Добавил:
kiopkiopkiop18@yandex.ru t.me/Prokururor I Вовсе не секретарь, но почту проверяю Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз: Предмет: Файл:

Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5849_Библиотеки_им_академика_М_И_Перельмана

.pdf
Скачиваний:
0
Добавлен:
30.08.2026
Размер:
49 Мб
Скачать
235. Warr, W. A. ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). J. Comput. Aided Mol. Des. 2009, 23, 195–198.
236. Singh, M. K., Srivastava, S., Raghava, G. P., and Varshney, G. C. HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies. Bioin- formatics 2006, 22, 253–255.
237. Wishart, D. S., et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007, 35, D521–D526.
238. Kanehisa, M. The KEGG database. Novartis Found. Symp. 2002, 247, 91–101; discus­sion 101–103, 119–128 244–252.
239. Feng, Z., Chen, L., Maddula, H., Akcan, O., Oughtred, R., Berman, H. M., and Westbrook, J. Ligand depot: a data warehouse for ligands bound to macromolecules. Bioinformatics 2004, 20, 2153–2155.
240. von Grotthuss, M., Pas, J., and Rychlewski, L. Ligand-Info, searching for similar small compounds using index profiles. Bioinformatics 2003, 19, 1041–1042.
241. MDPI. MDPI molecules. 2009.
242. Boyer, S., Arnby, C. H., Carlsson, L., Smith, J., Stein, V., and Glen, R. C. Reaction site mapping of xenobiotic biotransformations. J. Chem. Inf. Model 2007, 47, 583–590.
243. Masciocchi, J., Frau, G., Fanton, M., Sturlese, M., Floris, M., Pireddu, L., Palla, P., Cedrati, F., Rodriguez-Tome, P., and Moro, S. MMsINC: a large-scale chemoinformatics database. Nucleic Acids Res. 2009, 37, D284–D290.
244. MDSI. MicroSource Discovery Systems, Inc. database. 2009.
245. Sitzmann, M., Filippov, I. V., and Nicklaus, M. C. Internet resources integrating many small-molecule databases. SAR QSAR Environ. Res. 2008, 19, 1–9.
246. NTP U. National Toxicology Program (NTP) database. 2009. Available at http://ntp. niehs.nih.gov/.
247. Wheeler, D. L., et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2006, 34, D173–D180.
248. Klekota, J., Roth, F. P., and Schreiber, S. L. Query Chem: a Google-powered web search combining text and chemical structures. Bioinformatics 2006, 22, 1670–1673.
249. Hendlich, M., Bergner, A., Gunther, J., and Klebe, G. Relibase: design and development of a database for comprehensive analysis of protein–ligand interactions. J. Mol. Biol. 2003, 326, 607–620.
250. Diago, L. A., Morell, P., Aguilera, L., and Moreno, E. Setting up a large set of protein–ligand PDB complexes for the development and validation of knowledge-based docking algorithms. BMC Bioinformatics 2007, 8, 310.
251. Dunkel, M., Fullbeck, M., Neumann, S., and Preissner, R. SuperNatural: a searchable database of available natural compounds. Nucleic Acids Res. 2006, 34, D678–D683.
252. Goede, A., Dunkel, M., Mester, N., Frommel, C., and Preissner, R. SuperDrug: a conformational drug database. Bioinformatics 2005, 21
, 1751–1753.
253. Gunther, S., Hempel, D., Dunkel, M., Rother, K., and Preissner, R. SuperHapten: a comprehensive database for small immunogenic compounds. Nucleic Acids Res. 2007, 35, D906–D910.
254. Michalsky, E., Dunkel, M., Goede, A., and Preissner, R. SuperLigands—a database of ligand structures derived from the Protein Data Bank. BMC Bioinformatics 2005, 6, 122.
118
IN SILICO ADME/Tox PREDICTIONS
https://t.me/medicina_free
255. Schmidt, U., Struck, S., Gruening, B., Hossbach, J., Jaeger, I. S., Parol, R., Lindequist, U., Teuscher, E., and Preissner, R. SuperToxic: a comprehensive database of toxic com­pounds. Nucleic Acids Res. 2009, 37, D295–D299.
