Ryan Walsh

The failure of modern enzyme inhibition drug theory stems from the derivation of the inhibition equations. These equations do not recognize the difference between the chemical equilibrium, that defines the formation of the enzyme-inhibitor complex, and the effect an inhibitor has when it binds to the enzyme. Chemical equilibriums can only be used to determine the concentration of components found in a sample, but the traditional inhibition equations define inhibition with the enzyme-inhibitor binding equilibrium, destroying their usefulness in characterizing inhibition. This problem stems from the form the inhibition term takes in these equations. The traditional Inhibition term, when it was derived, was left in an arrangement that obscures its only function as a chemical equilibrium term. However, rearranging the inhibition term to establish its link to common binding curves allows us to analyse how this inhibition term affects the classic inhibition equations, pointing the way to correcting and simplifying the modeling of drug interactions. Here we will look at the derivations of biological binding curves and enzyme inhibition equations noting the forms chemical equilibrium terms take in biological equations. This will demonstrate the traditional inhibition term is just an inverted binding curve and thus a chemical equilibrium, which provides no information about the effect produced by the formation of the enzyme-inhibitor complex. From this analysis we will look at the simple alternative for these inhibition equations that clearly distinguishes between the chemical equilibrium and the effect inhibitor binding produces resulting in a single equation that defines inhibition and activation.

Alzheimer's disease is a neurodegenerative disorder characterized by the loss of memory and cognitive decline. Although the underlying mechanisms that cause Alzheimer's disease remain unknown, research suggests that dysregulation of processes related to neuronal growth and development may play a critical role. This dysregulation leads to a phenomenon called neuronal pruning, a developmental apoptotic process that shapes neural pathways by removing neurons that fail to form viable synaptic connections. While the accumulation of β-amyloid plaques and neurofibrillary tangles contributes to the progression of the disease, the processes that produce these disease markers, specifically the overproduction of amyloid precursor protein and microtubule growth through hyperphosphorylation of tau, also suggest a developmental basis for the disease. This developmental link also coincides with the dual role of acetylcholine, which acts as both a neurotransmitter and a regulator of growth and development. Therefore, it is postulated that risk factors for Alzheimer’s disease contribute to a hypocholinergic status, upsetting neuronal homeostasis, and ultimately resulting in a cascade of programmed neural death similar to developmental neuronal pruning. This theory has significant implications for understanding the initial stages of Alzheimer's disease and may provide a basis for improving preventative and therapeutic interventions.

Substrate modulation is the modification of an enzyme’s activity either through inhibition or activation by its substrate. Substrate modulation plays key roles in the functioning of complex biological systems, such as the regulation of acetylcholine, where the two cholinesterases with opposite substrate regulatory mechanisms exist to control acetylcholine’s concentration and distribution throughout the body. The importance of these systems is generally ignored in therapeutic development due to a lack of appropriate models for inhibitor interactions with enzymes that are modulated by their substrate. Here we will examine the most prevalent equation used to describe substrate modulation and by rearranging it we will demonstrate the ease with which it can be modified to model inhibitor or activator effects. In so doing we will establish an easy method for deriving the rearranged equation, providing an expandable framework for deriving equations with unlimited substrate modulation terms.

Characterization of enzyme inhibition in drug development is usually limited to a basic analysis with the classical inhibition models or simply the use of IC50 values. However, a better understanding of enzyme physiology and regulation is seen as key to unraveling and treating the processes associated with stubborn disease targets like Alzheimer’s disease. Recently it has been shown that enzyme regulation, through substrate, inhibitor or activator interactions can be modeled using the summation of binding curves. Here we examine the use of the modular equation permutations, that can be produced through binding curve summation, to fit and evaluate the interactions of abietic acid with protein tyrosine phosphatase nonreceptor type 11. This new sort of analysis will allow for improved insight into the physiological role enzymes play and the consequence their modulation may have in disease progression.

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1 + ([I]∕K_i)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1+([I]/K_i)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

The classical way of defining enzyme inhibition has obscured the distinction between inhibitory effect and the inhibitor binding constant. This article examines the relationship between the simple binding curve used to define biomolecular interactions and the standard inhibitory term (1+([I]/K_i)). By understanding how this term relates to binding curves which are ubiquitously used to describe biological processes, a modifier equation which distinguishes between inhibitor binding and the inhibitory effect, is examined. This modifier equation which can describe both activation and inhibition is compared to standard inhibitory equations with the development of global data fitting templates in Excel and via the global fitting of these equations to simulated and previously published datasets. In both cases, this modifier equation was able to match or outperform the other equations by providing superior fits to the datasets. The ability of this single equation to outperform the other equations suggests an over-complication of the field. This equation and the template developed in this article should prove to be useful tools in the study of enzyme inhibition and activation.

Mature granules and flocs from aerobic sludge were collected from the wastewater treatment plant (WWTP) treating both municipal and industrial effluents in Haining city China. This plant has been operating under conditions favoring aerobic granular sludge formation, for over 3 years, suggesting that the granules, which stably retained under changing effluent conditions, may contain unique microbial populations. Microbial analysis indicated that the granular sludge was primarily composed of Planctomycetes, Proteobacteria and Bacteroidetes from the bacterial phyla. Interestingly, microbial communities were also observed to be stratified between the structural features of the sludge. For example, Euryarchaeota was found to make up the majority of the archaea found in the granules while Methanosaeta was dominant in the flocs. Additionally, granules were found to contain, 34 phyla and 222 genera of bacteria, 4 phyla and 13 genera of fungi, and 2 phyla and 17 genera of archaea. While flocs contained, 32 phyla and 203 genera of bacteria, 6 phyla and 26 genera of fungi, and 2 phyla and 12 genera of archaea. This biodiversity signifying a preservation of bacterial and archaeal population in granules, and fungal populations in flocs may result from the sedimentary characteristics of the granules. This suggests microbes uniquely associated in the granules are playing a key role in structure formation and stability of the granular ecosystem, which is maintained by the longer sludge retention time.