Limited sampling strategies for estimation of cyclosporine exposure in pediatric hematopoietic stem cell transplant recipients : methodological improvement and introduction of sampling time deviation analysis.

Le suivi thérapeutique est recommandé pour l’ajustement de la dose des agents immunosuppresseurs. La pertinence de l’utilisation de la surface sous la courbe (SSC) comme biomarqueur dans l’exercice du suivi thérapeutique de la cyclosporine (CsA) dans la transplantation des cellules souches hématopoïétiques est soutenue par un nombre croissant d’études. Cependant, pour des raisons intrinsèques à la méthode de calcul de la SSC, son utilisation en milieu clinique n’est pas pratique. Les stratégies d’échantillonnage limitées, basées sur des approches de régression (R-LSS) ou des approches Bayésiennes (B-LSS), représentent des alternatives pratiques pour une estimation satisfaisante de la SSC. Cependant, pour une application efficace de ces méthodologies, leur conception doit accommoder la réalité clinique, notamment en requérant un nombre minimal de concentrations échelonnées sur une courte durée d’échantillonnage. De plus, une attention particulière devrait être accordée à assurer leur développement et validation adéquates. Il est aussi important de mentionner que l’irrégularité dans le temps de la collecte des échantillons sanguins peut avoir un impact non-négligeable sur la performance prédictive des R-LSS. Or, à ce jour, cet impact n’a fait l’objet d’aucune étude. Cette thèse de doctorat se penche sur ces problématiques afin de permettre une estimation précise et pratique de la SSC. Ces études ont été effectuées dans le cadre de l’utilisation de la CsA chez des patients pédiatriques ayant subi une greffe de cellules souches hématopoïétiques. D’abord, des approches de régression multiple ainsi que d’analyse pharmacocinétique de population (Pop-PK) ont été utilisées de façon constructive afin de développer et de valider adéquatement des LSS. Ensuite, plusieurs modèles Pop-PK ont été évalués, tout en gardant à l’esprit leur utilisation prévue dans le contexte de l’estimation de la SSC. Aussi, la performance des B-LSS ciblant différentes versions de SSC a également été étudiée. Enfin, l’impact des écarts entre les temps d’échantillonnage sanguins réels et les temps nominaux planifiés, sur la performance de prédiction des R-LSS a été quantifié en utilisant une approche de simulation qui considère des scénarios diversifiés et réalistes représentant des erreurs potentielles dans la cédule des échantillons sanguins. Ainsi, cette étude a d’abord conduit au développement de R-LSS et B-LSS ayant une performance clinique satisfaisante, et qui sont pratiques puisqu’elles impliquent 4 points d’échantillonnage ou moins obtenus dans les 4 heures post-dose. Une fois l’analyse Pop-PK effectuée, un modèle structural à deux compartiments avec un temps de délai a été retenu. Cependant, le modèle final - notamment avec covariables - n’a pas amélioré la performance des B-LSS comparativement aux modèles structuraux (sans covariables). En outre, nous avons démontré que les B-LSS exhibent une meilleure performance pour la SSC dérivée des concentrations simulées qui excluent les erreurs résiduelles, que nous avons nommée « underlying AUC », comparée à la SSC observée qui est directement calculée à partir des concentrations mesurées. Enfin, nos résultats ont prouvé que l’irrégularité des temps de la collecte des échantillons sanguins a un impact important sur la performance prédictive des R-LSS; cet impact est en fonction du nombre des échantillons requis, mais encore davantage en fonction de la durée du processus d’échantillonnage impliqué. Nous avons aussi mis en évidence que les erreurs d’échantillonnage commises aux moments où la concentration change rapidement sont celles qui affectent le plus le pouvoir prédictif des R-LSS. Plus intéressant, nous avons mis en exergue que même si différentes R-LSS peuvent avoir des performances similaires lorsque basées sur des temps nominaux, leurs tolérances aux erreurs des temps d’échantillonnage peuvent largement différer. En fait, une considération adéquate de l'impact de ces erreurs peut conduire à une sélection et une utilisation plus fiables des R-LSS. Par une investigation approfondie de différents aspects sous-jacents aux stratégies d’échantillonnages limités, cette thèse a pu fournir des améliorations méthodologiques notables, et proposer de nouvelles voies pour assurer leur utilisation de façon fiable et informée, tout en favorisant leur adéquation à la pratique clinique.

