• RADITYA ISWANDANA Department of Pharmaceutics and Pharmaceutical Technology, Laboratory of Pharmaceutical Technology and Formulation, Faculty of Pharmacy, Universitas Indonesia, Depok, 16424, Indonesia.
  • PERMATA AISYAH Department of Biomedical Computation, Laboratory of Biomedical Computation, Faculty of Pharmacy, Universitas Indonesia, Depok, 16424, Indonesia.
  • REZI RIADHI SYAHDI Department of Biomedical Computation, Laboratory of Biomedical Computation, Faculty of Pharmacy, Universitas Indonesia, Depok, 16424, Indonesia.



Absorption percentage, Absorption, distribution, metabolism, and excretion prediction, In silico, Oral systemic drugs, Physicochemical parameters, Pharmacokinetic parameters


Objective: This research aims to observe the pharmacokinetic parameters that can be predicted using a software, discover the best software to predict
pharmacokinetic properties, and analyze the correlation between pharmacokinetic parameters used as descriptors with absorption percentage
(%ABS) from references.
Methods: This research was conducted using Molinspiration, QikProp, admetSAR, SwissADME, Chemicalize, and pkCSM software. This research
analyzed 34 oral systemic drug compounds for absorption rate and six descriptors comprising molecular weight (MW), logP, hydrogen bond acceptor
(HBA), hydrogen bond donor (HBD), polar surface area (PSA), and pKa.
Results: SwissADME showed the most accurate prediction of MW, logP, and HBD. Chemicalize showed the most accurate prediction of HBA, PSA, and
pKa. Further, admetSAR showed the most accurate prediction of Caco-2 permeability. The highest R value was obtained from the correlation between
%ABS with Caco-2 permeability on 34 drug compounds (R=0.8211).
Conclusion: The highest R value was obtained from the correlation between %ABS with Caco2 permeability on 34 drug compounds (R=0.8211),
which showed a significant relationship (*p<0.001). This indicates that oral systemic drugs are affected by Caco-2 permeability. Moreover, the result of this research can be considered for the development of oral systemic drugs.


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Engman H. Intestinal barriers to oral drug absorption. Analysis


Song NN, Zhang S, Liu C. Overview of factors affecting oral drug

absorption. Asian J Drug Metab Pharmacokinet 2004;4:167-76.

Shargel L, Wu-Pong S, Yu A. Applied Biopharmaceutics and

Pharmacokinetics. Annals of Internal Medicine. 5th ed., Vol. 94.

New York: McGraw Hill; 2004. p. 826.

Beringer P. Remington: The Science and Practice of Pharmacy.

Philadelphia, PA: Lippincott Williams and Wilkins; 2011. p. 675.

Shekhawat PB, Pokharkar VB. Understanding peroral absorption:

Regulatory aspects and contemporary approaches to tackling solubility

and permeability hurdles. Acta Pharm Sin B 2017;7:260-80.

Manallack DT. The pK(a) distribution of drugs: Application to drug

discovery. Perspect Medicin Chem 2007;1:25-38.

Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. AdmetSAR:

A comprehensive source and free tool for assessment of chemical

ADMET properties. J Chem Inf Model 2012;52:3099-105.

Daina A, Michielin O, Zoete V. SwissADME: A free web tool to

evaluate pharmacokinetics, drug-likeness and medicinal chemistry

friendliness of small molecules. Sci Rep 2017;7:42717.

QikProp Ver. 3.5. New York: Schrödinger LLC; 2012. p. 6.

Available from:


Swain M. J Chem Inf Model 2012;52:613-5.

Pires DE, Blundell TL, Ascher DB. PkCSM: Predicting small-molecule

predicting small-molecule pharmacokinetic and toxicity properties

using graph-based signatures. J Med Chem 2015;58:4066-72.

Calculation of Molecular Properties and Bioactivity Score; 2018.

Available from:

[Last accessed on 2020 Mar 27].

Zhao YH, Abraham MH, Le J, Hersey A, Luscombe CN, Beck G, et al.

Rate-limited steps of human oral absorption and QSAR studies. Pharm

Res 2002;19:1446-57.

Lau ET, Giddings SJ, Mohammed SG, Dubois P, Johnson SK,

Stanley RA, et al. Encapsulation of hydrocortisone and mesalazine in

zein microparticles. Pharmaceutics 2013;5:277-93.

Takahashi K, Sakano H, Rytting JH, Numata N, Kuroda S, Mizuno N.

Influence of pH on the permeability of p-toluidine and aminopyrine

through shed snake skin as a model membrane. Drug Dev Ind Pharm


Fang B, Li P, Shi X, Chen F, Wang L. Incompatibilities of lornoxicam

with 4 antiemetic medications in polyole fi n bags during simulated

intravenous administration. Medicine (Baltimore) 2016;95:1-5.

