Gatarić, Biljana

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Primena tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova: identifikacija, klasifikacija i predviđanje faktora koji utiču na intestinalnu apsorpciju lekovitih supstanci

Gatarić, Biljana

(Универзитет у Београду, Фармацеутски факултет, 2021)

TY  - THES
AU  - Gatarić, Biljana
PY  - 2021
UR  - http://eteze.bg.ac.rs/application/showtheses?thesesId=8455
UR  - https://fedorabg.bg.ac.rs/fedora/get/o:24812/bdef:Content/download
UR  - http://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=49309449
UR  - https://nardus.mpn.gov.rs/handle/123456789/18879
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4038
AB  - Apsorpcija lekovite supstance iz gastrointestinalnog trakta zavisi od brojnih meĎusobno povezanih fizičkohemijskih, fizioloških, biofarmaceutskih i farmaceutsko-tehnoloških faktora. Primena savremenih tehnika za naprednu analizu podataka moţe da doprinese mehanističkom razumevanju fenomena uključenih u apsorpciju lekova i identifikaciji faktora od kojih ona zavisi. Osnovni cilj istraţivanja bio je procena mogućnosti primene različitih tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova kroz razvoj i validaciju modela za predviĎanje permeabilnosti, identifikaciju kritičnih vrednosti faktora od kojih zavisi apsorpcija i mogućnost predviĎanje uticaja hrane. Napredna analiza podataka sprovedena je na uzorku od 128 model supstanci grupisanih u tri seta podataka na osnovu opseţne analize dostupnih informacija o njihovim fizičkohemijskim, biofarmaceutskim i farmakokinetičkim karakteristikama. Primenom hijerarhijskog klasterovanja na glavnim komponentama pokazano je da je permeabilnost najvaţniji faktor za predviĎanje apsorpcije lekovitih supstanci nakon oralne primene. Kao osnov za klasifikaciju identifikovane su dve vrednosti koeficijenta permeabilnosti, 1 × 10-4 i 2,7 × 10-4 cm/s, što ukazuje na ternerni sistem klasifikacije i postojanje posebne grupe lekovitih supstanci sa umerenom permeabilnošću. Primenom algoritma slučajnih šuma razvijen je i validiran model koje je pokazao umerenu sposobnost predviĎanja efekta hrane na apsorpciju (kappa vrednost > 0,4). Četiri lekovite supstance za koje se u literaturi navodi da pokazuju nisku rastvorljivost i nisku permeabilnost (aciklovir, furosemid, valsartan i norfloksacin) su okarakterisane primenom fiziološki zasnovanih farmakokinetičkih modela u kombinaciji sa rezultatima in vitro ispitivanja brzine rastvaranja. Dobijeni rezultati ukazuju da su postojeći kriterijumi na kojima se zasniva procena rastvorljivosti u okviru Biofarmaceutskog sistema klasifikacije previše strogi i da ih je potrebno modifikovati kako bi se uspostavile biorelevantne granične vrednosti i kriterijumi za klasifikaciju lekova.
AB  - The absorption of drugs from gastrointestinal tract depends on a number of interrelated physicochemical, physiological, biopharmaceutical and formulation factors. The application of modern advanced data analysis techniques can contribute to a mechanistic understanding of the phenomena involved in drug absorption and the identification of the factors on which it depends. The main goal of the research was to evaluate the possibility of applying different advanced data analysis techniques in biopharmaceutical characterization of drugs through the development and validation of models for predicting permeability, identification of critical values of factors on which absorption depends and the possibility of predicting food effects. Advanced data analysis was performed on a sample of 128 model drug substances grouped into three data sets based on the extensive analysis of the available data on their physicochemical, biopharmaceutical and pharmacokinetic characteristics. The application of hierarchical clustering on principal components has shown that permeability is the most important factor for predicting the absorption of drugs after oral administration. Two values of the permeability coefficient, 1 × 10-4 and 2.7 × 10-4 cm/s, were identified as the basis for classification, which indicates the ternary classification system and the existence of a separate group of drugs with moderate permeability. Using a random forest algorithm, the model was developed and validated that showed a moderate ability to predict the effect of food on absorption (kappa value > 0.4). Four drugs reported in the literature to show low solubility and low permeability (acyclovir, furosemide, valsartan and norfloxacin) were characterized using physiologically based pharmacokinetic models in combination with the results of in vitro dissolution studies. The obtained results indicate that the existing criteria on which the solubility assessment is based within the Biopharmaceutical Classification System are too conservative and would need to be modified in order to establish biorelevant limit values and criteria for drug classification.
PB  - Универзитет у Београду, Фармацеутски факултет
T2  - Универзитет у Београду
T1  - Primena tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova: identifikacija, klasifikacija i predviđanje faktora koji utiču na intestinalnu apsorpciju lekovitih supstanci
UR  - https://hdl.handle.net/21.15107/rcub_nardus_18879
ER  - 
@phdthesis{
author = "Gatarić, Biljana",
year = "2021",
