Dataset Information

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping.

ABSTRACT: Our work proved that processing trajectory could effectively obtain a more reliable and robust quantitative model compared with the step-by-step optimization method. The use of systematic tracking was investigated as a tool to optimize modeling parameters including calibration method, spectral pretreatment and variable selection latent factors. The variable was selected by interval partial least-squares (iPLS), backward interval partial least-square (BiPLS) and synergy interval partial least-squares (SiPLS). The models were established by Partial least squares (PLS) and Bagging-PLS. The model performance was assessed by using the root mean square errors of validation (RMSEP) and the ratio of standard error of prediction to standard deviation (RPD). The proposed procedure was used to develop the models for near infrared (NIR) datasets of active pharmaceutical ingredients in tablets and chlorogenic acid of Lonicera japonica solution in ethanol precipitation process. The results demonstrated the processing trajectory has great advantages and feasibility in the development and optimization of multivariate calibration models as well as the effectiveness of bagging model and variable selection to improve prediction accuracy and robustness.

SUBMITTER: Zhao N

PROVIDER: S-EPMC6043861 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping.

Zhao Na N Ma Lijuan L Huang Xingguo X Liu Xiaona X Qiao Yanjiang Y Wu Zhisheng Z

Frontiers in chemistry 20180706

Our work proved that processing trajectory could effectively obtain a more reliable and robust quantitative model compared with the step-by-step optimization method. The use of systematic tracking was investigated as a tool to optimize modeling parameters including calibration method, spectral pretreatment and variable selection latent factors. The variable was selected by interval partial least-squares (iPLS), backward interval partial least-square (BiPLS) and synergy interval partial least-squ ...[more]

PMID: 30035108

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Project description:BackgroundEmergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation.MethodsWith this in mind, this study developed two-dimensional-quantitative structure-activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers.ResultsFive two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in "ortho" position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity.ConclusionsThe model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.

Dataset Information

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping.

Publications

Pharmaceutical Analysis Model Robustness From Bagging-PLS and PLS Using Systematic Tracking Mapping.

Similar Datasets

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