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How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans?

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Abstract

Optimizing the pharmacokinetics (PK) of a drug candidate to support oral dosing is a key challenge in drug development. PK parameters are usually estimated from the concentration–time profile following intravenous administration; however, traditional methods are time-consuming and expensive. In recent years, quantitative structure–pharmacokinetic relationship (QSPKR), an in silico tool that aims to develop a mathematical relationship between the structure of a molecule and its PK properties, has emerged as a useful alternative to experimental testing. Due to the complex nature of the various processes involved in dictating the fate of a drug, the development of adequate QSPKR models that can be used in real-world pre-screening situations has proved challenging. Given the crucial role played by a molecule’s ionization state in determining its PK properties, this work aims to build predictive QSPKR models for PK parameters in humans using an ionization state-based strategy. We divide a high-quality dataset into clusters based on ionization state at physiological pH and build global and ion subset-based ‘local’ models for three major PK parameters: plasma clearance (CL), steady-state volume of distribution (VDss), and half-life (t1/2). We use a robust methodology developed in our lab entitled ‘EigenValue ANalySis’ that accounts for the stereospecificity in drug disposition and use the support vector machine algorithm for model building. Our findings suggest that categorizing compounds in accordance with ionization state does not result in improved QSPKR models. The narrow ranges in the endpoints along with redundancies in the data adversely affect the ion subset-based QSPKR models. We suggest alternative approaches such as elimination route-based models that account for drug–transporter interactions for CL and chemotype-specific QSPKR for VDss.

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Data availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files). Data including molecule names and identifiers, csv files for training and test sets, eigenvalue descriptors, and supplementary figures may be freely accessed via the Mendeley Data Repository (https://doi.org/10.17632/wyknzwphzt.2). A preprint of a previous version of this manuscript has been uploaded on the Chemrxiv preprint server (https://doi.org/10.26434/chemrxiv.13560353.v1).

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Acknowledgements

The authors are thankful to the Department of Biotechnology (DBT), Government of India, for funding manpower and consumables for this research (Order No: BT/PR13600/BID/7/545/2015 dated 29/12/2016). We thank Prof. Kunal Roy, Jadavpur University, for providing access to DTC tools for model validation.

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Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Mumbai, India

    Anish Gomatam, Blessy Joseph, Krishna Iyer & Evans Coutinho

  2. Mumbai Educational Trust’s Institute of Pharmacy, Mumbai, India

    Poonam Advani

  3. Department of Pharmaceutical Chemistry, Vivekanand Education Society’s College of Pharmacy, Mumbai, India

    Mushtaque Shaikh

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  1. Anish Gomatam
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Correspondence to Evans Coutinho.

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Gomatam, A., Joseph, B., Advani, P. et al. How effective are ionization state-based QSPKR models at predicting pharmacokinetic parameters in humans?. Mol Divers 27, 1675–1687 (2023). https://doi.org/10.1007/s11030-022-10520-7

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