These diagenetic alterations can reduce total porosity, and more importantly,
reduce effective porosity available for fluid flow. To better predict permeability, effective
porosity needs to be estimated. A general equation is proposed to estimate effective
porosity. Permeability is predicted from effective porosity by empirical and theoretical
equations.
A new capillary pressure model is proposed. It is based on previous study, and
largely empirical. It is tested with over 200 samples covering a wide range of lithology
(clean sandstone, shaly sandstone, and carbonates dominated by intergranular pores).
Parameters in this model include: interfacial tension, contact angle, shape factor,
porosity, permeability, irreducible water saturation, and displacement pressure. These
parameters can be measured from routine core analysis, estimated from well log, and
assumed. An empirical equation is proposed to calculate displacement pressure from
porosity and permeability. The new capillary-pressure model is applied to evaluate
sealing capacity of seals, calculate transition zone thickness and saturation above free
water level in reservoirs. Good results are achieved through integration of well log data,
production data, core, and geological concepts.
5
Pereira, Janaina Luiza Lobato. "Permeability prediction from well log data using multiple regression analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3368.
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Thesis (M.S.)--West Virginia University, 2004.Title from document title page. Document formatted into pages; contains xiii, 82 p. : ill. (some col.), maps. Vita. Includes abstract. Includes bibliographical references (p. 41).
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Hahn, Christoph [Verfasser]. "A Simulation Approach of Permeability Prediction for RTM Process Simulation / Christoph Hahn." München : Verlag Dr. Hut, 2015. http://d-nb.info/1067708391/34.
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Xu, Jianlong. "Prediction of gas permeability in composite laminates using three-dimensional finite elements." [Gainesville, Fla.] : University of Florida, 2007. http://purl.fcla.edu/fcla/etd/UFE0021260.
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Ball, Leslie Derek. "Permeability prediction in a fluvial reservoir : the PUC-B sandstone, Sirt Basin, Libya." Thesis, Heriot-Watt University, 1997. http://hdl.handle.net/10399/1293.
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Goswami, Tarun. "Sublingual drug delivery: In vitro characterization of barrier properties and prediction of permeability." Scholarly Commons, 2008. https://scholarlycommons.pacific.edu/uop_etds/2370.
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Sublingual administration of drugs offers advantages including avoidance of first pass metabolism and quick absorption into the systemic circulation. In spite of being one of the oldest routes of drug delivery, there is dearth of literature on characterization of the barrier properties of the sublingual mucosa. Therefore, the aim of this research was to gain an insight into the barrier properties of the porcine sublingual mucosa. The studies conducted in this dissertation research focused on an important aspect of sublingual permeation, the dependence of permeability on different physicochemical properties of the permeant such as the degree of ionization, distribution coefficient and molecular weight/size on drug transport across sublingual mucosa. Further the data from the sublingual permeation of model compounds was used in development of a predictive model which provided us with some understanding regarding the important descriptors required for sublingual drug delivery. A series of β-blockers were employed as the model drugs to study the dependence of permeability on lipophilicity across the sublingual mucosa. Eighth different β-blockers with log D (distribution coefficient) values ranging from -1.30 to 1.37 were used in this study. The most hydrophilic drug atenolol showed the lowest permeability (0.19 ± 0.04 x 10 -6 ) cm/sec and the most lipophilic drug propranolol showed the highest permeability (38.25 ± 4.30 x 10 -6 ) cm/sec. The log-log plot of permeability coefficient and the distribution coefficient showed a linear relationship. It was concluded that the increase in lipophilicity results in improved partitioning across the lipid bilayers of sublingual mucosa which results in increased permeation for the drugs. As the sublingual mucosa contains a significant amount of the polar lipids bonded with water molecules, therefore, it was hypothesized that the hydrophilic or ionized permeants will have significant permeation across the sublingual mucosa. The objective of this research was to study the effect of ionization on permeation across sublingual mucosa using a model drug nimesulide. Based on the relationship between the permeability coefficient and distribution coefficient of nimesulide at different pH, the lipoidal route was suggested as the dominant transport route for nimesulide across the sublingual mucosa. The contribution of individual ionic species of nimesulide to the total drug flux was quantitatively delineated. It was observed that the ionized species of nimesulide contributes significantly to the total flux across the sublingual mucosa. The contribution of the ionized species to total flux was almost (90%) at a pH where the drug was almost completely ionized. Polyethylene glycols (PEGs) were used as the model permeants to study the dependence of permeability on molecular weight. An inverse relationship between molecular weight and permeability coefficients was observed. This relationship was used to estimate the molecular weight cut off for the sublingual mucosa. The molecular weight cut off was estimated to be around 1675 daltons. Further, the Renkin function was used to estimate the theoretical pore size of the sublingual mucosa and the pore size of the sublingual mucosa was estimated to be around 30–53 Å based on two separate calculations using the radius of gyration and Stokes-Einstein radius for PEG molecules, respectively. No specific model is present in literature to predict the in vitro sublingual drug permeability. In this dissertation a specific model was developed and validated by performing permeation studies of 14 small molecules across the porcine sublingual mucosa. It was shown that the lipophilicity (logD 6.8 ) and the number of hydrogen bond donors (HBD) were the most significant descriptors affecting sublingual permeability. Research conducted in this dissertation provided an in-depth understanding about the barrier properties of the porcine sublingual mucosa and role of different physicochemical properties on sublingual transport. Such an understanding will hopefully expand the suitable lead candidates for sublingual delivery.
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Goswani, Tarun. "Sublingual drug delivery : in vitro characterization of barrier properties and prediction of permeability." Scholarly Commons, 2008. https://scholarlycommons.pacific.edu/uop_etds/708.
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Gertz, Michael. "Prediction of intestinal availability in human from in vitro clearence and permeability data." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503642.
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Barnett, Nina (Kuentzer). "Permeability characterization and microvoid prediction during impregnation of fiber tows in dual-scale fabrics." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 2.70 Mb., 144 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1430754.
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Schuster, Stephanie Ann Foley Joe Preston. "Electrokinetic chromatography using novel unilamellar vesicles for unique separations and prediction of intestinal permeability /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/2579.
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Yasarer, Hakan I. "Characterizing the permeability of concrete mixes used in transportation applications: a neuronet approach." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/4314.
