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ARTICLES

Prediction of liquid chromatography retention times of erectile dysfunction drugs and analogues using chemometric approaches

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  • Narendrabhai, D.; Li, L.; Kee, C.; Ge, X.; Low, M.; Koh, H. Screening of Synthetic PDE-5 Inhibitors and Their Analogues as Adulterants: Analytical Techniques and Challenges. J. Pharm. Biomed. Anal. 2014, 87, 176–190.
  • Vickers, M. A.; Satyanarayana, R. Phosphodiesterase Type 5 Inhibitors for the Treatment of Erectile Dysfunction in Patients with Diabetes Mellitus. Int. J. Impot. Res. 2002, 14, 466–471.
  • Shindel, A. W. Update on Phosphodiesterase Type 5 Inhibitor Therapy Part 2: Updates on Optimal Utilization for Sexual Concerns and Rare Toxicities in this Class (CME). J. Sex Med. 2009, 6, 2352–2364.
  • Gur, S.; Kadowitz, P. J.; Gokce, A.; Sikka, S. C.; Lokman, U.; Hellstrom, W. J. G. Update on Drug Interactions with Phosphodiesterase-5 Inhibitors Prescribed as First-Line Therapy for Patients with Erectile Dysfunction or Pulmonary Hypertension. Curr. Drug Metab. 2013, 14, 265–269.
  • Venhuis, B. J.; de Kaste, D. Towards a Decade of Detecting New Analogues of Sildenafil, Tadalafil and Vardenafil in Food Supplements: A History, Analytical Aspects and Health Risks. J. Pharm. Biomed. Anal. 2012, 69, 196–208.
  • http://www.businesswire.com/news/home/20170127005600/en/World-Erectile-Dysfunction-Drugs-Market---Opportunities
  • Singh, S.; Prasad, B.; Savaliya, A. A.; Shah, R. P.; Gohil, V. M.; Kaur, A. Strategies for Characterizing Sildenafil, Vardenafil, Tadalafil and their Analogues in Herbal Dietary Supplements, and Detecting Counterfeit Products Containing these Drugs. TrAC - Trends Anal. Chem. 2009, 28, 13–28.
  • Lozv, M.; Zeng, Y.; Li, L.; Bloodworth, R. L. B. Safety and Quality Assessment of 175 Illegal Sexual Enhancement Products Seized in Red-Light Districts in Singapore. Drug Saf. 2009, 32, 1141–1146.
  • Alp, M.; Coşkun, M.; Göker, H. Isolation and Identification of a New Sildenafil Analogue Adulterated in Energy Drink: Propoxyphenyl Sildenafil. J. Pharm. Biomed. Anal. 2013, 72, 155–158.
  • Li, L.; Low, M. Y.; Ge, X.; Bloodworth, B. C.; Koh, H. L. Isolation and Structural Elucidation of a New Sildenafil Analogue from a Functional Coffee. Anal. Bioanal. Chem. 2013, 405, 4443–4450.
  • Calahan, J.; Howard, D.; Almalki, A. J.; Gupta, M. P.; Calderón, A. I. Chemical Adulterants in Herbal Medicinal Products: A Review. Planta Med. 2016, 82, 505–515.
  • Patel, D. N.; Low, W. L.; Tan, L. L.; Tan, M. M. B.; Zhang, Q.; Low, M. Y.; Chan, C. L.; Koh, H. L. Adverse Events Associated with the Use of Complementary Medicine and Health Supplements: An Analysis of Reports in the Singapore Pharmacovigilance Database from 1998 to 2009. Clin. Toxicol. (Phila.) 2012, 50, 481–489.
  • Liu, S. Y.; Woo, S. O.; Koh, H. L. HPLC and GC–MS Screening of Chinese Proprietary Medicine for Undeclared Therapeutic Substances. J. Pharm. Biomed. Anal. 2001, 24, 983–992.
  • Dumestre-Toulet, V.; Cirimele, V.; Gromb, S.; Belooussoff, T.; Lavault, D.; Ludes, B.; Kintz, P. Last Performance with VIAGRA: Post-Mortem Identification of Sildenafil and its Metabolites in Biological Specimens Including Hair Sample. Forensic Sci. Int. 2002, 126, 71–76.
