1. Microbiological assays are mostly conducted in the laboratory to check the bacterial growth inhibition in the presence of different drug concentration. This can be performed in a plates containing growth medium. ‘Zone of equivalence’ is found within the plates, which represents the concentration at which a particular microbial growth is inhibited by particular drug (Zuccheri and Asproulis, 2012). Various factors may affect the results. These are improper preparation of growth medium, improper selection of the population number, imbalanced moisture, pH, nutrient content of the growth medium. These errors can be minimized by maintaining proper sterile condition during growth medium preparation, correct selection of drug concentration.
2. LogP and logs coefficients are distinguished as the most important parameters for lipophilicity assessment, estimation of chemical compounds solubility and determination of pharmacokinetic properties. Tendency of molecules to get dissolve in lipids is generally calculated as logP. Tendency of molecules to get dissolve in water lipids is generally calculated as logS (Sun, 2004). So, when a drug molecule is developed, it is very much essential to understand the basic property of that particular molecule, whether that molecule will be taken up by the human system and distributed properly within a certain period of time, hence quantification of logP and logs is very much significant.
3. Prediction of absorption, distribution, metabolism and excretion and potential drug toxicity initially during the drug development process is important and the related research focus should be made to maximize the effectiveness of the candidate molecule. This is because it helps in predicting enzyme inhibition, such as: CYP3A4 Noninhibitor, CYP2D6 Noninhibitor, CYP2C19 Noninhibitor, CYP2C9 Noninhibitor, CYP1A2 Inhibitor. Fluvoxamine is a strong CYP1A2 Inhibitor and inhibits some of the cytochrome P450 enzymes, like: CYP3A4, CYP2C9 etc. Thus, fluvoxamine prevents few metabolic pathways from recompensing for CYP1A2 inhibition. This type of enzymatic inhibition or non-inhibition can be predicted with the help of screening tools, which is considered as advantage. Disadvantages may include unrestrained adsorption methods or matrix trapping or covalent bound or immobilization methods, low quantity enzymatic activity along with the limitation of mass transfer. ADME properties include absorption, distribution, metabolism and excretion and potential drug toxicity and these properties determine the drug disposition once a drug enters into the system. Total polar surface area or TPSA of a candidate molecule is identified as the sum of the surface over polar atoms, for example: nitrogen, oxygen, including attached hydrogens (Chatwal, 2010). TPSA is used to optimize the ability of a candidate drug to permeate cells. Molecules with more than 140Å squared are likely to be poor at cell membrane permeation. Lipinski’s rule of five evaluates drug-likeliness and find out whether a chemical compound with particular biological or pharmacological activity has characteristic properties, which would make the compound an expected orally active medicine in humans. Mostly orally consumed drugs are comparatively small and lipophilic molecules. The advantage of Lipinski’s rule of five is that it illustrates the significance of certain molecular properties for a particular drug pharmacokinetics inside the human system. This includes absorption, distribution, metabolism and excretion. The major disadvantage of this rule is that it does not calculate whether the candidate molecule is active pharmacologically. To predict a candidate molecule, the atomic environment in the candidate molecule is calculated and then database is searched for comparable surroundings. Occurrence ratio measures how often the similar surrounding has been discovered, compared with how frequently it has been found out in total, and then is calculated for individual atom in the candidate molecule (Carlsson et al., 2010). This ratio present comparative probability of metabolism taking place at individual atom but the disadvantage is that it does not predict whether the candidate molecule undergoes metabolism.
Carlsson, L., Spjuth, O., Adams, S., Glen, R. and Boyer, S. (2010). Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse.BMC Bioinformatics, 11(1), p.362.
Chatwal, G. (2010). Medicinal chemistry. Mumbai [India]: Himalaya Pub. House.
Sun, H. (2004). A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption. Journal of Chemical Information and Modeling, 44(2), pp.748-757.
Zuccheri, G. and Asproulis, N. (2012). Detection of pathogens in water using micro and nano-technology. London: IWA Publishing.
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