Optimal Sampling Schedule Research Articles

Immunological Monitoring in Hepatitis C Virus Controlled Human Infection Model.

Controlled human infection model trials for hepatitis C virus represent an important opportunity to identify correlates of protective immunity against a well-characterized inoculum of hepatitis C virus and how such responses are modified by vaccination. In this article, we discuss the approach to immunological monitoring during such trials, including a set of recommendations for optimal sampling schedule and preferred immunological assays to examine the different arms of the immune response. We recommend that this approach be adapted to different trial designs. Finally, we discuss how these studies can provide surrogate predictors of the success of candidate vaccines.

Accuracy and uncertainty analysis of reduced time point imaging effect on time-integrated activity for 177Lu-DOTATATE PRRT in patients and clinically realistic simulations

BackgroundDosimetry promises many advantages for radiopharmaceutical therapies but repeat post-therapy imaging for dosimetry can burden both patients and clinics. Recent applications of reduced time point imaging for time-integrated activity (TIA) determination for internal dosimetry following 177Lu-DOTATATE peptide receptor radionuclide therapy have shown promising results that allow for the simplification of patient-specific dosimetry. However, factors such as scheduling can lead to sub-optimal imaging time points, but the resulting impact on dosimetry accuracy is still under investigation. We use four-time point 177Lu SPECT/CT data for a cohort of patients treated at our clinic to perform a comprehensive analysis of the error and variability in time-integrated activity when reduced time point methods with various combinations of sampling points are employed.MethodsThe study includes 28 patients with gastroenteropancreatic neuroendocrine tumors who underwent post-therapy SPECT/CT imaging at approximately 4, 24, 96, and 168 h post-therapy (p.t.) following the first cycle of 177Lu-DOTATATE. The healthy liver, left/right kidney, spleen and up to 5 index tumors were delineated for each patient. Time-activity curves were fit with either monoexponential or biexponential functions for each structure, based on the Akaike information criterion. This fitting was performed using all 4 time points as a reference and various combinations of 2 and 3 time points to determine optimal imaging schedules and associated errors. 2 commonly used methods of single time point (STP) TIA estimation are also evaluated. A simulation study was also performed with data generated by sampling curve fit parameters from log-normal distributions derived from the clinical data and adding realistic measurement noise to sampled activities. For both clinical and simulation studies, error and variability in TIA estimates were estimated with various sampling schedules.ResultsThe optimal post-therapy imaging time period for STP estimates of TIA was found to be 3–5 days (71–126 h) p.t. for tumor and organs, with one exception of 6–8 days (144–194 h) p.t. for spleen with one STP approach. At the optimal time point, STP estimates give mean percent errors (MPE) within ± 5% and SD < 9% across all structures with largest magnitude error for kidney TIA (MPE = − 4.1%) and highest variability also for kidney TIA (SD = 8.4%). The optimal sampling schedule for 2TP estimates of TIA is 1–2 days (21–52 h) p.t. followed by 3–5 days (71–126 h) p.t. for kidney, tumor, and spleen. Using the optimal sampling schedule, the largest magnitude MPE for 2TP estimates is 1.2% for spleen and highest variability is in tumor with SD = 5.8%. The optimal sampling schedule for 3TP estimates of TIA is 1–2 days (21–52 h) p.t. followed by 3–5 days (71–126 h) p.t. and 6–8 days (144–194 h) p.t. for all structures. Using the optimal sampling schedule, the largest magnitude MPE for 3TP estimates is 2.5% for spleen and highest variability is in tumor with SD = 2.1%. Simulated patient results corroborate these findings with similar optimal sampling schedules and errors. Many sub-optimal reduced time point sampling schedules also exhibit low error and variability.ConclusionsWe show that reduced time point methods can be used to achieve acceptable average TIA errors over a wide range of imaging time points and sampling schedules while maintaining low uncertainty. This information can improve the feasibility of dosimetry for 177Lu-DOTATATE and elucidate the uncertainty associated with non-ideal conditions.