  • Lee, J.; Yoo, H. H.; Kang, M. Y.; Kim, D. H. Low-Energy Collision-Induced Dissociation of Sildenafil Thiono Analogues: Gas-Phase Intramolecular Nucleophilic Substitution Through Ion-Neutral Complexes Between a Cationic Substrate and a Thione-Containing Neutral Nucleophile. Rapid Commun. Mass Spectrom. 2005, 19, 1767–1770.
  • Zou, P.; Oh, S. S.; Hou, P.; Low, M. Y.; Koh, H. L. Simultaneous Determination of Synthetic Phosphodiesterase-5 Inhibitors Found in a Dietary Supplement and Pre-Mixed Bulk Powders for Dietary Supplements Using High-Performance Liquid Chromatography with Diode Array Detection and Liquid Chromatography–Electrospray Ionization–Tandem Mass Spectrometry. J. Chromatogr. A 2006, 1104, 113–122.
  • Trefi, S.; Routaboul, C.; Hamieh, S.; Gilard, V.; Malet-Martino, M.; Martino, R. Analysis of Illegally Manufactured Formulations of Tadalafil (Cialis®) by 1H NMR, 2D DOSY 1H NMR and Raman Spectroscopy. J. Pharm. Biomed. Anal. 2008, 47, 103–113.
  • de Veij, M.; Deneckere, A.; Vandenabeele, P.; de Kaste, D.; Moens, L. Detection of Counterfeit Viagra with Raman Spectroscopy. J. Pharm. Biomed. Anal. 2008, 46, 303–309.
  • Ng, C. S.; Law, T. Y.; Cheung, Y. K.; Ng, P. C.; Choi, K. K. Development of a Screening Method for the Detection of Analogues of Sildenafil and Vardenafil by the Use of Liquid Chromatograph Coupled with Triple Quadrupole Linear Ion Trap Mass Spectrometer. Anal. Methods 2010, 2, 890–896.
  • Man, C. N.; Nor, N. M.; Lajis, R.; Harn, G. L. Identification of Sildenafil, Tadalafil and Vardenafil by Gas Chromatography–Mass Spectrometry on Short Capillary Column. J. Chromatogr. A 2009, 1216, 8426–8430.
  • Song, F.; El-Demerdash, A.; Lee, S. J. Screening for Multiple Phosphodiesterase Type 5 Inhibitor Drugs in Dietary Supplement Materials by Flow Injection Mass Spectrometry and their Quantification by Liquid Chromatography–Tandem Mass Spectrometry. J. Pharm. Biomed. Anal. 2012, 70, 40–46.
  • Lee, E. S.; Lee, J. H.; Han, K. M.; Kim, J. W.; Hwang, I. S.; Cho, S.; Han, S. Y.; Kim, J. Simultaneous Determination of 38 Phosphodiestrase-5 Inhibitors in Illicit Erectile Dysfunction Products by Liquid Chromatography–Electrospray Ionization–Tandem Mass Spectrometry. J. Pharm. Biomed. Anal. 2013, 83, 171–178.
  • Lee, J. H.; Kim, N. S.; Han, K. M.; Kim, S. H.; Cho, S.; Kim, W. S. Monitoring by LC–MS/MS of 48 Compounds of Sildenafil, Tadalafil, Vardenafil and their Analogues in Illicit Health Food Products in the Korean Market Advertised as Enhancing Male Sexual Performance. Food Addit. Contam. A Chem. Anal. Control Expo Risk Assess 2013, 30, 1849–1857.
  • Gratz, S. R.; Gamble, B. M.; Flurer, R. A. Accurate Mass Measurement using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for Structure Elucidation of Designer Drug Analogs of Tadalafil, Vardenafil and Sildenafil in Herbal and Pharmaceutical Matrices. Rapid Commun. Mass Spectrom. 2006, 20, 2317–2327.
  • Patterson, R.; Mabe, P.; Mitchell, E. N.; Cory, W. Lifestyle Illicit Drug Seizures: A Routine ESI–LC–MS Method for the Identification of Sildenafil and Vardenafil. Forensic Sci. Int. 2012, 222, 83–88.
  • Lee, E. S.; Kim, J. W.; Lee, J. H.; Han, K. M.; Cho, S.; Hwang, I.; Han, S. Y.; Chae, K.; Kim, J. Identification of a New Tadalafil Analogue Found in a Dietary Supplement. Food Addit. Contam. A Chem. Anal. Control Expo Risk Assess 2013, 30, 621–626.