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Master of ScienceDepartment of Civil Engineering
Yacoub M. Najjar
Reliable and economical design of Portland Cement Concrete (PCC) pavement structural systems relies on various factors, among which is the proper characterization of the expected permeability response of the concrete mixes. Permeability is a highly important factor which strongly relates the durability of concrete structures and pavement systems to changing environmental conditions. One of the most common environmental attacks which cause the deterioration of concrete structures is the corrosion of reinforcing steel due to chloride penetration. On an annual basis, corrosion-related structural repairs typically cost millions of dollars. This durability problem has gotten widespread interest in recent years due to its incidence rate and the associated high repair costs. For this reason, material characterization is one of the best methods to reduce repair costs. To properly characterize the permeability response of PCC pavement structure, the Kansas Department of Transportation (KDOT) generally runs the Rapid Chloride Permeability test to determine the resistance of concrete to penetration of chloride ions as well as the Boil test to determine the percent voids in hardened concrete. Rapid Chloride test typically measures the number of coulombs passing through a concrete sample over a period of six hours at a concrete age of 7, 28, and 56 days. Boil Test measures the volume of permeable pore space of the concrete sample over a period of five hours at a concrete age of 7, 28, and 56 days. In this research, backpropagation Artificial Neural Network (ANN)-based and Regression-based permeability response prediction models for Rapid Chloride and Boil tests are developed by using the databases provided by KDOT in order to reduce or eliminate the duration of the testing period. Moreover, another set of ANN- and Regression-based permeability prediction models, based on mix-design parameters, are developed using datasets obtained from the literature. The backpropagation ANN learning technique proved to be an efficient methodology to produce a relatively accurate permeability response prediction models. Comparison of the prediction accuracy of the developed ANN models and regression models proved that ANN models have outperformed their counterpart regression-based models. Overall, it can be inferred that the developed ANN-Based permeability prediction models are effective and applicable in characterizing the permeability response of concrete mixes used in transportation applications.
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Karaman, Turker. "Prediction Of Multiphase Flow Properties From Nuclear Magnetic Resonance Imaging." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610382/index.pdf.
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In this study a hybrid Pore Network (PN) model that simulates two-phase (water-oil) drainage and imbibition mechanisms is developed. The developed model produces Nuclear Magnetic Resonance (NMR) T2 relaxation times using correlations available in the literature. The developed PN was calibrated using experimental relative permeability data obtained for Berea Sandstone, Kuzey Marmara Limestone, Yeniköy Dolostone and Dolomitic Limestone core plugs. Pore network body and throat parameters were obtained from serial computerized tomography scans and thin section images. It was observed that pore body and throat sizes were not statistically correlated. It was also observed that the developed PN model can be used to model different displacement mechanisms. By using the synthetic data obtained from PN model, an Artificial Neural Network (ANN) model was developed and tested. It has been observed that the developed ANN tool can be used to estimate oil &ndash
water relative permeability data very well (with less than 0.05 mean square error) given a T2 signal. It was finally concluded that the developed tools can be used to obtain multiphase flow functions directly from an NMR well log such as Combinable Magnetic Resonance (CMR).
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Liang, Zhirong. "Computer generation and application of 3-D reconstructed porous structure :: from 2-D images to the prediction of permeability /." Florianópolis, SC, 1997. http://repositorio.ufsc.br/xmlui/handle/123456789/77050.
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Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico.Made available in DSpace on 2012-10-17T00:20:46Z (GMT). No. of bitstreams: 0Bitstream added on 2016-01-08T21:22:26Z : No. of bitstreams: 1 139303.pdf: 10954842 bytes, checksum: 938a599a74b00751449e7e5bb7aa8917 (MD5)
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Hahn, Christoph [Verfasser], Klaus [Akademischer Betreuer] Drechsler, and Christophe [Akademischer Betreuer] Binetruy. "A Simulation Approach of Permeability Prediction for RTM Process Simulation / Christoph Hahn. Gutachter: Klaus Drechsler ; Christophe Binetruy. Betreuer: Klaus Drechsler." München : Universitätsbibliothek der TU München, 2014. http://d-nb.info/1066363560/34.
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Neuhoff, Sibylle. "Refined in vitro Models for Prediction of Intestinal Drug Transport : Role of pH and Extracellular Additives in the Caco-2 Cell Model." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis: Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5814.
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Aina, Clement Olajide 1963. "Permeability prediction in Benin River/Gbokoda field in Nigeria : a case study using facies derived from core studies and multiple regression of wireline data." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9529.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1999.Includes bibliographical references (leaves 60-63).
Detailed understanding of the heterogeneities and complexity of reservoir architecture and flow properties are crucial to development and exploitation of commercial hydrocarbon reservoirs. Thus, reservoir characterization and simulation studies are done on a continuous basis during the life of a field from initial exploration through appraisal, development and eventual abandonment. A key component of these studies is the knowledge of the reservoir permeability across the field. However, permeability is only measured directly at the pore scale from core, and since cores are rarely taken in a significant percentage of the wells in a field, estimation methods are commonly used to predict the permeability in wells without core data. These methods have included empirical and statistical approaches, as well as the emerging pattern recognition techniques. The accuracy of most methods are greatly enhanced when the reservoir is subdivided into units with common flow properties. In this thesis, a case study is carried out in the Benin River/Gbokoda field in Nigeria, with the aim of developing from existing tools, a facies based, simple to use, accurate and readily available technique to predict permeability in fields where there is at least one well that has core data for calibration of the reservoir properties and facies. The use of the facies data to constrain the prediction greatly improved the match between the predicted and the actual. The reservoir is subdivided into depositional groupings based on lithofacies and facies association, flow properties, and ease of recognition on wireline logs. Linear equations were developed from multiple regression of wire line log data to predict this groupings. The predicted groupings and the wireline Jog data were used in a multiple regression to develop another set of linear equations to predict permeability in each grouping. The equations produced were applied to a test well that had core data but was not used in the study. The predicted groupings and permeability from the test well was in very close agreement with the original data. The equations are next applied to other wells in the field.
by Clement Olajide Aina.
S.M.
20
Cao, Yichen. "APPLICATION OF LINEAR FREE ENERGY RELATIONSHIPS IN THE PREDICTION OF TRIGLYCERIDE/WATER PARTITION COEFFICIENTS AND LIPID BILAYER PERMEABILITY COEFFICIENTS OF SMALL ORGANIC MOLECULES AND PEPTIDES." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_diss/655.
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Computational methods such as linear free energy relationships (LFERs) offer a useful high-throughput solution to quickly evaluate drug developability, e.g. membrane permeability, organic solvent/water partition coefficients, and solubility. LFERs typically assume the contribution of structural components/functional groups to the overall properties of a given molecule to be constant and independent. This dissertation describes a series of studies in which linear free energy relationships were developed to predict solvation of small organic molecules in lipid formulations, specifically, triglyceride containing solvents and phospholipid-based liposomes. The formation of intermolecular HBs in triglyceride solvents (homogenous with H-bond accepting ability) and intramolecular HBs within the bilayer barrier domain (hydrocarbon-like) proved to be the major factors to consider in developing LFERs to account for the increased oil/water partition coefficients and enhanced bilayer permeability of small organic molecules.
The triglyceride solvent/water partition coefficients of a series of model compounds varying in polarity and H-bond donating/accepting capability were used to establish a correlation between the solvent descriptors and the ester concentration in these solvents using the Abraham LFER approach. The LFER analyses showed that the descriptors representing the polarizability and H-bond basicity of the solvents vary systematically with the ester concentration.
A fragment-based LFER to predict membrane permeability or 1,9- decadiene/water partition coefficients of small organic molecules including small peptides was systematically constructed using a total of 47 compounds. Significant nonadditivity was observed in peptides in that the contribution of the peptide backbone amide to the apparent transfer free energy from water into the bilayer barrier domain is considerably smaller than that of a “well-isolated” amide and greatly affected by adjacent polar substituents on the C-termini.