Influence of sampling schedules on [177Lu]Lu-PSMA dosimetry

BackgroundIndividualized dosimetry is recommended for [177Lu]Lu-PSMA radioligand therapy (RLT) which is resource-intensive and protocols are often not optimized. Therefore, a simulation study was performed focusing on the determination of efficient optimal sampling schedules (OSS) for renal and tumour dosimetry by investigating different numbers of time points (TPs).MethodsSampling schedules with 1–4 TPs were investigated. Time-activity curves of the kidneys and two tumour lesions were generated based on a physiologically based pharmacokinetic (PBPK) model and biokinetic data of 13 patients who have undergone [177Lu]Lu-PSMA I&T therapy. Systematic and stochastic noise of different ratios was considered when modelling time-activity data sets. Time-integrated activity coefficients (TIACs) were estimated by simulating the hybrid planar/SPECT method for schedules comprising at least two TPs. TIACs based on one single SPECT/CT measurement were estimated using an approximation for reducing the number of fitted parameters. For each sampling schedule, the root-mean-squared error (RMSE) of the deviations of the simulated TIACs from the ground truths for 1000 replications was used as a measure for accuracy and precision.ResultsAll determined OSS included a late measurement at 192 h p.i., which was necessary for accurate and precise tumour TIACs. OSS with three TPs were identified to be 3–4, 96–100 and 192 h with an additional SPECT/CT measurement at the penultimate TP. Kidney and tumour RMSE of 6.4 to 7.7% and 6.3 to 7.8% were obtained, respectively. Shortening the total time for dosimetry to e.g. 96 h resulted in kidney and tumour RMSE of 6.8 to 8.3% and 9.1 to 11%, respectively. OSS with four TPs showed similar results as with three TPs. Planar images at 4 and 68 h and a SPECT/CT shortly after the 68 h measurement led to kidney and tumour RMSE of 8.4 to 12% and 12 to 16%, respectively. One single SPECT/CT measurement at 52 h yielded good approximations for the kidney TIACs (RMSE of 7.0%), but led to biased tumour TIACs.ConclusionOSS allow improvements in accuracy and precision of renal and tumour dosimetry for [177Lu]Lu-PSMA therapy with potentially less effort. A late TP is important regarding accurate tumour TIACs.

Design optimal sampling plans for functional regression models

Functional regression models are widely considered in practice. To make a precise statistical inference, a good sampling schedule for collecting informative functional data is needed. However, there has not been much research on the optimal sampling schedule design for functional regression model so far. To address this design issue, an efficient computational approach is proposed for generating the best sampling plan in the function-on-function linear regression setting. The obtained sampling plan allows a precise estimation of the predictor function and a precise prediction of the response function. The proposed approach can also be applied to identify the optimal sampling plan for the problem with scalar-on-function linear regression model. Through case studies, this approach is demonstrated to outperform the methods proposed in the previous studies.

Technical Note: Optimal sampling schedules for kidney dosimetry based on the hybrid planar/SPECT method in 177 Lu-PSMA therapy.

Accurate and precise renal dosimetry during 177 Lu-labeled prostate-specific membrane antigen (PSMA) radioligand therapy is crucial for therapy decisions. Sampling schedules for estimating the necessary time-integrated activity coefficients (TIACs) are not optimized and standardized for clinical practice. Therefore, a simulation study to determine optimal sampling schedules (OSSs) was performed on 13 virtual 177 Lu-PSMA I&T therapy patients. A total of 880 clinically feasible sampling schedules for planar imaging (three time points) were investigated. To simulate the hybrid planar/SPECT method, an additional quantitative SPECT/CT measurement following one planar image was considered. For each sampling schedule and patient, the activity values were generated separately. Measurement noise was modeled by drawing random numbers of log-normal distributions. The used fractional standard deviations (FSD) differed depending on the imaging modality. For activity values assigned to planar imaging, systematic noise between 25% and 75% of the total noise was simulated. After fitting with a mono-exponential function, the root-mean-squared errors of the deviations of the simulated TIACs from the ground truth for 1000 replications were used to determine the OSS. The uncertainties of the TIACs and renal dose coefficients were estimated. For the hybrid planar/SPECT method, OSSs were determined to be (3-4, 72-76, 124-144) h post injection (p.i.) with the quantitative SPECT/CT scan shortly after the second measurement. The accuracy and precision of the determined TIACs were in the range of (-3.0± 6.2)% and (-1.0 ± 6.5)%. This precision was improved by a factor 2-3 compared to dosimetry based on planar images only. Similar results were obtained for the renal dose coefficients. The virtual patients' renal dose coefficients were (0.68 ±0.24) Gy/GBq indicating that a population-based method yields an uncertainty of 35%. Dosimetry based on the hybrid planar/SPECT method with OSS outperforms dosimetry based on planar images. The high variability in dose coefficients between the virtual patients demonstrates the need for individualized dosimetry.

