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Predicting the Drug Clearance Pathway with Structural Descriptors

  • Original Research Article
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European Journal of Drug Metabolism and Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background and Objective

The clearance, by renal elimination or hepatic metabolism, is one of the most important pharmacokinetic parameters of a drug. It allows the half-life, bioavailability, and drug–drug interactions to be predicted, and it can also affect the dose regimen of a drug. Predicting the clearance pathways of new chemical candidates during drug development is vital in order to minimize the risks of possible side effects and drug interactions. Many in vivo methods have been established to predict drug clearance in humans, and these mainly rely on data from in vivo studies in preclinical species—mainly rats, dogs, and monkeys. They are also time consuming and expensive. The aim of this study was to find the relationship between structural parameters of drugs and their clearance pathways.

Methods

The clearance pathway of each drug was obtained from the literature. Various structural descriptors [Abraham solvation parameters, topological polar surface area, numbers of hydrogen-bond donors and acceptors, number of rotatable bonds, molecular weight, logarithm of the partition coefficient (logP), and logarithm of the distribution coefficient at pH 7.4 (logD7.4)] were applied to develop a mechanistic model for predicting clearance pathways.

Results

The results of this study indicate that compounds with logD7.4 > 1 or with zero or one hydrogen-bond donor undergo hepatic metabolism, whereas the clearance pathway for chemicals with logD7.4 < − 2 is renal elimination. Furthermore, models established using logistic regression based on five structural parameters for compounds with – 2 < logD7.4 < 1 could be used in a clearance pathway prediction tool. The overall prediction accuracies of the first and second models were 84.8% and 84.4%, respectively.

Conclusion

The developed model can be used to find the clearance pathways of new drug candidates with acceptable accuracy. The main descriptors that are used to evaluate this parameter are the hydrophobicity and the number of hydrogen-bonding functional groups of the compound.

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Authors and Affiliations

  1. Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran

    Navid Kaboudi

  2. Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Navid Kaboudi

  3. Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Ali Shayanfar

  4. Editorial Office of Pharmaceutical Sciences Journal, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran

    Ali Shayanfar

Authors
  1. Navid Kaboudi
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Corresponding author

Correspondence to Ali Shayanfar.

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Funding

The research reported in this publication was supported by the Elite Researcher Grant Committee under award number 4000425 from the National Institute for Medical Research Development (NIMAD), Tehran, Iran.

Conflict of interest

Navid Kaboudi and Ali Shayanfar have no conflict of interest.

Ethical approval

The study was approved by the research ethics committees of the National Institute for Medical Research Development, Tehran, Iran (IR.NIMAD.REC.1400.037).

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Not applicable.

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Not applicable.

Availability of data and material

All data are available as a supplementary file (Table S1) on the journal’s website along with the published article.

Code availability

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Author contributions

Navid Kaboudi: data collection, data analysis and interpretation, drafting the article; Ali Shayanfar: design of the work, supervision of the project, critical revision of the article. All authors read and approved the final manuscript.

Supplementary Information

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Supplementary file 1 (PDF 820 KB)

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Kaboudi, N., Shayanfar, A. Predicting the Drug Clearance Pathway with Structural Descriptors. Eur J Drug Metab Pharmacokinet 47, 363–369 (2022). https://doi.org/10.1007/s13318-021-00748-3

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  • DOI: https://doi.org/10.1007/s13318-021-00748-3

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