Optimal experimental design for predator–prey functional response experiments

Functional response models are important in understanding predator–prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller sample sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs; these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator Notonecta glauca (an aquatic insect) preying on Asellus aquaticus (a small crustacean), and another experiment involving a ladybird beetle (Propylea quatuordecimpunctata L.) preying on the black bean aphid (Aphis fabae Scopoli). As a by-product, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014)

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late-stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well-established and regulatory-acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4-5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP-Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)-based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well-designed dose-finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale

Intravenous anakinra can achieve experimentally effective concentrations in the central nervous system within a therapeutic time window: results of a dose-ranging study

The naturally occurring antagonist of interleukin-1, IL-1RA, is highly neuroprotective experimentally, shows few adverse effects, and inhibits the systemic acute phase response to stroke. A single regime pilot study showed slow penetration into cerebrospinal fluid (CSF) at experimentally therapeutic concentrations. Twenty-five patients with subarachnoid hemorrhage (SAH) and external ventricular drains were sequentially allocated to five administration regimes, using intravenous bolus doses of 100 to 500 mg and 4 hours intravenous infusions of IL-1RA ranging from 1 to 10 mg per kg per hour. Choice of regimes and timing of plasma and CSF sampling was informed by pharmacometric analysis of pilot study data. Data were analyzed using nonlinear mixed effects modeling. Plasma and CSF concentrations of IL-1RA in all regimes were within the predicted intervals. A 500-mg bolus followed by an intravenous infusion of IL-1RA at 10 mg per kg per hour achieved experimentally therapeutic CSF concentrations of IL-1RA within 45 minutes. Experimentally, neuroprotective CSF concentrations in patients with SAH can be safely achieved within a therapeutic time window. Pharmacokinetic analysis suggests that IL-1RA transport across the blood–CSF barrier in SAH is passive. Identification of the practicality of this delivery regime allows further studies of efficacy of IL-1RA in acute cerebrovascular disease

Pharmacometrically Driven Optimisation of Dose Regimens in Clinical Trials

PhDThe dose regimen of a drug gives important information about the dose sizes, dose frequency and the duration of treatment. Optimisation of dose regimens is critical to ensure therapeutic success of the drug and to minimise its possible adverse effects. The central theme of this thesis is the Efficient Dosing (ED) algorithm - a computation algorithm developed by us for optimisation of dose regimens. In this thesis, we have attempted to develop a quantitative framework for measuring the efficiency of a dose regimen for specified criteria and computing the most efficient dose regimen using the ED algorithm. The criteria considered by us seek to prevent over- and under-exposure to the drug. For example, one of the criteria is to maintain the drug's concentration around a desired target level. Another criterion is to maintain the concentration within a therapeutic range or window. The ED algorithm and its various extensions are programmed in MATLAB R . Some distinguishing features of our methods are: mathematical explicitness in the optimisation process for a general objective function, creation of a theoretical base to draw comparisons among competing dose regimens, adaptability to any drug for which the PK model is known, and other computational features. We develop the algorithm further to compute the optimal ratio of two partner drugs in a fixed dose combination unit and the efficient dose regimens. In clinical trials, the parameters of the PK model followed by the drug are often unknown. We develop a methodology to apply our algorithm in an adaptive setting which enables estimation of the parameters while optimising the dose regimens for the typical subject in each cohort. A potential application of the ED algorithm for individualisation of dose regimens is discussed. We also discuss an application for computation of efficient dose regimens for obliteration of a pre-specified viral load.QMUL and Novarti

D-optimal design of a pediatric pharmacokinetic study of a fixed dose combination product of rifampicin-pyrazinamide-isoniazid for the treatment of tuberculosis