In order to explain the phenomenon of nonadditivity, the formation of intramolecular HBs and inductive effects of neighboring polar groups on backbone amide, were investigated using FTIR and MD simulations. Both spectroscopic and computational results provided supportive evidence for the hypothesis that the formation of intramolecular HBs in peptides is the main reason for the observed nonadditivity of Δ(ΔG°)-CONH-. The MD simulation results showed that the inductive effect of neighboring groups is not as important as the effect of intramolecular HBs.
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Bergström, Christel A. S. "Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption." Doctoral thesis, Uppsala University, Department of Pharmacy, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3593.
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New effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the number of new drugs undergoing clinical trial has not augmented at the same pace, which in part has been attributed to poor absorption of the compounds.
The main objective of this thesis was to investigate whether computer-based models devised from calculated molecular descriptors can be used to predict aqueous drug solubility, an important property influencing the absorption process. For this purpose, both experimental and computational studies were performed. A new small-scale shake flask method for experimental solubility determination of crystalline compounds was devised. This method was used to experimentally determine solubility values used for the computational model development and to investigate the pH-dependent solubility of drugs. In the computer-based studies, rapidly calculated molecular descriptors were used to predict aqueous solubility and the melting point, a solid state characteristic of importance for the solubility. To predict the absorption process, drug permeability across the intestinal epithelium was also modeled.
The results show that high quality solubility data of crystalline compounds can be obtained by the small-scale shake flask method in a microtiter plate format. The experimentally determined pH-dependent solubility profiles deviated largely from the profiles predicted by a traditionally used relationship, highlighting the risk of data extrapolation. The in silico solubility models identified the non-polar surface area and partitioned total surface areas as potential new molecular descriptors for solubility. General solubility models of high accuracy were obtained when combining the surface area descriptors with descriptors for electron distribution, connectivity, flexibility and polarity. The used descriptors proved to be related to the solvation of the molecule rather than to solid state properties. The surface area descriptors were also valid for permeability predictions, and the use of the solubility and permeability models in concert resulted in an excellent theoretical absorption classification. To summarize, the experimental and computational models devised in this thesis are improved absorption screening tools applicable to the lead optimization in the drug discovery process.
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Yildirim, Akbas Ceylan. "Determination Of Flow Units For Carbonate Reservoirs By Petrophysical - Based Methods." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606343/index.pdf.
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Characterization of carbonate reservoirs by flow units is a practical way of reservoir zonation. This study represents a petrophysical-based method that uses well loggings and core plug data to delineate flow units within the most productive carbonate reservoir of Derdere Formation in Y field, Southeast Turkey.
Derdere Formation is composed of limestones and dolomites. Logs from the 5 wells are the starting point for the reservoir characterization. The general geologic framework obtained from the logs point out for discriminations within the formation. 58 representative core plug data from 4 different wells are utilized to better understand the petrophysical framework of the formation. The plots correlating petrophysical parameters and the frequency histograms suggest the presence of distinctive reservoir trends. These discriminations are also represented in Winland porosity-permeability crossplots resulted in clusters for different port-sizes that are responsible for different flow characteristics. Although the correlation between core plug porosity and air permeability yields a good correlation coefficient, the formation has to be studied within units due to differences in port-sizes and reservoir process speed.
Linear regression and multiple regression analyses are used for the study of each unit. The results are performed using STATGRAPH Version Plus 5.1 statistical software. The permeability models are constructed and their reliabilities are compared by the regression coefficients for predictions in un-cored sections.
As a result of this study, 4 different units are determined in the Derdere Formation by using well logging data, and core plug analyses with the help of geostatistical methods. The predicted permeabilities for each unit show good correlations with the calculated ones from core plugs. Highly reliable future estimations can be based on the derived methods.
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Laver, Richard George. "Long-term behaviour of twin tunnels in London clay." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/237245.
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The assessment of ageing tunnels requires a deeper understanding of the long-term behaviour of twin tunnels, whilst lack of permeability data limits the accuracy of long-term predictions. This thesis therefore investigates long-term twin-tunnel behaviour through finite-element parametric analyses, and provides additional pereability data through laboratory studies. Permeability tests are performed on fissured London Clay, exploring the effect of isotropic stress cycles on the permeability of fissures. A model explaining the permeability-stress relationship is proposed to explain irrecoverable changes observed in fissure permeability, and is formulated mathematically for numerical implementation. Laboratory investigations are performed on grout from the London Underground tunnels, investigating permeability, porosity, microstructure and composition. A deterioration process is proposed to explain observations, consisting of acid attack and leaching. The deterioration had appeared to transform the grout from impermeable to permeable relative to the soil. The change in grout permeability with time would strongly influence long-term movements. The long-term behaviour of single tunnels is investigated in a finite-element parametric study. A new method is formulated to predict long-term horizontal and vertical surface displacements after excavation of a single tunnel, and incorporates an improved measure of relative soil-lining permeability. The study also predicts significant surface movements during the consolidation period, contradicting the lack of further building damage observed in the field. A further parametric study also investigates the long-term behaviour of twin tunnels. Key interaction mechanisms are identified, leading to the postulation of the long-term interaction behaviour under different tunnelling conditions. Long-term interaction is found to be complex and significant, and should be accounted for in numerical simulations.
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Hoffmann, Angelika [Verfasser], Claus [Akademischer Betreuer] Zimmer, and Konstantin [Akademischer Betreuer] Holzapfel. "Validation of dynamic contrast enhanced MR blood-brain barrier permeability measurements and prediction of hemorrhagic transformation in an adult rat model of ischemic stroke / Angelika Hoffmann. Gutachter: Claus Zimmer ; Konstantin Holzapfel. Betreuer: Claus Zimmer." München : Universitätsbibliothek der TU München, 2013. http://d-nb.info/1034420836/34.
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Soares, Rosana Nobre. "Landscape Permeability Improves Climate-Based Predictions of Butterfly Species Persistence." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35528.
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Habitat modification alters species' capacities to track shifting climatic conditions. Broad-scale analyses that explore demographical responses to on-going climate change tend to neglect the influence of the underlying landscape pattern. However, many landscapes are fragmented by human activities, which might make dispersal for many species more challenging. Determining the extent to which landscape factors affect broad-scale distributional patterns has implications for our ability to predict realistic climate change impacts on species. Here, we constructed species-specific measurements of landscape permeability for 96 butterfly species in southern Ontario to test whether this landscape characteristic affected species' distributions at macroecological scales. We used multiple logistic regression models to test for the effects of permeability and its interaction with temperature on butterfly species presence/absence. We found that 48% of butterfly species responded to landscape permeability alone or in interaction with temperature. In general, the effect was positive (87%) and species were more likely to be present with increasing landscape permeability. For 61% of the species that responded to broad-scale landscape permeability, the interaction of temperature with permeability was statistically significant. In warm areas, species were more likely to be present if landscape permeability was high. Landscape permeability explained 3-43% of residual variability in species' presences after accounting for temperature. Finally, we show how fine-scale permeability measurements can be combined with large-scale patterns of diversity to inform conservation efforts. Landscape permeability can affect species' distributions at broad-scales and understanding factors that potentially influence species' dispersal can improve predictions for how species respond to changing climatic conditions.
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Onur, Emine Mercan. "PREDICTING THE PERMEABILITY OF SANDY SOILS FROM GRAIN SIZE DISTRIBUTIONS." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1389550812.
