Badania in silico w przewidywaniu zdolności przenikania leków przez barierę krew-mózg

• Authors: Elżbieta Brzezińska

Title variants

EN

In silico prediction of blood-brain barrier penetration of drugs

• Languages of publication: PL

Abstracts

PL

Bariera krew-mózg (ang. Blood-brain barrier - BBB) jest złożonym systemem, który oddziela ośrodkowy układ nerwowy (OUN) od krwioobiegu. Zdolność przenikania bariery krew-mózg (ang. Blood-brain barrier permeability – BBBp) stanowi jedną z najważniejszych właściwości farmakokinetycznych dla leków działających ośrodkowo. Równocześnie, poziom przenikania do mózgu leków działających poza OUN powinien być niski, dla uniknięcia ośrodkowych działań niepożądanych. Ustalenie BBBp substancji leczniczej jest ważnym elementem projektowania leków. Najczęściej używanym wskaźnikiem poziomu przenikania jest współczynnik rozdziału pomiędzy mózg i krew (log BB). Modele matematyczne ilościowej zależności pomiędzy strukturą i aktywnością (ang. quantitative structure-activity relationship - QSAR) dają możliwość przewidywania parametru log BB na podstawie badania struktury związku chemicznego. Doświadczalne ustalanie wartości log BB jest trudne, czasochłonne i pracochłonne. Bardzo przydatna jest więc możliwość przewidywania współczynnika rozdziału związku pomiędzy mózg i krew, na podstawie właściwości fizykochemicznych lub ich struktury. Znacząca rola różnych deskryptorów molekularnych w przewidywaniu log BB została udowodniona w wielu doświadczeniach. W niniejszej pracy opisano najważniejsze z parametrów, często używanych do tworzenia modeli QSAR oraz popularne metody modelowania QSAR. Stosowanie modeli in silico, opartych na metodach QSAR, jest bardzo rozpowszechnione. We wstępnej fazie poszukiwania leku użyteczność tych metod jest ograniczona brakiem dostępu do danych z badań in vivo.

EN

Blood-brain barrier (BBB) is a complex cellular system, which separates the brain and central nervous system (CNS) from the bloodstream. BBB permeability (BBBp) is one of the most important pharmacokinetic properties not only for CNS-active drugs. The brain penetration of CNS-nonactive drugs should be very low to minimize the unwanted CNS side effects. Determination of BBBp of therapeutic compounds is an important component in the design of drugs. Usually the blood-brain partition coefficient (log BB) is used to determine BBB permeability of chemical compounds. Quantitative structure-activity relationship (QSAR) models offer predicting log BB from the molecular structure of a compound. Experimental determination of log BB of the compound is difficult, labour-consuming and time-consuming. It is desirable to predict the blood-brain partition coefficient of compounds from their molecular structures or from physicochemical properties. Various descriptors have been revealed in many studies to be important for predicting BBBp of small molecules via passive diffusion. The most important descriptors usually used to build QSAR models and the QSAR modeling methods were presented in this work. The in silico models based on QSAR are frequently used, but are limited by the restricted accessibility of in vivo data during the early drug discovery phase.

Keywords

EN

QSAR modeling; bariera krew-mózg; blood-brain barrier (BBB); deskryptorymolekularne; modelowanie QSAR; molecular descriptors

Discipline

• MEDICINE: MEDICINE

• Publisher: Łódzkie Towarzystwo Naukowe
• Journal: Folia Medica Lodziensia
• Year: 2011
• Volume: 38
• Issue: 2
• Pages: 117-144

Contributors

author

Elżbieta Brzezińska

• Zakład Chemii Analitycznej Katedry Chemii Medycznej, Uniwersytet Medyczny w Łodzi

