MATHEMATICAL CORRELATION OF IC50 AND MIC OF HYDROPHOBIC ANTIBIOTICS FOLLOWING COMPETITIVE INHIBITION IN THE DISK DIFFUSION METHOD
DOI:
https://doi.org/10.63456/aamc-2-2-114Keywords:
MIC, , Zone of Inhibition,, Binding affinity, antibiotic resistance, IC50, Competative Inhibition, Disk diffusion.Abstract
Objective: This study explores the mathematical link between binding affinity, drug resistance, IC50, MIC, and Zone of Inhibition (ZOI) in the disc diffusion method for antimicrobial susceptibility testing. We consider ZOI (x) as a response to antibiotic concentration, showing that x is proportional to –log (MIC) and correlated with the binding free energy (exp ΔG). Our findings establish a clear relationship between binding affinity, MIC, ZOI, and antibiotic concentration (c) for hydrophobic antibiotics under competitive inhibition in the disk diffusion method, the value of half Maximal inhibitory concentration IC50 is equal to the twice of minimum inhibitory concentration to the multiple of exponent of fraction of Zone of Inhibition ‘x’ and dissipative term “V” by twice the diffusion term. It will Contributing to a better understanding of resistance mechanisms.
Methods: The mathematical derivations are been validated by the published data available from the journal such as MIC, diffusion constant (D), ZOI (x) and IC50 values and Dissipative Value (V) has been calculated and calculated, IC50 values are compared with reported values.
Result: The data had been validated for ∆G, Dissipative Value (V), IC50 values from predictive to reported it had been observed that while calculating IC50 values of resistance and non-resistance susceptibility, the MIC value for resistance group is more hence the value of IC50 will depreciates and depend more on values of ZOI (x) and V the dissipative term Table 1 revels while analysing the ratio between resistance and non-resistance MIC and IC50 shows direct relationship it can be cross verified by reported and calculated values, Levofloxacin IC50 values ranges from 9.81- 3.2 µg/ml while calculated value is 10-1.6 µg/ml, binding energy ∆G reported is -6.57 and calculated is -7.72 to 7.82 K Cal/mol (Table 1) similarly with ciprofloxacin the value of IC50 for non-resistant to resistant is 3.5- 10 µg/ml where Mic is 0.001-0.5 µg/ml, binding energy reported -7.13 and calculated was 8.37 Kcal/mol. In the case of Tetracycline, the value of reported IC50 was 35- 47 µg/ml and calculated is 54-60 µg/ml and value of reported and calculated ∆G is -12.48 and -7.62 to 7.07K Cal/mol respectively. The reason behind difference of ∆G is, large value of V 4.16 and 2.23 for tetracycline.
Significance: The equations and derivation give better understanding between ZOI (x) and Dissipative Value (V) we can predict the resistance and factors effecting resistance which give low value of ZOI (x) against same concentration of antibiotics against non-resistance bacteria
References
[1] Wiegand, K. Hilpert, and R. E. W. Hancock, ‘Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances’, Nat. Protoc., vol. 3, no. 2, pp. 163–175, Feb. 2008, doi: 10.1038/NPROT.2007.521.
[2] O. Gefen, B. Chekol, J. Strahilevitz, and N. Q. Balaban, ‘TDtest: Easy detection of bacterial tolerance and persistence in clinical isolates by a modified disk-diffusion assay’, Sci. Rep., vol. 7, Feb. 2017, doi: 10.1038/srep41284.
[3] R. N. Jones, A. L. Barry, and C. Thornsberry, ‘Disk agar diffusion susceptibility testing with 30-micrograms ceftazidime disks: confirmation of interpretive breakpoints and quality control guidelines’, J. Clin. Microbiol., vol. 18, no. 1, pp. 211–214, Jul. 1983, doi: 10.1128/jcm.18.1.211-214.1983.
[4] S. M. Abdelaziz, K. M. Aboshanab, I. S. Yahia, M. A. Yassien, and N. A. Hassouna, ‘Correlation between the Antibiotic Resistance Genes and Susceptibility to Antibiotics among the Carbapenem-Resistant Gram-Negative Pathogens’, Antibiotics, vol. 10, no. 3, pp. 1–15, 2021, doi: 10.3390/ANTIBIOTICS10030255.
[5] B. Bonev, J. Hooper, and J. Parisot, ‘Principles of assessing bacterial susceptibility to antibiotics using the agar diffusion method’, J. Antimicrob. Chemother., vol. 61, no. 6, pp. 1295–1301, Jun. 2008, doi: 10.1093/jac/dkn090.
[6] Chopra and M. Roberts, ‘Tetracycline Antibiotics: Mode of Action, Applications, Molecular Biology, and Epidemiology of Bacterial Resistance’, Microbiology and Molecular Biology Reviews, vol. 65, no. 2, pp. 232–260, Jun. 2001, doi: 10.1128/mmbr.65.2.232-260.2001.
[7] K. Kwiecień et al., ‘Insight in Superiority of the Hydrophobized Gentamycin in Terms of Antibiotics Delivery to Bone Tissue’, Int. J. Mol. Sci., vol. 23, no. 20, p. 12077, Oct. 2022, doi: 10.3390/IJMS232012077.
[8] M. Kemme and R. Heinzel-Wieland, ‘Quantitative assessment of antimicrobial activity of PLGA films loaded with 4-hexylresorcinol’, J. Funct. Biomater., vol. 9, no. 1, Jan. 2018, doi: 10.3390/jfb9010004.
