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Data Analysis
Published in Clive R. Bagshaw, Biomolecular Kinetics, 2017
Chapter 2 provided examples of reaction profiles generated from a defined kinetic scheme with known parameters (i.e., starting concentrations and rate constants). In this chapter, we examine the more difficult problem of finding an appropriate kinetic scheme and associated parameters from experimentally obtained reaction profiles. Here, we will restrict the analysis to deterministic results from ensemble measurements. Analysis of stochastic reactions at the single-molecule level requires a different treatment and is covered in Chapter 9. In fitting experimental data to a model, we can never be certain whether a particular kinetic scheme is correct, only if it is compatible. In general, we can only rule out incorrect schemes that fail to account for the data. For a given kinetic scheme, finding the most likely values for the parameters and the confidence in them is a retractable problem. This simpler problem will be discussed first. Here, a kinetic scheme is defined as a series of states and the rate constants that define their interconversion. Assigning specific structures or conformations to the states defined by the kinetic scheme, to give a molecular mechanism, requires consideration of the information contained within the signal(s) and/or independent information. In some cases, for example fluorescence intensity signals, the intrinsic structural information content is low. However, the kinetic profiles themselves may contain inherent information about the number of states, even if these states can only be defined in an abstract way (e.g., a two-step binding mechanism as in Equation 2.30 and Figures 2.12 to 2.16).
Numerical analysis of time-dependent inhibition kinetics: comparison between rat liver microsomes and rat hepatocyte data for mechanistic model fitting
Published in Xenobiotica, 2020
Chuong Pham, Swati Nagar, Ken Korzekwa
TDI experiments and subsequent modeling utilized a two-step incubation method: a primary preincubation with the inhibitor TAO at various concentrations and time points, followed by a secondary incubation with the substrate MDZ. Percent remaining activity (PRA) for each TAO concentration was plotted versus preincubation time. All experimental PRA plots showed a concave upward shape indicative of quasi-irreversible inhibition (Figures 2, 3 and 4) (Korzekwa et al., 2014). Using the RLM data to parameterize the quasi-irreversible kinetic scheme (Figure 1A), the model provided a better fit to the induced set compared to the uninduced set (R2: 0.973 and 0.962 respectively, Figure 2A and B, Table 2). However, the model poorly fit the 5 min preincubation data points (Figure 2A). The plot suggested a slight lag before inactivation. We hypothesized the lag was due to TAO n-dealkylation and oxidation before nitroso/heme MIC formation. Therefore, we applied a sequential (Seq) kinetic scheme adding inhibitor metabolite formation (M) prior to the metabolic intermediate complexation (E*, Figure 1B). The sequential model generated a better fit for the early preincubation times in RLM compared to the direct MIC formation model (Figure 2C and D), and a slightly better fit overall based on AICc (−DEX Direct: −166.1, Seq: −170.3; +DEX Direct: −116.5, Seq: −118.6).