Biomedical subjects

Scott Marshall

Publications and source records attributed to Scott Marshall.

6 recordsLinked to original sources

PubMed2007

The application of a Bayesian approach to the analysis of a complex, mechanistically based model.

The Bayesian approach has been suggested as a suitable method in the context of mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling, as it allows for efficient use of both data and prior knowledge regarding the drug or disease state. However, to this day, published examples of its application to real PK-PD problems have been scarce. We present an example of a fully Bayesian re-analysis of a previously published mechanistic model describing the time course of circulating neutrophils in stroke patients and healthy individuals. While priors could be established for all population parameters in the model, not all variability terms were known with any degree of precision. A sensitivity analysis around the assigned priors used was performed by testing three different sets of prior values for the population variance terms for which no data were available in the literature: "informative", "semi-informative", and "noninformative", respectively. For all variability terms, inverse gamma distributions were used. It was possible to fit the model to the data using the "informative" priors. However, when the "semi-informative" and "noninformative" priors were used, it was impossible to accomplish convergence due to severe correlations between parameters. In addition, due to the complexity of the model, the process of defining priors and running the Markov chains was very time-consuming. We conclude that the present analysis represents a first example of the fully transparent application of Bayesian methods to a complex, mechanistic PK-PD problem with real data. The approach is time-consuming, but enables us to make use of all available information from data and scientific evidence. Thereby, it shows potential both for detection of data gaps and for more reliable predictions of various outcomes and "what if" scenarios.

Algorithms↗

PubMed2006

Skin explant model of human graft-versus-host disease: prediction of clinical outcome and correlation with biological risk factors.

A human skin explant model has been used to predict the clinical outcome and to study the immunopathology of human graft-versus-host disease (GVHD). Whether the model gives the same predictive effect for GVHD in different hematopoietic stem cell transplantation (HSCT) settings has not been assessed. It is also unknown whether the skin explant result reflects the known biological risk factors for clinical GVHD. In this study, the skin explant model was used to detect graft-versus-host reactions (GVHR) in vitro for 225 eligible patient/donor pairs. The predicted skin GVHR grade was correlated with the outcome of clinical GVHD, as well as HLA matching status, sex mismatches, and patient age. In sibling HSCT under either myeloablative or reduced-intensity conditioning, a significant correlation was observed between the predicted skin GVHR and clinical GVHD (P < .001 and P = .033, respectively). In HSCT using unrelated donors, the involvement of T-cell depletion led to a sharp increase in false-positive GVHR results, and no correlation was observed between the predicted skin GVHR and clinical GVHD. The skin GVHR grade correlated significantly with the HLA matching status (HLA-matched sibling pairs, HLA-matched unrelated pairs, and HLA-unmatched unrelated pairs). Furthermore, HLA-matched sibling pairs with a female-to-male sex mismatch had a significantly higher overall skin GVHR grade and a higher ratio of high- versus low-grade skin GVHR than the sibling pairs with all other sex combinations. Patient age was not reflected in the skin explant result. In conclusion, the predictive value of the skin explant model for aGVHD varies depending on the clinical transplant protocols, such as the type of GVHD prophylaxis used. Nevertheless, the skin explant model remains a unique in vitro system that provides an in situ histopathologic readout for studying alloreactivity and human GVHD. The model has also the potential to aid the development of novel prophylaxis and treatment for GVHD.

Adolescent↗

PubMed2006

A Bayesian design and analysis for dose-response using informative prior information.

We wish to use prior information on an existing drug in the design and analysis of a dose-response study for a new drug candidate within the same pharmacological class. Using the Bayesian methodology, this prior information can be used quantitatively and the randomization can be weighted in favor of the new compound, where there is less information. An Emax model is used to describe the dose-response of the existing drug. The estimates from this model are used to provide informative prior information used for the design and analysis of the new study to establish the relative potency between the new compound and the existing drug therapy. The assumption is made that the data from previous trials and the new study are exchangeable. The impact of departures from this assumption can be quantified through simulations and by assessing the operating characteristics of various scenarios. Simulations show that relatively modest sample sizes can yield informative results about the magnitude of the relative potency using this approach. The operating characteristics are good when assessing model estimates against clinically important changes in relative potency.

Algorithms↗

PubMed2006

Role of mechanistically-based pharmacokinetic/pharmacodynamic models in drug development : a case study of a therapeutic protein.