References

• Abbott N. Prediction of blood-brain barrier permeation in drug discovery from in vivo, in vitro and in silico models. Drug Discov Today Technolo. 2004; 1: 407 416.
• Goodwin J, Clark D. In silico predictions of blood-brain barrier penetration: Considerations to “keep in mind”. J Pharmacol Exp Ther. 2005; 315: 477-483.
• Fan Y, Unwalla R, Denny RA, Di L, Kerns EH, Diller DJ, Humblet C. Insights for predicting blood-brain barrier penetration of CNS targeted molecules using QSPR approaches. J Chem Inf Model. 2010; 50: 1123-1133.
• Kim RB. Organic anion-transporting polypeptide (OATP) transporter family and drug disposition. Eur J Clin Invest. 2003; 33:1-5.
• Sun H, Dai H, Shaik N, Elmquist WF. Drug efflux transporters in the CNS. Adv Drug Deliv Rev. 2003; 55: 83-105.
• Mori S, Takanaga H, Ohtsuki S. Rat organic anion transporter 3 (rOAT3) is responsible for brain-to-blood efflux of homovanilic acid at the albuminal membrane of brain capillary endothelial cells. J Cereb Blood Flow Metab. 2003; 23: 432-440.
• Begley DJ, Brightman MW. Structural and functional aspects of the blood-brain barrier. Prog Drug Res. 2003; 61: 39-78.
• 8. silico methods for small molecule transfer across the BBB. J Pharm Sci. 2009; 98: 4429 4468.
• Pangalos MN, Schechter LE, Hurko O. Drug development for CNS disorders: Strategies for balancing risk and reducing attrition. Nat Rev Drug Discov. 2007; 6:521- 532.
• Di L, Kerns EH, Bezar IF, Petusky SL, Huang Y. Comparison of blood-brain barrier permeability assays: In situ brain perfusion, MDR1-MDCKII and PAMPA-BBB. J Pharm Sci. 2009; 98: 1980-1991.
• Subramanian G, Kitchen DB. Computational models to predict blood-brain barrier permeation and CNS activity. Comp Aided Mol Des. 2003; 17: 643-664.
• of thumb. J Med Chem. 2008; 51: 817-834.
• Lanevskij K, Japertas P, Didziapetris R, Petrauskas A. Ionization-specific prediction of blood-brain permeability. J Pharm Sci. 2009; 98: 122-134.
• Lobell M, Molnár L, Keserü GM. Recent advances in the prediction of blood-brain partitioning from molecular structure. J Pharm Sci. 2003; 92: 360-370.
• Doran A, Obach RS, Smith BJ, Hosea NA, Becker S, Callegari E i wsp. The impact of P-glycoprotein on the disposition of drug targeted for indications of the central nervous system: Evaluation using the MDR1A/1B knockout mouse model. Drug Metab Dispos. 2005; 33: 165-174.
• Discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates. J Chem Inf Comput Sci. 2004; 44: 239-248.
• Van de Waterbeemd WH, Camenisch G, Folkers G, Chretien JR, Rayevsky O. Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J Drug Target. 1998; 6: 151-165.
• Zhao YH, Abraham MH, Ibrahim A, Fish P, Cole S, Lewis ML, de Groot MJ, Reynolds DP. Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation scheme. J Chem Inf Model. 2007; 47: 170-175.
• Cruciani G, Pastor M, Guba W. A new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sci. 2000; 2: 29-39.
• Li H, Yap C, Ung CY, Xue Y, Cao Z, Chen YZ. 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. 2005; 45: 1376-1384.
• Clark DE. In silico prediction of blood-brain barrier permeation. Drug Discov Today. 2003; 8: 927-933.
• Vilar S, Chakrabarti M, Costanzi S. Prediction of passive blood-brain partitioning: Straightforward and effective classification models based on in silico derived physicochemical descriptors. J Mol Graph Model. 2010; 28: 899-903.
• Fu X, Wang G, Shan H, Liang W, Gao J. Predicting blood-brain barrier penetration from molecular weight and number of polar atoms. Eur J Pharm Biopharm. 2008; 70: 462-466.
• Lanevskij K, Dapkunas J, Juska L, Japertas P, Didziapetris R. QSAR Analysis of Blood-Brain Distribution: The Influence of Plasma and Brain Tissue Binding. J Pharm Sci. 2011; 100: 2147-2159.
• Kelder J, Grootenhuis PDJ, Bajada DM, Delbressine LPC, Ploemen JP. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res. 1999; 16: 1514-1519.
• Doan KM, Humphreys JE, Webster LO, Wring SA, Shampine LJ, Serabjit-Singh CJ i wsp. Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs. J Pharmacol Exp Ther. 2002; 303: 1029-1037.
• Norinder U, Haberlein M. Computational approaches to the prediction of the bloodbrain distribution. Adv Drug Deliv Rev. 2002; 54:291-313.
• Ajay, Bermis GW, Murcko MA. Designing libraries with CNS Activity. J Med Chem. 1999; 42:4942-4951.
