The conserved Lysine69 residue plays a catalytic role in Mycobacterium tuberculosis shikimate dehydrogenase

Application of modified Michaelis - Menten equations for determination of enzyme inducing and inhibiting drugs

BACKGROUND

Pharmacokinetics (PK) is the process of absorption, distribution, metabolism and elimination (ADME) of drugs. Some drugs undergo zero-order kinetics (ethyl alcohol), first order kinetics (piroxicam) and mixed order kinetics (ascorbic acid). Drugs that undergo Michaelis-Menten metabolism are characterized by either increased or decreased metabolism constant (Km) and maximum velocity (Vmax) of enzyme reaction. Hence literatures were searched with a view to translating in vitro-in vivo enzyme kinetics to pharmacokinetic/pharmacodynamic parameters for determination of enzyme inducing and inhibiting drugs, in order to achieve optimal clinical efficacy and safety.

METHODS

A narrative review of retrospective secondary data on drugs, their metabolites, Vmax and Km, generated in the laboratory and clinical environments was adopted, using inclusion and exclusion criteria. Key word search strategy was applied, to assess databases of published articles on enzyme inducing and inhibiting drugs, that obey Michaelis-Menten kinetics. In vitro and in vivo kinetic parameters, such as concentration of substrate, rate of endogenous substrate production, cellular metabolic rate, initial velocity of metabolism, intrinsic clearance, percent saturation and unsaturation of the enzyme substrate, were calculated using original and modified formulas. Years and numbers of searched publications, types of equations and their applications were recorded.

RESULTS

A total of fifty-six formulas both established and modified were applied in the present study. Findings have shown that theophylline, voriconazole, phenytoin, thiopental, fluorouracil, thyamine and thymidine are enzyme inducers whereas, mibefradil, metronidazole, isoniazid and puromicin are enzyme inhibitors. They are metabolized and eliminated according to Michaelis-Menten principle. The order could be mixed but may change to zero or first order, depending on drug concentration, frequency and route of drug administration.

CONCLUSION

Hence, pharmacokinetic-pharmacodynamic translation can be optimally achieved by incorporating, newly modified Michaelis-Menten equations into pharmacokinetic formulas for clinical efficacy and safety of the enzyme inducing and inhibiting therapeutic agents used in laboratory and clinical settings.

Mycobacterium tuberculosis Shikimate Pathway Enzymes as Targets for the Rational Design of Anti-Tuberculosis Drugs

Roughly a third of the world’s population is estimated to have latent Mycobacterium tuberculosis infection, being at risk of developing active tuberculosis (TB) during their lifetime. Given the inefficacy of prophylactic measures and the increase of drug-resistant M. tuberculosis strains, there is a clear and urgent need for the development of new and more efficient chemotherapeutic agents, with selective toxicity, to be implemented on patient treatment. The component enzymes of the shikimate pathway, which is essential in mycobacteria and absent in humans, stand as attractive and potential targets for the development of new drugs to treat TB. This review gives an update on published work on the enzymes of the shikimate pathway and some insight on what can be potentially explored towards selective drug development.

Computational approach identifies protein off-targets for Isoniazid-NAD adduct: hypothesizing a possible drug resistance mechanism in Mycobacterium tuberculosis

Isoniazid is an important antitubercular molecule identified as a drug of choice in tuberculosis treatment. As such, INH is an inactive prodrug; it acquires an active conformation by forming an adduct with NAD. The adduct targets inhA protein, a reductase responsible for fatty acid chain elongation in the cell wall of Mycobacterium tuberculosis. Resistance to INH is majorly contributed by mutations in inhA, katG and geneic and non-geneic regions associated with efflux genes. Despite being widespread, the mechanism of resistance remains unknown in ∼15% of INH-resistant strains. Studies report that an intracellular increase in NADH concentration prevents inhA inhibition, leading to INH resistance. In the pursuit of finding possible resistance mechanisms, we set out to find NAD binding proteins to explore similarities in structure and NAD binding property of these proteins with that of inhA. We identified 172 NAD binding proteins, of which 53 were identified to have sequence or structural similarity to inhA. By performing docking analysis on selected proteins, we identified INH-adduct to have good binding affinity despite very minimal structural similarity to inhA. This analysis was further supported by principal component analysis, which identified 65 proteins with NAD binding conformation similar to that of inhA. These findings prompt us to hypothesize that upon exposure to INH, bacteria tries to reduce inhA susceptibility by inducing expression of these NAD binding proteins through increase in NADH concentration. This in turn favours off-target binding and leads to decreased binding and potency of INH, thus contributing indirectly to INH resistance.Communicated by Ramaswamy H. Sarma.

