Molecular properties that influence the oral bioavailability of drug candidates
Abstract
Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two criteria of (1) 10 or fewer rotatable bonds and (2) polar surface area equal to or less than 140 A(2) (or 12 or fewer H-bond donors and acceptors) will have a high probability of good oral bioavailability in the rat. Data sets for the artificial membrane permeation rate and for clearance in the rat were also examined. Reduced polar surface area correlates better with increased permeation rate than does lipophilicity (C log P), and increased rotatable bond count has a negative effect on the permeation rate. A threshold permeation rate is a prerequisite of oral bioavailability. The rotatable bond count does not correlate with the data examined here for the in vivo clearance rate in the rat.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In drug development, 500 Da is often quoted as the maximum molecular weight parameter, but molecules with higher molecular weights can be absorbed, and the limits of oral bioavailability appear to extend to about MW ≤ 1000 Da.
“In the development of new drugs, 500 Da is often quoted as the maximum molecular weight parameter. However, it has been observed that molecules with higher molecular weights are also capable of being absorbed, and the limits of oral bioavailability appear to extend to about MW ≤ 1000 Da [70,71,72].”
From The EU’s Per- and Polyfluoroalkyl Substances (PFAS) Ban: A Case of Policy over Science (2023) · cites this paper as [70]
The lipophilic nature and low polarity of sesquiterpenes render them bioavailable at specific sites.
“The lipophilic nature and low polarity of sesquiterpenes render them bioavailable at specific sites [105,153].”
From Acetylcholinesterase Inhibitory Potential of Various Sesquiterpene Analogues for Alzheimer’s Disease Therapy (2021) · cites this paper as [153]
Cites
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,484 · see citing works
Goodman and Gilman's the Pharmacological Basis of Therapeutics
Stigelman, W. H., & Pharm, D. (1986) · Military Medicine · doi:10.1093/milmed/151.1.386 · Cited by 6,796 · see citing works
Drug-like properties and the causes of poor solubility and poor permeability
Lipinski, C. A. (2000) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/s1056-8719(00)00107-6 · Cited by 3,776 · see citing works
Ertl, P., Rohde, B., & Selzer, P. M. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000942e · Cited by 3,149 · see citing works
Prediction of Drug Absorption Using Multivariate Statistics
Egan, W. J., Merz, K. M., & Baldwin, J. J. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000292e · Cited by 2,015 · see citing works
Kansy, M., Senner, F., & Gubernator, K. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970530e · Cited by 1,304 · see citing works
Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans
Palm, K., Stenberg, P., Luthman, K., & Artursson, P. (1997) · Pharmaceutical Research · doi:10.1023/a:1012188625088 · Cited by 762 · see citing works
Clark, D. E. (1999) · Journal of Pharmaceutical Sciences · doi:10.1021/js9804011 · Cited by 632 · see citing works
Wohnsland, F., & Faller, B. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm001020e · Cited by 496 · see citing works
Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?
Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484 · see citing works
Correlation of Drug Absorption with Molecular Surface Properties
Palm, K., Luthman, K., Unge, A.-L., Strandlund, G., & Artursson, P. (1996) · Journal of Pharmaceutical Sciences · doi:10.1021/js950285r · Cited by 412 · see citing works
Is the Role of the Small Intestine in First-Pass Metabolism Overemphasized?
