1
|
Passarin PBS, Lourenço FR. Enhancing analytical development in the pharmaceutical industry: A DoE-QSRR model for virtual Method Operable Design Region assessment. J Pharm Biomed Anal 2024; 239:115907. [PMID: 38103415 DOI: 10.1016/j.jpba.2023.115907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/17/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
Recently, the pharmaceutical industry has increasingly adopted the Analytical Quality by Design (AQbD) approach for analytical development. To facilitate AQbD approach implementation in the development of chromatographic methods for determining cephalosporin antibiotics, an in silico tool capable of performing virtual DoEs was developed enabling to obtain virtual operable regions of method. To this end, the drugs cephalexin, cefazolin, cefotaxime and ceftriaxone were analyzed using four experimental designs, deriving a DoE-QSRR model and employing Monte Carlo method. The DoE-QSRR model and virtual DoEs were validated using data not used in model's construction, obtaining coefficients of determination of 84.72 % for DoE-QSRR model and over 77 % for virtual DoEs. Virtual MODRs were constructed using data from the virtual DoEs. The virtual MODRs were validated by comparing them with experimental MODRs under various scenarios, with overlap areas reaching values exceeding 84 %. Therefore, the in silico tool was considered suitable for indicating analyte trends under different analytical conditions, being capable of performing virtual DoEs for cephalosporin drugs with sufficient assertiveness to guide analytical development and allow obtaining a MODR capable of providing results of adequate quality.
Collapse
Affiliation(s)
- Paula Beatriz Silva Passarin
- Faculty of Pharmaceutical Sciences, Department of Pharmacy, University of São Paulo, Avenida Professor Lineu Prestes 508, Butantan, São Paulo, SP, Brazil
| | - Felipe Rebello Lourenço
- Faculty of Pharmaceutical Sciences, Department of Pharmacy, University of São Paulo, Avenida Professor Lineu Prestes 508, Butantan, São Paulo, SP, Brazil.
| |
Collapse
|
2
|
Kumari P, Van Laethem T, Duroux D, Fillet M, Hubert P, Sacré PY, Hubert C. A multi-target QSRR approach to model retention times of small molecules in RPLC. J Pharm Biomed Anal 2023; 236:115690. [PMID: 37688907 DOI: 10.1016/j.jpba.2023.115690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/11/2023]
Abstract
Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).
Collapse
Affiliation(s)
- Priyanka Kumari
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium; Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium.
| | - Thomas Van Laethem
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium; Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Diane Duroux
- ETH AI Center, OAT X11, Andreasstrasse 5, 8092 Zürich
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Phillipe Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Pierre-Yves Sacré
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Cédric Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium.
| |
Collapse
|