256. TimTec. TimTec LLC databases. 2009.
257. Wexler, P. TOXNET: the National Library of Medicine’s toxicology database. Am. Fam. Physician 1995, 52, 1677–1678.
258. Toxicity, Datasets. Chemoinformatics.org website. Available at http://cheminformatics. org/datasets/index.shtml#tox, 2009.
259. ChemDB. ChemDB: The UC Irvine ChemDB datasets. 2009.
260. Briggs, K. A. Vitic—a data source for (quantitative) structure–activity relationship modelling. Toxicol. Abstr. 2007.
261. Irwin, J. J. and Shoichet, B. K. ZINC—a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model 2005, 45, 177–182.
262. Lagorce, D., Sperandio, O., Galons, H., Miteva, M., and Villoutreix, B. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects,
2008.
263. Song, C. M., Bernardo, P. H., Chai, C. L., and Tong,J. C. CLEVER: pipeline for designing in silico chemical libraries. J. Mol. Graph. Model 2009, 27, 578–583.
264. ToxTree, ToxtreeIdeaconsult Ltd. 2009.
265. Cramer, G. M., Ford, R. A., and Hall, R. L., Estimation of toxic hazard—a decision tree approach. Food Cosmet. Toxicol. 1978, 16, 255–276.
266. Ingsriswang, S., and Pacharawongsakda, E., sMOL Explorer: an open source, web­enabled database and exploration tool for Small MOLecules datasets. Bioinformatics 2007, 23, 2498–2500.
267. Cheng, T., Zhao, Y., Li, X., Lin, F., Xu, Y., Zhang, X., Li, Y., Wang, R., and Lai, L., Computation of octanol–water partition coefficients by guiding an additive model with knowledge. J. Chem. Inf. Model 2007, 47, 2140–2148.
268. Girke, T., Cheng, L. C., and Raikhel, N., ChemMine. A compound mining database for chemical genomics. Plant Physiol. 2005, 138, 573–577.
269. Bolelli, L., Lu, X., Liu, Y., Jaiswal, A., Bai, K., Councill, I., Mitra, P.,Wang,J. Z., Mueller, K., Kubicki, J., Garrison, B., Bandstra J., and Giles, C. L. ChemXSeer: A Chemistry Web Portal for Scientific Literature and Datasets In Open Repositories Conference, San Antonio, TX, 2007.
270. Helma, C. Lazy structure–activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity. Mol. Divers. 2006, 10, 147–158.
271. Sander, T., Freyss, J., von Korff, M., Reich, J. R., and Rufener, C. OSIRIS, an entirely in-house developed drug discovery informatics system. J. Chem. Inf. Model 2009, 49(2), 232–246.
272. Moda, T. L., Torres, L. G., Carrara, A. E., and Andricopulo, A. D. PK/DB: database for pharmacokinetic properties and predictive in silico ADME models. Bioinformatics 2008, 24, 2270–2271.
273. Alland, C., Moreews, F., Boens, D., Carpentier, M., Chiusa, S., Lonquety, M., Renault, N., Wong, Y., Cantalloube, H., Chomilier, J., Hochez, J., Pothier, J., Villoutreix, B. O., Zagury, J. F., and Tuffery, P. RPBS: a web resource for structural bioinformatics. Nucleic Acids Res. 2005, 33, W44–W49.
REFERENCES 119
https://t.me/medicina_free
274. Sperandio, O., Petitjean, M., and Tuffery, P. wwLigCSRre: a 3D ligand-based server for hit identification and optimization. Nucleic Acids Res. 2009, 37, W504–W509.
275. Varnek, A., Fourches, D., Horvath, D., Klimchuk, O., Gaudin, C., Vayer, P., Solov’ev, V., Hoonakker, F., Tetko, I. V., and Marcou, G. ISIDA—platform for virtual screening based on fragment and pharmacophoric descriptors. Curr. Comput. Aided Drug Des. 2008, 4, 191–198.
