Several approaches for determining an enzyme’s kinetic parameter K_m (Michaelis constant) from progress curves have been developed in recent decades. In the present article, we compare different approaches on a set of experimental measurements of lactonase activity of paraoxonase 1 (PON1): (1) a differential-equation-based Michaelis–Menten (MM) reaction model in the program Dynafit; (2) an integrated MM rate equation, based on an approximation of the Lambert W function, in the program GraphPad Prism; (3) various techniques based on initial rates; and (4) the novel program “iFIT”, based on a method that removes data points outside the area of maximum curvature from the progress curve, before analysis with the integrated MM rate equation. We concluded that the integrated MM rate equation alone does not determine kinetic parameters precisely enough; however, when coupled with a method that removes data points (e.g., iFIT), it is highly precise. The results of iFIT are comparable to the results of Dynafit and outperform those of the approach with initial rates or with fitting the entire progress curve in GraphPad Prism; however, iFIT is simpler to use and does not require inputting a reaction mechanism. Removing unnecessary points from progress curves and focusing on the area around the maximum curvature is highly advised for all researchers determining K_m values from progress curves.

Cognitive impairment is a common non-motor symptom of Parkinson’s disease (PD), which often progresses to PD dementia. PD patients with and without dementia may differ in certain biochemical parameters, which could thus be used as biomarkers for PD dementia. The enzyme paraoxonase 1 (PON1) has previously been investigated as a potential biomarker in the context of other types of dementia. In a cohort of PD patients, we compared a group of 89 patients with cognitive impairment with a group of 118 patients with normal cognition. We determined the kinetic parameters K_m and V_max for PON1 for the reaction with dihydrocoumarin and the genotype of four single nucleotide polymorphisms in PON1. We found that no genotype or kinetic parameter correlated significantly with cognitive impairment in PD patients. However, we observed associations between PON1 rs662 and PON1 K_m (p < 10⁻¹⁰), between PON1 rs662 and PON1 V_max (p = 9.33 × 10⁻⁷), and between PON1 rs705379 and PON1 V_max (p = 2.21 × 10⁻¹⁰). The present study is novel in three main aspects. (1) It is the first study to investigate associations between the PON1 genotype and enzyme kinetics in a large number of subjects. (2) It is the first study to report kinetic parameters of PON1 in a large number of subjects and to use time-concentration progress curves instead of initial velocities to determine K_m and V_max in a clinical context. (3) It is also the first study to calculate enzyme-kinetic parameters in a clinical context with a new algorithm for data point removal from progress curves, dubbed iFIT. Although our results suggest that in the context of PD, there is no clinically useful correlation between cognitive status on the one hand and PON1 genetic and enzyme-kinetic parameters on the other hand, this should not discourage future investigation into PON1’s potential associations with other types of dementia.

The area where progress curve exhibits maximum curvature contains the most information about kinetic parameters. To determine these parameters more accurately from progress curves, we propose an iterative approach that calculates the area of maximum curvature based on an estimate of kinetic parameters and then recalculates the parameters based on time-concentration data points within this area. Based on this algorithm, we developed a computer script called iFIT as a free web application at http://www.i-fit.si. The benefits of working with iFIT are that it decreases the importance of initial substrate concentration and the impact of certain side reactions on the final calculated kinetic parameters.