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Physical Properties in Drug Design

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Tactics in Contemporary Drug Design

Part of the book series: Topics in Medicinal Chemistry ((TMC,volume 9))

Abstract

The physical properties of investigational molecules in drug discovery programmes have been the subjects of intense scrutiny, largely due to a propensity for the pursuit of examples where they are sub-optimal. This chapter introduces the noteworthy contributions that identified the shortcomings and then defines and discusses the key physical parameters (lipophilicity, pK a and solubility) and contemporary developments in their measurement and use. These physical characteristics impact the passage of a drug molecule from the administered dose to the site of action, profoundly influencing its pharmacokinetics and pharmacology. In particular, lipophilicity has a major influence on various parameters used to assess the developability of experimental molecules; the additional impact of aromaticity or flatness in structures and differentiation between the roles intrinsic (log P) and effective (log D) are also illustrated. In conclusion, the combined influences of good properties in efficient molecules are presented as powerful indicators of quality.

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Abbreviations

ADME (or ADMET):

Absorption, distribution, metabolism and elimination

BBB:

Blood–brain barrier

BEI:

Binding efficiency index

CHI:

Chromatographic hydrophobicity index

CLND:

Chemiluminescent nitrogen detection

cmr:

Calculated molar refraction

DCS:

Developability classification system

DMPK:

Drug metabolism and pharmacokinetics

FaSSIF:

Fasted state simulated intestinal fluids

FeSSIF:

Fed state simulated intestinal fluids

GSE:

General solubility equation

GSK:

GlaxoSmithKline

hERG:

Human ether-a-go-go-related gene

HSA:

Human serum albumin

IAM:

Immobilised artificial membrane

ITC:

Isothermal titration calorimetry

LE:

Ligand efficiency

LLE:

Ligand lipophilicity efficiency

MPbAP:

Melting point based absorption potential

OW:

Octanol/water

PAMPA:

Parallel artificial membrane permeation assays

PFI:

Property forecast index

QED:

Quantitative estimate of drug-likeness

QSAR:

Quantitative structure activity relationships

QSPR:

Quantitative structure property relationships

SGF:

Simulated gastric fluid

SILE:

Size-independent ligand efficiency

References

  1. Meanwell NA (2011) Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol 24:1420–1456

    CAS  Google Scholar 

  2. Hansch C, Bjorkroth J, Leo A (1987) Hydrophobicity and central nervous system agents: on the principle of minimal hydrophobicity. J Pharm Sci 76:663–687

    CAS  Google Scholar 

  3. Hann MM (1994) Considerations for the use of computational chemistry techniques by medicinal chemists. In: King FD (ed) Medicinal chemistry, principles and practice. RSC, Cambridge, pp 130–142

    Google Scholar 

  4. Lipinski CA et al (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    CAS  Google Scholar 

  5. Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881–890

    CAS  Google Scholar 

  6. Hann MM (2011) Molecular obesity, potency and other addictions in drug discovery. Med Chem Comm 2:349–355

    CAS  Google Scholar 

  7. Hubbard RE, Murray JB (2011) Experiences in fragment-based lead discovery. Methods Enzymol 493:509–531

    CAS  Google Scholar 

  8. Congreve M et al (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680

    CAS  Google Scholar 

  9. Erlanson DA et al (2004) Fragment-based drug discovery. J Med Chem 47:3463–3482

    CAS  Google Scholar 

  10. Muresan S, Sadowski J (2008) Properties guiding drug- and lead-likeness. In: Mannhold R (ed) Molecular drug properties – measurement and prediction. Wiley-VCH, Weinheim, pp 439–461

    Google Scholar 

  11. Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430–431

    Google Scholar 

  12. Leeson PD, Empfield JR (2010) Reducing the risk of drug attrition associated with physicochemical properties. Annu Rep Med Chem 45:393–407

    CAS  Google Scholar 

  13. Van De Waterbeemd H et al (2001) Property-based design: optimization of drug absorption and pharmacokinetics. J Med Chem 44:1313–1333