276. Sanderson, D. M. and Earnshaw, C. G. Computer prediction of possible toxic action from chemical structure; the DEREK system. Hum. Exp. Toxicol. 1991, 10, 261–273.
277. Klopman, G., Chakravarti, S. K., Zhu, H., Ivanov, J. M., and Saiakhov, R. D. ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases. J. Chem. Inf. Comput. Sci. 2004, 44, 704–715.
278. Inpharmatica. ADMENSA INTERACTIVE. Available at http://www.admensa.com/ admensa_old.htm, 2009.
279. FILTER. OpenEye Scientific Software. Available at http://www.eyesopen.com/products/ applications/filter.html, 2009.
280. Schr€odinger. QikProp, 2009.
281. Accelrys. Discovery Studio 2.0. Available at http://accelrys.com/products/discovery­studio/, 2009.
282. Discovery, M., VolSurf þ . Available at http://www.moldiscovery.com, 2009.
283. Milletti, F., Storchi, L., Sforna, G., and Cruciani, G. New and original pK
a
prediction
method using grid molecular interaction fields. J. Chem. Inf. Model 2007, 47, 2172–2181.
284. Woo, Y. T., Lai, D. Y., Argus, M. F., and Arcos, J. C. Development of structure–activity relationship rules for predicting carcinogenic potential of chemicals. Toxicol. Lett. 1995, 79, 219–228.
285. q-pharm. Quantum Pharmaceuticals. Available at http://q-pharm.com/, 2009.
286. MOE. Chemical Computing Group. Available at http://www.chemcomp.com, 2009.
287. ChemAxon. Available at www.chemaxon.com, 2009.
288. Bemis, G. W. and Murcko, M. A. The properties of known drugs. 1. Molecular frame­works. J. Med. Chem. 1996, 39, 2887–2893.
289. Bemis, G. W. and Murcko, M. A. Properties of known drugs. 2. Side chains. J. Med. Chem. 1999, 42, 5095–5099.
290. Krier, M. and Hutter, M. C. Bioisosteric similarity of molecules based on structural alignment and observed chemical replacements in drugs. J. Chem. Inf. Model 2009, 49(5), 1280–1297.
291. Vainio, M. J. and Johnson, M. S. Generating conformer ensembles using a multiobjective genetic algorithm. J. Chem. Inf. Model 2007, 47, 2462–2474.
292. Schuttelkopf, A. W. and van Aalten, D. M. PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr. D Biol. Crystallogr. 2004, 60, 1355–1363.
293. Leite, T. B., Gomes, D., Miteva, M. A., Chomilier, J., Villoutreix, B. O., and Tuffery, P. Frog: a FRee Online druG 3D conformation generator. Nucleic Acids Res. 2007, 35, W568–W572.
294. Lobell, M., Hendrix, M., Hinzen, B., Keldenich, J., Meier, H., Schmeck, C., Schohe­Loop, R., Wunberg, T., and Hillisch, A. In silico ADMET traffic lights as a tool for the prioritization of HTS hits. ChemMedChem. 2006, 1, 1229–1236.
120
IN SILICO ADME/Tox PREDICTIONS
https://t.me/medicina_free
295. Hennemann, M., Friedl, A., Lobell, M., Keldenich, J., Hillisch, A., Clark, T., and Goller, A. H. CypScore: quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory. ChemMedChem. 2009, 4, 657–669.
296. Pearce, B. C., Sofia, M. J., Good, A. C., Drexler, D. M., and Stock, D. A. An empirical process for the design of high-throughput screening deck filters. J. Chem. Inf. Model 2006, 46, 1060–1068.
297. Stahl, M., Guba, W.,and Kansy,M. Integrating molecular design resources within modern drug discovery research: the Roche experience. Drug Discov. Today 2006, 11, 326–333.
298. Alanine, A., Nettekoven, M., Roberts, E., and Thomas, A. W. Lead generation–enhancing the success of drug discovery by investing in the hit to lead process. Comb. Chem. High Throughput Screen 2003, 6, 51–66.
299. Fischer, H. CAFCA a novel tool for the calculation of amphiphilic properties of charged drug molecules. Chimia 2000, 54, 640–645.
