Lipophilicity descriptors correlate uniquely with pharmacokinetic and blood-brain barrier penetration parameters for selected antipsychotic drugs
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Keywords

lipophilicity,
hydrophobicity
biomimetic
pharmacokinetics
permeability
antipsychotics

How to Cite

Idowu, S., Adeyemo, M., & Balogun, F. (2021). Lipophilicity descriptors correlate uniquely with pharmacokinetic and blood-brain barrier penetration parameters for selected antipsychotic drugs. African Journal of Biomedical Research, 24(2), 173–179. Retrieved from http://ojshostng.com/index.php/ajbr/article/view/1685

Abstract

Lipophilicity is an important physicochemical parameter of biological relevance; although its in- vivo predictive capability is dependent on accuracy and reliability of platforms used for its determination. This work examines biomimetic attribute of isocratic chromatographic hydrophobicity index (ICHI), experimental logarithm of octanol – water partition coefficient (LogP) and some computed lipophilicity indices for eight (8) selected antipsychotic agents and their predictive capability in drug discovery. The retention behavior of 5 first-generation and 3 second-generation antipsychotics was determined on reversed-phase chromatographic platform using methanol-phosphate buffer (pH 6.8) mobile phase. The retardation factor obtained was transformed to Rm, and plotted against volume fraction of organic modifier in the mobile phase to generate linear graph whose x- intercept is ICHI. Experimental LogP values were curled from literature while computed LogP were obtained using respective software. The experimentally determined LogPoctanol/water and ICHI were first correlated with index  of  brain  permeability  (BBB);  before  all  lipophilicity indices were comparatively evaluated and correlated with in-vivo-normalized pharmacokinetic parameters curled from literature. ICHI gave better correlation with BBB index (r = 0.976) compared to Log Poctanol/water (r = 0.557). Comparative lipophilicity evaluation shows clustered pattern for second generation antipsychotics compared to first generation. In vivo correlation was poorer for the 8 drugs (r < 0.7), better with subset of phenothiazine homologues (r = 0.51 to 0.97). The ALogP, LogPoctanol/water, cLogP and ICHI gave highest correlation with the pharmacokinetic parameters. The biomimetic attributes of ICHI is better than for LogPoctanol/water in predicting brain permeability,  but lower for in-vivo pharmacokinetic prediction.

 

 

 

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References

Ballas C. and Dinges D. F. (2009) Stimulant and Wake-Promoting Substances In: L. R. Squire

(Eds.) Encyclopedia of Neuroscience pp. 419 – 424 United State: Academic Press Available online at: https://doi.org/10.1016/B978-008045046-9.0051-6

Bhosale S.H., Kanhed A.M., Dash R.C., Suryawanshi M.R. and Mahadik K.R. (2014) Design, Synthesis, Pharma-cological Evaluation and Computational Studies of 1-(Biphenyl-4-yl)-2-[4- (substituted phenyl)-piperazin-1-yl] Etha- nones as Potential Antipsychotics European Journal of Medicinal Chemistry, 74, 358 - 365 Available online at: http://dx.doi.org/10.1016/j.ejmech.2013.12.043

Chavda V. P., Shah D., Tandel H. and Soniwala M. (2016) In Vitro-In Vivo correlation (IVIVC): A strategic tool in drug product development Research and Reviews: A Journal of Drug Formulation, Development and Production 3(3), 31 – 54

Constantinescu T., Lungu C. N. and Lung I. (2019) Lipophilicity as a central component of

Drug-like properties of Chalchones and Flavonoid derivatives Molecules 24(1505), 1 – 11 https://doi.org/10.3390/molecules24081505

Dambolena J. S., Zunino M. P., Herrera J. M., Pizzolitto R. P., Areco V. A. and Zygadlo J. A. (2016) Terpenes: Natural Products for Controlling Insects of Importance to Human Health – A Structure-Activity Relationship Study Psyche 2016, 1 – 17 Available online at:

http://dx.doi.org/10.1155/2016/4595823

Giaginis C., Tsopelas F., Tsantili-Kakoulidou A. (2018) The impact of lipophilicity in drug discovery: Rapid measurements by means of Reversed-Phase HPLC Methods in Molecular Biology 1824, 217 – 228.

Gleeson M. P., Leeson P. D., van der Waterbeemd H. (2015) Physicochemical properties and compound quality In: The handbook of Medicinal Chemistry. pp 1 – 31 x The Royal Society of Chemistry Publications: London.

Global Disease Burden (2017) Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic

analysis for the Global Burden of Disease Study 2016 Global Health Metrics 390(10100), P1211

– 1259 Available online at: https://doi.org/10.1016/S0140-6736(17)32154-2

Gomeni R., Fang L. L., Bressolie-Gomeni F., Spencer T. J., Faraone S. V. and Babiskin A. (2019) A general framework for assessing in vitro/In vivo correlation as a tool for maximizing the benefit-risk ratio of a treatment using a convolution-based modeling approach. CPT Pharmacometrics and System Pharmacology 8, 97 – 106 Available online at:

https://doi.org/10.1002/psp4.12378

Hau K. M., Connell D. W. and Richardson B. J. (1999) Quantitative Structure Activity relationships for Nasal pungency thresholds of volatile organic compounds Toxicological Sciences 47, 93 - 98

Hill A. P. and Young R. J. (2010) Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity Drug Discov. Today 15, 648 – 655

Hopkins, A. L., Keseru, G. M., Leeson, P. D., Rees, D. C. and Reynolds, C. H. (2014) The role of Ligand Efficiency Measures in Drug Discovery Nat. Rev. Drug Discov.13(2), 105 – 121

Available online at: https://doi.org/10.1038/nrd4163

Leeson P.D. and Young, R. J. (2015) Molecular Property Design: Does Everyone Get It? ACS Med. Chem. Lett. 6 (2015), 722-725.