Avdeef A, Berger CM. pH-metric solubility. 3. Dissolution titration

template method for solubility determination. Eur J Pharm Sci


Rodríguez-Barrientos D, Rojas-Hernández A, Gutiérrez A, Moya-

Hernández R, Gómez-Balderas R, Ramírez-Silva MT. Determination of

pKa values of tenoxicam from 1H NMR chemical shifts and of oxicams

from electrophoretic mobilities (CZE) with the aid of programs SQUAD

and HYPNMR. Talanta 2009;80:754-62.

Oliveira ÉD, Azevedo RD, Bonfilio R, De Oliveira DB,

Ribeiro GP, De Araújo MB. Dissolution test optimization for meloxicam

in the tablet pharmaceutical form. Braz J Pharm Sci 2009;45:67-73.

Juranic I, Dzeletovic D, Jovanovic J. Protolytic constants of nizatidine,

ranitidine and N, N ’-DIMETHYL-2-nitro-1, 1-ethenediamine.

Spectrophotometric and theoretical investigation. J Pharm Biomed

Anal 2015;15:1-18.

Ahmadi F, Karamian E. Computational aided-molecular imprinted

polymer design for solid phase extraction of metaproterenol from

plasma and determination by voltammetry using modified carbon

nanotube electrode. Iran J Pharm Res 2014;13:417-29.

Sheshala R. Validated high performance liquid chromatography

(HPLC) method for the determination of sumatriptan in rabbit plasma:

Application to pharmacokinetic study. Afr J Pharm Pharmacol


Nidhi K, Indrajeet S, Khushboo M, Gauri K, Sen DJ.

Hydrotropy: A promising tool for solubility enhancement: A review.

Int J Drug Dev Res 2011;3:26-33.

Gao H, Yao L, Mathieu HW, Zhang Y, Maurer TS, Troutman MD, et al.

In silico modeling of nonspecific binding to human liver microsomes.

Pharmacology 2008;36:2130-5.

Al-Deen AA, Dayo A, Ghoto MA, Arain MI, Parveen AQ. In vitro study

of stability, quality and quantity of some clinically and non-clinically

used cortisones from pharmaceutical preparations. Int J Biol Pharm

Allied Sci 2014;3:2720-33.

Du-Cuny L. Aqueous Solubility of Drug-like Compounds (Doctoral

Dissertation, Universitäts-und Landesbibliothek Bonn). Univ

Darmstadt; 2006.

Ahmad T. Modeling of ibuprofen II: Effect of pH on the adsorption

behavior on reversed phase liquid chromatography. Int J Appl Sci

Technol 2012;2:49-56.

Fillet M, Bechet I, Piette V, Crommen J. Separation of nonsteroidal

anti-inflammatory drugs by capillary electrophoresis using nonaqueous

electrolytes. Electrophoresis 1999;20:1907-15.

Comer JE, Manallack D. Ionization constants and ionization profiles.

Ref Modul Chem Mol Sci Chem Eng 2014; 8:357-97.

Raval G. Thermodynamic and Spectroscopic Studies on the Molecular

Interaction of Doxorubicin (DOX) with Negatively Charged Polymeric

Nanoparticles. University Toronto Master’s Theses; 2012. p. 65.

Margalit E, Kugler LJ, Brumm MV, Meza JL, Kompella UB,

Escobar ER, et al. The safety of intraocular ketorolac in rabbits.

Investig Ophthalmol Vis Sci 2006;47:2093-9.

Roche VF. The chemically elegant proton pump inhibitors. Am J Pharm

Educ 2006;70:101.

Yamashita F, Wanchana S, Hashida M. Quantitative structure/property

relationship analysis of Caco-2 permeability using a genetic algorithmbased

partial least squares method. Kyoto Univ 2002;91:2230-9.

Castillo-Garit JA, Marrero-Ponce Y, Torrens F. Estimation of ADME

properties in drug discovery: Predicting Caco-2 cell permeability using

atom-based stochastic and non-stochastic linear indices. Int Electron

Conf Synth Org Chem 2007;97:1-30.

Asuero AG, Sayago A, Gonz´alez AG. The correlation coefficient : An

overview. The correlation coefficient : An overview. J Crit Rev Anal

Chem 2006;36:41-59.

Settimo L, Bellman K, Knegtel RM. Comparison of the accuracy of

experimental and predicted pKa values of basic and acidic compounds.

Springer Sci 2013;31:86-8.

Lee AC, Crippen GM. Predicting pKa. Univ Michigan 2013;49:2013-33.

Frimayanti N, Yam ML, Lee HB, Othman R, Zain SM, Rahman NA.

Validation of quantitative structure activity relationship (QSAR) model

for photosensitizer activity prediction. Int J Mol Sci 2011;12:8626-44.

Alexander DL, Tropsha A, Winkler DA. Beware of R2: Simple,

unambiguous assessment of the prediction accuracy of QSAR and

QSPR models. J Chem Inf Model 2015;55:1316-22.



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