Editorial

P-glycoprotein transporter in drug development

Veda Prachayasittikul1,2, Virapong Prachayasittikul1[*]

1Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand

2Dental Hospital Mahidol University Faculty of Dentistry, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand

EXCLI J 2016;15:Doc113

 

Drug discovery and development is a complex and time consuming process which requires multidisciplinary expertise (Prachayasittikul et al., 2015[35]). It is true that bioactive compounds will become useless if their pharmacokinetic properties are not adequate. Pharmacokinetic properties include absorption (A), distribution (D), metabolism (M), excretion (E) and toxicity (T), or ADMET. ADMET properties influence clinical efficacy and toxicity of drugs, because they determine how much and how fast the administered drug enters the cell to reach the target site of action where it exhibits pharmacological effects, as well as control drug metabolism and elimination (van de Waterbeemd and Gifford, 2003[46]). In clinical aspect, ADMET properties determine route of administration, administered dose, and frequency of administration (van de Waterbeemd and Gifford, 2003[46]). The ADMET properties are affected by many factors including physicochemical/molecular properties of the drug (van de Waterbeemd et al., 2001[47]) and drug transporters (Lee and Kim, 2004[22]; Murakami and Takano, 2008[28]; Ueno et al., 2010[45]). Therefore, understanding the ADMET properties of candidate compounds is essential for successful drug development in terms of saving time and economic cost. In this regard, pharmacokinetic properties are important factors that need to be considered in early stages of drug development to increase the success rate and minimize financial cost (van de Waterbeemd and Gifford, 2003[46]). Computational or in silico approaches are effective tools for facilitating drug discovery and development (Prachayasittikul et al., 2015[35]). Computational methods are employed in many stages of drug development process, including primary ADMET screening (van de Waterbeemd and Gifford, 2003[46]).

P-glycoprotein (Pgp) is a good example of clinical relevant drug transporter (Amin, 2013[4]; Srivalli and Lakshmi, 2012[39]; Wessler et al., 2013[52]) due to its broad-specific nature and its influence on ADMET properties of drugs (Srivalli and Lakshmi, 2012[39]). Pgp belongs to the ATP-binding cassette (ABC) superfamily (Hennessy and Spiers, 2007[16]) and is encoded by multidrug resistance (mdr) genes. Pgp expresses in many pharmacokinetic-related organs and physical barriers such as gastrointestinal (GI) tract, blood-brain-barrier (BBB), kidney, liver, endothelium and placenta (Fardel et al., 2012[11]). Pgp functions to limit cellular uptake, distribution, excretion and toxicity of a wide range of structurally unrelated hydrophobic substances, pollutants and drugs (Amin, 2013[4]) by acting as a unidirectional efflux pump, which extrudes its substrate from inside to outside of cells (Aller et al., 2009[3]). It is also recommended by the Food and Drug Administration (FDA) that a screening to ensure whether the candidate bioactive compounds are substrates of the Pgp should be conducted as early as possible during drug discovery pipeline (U.S. Food and Drug Administration, 2012[44]). Many experimental assays are available to determine interaction of the compounds against Pgp transporter (Pgp endpoint), however, discordance of experimental condition leads to conflict report of the Pgp endpoints (Polli et al., 2001[32]). Hence, classification of Pgp-interacting compounds is challenging (Wang et al., 2005[49]) and is a growing research area. Recently, many computational approaches such as quantitative structure activity relationship (Ghandadi et al., 2014[13]; Palestro et al., 2014[30]; Shen et al., 2014[38]), classification models (Adenot and Lahana, 2004[2]; Chen et al., 2011[8]; Klepsch et al., 2014[17]; Levatić et al., 2013[23]; Li et al., 2014[24]; Penzotti et al., 2002[31]; Prachayasittikul et al., 2015[34]; Wang et al., 2011[51]), molecular docking (Ghandadi et al., 2014[13]; Palestro et al., 2014[30]; Zeino et al., 2014[53]), and substructure analysis (Prachayasittikul et al., 2016[33]; Wang et al., 2011[51]; Klepsch et al., 2014[17]) have been successfully employed to provide deeper understanding about this promiscuous protein.