Lobo S. (2020) Is there enough focus on lipophilicity in drug discovery? Expert Opin. Drug

Discov. 15(3), 261 – 263 Available online at: http://doi.org/10.1080/17460441.2020.1691995

Loscher W. (2005) Blood-brain barrier active efflux transporters: ATP-binding cassette gene

family NeuroRX 2, 86 – 98 http://dx.doi.org/10.1602/neurorx.2.1.86

Marta T., Eva R., Xian W., Monica B., Biuse G., Abel E., et al. (2014) The blood-brain barrier: Structure, function and therapeutic approaches to cross it Molecular Membrane Biology 31(5):

– 167 Available online at: https://doi.org/10.3109/09687688.2014.937468

Meanwell N. A. (2011) Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety Chem. Res. Toxicol. 24,

– 1456

Meanwell N. A. (2016) Improving drug design: An update on Recent Applications of Efficiency metrics, strategies for replacing problematic elements, and compounds in nontraditional drug space Chem. Res. Toxicol. 29(4), 564 – 616 Available online at: https://doi.org/10.1016/S1567-

(00)80015-7

Mikitsh J. L. and Chacko A. (2014) Pathways for small molecule delivery to the Central Nervous

System across the blood-brain barrier Perspectives in Medicinal Chemistry 6, 11–24 Available online at: https://doi.org/10.4137/PMC.S13384

Morak-Mlodawska B., Pluta K and Jelen M. (2020) Evaluation of the lipophilicity of new anticancer 1,2,3-Triazole-dipyridothiazine hybrids using RPTLC and different computational

methods Processes 8(858), 1 – 11 Available online at: https://doi.org/10.3390/pr8070858

Nielsen R. E. and Nielsen J. (2009) Antipsychotic Drug Treatment for Patients with schizophrenia: Theoretical Background, Clinical Considerations and Patient Preferences

Available online at: http://doi.org/10.4137/CMT.S2175

Paul A. (2019) Drug Absorption and Bioavailability. In: Raj G., Raveendran R. (eds) Introduction to Basics of Pharmacology and Toxicology. Springer Publishers: Singapore.

https://doi.org/10.1007/978-981-32-9779-1_5

Peruskovic D. S., Stevanovic N. R., Lolic A. D., Nikolic M. R. and Baosic R. M. (2014) Quantitative Structure-Activity Relationship study of some antipsychotics by Multiple Linear Regression Am. J. Analyt Chem. 5, 335 – 342 Available online at:

http://dx.doi.org/10.4236/ajac.2014.55041

Šegan S., Božinovi´c N., Opsenica I., Andri´c F. (2017) Consensus-based comparison of chromatographic and computationally estimated lipophilicity of benzothiepino[3,2-c]pyridine derivatives as potential antifungal drugs Journal of Separation Science 40, 2089–2096.

Souza E. S., Zaramello L., Kuhnen C. A. and Junkes B. D. (2011) Coefficient for aliphatic organic compounds using semi-empirical electrotopological index Int. J. Mol. Sci. 12(10), 7250

– 7264 Available online at: https://doi.org/10.3390/ijms12107250

Tsopelas F., Giaginis C., Tsantili-Kakoulidou A. (2017) Lipophilicity and biomimetic properties to support drug discovery Expert Opin. Discov. 12(9), 885 – 896.

Valko K., Bevan C. and Reynolds D. (1997) Chromatographic Hydrophobicity Index by Fast- gradient RP-HPLC: A high-throughput alternative to log P/log D Analytical Chemistry 69, 2022

– 2029

Valko K., Teague S. and Pidgeon C. (2017) In vitro membrane binding and protein binding

(IAM MB/PB technology) to estimate in vivo distribution: applications in early drug discovery

ADMET 5(1), 14 - 38 Available online at: https://doi.org/10.5599/admet.5.1.373

Xuefeng L., Gaoyong Z., Jinfeng D., Xiaohai Z., Xiaoci Y. and Mingdao L. (2006) Correlation of critical micelle concentration of sodium alkyl benzenesulfonates with molecular descriptors. Wuhan University Journal of Natural Science 11, 409 – 414 Available online at:

https://doi.org/10.1007/BF02832133

Young R. J., Green D. V., Luscombe C. N. and Hill A. P. (2011) Getting physical in drug discovery II: the impact of chromatographic hydrophobicity measurements and aromaticity Drug Discov. Today 16, 822 – 830

Varshney A., Sen P., Ahmad E., Rehan M., Subbarao N. and Khan R. H. (2010) Ligand Binding Strategies of Human Serum Albumin: How can the cargo be utilized? Chirality 22, 77 – 87 http://dx.doi/10.1002/chir.20709

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