• Mehdipour AR, Hamidi M. Brain drug targeting: a computational approach for overcoming blood-brain barrier. Drug Discov Today. 2009; 14: 1030-1036.
• Winkler DA, Burden FR. Modelling blood-brain barrier partitioning using Bayesian neural nets. J Mol Graph Model. 2004; 22: 499-505.
• Wan H, Rehngren M, Giordanetto F, Bergström F, Tunek A. High-throughput screening of drug-brain tissue binding and in silico prediction for assessment of central nervous system drug delivery. J Med Chem. 2007; 50:4606-4615.
• Hemmateenejad B. correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation. Chemom Intel Lab Syst. 2004; 75: 231-245.
• Hemmateenejad B, Miri R, Safarpour MA, Mehdipour AR. Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling. J Comput Chem. 2006; 27: 1125-1135.
• Van Damme S, Langenaeker W, Bultinck P. Prediction of blood-brain partitioning: A model based on ab initio calculated quantum chemical descriptors. J Mol Graph Model. 2008; 26: 1223-1236.
• Zhang H. A new nonlinear equation for the tissue/blood partition coefficients of neutral compounds. J Pharm Sci. 2004; 93: 1595-1604.
• Zhang L, Zhu H, Opera TI, Golbraikh A, Tropsha A. QSAR modeling of the blood brain permeability for diverse organic compounds. Pharm Res. 2008; 25: 1902 1914.
• Hughes JD. Physicochemical drug properties associated with in vivo toxicological outcomes. Bioorg Med Chem Lett. 2008; 18: 4872-4875.
• Abraham M, Ibrahim A, Zhao YH, Acree WE. A data base for partition of volatile organic compounds and drugs from blood/plasma/serum to brain, and an LFER analysis of the data. J Pharm Sci. 2006; 95: 2091-2100.
• Jeffrey P, Summerfield S. Assessment of the blood-brain barrier in CNS drug discovery. Neurobiol Dis. 2010; 37: 33-37.
• Hammarlund-Udenaes M, Friden M, Sywänen S, Gupta A. On the rate and extent of drug delivery to the brain. Pharm Res. 2008; 25: 1737-1750.
• Wan H, Ahman M, Holmen AG. Relationship between brain tissue partitioning and microemulsion retention factors of CNS drugs. J Med Chem. 2009; 52: 1693 1700.
• Kalvass JC, Maurer TS, Pollack GM. Use of plasma and brain unbound fraction to assess the extent of brain distribution of 34 drugs: Comparison of unbound concentration ratios to in vivo p-glycoprotein efflux ratios. Drug Metab Disps. 2007; 35: 660-666.
• Klon AE. Computational Models for Central nervous system penetration. Curr Comput-Aided Drug Des. 2009; 5: 71-89.
• Reiche A, Begley DJ. Potential of immobilized artificial membranes for predicting drug penetration across the blood-brain barrier. Pharm. Res. 1998; 15: 1270-1274
• Sugano K, Hamada H, Machida M, Ushio H. High throughput prediction of oral absorption: improvement of the composition of the lipid solution used in parallel artificial membrane permeation assay. J. Biomol. Screen. 2001; 6: 189-196.
• Yoon CH, Shin BS, Chang HS, Kwon LS, Kim HY, Yoo SE i wsp. Rapid screening of drug absorption potential using the immobilized artificial membrane phosphatidylcholine column and molar volume. Chromatographia. 2006; 60: 399 204.
• Crivori P, Cruciani G, Carrupt PA, Testa B. Predicting blood-brain barrier permeation from three-dimensional molecular structure. J Med Chem. 2000; 43: 2204-2216.
• Habgood MD, Begley DJ, Abbott NJ. Determinants of passive drug entry into the central nervous system. Cell. Mol. Neurobiol. 2000; 20: 231-253.
• Levin VA. Relationship of octanol/water partition coefficient and molecular weight to rat brain capillary permeability. J Med Chem. 1980; 23: 682-684.
• Young RC. Development of a new physicochemical model of brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists. J Med Chem. 1988; 31: 656-671.
• Liu X, Tu M, Kelly RS, Chen C, Smith BJ. Development of the computational approach to predict blood-brain barrier permeability. Drug Metab Dispos. 2004; 32: 132-139.
• Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001; 46: 3-26.
• Hou TJ, Xu J. ADME Evaluation in drug discovery. 3 Modelling blood-brain barrier partitioning using simple molecular descriptors. J Chem Inf Comput Sci. 2003 43: 2137-2152.
• Iyer M, Mishru R, Han Y, Hopfinger AJ. Predicting blood-brain barrier partitioning of organic molecules using membrane-interaction QSAR analysis. Pharm Res. 2002; 19: 1611-1621.
• Abraham MH, Chadha HS, Mitchell RC. Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. J Pharm Sci. 1994; 83: 1257-1268.
• Abraham MH. Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical process. Chem Soc Rev. 1993; 22: 73-83.