Extreme Nonuniform Sampling for Protein NMR Dynamics Studies in Minimal Time.

NMR spectroscopy is an extraordinarily rich source of quantitative dynamics of proteins in solution using spin relaxation or chemical exchange saturation transfer (CEST) experiments. However, 15N-CEST measurements require prolonged multidimensional, so-called pseudo-3D HSQC experiments where the pseudo dimension is a radio frequency offset Δω of a weak 15N saturation field. Nonuniform sampling (NUS) approaches have the potential to significantly speed up these measurements, but they also carry the risk of introducing serious artifacts and the systematic optimization of nonuniform sampling schedules has remained elusive. It is demonstrated here how this challenge can be addressed by using fitted cross-peaks of a reference 2D HSQC experiment as footprints, which are subsequently used to reconstruct cross-peak amplitudes of a pseudo-3D data set as a function of Δω by a linear least-squares fit. It is shown for protein Im7 how the approach can yield highly accurate CEST profiles based on an absolutely minimally sampled (AMS) data set allowing a speed-up of a factor 20-30. Spectrum-specific optimized nonuniform sampling (SONUS) schemes based on the Cramer-Rao lower bound metric were critical to achieve such a performance, revealing also more general properties of optimal sampling schedules. This is the first systematic exploration and optimization of NUS schedules for the dramatic speed-up of quantitative multidimensional NMR measurements that minimize unwanted errors.

A simulation-based method to determine optimal sampling schedules for dosimetry in radioligand therapy

AimFor dosimetry in radioligand therapy, the time-integrated activity coefficients (TIACs) for organs at risk and for tumour lesions have to be determined. The used sampling scheme affects the TIACs and therefore the calculated absorbed doses. The aim of this work was to develop a general and flexible method, which analyses numerous clinically applicable sampling schedules using true time-activity curves (TACs) of virtual patients. MethodNine virtual patients with true TACs of the tumours were created using a physiologically-based pharmacokinetic (PBPK) model and individual biokinetic data of five patients with neuroendocrine tumours and four with meningioma. 111In-DOTATATE was used for pre-therapeutic dosimetry. In total, 15,120 sampling schemes, each consisting of 4 time points, were investigated. Gaussian noise of different levels was added to the corresponding true time-activity points. A bi-exponential function was used to fit the simulated time-activity data. For each investigated sampling schedule, 1000 replications were performed. Patient-specific and population-specific optimal sampling schedules were determined using the relative root-mean-square error (rRMSE). Furthermore, the fractions of TIACs a˜ deviating >5% (fΔa˜>5%) and >10% (fΔa˜>10%) from the true TIAC a˜true were used for additional evaluations e.g. to investigate the effect of varying single time points. ResultsAlmost all patient-specific and all population-specific optimal sampling schedules have t4≥96h for all noise levels. Changing the latest time point from the population-specific optimal time to e.g. 48h leads to a median increase of fΔa˜>10% from 0.1% to 88% for the lowest investigated noise level. Using the determined population-specific optimal schedules, results in more accurate and precise results than established schedules from the literature. ConclusionA method of determining the optimal sampling schedule for dosimetry, which considers clinical working hours and measurement uncertainties, has been developed and applied. The simulation study shows that optimised sampling schedules result in high accuracy and precision of the determined TIACs.

Nonuniform Sampling for NMR Spectroscopy.