    Google Scholar 

  14. Tarcsay Á, Nyíri K, Keserű GM (2012) Impact of lipophilic efficiency on compound quality. J Med Chem 55:1252–1260

    CAS  Google Scholar 

  15. Gleeson MP et al (2011) Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat Rev Drug Discov 10:197–208

    CAS  Google Scholar 

  16. Hill AP, Young RJ (2010) Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity. Drug Discov Today 15:648–655

    CAS  Google Scholar 

  17. Roden DM, George AL Jr (2002) The genetic basis of variability in drug responses. Nat Rev Drug Discov 1:37–44

    CAS  Google Scholar 

  18. Faller B et al (2011) Evolution of the physicochemical properties of marketed drugs: can history foretell the future? Drug Discov Today 16:976–984

    CAS  Google Scholar 

  19. Zhao H (2010) Lead optimization in the nondrug-like space. Drug Discov Today 16:158–163

    Google Scholar 

  20. Van de Waterbeemd H, Gifford E (2003) Admet in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204

    Google Scholar 

  21. Bickerton GR et al (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98

    CAS  Google Scholar 

  22. Kerns EH, Di L (2004) Physicochemical profiling: overview of the screens. Drug Discov Today Technol 1:343–348

    CAS  Google Scholar 

  23. Kerns EH, Di L (2004) Drug-like properties: concepts, structure design and methods: from ADME to toxicity optimization. Academic, Amsterdam, Boston

    Google Scholar 

  24. Wan H, Holmen AG (2009) High throughput screening of physicochemical properties and in vitro ADME profiling in drug discovery. Comb Chem High Throughput Screen 12:315–329

    CAS  Google Scholar 

  25. Smith RN et al (1975) Selection of a reference partitioning system for drug design work. J Pharm Sci 64:599–606

    CAS  Google Scholar 

  26. Tute MS (1996) Lipophilicity: a history. In: Mannhold R et al (eds) Methods and principles in medicinal chemistry. Wiley, New York, pp 7–26

    Google Scholar 

  27. Fujita T, Iwasha J, Hansch C (1964) A new substituent constant, π, derived from partition coefficients. J Am Chem Soc 86:5175–5180

    CAS  Google Scholar 

  28. Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71:525–616

    CAS  Google Scholar 

  29. Young RJ et al (2011) Getting physical in drug discovery II: the impact of chromatographic hydrophobicity measurements and aromaticity. Drug Discov Today 16:822–830

    CAS  Google Scholar 

  30. He Y-L et al (1998) Species differences in size discrimination in the paracellular pathway reflected by oral bioavailability of polyethylene glycol and D-peptides. J Pharm Sci 87:626–633

    CAS  Google Scholar 

  31. Kirch W, Görg KG (1982) Clinical pharmacokinetics of atenolol. Eur J Drug Metab Pharmacokinet 7:81–91

    CAS  Google Scholar 

  32. Bunnage ME et al (2007) Discovery of potent & selective inhibitors of activated thrombin-activatable fibrinolysis inhibitor for the treatment of thrombosis. J Med Chem 50:6095–6103

    CAS  Google Scholar 

  33. Hansch C, Leo A (1979) Substituent constants for correlation analysis in chemistry and biology. Wiley, New York

    Google Scholar 

  34. Leo AJ (1993) Calculating log POct from structures. Chem Rev 93:1281–1306

    CAS  Google Scholar 

  35. Rekker RE et al (1993) On the reliability of calculated log P-values: Rekker, Hansch-Leo and Suzuki approach. Quant Struct Act Relat 12:152–157

    CAS  Google Scholar 

  36. Ribeiro MMB et al (2010) Drug–lipid interaction evaluation: why a 19th century solution? Trends Pharmacol Sci 31:449–454

    CAS  Google Scholar 

  37. Wenlock MC, Barton P, Luker T (2011) Lipophilicity of acidic compounds: impact of ion pair partitioning on drug design. Bioorg Med Chem Lett 21:3550–3556

    CAS  Google Scholar 

  38. Valkó K (2004) Application of high-performance liquid chromatography based measurements of lipophilicity to model biological distribution. J Chromatogr A 1037:299–310

    Google Scholar 

  39. Valkó K et al (1997) Chromatographic hydrophobicity index by fast-gradient RP-HPLC: a high-throughput alternative to log P/log D. Anal Chem 69:2022–2029