• Abraham MH, Chadha HS, Mitchell RC. Hydrogen-bonding. Part 36. Determination of blood brain distribution using octanol-water partition coefficients. Drug Des Discov. 1995; 13: 123-131.
• Abraham MH, Takacs-Novak K, Mitchell RC. On the partitioning of ampholytes: application to blood-brain distribution. J Pharm Sci. 1997; 86: 310-315.
• Abraham MH, Ibrahim A, Zissimos AM, Zhao YH, Comer J, Reynolds DP. Application of hydrogen bonding calculations in property based drug design. Drug Discov Today. 2002; 7: 1056-1063.
• Abraham MH, Acree WE, Leo AJ, Hoekman D, Cavanaugh JE. Water-solvent partition coefficients and Delta Log P values as predictors for blood-brain distribution; application of the Akaike information criteria. J Pharm Sci. 2010; 99: 2492-2501.
• Luco JM. Prediction of the brain-blood distribution of a large set of drugs from the structurally derived descriptors using partial least-squares (PLS) modelling. J Chem Inf Comp Sci. 1999; 39: 396-404.
• Kortagere S, Chekmarev D, Welsh WJ, Ekins S. New predictive models for blood-brain barrier permeability of drug-like models. Pharm Res. 2008; 25: 1836 1845.
• Cuadrado MU, Ruiz IL, Gomez-Nieto MA. QSAR models based on isomorphic and nonisomorphic data fusion for predicting the blood brain barrier permeability. J Comput Chem. 2007; 28: 1252-1260.
• Dureja H, Madan AK. Validation of topochemical models for the prediction of permeability through the blood-brain barrier. Acta Pharm. 2007; 57: 451-467.
• Labute P. A widely applicable set og descriptors. J Mol Graphics Modell. 2000; 18: 464-477.
• Litman T, Zeuthen T, Skovsagaard T, Stein WD. Structure-activity relationship of Pglycoprotein interacting drugs: Kinetic characterization of their effects on ATPase activity. Biochem Biophys Acta. 1997; 1361: 159-168.
• Clark DE. Rapid calculation of polar molecular surface area and its application of transport phenomena. 2. Prediction of blood-brain barrier penetration. J Pharm Sci. 1999; 88: 815-821.
• Osterberg T, Norinder U. Prediction of polar surface area and drug transport processes using simple parameters and PLS statistics. J Chem Inf Comput Sci. 2000; 40: 1408-1411.
• Chen Y, Zhu Q, Pan J, Yang Y, Wu X. A prediction model for blood-brain barrier permeation and analysis on its parameter biologically. Comput Methods Programs Biomed. 2009; 95: 280-287.
• Luco JM, Marchevsky E. QSAR studies on blood-brain barrier permeation. Curr Comp Aided Drug Des. 2006; 2: 31-35.
• Ertl P, Rohde B, Selzer P. Fast calculations of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport. J Med Chem. 2000; 43: 3714-3717.
• Geldenhuys WJ, Manda VK, Mittapalli RK, Van der Schyf CJ, Crooks PA, Dwoskin LP, Allen DD, Lockman PR. Predictive screening model for potential vector-mediated transport of cationic substrates at the blood-brain barrier choline transporter. Bioorg Med Chem Lett. 2010; 20: 870-877.
• Rose K, Hall LH, Kier LB. Modeling blood-brain barrier partitioning using the electrotopological state. J Chem Inf Comput Sci. 2002; 42: 651-666.
• Hall LH, Kier LB. MDL QSAR modeling blood-brain barrier partitioning. 2002 http://www.mdl.com/products/pdfs/MDLQSARReprint.pdf
• Stouch TR, Gudmundsson O. progress in understanding the structure-activity relationship of P-glycoprotein. Adv Drug Deliv Rev. 2002; 54: 315-328.
• Qin LT, Liu SS, Liu HI. QSPR model for bioconcentration factors of non-polar organic compounds using electronegativity distance vector descriptors. Mol Divers. 2010; 14: 67-80.
• Liu SS, Yin CS, Wang LS. Combined MEDV-GA-MLR method for QSAR of three panels of steroids, dipeptides and COX-2 inhibitors. J Chem Inf Comput Sci. 2002; 42: 749-756.
• Suzuki T, Fukazawa N, San nohe K, Sato W, Yano O, Tsuruo T. Structure-activity relationship of newly synthesized quinolone derivatives for reversal of multidrug resistance in cancer. J Med Chem. 1997; 40: 2047-2052.
• Zhang YH, Xia ZN, Qin LT, Liu SS. Prediction of blood-brain partitioning: A model based on molecular electronegativity distance vector descriptors. J Mol Graph Model. 2010; 29: 214-220.
• Nielsen PA, Andersson O, Hansen SH, Simonsen KB, Anderson G. Models for predicting blood-brain barrier permeation. Drug Discov Today. 2011; 16: 472 475.
• Abraham MH. The factors that influence permeation across the blood-brain barrier. Eur J Med Chem. 2004; 39: 235-240.

• Document Type: paper