abstract = "Apsorpcija lekovite supstance iz gastrointestinalnog trakta zavisi od brojnih meĎusobno povezanih fizičkohemijskih, fizioloških, biofarmaceutskih i farmaceutsko-tehnoloških faktora. Primena savremenih tehnika za naprednu analizu podataka moţe da doprinese mehanističkom razumevanju fenomena uključenih u apsorpciju lekova i identifikaciji faktora od kojih ona zavisi. Osnovni cilj istraţivanja bio je procena mogućnosti primene različitih tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova kroz razvoj i validaciju modela za predviĎanje permeabilnosti, identifikaciju kritičnih vrednosti faktora od kojih zavisi apsorpcija i mogućnost predviĎanje uticaja hrane. Napredna analiza podataka sprovedena je na uzorku od 128 model supstanci grupisanih u tri seta podataka na osnovu opseţne analize dostupnih informacija o njihovim fizičkohemijskim, biofarmaceutskim i farmakokinetičkim karakteristikama. Primenom hijerarhijskog klasterovanja na glavnim komponentama pokazano je da je permeabilnost najvaţniji faktor za predviĎanje apsorpcije lekovitih supstanci nakon oralne primene. Kao osnov za klasifikaciju identifikovane su dve vrednosti koeficijenta permeabilnosti, 1 × 10-4 i 2,7 × 10-4 cm/s, što ukazuje na ternerni sistem klasifikacije i postojanje posebne grupe lekovitih supstanci sa umerenom permeabilnošću. Primenom algoritma slučajnih šuma razvijen je i validiran model koje je pokazao umerenu sposobnost predviĎanja efekta hrane na apsorpciju (kappa vrednost > 0,4). Četiri lekovite supstance za koje se u literaturi navodi da pokazuju nisku rastvorljivost i nisku permeabilnost (aciklovir, furosemid, valsartan i norfloksacin) su okarakterisane primenom fiziološki zasnovanih farmakokinetičkih modela u kombinaciji sa rezultatima in vitro ispitivanja brzine rastvaranja. Dobijeni rezultati ukazuju da su postojeći kriterijumi na kojima se zasniva procena rastvorljivosti u okviru Biofarmaceutskog sistema klasifikacije previše strogi i da ih je potrebno modifikovati kako bi se uspostavile biorelevantne granične vrednosti i kriterijumi za klasifikaciju lekova., The absorption of drugs from gastrointestinal tract depends on a number of interrelated physicochemical, physiological, biopharmaceutical and formulation factors. The application of modern advanced data analysis techniques can contribute to a mechanistic understanding of the phenomena involved in drug absorption and the identification of the factors on which it depends. The main goal of the research was to evaluate the possibility of applying different advanced data analysis techniques in biopharmaceutical characterization of drugs through the development and validation of models for predicting permeability, identification of critical values of factors on which absorption depends and the possibility of predicting food effects. Advanced data analysis was performed on a sample of 128 model drug substances grouped into three data sets based on the extensive analysis of the available data on their physicochemical, biopharmaceutical and pharmacokinetic characteristics. The application of hierarchical clustering on principal components has shown that permeability is the most important factor for predicting the absorption of drugs after oral administration. Two values of the permeability coefficient, 1 × 10-4 and 2.7 × 10-4 cm/s, were identified as the basis for classification, which indicates the ternary classification system and the existence of a separate group of drugs with moderate permeability. Using a random forest algorithm, the model was developed and validated that showed a moderate ability to predict the effect of food on absorption (kappa value > 0.4). Four drugs reported in the literature to show low solubility and low permeability (acyclovir, furosemide, valsartan and norfloxacin) were characterized using physiologically based pharmacokinetic models in combination with the results of in vitro dissolution studies. The obtained results indicate that the existing criteria on which the solubility assessment is based within the Biopharmaceutical Classification System are too conservative and would need to be modified in order to establish biorelevant limit values and criteria for drug classification.",
publisher = "Универзитет у Београду, Фармацеутски факултет",
journal = "Универзитет у Београду",
title = "Primena tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova: identifikacija, klasifikacija i predviđanje faktora koji utiču na intestinalnu apsorpciju lekovitih supstanci",
url = "https://hdl.handle.net/21.15107/rcub_nardus_18879"
}
Gatarić, B.. (2021). Primena tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova: identifikacija, klasifikacija i predviđanje faktora koji utiču na intestinalnu apsorpciju lekovitih supstanci. in Универзитет у Београду
Универзитет у Београду, Фармацеутски факултет..
https://hdl.handle.net/21.15107/rcub_nardus_18879
Gatarić B. Primena tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova: identifikacija, klasifikacija i predviđanje faktora koji utiču na intestinalnu apsorpciju lekovitih supstanci. in Универзитет у Београду. 2021;.
https://hdl.handle.net/21.15107/rcub_nardus_18879 .
Gatarić, Biljana, "Primena tehnika za naprednu analizu podataka u biofarmaceutskoj karakterizaciji lekova: identifikacija, klasifikacija i predviđanje faktora koji utiču na intestinalnu apsorpciju lekovitih supstanci" in Универзитет у Београду (2021),
https://hdl.handle.net/21.15107/rcub_nardus_18879 .