    Google Scholar 

  40. Albert A, Serjeant EP (1984) The determination of ionization constants, 3rd edn. Chapman and Hall, New York

    Google Scholar 

  41. Avdeef A, Bucher JJ (1978) Accurate measurements of the concentration of hydrogen ions with a glass electrode. Anal Chem 50:2137–2142

    CAS  Google Scholar 

  42. Avdeef A et al (2000) pH-metric solubility: correlation between the acid–base titration and the saturation shake-flask solubility-pH methods. Pharm Res 17:85–89

    CAS  Google Scholar 

  43. Allen RI et al (1998) Multiwavelength spectrophotometric determination of acid dissociation constants of ionisable drugs. J Pharm Biomed Anal 17:699–712

    CAS  Google Scholar 

  44. Gift AD et al (2012) Experimental determination of pK a values by use of NMR chemical shifts, revisited. J Chem Educ 89:1458–1460

    CAS  Google Scholar 

  45. Cleveland JA et al (1993) Automated pK a determination at low solute concentrations by capillary electrophoresis. J Chromatogr A 652:301–308

    CAS  Google Scholar 

  46. Box K et al (2003) High throughput measurement of pK a values in a mixed-buffer linear pH gradient system. Anal Chem 75:883–892

    CAS  Google Scholar 

  47. Perrin DD, Dempsey B, Serjeant EP (1981) pK a prediction for organic acids and bases. Chapman and Hall, London

    Google Scholar 

  48. Di L, Fish PV, Mano T (2012) Bridging solubility between drug discovery and development. Drug Discov Today 17:486–495

    CAS  Google Scholar 

  49. Sugano K et al (2007) Solubility and dissolution profile assessment in drug discovery. Drug Metab Pharmacokinet 22:225–254

    CAS  Google Scholar 

  50. Huang LF, Tong WQ (2004) Impact of solid state properties on developability assessment of drug candidates. Adv Drug Deliv Rev 56:321–334

    CAS  Google Scholar 

  51. Bhattachar SN et al (2006) Evaluation of the chemiluminescent nitrogen detector for solubility determinations to support drug discovery. J Pharm Biomed Anal 41:152–157

    CAS  Google Scholar 

  52. Jain N, Yalkowsky SH (2001) Estimation of the aqueous solubility I: application to organic non-electrolytes. J Pharm Sci 90:234–252

    CAS  Google Scholar 

  53. European pharmacopeia. http://pharmeuropa.edqm.eu/home/

  54. Bergström CA et al (2007) Poorly soluble marketed drugs display solvation limited solubility. J Med Chem 50:5858–5862

    Google Scholar 

  55. Ishikawa, Hashimoto (2011) Improvement in aqueous solubility in small molecule drug discovery programs by disruption of molecular planarity and symmetry. J Med Chem 54:1539–1554

    CAS  Google Scholar 

  56. Chu KA, Yalkowsky SH (2009) An interesting relationship between drug absorption and melting point. Int J Pharm 373:24–40

    CAS  Google Scholar 

  57. Llinàs A, Glen RC, Goodman JM (2008) Can you predict solubilities of thirty-two molecules using a database of one hundred reliable measurements? J Chem Inf Model 48:1289–1303

    Google Scholar 

  58. Elder D, Holm R (2013) Aqueous solubility: simple predictive methods (in silico, in vitro and bio-relevant approaches). Int J Pharm 453:3–11

    CAS  Google Scholar 

  59. Jantratid E et al (2008) Dissolution media simulating conditions in the proximal human gastrointestinal tract: an update. Pharm Res 25:1663–1676

    CAS  Google Scholar 

  60. Holm R et al (2013) Bile salts and their importance for drug absorption. Int J Pharm 453:44–45

    CAS  Google Scholar 

  61. Bevernage J et al (2013) Evaluation of gastrointestinal drug supersaturation and precipitation: strategies and issues. Int J Pharm 453:25–35

    CAS  Google Scholar 

  62. Ritchie TJ, Macdonald SJF (2009) The impact of aromatic ring count on compound developability – are too many aromatic rings a liability in drug design? Drug Discov Today 14:1011–1020

    CAS  Google Scholar 

  63. Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52:6752–6756

    CAS  Google Scholar 

  64. Yang Y et al (2012) Beyond size, ionization state, and lipophilicity: influence of molecular topology on absorption, distribution, metabolism, excretion, and toxicity for drug-like compounds. J Med Chem 55:3667–3677