Lin, J. H., Chiba, M., & Baillie, T. A. (1999) · Pharmacological Reviews · doi:10.1016/s0031-6997(24)01402-9 · Cited by 354 · see citing works
QSAR Model for Drug Human Oral Bioavailability
Yoshida, F., & Topliss, J. G. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm0000564 · Cited by 302 · see citing works
Experimental and Computational Screening Models for the Prediction of Intestinal Drug Absorption
Stenberg, P., Norinder, U., Luthman, K., & Artursson, P. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm001101a · Cited by 253 · see citing works
Pharmacokinetic and Pharmacodynamic Implications of P‐glycoprotein Modulation
Matheny, C. J., Lamb, M. W., Brouwer, K. L. R., & Pollack, G. M. (2001) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1592/phco.21.9.778.34558 · Cited by 224 · see citing works
Sugano, K., Hamada, H., Machida, M., & Ushio, H. (2001) · SLAS DISCOVERY · doi:10.1177/108705710100600309 · Cited by 207 · see citing works
Design principles for orally bioavailable drugs
Navia, M. A., & Chaturvedi, P. R. (1996) · Drug Discovery Today · doi:10.1016/1359-6446(96)10020-9 · Cited by 203 · see citing works
How Structural Features Influence the Biomembrane Permeability of Peptides
Burton, P. S., Conradi, R. A., Ho, N. F. H., Hilgers, A. R., & Borchardt, R. T. (1996) · Journal of Pharmaceutical Sciences · doi:10.1021/js960067d · Cited by 177 · see citing works
Prediction of pharmacokinetic properties using experimental approaches during early drug discovery
Chaturvedi, P. R., Decker, C. J., & Odinecs, A. (2001) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(00)00228-3 · Cited by 118 · see citing works
Utility of metabolic stability screening: comparison ofin vitroandin vivoclearance
Clarke, S. E., & Jeffrey, P. (2001) · Xenobiotica · doi:10.1080/00498250110057350 · Cited by 111 · see citing works
Virtual screening of intestinal drug permeability
Stenberg, P., Luthman, K., & Artursson, P. (2000) · Journal of Controlled Release · doi:10.1016/s0168-3659(99)00239-4 · Cited by 107 · see citing works
Azepanone-Based Inhibitors of Human and Rat Cathepsin K
Marquis, R. W., Ru, Y., LoCastro, S. M., et al. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm000481x · Cited by 102 · see citing works
Structure and electrochemical properties of microfiltration filter-lipid membrane systems
Thompson, M. E., Lennox, R. B., & McClelland, R. A. (1982) · Analytical Chemistry · doi:10.1021/ac00238a024 · Cited by 100 · see citing works
Okumu, F. W., Pauletti, G. M., VanderVelde, D. G., et al. (1997) · Pharmaceutical Research · doi:10.1023/a:1012092409216 · Cited by 92 · see citing works
Prediction of Membrane Permeability to Peptides from Calculated Dynamic Molecular Surface Properties
Stenberg, P., Luthman, K., & Artursson, P. (1999) · Pharmaceutical Research · doi:10.1023/a:1018816122458 · Cited by 83 · see citing works
Clarke, S. E., Baldwin, S. J., Bloomer, J. C., et al. (1994) · Chemical Research in Toxicology · doi:10.1021/tx00042a018 · Cited by 59 · see citing works
Sugawara, M., Takekuma, Y., HarumiYamada, et al. (1998) · Journal of Pharmaceutical Sciences · doi:10.1021/js970451q · Cited by 53 · see citing works
Strategies toward predicting peptide cellular permeability from computed molecular descriptors
Goodwin, J. T., Conradi, R. A., Burton, P. S., et al. (1999) · Journal of Peptide Research · doi:10.1034/j.1399-3011.1999.00072.x · Cited by 38 · see citing works
Gao, J., Sudoh, M., BORCHARDT, R. T., et al. (2001) · Journal of Peptide Research · doi:10.1046/j.1397-002x.2001.00000.x · Cited by 33 · see citing works
Heine, D. R., Denton, M. B., & Schlabach, T. D. (1982) · Analytical Chemistry · doi:10.1021/ac00238a025 · Cited by 32 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367 · see citing works
The influence of drug-like concepts on decision-making in medicinal chemistry
Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403 · see citing works
An oral SARS-CoV-2 M <sup>pro</sup> inhibitor clinical candidate for the treatment of COVID-19
