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AAPS PharmSci (2)
Balant, Luc (1)
Hadjitodorov, S. T. (1)
Nestorov, Ivan (1)
Peck, Carl (1)
Petrov, I. (1)
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Physiologically based pharmacokinetics in Drug Development and Regulatory Science: A workshop report (Georgetown University, Washington, DC, May 29–30, 2002)
A 2-day workshop on “Physiologically Based Pharmacokinetics (PBPK) in Drug Development and Regulatory Science” came to a successful conclusion on May 30, 2002, in Washington, DC. More than 120 international participants from the environmental and predominantly pharmaceutical industries, Food and Drug Administration (FDA), and universities attended this workshop, organized by the Center for Drug Development Science, Georgetown University, Washington, DC. The first of its kind specifically devoted to the subject, this intensive workshop, comprising 7 plenary presentations and 10 breakout sessions addressed 2 major objectives: (1) to “define demonstrated and potential contributions of PBPK in drug development and regulatory science,” and (2) to “assess current PBPK methodologies with the identification of their limitations and outstanding issues.” This report summarizes the presentations and recommendations that emerged from the workshop, while providing key references, software, and PBPK data sources in the appendices. The first day was initially devoted to presentations setting the stage and providing demonstrated applications to date. This was followed by breakout sessions that considered further opportunities and limitations, and which extended into Day 2 to deal with developments in methodologies and tools. Although the primary emphasis was on pharmacokinetics, consideration was also given to its integration specifically with mechanism-based pharmacodynamics.
Empirical versus mechanistic modelling: Comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates
Hadjitodorov, S. T.
The aim of the current study was to compare the predictive performance of a mechanistically based model and an empirical artificial neural network (ANN) model to describe the relationship between the tissue-to-unbound plasma concentration ratios (Kpu's) of 14 rat tissues and the lipophilicity (LogP) of a series of nine 5-n-alkyl-5-ethyl barbituric acids. The mechanistic model comprised the water content, binding capacity, number of the binding sites, and binding association constant of each tissue. A backpropagation ANN with 2 hidden layers (33 neurons in the first layer, 9 neurons in the second) was used for the comparison. The network was trained by an algorithm with adaptive momentum and learning rate, programmed using the ANN Toolbox of MATLAB. The predictive performance of both models was evaluated using a leave-one-out procedure and computation of both the mean prediction error (ME, showing the prediction bias) and the mean squared prediction error (MSE, showing the prediction accuracy). The ME of the mechanistic model was 18% (range, 20 to 57%), indicating a tendency for overprediction; the MSE is 32% (range, 6 to 104%). The ANN had almost no bias: the ME was 2% (range, 36 to 64%) and had greater precision than the mechanistic model, MSE 18% (range, 4 to 70%). Generally, neither model appeared to be a significantly better predictor of the Kpu's in the rat.
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