The importance of Pgp is not only limited for ADMET issue, but also extended to an area of multidrug resistance (MDR) cancer (Hennessy and Spiers, 2007[16]). The linkage between Pgp overexpression and MDR cancer has been demonstrated in literatures (Abolhoda et al., 1999[1]; Thomas and Coley, 2003[42]). Increased efflux activity of the cancer cell is one of mechanisms behind drug resistance (Schinkel and Jonker, 2012[37]; Szakács et al., 2006[41]). The cancer cells derived from tissues that naturally express Pgp (i.e., kidney, colon, liver, and pancreas) have high potential to develop intrinsic drug resistance, even before exposing to anticancer agents (Sun et al., 2004[40]). Unlikely, low level of Pgp expression is found in an early diagnostic stage of cancer cells of non-Pgp expressed origin, but Pgp expression increase and the resistance is developed after treating with anticancer drugs (Fardel et al., 1996[12]; Thomas and Coley, 2003[42]). Besides exposure to anticancer agents, Pgp expression can be induced by hypoxic condition of the cancer cells (Trédan et al., 2007[43]). Pgp overexpression is found in many types (Drach et al., 1995[10]) and many stages (Krishna and Mayer, 2000[18]) of cancer cells. In addition, many clinically used anticancer agents are substrates of Pgp (Drach et al., 1995[10]). In this regard, delivery of the administered anticancer drug to target site of action is impaired thereby leading to decreased intracellular drug concentration and ineffective treatment outcome (Srivalli and Lakshmi, 2012[39]). Hence, an inhibition of Pgp function is an attractive strategy toward MDR (Szakács et al., 2006[41]). Many Pgp inhibitors (including small molecules, natural compounds, and pharmaceutical excipients (Srivalli and Lakshmi, 2012[39])) have been developed for a combination use with anticancer drugs that are substrates of the Pgp to combat resistance (Szakács et al., 2006[41]). However, the outcome remains apart from satisfaction (Szakács et al., 2006[41]).

Antimicrobial resistance is another global issue with prime concern. Efflux pump is noted to be one of the factors contributing to drug resistance of microorganisms (Rouveix, 2007[36]). Similar to MDR cancer, the MDR microorganisms express the broad-specific Pgp efflux on their components, therefore, a wide range of structurally unrelated hydrophobic antimicrobials can be extruded out of the bacterial cells (Rouveix, 2007[36]). This phenomenon limits access of the drug to target site of action and deteriorates antimicrobial effects (Rouveix, 2007[36]). Beside the search for novel antimicrobials against resistant strains, the development of efflux inhibitors (i.e., Pgp inhibitors) for co-administration with the currently used antimicrobials is considered to be an effective treatment strategy that could restore and improve effectiveness of the standard antimicrobial agents.

It should not be overlooked that Pgp plays important roles in ADMET profiles of the administered drugs. Thus, drug-drug interaction, adverse effects and toxicities are the issues that should be concerned when many drugs are co-administered (Amin, 2013[4]; Aszalos, 2007[6]). In particular, dose adjustment and monitoring are recommended when drugs with narrow therapeutic window are co-administered with strong Pgp inhibitors (Wessler et al., 2013[52]).

In addition to the search of novel Pgp inhibitors, modulation of Pgp expression is another strategy towards therapeutics. Abnormal Pgp expression, either increased or decreased expression, is noted to be a pathological factor of many diseases. Overexpression of Pgp in blood-brain barrier (BBB) is found in non-responsive refractory epilepsy patients and is noted to be a contributing factor of resistance against anti-epileptic drugs (Lazarowski et al., 2007[21]; Li et al., 2014[25]). Similar to cancer, Pgp expression is enhanced under the hypoxic condition, which is triggered by recurrent seizure (Li et al., 2014[25]). In this regard, suppression of Pgp expression may be an attractive treatment choice.

