Integrating Dynamic Positron Emission Tomography and Conventional Pharmacokinetic Studies to Delineate Plasma and Tumor Pharmacokinetics of FAU, a Prodrug Bioactivated by Thymidylate Synthase

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Abstract

FAU, a pyrimidine nucleotide analog, is a prodrug bioactivated by intracellular thymidylate synthase to form FMAU that is incorporated into DNA causing cell death. This study presents a model-based approach to integrating dynamic positron emission tomography (PET) and conventional plasma pharmacokinetic studies to characterize the plasma and tissue pharmacokinetics of FAU and FMAU. Twelve cancer patients were enrolled into a phase I study, where conventional plasma pharmacokinetic evaluation of therapeutic FAU (50 – 1600 mg/m 2) and dynamic PET assessment of 18F-FAU were performed. A parent-metabolite population pharmacokinetic model was developed to simultaneously fit PET-derived tissue data and conventional plasma pharmacokinetic data. The developed model enabled separation of PET-derived total tissue concentrations into the parent drug and metabolite components. The model provides quantitative, mechanistic insights into the bioactivation of FAU and retention of FMAU in normal and tumor tissues, and has potential utility to predict tumor responsiveness to FAU treatment.

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T Maiwald, J. Timmer, C. Kreutz … (2007)

Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself in functionally related parameters, which are difficult to detect. We present the method of mean optimal transformations, a non-parametric bootstrap-based algorithm for identifiability testing, capable of identifying linear and non-linear relations of arbitrarily many parameters, regardless of model size or complexity. This is performed with use of optimal transformations, estimated using the alternating conditional expectation algorithm (ACE). An initial guess or prior knowledge concerning the underlying relation of the parameters is not required. Independent, and hence identifiable parameters are determined as well. The quality of data at disposal is included in our approach, i.e. the non-linear model is fitted to data and estimated parameter values are investigated with respect to functional relations. We exemplify our approach on a realistic dynamical model and demonstrate that the variability of estimated parameter values decreases from 81 to 1% after detection and fixation of structural non-identifiabilities.

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Positron emission tomography microdosing: a new concept with application in tracer and early clinical drug development.

Bengt Långström, Anders Grahnen, Mats Bergström (2003)

The realisation that new chemical entities under development as drug candidates fail in three of four cases in clinical trials, together with increased costs and increased demands of reducing preclinical animal experiments, have promoted concepts for improvement of early screening procedures in humans. Positron emission tomography (PET) is a non-invasive imaging technology, which makes it possible to determine drug distribution and concentration in vivo in man with the drug labelled with a positron-emitting radionuclide that does not change the biochemical properties. Recently, developments in the field of rapid synthesis of organic compounds labelled with positron-emitting radionuclides have allowed a substantial number of new drug candidates to be labelled and potentially used as probes in PET studies. Together, these factors led to the logical conclusion that early PET studies, performed with very low drug doses-PET-microdosing-could be included in the drug development process as one means for selection or rejection of compounds based on performance in vivo in man. Another important option of PET, to evaluate drug interaction with a target, utilising a PET tracer specific for this target, necessitates a more rapid development of such PET methodology and validations in humans. Since only very low amounts of drugs are used in PET-microdosing studies, the safety requirements should be reduced relative to the safety requirements needed for therapeutic doses. In the following, a methodological scrutinising of the concept is presented. A complete pre-clinical package including limited toxicity assessment is proposed as a base for the regulatory framework of the PET-microdosing concept.

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Extensions to the Visual Predictive Check to facilitate model performance evaluation

Teun Post, Jan I. Freijer, Bart A. Ploeger … (2008)

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example.