Nonuniform sampling was first proposed more than 40 years ago as an alternate method for sampling two-dimensional NMR data was initially pursued by only a small number of scientists. However, it has been gradually adopted after it was shown that major gains in measuring time and spectrum resolution can be obtained. Furthermore, migration of NMR software to the Unix environment facilitated development of new processing tools, and there is now a selection of programs available that yield high-quality reconstructions of NUS data. Moreover, it became obvious that recording high-resolution 3D and 4D protein NMR spectra at the resolution provided by modern high-field instruments was not possible with uniform sampling. It has become apparent that sparse, low dynamic-range NMR spectra, in particular, the triple resonance experiments are all best recorded with NUS. Optimal sampling schedules can yield benefits with respect to detecting weak peaks in high dynamic-range spectra and therefore a careful use of 2D HSQC-like spectra of mixed concentrations of small molecules is feasible. It is not yet clear whether crowded high dynamic-range spectra, such as NOESYs with many cross-peaks, benefit from NUS. On the other hand, NUS appears to be the best option for recording such high dynamic-range spectra if crowding can be reduced by shorten NOESY mixing times and/or by reducing overlap through isotopic labeling in high-resolution 3D and 4D experiments. Here we discuss principals and applications of the method, trying to provide a wide overview, but are biased by our own approaches. Thus, we will obviously miss important developments elsewhere and do not claim to be comprehensive.

FPCA-based Method to Select Optimal Sampling Schedules That Capture Between-subject Variability in Longitudinal Studies

A critical component of longitudinal study design involves determining the sampling schedule. Criteria for optimal design often focus on accurate estimation of the mean profile, although capturing the between-subject variance of the longitudinal process is also important since variance patterns may be associated with covariates of interest or predict future outcomes. Existing design approaches have limited applicability when one wishes to optimize sampling schedules to capture between-individual variability. We propose an approach to derive optimal sampling schedules based on functional principal component analysis (FPCA), which separately characterizes the mean and the variability of longitudinal profiles and leads to a parsimonious representation of the temporal pattern of the variability. Simulation studies show that the new design approach performs equally well compared to an existing approach based on parametric mixed model (PMM) when a PMM is adequate for the data, and outperforms the PMM-based approach otherwise. We use the methods to design studies aiming to characterize daily salivary cortisol profiles and identify the optimal days within the menstrual cycle when urinary progesterone should be measured.

Target attainment analysis and optimal sampling designs for population pharmacokinetic study on piperacillin/tazobactam in neonates and young infants.

Population pharmacokinetic (popPK) analyses for piperacillin/tazobactam in neonates and infants of less than 2months of age have been performed by our group previously. The results indicate that a dose of 44.44/5.56mg/kg piperacillin/tazobactam every 8 or 12h may not be enough for controlling infection in this population. In order to determine the appropriate dosing regimen and to provide a rationale for the development of dosing guidelines suitable for this population, further popPK studies of piperacillin/tazobactam would need to be conducted. The aim of the present study was to determine the appropriate dosing regimen and optimal sampling schedules in neonates and infants of less than 2months of age. Pharmacodynamic profiling of piperacillin using Monte Carlo simulation was performed to explore the target attainment probability of different dosing regimens for infections caused by different isolated pathogens. D-optimal designs for piperacillin and tazobactam were conducted separately, and the times that overlapped were chosen as the final sampling scheme for future popPK studies in neonates and young infants of less than 2months of age. Our findings revealed that compared to the current empirical piperacillin/tazobactam dose regimen (50mg/kg every 12h by 5-min infusion in our hospital), the clinical outcome could be improved by increasing doses, increasing administration frequency, and prolonging intravenous infusion in neonates and infants of less than 2months of age. The following optimal sampling windows were chosen as the final sampling scheme: 0.1-0.11, 0.26-0.29, 0.97-2.62, and 7.95-11.9h administered every 12h with 5-min infusion; 0.1-0.12, 0.39-0.56, 2.86-4.95, and 8.91-11.8h administered every 12h with 3-h infusion; 0.1-0.11, 0.22-0.29, 0.91-1.96, and 5.56-7.93h administered every 8h with 5-min infusion; 0.1-0.11, 0.38-0.48, 2.54-3.82, and 6.86-7.93h administered every 8h with 3-h infusion; 0.1-0.11, 0.25-0.28, 0.84-1.69, and 4.55-5.94h administered every 6h with 5-min infusion; and 0.1-0.11, 0.37-0.54, 3.13-3.72, and 5.57-5.99h administered every 6h with 3-h infusion. The dosing regimen and sampling schedules proposed in this study should be evaluated in future popPK studies of piperacillin/tazobactam in neonates and infants. To the best of our knowledge, this is the first study that combined optimal sampling design with Monte Carlo simulation for designing popPK studies of piperacillin/tazobactam.