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Zhang, Keda [Verfasser]. "Predicting skin permeability of neutral species and ionic species / Keda Zhang." Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2013. http://d-nb.info/1033670235/34.
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Shahzad, Yasser. "Micellar chromatographic partition coefficients and their application in predicting skin permeability." Thesis, University of Huddersfield, 2013. http://eprints.hud.ac.uk/id/eprint/23480/.
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The major goal for physicochemical screening of pharmaceuticals is to predict human drug absorption, distribution, elimination, excretion and toxicity. These are all dependent on the lipophilicity of the drug, which is expressed as a partition coefficient i.e. a measure of a drug’s preference for the lipophilic or hydrophilic phases. The most common method of determining a partition coefficient is the shake flask method using octanol and water as partitioning media. However, this system has many limitations when modeling the interaction of ionised compounds with membranes, therefore, unreliable partitioning data for many solutes has been reported. In addition to these concerns, the procedure is tedious and time consuming and requires a high level of solute and solvent purity. Micellar liquid chromatography (MLC) has been proposed as an alternative technique for measuring partition coefficients utilising surfactant aggregates, known as micelles. This thesis investigates the application of MLC in determining micelle-water partition coefficients (logPMW) of pharmaceutical compounds of varying physicochemical properties. The effect of mobile phase pH and column temperature on the partitioning of compounds was evaluated. Results revealed that partitioning of drugs solely into the micellar core was influenced by the interaction of charged and neutral species with the surface of the micelle. Furthermore, the pH of the mobile phase significantly influenced the partitioning behaviour and a good correlation of logPMW was observed with calculated distribution coefficient (logD) values. More interestingly, a significant change in partitioning was observed near the dissociation constant of each drug indicating an influence of ionised species on the association with the micelle and retention on the stationary phase. Elevated column temperatures confirmed partitioning of drugs considered in this study was enthalpically driven with a small change in the entropy of the system because of the change in the nature of hydrogen bonding. Finally, a quantitative structure property relationship was developed to evaluate biological relevance in terms of predicting skin permeability of the newly developed partition coefficient values. This study provides a better surrogate for predicting skin permeability based on an easy, fast and cheap experimental methodology, and the method holds the predictive capability for a wider population of drugs. In summary, it can be concluded that MLC has the ability to generate partition coefficient values in a shorter time with higher accuracy, and has the potential to replace the octanol-water system for pharmaceutical compounds.
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Riera, Alexis J. "Predicting permeability and flow capacity distribution with back-propagation artificial neural networks." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1309.
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Thesis (M.S.)--West Virginia University, 2000.Title from document title page. Document formatted into pages; contains xii, 86 p. : ill. (some col.), maps. Includes abstract. Includes bibliographical references (p. 61-63).
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Nines, Shawn D. "Predicting a detailed permeability profile from minipermeameter measurements and well log data." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1690.
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Thesis (M.S.)--West Virginia University, 2000.Title from document title page. Document formatted into pages; contains ix, 111 p. : ill. (some col.), map. Includes abstract. Includes bibliographical references (p. 110-111).
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Ogata, Sho. "Development of Coupled Thermal-Hydraulic-Mechanical-Chemical Models for Predicting Rock Permeability Change." Doctoral thesis, Kyoto University, 2019. http://hdl.handle.net/2433/244532.
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京都大学0048
新制・課程博士
博士(工学)
甲第22051号
工博第4632号
新制||工||1722(附属図書館)
京都大学大学院工学研究科都市社会工学専攻
(主査)教授 岸田 潔, 教授 木村 亮, 教授 小池 克明
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DFAM
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Lind, Eleonora. "Predicting and optimising acoustical and vibrational performance of open porous foams." Licentiate thesis, Stockholm : Department of Aeronautics and Vehicle Engineering, Kungliga Tekniska högskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4820.
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Kokate, Amit. "Drug permeability across the buccal mucosa: Role of ionized species activity and development of a predictive model." Scholarly Commons, 2007. https://scholarlycommons.pacific.edu/uop_etds/2354.
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Based on the biochemical composition and structure of the buccal mucosa, drugs can permeate by the lipoidal and/or aqueous pathways. In this regard, the buccal mucosa is similar to skin. As the unionized drug form is the major permeant across skin, flux depends predominantly on the thermodynamic activity of this species. In contrast, ionized drug has been demonstrated to contribute significantly to the permeability across the buccal mucosa due to the presence of large amounts of polar lipids. The contributions of the individual activities of these species is however, not known. Therefore, the first objective of this study was to delineate the thermodynamic activities of ionized and unionized species and to determine their role in governing the total flux across buccal membrane. The flux of model acidic (nimesulide) and basic (bupivacaine) drugs across buccal mucosa either increased (nimesulide) or decreased (bupivacaine) with pH under saturated conditions depending on an increase (nimesulide) or decrease (bupivacaine) in the degree of saturation of ionized species (DS ionized ). At sub-saturated drug concentrations, a decrease in nimesulide flux and an increase in bupivacaine flux were observed with pH due to corresponding changes in DS unionized . For nimesulide and bupivacaine, the contributions of the ionized and unionized species to total flux are equal when 90% of the drug is in the ionized form. In conclusion, the contribution of the ionized form activity to flux was significant. A lack of a specific model for predicting buccal permeability has led to the use of transdermal models such as the Potts-Guy model. However, it is hypothesized that based on the above conclusion, this model might lead to erroneous permeability predictions. In the second part of this dissertation, a specific model was developed and validated by performing permeation studies of 15 small molecules across porcine buccal mucosa. Molecular volume, lipophilicity, number of hydrogen bond donors and number of rotatable bonds were found to be the most significant descriptors governing buccal permeability (logK p ) based on stepwise regression analysis. An excellent fit with an adjusted R 2 of 0.946 and a Q 2 of 0.882 were obtained. A good correlation was observed between the observed and predicted logK p values for an external data set.
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Bentham, Lucy Claudine. "The use of in vitro unbound drug fraction and permeability in predicting central nervous system drug penetration." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/the-use-of-in-vitro-unbound-drug-fraction-and-permeability-in-predicting-central-nervous-system-drug-penetration(1a826372-0843-4562-a6f3-14655ae9d8dc).html.