300. Davies, J. W., Glick, M., and Jenkins, J. L. Streamlining lead discovery by aligning in silico and high-throughput screening. Curr. Opin. Chem. Biol. 2006, 10, 343–351.
301. Steinmeyer, A. The hit-to-lead process at Schering AG: strategic aspects. ChemMed- Chem. 2006, 1, 31–36.
302. Giacomini, K. M., Krauss, R. M., Roden, D. M., Eichelbaum, M., Hayden, M. R., and Nakamura, Y. When good drugs go bad. Nature 2007, 446, 975–977.
303. Gultekin, F. and Hicyilmaz, H. Renal deterioration caused by carcinogens as a conse­quence of free radical mediated tissue damage: a review of the protective action of melatonin. Arch. Toxicol. 2007, 81, 675–681.
304. Metosh-Dickey, C. A., Mason, R. P., and Winston, G. W. Nitroarene reduction and generation of free radicals by cell-free extracts of wild-type, and nitroreductase-deficient and -enriched Salmonella typhimurium strains used in the umu gene induction assay. Toxicol. Appl. Pharmacol. 1999, 154, 126–134.
305. Segers, K., Sperandio, O., Sack, M., Fischer, R., Miteva, M. A., Rosing, J., Nicolaes, G. A., and Villoutreix, B. O. Design of protein membrane interaction inhibitors by virtual ligand screening, proof of concept with the C2 domain of factor V. Proc. Natl. Acad. Sci. USA 2007, 104, 12697–12702.
306. Hodgetts, K. J., Yoon, T., Huang, J., Gulianello, M., Kieltyka, A., Primus, R., Brodbeck, R., De Lombaert, S., and Doller, D. 2-Aryl-3, 6-dialkyl-5-dialkylaminopyrimidin-4­ones as novel crf-1 receptor antagonists. Bioorg. Med. Chem. Lett. 2003, 13, 2497–2500.
307. Egan, W. J., Merz, K. M., Jr., and Baldwin, J. J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000, 43, 3867–3877.
308. Benigni, R. and Bossa, C. Predictivity of QSAR. J. Chem. Inf. Model 2008, 48, 971–980.
309. Kortagere, S., Chekmarev, D., Welsh, W. J., and Ekins, S. New predictive models for blood–brain barrier permeability of drug-like molecules. Pharm. Res. 2008, 25, 1836–1845.
310. Sch€olkopf, B., Smola, A., and Muller, K. R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998, 10, 1299–1319.
311. Rosipal, R. and Kramer, N. Overview and recent advances in partial least squares, in subspace, latent structure and feature selection techniques. Lect. Notes Comput. Sci., 2006, 34–51.
REFERENCES 121
https://t.me/medicina_free
312. Chohan, K. K., Paine, S. W.,Mistry, J., Barton, P., and Davis, A. M. A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries. J. Med. Chem. 2005, 48, 5154–5161.
313. Luco, J. M. Prediction of the brain-blood distribution of a large set of drugs from structurally derived descriptors using partial least-squares (PLS) modeling. J. Chem. Inf. Comput. Sci. 1999, 39, 396–404.
314. Obrezanova, O., Gola, J. M., Champness, E. J., and Segall, M. D. Automatic QSAR modeling of ADME properties: blood–brain barrier penetration and aqueous solubility. J. Comput. Aided Mol. Des. 2008, 22, 431–440.
315. Kriegl, J. M., Eriksson, L., Arnhold, T., Beck, B., Johansson, E., and Fox, T. Multivariate modeling of cytochrome P450 3A4 inhibition. Eur J. Pharm. Sci. 2005, 24, 451–463.
316. Durand, J.-F. and Sabatier, R. Additive splines for partial least squares regression. J. Am. Stat. Assoc. 1997, 92, 1546–1554.
317. Hou, T., Wang, J., and Li, Y. ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine. J. Chem. Inf. Model 2007, 47, 2408–2415.
318. Karatzoglou, A., Meyer, D., and Hornik, K. Support Vector Machines in R, Research Report Series, Wirstchaftuniversitat Wien, 2005.