An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology

Gatarić, Biljana; Parojčić, Jelena

(Springer International Publishing, 2020)

TY  - JOUR
AU  - Gatarić, Biljana
AU  - Parojčić, Jelena
PY  - 2020
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3488
AB  - Drug absorption is a complex process governed by a number of interrelatedphysicochemical, biopharmaceutical, and pharmacokinetic factors. In order to explore complexrelationships among these factors, multivariate exploratory analysis was performed on thedataset of drugs with diverse bioperformance. The investigated dataset included subset of drugsfor which bioequivalence between solid dosage form and oral solution has been reported, andsubset of drugs described in the literature as low solubility/low permeability compounds.Discriminatory power of hierarchical clustering on principal components was somewhat higherwhen applied on the data subsets of drugs with similar bioperformance, while analysis of theintegrated dataset indicated existence of two groups of drugs with the boundaries reflected in Peffvalue of approximately 2 × 10−4cm/s and Fa and Fm values higher than 85% and 50%,respectively. Majority of the investigated drugs within the integrated dataset were grouped withintheir initial subset indicating that overall drug bioperformance is closely related to itsphysicochemical, biopharmaceutical and pharmacokinetic properties. Classification modelsconstructed using the random forest (RF) and support vector machine with polynomial kernelfunction were able to predict food effect based on drug dose/solubility ratio (D/S), effectivepermeability (Peff), percent of dose metabolized (Fm), and elimination half-life (τ1/2). Althoughboth models performed well during training and testing, only RF kept satisfying performancewhen applied on the external dataset (kappa value > 0.4). The results obtained indicate that datamining can be employed as useful tool in biopharmaceutical drug characterization which meritsfurther investigation
PB  - Springer International Publishing
T2  - APPS Journal
T1  - An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology
VL  - 22
IS  - 1
DO  - 10.1208/s12248-019-0394-y
ER  - 
@article{
author = "Gatarić, Biljana and Parojčić, Jelena",
year = "2020",
abstract = "Drug absorption is a complex process governed by a number of interrelatedphysicochemical, biopharmaceutical, and pharmacokinetic factors. In order to explore complexrelationships among these factors, multivariate exploratory analysis was performed on thedataset of drugs with diverse bioperformance. The investigated dataset included subset of drugsfor which bioequivalence between solid dosage form and oral solution has been reported, andsubset of drugs described in the literature as low solubility/low permeability compounds.Discriminatory power of hierarchical clustering on principal components was somewhat higherwhen applied on the data subsets of drugs with similar bioperformance, while analysis of theintegrated dataset indicated existence of two groups of drugs with the boundaries reflected in Peffvalue of approximately 2 × 10−4cm/s and Fa and Fm values higher than 85% and 50%,respectively. Majority of the investigated drugs within the integrated dataset were grouped withintheir initial subset indicating that overall drug bioperformance is closely related to itsphysicochemical, biopharmaceutical and pharmacokinetic properties. Classification modelsconstructed