Simulations of Shikimate Dehydrogenase from Mycobacterium tuberculosis in Complex with 3-Dehydroshikimate and NADPH Suggest Strategies for MtbSDH Inhibition

Shikimate dehydrogenase (SDH) from Mycobacterium tuberculosis ( MtbSDH), encoded by the aroE gene, is essential for viability of M. tuberculosis but absent from humans. Therefore, it is a potentially promising target for antituberculosis agent development. Molecular-level understanding of the interactions of MtbSDH with its 3-dehydroshikimate (DHS) substrate and NADPH cofactor will help in the design of novel and effective MtbSDH inhibitors. However, this is limited by the lack of relevant crystal structures for MtbSDH complexes. Here, molecular dynamics (MD) simulations were performed to generate these MtbSDH complexes and investigate interactions of MtbSDH with substrate and cofactor and the role of MtbSDH dynamics within these. The results indicate that, while structural rearrangements are not necessary for DHS binding, reorientation of individual side chains in the NADPH binding pocket is involved in ternary complex formation. The mechanistic roles for Lys69, Asp105, and Ala213 were investigated by generating Lys69Ala, Asp105Asn, and Ala213Leu mutants in silico and investigating their complexes with DHS and NADPH. Our results show that Lys69 plays a dual role, in positioning NADPH and in catalysis. Asp105 plays a crucial role in positioning both the ε-amino group of Lys69 and nicotinamide ring of NADPH for MtbSDH catalysis but makes no direct contribution to DHS binding. Ala213 is the selection key for NADPH binding with the nicotinamide ring in the proS, rather than proR, conformation in the MtbSDH complex. Our results identify three strategies for MtbSDH inhibition: prevention of MtbSDH binary and ternary complex formation by blocking DHS and NADPH binding (first and second strategies, respectively) and the prevention of MtbSDH complex formation with either DHS or NADPH by blocking both DHS and NADPH binding (third strategy). Further, based on this third strategy, we propose guidelines for the rational design of "hybrid" MtbSDH inhibitors able to bind in both the substrate (DHS) and cofactor (NADPH) pockets, providing a new avenue of exploration in the search for anti-TB therapeutics.

In silico identification of potential inhibitors against shikimate dehydrogenase through virtual screening and toxicity studies for the treatment of tuberculosis

The present study attempts to identify the novel inhibitors of shikimate dehydrogenase (SD), the enzyme that catalyzes the fourth reaction in the shikimate pathway, through virtual screening and toxicity studies. Crystal structure of SD was obtained from Protein Data Bank (PDB ID 4P4G, 1.7 Å) and subjected to energy minimization and structure optimization. A total of 13,803 compounds retrieved from two public databases and used for the virtual screening based on physicochemical properties (Lipinski rule of five) and molecular docking analyses. A total of 26 compounds with good AutoDock binding energies values ranging between - 12.03 and - 8.33 kcal/mol was selected and further filtered for absorption distribution metabolism excretion and toxicity analyses (ADMET). In this, eight compounds were selected, which satisfied all the ADME and toxicity analysis properties. Three compounds with better AutoDock binding energies values (ZINC12135132, - 12.03 kcal/mol; ZINC08951370, - 10.04 kcal/mol; and ZINC14733847, 9.82 kcal/mol) were considered for molecular dynamic (MD) simulation and molecular generalized born surface area (MM-GBSA) analyses. The results of the analyses revealed that the two ligands (ZINC12135132 and ZINC08951370) had better inhibitory activities within their complexes, after the 50-ns MD simulation, which suggested that the complexes formed stable conformation. It is noteworthy that compounds identified by docking, MD simulation, and MM-GBSA methods could be a drug for tuberculosis which required further experimental validation.

The shikimate dehydrogenase family: functional diversity within a conserved structural and mechanistic framework

Shikimate dehydrogenase (SDH) catalyzes the NADPH-dependent reduction of 3-deydroshikimate to shikimate, an essential reaction in the biosynthesis of the aromatic amino acids and a large number of other secondary metabolites in plants and microbes. The indispensible nature of this enzyme makes it a potential target for herbicides and antimicrobials. SDH is the archetypal member of a large protein family, which contains at least four additional functional classes with diverse metabolic roles. The different members of the SDH family share a highly similar three-dimensional structure and utilize a conserved catalytic mechanism, but exhibit distinct substrate preferences, making the family a particularly interesting system for studying modes of substrate recognition used by enzymes. Here, we review our current understanding of the biochemical and structural properties of each of the five previously identified SDH family functional classes.