Quantifying Uncertainty in Predictions of Hepatic Clearance

Preclinical predictions of human pharmacokinetic parameters are routinely used in pharmaceutical research and development. In particular, pharmacokinetic predictions are critical in the decision to advance a potential drug to the clinic, to determine appropriate dosing regimens for first-in-human studies, and as a component of translational pharmacology models. Although the associated biological and mathematical models have been extensively discussed in the pharmacokinetic literature, relatively little work has been done to explicitly relate the estimation error of these methods to the underlying experimental variability. This article proposes and evaluates Bayesian models for this purpose.

We apply our methodology to a dataset describing both preclinical and clinical pharmacokinetic experimentation for 12 different anonymized drugs. For each drug and for each preclinical mode of prediction, a credible interval is computed and compared against estimates obtained by direct experimentation with human subjects in the clinic. We conclude that many apparent translational differences may be readily explained as a function of experimental error.

We view this problem as representative of a larger class of statistical problems in translational medicine, where the mathematics of translation from one species to another requires multiple experimentally estimated scaling factors.     

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.     

Nonrelapse Mortality and Mycophenolic Acid Exposure in Nonmyeloablative Hematopoietic Cell Transplantation

We evaluated the pharmacodynamic relationships between mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), and outcomes in 308 patients after nonmyeloablative hematopoietic cell transplantation. Patients were conditioned with total body irradiation ± fludarabine, received grafts from HLA-matched related (n = 132) or unrelated (n = 176) donors, and received postgrafting immunosuppression with MMF and a calcineurin inhibitor. Total and unbound MPA pharmacokinetics were determined to day 25; maximum a posteriori Bayesian estimators were used to estimate total MPA concentration at steady state (C_ss). Rejection occurred in 9 patients, 8 of whom had a total MPA C_ss less than 3 μg/mL. In patients receiving a related donor graft, MPA C_ss was not associated with clinical outcomes. In patients receiving an unrelated donor graft, low total MPA C_ss was associated with increased grades III to IV acute graft-versus-host disease and increased nonrelapse mortality but not with day 28 T cell chimerism, disease relapse, cytomegalovirus reactivation, or overall survival. We conclude that higher initial oral MMF doses and subsequent targeting of total MPA C_ss to greater than 2.96 μg/mL could lower grades III to IV acute graft-versus-host disease and nonrelapse mortality in patients receiving an unrelated donor graft.     

The importance of evaluating findings given activity level propositions in order to avoid misleading evidence

ABSTRACT

The advancement of DNA technology comes with the increased sensitivity of amplification systems, where DNA traces are routinely detected without a known biological source. These systems also have increased discriminating capacity, providing larger likelihood ratios (LRs) when a corresponding DNA profile is observed. Questions in court are shifting from identity to transfer mechanism, where the presence of an individual’s DNA is conceded by both parties, but the activities that led to its deposition is in dispute. One way of handling propositions developed at the activity level is with the use of graphical structures known as Bayesian Networks (BNs). The following is an evaluation of a case, given activity level propositions, through the application of BNs. Alternative case findings will be explored for the given scenario to show the potential value of the DNA evidence for different outcomes within the broader case context.     

Not just data: A method for improving prediction with knowledge

Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as ‘latent’ variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.     

Development and evaluation of RapTAT: A machine learning system for concept mapping of phrases from medical narratives

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system. It was also assessed on the manually annotated, 2010 i2b2 challenge data. Performance was established with regard to precision, recall, and F-measure for each of the concepts within the VA documents using bootstrapping. Within that corpus, concepts identified by MCVS were broadly distributed throughout SNOMED CT, and the token-order-specific language model achieved better performance based on precision, recall, and F-measure (0.95 ± 0.15, 0.96 ± 0.16, and 0.95 ± 0.16, respectively; mean ± SD) than the bag-of-words based, naïve Bayes model (0.64 ± 0.45, 0.61 ± 0.46, and 0.60 ± 0.45, respectively) that has previously been used for concept mapping. Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model. RapTAT required just 7.2 ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.     

A Bayesian Approach on Sample Size Calculation for Comparing Means

ABSTRACT

In clinical research, parameters required for sample size calculation are usually unknown. A typical approach is to use estimates from some pilot studies as the true parameters in the calculation. This approach, however, does not take into consideration sampling error. Thus, the resulting sample size could be misleading if the sampling error is substantial. As an alternative, we suggest a Bayesian approach with noninformative prior to reflect the uncertainty of the parameters induced by the sampling error. Based on the informative prior and data from pilot samples, the Bayesian estimators based on appropriate loss functions can be obtained. Then, the traditional sample size calculation procedure can be carried out using the Bayesian estimates instead of the frequentist estimates. The results indicate that the sample size obtained using the Bayesian approach differs from the traditional sample size obtained by a constant inflation factor, which is purely determined by the size of the pilot study. An example is given for illustration purposes.     

A Bayesian Meta-analysis Method for Estimating Risk Difference of Rare Events

Bayesian meta-analysis has been more frequently utilized for synthesizing safety and efficacy information to support landmark decision-making due to its flexibility of incorporating prior information and availability of computing software. However, when the outcome is binary and the events are rare, where event counts can be zero, conventional meta-analysis methods including Bayesian methods may not work well. Several methods have been proposed to tackle this issue but the prior knowledge of event rate was not utilized to increase precision of risk difference estimates. To better estimate risk differences, we propose a new Bayesian method, Beta prior BInomial model for Risk Differences (B-BIRD), which takes into account the prior information of rare events. B-BIRD is illustrated using a real data set of 48 clinical trials about a type 2 diabetes drug. In simulation studies, it performs well in low event rate settings.     

New insight into the epidemiology of rabbit hemorrhagic disease viruses in Portugal: Retrospective study reveals the circulation of genogroup 5 (G5) in Azores and discloses the circulation of G1 and G6 strains in mainland until 2008

The genetic relationships between 10 rabbit hemorrhagic disease strains collected in Portugal between 2006 and 2013, originated in the mainland and Azorean islands, were investigated based on the vp60 gene variability. A genetic diversity ranging from 2% to 13% was determined among the 10-vp60 complete sequences revealing a significant level of genetic heterogeneity between same strains. Phylogenetic Bayesian analysis showed that the Portuguese RHDV strains fell within different genogroups, namely G1, G5 and G6. Interestingly, all strains obtained from Azores, where RHDV was first detected in 1988, belong to G5 genogroup. G5 strains, that were not identified in the continent so far, seem to be the dominant group in these Atlantic islands.G1-related strains belonging to the Iberian group 3 (n = 3) and G6 (RHDVa) strains (n = 2) were identified among the samples originated in mainland which were collected between 2006 and 2008. Although the presence of G1 and G6 in Portugal had been shown before, our data refines the time of circulation of these strains until at least 2008.In summary, this study revises the epidemiological information of RHDV in Portugal since it reports for the first time the presence of G5 strains in Azores and demonstrates the circulation of G1 and G6 strains in mainland Portugal until the late 2000s.