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The permeation of drugs across the blood-brain barrier (BBB) is a prerequisite for central nervous system (CNS) drug penetration. The BBB, possessing efflux transporters and tight junctions, limits drug penetration to the brain. Consequently, the discovery of novel drugs to treat CNS diseases remains problematic and is lagging behind other therapeutic areas. In vitro assays have progressed understanding of the factors that govern brain penetration. Central nervous system drug penetration is now thought to be modulated by three main processes, namely BBB permeability, active transport at the BBB and drug binding in blood and brain tissue. A more integrated approach to CNS drug discovery programmes is emerging which encompasses these processes in order to examine the rate and extent of drug brain penetration across species and improve predictions in human.A primary porcine in vitro BBB model was developed and characterised for the prediction of CNS drug permeability in vivo. Characterisation confirmed that the model exhibited physiologically realistic cell architecture, the formation of tight junction protein complexes, transcellular electrical resistance consistently >2000 Ω.cm2, functional expression the P-gp efflux transporter and ?-glutamyl transpeptidase and alkaline phosphatase activities.Transport of 12 centrally acting test drugs was investigated across four in vitro BBB models in order make comparisons between models and to generate in vitro permeability and efflux measurements. Blood-brain barrier permeability and active efflux processes are two major influences on the rate of drug penetration across the BBB. Species differences in fublood and fubrain, two prime influences on the extent of drug penetration, were investigated using equilibrium dialysis. Fraction unbound in brain was shown to be comparable across species suggesting that species differences in brain penetration could be due to variation in fublood for drugs that cross the BBB by passive diffusion, and/or species differences in transporter characteristics for drugs that are subject to active transport processes at the BBB. An in-house hybrid-PBPK rat CNS model was used to predict calculated rat Kp,uu using in vitro permeability, efflux, fublood and fubrain parameters generated during this work. The predicted Kp,uu generated using the rat CNS hybrid-PBPK model were within 3-fold of calculated Kp,uu. The rat CNS hybrid-PBPK model has potential use, as a tool for drug discovery scientists to aid the prediction of the extent of drug penetration in the early stages of drug discovery.This work has demonstrated that in vitro permeability and unbound drug fraction can be used to predict CNS drug penetration.
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Richter, Robert [Verfasser]. "Predicting bacterial accumulation of anti-infectives by measuring permeability across surrogates of the Gram-negative bacterial cell envelope / Robert Richter." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1227925352/34.
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Woudberg, Sonia. "Comparative analysis of predictive equations for transfer processes in different porous structures." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71862.
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Thesis (PhD)--Stellenbosch University, 2012.ENGLISH ABSTRACT: Research on transfer processes in various types of porous media has become important for the optimization of high technology engineering devices and processes. In this study the micro-structural parameters of different types of porous media, namely granular media, foamlike media and fibre beds, are characterized and quantified. Existing analytical modelling procedures for the three different types of porous media have been unified and refined to improve their predictive capabilities. Deterministic equations are proposed for predicting the streamwise pressure gradient, permeability and inertial coefficient of each type of porous medium. The equations are applicable over the entire porosity range and steady laminar flow regime and well suited as drag models in numerical computations. It is shown that the improved granular model can be regarded as qualitative and quantitative proof of the extensively used semi-empirical Ergun equation. The proposed model is used to provide physical meaning to the empirical coefficients. An Ergun-type equation is also proposed for foamlike media by remodelling the interstitial geometric configuration and accompanying flow conditions. The range of applicability of the existing foam model has been extended by incorporating the effect of developing flow in the pressure drop prediction. An equation is proposed in which the variation in the cross-sectional shape of the fibres can be incorporated into the interstitial form drag coefficient used in the foam model. This serves as an improvement on the constant value previously used. The existing foam model is also adapted to account for anisotropy resulting from compression. Two case studies are considered, namely compression of a non-woven glass fibre filter and compression of a soft polyester fibre material. The significant effect of compression on permeability is illustrated. In each case study the permeability values range over more than an order of magnitude for the narrow porosity ranges involved. The pressure drop prediction of the foam model is furthermore adapted to account for the combined effects of compression and developing flow. The newly proposed model diminishes the significant over-prediction of the existing foam model. An equation is furthermore proposed for predicting the permeability of Fontainebleau sandstones in which the effect of blocked throats is accounted for. Lastly, equations are proposed for predicting diffusivity ratios of unconsolidated arrays of squares and cubes. The prediction of the diffusivity ratio proposed in the present study, as opposed to model predictions from the literature, takes into account diffusion that may take place in stagnant fluid volumes. It is shown that a specific weighted average model proposed in the literature is not adequate to predict the diffusivity ratio of fully staggered arrays of squares, since it is shown not to be applicable to rectangular unit cells. Instead a new weighted average model is proposed which is applicable over the entire porosity range and for both staggered and non-staggered arrays of solid squares and cubes. The proposed weighted average model provides satisfactory agreement with experimental data from the literature and numerical data generated in the present study.
AFRIKAANSE OPSOMMING: Navorsing op oordragsprosesse in verskeie tipes poreuse media het belangrik geword vir die optimisering van ho¨e-tegnologie ingenieurstoestelle- en prosesse. In hierdie studie word die mikro-struktuur parameters van verskillende tipes poreuse media, naamklik korrelagtige media, sponsatige media en veselbeddens gekarakteriseer en gekwantifiseer. Bestaande analitiese modelleringsprosedures vir die drie verskillende tipes poreuse media is verenig en verfyn om die voorspelbare bekwaamheid daarvan te verbeter. Deterministiese vergelykings is voorgestel vir die voorspelling van die stroomsgewyse gradi¨ent, permeabiliteit en inersi¨ele ko¨effisi¨ent van elke tipe poreuse medium. Die vergelykings is geldig oor die hele porositeitsgrens en gestadigde laminˆere vloeigrens en goed geskik as weerstandsmodelle in numeriese berekeninge. Dit is aangetoon dat die verbeterde korrelmodel beskou kan word as kwalitatiewe en kwantitatiewe bewys van die ekstensiewe gebruikte semi-empiriese Ergun vergelyking. Die voorgestelde model is gebruik om fisiese betekenis aan die empiriese ko¨effisi¨ente te gee. ’n Ergun-tipe vergelyking is ook voorgestel vir sponsagtige media deur hermodellering van die tussenruimtelike geometriese konfigurasie en gepaardgaande vloeikondisies. Die grense van toepaslikheid van die bestaande sponsmodel is uitgebrei deur die inkorporering van die effek van ontwikkelende vloei in die voorspelling van die drukval. ’n Vergelyking is voorgestel waarin die variasie in die deursnit vorm van die vesels ingesluit is in die sponsmodel. Dit dien as verbetering op die konstante waarde wat voorheen gebruik is. Die bestaande sponsmodel is ook aangepas om voorsiening te maak vir anisotropie as gevolg van kompressie. Twee gevallestudies is oorweeg, naamlik kompressie van ’n nie-geweefde glasvesel filter en kompressie van ’n sagte polyester veselmateriaal. Die beduidende effek van kompressie op permeabiliteit is aangetoon. In elke gevallestudie strek die permeabiliteit waardes oor meer as ’n grootte orde vir die skrale porositeitgrense betrokke. Die drukvalvoorspelling van die sponsmodel is verder aangepas om voorsiening te maak vir die gekombineerde effekte van kompressie en ontwikkelende vloei. Die nuwe voorgestelde model verminder die beduidende oor-voorspelling van die bestaande sponsmodel. ’n Vergelyking is verder voorgestel vir die voorspelling van die permeabiliteit van Fontainebleau sandsteen waarin die effek van geblokte porie¨e in ag geneem is. Laastens is vergelykings voorgestel vir die voorspelling van die diffusiwiteitsverhoudings van nie-konsoliderende rangskikkings van vierkante en kubusse. Die diffusiwiteitsverhouding voorspel in die huidige studie, teenoor modelvoorspellings vanaf die literatuur, neem diffusie in ag wat plaasvind in die stagnante vloeistofvolumes. Dit is aangetoon dat ’n geweegde gemiddelde model, voorgestel in die literatuur, nie in staat is om die diffusiwiteitsverhouding van ten volle verspringende rangskikkings van vierkante te voorspel nie, aangesien dit nie toepaslik is vir reghoekige eenheidselle nie. ’n Nuwe geweegde model is in plaas daarvan voorgestel wat toepaslik is oor die hele porositeitsgrens en vir beide verspringende en nieverspringende rangskikkings van soliede vierkante en kubusse. Die voorgestelde geweegde gemiddelde model bied bevredigende ooreenstemming met eksperimentele data uit die literatuur en numeriese data gegenereer in die huidige studie.