using the random forest (RF) and support vector machine with polynomial kernelfunction were able to predict food effect based on drug dose/solubility ratio (D/S), effectivepermeability (Peff), percent of dose metabolized (Fm), and elimination half-life (τ1/2). Althoughboth models performed well during training and testing, only RF kept satisfying performancewhen applied on the external dataset (kappa value > 0.4). The results obtained indicate that datamining can be employed as useful tool in biopharmaceutical drug characterization which meritsfurther investigation",
publisher = "Springer International Publishing",
journal = "APPS Journal",
title = "An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology",
volume = "22",
number = "1",
doi = "10.1208/s12248-019-0394-y"
}
Gatarić, B.,& Parojčić, J.. (2020). An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology. in APPS Journal
Springer International Publishing., 22(1).
https://doi.org/10.1208/s12248-019-0394-y
Gatarić B, Parojčić J. An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology. in APPS Journal. 2020;22(1).
doi:10.1208/s12248-019-0394-y .
Gatarić, Biljana, Parojčić, Jelena, "An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology" in APPS Journal, 22, no. 1 (2020),
https://doi.org/10.1208/s12248-019-0394-y . .
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Application of data mining approach to identify drug subclasses based on solubility and permeability

Gatarić, Biljana; Parojčić, Jelena

(Wiley, Hoboken, 2019)

TY  - JOUR
AU  - Gatarić, Biljana
AU  - Parojčić, Jelena
PY  - 2019
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3294
AB  - Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P-eff). The P-eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P-eff over D/S. Two P-eff cut-off values (1 x 10(-4) and 2.7 x 10(-4) cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.
PB  - Wiley, Hoboken
T2  - Biopharmaceutics & Drug Disposition
T1  - Application of data mining approach to identify drug subclasses based on solubility and permeability
VL  - 40
IS  - 2
SP  - 51
EP  - 61
DO  - 10.1002/bdd.2170
ER  - 
@article{
author = "Gatarić, Biljana and Parojčić, Jelena",
year = "2019",
abstract = "Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P-eff). The P-eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P-eff over D/S. Two P-eff cut-off values (1 x 10(-4) and 2.7 x 10(-4) cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.",
publisher = "Wiley, Hoboken",
journal = "Biopharmaceutics & Drug Disposition",
title = "Application of data mining approach to identify drug subclasses based on solubility and permeability",
volume = "40",
number = "2",
pages = "51-61",
doi = "10.1002/bdd.2170"
}
Gatarić, B.,& Parojčić, J.. (2019). Application of data mining approach to identify drug subclasses based on solubility and permeability. in Biopharmaceutics & Drug Disposition
Wiley, Hoboken., 40(2), 51-61.
https://doi.org/10.1002/bdd.2170
Gatarić B, Parojčić J. Application of data mining approach to identify drug subclasses based on solubility and permeability. in Biopharmaceutics & Drug Disposition. 2019;40(2):51-61.
doi:10.1002/bdd.2170 .
Gatarić, Biljana, Parojčić, Jelena, "Application of data mining approach to identify drug subclasses based on solubility and permeability" in Biopharmaceutics & Drug Disposition, 40, no. 2 (2019):51-61,
https://doi.org/10.1002/bdd.2170 . .
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