The Importance of Temporal Design: How Do Measurement Intervals Affect the Accuracy and Efficiency of Parameter Estimates in Longitudinal Research?

The timing (spacing) of assessments is an important component of longitudinal research. The purpose of the present study is to determine methods of timing the collection of longitudinal data that provide better parameter recovery in mixed effects nonlinear growth modeling. A simulation study was conducted, varying function type, as well as the number of measurement occasions, in order to examine the effect of timing on the accuracy and efficiency of parameter estimates. The number of measurement occasions was associated with greater efficiency for all functional forms and was associated with greater accuracy for the intrinsically nonlinear functions. In general, concentrating measurement occasions toward the left or at the extremes was associated with increased efficiency when estimating the intercepts of intrinsically linear functions, and concentrating values where the curvature of the function was greatest generally resulted in the best recovery for intrinsically nonlinear functions. Results from this study can be used in conjunction with theory to improve the design of longitudinal research studies. In addition, an R program is provided for researchers to run customized simulations to identify optimal sampling schedules for their own research.

Optimal design of T&lt;SUP align="right"&gt;2&lt;/SUP&gt; monitoring chart for chemical processes

In this paper, the optimal design of multivariate statistics-based monitoring charts for large scale systems is investigated. A cost function that explicitly accounts for the different quality monitoring costs is used to determine the parameters of the Hotelling's T2 monitoring chart. The proposed approach is used to economically monitor the products in the Tennessee Eastman process (TEP). The implementation of a multivariate economical criterion in the design of the Hotelling's T2 control chart provides an improved basis for the evaluation and repairs of out of control states and results in monitoring's cost minimisation through optimal sampling schedules. Due to the nature of the used economic model, the decision of switching from traditional statistical design to economic design requires the balance between the gained cost saving and the expected statistical performance of the T2 monitoring chart. Finally, in order to evaluate the reliability of the estimated optimal parameters, a detailed sensitivity analysis is presented.

Precision‐guided sampling schedules for efficient T1ρ mapping

PurposeTo describe, assess, and implement a simple precision estimation framework for optimization of spin‐lock time (TSL) sampling schedules for quantitative T1ρ mapping using a mono‐exponential signal model.Materials and MethodsA method is described for estimating T1ρ precision, and a cost function based on the precision estimates is evaluated to determine efficient TSL sampling schedules. The validity of the framework was tested by imaging a phantom with various sampling schedules and comparing theoretical and experimental precision values. The method utility was demonstrated with in vivo T1ρ mapping of brain tissue using a similar procedure as the phantom experiment. To assist investigators, optimal sampling schedules are tabulated for various tissue types and an online calculator is implemented.ResultsTheoretical and experimental precision values followed similar trends for both the phantom and in vivo experiments. The mean absolute percentage error (MAPE) of theoretical estimates of T1ρ map signal‐to‐noise ratio (SNR) was typically 5% in the phantom experiment and 33% in the in vivo demonstration. In both experiments, optimal TSL schedules yielded greater T1ρ map SNR efficiency than typical schedules.ConclusionThe framework can be used to improve the imaging efficiency of T1ρ mapping protocols and to guide selection of imaging parameters. J. Magn. Reson. Imaging 2015;41:242–250. © 2014 Wiley Periodicals, Inc.

Development of an optimal sampling schedule for children receiving ketamine for short-term procedural sedation and analgesia.