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Ashrafi, Parivash. "Predicting the absorption rate of chemicals through mammalian skin using machine learning algorithms." Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17310.
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Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.
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Merget, Benjamin [Verfasser], and Christoph [Gutachter] Sotriffer. "Computational methods for assessing drug-target residence times in bacterial enoyl-ACP reductases and predicting small-molecule permeability for the \(Mycobacterium\) \(tuberculosis\) cell wall / Benjamin Merget ; Gutachter: Christoph Sotriffer." Würzburg : Universität Würzburg, 2016. http://d-nb.info/1125884541/34.
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Chen, Chang-Hsu, and 陳昶旭. "Committee-Machine-based Models for Permeability Prediction." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/26794238649768153451.
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博士國立成功大學
資源工程學系碩博士班
94
This study aims to employ committee machines (CMs) to propose novel models for permeability estimation. In this study, conventional empirical formulas and backpropagation neural networks (BPNNs) were used as members of the CMs. These CM-based models were developed to improve prediction for formation permeability. The proposed CM-based models included the CMEF model (CM with empirical formulas), CNNH model (committee neural network [CNN] with holdout method), CNNKCV model (CNN with K-fold cross-validation), CNNB model (CNN with bagging), and CNNDC model (CNN with divide and conquer). In this study, all the CM-based models were developed to predict the permeability directly from well logs without explicit knowledge of the fluid and rock properties. This study demonstrated that the CM-based models are more robust and accurate than the conventional empirical formulas and a single BPNN. The CM-based models were successfully applied to analyze field data consisting of eight well logs and core-measured permeability. The eight logs used included Caliper (CALI), Gamma Ray (GR), Laterolog Deep Resistivity (LLD), Micro Spherically Focused Resistivity (MSFL), Laterolog Shallow Resistivity (LLS), Interval Transit Time (DT), Bulk Density (RHOB), and Thermal Neutron Porosity (NPHI) logs. The CMEF model, combining outputs from three empirical formulas, was superior to the three individual empirical formulas acting alone. As for the CNNH and CNNKCV models, BPNNs in the CNNH model were trained by varying the initial weights, whereas BPNNs in the CNNKCV model were trained by varying the combined training data. The results from the CNNH and CNNKCV models demonstrated the power of CM by obtaining larger R2 value compared with a single BPNN. The CNNB model was developed to leverage the limited core data pairs by applying the bagging (bootstrap aggregating) technique. This model demonstrated its effect with a small data set by obtaining a considerably larger R2 value compared with a model without bagging. The CNNDC model was provided to demonstrate the benefits of modularity by decomposing the permeability range into two sub-ranges to increase the resolution. This implemented model improved the accuracy for permeability prediction and also earned its expected result by achieving the best generalization.
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Lee, Ming-Han, and 李明翰. "In Silico Prediction of Jejunum Permeability by Hierarchical Support Vector Regression." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yr7ay4.
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碩士國立東華大學
化學系
106
Permeability is an important parameter for oral drugs. Intestinal absorption is Closely related to permeability in drugs. Generally speaking, oral drug intestinal permeability measured with two method, in vivo method and in vitro method. Pros of in vivo method are experiment data closer to the reality. Cons are need a lot of time and money. In contrast, Pros of in vitro method are efficient than the first one. Cons are large measurement deviation with unreal situation. An in silico model was produced to quantitatively the oral drug permeability using the hierarchical support vector regression (HSVR) scheme based on the in vivo rat experimental data collected from the literatures(1997-2017). The predictions by HSVR are in good performance with the experiment for those drugs in the training set (n = 53, r2 = 0.93, q2cv = 0.84, RMSE = 0.17, s = 0.07) and test set (n = 13, q2 = 0.75–0.85, RMSE = 0.26, s = 0.14) and outlier (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). Thus, it can be asserted that HSVR can quickly and accurately predict the drug permeability. In addition, the derived model can be adopted as the preliminary metric to carry out biopharmaceutics drug disposition classification system (BDDCS).
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Korner, Jaime L. "Biomimetic artificial cell plasma membranes-on-a-chip for drug permeability prediction." Thesis, 2021. http://hdl.handle.net/1828/13365.
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The drug development process is notoriously long and expensive. During preclinical studies, inaccurate prediction of pharmacokinetic properties such as the ability of a drug candidate to passively permeate cell plasma membranes contributes to the high failure rate of drug candidates during clinical trials. Passive drug permeability is currently predicted using in vitro techniques such as parallel artificial membrane permeability assays, or PAMPA. In PAMPA, drug transport is predicted between aqueous compartments via a synthetic filter filled with a phospholipid solution in an organic solvent. The lack of translatability of preclinical predictions to humans can be attributed, in part, to lack of biological similarly between models used for permeability prediction and cell plasma membranes in vivo. Here, I demonstrate a new method for pharmacokinetic prediction, built by using droplet interface bilayers (DIBs) as human-mimetic artificial cell membranes. DIBs are bilayer sections created at the interface of two aqueous droplets. In the literature, DIBs have been used as artificial cell plasma membranes to study, for example, electrophysiological properties, protein insertion, water permeability, and molecular transport. DIBs can be formed between droplets of differing composition such that one droplet can be used as a donor compartment and the other as an acceptor compartment for the quantification of molecular transport across the artificial cell membrane. DIBs have previously been used to measure the passive permeability of numerous fluorophores as well as the drugs caffeine and doxorubicin. However, the extent to which DIBs have been tuned to mimic human cell plasma membranes and transport across them is limited. I present here the use of microfluidic platforms for bespoke DIB formation, where variables such as temperature, bilayer composition, and droplet contents are customized to create biomimetic cells-on-a-chip. These artificial cells are then used to measure molecular transport with the aim of predicting permeability.