Besides degenerate effects, Pgp is also noted for its protective roles. Protective role of Pgp is demonstrated in Alzheimer's disease (AD) and placenta protective mechanism. Amyloid-β is a pathologic protein of AD and its accumulation leads to neuronal damages (Hardy and Selkoe, 2002[14]). Pgp efflux pump facilitates clearance of amyloid-β from the brain and plays critical role in pathogenesis and progression of AD (Kuhnke et al., 2007[19]; Lam et al., 2001[20]). Pgp expression was found to be inversely correlated with amyloid-β deposition (Cirrito et al., 2005[9]; Hartz et al., 2010[15]; Vogelgesang et al., 2002[48]). Thus, increasing cerebrovascular Pgp expression is suggested to be an alternative therapeutic target for treatment and delay progression of AD (Brenn et al., 2014[7]). Likewise, placental Pgp efflux prevents fetus from xenobiotics, toxicants and drugs (Anger et al., 2012[5]). The protective effect of placental Pgp is correlated with level of Pgp expression. While hypoxic condition provoked increased Pgp expression (Trédan et al., 2007[43]), oxidative stress environment is noted to suppress expression and inhibit efflux function of Pgp (Li et al., 2011[27]; Wang et al., 2009[49]). Oxidative stress is one of the most common factors contributing to placental injuries and other harmful effects during pregnancy (Myatt and Cui, 2004[29]). Recent study revealed that placental Pgp expression is decreased under oxidative stress condition, and the level of Pgp expression can be restored with antioxidant agent (Li et al., 2014[26]). Hence, upregulation of placental Pgp expression may be a strategy for preventing adverse conditions and diseases in pregnancy (Li et al., 2014[26]).

In summary, Pgp is a drug transporter of clinical importance in which many aspects of this transporter and its interacting ligands are need to be fully elucidated. Clinical relevance of Pgp and therapeutic applications of its interacting ligands render the study regarding this transporter an active research area with continual interest. The study relating to Pgp expression also could be of great benefit for understanding the unsolved problems. In clinical aspect, adjustment of dosing regimen and careful drug monitoring also take part in an effective treatment along with a maximum safety.

Acknowledgements

This project is supported by the Office of the Higher Education Commission, Mahidol University under the National Research Universities Initiative and Annual Government Grant under Mahidol University (2556-2558 B.E.).

Conflict of interests

The authors declare they have no conflict of interest.

 