Quantifying nitrous oxide emissions from sugarcane cropping systems : Optimum sampling time and frequency

Nitrous oxide (N2O) emissions from soil are often measured using the manual static chamber method. Manual gas sampling is labour intensive, so a minimal sampling frequency that maintains the accuracy of measurements would be desirable. However, the high temporal (diurnal, daily and seasonal) variabilities of N2O emissions can compromise the accuracy of measurements if not addressed adequately when formulating a sampling schedule. Assessments of sampling strategies to date have focussed on relatively low emission systems with high episodicity, where a small number of the highest emission peaks can be critically important in the measurement of whole season cumulative emissions. Using year-long, automated sub-daily N2O measurements from three fertilised sugarcane fields, we undertook an evaluation of the optimum gas sampling strategies in high emission systems with relatively long emission episodes. The results indicated that sampling in the morning between 09:00–12:00, when soil temperature was generally close to the daily average, best approximated the daily mean N2O emission within 4–7% of the ‘actual’ daily emissions measured by automated sampling. Weekly sampling with biweekly sampling for one week after >20 mm of rainfall was the recommended sampling regime. It resulted in no extreme (>20%) deviations from the ‘actuals’, had a high probability of estimating the annual cumulative emissions within 10% precision, with practicable sampling numbers in comparison to other sampling regimes. This provides robust and useful guidance for manual gas sampling in sugarcane cropping systems, although further adjustments by the operators in terms of expected measurement accuracy and resource availability are encouraged. By implementing these sampling strategies together, labour inputs and errors in measured cumulative N2O emissions can be minimised. Further research is needed to quantify the spatial variability of N2O emissions within sugarcane cropping and to develop techniques for effectively addressing both spatial and temporal variabilities simultaneously.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Novel Approaches to the Sampling of Atmospheric Aerosols and Determination of Chemical Composition

The Earth s climate is a highly dynamic and complex system in which atmospheric aerosols have been increasingly recognized to play a key role. Aerosol particles affect the climate through a multitude of processes, directly by absorbing and reflecting radiation and indirectly by changing the properties of clouds. Because of the complexity, quantification of the effects of aerosols continues to be a highly uncertain science. Better understanding of the effects of aerosols requires more information on aerosol chemistry. Before the determination of aerosol chemical composition by the various available analytical techniques, aerosol particles must be reliably sampled and prepared. Indeed, sampling is one of the most challenging steps in aerosol studies, since all available sampling techniques harbor drawbacks. In this study, novel methodologies were developed for sampling and determination of the chemical composition of atmospheric aerosols. In the particle-into-liquid sampler (PILS), aerosol particles grow in saturated water vapor with further impaction and dissolution in liquid water. Once in water, the aerosol sample can then be transported and analyzed by various off-line or on-line techniques. In this study, PILS was modified and the sampling procedure was optimized to obtain less altered aerosol samples with good time resolution. A combination of denuders with different coatings was tested to adsorb gas phase compounds before PILS. Mixtures of water with alcohols were introduced to increase the solubility of aerosols. Minimum sampling time required was determined by collecting samples off-line every hour and proceeding with liquid-liquid extraction (LLE) and analysis by gas chromatography-mass spectrometry (GC-MS). The laboriousness of LLE followed by GC-MS analysis next prompted an evaluation of solid-phase extraction (SPE) for the extraction of aldehydes and acids in aerosol samples. These two compound groups are thought to be key for aerosol growth. Octadecylsilica, hydrophilic-lipophilic balance (HLB), and mixed phase anion exchange (MAX) were tested as extraction materials. MAX proved to be efficient for acids, but no tested material offered sufficient adsorption for aldehydes. Thus, PILS samples were extracted only with MAX to guarantee good results for organic acids determined by liquid chromatography-mass spectrometry (HPLC-MS). On-line coupling of SPE with HPLC-MS is relatively easy, and here on-line coupling of PILS with HPLC-MS through the SPE trap produced some interesting data on relevant acids in atmospheric aerosol samples. A completely different approach to aerosol sampling, namely, differential mobility analyzer (DMA)-assisted filter sampling, was employed in this study to provide information about the size dependent chemical composition of aerosols and understanding of the processes driving aerosol growth from nano-size clusters to climatically relevant particles (>40 nm). The DMA was set to sample particles with diameters of 50, 40, and 30 nm and aerosols were collected on teflon or quartz fiber filters. To clarify the gas-phase contribution, zero gas-phase samples were collected by switching off the DMA every other 15 minutes. Gas-phase compounds were adsorbed equally well on both types of filter, and were found to contribute significantly to the total compound mass. Gas-phase adsorption is especially significant during the collection of nanometer-size aerosols and needs always to be taken into account. Other aims of this study were to determine the oxidation products of β-caryophyllene (the major sesquiterpene in boreal forest) in aerosol particles. Since reference compounds are needed for verification of the accuracy of analytical measurements, three oxidation products of β-caryophyllene were synthesized: β-caryophyllene aldehyde, β-nocaryophyllene aldehyde, and β-caryophyllinic acid. All three were identified for the first time in ambient aerosol samples, at relatively high concentrations, and their contribution to the aerosol mass (and probably growth) was concluded to be significant. Methodological and instrumental developments presented in this work enable fuller understanding of the processes behind biogenic aerosol formation and provide new tools for more precise determination of biosphere-atmosphere interactions.