319. Breiman, L., Friedman, J., Olshen, R., and Stone, C. Classification and Regression Trees, New edition (1-1-1984) ed, ed Hall/CRC, Boca Raton, FL, 1984, p. 368.
320. Young, S. S. and Hawkins, D. M. Analysis of a 2(9) full factorial chemical library.J. Med. Chem. 1995, 38, 2784–2788.
321. Rusinko, A., 3rd, Farmen, M. W., Lambert, C. G., Brown, P. L., and Young, S. S. Analysis of a large structure/biological activity data set using recursive partitioning. J. Chem. Inf. Comput. Sci. 1999, 39, 1017–1026.
322. van Rhee, A. M. Use of recursion forests in the sequential screening process: consensus selection by multiple recursion trees. J. Chem. Inf. Comput. Sci. 2003, 43, 941–948.
323. Klekota, J. and Roth, F. P. Chemical substructures that enrich for biological activity. Bioinformatics 2008, 24, 2518–2525.
324. Schneider, N., Jackels, C., Andres, C., and Hutter, M. C. Gradual in silico filtering for druglike substances. J. Chem. Inf. Model 2008, 48, 613–628.
325. Deconinck, E., Zhang, M. H., Coomans, D., and Vander Heyden, Y. Classification tree models for the prediction of blood–brain barrier passage of drugs. J. Chem. Inf. Model 2006, 46, 1410–1419.
326. Lamanna, C., Bellini, M., Padova, A., Westerberg, G., and Maccari, L. Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. J. Med. Chem. 2008, 51, 2891–2897.
327. Gleeson, M. P., Waters, N. J., Paine, S. W., and Davis, A. M. In silico human and rat vs. quantitative structure–activity relationship models. J. Med. Chem. 2006, 49, 1953–1963.
328. de Cerqueira Lima, P., Golbraikh, A., Oloff, S., Xiao, Y., and Tropsha, A. Combinatorial QSAR modeling of P-glycoprotein substrates. J. Chem. Inf. Model 2006,
46, 1245–1254.
329. Mente, S. R. and Lombardo, F. A recursive-partitioning model for blood–brain barrier permeation. J. Comput. Aided Mol. Des. 2005, 19, 465–481.
330. Sakiyama, Y., Yuki, H., Moriya, T., Hattori, K., Suzuki, M., Shimada, K., and Honma, T. Predicting human liver microsomal stability with machine learning techniques. J. Mol. Graph Model 2008, 26, 907–915.
122
IN SILICO ADME/Tox PREDICTIONS
https://t.me/medicina_free
331. Yamashita, F., Hara, H., Ito, T., and Hashida, M. Novel hierarchical classification and visualization method for multiobjective optimization of drug properties: application to structure-activity relationship analysis of cytochrome P450 metabolism. J. Chem. Inf. Model 2008, 48, 364–369.
332. Arimoto, R., Prasad, M. A., and Gifford, E. M. Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors. J. Biomol. Screen 2005, 10, 197–205.
333. Breiman, L., Friedman, J., Olshen, R. A., and Stone, C. J. Classification and Regression Trees, Wadsworth and Brooks, Florence, KY, 1984.
334. Quinlan, J. R. C4.5: Programs for machine learning. Mach. Learn. 1993, 16, 235–240.
335. Breiman, L. Random forests. Mach Learn. 2001, 45, 5–32.
336. Rosenblatt, F. Principles of Neurodynamics, 1960. Spartan Books, Washington, DC,
1962.
337. Huuskonen, J., Salo, M., and Taskinen, J. Neural network modeling for estimation of the aqueous solubility of structurally related drugs. J. Pharm. Sci. 1997, 86, 450–454.
338. Devillers, J., Domine, D., Guillon, C., and Karcher, W. Simulating lipophilicity of organic molecules with a back-propagation neural network. J. Pharm. Sci. 1998, 87, 1086–1090.
339. Bruneau, P. Search for predictive generic model of aqueous solubility using Bayesian neural nets. J. Chem. Inf. Comput. Sci. 2001, 41, 1605–1616.
