In silico approaches for predicting adme properties

Challenges and Advances in Computational Chemistry and Physics

Volumn 8, Issue , 2010, Pages 283-304

  Madden, Judith C ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

ADME; Biokinetics; In silico methods

Indexed keywords

EID: 85073143422     PISSN: 25424491     EISSN: 25424483     Source Type: Book Series    
DOI: 10.1007/978-1-4020-9783-6_10     Document Type: Chapter

References (63)

1. Kerns EH, Di L (2008) Drug-like properties: Concepts, structure design and methods. Elsevier, Burlington, USA
2. d’Yvoire MB, Prieto P, Blaauboer BJ et al. (2007) Physiologically-based kinetic modelling (PBK modelling): Meeting the 3Rs agenda. The report and recommendations of ECVAM workshop 63. ATLA 35:661–671
3. Szakacs G, Varadi A, Ozvegy-Laczka C, Sarkadi B (2008) The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME-Tox). DDT 13:379–393
4. Wilkinson GG (2001) Pharmacokinetics: The dynamics of drug absorption, distribution and elimination. In: Hardman J, Limbird E (eds) Goodman and Gilman’s the pharmacological basis of therapeutics, McGraw-Hill, New York, pp 3–29
5. Gibbs S, van de Sandt JJM, Merk HF et al. (2007) Xenobiotic metabolism in human skin and 3-D human constructs: A review. Curr Drug Metab 8:758–772
6. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25
7. Lobell M, Hendrix M, Hinzen B, Keldenrich J (2006) In Silico ADMET traffic lights as a tool for the prioritization of HTS hits. Chem Med Chem 1:1229–1236
8. Gleeson MP (2008) Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem 51:817–834
9. Hou T, Wang J, Zhang W et al. (2006) Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem 13:2653–2667
10. Lian G, Chen L, Han L (2008) An evaluation of mathematical models for predicting skin permeability. J Pharm Sci 97:584–598
11. Basak SC, Mills D Mumtaz MM (2007) A quantitative structure-activity relationship (QSAR) study of dermal absorption using theoretical molecular descriptors. SAR QSAR Env Res 18:45–55
12. Veber DF, Johnson SR, Cheng H-Y et al. (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623
13. Moda TL, Monanari CA, Andricopulo AD (2007) Hologram QSAR model for the prediction of human oral bioavailability. Bioorg Med Chem 15:7738–7745
14. Colmenarejo G (2003) In silico prediction of drug-binding strengths to human serum albumin. Med Res Rev 23:275–301
15. Votano JR, Parham M, Hall ML et al. (2006) QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J Med Chem 49: 7169–7181
16. Ghafourian T, Barzegar-Jalali M, Hakimiha N et al. (2004) Quantitative structure-pharmacokinetic relationship modelling: apparent volume of distribution. J Pharm Pharmacol 56:339–350
17. Lombardo F, Obach RS, Shalaeva MY, Gao F (2004) Prediction of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics. J Med Chem 47:1242–1250
18. Gleeson MP, Waters NJ, Paine SW et al. (2006) In silico human and rat Vss quantitative structure-activity relationship models. J Med Chem 49:1953–1963
19. Sui XF, Sun J, Wu X et al. (2008) Predicting the volume of distribution of drugs in humans. Curr Drug Metab 9:574–580
20. Zhang H (2005) A new approach for the tissue-blood partition coefficients of neutral and ionized compounds. J Chem Inf Model 45:121–127
21. Abraham MH, Ibrahim A, Acree WE Jr (2006) Air to brain, blood to brain and plasma to brain distribution of volatile organic compounds: linear free energy analyses. Eur J Med Chem 41: 494–502
22. Basak SC, Mills D, Gute BD (2006) Prediction of tissue–air partition coefficients – theoretical vs experimental methods. QSAR SAR Env Res 17:515–532
23. Norinder U, Haberlein M (2002) Computational approaches to the prediction of the blood-brain distribution. Adv Drug Deliv Rev 54:291–313
24. Konovalov DA, Coomans D, Deconinck E, Heyden YV (2007) Benchmarking of QSAR models for blood-brain barrier permeation. J Chem Inf Model 47:1648–1656
25. Li H, Yap CW, Ung CY et al. (2005) Effect of selection of molecular descriptors on the prediction of blood brain barrier penetrating and nonpenetrating agents by statistical learning methods. J Chem Inf Model 45:1376–1384
26. Zhao YH, Abraham MH, Ibrahim A et al. (2007) Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes. J Chem Inf Model 47:170–175
27. Hewitt M, Madden JC, Rowe PH, Cronin MTD (2007) Structure-based modelling in reproductive toxicology: (Q)SARs for the placental barrier. SAR QSAR Env Res 18:57–76
28. Chang C, Ray A, Swaan P (2005) In Silico strategies for modeling membrane transporter function. DDT 10:663–671
29. Huang J, Ma G, Muhammad I, Cheng Y (2007) Identifying P-glycoprotein substrates using a support vector machine optimised by a particle swarm. J Chem Inf Mod 47:1638–1647
30. Cabrera MA, Gonzalez I, Fernandez C et al. (2006) A topological substructural approach for the prediction of P-glycoprotein substrates. J Pharm Sci 95:589–606
31. Manga N, Duffy JC, Rowe PH, Cronin MTD (2003) A hierarchical QSAR model for urinary excretion of drugs in humans as a predictive tool for biotransformation. QSAR Comb Sci 22:263–273