References

1. Abolhoda A, Wilson AE, Ross H, Danenberg PV, Burt M, Scotto KW. Rapid activation of MDR1 gene expression in human metastatic sarcoma after in vivo exposure to doxorubicin. Clin Cancer Res. 1999;5:3352-6.
2. Adenot M, Lahana R. Blood-brain barrier permeation models: discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates. J Chem Inf Comput Sci. 2004;44:239-48.
3. Aller SG, Yu J, Ward A, Weng Y, Chittaboina S, Zhuo R, et al. Structure of P-glycoprotein reveals a molecular basis for poly-specific drug binding. Science. 2009;323:1718-22.
4. Amin ML. P-glycoprotein inhibition for optimal drug delivery. Drug Target Insights. 2013;7:27-34.
5. Anger GJ, Cressman AM, Piquette-Miller M. Expression of ABC efflux transporters in placenta from women with insulin-managed diabetes. PLoS ONE. 7(4):e35027.
6. Aszalos A. Drug-drug interactions affected by the transporter protein, P-glycoprotein (ABCB1, MDR1). I. Preclinical aspects. Drug Discov Today. 2007;12:833-7.
7. Brenn A, Grube M, Jedlitschky G, Fischer A, Strohmeier B, Eiden M, et al. St. John's Wort reduces beta-amyloid accumulation in a double transgenic Alzheimer's disease mouse model-role of P-glycoprotein. Brain Pathol. 2014;24:18-24.
8. Chen L, Li Y, Zhao Q, Peng H, Hou T. ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and naive bayesian classification techniques. Mol Pharm. 2011;8:889-900.
9. Cirrito JR, Deane R, Fagan AM, Spinner ML, Parsadanian M, Finn MB, et al. P-glycoprotein deficiency at the blood-brain barrier increases amyloid-β deposition in an Alzheimer disease mouse model. J Clin Invest. 2005;115:3285-90.
10. Drach D, Zhao S, Drach J, Andreeff M. Low incidence of MDR1 expression in acute promyelocytic leukaemia. Br J Haematol. 1995;90:369-74.
11. Fardel O, Kolasa E, Le Vee M. Environmental chemicals as substrates, inhibitors or inducers of drug transporters: implication for toxicokinetics, toxicity and pharmacokinetics. Expert Opin Drug Metab Toxicol. 2012;8:29-46.
12. Fardel O, Lecureur V, Guillouzo A. The P-glycoprotein multidrug transporter. Gen Pharmacol. 1996;27:1283-91.
13. Ghandadi M, Shayanfar A, Hamzeh-Mivehroud M, Jouyban A. Quantitative structure activity relationship and docking studies of imidazole-based derivatives as P-glycoprotein inhibitors. Med Chem Res. 2014;23:4700-12.
14. Hardy J, Selkoe DJ. The Amyloid hypothesis of Alzheimer's Disease: progress and problems on the road to therapeutics. Science. 2002;297:353-6.
15. Hartz AMS, Miller DS, Bauer B. Restoring blood-brain barrier P-glycoprotein reduces brain amyloid-β in a mouse model of Alzheimer's disease. Mol Pharmacol. 2010;77:715-23.
16. Hennessy M, Spiers JP. A primer on the mechanics of P-glycoprotein the multidrug transporter. Pharmacol Res. 2007;55:1-15.
17. Klepsch F, Vasanthanathan P, Ecker GF. Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors. J Chem Inf Model. 2014;54:218-29.
18. Krishna R, Mayer LD. Multidrug resistance (MDR) in cancer. Mechanisms, reversal using modulators of MDR and the role of MDR modulators in influencing the pharmacokinetics of anticancer drugs. Eur J Pharm Sci. 2000;11:265-83.
19. Kuhnke D, Jedlitschky G, Grube M, Krohn M, Jucker M, Mosyagin I, et al. MDR1-P-glycoprotein (ABCB1) mediates transport of Alzheimer's amyloid-β peptides - Implications for the mechanisms of Aβ clearance at the blood-brain barrier. Brain Pathol. 2007;17:347-53.
20. Lam FC, Liu R, Lu P, Shapiro AB, Renoir JM, Sharom FJ, et al. β-Amyloid efflux mediated by P-glycoprotein. J Neurochem. 2001;76:1121-8.
21. Lazarowski A, Czornyj L, Lubienieki F, Girardi E, Vazquez S, D'Giano C. ABC Transporters during Epilepsy and Mechanisms Underlying Multidrug Resistance in Refractory Epilepsy. Epilepsia. 2007;48:140-9.
22. Lee W, Kim RB. Transporters and renal drug elimination. Annu Rev Pharmacol Toxicol. 2004;44:137-66.
23. Levatić J, Ćurak J, Kralj M, Šmuc T, Osmak M, Supek F. Accurate models for P-gp drug recognition induced from a cancer cell line cytotoxicity screen. J Med Chem. 2013;56:5691-708.
24. Li D, Chen L, Li Y, Tian S, Sun H, Hou T. ADMET evaluation in drug discovery. 13. Development of in silico prediction models for p-glycoprotein substrates. Mol Pharm. 2014;11:716-26.
25. Li Y, Chen J, Zeng T, Lei D, Chen L, Zhou D. Expression of HIF-1alpha and MDR1/P-glycoprotein in refractory mesial temporal lobe epilepsy patients and pharmacoresistant temporal lobe epilepsy rat model kindled by coriaria lactone. Neurol Sci. 2014;35:1203-8.
26. Li Y, Fang J, Zhou K, Wang C, Mu D, Hua Y. Evaluation of oxidative stress in placenta of fetal cardiac dysfunction rat model and antioxidant defenses of maternal vitamin C supplementation with the impacts on P-glycoprotein. J Obstet Gynaecol Res. 2014;40:1632-42.