Sliding mode for detection and accommodation of computation time delay fault

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Sampling times for monitoring tacrolimus in stable adult liver transplant recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R-2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

High resolution optical time-domain reflectometry based on correlation utilizing an all-fiber chaotic source

We propose a high-resolution optical time domain reflectometry (OTDR) based on an all-fiber supercontinuum source. The source simply consists of a laser with moderate power and a section of fiber which has a zero dispersion wavelength near the laser's central wavelength. Spectrum and time domain properties of the source are investigated, showing that the source has great capability in nonlinear optics, such as correlation OTDR. We analyze one of the key factors limiting the operational range of such an OTDR, i.e., sampling time. Finally, we experimentally demonstrate a correlation OTDR with 25km sensing range and 5.3cm spatial resolution, as a verification of theoretical analysis.

Improved dynamic headspace sampling and detection using capillary microextraction of volatiles coupled to gas chromatography mass spectrometry

Sampling and preconcentration techniques play a critical role in headspace analysis in analytical chemistry. My dissertation presents a novel sampling design, capillary microextraction of volatiles (CMV), that improves the preconcentration of volatiles and semivolatiles in a headspace with high throughput, near quantitative analysis, high recovery and unambiguous identification of compounds when coupled to mass spectrometry. The CMV devices use sol-gel polydimethylsiloxane (PDMS) coated microglass fibers as the sampling/preconcentration sorbent when these fibers are stacked into open-ended capillary tubes. The design allows for dynamic headspace sampling by connecting the device to a hand-held vacuum pump. The inexpensive device can be fitted into a thermal desorption probe for thermal desorption of the extracted volatile compounds into a gas chromatography-mass spectrometer (GC-MS). The performance of the CMV devices was compared with two other existing preconcentration techniques, solid phase microextraction (SPME) and planar solid phase microextraction (PSPME). Compared to SPME fibers, the CMV devices have an improved surface area and phase volume of 5000 times and 80 times, respectively. One (1) minute dynamic CMV air sampling resulted in similar performance as a 30 min static extraction using a SPME fiber. The PSPME devices have been fashioned to easily interface with ion mobility spectrometers (IMS) for explosives or drugs detection. The CMV devices are shown to offer dynamic sampling and can now be coupled to COTS GC-MS instruments. Several compound classes representing explosives have been analyzed with minimum breakthrough even after a 60 min. sampling time. The extracted volatile compounds were retained in the CMV devices when preserved in aluminum foils after sampling. Finally, the CMV sampling device were used for several different headspace profiling applications which involved sampling a shipping facility, six illicit drugs, seven military explosives and eighteen different bacteria strains. Successful detection of the target analytes at ng levels of the target signature volatile compounds in these applications suggests that the CMV devices can provide high throughput qualitative and quantitative analysis with high recovery and unambiguous identification of analytes.