Ο σκοπός αυτής της εργασίας είναι να σχεδιαστεί μια παιδιατρική φαρμακοκινητική μελέτη αραιής δειγματοληψίας ενός φαρμακευτικού σκευάσματος σταθερού συνδυασμού δόσης, ισονιαζίδης, ριφαμπικίνης και πυραζιναμίδης για την θεραπεία της φυματίωσης. Θεωρείται ότι θα δοθεί μια μοναδική χορήγηση του φαρμακευτικού σκευάσματος σε δυο κοορτές των πενήντα(50) παιδιών η καθεμία. Μη γραμμικά μοντέλα μικτών επιδράσεων χρησιμοποιήθηκαν προκείμενου να περιγράψουν την δομή καθενός από τα φαρμακευτικά μοντέλα. Καθορίσαμε ένα μοναδικό δειγματοληπτικό σχήμα για τα τρία φάρμακα, έτσι ώστε οι παράμετροι των φαρμακοκινητικών μοντέλων να εκτιμηθούν με υψηλή ακρίβεια. Eφαρμόστηκε μια μέθοδος βασισμένη στον Πίνακα Πληροφορίας του Fisher (Fisher Information Matrix) για μη γραμμικά μοντέλα μικτών επιδράσεων προκειμένου να βελτιστοποιηθούν οι χρόνοι δειγματοληψίας αποκτώντας αποτελεσματικές εκτιμήσεις των παραμέτρων. Η προσέγγιση αυτή βασίζεται στην ανισότητα του Rao-Cramer κατά την οποία o αντίστροφος του Πίνακα Πληροφορίας του Fisher είναι το κάτω φράγμα του πίνακα διασποράς- συν διασποράς κάθε αμερόληπτού εκτιμητή των παραμέτρων. Το κριτήριο που χρησιμοποιήθηκε για την βελτιστοποίηση είναι το D-βέλτιστο. Ένας σχεδιασμός θεωρείται D-βέλτιστος εάν μεγιστοποιεί την ορίζουσα του Πίνακα Πληροφοριών του Fisher. Ο αλγόριθμος particle swarm optimization (PSO) εφαρμόστηκε για την διαδικασία της βελτιστοποίησης καθώς και όλη η εφαρμογή διεξήχθηκε στο προγραμματιστικό πακέτο MATLAB. Ο τελικός σχεδιασμός αξιολογήθηκε μέσω προσομοιώσεων και εκτιμήσεων στο πακέτο NONMEM. Bootstrap, Visual predictive check plots και γραφήματα καλής προσαρμογής δημιουργήθηκαν. Τελικά σχεδιάστηκε, μια φαρμακοκινητική μελέτη με 4 δείγματα αίματος ανά ασθενή. Οι βέλτιστοι χρόνοι δειγματοληψίας για την πρώτη κοορτή είναι στις 0.10 , 0.13 ,0.55 και 4.28 ώρες και οι βέλτιστοι χρόνοι δειγματοληψίας για την δεύτερη κοορτή είναι στις 0.57 , 1.38, 2.25 and 6 ώρες. Η αξιολόγηση των τριών φαρμακοκινητικών μοντέλων έδειξε ακριβή αποτελέσματα, όσο αναφορά τις εκτιμήσεις των παραμέτρων. Κλείνοντας, εάν αυτή η κλινική μελέτη πραγματοποιηθεί, τότε το φαρμακευτικό αυτό σκεύασμα σταθερού συνδυασμού δόσης για την θεραπεία της φυματίωσης, θα μπορεί να πάρει έγκριση για άδεια κυκλοφορίας στην Ευρώπη και την Αμερική, το οποίο μέχρι τώρα δεν είναι διαθέσιμο.The objective of this dissertation was to design a sparse sampling pediatric pharmacokinetic study for a fixed dose combination product of isoniazid (Η), rifampicin (R) and pyrazinamide (Z) for the treatment of tuberculosis. A single dose of FDC tablet was supposed to be given into two cohorts of fifty (50) children each. Non-linear mixed effects models were used to describe the structure of each drug model. We determined a unique optimal sampling schedule for the three drugs, such that the parameters of the PK models of each drug are estimated with high precision. We applied a method based on an expression for the Fisher Information Matrix (FIM) for non-linear mixed effects to improve the sampling design so as to obtain efficient parameter estimates. The approach is based on Rao-Cramer inequality which states that the inverse of FIM is the lower bound of the variance-covariance matrix of any unbiased estimators of the parameters. The criterion used for the optimization is D-optimality; a design is considered D-optimal if it maximizes the determinant of the Fisher information matrix. The particle swarm optimization (PSO) algorithm was applied for the optimization procedure while all implementation was conducted in MATLAB. The final design was evaluated by simulations and estimation with NONMEM. Bootstrap, Visual Predictive Check (VPC) plots and Goodness of fit plots were generated. A pharmacokinetic study with 4 blood samples per subject was eventually designed. The optimal blood sampling times for the first cohort is0.10h, 0.13h, 0.55h and 4.28h and optimal blood sampling times for Cohort 2 is 0.57 h, 1.38h, 2.25h and 6h.The evaluation of the 3 pharmacokinetic models showed accurate results, as regards the parameter estimates. Finally, if the clinical trial that we designed is implemented, it could be used for taking Marketing Authorization Approval of first-line paediatric fixed dose combination product for the treatment of tuberculosis in Europe and USΑ, which is currently unavailable