In Chapter 2, I investigate the effectiveness of literature methods for the modification of polydimethylsiloxane (PDMS) microfluidic device channels for aqueous droplet formation and storage. While numerous techniques have been presented as mitigation strategies for common challenges in droplet microfluidics, it is not clear from the literature if any of these methods would be effective or necessary for the formation and analysis of DIBs. With the aim of facilitating aqueous droplet formation, I tested the effect of PDMS silanization using trichloro(1H,1H,2H,2H-perfluorooctyl)silane (PFOS) on surface hydrophobicity and oleophobicity. To assess their effect on reducing the rate of aqueous droplet evaporation, I tested surface treatment of PDMS with Teflon AF or Aquapel. I also tested modifications to the device fabrication process by bonding a glass coverslip to the surface of the device and soaking the device in water overnight. To quantify changes in PDMS surface chemistry, I performed contact angle measurements, aqueous droplet formation experiments, and measurements of droplet size during on-chip storage. I determined that baking PDMS microfluidic devices at 65 C overnight produced channel surfaces which allowed for aqueous droplet formation and storage. In Chapter 3 I present a systematic study on the role of temperature in DIB formation using naturally derived phospholipids. The use of increased temperature to form DIBs using total lipid extracts has previously been demonstrated, but has never before been investigated systematically using naturally derived phospholipids and bespoke formulations thereof. I hypothesized that, in order to form complete phospholipid monolayers and DIBs, the microfluidic device must be held at the phase transition temperature of the phospholipids. Using a custom-built heating platform, I tested DIB formation over a range of temperatures to determine conditions which allowed DIB formation rather than droplet coalescence. I show that temperature is a key parameter for DIB formation using naturally derived phospholipids in a microfluidic device. In Chapter 4, I demonstrate the use of DIBs as a new type of pharmacokinetic compartment model for intestinal absorption. Using three-droplet networks, the components of which were designed to mimic the intestinal space, the enterocyte cytosol, and the blood, I measured fluorescein permeability across intestine-mimetic DIBs. The model was able to predict the transport of fluorescein more accurately than the current state-of-the-art technique, PAMPA. Chapter 5 describes the development of complex DIB models for pharmacologically relevant membranes as well as an investigation into novel methods of drug transport detection on-chip. I created a new DIB model for the small intestine, incorporating more components of the enterocyte plasma membrane such as cholesterol. Measurement of calcein permeability served as a control experiment, as calcein does not cross cell plasma membranes. Measurement of fluorescein permeability yielded a significantly shorter permeation half-life than was determined in Chapter 4, indicating an increase in permeability with the more complex, biomimetic phospholipid formulation. I also developed sex-specific models for intestinal absorption to investigate the effect of sex-based membrane differences on permeability. This relationship has never before been explored in the literature. In comparison to the initial intestinal phospholipid formulation, the sex-specific formulations contained acyl chain tail groups which have been found in different ratios in male and female cells. A significantly longer half-life for fluorescein permeability was found in female intestine-mimetic DIBs, mirroring the slower drug absorption observed in female patients. I also used DIBs to model blood-brain barrier permeability. I demonstrate this application using two different brain lipid extracts, polar and total brain lipids. Polar brain lipids have previously been used in PAMPA to predict blood-brain barrier permeability, but have been found to overpredict the permeation of charged molecules in comparison to custom lipid formulations which mimic the composition of human brain endothelial cells. Permeability measurements in DIBs formed using polar brain lipids gave results which agree with PAMPA, as DIBs formed using polar brain lipids were permeable to fluorescein, but those formed using total brain lipids were not. Blood-brain barrier-mimetic DIBs formed using either lipid extract are impermeable to calcein and FITC-dextrans (both 40 and 500 kDa). I also show the formation of the first DIBs to be created using a total lipid extract from human cells as well as their impermeability to calcein. The extract tested was prepared from testicular Sertoli cells, which exhibit properties similar to the blood-brain barrier, but future work will focus on extracts prepared from human brain endothelial cells. Finally, I explore new options for the on-chip detection of the transport of nonfluorescent molecules. To move away from reliance on fluorescent molecules for permeability measurements, I selected three fluorogenic molecular recognition agents (fluorescamine, Chromeo P540, and DimerDye 4) whose fluorescence signal is activated by amine groups. None of the tested methods proved to be viable in DIBs, potentially due to slow permeation, low quantum yield, and side reactions with phospholipids.
Overall, I demonstrate here the microfluidic formation and application of several novel types of biomimetic DIBs to permeability prediction. My work shows that DIBs can be used to predict permeability and mirror effects observed in vivo. Future work will focus on the development of new methods for the detection of drug transport and the application of the pharmacokinetic compartment models presented to predicting drug permeability. Further work using total lipid extracts prepared from human cells will also be vital to enhancing the use of biomimetic DIBs as pharmacokinetic permeability prediction tools. As their biological similarity and capacity to accurately predict transport increase, so will the potential of DIBs to improve the accuracy and translatablity of preclinical drug development.Graduate
2022-08-09
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Chen, Po-Hong, and 陳柏宏. "In Silico Prediction of Blood–Brain Barrier Permeability by Machine Learning Methods." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66161949230564256711.
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碩士國立東華大學
化學系
97
Summary Background and purpose. The objective of this investigation was to yield a generalized in silico model to quantitatively predict blood–brain barrier permeability to facilitate drug discovery. Experimental approach. Machine learning methods hierarchical support vector regression, support vector machine, radial basis function neural network and genetic function approximation were employed to generate the prediction model based on the data compiled from the literature. Key results. The prediction by the HSVR model are in agreement with the experimental observations for those molecules in the training set (n = 173, r2 = 0.86, q2 = 0.80, RMSE = 0.25, MAE = 0.18, s = 0.17), the test set (n = 57, r2 = 0.81, RMSE = 0.25, MAE = 0.19, s = 0.15) and the outlier set (n = 23, r2 = 0.80, RMSE = 0.41, MAE = 0.36, s = 0.20). Conclusions and Implications. The HSVR model outperformed the other machine learning models developed in this investigation, which, in turn, executed better than other published prediction models in most of cases; and thus can be utilized for predicting blood–brain barrier permeability, high-throughput screening and data mining to facilitate drug discovery.
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Peng, Chu-Yuan, and 彭琡媛. "In Silico Prediction of Blood-Brain Barrier Permeability by Hierarchical Support Vector Machine." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/36432s.
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碩士國立東華大學
化學系
102
In this study, the hierarchical support vector regression to latent structures (HSVR) technique was used for modeling the logarithm of brain-blood concentration ratio (BB) of 164 structurally diverse compounds. The HSVR model was based on molecular descriptors that can be calculated for compounds simply from knowledge of its molecular structure, and the model included several topological and constitutional descriptors. The HSVR analysis resulted in a significant global model with the following statistics in the training set (n=126 r2=0.89, =0.80, RMSE= 0.30, s=0.25), test set (n=30 q2=0.82-0.85, RMSE= 0.35, s=0.21), and outlier set (n=8 q2=0.81-0.92, RMSE= 0.26, s=0.13). When compared with various published MLR models, the HSVR derived in this study performed best in every aspect. Taking into account the derived HSVR model, it may be of general utility in predicting logarithm BB ratios for a very wide range of new drugs.
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Gladkikh, Mikhail Nikolaevich. "A priori prediction of macroscopic properties of sedimentary rocks containing two immiscible fluids." Thesis, 2005. http://hdl.handle.net/2152/1551.
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Jhan, Ci-Jen, and 詹奇君. "Prediction of Permeability Surface Area Product of Blood Brain Barrier Using Hierarchical Support Vector Regression." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/59kabu.
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碩士國立東華大學
化學系
105
Permeability surface area product (PS) represents the uptake clearance across the BBB. Moreover, log PS is one of parameter of a compound to assess the brain penetration properties in drug discovery. Log PS value of a compound can be measured by several in vivo methods. In addition, the activities of molecules estimated by more accurately in situ brain perfusion were employed. However, the in vivo BBB permeability assay is hard to determination due to the labor intensive and low throughput. Since in silico approach can provide a fast way to obtain activities, the hierarchical support vector regression (HSVR) was adopted in this study to predict the log PS based on rational descriptor selection. Those collected molecules have good predicted values by the generated HSVR model in training set (). As a result, the derived HSVR model can provide a better way to predict logarithm PS values and facilitate the drug discovery and development.