    CAS  Google Scholar 

  65. Lovering F (2013) Escape from Flatland 2: complexity and promiscuity. Med Chem Comm 4:515

    CAS  Google Scholar 

  66. Ritchie TJ et al (2011) The impact of aromatic ring count on compound developability – further insights by examining carbo- and hetero- aromatic and aliphatic ring types. Drug Discov Today 16:164–171

    CAS  Google Scholar 

  67. Kimura T, Higaki K (2002) Gastrointestinal transit and drug absorption. Biol Pharm Bull 25:149–164

    CAS  Google Scholar 

  68. Davies B, Morris T (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093–1095

    CAS  Google Scholar 

  69. Riley RJ et al (2002) The influence of DMPK as an integrated partner in modern drug discovery. Curr Drug Metab 3:527–550

    CAS  Google Scholar 

  70. Schiller C et al (2005) Intestinal fluid volumes and transit of dosage forms as assessed by magnetic resonance imaging. Aliment Pharmacol Ther 22:971–979

    CAS  Google Scholar 

  71. Uetrecht JP, Trager W (2007) Conjugation pathways. Drug metabolism, chemical and enzymatic aspects. Informa Healthcare, New York, pp 130–144

    Google Scholar 

  72. Wager TT et al (2010) Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of drug-like properties. ACS Chem Neurosci 1:435–449

    CAS  Google Scholar 

  73. Butler JM, Dressman JB (2010) The developability classification system: application of biopharmaceutics concepts to formulation development. J Pharm Sci 99:4940–4954

    CAS  Google Scholar 

  74. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715

    CAS  Google Scholar 

  75. Waring MJ (2010) Lipophilicity in drug discovery. Expert Opin Drug Discov 5:235–248

    CAS  Google Scholar 

  76. Hann MM, Keserű GM (2012) Finding the sweet spot – the role of nature and nurture in medicinal chemistry. Nat Rev Drug Discov 11:355–365

    CAS  Google Scholar 

  77. Waring MJ (2009) Defining optimum lipophilicity and molecular weight ranges for drug candidates – molecular weight dependent lower logD limits based on permeability. Bioorg Med Chem Lett 19:2844–2851

    CAS  Google Scholar 

  78. Gleeson MP (2008) Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem 51:817–834

    CAS  Google Scholar 

  79. Kenny PW, Montanari CA (2013) Inflation of correlation in the pursuit of drug-likeness. J Comput Aided Mol Des 27:1–13

    CAS  Google Scholar 

  80. Sugano K et al (2010) Coexistence of passive and carrier-mediated processes in drug transport. Nat Rev Drug Discov 9:597–614

    CAS  Google Scholar 

  81. Gleeson MP, Hersey A, Hannongbua S (2011) In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Top Med Chem 11:358–381

    CAS  Google Scholar 

  82. Kell DB, Dobson PD, Oliver SG (2011) Pharmaceutical drug transport: the issues and the implications that it is essentially carrier-mediated only. Drug Discov Today 16:704–714

    CAS  Google Scholar 

  83. Di L et al (2012) Evidence-based approach to assess passive diffusion and carrier-mediated drug transport. Drug Discov Today 17:905–912

    CAS  Google Scholar 

  84. Kubinyi H (1978) Drug partitioning: relationships between forward and reverse rate constants and partition coefficient. J Pharm Sci 67:262–263

    CAS  Google Scholar 

  85. Kubinyi H (1979) Lipophilicity and drug activity. Prog Drug Res 23:97–198

    CAS  Google Scholar 

  86. Glen RC et al (1995) Computer-aided design and synthesis of 5-substituted tryptamines and their pharmacology at the 5-HT1D receptor: discovery of compounds with potential anti-migraine properties. J Med Chem 38:3566–3580

    CAS  Google Scholar 

  87. Young RJ (2011) The successful quest for oral factor Xa inhibitors; learnings for all of medicinal chemistry? Bioorg Med Chem Lett 21:6228–6235

    CAS  Google Scholar 

  88. Johnson TW et al (2009) Using the Golden Triangle to optimize clearance and oral absorption. Bioorg Med Chem Lett 19:5560–5564