340. Sadowski, J. and Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 1998, 41, 3325–3329.
341. Tetko, I. V. and Tanchuk, V. Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. J. Chem. Inf. Comput. Sci. 2002, 42, 1136–1145.
342. Votano, J. R., Parham, M., Hall, L. M., Hall, L. H., Kier, L. B., Oloff, S., and Tropsha, A. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem. 2006, 49, 7169–7181.
343. Jung, E., Kim, J., Kim, M., Jung, D. H., Rhee, H., Shin, J. M., Choi, K., Kang, S. K., Kim, M. K., Yun, C. H., Choi, Y. J., and Choi, S. H. Artificial neural network models for prediction of intestinal permeability of oligopeptides. BMC Bioinformatics 2007, 8, 245.
344. Polley, M. J., Burden, F. R., and Winkler, D. A. Predictive human intestinal absorption— QSAR models using Bayesian regularized neural networks. Aust. J. Chem. 2005, 58, 859–863.
345. Bazeley,P. S., Prithivi, S., Struble, C. A., Povinelli, R. J., and Sem, D. S. Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling. J. Chem. Inf. Model 2006, 46, 2698–2708.
346. Molnar, L. and Keseru, G. M. A neural network based virtual screening of cytochrome P450 3A4 inhibitors. Bioorg. Med. Chem. Lett. 2002, 12, 419–421.
347. Wang, Y. H., Li, Y., Li, Y. H., Yang, S. L., and Yang, L. Modeling K(m) values using electrotopological state: substrates for cytochrome P450 3A4-mediated metabolism. Bioorg. Med. Chem. Lett. 2005, 15
, 4076–4084.
348. Sadowski, J. Database Profiling by Neural Networks, In Virtual Screening for Bioactive Molecules, Wiley, New York, 2000.
349. Gregori-Puigjane, E. and Mestres, J. Coverage and bias in chemical library design. Curr. Opin. Chem. Biol. 2008, 12, 359–365.
REFERENCES 123
https://t.me/medicina_free
350. Caffrey, C. R., Rohwer, A., Oellien, F., Marhofer, R. J., Braschi, S., Oliveira, G., McKerrow, J. H., and Selzer, P. M. A comparative chemogenomics strategy to predict potential drug targets in the metazoan pathogen, Schistosoma mansoni. PLoS One 2009, 4, e4413.
351. de Wildt, S. N., Ito, S., and Koren, G. Challenges for drug studies in children: CYP3A phenotyping as example. Drug Discov. Today 2009, 14, 6–15.
352. Ince, I., de Wildt, S. N., Tibboel, D., Danhof, M., and Knibbe, C. A. Tailor-made drug treatment for children: creation of an infrastructure for data-sharing and population PK-PD modeling. Drug Discov. Today 2009, 14, 316–320.
353. Hilmer, S. N. ADME-tox issues for the elderly. Expert Opin. Drug Metab. Toxicol. 2008, 4, 1321–1331.
354. Jamei, M., Dickinson, G. L., and Rostami-Hodjegan, A. A framework for assessing inter­individual variability in pharmacokinetics using virtual human populations and integrat­ing general knowledge of physical chemistry, biology, anatomy,physiology and genetics: A tale of bottom-upvs. top-downrecognition of covariates. Drug Metab. Pharma- cokinet. 2009, 24, 53–75.
355. Jamei, M., Marciniak, S., Feng, K., Barnett, A., Tucker, G., and Rostami-Hodjegan, A. The Simcyp((R)) population-based ADME Simulator. Expert Opin. Drug Metab. Toxicol. 2009, 5(2), 211–223.
356. Hodge, A. E., Altman, R. B., and Klein, T. E. The PharmGKB: integration, aggregation, and annotation of pharmacogenomic data and knowledge. Clin. Pharmacol. Ther. 2007, 81, 21–24.
357. Weskamp, N., Hullermeier, E., and Klebe, G. Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins 2009, 76, 317–330.
358. Yildirim, M. A., Goh, K. I., Cusick, M. E., Barabasi, A. L., and Vidal, M. Drug-target network. Nat. Biotechnol. 2007, 25, 1119–1126.
359. Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J., and Bork, P. Drug target identification using side-effect similarity. Science 2008, 321, 263–266.
360. Scheiber, J., Jenkins, J. L., Sukuru, S. C., Bender, A., Mikhailov, D., Milik, M., Azzaoui, K., Whitebread, S., Hamon, J., Urban, L., Glick, M., and Davies, J. W. Mapping Adverse Drug Reactions in Chemical Space. J. Med. Chem. 2009, 52, 3103–3107.
361. Schimmel, K. J., Richel, D. J., van den Brink, R. B., and Guchelaar, H. J. Cardiotoxicity of cytotoxic drugs. Cancer Treat. Rev. 2004, 30, 181–191.
362. Mirza, A., Desai, R., and Reynisson, J. Known drug space as a metric in exploring the boundaries of drug-like chemical space. Eur. J. Med. Chem. 2009, 44(12), 5006–5011.
363. Hohman, M., Gregory, K., Chibale, K., Smith, P. J., Ekins, S., and Bunin, B. Novel web­based tools combining chemistry informatics, biology and social networks for drug discovery. Drug Discov. Today 2009, 14, 261–270.
364. Yang, L., Luo, H., Chen, J., Xing, Q., and He, L. SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome. Nucleic Acids Res. 2009, 37, W406–W412.
124
IN SILICO ADME/Tox PREDICTIONS
https://t.me/medicina_free
3
ABSORPTION AND PHYSICOCHEMICAL PROPERTIES OF THE NCE
JON SELBO AND PO-CHANG CHIANG
3.1. INTRODUCTION
Attrition of drug candidates during the discovery and development process is a serious problem for the pharmaceutical industry. Other than a lack of in vivo efficacy or unintended toxicological issues, failures are often associated with inappropriate physicochemical characteristics contributing to poor absorption and poor pharma­cokinetics [1–3]. The absorption of any chemical entity reflects a very complex series of actions and efficacy can be affected by factors acting in concert or independently. In human physiology, for example, the physicochemical properties of the active ingredient, active or passive transport, disease state, formulation, and dose are often important for bioavailability. Other factors such as dosing interval, fed or fasted state, age, and gender can each affect the oral bioavailability of a given drug. Due to this complexity, optimizing absorption of a drug or formulation often requires full knowledge of how these variables interact. For a given compound it may take years of research before such comprehensive knowledge can be accu­mulated. In the absence of such detailed information on a new compound, optimization of a candidate to fit certain physicochemical properties and the corresponding first in human formulation is often based on common considerations of factors that affect absorption.
ADMET for Medicinal Chemists: A Practical Guide, Edited by Katya Tsaioun and Steven A. Kates Copyright 2011 John Wiley & Sons, Inc.
125
https://t.me/medicina_free
3.2. PHYSICOCHEMICAL PROPERTIES
During the 1990s almost 40% of the failure in clinical trails was attributed to poor absorption and poor pharmacokinetics [4–6]. In response, pharmaceutical researchers began to focus on a better understanding of target selectivity, toxicological, and physicochemical (pharmacokinetics related) properties of new chemical entities (NCEs) [7, 8]. For example, approaches such as ‘‘property-based design’’ [8], a method for understanding how medicinal chemists could manipulate critical combi­nations of physical and structural properties that contribute to ‘‘drug-like properties,’’ were adopted. The outcome was profound and within a decade, candidate attrition for poor absorption and poor pharmacokinetics properties was reduced to less than 10% of the total failures [4–6].
There have been numerous attempts to predict the properties that are the most desirable for a good drug candidate. Analysis of the structures of orally administered drugs, and of drug candidates, headed by Lipinski and his colleagues [9] led to the establishment of the Lipinski rule of five. The rule states that an orally active drug should have as little violations of these guidelines as possi ble:
.
molecular weight less than 500,
.
log P (octanol/water partition coefficient) less than 5,
.
no more than five hydrogen bond donors,
.
no more than 10 hydrogen bond acceptors.
Other parameters such as polar surface area (PSA) and molecular rigidity as indicated by the number of rotatable bonds (NRB) [10] have also been associated with drugability of NCEs.