27. Li Y, Yan YE, Wang H. Enhancement of placental antioxidative function and P-gp expression by sodium ferulate mediated its protective effect on rat IUGR induced by prenatal tobacco/alcohol exposure. Environ Toxicol Pharmacol. 2011;32:465-71.
28. Murakami T, Takano M. Intestinal efflux transporters and drug absorption. Expert Opin Drug Metab Toxicol. 2008;4:923-39.
29. Myatt L, Cui X. Oxidative stress in the placenta. Histochem Cell Biol. 2004;122:369-82.
30. Palestro PH, Gavernet L, Estiu GL, Bruno Blanch LE. Docking applied to the prediction of the affinity of compounds to P-glycoprotein. Biomed Res Int. 2014;2014:358425.
31. Penzotti JE, Lamb ML, Evensen E, Grootenhuis PDJ. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J Med Chem. 2002;45:1737-40.
32. Polli JW, Wring SA, Humphreys JE, Huang L, Morgan JB, Webster LO, et al. Rational use of in vitro P-glycoprotein assays in drug discovery. J Pharmacol Exp Ther. 2001;299:620-8.
33. Prachayasittikul V, Mandi P, Prachayasittikul S, Prachayasittikul V, Nantasenamat C. Exploring the chemical space of P-glycoprotein interacting compounds. Mini Rev Med Chem. 2016;16 (Epub ahead of print). doi:10.2174/1389557516666160121120344.
34. Prachayasittikul V, Worachartcheewan A, Shoombuatong W, Prachayasittikul V, Nantasenamat C. Classification of P-glycoprotein-interacting compounds using machine learning methods. EXCLI J. 2015;14:958-70.
35. Prachayasittikul V, Worachartcheewan A, Shoombuatong W, Songtawee N, Simeon S, Prachayasittikul V, et al. Computer-aided drug design of bioactive natural products. Curr Top Med Chem. 2015;15:1780-800.
36. Rouveix B. Clinical implications of multiple drug resistance efflux pumps of pathogenic bacteria. J Antimicrob Chemother. 2007;59:1208-9.
37. Schinkel AH, Jonker JW. Mammalian drug efflux transporters of the ATP binding cassette (ABC) family: An overview. Adv Drug Deliv Rev. 2012;64:138-53.
38. Shen J, Cui Y, Gu J, Li Y, Li L. A genetic algorithm-back propagation artificial neural network model to quantify the affinity of flavonoids toward P-glycoprotein. Comb Chem High Throughput Screen. 2014;17:162-72.
39. Srivalli KMR, Lakshmi PK. Overview of P-glycoprotein inhibitors: a rational outlook. Braz J Pharm Sci. 2012;48:353-67.
40. Sun J, He ZG, Cheng G, Wang SJ, Hao XH, Zou MJ. Multidrug resistance P-glycoprotein: Crucial significance in drug disposition and interaction. Med Sci Monit. 2004;10:RA5-14.
41. Szakács G, Paterson JK, Ludwig JA, Booth-Genthe C, Gottesman MM. Targeting multidrug resistance in cancer. Nat Rev Drug Discov. 2006;5:219-34.
42. Thomas H, Coley HM. Overcoming multidrug resistance in cancer: an update on the clinical strategy of inhibiting P-glycoprotein. Cancer Control. 2003;10:159-65.
43. Trédan O, Galmarini CM, Patel K, Tannock IF. Drug resistance and the solid tumor microenvironment. J Natl Cancer Inst. 2007;99:1441-54.
44. U.S. Food and Drug Administration. Guidance for industry : Drug interaction studies - study design, data analysis, implications for dosing, and labeling recommendations. Silver Spring, MD: FDA, 2012.
45. Ueno M, Nakagawa T, Wu B, Onodera M, Huang CL, Kusaka T, et al. Transporters in the brain endothelial barrier. Curr Med Chem. 2010;17:1125-38.
46. van de Waterbeemd H, Gifford E. ADMET in silico modelling: Towards prediction paradise? Nat Rev Drug Discov. 2003;2:192-204.
47. van de Waterbeemd H, Smith DA, Beaumont K, Walker DK. Property-based design: Optimization of drug absorption and pharmacokinetics. J Med Chem. 2001;44:1313-33.
48. Vogelgesang S, Cascorbi I, Schroeder E, Pahnke J, Kroemer HK, Siegmund W, et al. Deposition of Alzheimer's β-amyloid is inversely correlated with P-glycoprotein expression in the brains of elderly non-demented humans. Pharmacogenetics. 2002;12:535-41.
49. Wang T, Chen M, Yan YE, Xiao FQ, Pan XL, Wang H. Growth retardation of fetal rats exposed to nicotine in utero: Possible involvement of CYP1A1, CYP2E1, and P-glycoprotein. Environ Toxicol. 2009;24:33-42.
50. Wang YH, Li Y, Yang SL, Yang L. Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach. J Chem Inf Model. 2005;45:750-7.
51. Wang Z, Chen Y, Liang H, Bender A, Glen RC, Yan A. P-glycoprotein substrate models using support vector machines based on a comprehensive data set. J Chem Inf Model. 2011;51:1447-56.
52. Wessler JD, Grip LT, Mendell J, Giugliano RP. The P-glycoprotein transport system and cardiovascular drugs. J Am Coll Cardiol. 2013;61:2495-502.
53. Zeino M, Saeed MEM, Kadioglu O, Efferth T. The ability of molecular docking to unravel the controversy and challenges related to P-glycoprotein - A well-known, yet poorly understood drug transporter. Invest New Drugs. 2014;32:618-25.
 
 
 

[*] Corresponding Author:

Virapong Prachayasittikul, Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Telephone: 662-441-4376, Fax: 662-441-4380, eMail: virapong.pra@mahidol.ac.th