Blood–brain barrier permeability mechanisms in view of quantitative structure–activity relationships (QSAR)

Buják, Renata; Struck-Lewicka, Wiktoria; Kaliszan, Michał; Kaliszan, Roman; Markuszewski, Michał J.

doi:10.1016/j.jpba.2015.01.046

Cited by 42 publications

(27 citation statements)

References 43 publications

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“…The PLS response plot is presented in Figure 2, which shows the linear regression between the predicted (calculated response) and experimental log BB values of the 23 compounds from Table 3. The residual versus leverage plot in Figure 3 proves that the model that was obtained is valid for the domain in which it was developed [44]. The warning leverage limit (h*) was calculated according to:…”

Section: Chromatographic Resultsmentioning

confidence: 69%

“…Efforts to predict the biological activity (including the BBB permeation) based on the properties of substances led to the development of Quantitative Structure Activity Relationships (QSARs) and Quantitative Retention Activity Relationships (QRARs) methodology [37,[44][45][46][47][48]. Various models and approaches have been developed to predict the penetration of the blood-brain barrier based on various physicochemical properties of molecules, including the lipophilicity, molecular size, polarizability, polar surface area and the number of groups that can establish potential hydrogen bonds [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63].…”

Section: Establishment Of Qsars Modelsmentioning

confidence: 99%

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Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

Janicka

Sztanke

2020

Molecules

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The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = Cblood/Cbrain) values (R2 = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log kw), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R2 > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.

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Section: Chromatographic Resultsmentioning

confidence: 69%

Section: Establishment Of Qsars Modelsmentioning

confidence: 99%

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

Janicka

Sztanke

2020

Molecules

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“…In such cases, data-driven approaches can be useful, because strong mechanistic understanding is not required for these approaches. The relevance of such empirical, data-driven modeling for predicting partitioning into the blood-brain barrier has already been widely demonstrated (13,(16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid

et al. 2015

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Purpose Obtaining pharmacologically relevant exposure levels of antibiotics in the epithelial lining fluid (ELF) is of critical importance to ensure optimal treatment of lung infections. Our objectives were to develop a model for the prediction of the ELF-plasma concentration ratio (EPR) of antibiotics based on their chemical structure descriptors (CSDs). Methods EPR data was obtained by aggregating ELF and plasma concentrations from historical clinical studies investigating antibiotics and associated agents. An elastic net regularized regression model was used to predict EPRs based on a large number of CSDs. The model was tuned using leave-one-drug-out cross validation, and the predictions were further evaluated using a test dataset. Results EPR data of 56 unique compounds was included. A high degree of variability in EPRs both between-and within drugs was apparent. No trends related to study design or pharmacokinetic factors could be identified. The model predicted 80% of the within-drug variability (R 2 WDV ) and 78.6% of drugs were within 3-fold difference from the observations. Key CSDs were related to molecular size and lipophilicity. When predicting EPRs for a test dataset the R 2 WDV was 75%. Conclusions This model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.

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“…k-fold CV, leave-one-out CV (LOOCV) or leave-many-out CV (LMOCV); or a validation using external set of data. The concept of cross-validation technique is based on fitting the model to the objects from n − 1 data samples and evaluating the predictive accuracy on the heldout samples [32]. Overfitting phenomenon is closely related to the cross-validation concept and is observed when the developed model describes well the relationship between predictors and dependent variable, but may not be valid for the prediction of new compounds.…”

Section: Model Evaluationmentioning

confidence: 99%

Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography

Daghir-Wojtkowiak

Wiczling

Bocian

et al. 2015

Journal of Chromatography A

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Blood–brain barrier permeability mechanisms in view of quantitative structure–activity relationships (QSAR)

Cited by 42 publications

References 43 publications

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid

Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography

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