    CAS  Google Scholar 

  89. Obach RL et al (2005) In vitro cytochrome P450 inhibition data and the prediction of drug–drug interactions: qualitative relationships, quantitative predictions, and the rank-order approach. Clin Pharmacol Ther 78:582–592

    CAS  Google Scholar 

  90. Lewis DFV et al (2007) Quantitative structure-activity relationships (QSARs) in inhibitors of various cytochromes P450: the importance of compound lipophilicity. J Enzyme Inhib Med Chem 22:1–6

    CAS  Google Scholar 

  91. Lewis DFV et al (2004) Compound lipophilicity for substrate binding to human P450s in drug metabolism. Drug Discov Today 9:530–537

    CAS  Google Scholar 

  92. Lewis DFV, Dickins M (2002) Substrate SAR in human p450s. Drug Discov Today 7:918–925

    CAS  Google Scholar 

  93. Jamieson C et al (2006) Medicinal chemistry of hERG optimizations: highlights and hang-ups. J Med Chem 49:5029–5046

    CAS  Google Scholar 

  94. Waring MJ, Johnstone C (2007) A quantitative assessment of hERG liability as a function of lipophilicity. Bioorg Med Chem Lett 17:1759–1764

    CAS  Google Scholar 

  95. Wood A, Armour D (2005) The discovery of the CCR5 receptor antagonist, UK-427,857, a new agent for the treatment of HIV infection and AIDS. Prog Med Chem 43:239–271

    CAS  Google Scholar 

  96. Diller DJ (2009) In silico hERG modelling: challenges and progress. Curr Comput Aided Drug Des 5:106–121

    CAS  Google Scholar 

  97. Tarcsay Á, Keserű GM (2013) Contributions of molecular properties to drug promiscuity. J Med Chem 56:1789–1795

    CAS  Google Scholar 

  98. Hopkins AL, Mason JS, Overington JP (2006) Can we rationally design promiscuous drugs? Curr Opin Struct Biol 16:127–136

    CAS  Google Scholar 

  99. Hughes JD et al (2008) Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg Med Chem Lett 18:4872–4875

    CAS  Google Scholar 

  100. Morphy R, Rankovic Z (2007) Fragments, network biology and designing multiple ligands. Drug Discov Today 12:156–160

    CAS  Google Scholar 

  101. Azzaoui K et al (2007) Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem 2007(2):874–880

    Google Scholar 

  102. Peters J-U et al (2009) Pharmacological promiscuity: dependence on compound properties and target specificity in a set of recent Roche compounds. ChemMedChem 4:680–686

    CAS  Google Scholar 

  103. Yang Y et al (2010) Investigation of the relationship between topology and selectivity for drug-like molecules. J Med Chem 53:7709–7714

    CAS  Google Scholar 

  104. Peters J-U et al (2012) Can we discover pharmacological promiscuity early in the drug discovery process? Drug Discov Today 17:325–335

    Google Scholar 

  105. Lounkine E et al (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486:361–367

    CAS  Google Scholar 

  106. Leeson PD et al (2011) Impact of ion class and time on oral drug molecular properties. Med Chem Comm 2:91–105

    CAS  Google Scholar 

  107. Leach AR, Hann MM (2011) Molecular complexity and fragment-based drug discovery: ten years on. Curr Opin Chem Biol 15:489–496

    CAS  Google Scholar 

  108. Trainor GL (2007) The importance of plasma protein binding in drug discovery. Expert Opin Drug Discov 2:51–64

    CAS  Google Scholar 

  109. Valkó K et al (2003) Fast gradient HPLC method to determine compounds binding to human serum albumin: relationships with octanol/water and immobilized artificial membrane lipophilicity. J Pharm Sci 92:2236–2248

    Google Scholar 

  110. Smith DA et al (2010) The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discov 9:929–939

    CAS  Google Scholar 

  111. Riley RJ et al (2005) A unified model for predicting human hepatic metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab Dispos 33:1304–1311

    CAS  Google Scholar 

  112. Braggio et al (2010) Drug efficiency: a new concept to guide lead optimization programs towards the selection of better clinical candidates. Expert Opin Drug Discov 5:609–618