32. Agatonovic-Kustrin S, Ling LH, Tham SY, Alany RG (2002) Molecular descriptors that influence the amount of drugs transfer into human breast milk. J Pharm Biomed Anal 29:103–119
33. Afzelius L, Arnby CH, Broo A et al. (2007) State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications. Drug Metab Rev 39:61–86
34. Madden JC, Cronin MTD (2006) Structure-based methods for the prediction of drug metabolism. Expert Opin Drug Metab Toxicol 2:545–557
35. Payne M (2004) Computer-based methods for the prediction of chemical metabolism and biotransformation within biological organisms. In: Cronin MTD, Livingstone DJ (eds) Predicting chemical toxicity and fate. CRC Press, Boca Raton
36. Manga N, Duffy JC, Rowe PH, Cronin MTD (2005) Structure-based methods for the prediction of the dominant P450 enzyme in human drug biotransformation: Consideration of CYP3A4, CYP2C9, CYP2D6. SAR QSAR Env Res 16:43–61
37. Yap CW, Li ZR, Chen YZ (2006) Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. J Mol Graph Mod 24:383–395
38. Quinones C, Caceres J, Stud M, Martinez A (2000) Prediction of drug half-life values of antihistamines based on the CODES/neural network model. Quant Struct Act Relat 19:448–454
39. Quinones-Torrelo C, Sagrado S, Villaneuva-Camanas RM, Medina-Hernandez MJ (2001) Retention pharmacokinetic and pharmacodynamic parameter relationships of antihistmaine drugs using biopartitioning micellar chromatography. J Chromatogr B 761:13–26
40. Netzeva TI, Worth AP, Aldenberg T et al. (2005) Current status of methods for defining the applicability domain of (quantitative) structure–activity relationships. The report and recommendations of ECVAM Workshop 52. ATLA 33:155–173
41. Hou T, Wang J, Zhang W, Xu X (2007) ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. J Chem Inf Model 47:208–218
42. Hou T, Wang J, Zhang W, Xu X (2007) ADME evaluation in drug discovery. 6. Can oral bioavailability in humans be effectively predicted by simple molecular property-based rules. J Chem Inf Model 47:460–463
43. Abraham MH, Ibrahim A, Zhao Y, Acree WE Jr (2006) A database for partition of volatile organic compounds and drugs from blood/plasma/serum to brain and an LFER analysis of the data. J Pharm Sci 95:2091–2100
44. Kalgutkar AS, Gardner I, Obach S et al. (2005) A comprehensive listing of bioactivation pathways of organic functional groups. Curr Drug Metab 6:161–225
45. Turner JV, Maddalena DJ, Cutler DJ (2004) Pharmacokinetic parameter prediction from drug structure using artificial neural networks. Int J Pharmac 270:209–219
46. Takano M, Yumoto R, Murakami T (2006) Expression and function of efflux transporters in the intestine. Pharmacol Ther 109:137–161
47. Thummel KE, Shen GG (2001) Design and optimization of dosage regimens: Pharmacokinetic data. In: Hardman J, Limbird E (eds) Goodman and Gilman’s the pharmacological basis of therapeutics. McGraw-Hill, New York, pp 1917–2023
48. Schultz M, Schmoldt A (1997) Therapeutic and toxic blood concentrations of more than 500 drugs. Pharmazie 52(12):895–911
49. Hollósy F, Valkó K, Hersey A et al. (2006) Estimation of volume of distribution in humans from high throughput HPLC-based measurements of human serum albumin binding and immobilised artificial membrane partitioning. J Med Chem 49:6958–6971
50. Ekins S (2006) Systems-ADME/Tox: Resources and network approaches. J Pharmacol Toxicol Meth 53:38–66
51. de Groot M (2006) Designing better drugs: Predicting cytochrome P450 metabolism. Drug Disc Today 11:601–606
52. Banik GM (2004) In silico ADME-Tox prediction: the more the merrier. Curr Drug Disc 4:31–34
53. Ekins S, Waller CL, Swann PW (2000) Progress in predicting human ADME parameters in silico. J Pharmacol Toxicol Meth 44:251–272
54. Duffy JC (2004) Prediction of pharmacokinetic parameters in drug design and toxicology. In: Cronin MTD, Livingstone DJ (eds) Predicting chemical toxicity and fate. CRC Press, Boca Raton
55. Gola J, Obrezanova O, Champness E, Segall M (2006) ADMET property prediction: The state of the art and current challenges. QSAR Comb Sci 25:1172–1180
56. Chohan KK, Paine SW, Water, NJ (2006) Quantitative structure activity relationships in drug metabolism. Curr Top Med Chem 6:1569–1578
57. Winkler DA (2004) Neural networks in ADME and toxicity prediction. Drugs Fut 29:1043–1057
58. Dearden JC (2007) In silico prediction of ADMET properties: How far have we come? Expert Opin Drug Metab Toxicol 3:635–639
59. Enoch SJ, Cronin MTD, Schultz TW, Madden JC (2008) An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis. Chemosphere 71:1225–1232
60. Rodgers SL, Davis AM, van de Waterbeemd H (2007) Time-series QSAR analysis of human plasma protein binding data. QSAR Comb Sci 26:511–521
61. Ekins S, Andreyev S, Ryabov A et al. (2005) Computational prediction of human drug metabolism. Expert Opin Drug Metab Toxicol 1:303–324
62. Stouch TR, Kenyon JR, Johnson SR et al. (2003) In Silico ADME/Tox: Why models fail. J Comput-Aid Mol Des 17:83–92
63. R® population based ADME simulator. Expert Opin Drug Metab Toxicol 5:211–223