  • Jakab, A.; Schubert, G.; Prodan, M.; Forgács, E. Determination of the Retention Behavior of Barbituric Acid Derivatives in Reversed-Phase High-Performance Liquid Chromatography by using Quantitative Structure–Retention Relationships. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2002, 770, 227–236.
  • Alves de Lima Ribeiro, F.; Ferreira, M. M. C. QSPR Models of boiling Point, Octanol–Water Partition Coefficient and Retention Time Index of Polycyclic Aromatic Hydrocarbons. J. Mol. Struct. Theochem. 2003, 663, 109–126.
  • Petritis, K.; Kangas, L. J.; Yan, B.; Monroe, M. E.; Strittmatter, E. F.; Qian, W. J.; Adkins, J. N.; Moore, R. J.; Xu, Y.; Lipton, M. S.; Camp, D. G.; Smith, R. D. Improved Peptide Elution Time Prediction for Reversed-Phase Liquid Chromatography–MS by Incorporating Peptide Sequence Information. Anal. Chem. 2006, 78, 5026–5039.
  • Kaliszan, R. QSRR: Quantitative Structure–(Chromatographic) Retention Relationships. Chem. Rev. 2007, 107, 3212–3246.
  • Quiming, N. S.; Denola, N. L.; Samsuri, S. R. B.; Saito, Y.; Jinno, K. Development of Retention Prediction Models for Adrenoreceptor Agonists and Antagonists on a polyvinyl Alcohol-Bonded Stationary Phase in Hydrophilic Interaction Chromatography. J. Sep. Sci. 2008, 31, 1537–1549.
  • Ghasemi, J.; Saaidpour, S. QSRR Prediction of the Chromatographic Retention Behavior of Painkiller Drugs. J. Chromatogr. Sci. 2009, 47, 156–163.
  • Ghasemi, J. B.; Ahmadi, S.; Brown, S. D. A Quantitative Structure–Retention Relationship Study for Prediction of Chromatographic Relative Retention Time of Chlorinated Monoterpenes. Environ. Chem. Lett. 2011, 9, 87–96.
  • Akbar, J.; Iqbal, S.; Batool, F.; Karim, A.; Chan, K. Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A quantitative Structure–Retention Relationship (QSRR) Approach. Int. J. Mol. Sci. 2012, 13, 15387–15400.
  • Goryński, K.; Bojko, B.; Nowaczyk, A.; Buciński, A.; Pawliszyn, J.; Kaliszan, R. Quantitative Structure–Retention Relationships Models for Prediction of High Performance Liquid Chromatography Retention Time of Small Molecules: Endogenous Metabolites and Banned Compounds. Anal. Chim. Acta 2013, 797, 13–19.
  • Noorizadeh, H.; Farmany, A.; Narimani, H.; Noorizadeh, M. QSRR using Evolved Artificial Neural Network for 52 Common Pharmaceuticals and Drugs of Abuse in Hair from UPLC–TOF–MS. Drug Test Anal. 2013, 5, 320–324.
  • Carlucci, G.; D’Archivio, A. A.; Maggi, M. A.; Mazzeo, P.; Ruggieri, F. Investigation of Retention Behaviour of Non-Steroidal Anti-Inflammatory Drugs in High-Performance Liquid Chromatography by using Quantitative Structure–Retention Relationships. Anal. Chim. Acta 2007, 601, 68–76.
  • Ghasemi, J.; Ahmadi, S.; Torkestani, K. Simultaneous Determination of Copper, Nickel, Cobalt and Zinc using Zincon as a Metallochromic Indicator with Partial Least Squares. Anal. Chim. Acta 2003, 487, 181–188.
  • Ghasemi, J.; Saaidpour, S.; Brown, S. D. QSPR Study for Estimation of Acidity Constants of Some Aromatic Acids Derivatives using Multiple Linear Regression (MLR) Analysis. J. Mol. Struct. Theochem. 2007, 805, 27–32.
  • Leardi, R.; Lupiáñez González, A. Genetic Algorithms Applied to Feature Selection in PLS Regression: How and When to Use Them. Chemom. Intell. Lab Syst. 1998, 41, 195–207.
  • Niculescu, S. P. Artificial Neural Networks and Genetic Algorithms in QSAR. J. Mol. Struct. Theochem. 2003, 622, 71–83.