    CAS  Google Scholar 

  113. Montanari D et al (2011) Application of drug efficiency index in drug discovery: a strategy towards low therapeutic dose. Expert Opin Drug Discov 6:913–920

    CAS  Google Scholar 

  114. Valkó K et al (2012) In vitro measurement of drug efficiency index to aid early lead optimization. J Pharm Sci 101:4155–4169

    Google Scholar 

  115. Valkó K, Nunhuck SB, Hill AP (2011) Estimating unbound volume of distribution and tissue binding by in vitro HPLC-based human serum albumin and immobilized artificial membrane-binding measurements. J Pharm Sci 100:849–862

    Google Scholar 

  116. Hopkins AL et al (2014) The role of ligand efficiency measures in drug discovery. Nat Rev Drug Discov 13:105–121

    Google Scholar 

  117. Reynolds CH et al (2008) Ligand binding efficiency: trends, physical basis, and implications. J Med Chem 51:2432–2438

    CAS  Google Scholar 

  118. Nissink JWM (2009) Simple size-independent measure of ligand efficiency. J Chem Inf Model 49:1617–1622

    CAS  Google Scholar 

  119. Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 41:856–864

    CAS  Google Scholar 

  120. Mortenson PN, Murray CW (2011) Assessing the lipophilicity of fragments and early hits. J Comput Aided Mol Des 25:663–667

    CAS  Google Scholar 

  121. Wyatt PG et al (2008) Identification of N-(4-Piperidinyl)-4-(2,6-dichlorobenzoylamino)-1H-pyrazole-3-carboxamide (AT7519), a novel cyclin dependent kinase inhibitor using fragment-based X-ray crystallography and structure based drug design. J Med Chem 51:4986–4999

    CAS  Google Scholar 

  122. Gill AL et al (2005) Identification of novel p38alpha MAP kinase inhibitors using fragment-based lead generation. J Med Chem 48:414–426

    CAS  Google Scholar 

  123. Freeman-Cook KD, Hoffman RL, Johnson TW (2013) Lipophilic efficiency: the most important efficiency metric in medicinal chemistry. Future Med Chem 5:113–115

    CAS  Google Scholar 

  124. Gill AL et al (2007) A comparison of physicochemical property profiles of marketed oral drugs and orally bioavailable anti-cancer protein kinase inhibitors in clinical development. Curr Top Med Chem 7:1408–1422

    CAS  Google Scholar 

  125. McTigue M et al (2012) Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proc Natl Acad Sci U S A 109:18281–18289

    CAS  Google Scholar 

  126. Shultz MD (2013) Setting expectations in molecular optimizations: strengths and limitations of commonly used composite parameters. Bioorg Med Chem Lett 23:5980–5991

    CAS  Google Scholar 

  127. Freire E (2008) Do enthalpy and entropy distinguish first in class from best in class? Drug Discov Today 13:869–874

    CAS  Google Scholar 

  128. Shultz MD (2013) The thermodynamic basis for the use of lipophilic efficiency (LipE) in enthalpic optimizations. Bioorg Med Chem Lett 23:5992–6000

    CAS  Google Scholar 

  129. Morgan P et al (2012) Can the flow of medicines be improved? fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov Today 17:419–424

    CAS  Google Scholar 

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Acknowledgements

The educational help of the many who have turned a maths-averse organic chemist into a medicinal chemist conversant in physical properties is gratefully acknowledged. In particular long-time friend and mentor Alan Hill has been the source of much knowledge and inspiration. The expertise of, and stimulating conversations with, Paul Leeson, Chris Luscombe, Darren Green, Mike Hann, Klára Valkó, Andrew Leach and Tim Ritchie have also contributed much to the growing debate and wider acceptance of the impact of physical properties.

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  1. MDR Medicinal Chemistry, GlaxoSmithKline R&D, Stevenage, Hertfordshire, UK, SG1 2NY

    Robert J. Young

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  1. Bristol-Myers Squibb, Wallingford, Connecticut, USA

    Nicholas A. Meanwell

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Young, R.J. (2014). Physical Properties in Drug Design. In: Meanwell, N. (eds) Tactics in Contemporary Drug Design. Topics in Medicinal Chemistry, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/7355_2013_35

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