[9] P. K. Robinson, ‘Enzymes: principles and biotechnological applications’, Essays Biochem., vol. 59, pp. 1–41, Nov. 2015, doi: 10.1042/BSE0590001.
[10] P. J. F. Henderson, ‘A Linear Equation that Describes the Steady-State Kinetics of Enzymes and Subcellular Particles Interacting with Tightly Bound Inhibitors’, 1972.
[11] O. E. Akanbi, H. A. Njom, J. Fri, A. C. Otigbu, and A. M. Clarke, ‘Antimicrobial Susceptibility of Staphylococcus aureus Isolated from Recreational Waters and Beach Sand in Eastern Cape Province of South Africa’, Int. J. Environ. Res. Public Health, vol. 14, no. 9, Sep. 2017, doi: 10.3390/IJERPH14091001.
[12] B. Kowalska-Krochmal and R. Dudek-Wicher, ‘The Minimum Inhibitory Concentration of Antibiotics: Methods, Interpretation, Clinical Relevance’, Pathogens, vol. 10, no. 2, pp. 1–21, Feb. 2021, doi: 10.3390/PATHOGENS10020165.
[13] U. Ryde and P. Söderhjelm, ‘Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods’, Chem. Rev., vol. 116, no. 9, pp. 5520–5566, May 2016, doi: 10.1021/ACS.CHEMREV.5B00630/ASSET/IMAGES/LARGE/CR-2015-00630W_0007.JPEG.
[14] H. Hata, D. P. Tran, M. M. Sobeh, A. Kitao, and M. M. Sobeh, ‘Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model’, Biophys. Physicobiol., vol. 18, pp. 305–316, 2021, doi: 10.2142/biophysico.bppb-v18.037.
[15] F. Clarelli et al., ‘Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones’, PLoS Comput. Biol., vol. 16, no. 8 August, Aug. 2020, doi: 10.1371/JOURNAL.PCBI.1008106.
[16] H. Yang et al., ‘Accurate quantitative determination of affinity and binding kinetics for tight binding inhibition of xanthine oxidase’, Biomedicine & Pharmacotherapy, vol. 139, p. 111664, Jul. 2021, doi: 10.1016/J.BIOPHA.2021.111664.
[17] C. Yung-Chi and W. H. Prusoff, ‘Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction’, Biochem. Pharmacol., vol. 22, no. 23, pp. 3099–3108, Dec. 1973, doi: 10.1016/0006-2952(73)90196-2.
[18] Veiga et al., ‘Colorimetric microdilution assay: Validation of a standard method for determination of MIC, IC50%, and IC90% of antimicrobial compounds’, J. Microbiol. Methods, vol. 162, pp. 50–61, Jul. 2019, doi: 10.1016/J.MIMET.2019.05.003.
[19] M. Takei, H. Fukuda, R. Kishii, and M. Hosaka, ‘Target preference of 15 quinolones against Staphylococcus aureus, based on antibacterial activities and target inhibition’, Antimicrob. Agents Chemother., vol. 45, no. 12, pp. 3544–3547, 2001, doi: 10.1128/AAC.45.12.3544-3547.2001.
[20] Z. A. Kanafani et al., ‘Molecular characterization and differential effects of levofloxacin and ciprofloxacin on the potential for developing quinolone resistance among clinical Pseudomonas aeruginosa isolates’, Front. Microbiol., vol. 14, 2023, doi: 10.3389/fmicb.2023.1209224.
[21] J. L. Gray et al., ‘Synthesis and biological testing of non-Fluorinated analogues of levofloxacin’, Bioorg. Med. Chem. Lett., vol. 13, no. 14, pp. 2373–2375, 2003, doi: https://doi.org/10.1016/S0960-894X(03)00399-8.
[22] E. Hain, H. Adejumo, B. Anger, J. Orenstein, and L. Blaney, ‘Advances in antimicrobial activity analysis of fluoroquinolone, macrolide, sulfonamide, and tetracycline antibiotics for environmental applications through improved bacteria selection’, J. Hazard. Mater., vol. 415, p. 125686, Aug. 2021, doi: 10.1016/J.JHAZMAT.2021.125686.
[23] J. Song, M.-S. Kook, B.-H. Kim, Y.-I. Jeong, and K.-J. Oh, ‘Ciprofloxacin-Releasing ROS-Sensitive Nanoparticles Composed of Poly(Ethylene Glycol)/Poly(D,L-lactide-co-glycolide) for Antibacterial Treatment’, 2021, doi: 10.3390/ma14154125.
[24] M. Müller, J. E. Weigand, O. Weichenrieder, and B. Suess, ‘Thermodynamic characterization of an engineered tetracycline-binding riboswitch’, Nucleic Acids Res., vol. 34, no. 9, pp. 2607–2617, 2006, doi: 10.1093/nar/gkl347.
[25] J. Park et al., ‘Plasticity, dynamics, and inhibition of emerging tetracycline resistance enzymes’, Nat. Chem. Biol., vol. 13, no. 7, pp. 730–736, Jul. 2017, doi: 10.1038/nchembio.2376.
[26] R. Attaallah and A. Amine, ‘The kinetic and analytical aspects of enzyme competitive inhibition: Sensing of tyrosinase inhibitors’, Biosensors (Basel)., vol. 11, no. 9, Sep. 2021, doi: 10.3390/BIOS11090322.
[27] L. Poghosyan and A. Poghosyan, ‘Asymptotic estimates for the quasi-periodic interpolations’, 2013.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Viqar Agha, Mohammad Asif (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.

Licensing: Creative Commons Attribution 4.0 International License (CC BY 4.0)