Evaluation of non-contact sampling and detection of explosives using receiver operating characteristic curves

The growing need for fast sampling of explosives in high throughput areas has increased the demand for improved technology for the trace detection of illicit compounds. Detection of the volatiles associated with the presence of the illicit compounds offer a different approach for sensitive trace detection of these compounds without increasing the false positive alarm rate. This study evaluated the performance of non-contact sampling and detection systems using statistical analysis through the construction of Receiver Operating Characteristic (ROC) curves in real-world scenarios for the detection of volatiles in the headspace of smokeless powder, used as the model system for generalizing explosives detection. A novel sorbent coated disk coined planar solid phase microextraction (PSPME) was previously used for rapid, non-contact sampling of the headspace containers. The limits of detection for the PSPME coupled to IMS detection was determined to be 0.5-24 ng for vapor sampling of volatile chemical compounds associated with illicit compounds and demonstrated an extraction efficiency of three times greater than other commercially available substrates, retaining >50% of the analyte after 30 minutes sampling of an analyte spike in comparison to a non-detect for the unmodified filters. Both static and dynamic PSPME sampling was used coupled with two ion mobility spectrometer (IMS) detection systems in which 10-500 mg quantities of smokeless powders were detected within 5-10 minutes of static sampling and 1 minute of dynamic sampling time in 1-45 L closed systems, resulting in faster sampling and analysis times in comparison to conventional solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis. Similar real-world scenarios were sampled in low and high clutter environments with zero false positive rates. Excellent PSPME-IMS detection of the volatile analytes were visualized from the ROC curves, resulting with areas under the curves (AUC) of 0.85-1.0 and 0.81-1.0 for portable and bench-top IMS systems, respectively. Construction of ROC curves were also developed for SPME-GC-MS resulting with AUC of 0.95-1.0, comparable with PSPME-IMS detection. The PSPME-IMS technique provides less false positive results for non-contact vapor sampling, cutting the cost and providing an effective sampling and detection needed in high-throughput scenarios, resulting in similar performance in comparison to well-established techniques with the added advantage of fast detection in the field.

Assessment of the cortisol awakening response: Real-time analysis and curvilinear effects of sample timing inaccuracy

The cortisol awakening response (CAR) is typically measured in the domestic setting. Moderate sample timing inaccuracy has been shown to result in erroneous CAR estimates and such inaccuracy has been shown partially to explain inconsistency in the CAR literature. The need for more reliable measurement of the CAR has recently been highlighted in expert consensus guidelines where it was pointed out that less than 6% of published studies provided electronic-monitoring of saliva sampling time in the post-awakening period. Analyses of a merged data-set of published studies from our laboratory are presented. To qualify for selection, both time of awakening and collection of the first sample must have been verified by electronic-monitoring and sampling commenced within 15 min of awakening. Participants (n = 128) were young (median age of 20 years) and healthy. Cortisol values were determined in the 45 min post-awakening period on 215 sampling days. On 127 days, delay between verified awakening and collection of the first sample was less than 3 min (‘no delay’ group); on 45 days there was a delay of 4–6 min (‘short delay’ group); on 43 days the delay was 7–15 min (‘moderate delay’ group). Cortisol values for verified sampling times accurately mapped on to the typical post-awakening cortisol growth curve, regardless of whether sampling deviated from desired protocol timings. This provides support for incorporating rather than excluding delayed data (up to 15 min) in CAR analyses. For this population the fitted cortisol growth curve equation predicted a mean cortisol awakening level of 6 nmols/l (±1 for 95% CI) and a mean CAR rise of 6 nmols/l (±2 for 95% CI). We also modelled the relationship between real delay and CAR magnitude, when the CAR is calculated erroneously by incorrectly assuming adherence to protocol time. Findings supported a curvilinear hypothesis in relation to effects of sample delay on the CAR. Short delays of 4–6 min between awakening and commencement of saliva sampling resulted an overestimated CAR. Moderate delays of 7–15 min were associated with an underestimated CAR. Findings emphasize the need to employ electronic-monitoring of sampling accuracy when measuring the CAR in the domestic setting.