Intravenous racemic ketamine is commonly administered for procedural sedation, although few pharmacokinetic studies have been conducted among children. Moreover, an optimal sampling schedule has not been derived to enable the conduct of pharmacokinetic studies that minimally inconvenience study participants. Concentration-time data were obtained from 57 children who received 1-1.5 mg·kg(-1) of racemic ketamine as an intravenous bolus. A population pharmacokinetic analysis was conducted using nonlinear mixed effects models, and the results were used as inputs to develop a D-optimal sampling schedule. The pharmacokinetics of ketamine were described using a two-compartment model. The volume of distribution in the central and peripheral compartments were 20.5 l∙70 kg(-1) and 220 l∙70 kg(-1), respectively. The intercompartmental clearance and total body clearance were 87.3 and 87.9 l·h(-1) ∙70 kg(-1), respectively. Population parameter variability ranged from 34% to 98%. Initially, blood samples were drawn on 3-6 occasions spanning a range of 14-152 min after dosing. Using these data, we determined that four optimal sampling windows occur at 1-5, 5.5-7.5, 10-20, and 90-180 min after dosing. Monte Carlo simulations indicated that these sampling windows produced precise and unbiased ketamine pharmacokinetic parameter estimates. An optimal sampling schedule was developed that allowed assessment of the pharmacokinetic parameters of ketamine among children requiring short-term procedural sedation.

Precision‐guided sampling schedules for efficient T1ρ mapping

To describe, assess, and implement a simple precision estimation framework for optimization of spin-lock time (TSL) sampling schedules for quantitative T1ρ mapping using a mono-exponential signal model. A method is described for estimating T1ρ precision, and a cost function based on the precision estimates is evaluated to determine efficient TSL sampling schedules. The validity of the framework was tested by imaging a phantom with various sampling schedules and comparing theoretical and experimental precision values. The method utility was demonstrated with in vivo T1ρ mapping of brain tissue using a similar procedure as the phantom experiment. To assist investigators, optimal sampling schedules are tabulated for various tissue types and an online calculator is implemented. Theoretical and experimental precision values followed similar trends for both the phantom and in vivo experiments. The mean absolute percentage error (MAPE) of theoretical estimates of T1ρ map signal-to-noise ratio (SNR) was typically 5% in the phantom experiment and 33% in the in vivo demonstration. In both experiments, optimal TSL schedules yielded greater T1ρ map SNR efficiency than typical schedules. The framework can be used to improve the imaging efficiency of T1ρ mapping protocols and to guide selection of imaging parameters.

Comparison of an assumption‐free Bayesian approach with Optimal Sampling Schedule to a maximum a posteriori Approach for Personalizing Cyclophosphamide Dosing

Variable metabolism, dose-dependent efficacy, and a narrow therapeutic target of cyclophosphamide (CY) suggest that dosing based on individual pharmacokinetics (PK) will improve efficacy and minimize toxicity. Real-time individualized CY dose adjustment was previously explored using a maximum a posteriori (MAP) approach based on a five serum-PK sampling in patients with hematologic malignancy undergoing stem cell transplantation. The MAP approach resulted in an improved toxicity profile without sacrificing efficacy. However, extensive PK sampling is costly and not generally applicable in the clinic. We hypothesize that the assumption-free Bayesian approach (AFBA) can reduce sampling requirements, while improving the accuracy of results. Retrospective analysis of previously published CY PK data from 20 patients undergoing stem cell transplantation. In that study, Bayesian estimation based on the MAP approach of individual PK parameters was accomplished to predict individualized day-2 doses of CY. Based on these data, we used the AFBA to select the optimal sampling schedule and compare the projected probability of achieving the therapeutic end points. By optimizing the sampling schedule with the AFBA, an effective individualized PK characterization can be obtained with only two blood draws at 4 and 16 hours after administration on day 1. The second-day doses selected with the AFBA were significantly different than the MAP approach and averaged 37% higher probability of attaining the therapeutic targets. The AFBA, based on cutting-edge statistical and mathematical tools, allows an accurate individualized dosing of CY, with simplified PK sampling. This highly accessible approach holds great promise for improving efficacy, reducing toxicities, and lowering treatment costs.