BACKGROUND AND OBJECTIVE: This case study describes the pharmacokinetic and pharmacodynamic modelling undertaken during the development programme for UK-279,276 (neutrophil inhibitory factor), focusing on the transition from early empirical-based models to a final mechanistic-based model. UK-279,276 binds to the CD11b/CD18 (MAC-1) on neutrophils and was under development for the treatment of ischaemic stroke. METHODS: The aims, data, models, results and value-to-drug development process across four stages of model development are described: (i) the validation of the pharmacokinetic assay; (ii) the development and application of an empirical patient pharmacokinetic/pharmacodynamic model; (iii) the development of a mechanistic-based model to bridge between patients and healthy volunteers; and (iv) propagation of the stage III model to a large efficacy study. The analyses utilised available concentration measurements (stages I-IV), CD11b receptor occupancy data (stages I-III) and neutrophil count data (stages III-IV) from three healthy volunteers (study 1, n=51; study 2, n=31; study 4, n=15) and two patient studies (study 3, n=169; study 5, n=992). In studies 1-4, subjects received placebo or between three and six doses of UK-279,276 covering a range of 0.006 and 1.5 mg/kg as a single 15-minute intravenous infusion. In study 5, subjects received placebo or one of 15 possible doses of UK-279,276 (10--20mg) assigned through adaptive design and administered as a single 15-minute intravenous infusion. All model building was conducted using NONMEM version VI (beta). The empirical pharmacokinetic/pharmacodynamic model developed during stage I was used to demonstrate that the pharmacokinetic assay was measuring biologically active drug. Simulations from the stage II model, developed from study 3, were used in the design of study 5. The model supported the switch to a fixed-dose regimen and the selection of the maximum dose and dosage increments. The common mechanistic-based model developed during stage III was used to support the 'comparability strategy' for UK-279,276 and provided insight into the underlying clearance mechanisms. At stage 4, the prior functionality available with NONMEM was used to successfully propagate the model from stage III in order to analyse the pharmacokinetic data from study 5. The analysis indicated that the exposure in study 5 was consistent with prior data. The role of empirical-based models in providing the learning for future mechanistic model development was highlighted. Similarly, the qualitative and quantitative aspects to knowledge propagation and the ultimate benefits from the development of the mechanistic-based model were demonstrated. While the empirical-based models were used to guide some early drug development decisions for UK-279,276, the development of the mechanistic-based model was valuable in linking the complex pharmacokinetics/pharmacodynamics of UK-279,276 across the phases of drug development.

Adult↗

PubMed2005

Bridging the pharmacokinetics and pharmacodynamics of UK-279,276 across healthy volunteers and stroke patients using a mechanistically based model for target-mediated disposition.

PURPOSE: UK-279,276 is a recombinant glycoprotein and is a selective antagonist of CD11b, which in preclinical models of acute stroke blocks the infiltration of activated neutrophils into the site of infarction. Binding of UK-279,276 to the CD11b receptors is hypothesized to facilitate its elimination. The event of an acute stroke leads to proliferation of neutrophils and an up-regulation of CD11b, which results in different pharmacokinetics/pharmacodynamics (PK/PD) in patients than in healthy volunteers. The aim of this current analysis was to develop a mechanistically based model to bridge the differences between healthy volunteers and patients. METHODS: PK samples, neutrophil counts, and total number and number of free CD11b receptors per neutrophils from three healthy volunteer studies (n=98) and one patient study (n=169) were modeled using the mixed effects modeling software NONMEM version VI (beta). Three mechanistic submodels were developed based on underlying physiology and pharmacology: (1) neutrophil maturation and proliferation, (2) CD11b up-regulation, and (3) three clearance pathways for UK-279-276 including CD11b-mediated elimination. RESULTS: The model accurately described the time course of CD11b expression, CD11b binding, and the measured PK of UK-279,276 and accounted for the PK/PD differences between healthy volunteers and patients. CONCLUSIONS: A complex mechanistic model that closely resembled the "true" underlying system provided an effective bridge between healthy volunteers and patients by appropriately accounting for the underlying disease-dependent target mediated disposition.

CD11b Antigen↗

PubMed2005

A longitudinal model for non-monotonic clinical assessment scale data.

Clinical assessment scales, where subitem ratings are added and summarized as a total score, are convenient tools for monitoring disease progression and often used to measure the effect of drug treatment in clinical trials. Statistical evaluation of any beneficial treatment effects tends to focus on single-valued summary measures, for example, the difference between the score at the end of treatment and the score at baseline. Such analyses ignore potentially important features of the data, e.g. early vs. late recoveries. It is therefore of interest to develop longitudinal models that make more efficient use of the information present in non-monotonic clinical assessment scale data. We propose a two-part modeling approach for the modeling of this type of data. Non-monotonicity is managed by regarding score changes as Markovian transition events. A set of probabilistic models are used to describe the occurrences of the transitions. Continuous models are used to describe the magnitude of the scale score change, given the observed transition. In this manner, a non-monotonic disease progression is handled more efficiently than if other available methods are used. We illustrate this approach using data from a recent phase II study of a drug used in the treatment of stroke, where stroke severity was measured on the Scandinavian Stroke Scale (SSS). This scale consists of nine subitems: consciousness, eye movements, hand/arm/leg motor performance, orientation, speech, facial palsy, and gait. The data were non-monotonic, since there was at any time a risk of a score decline, despite a general tendency towards healing. The two-part probabilistic/continuous model fit the data well and proved to be robust in model-checking procedures such as posterior predictive checks and bootstrapping. The models derived using this approach could potentially accommodate drug effects, not only in terms of score improvement at end of study, but also on the onset of recovery, on dropout and on the probability of unfavorable progression patterns. In addition, it is possible to use the resulting for simulation of the prospective outcome of future studies. We conclude that this approach has considerable potential for more efficient use of information in longitudinal modeling of non-monotonic clinical assessment scale data.

Algorithms↗