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Prasetyo, Utomo Chandra. "The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir." Thesis, 2011. http://hdl.handle.net/10754/209392.
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Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers concerning this important process and has wider implications for oil reservoir management efficiency.
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Zhang, K., M. H. Abraham, and Xiangli Liu. "An Equation for the Prediction of Human Skin Permeability of Neutral Molecules, Ions and Ionic Species." 2017. http://hdl.handle.net/10454/11486.
Full textAbstract:
yesExperimental values of permeability coefficients, as log Kp, of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37 °C for the effect of temperature. The values of log Kp for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log Kp values have been regressed against Abraham solute descriptors to yield a correlation equation with R2 = 0.866 and SD = 0.432 log units. The equation can provide valid predictions for log Kp of neutral molecules, ions and ionic species, with predictive R2 = 0.858 and predictive SD = 0.445 log units calculated by the leave-one-out statistics. The predicted log Kp values for Na+ and Et4N+ are in good agreement with the observed values. We calculated the values of log Kp of ketoprofen as a function of the pH of the donor solution, and found that log Kp markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log Kp was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log Kp.
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Τουμπάνου, Ιωάννα. "Διαπερατότητα άμμων : μέτρηση, πρόβλεψη, εφαρμογή." Thesis, 2015. http://hdl.handle.net/10889/8750.
Full textAbstract:
Αντικείμενο της παρούσας Μεταπτυχιακής Διπλωματικής Εργασίας είναι η διερεύνηση της τιμής του συντελεστή διαπερατότητας άμμων διαφορετικής κοκκομετρικής σύνθεσης, η αξιολόγηση των αποτελεσμάτων και η σύγκριση τους με τιμές που προκύπτουν εφαρμόζοντας γνωστές εξισώσεις πρόβλεψης της τιμής του συντελεστή διαπερατότητας και, τέλος, η ειδική εφαρμογή των αποτελεσμάτων σε μια προσπάθεια διαμόρφωσης μοντέλου πρόβλεψης της ενεσιμότητας αιωρημάτων τσιμέντου σε άμμους.Purpose of the Thesis is to investigate the value of the coefficient of sands permeability, the evaluation of results and their comparison with values obtained by applying known predictive formulas of coefficient of permeability and finally, the implementation of the results in creating a predicting model of the groutability of cement suspensions in sands.
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Salimifard, Babak. "Predicting permeability from other petrophysical properties." 2015. http://hdl.handle.net/1993/30645.
Full textAbstract:
Understanding pore network structure of a porous medium and fluid flow in the pore network has been an interest to researchers for decades. This study focuses on the characterization and simulation of the pore networks in petroleum reservoir rocks using conventional characterization techniques. A Representative Elemental Volume (REV) model is developed which simulates the pore network as a series of non-interconnected capillary tubes of varying sizes. The model implements mercury porosimetry (MP) results and capillary pressure principles to calculate the size of each bundle of capillary tubes based on a pore throat size distribution produced by the MP experiment. It also implements electrical properties of the rocks to estimate the average length of the capillary tubes. To verify the validity of the simulated network, permeability is calculated for the simulated network using Poiseuille’s flow principles for capillary tubes. Preliminary work showed that the model is capable of simulating the pore network reasonably well because permeability estimations for the simulated network matched measurements. In this study, MP and nuclear magnetic resonance (NMR) tests as well as centrifuge and permeability tests are performed on a suite of 11 sandstone and carbonate rock samples. Because electrical tests were not available, average length of flow paths is calculated with an alternative method that uses porosity to calculate tortuosity. Permeability estimations of the simulated network are compared with measurements. Estimations are also compared to other predictions using methods that implement MP and NMR data to simulate the pore network and the results show that the developed REV model out performs all the other techniques.October 2015
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Xu, Chicheng. "Reservoir description with well-log-based and core-calibrated petrophysical rock classification." 2013. http://hdl.handle.net/2152/21315.
Full textAbstract:
Rock type is a key concept in modern reservoir characterization that straddles multiple scales and bridges multiple disciplines. Reservoir rock classification (or simply rock typing) has been recognized as one of the most effective description tools to facilitate large-scale reservoir modeling and simulation. This dissertation aims to integrate core data and well logs to enhance reservoir description by classifying reservoir rocks in a geologically and petrophysically consistent manner. The main objective is to develop scientific approaches for utilizing multi-physics rock data at different time and length scales to describe reservoir rock-fluid systems. Emphasis is placed on transferring physical understanding of rock types from limited ground-truthing core data to abundant well logs using fast log simulations in a multi-layered earth model. Bimodal log-normal pore-size distribution functions derived from mercury injection capillary pressure (MICP) data are first introduced to characterize complex pore systems in carbonate and tight-gas sandstone reservoirs. Six pore-system attributes are interpreted and integrated to define petrophysical orthogonality or dissimilarity between two pore systems of bimodal log-normal distributions. A simple three-dimensional (3D) cubic pore network model constrained by nuclear magnetic resonance (NMR) and MICP data is developed to quantify fluid distributions and phase connectivity for predicting saturation-dependent relative permeability during two-phase drainage. There is rich petrophysical information in spatial fluid distributions resulting from vertical fluid flow on a geologic time scale and radial mud-filtrate invasion on a drilling time scale. Log attributes elicited by such fluid distributions are captured to quantify dynamic reservoir petrophysical properties and define reservoir flow capacity. A new rock classification workflow that reconciles reservoir saturation-height behavior and mud-filtrate for more accurate dynamic reservoir modeling is developed and verified in both clastic and carbonate fields. Rock types vary and mix at the sub-foot scale in heterogeneous reservoirs due to depositional control or diagenetic overprints. Conventional well logs are limited in their ability to probe the details of each individual bed or rock type as seen from outcrops or cores. A bottom-up Bayesian rock typing method is developed to efficiently test multiple working hypotheses against well logs to quantify uncertainty of rock types and their associated petrophysical properties in thinly bedded reservoirs. Concomitantly, a top-down reservoir description workflow is implemented to characterize intermixed or hybrid rock classes from flow-unit scale (or seismic scale) down to the pore scale based on a multi-scale orthogonal rock class decomposition approach. Correlations between petrophysical rock types and geological facies in reservoirs originating from deltaic and turbidite depositional systems are investigated in detail. Emphasis is placed on the cause-and-effect relationship between pore geometry and rock geological attributes such as grain size and bed thickness. Well log responses to those geological attributes and associated pore geometries are subjected to numerical log simulations. Sensitivity of various physical logs to petrophysical orthogonality between rock classes is investigated to identify the most diagnostic log attributes for log-based rock typing. Field cases of different reservoir types from various geological settings are used to verify the application of petrophysical rock classification to assist reservoir characterization, including facies interpretation, permeability prediction, saturation-height analysis, dynamic petrophysical modeling, uncertainty quantification, petrophysical upscaling, and production forecasting.text