Validation of a Novel Approach for Dose Individualization in Pharmacotherapy Using Gabapentin in a Proof of Principles Study

To demonstrate the premise of individualized dosing charts (IDCs) as a clinical-bedside decision-support tool to individualize dosage regimens for drugs in which the interpatient variability is controlled by the pharmacokinetic (PK) behavior of the patient, to calculate the optimal sampling schedule (OSS), which minimizes the number of blood samples per patient. The approach is illustrated with available PK data for gabapentin. Retrospective proof of principles study using gabapentin PK data from a published clinical trial. Nineteen subjects in a trial designed to uncover the importance of the genetic contributions to variability in gabapentin absorption, renal elimination, and transport; subjects were monitored for 36hours after administration of a single dose of gabapentin 400mg, and plasma concentrations were determined at 14 time points. When the PK profiles were different between subjects, the IDCs are dramatically different from each other and from the IDC for an "average" patient representing the patient population. The dose amount and dosing interval must be adjusted to maximize the probability of staying within the target concentration range. An optimal sampling methodology based on the assumption-free Bayesian approach is used to distinguish the PK profile of an individual patient from the patient population. In the case of gabapentin, only two optimally selected test blood samples, at 1.5 and 6hours after administration of a single doses, were necessary. The average sensitivity and the average specificity of the OSS was 99% and 96%, respectively. IDCs display the risk of a patient violating the target concentration range for any dosage regimen. They can be used as a clinical-bedside decision-support tool in a patient-physician partnership to decide on a dose amount and dosing interval that are medically acceptable while practical and convenient to ensure compliance. By using the assumption-free Bayesian approach and the OSS, the number of samples required from a new patient to individualize the dosage regimen can be reduced significantly while preserving high levels of sensitivity and specificity. Prospective studies are being planned to validate the encouraging results. This approach can be extended to any drug if PK data and a target concentration range are available for either therapeutic drug monitoring or target concentration intervention.

Optimal sampling schedule for chemical exchange saturation transfer

The sampling schedule for chemical exchange saturation transfer imaging is normally uniformly distributed across the saturation frequency offsets. When this kind of evenly distributed sampling schedule is used to quantify the chemical exchange saturation transfer effect using model-based analysis, some of the collected data are minimally informative to the parameters of interest. For example, changes in labile proton exchange rate and concentration mainly affect the magnetization near the resonance frequency of the labile pool. In this study, an optimal sampling schedule was designed for a more accurate quantification of amine proton exchange rate and concentration, and water center frequency shift based on an algorithm previously applied to magnetization transfer and arterial spin labeling. The resulting optimal sampling schedule samples repeatedly around the resonance frequency of the amine pool and also near to the water resonance to maximize the information present within the data for quantitative model-based analysis. Simulation and experimental results on tissue-like phantoms showed that greater accuracy and precision (>30% and >46%, respectively, for some cases) were achieved in the parameters of interest when using optimal sampling schedule compared with evenly distributed sampling schedule. Hence, the proposed optimal sampling schedule could replace evenly distributed sampling schedule in chemical exchange saturation transfer imaging to improve the quantification of the chemical exchange saturation transfer effect and parameter estimation.

A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies

Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalog of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical population pharmacokinetic study, and compare our results with those obtained via a simulation method from the literature.

Optimal use of GPS transmitter for estimating species migration rate

A common approach to learning about species movements is to tag individuals with a GPS transmitter. Here we provide methodology which determines the optimal programming of the times for such a device, and in doing so allow an assessment of the benefit provided over equidistant sampling schedules. We provide an algorithm (and MATLAB code) that computes the optimal patch in which to tag an individual, in addition to the optimal timing and number of samples in order to best estimate three parameters describing species-habitat migration rate (assuming a common form of migration). We use this algorithm to identify some basic conditions of a network that ensure identifiability of model parameters: at least four distinct inter-patch distances. We subsequently apply our algorithm to a number of randomly-generated networks, and demonstrate the efficiency gains from optimising various components of the sampling schedule. Finally, we determine the optimal sampling schedule for a real network: the spotted owl (Strix occidentalis occidentalis) in Southern California. The comparison of random and real networks demonstrates the improvement in efficiency as the size and heterogeneity of the underlying network increases. We hope this methodology is used to make better use of existing technologies, leading to improved understanding and conservation of species.