Bayesian experimental design for implicit models using mutual information

Scientists regularly face the challenging task of designing experiments in such a way that the collected data is informative and useful. The field of Bayesian experimental design formalises this task by phrasing it as an optimisation problem. Here, the goal is to maximise a utility function that describes the value of an experimental design according to the scientific aims of the experiment. The mutual information, which quantifies the expected information gain about variables of interest, is a principled choice of utility function that has seen extensive use in literature. However, computing the mutual information is intractable for all but the most simple computational models of nature. Indeed, as our scientific theories improve, scientists are increasingly devising models that have intractable likelihood functions, so-called implicit models. The increased realistic behaviour of implicit models comes at the cost of severely complicating the Bayesian design of experiments. The work presented in this thesis provides several solutions to Bayesian experimental design for implicit models using the mutual information utility function. Although a desirable quantity, mutual information is generally prohibitively expensive to compute because it involves posterior distributions, which are naturally intractable for implicit models. Therefore, existing literature has, mostly, either considered special settings where mutual information can be approximated, or utilised other simplified utility functions altogether. First, we present a method of approximating the mutual information using density ratio estimation techniques, where the only requirement is that we can sample data from the computational model, which is naturally satisfied for implicit models. This allows us to efficiently estimate the mutual information and then solve the Bayesian experimental design problem by maximising it by means of gradient-free optimisation techniques. Following this, we present an extension that concerns sequential Bayesian experimental design, where the aim is to find optimal designs and gather data in a sequential manner. We use the density ratios learned through the aforementioned approach to update our beliefs of the variable of interest at every iteration, which then repeatedly changes the optimisation landscape. Similar to before, we optimise the sequential mutual information at every iteration using gradient-free techniques. Next, we present a method where we construct a lower bound on the mutual information that is parametrised by a neural network. Neural networks provide great flexibility and, more importantly, allow us to back-propagate from the lower bound estimate to the experimental designs. We can therefore simultaneously tighten and maximise the mutual information lower bound using stochastic gradient-ascent. As opposed to previous gradient-free approaches, this results in greater scalability with respect to the number of experimental design dimensions. Following this, we provide a general framework that accommodates the use of (a) several lower bounds with different bias-variance trade-offs and (b) several important scientific tasks instead of only a single one (as is common in exist ing literature), such as parameter estimation, distinguishing between competing models and improving future predictions. Lastly, we present an application of this approach to cognitive science, where we design behavioural experiments with the aim of estimating parameters of and distinguishing between cognitive models. We showcase the advantages of our method by performing real-world experiments with human participants, demonstrating how scientists can use and profit from Bayesian experimental design methods in practice, even when likelihood functions are intractable

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstracts

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstract

CASH: CORRELATING, ANALYTICAL, SENSORY AND HEDONIC DATA IN GREEN TEA

Ph.DDOCTOR OF PHILOSOPH

Developing multiparametric and novel magnetic resonance imaging biomarkers for prostate cancer

Whilst biomarker research is gaining momentum within the cancer sciences, disappointingly few biomarkers are successfully translated into clinical practice, which is partly due to lack of rigorous methodology. In this thesis, I aim to systematically study several quantitative magnetic resonance imaging (MRI) biomarkers (QIBs), at various stages of biomarker development for use as tools in the assessment of local and metastatic prostate cancer according to clinical need. I initially focus on QIBs derived from conventional multiparametric (mp) prostate MRI sequences, namely T2 weighted (T2W), apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE). Firstly, by optimising analytical methods used throughout the thesis, deciding which approach is more reliable between single-slice region-of-interest vs. contouring the whole tumour volume using two different software packages. I then consider whether metric reproducibility can be improved by normalisation to different anatomical structures, and assess whether it is preferable to use statistics derived from imaging histograms rather than the current convention of using mean values. I combine multiple QIBs in a logistic regression model to predict a Gleason 4 component in known prostate cancer, which represents an unmet clinical need, as noninvasive tools to distinguish these more aggressive tumours do not currently exist. I subsequently ‘technically validate’ a novel microstructural diffusion-weighted MRI technique called VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumours) to detect aggressive prostate cancer as part of a prospective cohort study. I assess the image quality, contrast-to-noise ratio, repeatability and performance of quantitative parametric VERDICT maps to discriminate between Gleason grades vs. the current best performing, but still imperfect tool of ADC. In the final two results chapters, motivated by the limited diagnostic accuracy of the prostate cancer staging modalities in current clinical use, I investigate the ability of mp whole-body (WB) MRI to stage aggressive cancer outside the prostate in patients with a high risk of metastases at primary diagnosis, and in biochemical failure following prostatectomy