  • Malenović, A.; Jančić-Stojanović, B.; Kostić, N.; Ivanović, D.; Medenica, M. Optimization of Artificial Neural Networks for Modeling of Atorvastatin and its Impurities Retention in Micellar Liquid Chromatography. Chromatographia 2011, 73, 993–998.
  • Deconinck, E.; Sacré, P. Y.; Coomans, D.; De Beer, J. Classification Trees Based on Infrared Spectroscopic Data to Discriminate Between Genuine and Counterfeit Medicines. J. Pharm. Biomed. Anal. 2012, 57, 68–75.
  • Deconinck, E.; Sacré, P. Y.; Courselle, P.; de Beer, J. O. Chemometrics and Chromatographic Fingerprints to Discriminate and Classify Counterfeit Medicines Containing PDE-5 Inhibitors. Talanta 2012, 100, 123–133.
  • Virtual Computational Chemistry Laboratory. http://www.vcclab.org/VCCLAB.
  • Tetko, I. V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V. A.; Radchenko, E. V.; Zefirov, N. S.; Makarenko, A. S.; Tanchuk, V. Y.; Prokopenko, V. V. Virtual Computational Chemistry Laboratory—Design and Description. J. Comput. Aided Mol. Des. 2005, 19, 453–463.
  • Gaussian 03, Revision A.1, Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A. Jr.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. G.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; and Pople, J. A.. 2003, Gaussian, Inc., Pittsburgh PA.
  • Rinnan, A.; Christensen, N. J.; Engelsen, S. B. How the Energy Evaluation Method Used in the Geometry Optimization Step Affect the Quality of the Subsequent QSAR/QSPR Models. J. Comput. Aided Mol. Des. 2010, 24, 17–22.
  • Leardi, R.; Boggia, R.; Terrile, M. Genetic Algorithms as a Strategy for Feature Selection. J. Chemometr. 1992, 6, 267–281.
  • Hunger, J.; Huttner, G. Optimization and Analysis of Force Field Parameters by Combination of Genetic Algorithms and Neural Networks. J. Comput. Chem. 1999, 20, 455–471.
  • Xu, L.; Zhang, W. J. Comparison of Different Methods for Variable Selection. Anal. Chim. Acta 2001, 446, 475–481.
  • Roy, K. QSAR Tools. http://dtclab.webs.com/software-tools.
  • Reddy, A. S.; Kumar, S.; Garg, R. Hybrid-Genetic Algorithm Based Descriptor Optimization and QSAR Models for Predicting the Biological Activity of Tipranavir Analogs for HIV Protease Inhibition. J. Mol. Graph Model 2010, 28, 852–862.
  • Saíz-Urra, L.; González, M. P.; Teijeira, M. 2D-Autocorrelation Descriptors for Predicting Cytotoxicity of Naphthoquinone Ester Derivatives Against Oral Human Epidermoid Carcinoma. Bioorg. Med. Chem. 2007, 15, 3565–3571.
  • Todeschini, R.; Viviana, C. Molecular Descriptors for Chemoinformatics. 2nd ed. Weinheim, Germany: Wiley-VCH, 2010.
  • Pearlman, R. S.; Smith, K. M. Metric Validation and the Receptor-Relevant Subspace Concept. J. Chem. Inf. Comput. Sci. 1999, 39, 28–35.
  • Dehmer, M.; Emmert-Streib, F.; Tripathi, S. Large-Scale Evaluation of Molecular Descriptors by Means of Clustering. Plos One 2013, 8, e83956.
  • Todeschini, R.; Consonni, V. VIII. 2. Descriptors from Molecular Geometry. In: Handbook of Chemoinformatics: From Data to Knowledge; Gasteiger, J., Ed.; Erlangen, Germany: University of Erlangen-Nürnberg, 2003; Vol. 4, 1004–1033 pp.
  • Hong, H.; Slavov, S.; Ge, W.; Qian, F.; Su, Z.; Fang, H.; Cheng, Y.; Perkins, R.; Shi, L.; Tong, W. 2011. Mold2 Molecular Descriptors for QSAR. In: Statistical Modelling of Molecular Descriptors in QSAR/QSPR; Varmuza, K., Bonchev, D., Gasteiger, H. A., Eds.; Weinheim, Germany: Wiley-Blackwell, Chapter 3.
  • Gramatica, P. WHIM Descriptors of Shape. QSAR Comb. Sci. 2006, 25, 327–332.

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