Prodrugs for Masking the Bitter Taste of Drugs

Karaman, Rafik

doi:10.5772/58404

Cited by 8 publications

(4 citation statements)

References 144 publications

(180 reference statements)

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“…Also, another active compound performing a pharmaceutical action can be linked, forming a mutual prodrug which simultaneously releases two active molecules after transformation [ 3 ]. Development of prodrugs is a way of addressing issues met during the early stages of API development such as low solubility [ 4 ], chemical instability [ 5 ], and bad taste [ 6 ]; also, changes of pharmacokinetic properties may be achieved, including permeation through the gastrointestinal tract [ 7 ], blood–brain penetration [ 8 ], modification of therapeutic activity time [ 9 ], and pharmacodynamic properties, including toxicity reduction of some antitumor drugs [ 10 ] and improvement of therapeutic index [ 11 ]. Development of prodrugs by linking with lipophilic moiety may have a negative impact on solubility compared to the parent molecule.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis

et al. 2019

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The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, including cefuroxime axetil, cefetamet pivoxil, and pivampicillin, as well as CDs, including αCD, βCD, γCD, hydroxypropyl-αCD, methyl-βCD, hydroxypropyl-βCD, and hydroxypropyl-γCD, were prepared in all combinations. Thermograms confirming the formation of cyclodextrin complexes were obtained using differential scanning calorimetry. Transmission Fourier-transform infrared (tFTIR) and complementary attenuated total reflectance FTIR (ATR) coupled with machine learning were techniques chosen as a nondestructive alternative. The machine learning algorithm was used to determine the formation of complexes in samples using solely their tFTIR and ATR spectra at the prediction stage. Parameterized method 7 (PM7) was used to support the analysis by molecular modeling of the complexes. The model developed through machine learning properly distinguished samples with formed complexes form noncomplexed samples with a cross-validation accuracy of 90.4%. Analysis of the contribution of spectral bands to the model indicated interactions of ester groups of β-lactam antibiotics with CDs, as well as some interactions of cephem ring in cefetamet pivoxil and penam moiety in pivampicillin. Molecular modeling with PM7 helped to explain experimental results and allowed to propose possible binding modes.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis

et al. 2019

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“…Therefore, two new novel approaches have been designed (1) bitterless prodrugs that attach a promoiety to the active drug to bind to the bitter taste receptor and (2) bitter taste antagonists, which can be achieved by modifications to the structure and size of the bitter compound. Computational methods were used to investigate several intramolecular processes for the design of an efficient chemical device to be linked to a bitter drug such that the resulting prodrug cannot bind to the active site of the bitter taste receptor (thus masking the bitter taste), and upon passing from the oral cavity to the stomach, it interconverts to the bitter parent drug [ 139 , 148 , 149 ].…”

Section: The Prodrug Approachmentioning

confidence: 99%

Enzyme Models—From Catalysis to Prodrugs

Breijyeh

2021

Molecules

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Enzymes are highly specific biological catalysts that accelerate the rate of chemical reactions within the cell. Our knowledge of how enzymes work remains incomplete. Computational methodologies such as molecular mechanics (MM) and quantum mechanical (QM) methods play an important role in elucidating the detailed mechanisms of enzymatic reactions where experimental research measurements are not possible. Theories invoked by a variety of scientists indicate that enzymes work as structural scaffolds that serve to bring together and orient the reactants so that the reaction can proceed with minimum energy. Enzyme models can be utilized for mimicking enzyme catalysis and the development of novel prodrugs. Prodrugs are used to enhance the pharmacokinetics of drugs; classical prodrug approaches focus on alternating the physicochemical properties, while chemical modern approaches are based on the knowledge gained from the chemistry of enzyme models and correlations between experimental and calculated rate values of intramolecular processes (enzyme models). A large number of prodrugs have been designed and developed to improve the effectiveness and pharmacokinetics of commonly used drugs, such as anti-Parkinson (dopamine), antiviral (acyclovir), antimalarial (atovaquone), anticancer (azanucleosides), antifibrinolytic (tranexamic acid), antihyperlipidemia (statins), vasoconstrictors (phenylephrine), antihypertension (atenolol), antibacterial agents (amoxicillin, cephalexin, and cefuroxime axetil), paracetamol, and guaifenesin. This article describes the works done on enzyme models and the computational methods used to understand enzyme catalysis and to help in the development of efficient prodrugs.

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“…Remarkably, prodrugs are rapidly evolving from something serendipitously discovered in the past to something rational and well-planned nowadays. This is reflected in the growing percentage of prodrugs that are approved by drug agencies year after year . Over the last decade, prodrugs represent 12% of new therapeutical compounds approved by the Food and Drug Administration (FDA), though this number is expected to grow exponentially.…”

Section: Introductionmentioning

confidence: 99%

Adenosine-Dependent Activation Mechanism of Prodrugs Targeting an Aminoacyl-tRNA Synthetase

et al. 2023

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Prodrugs have little or no pharmacological activity and are converted to active drugs in the body by enzymes, metabolic reactions, or through human-controlled actions. However, prodrugs promoting their chemical bioconversion without any of these processes have not been reported before. Here, we present an enzyme-independent prodrug activation mechanism by boron-based compounds (benzoxaboroles) targeting leucyl-tRNA synthetase (LeuRS), including an antibiotic that recently has completed phase II clinical trials to cure tuberculosis. We combine nuclear magnetic resonance spectroscopy and X-ray crystallography with isothermal titration calorimetry to show that these benzoxaboroles do not bind directly to their drug target LeuRS, instead they are prodrugs that activate their bioconversion by forming a highly specific and reversible LeuRS inhibition adduct with ATP, AMP, or the terminal adenosine of the tRNALeu. We demonstrate how the oxaborole group of the prodrugs cyclizes with the adenosine ribose at physiological concentrations to form the active molecule. This bioconversion mechanism explains the remarkably good druglike properties of benzoxaboroles showing efficacy against radically different human pathogens and fully explains the mechanism of action of these compounds. Thus, this adenosine-dependent activation mechanism represents a novel concept in prodrug chemistry that can be applied to improve the solubility, permeability and metabolic stability of challenging drugs.

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Prodrugs for Masking the Bitter Taste of Drugs

Cited by 8 publications

References 144 publications

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis

Enzyme Models—From Catalysis to Prodrugs

Adenosine-Dependent Activation Mechanism of Prodrugs Targeting an Aminoacyl-tRNA Synthetase

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