Enzyme inhibitor data

Substrate and product inhibitions analyses involved considerations of competitive, uncompetitive, non-competitive and mixed inhibition models. The kinetic studies of the enantiomeric hydrolysis reaction in the membrane reactor included inhibition effects by substrate (ibuprofen ester) and product (2-ethoxyethanol) while varying substrate concentration (5-50 mmol-I ). The initial reaction rate obtained from experimental data was used in the primary (Hanes-Woolf plot) and secondary plots (1/Vmax versus inhibitor concentration), which gave estimates of substrate inhibition (K[s) and product inhibition constants (A jp). The inhibitor constant (K[s or K[v) is a measure of enzyme-inhibitor affinity. It is the dissociation constant of the enzyme-inhibitor complex. 

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To account for differences in the Hill coefficient, enzyme inhibition data are best ht to Equation (5.4) or (5.5). In measuring the concentration-response function for small molecule inhibitors of most target enzymes, one will hnd that the majority of compounds display Hill coefficient close to unity. However, it is not uncommon to hnd examples of individual compounds for which the Hill coefficient is signihcandy greater than or less than unity. When this occurs, the cause of the deviation from expected behavior is often reflective of non-ideal behavior of the compound, rather than a true reflection of some fundamental mechanism of enzyme-inhibitor interactions. Some common causes for such behavior are presented below. 

Figure 7.6 Double reciprocal plot for a tight binding competitive enzyme inhibitor, demonstrating the curvature of such plots. The dashed lines represent an attempt to fit the data at lower substrate concentrations to linear equations. This highlights how double reciprocal plots for tight binding inhibitors can be misleading, especially when data are collected only over a limited range of substrate concentrations.

The great power of mechanistic enzymology in drug discovery is the quantitative nature of the information gleaned from these studies, and the direct utility of this quantitative data in driving compound optimization. For this reason any meaningful description of enzyme-inhibitor interactions must rest on a solid mathematical foundation. Thus, where appropriate, mathematical formulas are presented in each chapter to help the reader understand the concepts and the correct evaluation of the experimental data. To the extent possible, however, I have tried to keep the mathematics to a minimum, and instead have attempted to provide more descriptive accounts of the molecular interactions that drive enzyme-inhibitor interactions. 

For example, experimental data might reveal that a novel enzyme inhibitor causes a concentration-dependent increase in Km, with no effect on and with Lineweaver-Burk plots indicative of competitive inhibition. Flowever, even at very high inhibitor concentrations and very low substrate concentrations, it is observed that the degree of inhibition levels off when some 60% of activity still remains. Furthermore, it has been confirmed that only one enzyme is present, and all appropriate blank rates have been accounted for. It is clear that full competitive inhibition cannot account for such observations because complete inhibition can be attained at infinitely high concentrations of a full competitive inhibitor. Thus, it is likely that the inhibitor binds to the enzyme at an allosteric site. 

When data in the presence of an enzyme inhibitor are presented in the form of a Lineweaver-Burk plot, a series of straight lines should be obtained. The slopes of these hnes may or may not change, and the hnes may or may not intersect at a common point. The relationships between slopes, intersection points, and inhibitor mechanisms are outlined later. Further information regarding these mechanisms, including velocity equations describing data obtained in the presence of inhibitors with diverse mechanisms, can be found in (Segel, 1993). 

More is known as to the pH optimum (usually, pH 4.5-5.5), the requirement for ions, the substrate specificity, and kinetic data on siali-dases, as such results, extensively reviewed,55,"<1, H7 313 can also be obtained by use of crude or partially purified enzymes. The data obtained with impure enzymes must he interpreted with care, as the presence of various sialidases, of other glycosidases modifying the substrate during incubation, or of hypothetical activators and inhibitors may lead to large errors. Correspondingly, such studies require well characterized substrates, and identification of the enzyme reaction-produets. 

ACE Inhibitors in Diabetic Nephropathy Trialist Group Should all patients with type 1 diabetes mellitus and microalbuminuria receive angiotensin-converting enzyme inhibitors A meta-analysis of individual patient data. Ann Intern Med 2001 134 370. 

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