More recently, PSA and molecular flexibility (measured as NRB) have been demonstrated to be important predictors of good oral bioavailability [11]. After analyzing more than 1100 drug candidates in rats, Veber et al. found that increasing molecular rigidity had a positive impact on bioavailability while increasing the polar surface area lowered bioavailability. They suggested that compounds with 10 or fewer rotatable bonds and a polar surface area below 140 A
˚
2
would have a higher chance of being orally bioavailable. Molecular weight, independent of NRB, did not appear to correlate with oral bioavailability. At first glance, this result appears to be inconsistent with Lipinski’s molecular weight rule. However, given that the major contributors to the PSA are hydrogen-bond donors or acceptors and NRB count frequently increas es with MW, these findings are still fairly consistent. PSA has also been reported to correlate with transporter activities within a given molecular scaffold that can be useful in building structure–activities relationships (SAR) [12].
It is worth mentioning that statistical analyses of over 1000 marketed drugs and clinical candidates indicate that lower MW, balanced log P, and greater rigidity remain important features of oral drug molecules [12–15]. Authors of those articles tracked the changes of computed property profiles (MW, Clog P, PSA, etc.) relative to the stage of clinical development candidates. By examining a large database of
126 ABSORPTION AND PHYSICOCHEMICAL PROPERTIES OF THE NCE
https://t.me/medicina_free
compounds in clinical development, the authors found that as the stage of develop­ment progresses (preclinical, Phase 1, Phase 2, Phase 3, and launch), compounds that are advanced further have lower molecular weight, Clog P, and polar surf ace area [16]. Compounds with excessive molecular weight (>500) or high lipophilicity (Clog P > 5), tended to be highly disfavored in clinical development [14, 16]. This suggests that physicochemical properties are intimately linked to physiological control.
Vieth et al. [17, 18] compiled a database of physicochemical properties of marketed drugs, compounds in clinical development, and related molecules known to have biological activity, but not moving forward for clinical development. They distinguished between oral and other routes of administration for the known drugs. Their goal was to establish the optimal physicochemical parameters associated with compounds with good pharmacokinetic properties versus those with poor pharma­cokinetic properties [16]. Over 1700 compounds were used in their study. It was not surprising that they found that when compared with oral drugs, injectable drugs have significantly higher MW, number of H-bond acceptors/donors, rotatable bonds, aromatic rings, and much lower Clog P. Differences were also distinguished for absorbent and topical compounds compared with oral, although these differences were not as large as those found with injectable drugs. The authors also reported that the average property distributions of oral drugs remains fairly constant over time, suggesting that the properties of successful drug candidates are in a narrowly defined property space, which is in agreement with simple rules such as Lipinski’s, NRB, and PSA correlations.
Physicochemical properties have also been correlated with the in vivo performance of drug candidates. For example, lipophilicity plays an important role in metabolism by Cytochrome P450 enzymes. Theses enzymes, which mediate the clearance of more than 50% of marketed drugs, have lipophilic active sites that accommodate and metabolize drug molecules. In general, drug clearance increases with the increase of lipophilicity. Common examples include the barbituric acid series [19, 20], b-adre­noceptor antagonists and calcium channel blockers [21], and the diverse structures of CYP3A4 substrates [20]. Other investigations correlating plasma protein binding with physicochemical properties have been reported as well [22, 23].
Compounds may fail for a number of reasons in clinical development. Factors such as no or low efficacy, toxicity, or exposure [16, 24] are likely causes. However, certain physical properties are favored for clinical development of human therapeutic agents. In order to ensure the integration of these drug-like properties into the drug design strategy, a strong emphasis on drug ‘‘developability’’ has been raised within the pharmaceutical industry [25–28]. Empirical rules such as the rule-of-five are now widely applied by many pharmaceutical companies as a first in silico filter that flags compounds with potential issues for further development.
3.3. STABILITY
Another important property that impacts bioavailability is the chemical and physical (especially gastrointestinal (GI)) stability of a drug candidate. Chemical stability is
STABILITY 127
https://t.me/medicina_free