Improved Dynamic Headspace Sampling and Detection using Capillary Microextraction of Volatiles Coupled to Gas Chromatography Mass Spectrometry

Sampling and preconcentration techniques play a critical role in headspace analysis in analytical chemistry. My dissertation presents a novel sampling design, capillary microextraction of volatiles (CMV), that improves the preconcentration of volatiles and semivolatiles in a headspace with high throughput, near quantitative analysis, high recovery and unambiguous identification of compounds when coupled to mass spectrometry. The CMV devices use sol-gel polydimethylsiloxane (PDMS) coated microglass fibers as the sampling/preconcentration sorbent when these fibers are stacked into open-ended capillary tubes. The design allows for dynamic headspace sampling by connecting the device to a hand-held vacuum pump. The inexpensive device can be fitted into a thermal desorption probe for thermal desorption of the extracted volatile compounds into a gas chromatography-mass spectrometer (GC-MS). The performance of the CMV devices was compared with two other existing preconcentration techniques, solid phase microextraction (SPME) and planar solid phase microextraction (PSPME). Compared to SPME fibers, the CMV devices have an improved surface area and phase volume of 5000 times and 80 times, respectively. One (1) minute dynamic CMV air sampling resulted in similar performance as a 30 min static extraction using a SPME fiber. The PSPME devices have been fashioned to easily interface with ion mobility spectrometers (IMS) for explosives or drugs detection. The CMV devices are shown to offer dynamic sampling and can now be coupled to COTS GC-MS instruments. Several compound classes representing explosives have been analyzed with minimum breakthrough even after a 60 min. sampling time. The extracted volatile compounds were retained in the CMV devices when preserved in aluminum foils after sampling. Finally, the CMV sampling device were used for several different headspace profiling applications which involved sampling a shipping facility, six illicit drugs, seven military explosives and eighteen different bacteria strains. Successful detection of the target analytes at ng levels of the target signature volatile compounds in these applications suggests that the CMV devices can provide high throughput qualitative and quantitative analysis with high recovery and unambiguous identification of analytes.

The application of profluorescent nitroxides to detect reactive oxygen species derived from combustion-generated particulate matter: Cigarette smoke – a case study

Reactive oxygen species (ROS) and related free radicals are considered to be key factors underpinning the various adverse health effects associated with exposure to ambient particulate matter. Therefore, measurement of ROS is a crucial factor for assessing the potential toxicity of particles. In this work, a novel profluorescent nitroxide, BPEAnit, was investigated as a probe for detecting particle-derived ROS. BPEAnit has a very low fluorescence emission due to inherent quenching by the nitroxide group, but upon radical trapping or redox activity, a strong fluorescence is observed. BPEAnit was tested for detection of ROS present in mainstream and sidestream cigarette smoke. In the case of mainstream cigarette smoke, there was a linear increase in fluorescence intensity with an increasing number of cigarette puffs, equivalent to an average of 101 nmol ROS per cigarette based on the number of moles of the probe reacted. Sidestream cigarette smoke sampled from an environmental chamber exposed BPEAnit to much lower concentrations of particles, but still resulted in a clearly detectible increase in fluorescence intensity with sampling time. It was calculated that the amount of ROS was equivalent to 50 ± 2 nmol per mg of particulate matter; however, this value decreased with ageing of the particles in the chamber. Overall, BPEAnit was shown to provide a sensitive response related to the oxidative capacity of the particulate matter. These findings present a good basis for employing the new BPEAnit probe for the investigation of particle-related ROS generated from cigarette smoke as well as from other combustion sources.

Genetic Diversity of Rice Tungro Spherical Virus in Tungro-Endemic Provinces of the Philippines and Indonesia

The two adjacent genes of coat protein 1 and 2 of rice tungro spherical virus (RTSV) were amplified from total RNA extracts of serologically indistinguishable field isolates from the Philippines and Indonesia, using reverse transcriptase polymerase chain reaction (RT-PCR). Digestion with HindIII and BstYI restriction endonucleases differentiated the amplified DNA products into eight distinct coat protein genotypes. These genotypes were then used as indicators of virus diversity in the field. Inter- and intra-site diversities were determined over three cropping seasons. At each of the sites surveyed, one or two main genotypes prevailed together with other related minor or mixed genotypes that did not replace the main genotype over the sampling time. The cluster of genotypes found at the Philippines sites was significantly different from the one at the Indonesia sites, suggesting geographic isolation for virus populations. Phylogenetic studies based on the nucleotide sequences of 38 selected isolates confirm the spatial distribution of RTSV virus populations but show that gene flow may occur between populations. Under the present conditions, rice varieties do not seem to exert selective pressure on the virus populations. Based on the selective constraints in the coat protein amino acid sequences and the virus genetic composition per site, a negative selection model followed by random-sampling events due to vector transmissions is proposed to explain the inter-site diversity observed