Owen, D. R., Allerton, C. M. N., Anderson, A. S., Aschenbrenner, L., Avery, M., Berritt, S., Boras, B., Cardin, R. D., Carlo, A., Coffman, K. J., et al. (2021) · Science · doi:10.1126/science.abl4784 · Cited by 2,033 · see citing works
Wu, C.-Y., & Benet, L. Z. (2005) · Pharmaceutical Research · doi:10.1007/s11095-004-9004-4 · Cited by 1,401 · see citing works
Getting Across the Cell Membrane: An Overview for Small Molecules, Peptides, and Proteins
Yang, N. J., & Hinner, M. J. (2015) · Methods in Molecular Biology · doi:10.1007/978-1-4939-2272-7_3 · Cited by 826 · see citing works
Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates
Doak, B. C., Over, B., Giordanetto, F., & Kihlberg, J. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.08.013 · Cited by 835 · see citing works
Role of computer-aided drug design in modern drug discovery
Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762 · see citing works
Halogen atoms in the modern medicinal chemistry: hints for the drug design
Hernandes, M., Cavalcanti, S. M., Moreira, D. R., de Azevedo Junior, W., & Leite, A. C. (2010) · Current Drug Targets · doi:10.2174/138945010790711996 · Cited by 723 · see citing works
Diversity-oriented synthesis: Exploring the intersections between chemistry and biology
Tan, D. S. (2005) · Nature Chemical Biology · doi:10.1038/nchembio0705-74 · Cited by 605 · see citing works
Properties of FDA-approved small molecule protein kinase inhibitors
Roskoski, R. (2019) · Pharmacological Research · doi:10.1016/j.phrs.2019.03.006 · Cited by 554 · see citing works
Small-molecule kinase inhibitors: an analysis of FDA-approved drugs
Wu, P., Nielsen, T. E., & Clausen, M. H. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2015.07.008 · Cited by 457 · see citing works
Fragment-Based Approaches in Drug Discovery and Chemical Biology
Scott, D. E., Coyne, A. G., Hudson, S. A., & Abell, C. (2012) · Biochemistry · doi:10.1021/bi3005126 · Cited by 460 · see citing works
Edmondson, S. D., Yang, B., & Fallan, C. (2019) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2019.04.030 · Cited by 422 · see citing works
Current strategies for the design of PROTAC linkers: a critical review
Troup, R. I., Fallan, C., & Baud, M. G. J. (2020) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2020.00018 · Cited by 424 · see citing works
Drug research: myths, hype and reality
Kubinyi, H. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1156 · Cited by 348 · see citing works
Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS)
Varma, M. V., Steyn, S. J., Allerton, C., & El-Kattan, A. F. (2015) · Pharmaceutical Research · doi:10.1007/s11095-015-1749-4 · Cited by 334 · see citing works
A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
Gayvert, K. M., Madhukar, N. S., & Elemento, O. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.07.023 · Cited by 310 · see citing works
Ursu, O., Rayan, A., Goldblum, A., & Oprea, T. I. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.52 · Cited by 276 · see citing works
PAMPA—critical factors for better predictions of absorption
Avdeef, A., Bendels, S., Di, L. I., Faller, B., Kansy, M., Sugano, K., & Yamauchi, Y. (2007) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21068 · Cited by 240 · see citing works
Review and analysis of FDA approved drugs using lipid-based formulations
Savla, R., Browne, J., Plassat, V., Wasan, K. M., & Wasan, E. K. (2017) · Drug Development and Industrial Pharmacy · doi:10.1080/03639045.2017.1342654 · Cited by 225 · see citing works
Solution Conformations Shed Light on PROTAC Cell Permeability
Atilaw, Y., Poongavanam, V., Svensson Nilsson, C., Nguyen, D., Giese, A., Meibom, D., Erdelyi, M., & Kihlberg, J. (2021) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00556 · Cited by 208 · see citing works
Drug permeability profiling using cell-free permeation tools: overview and applications
Berben, P., Bauer-Brandl, A., Brandl, M., Faller, B., Flaten, G. E., Jacobsen, A.-C., Brouwers, J., & Augustijns, P. (2018) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2018.04.016 · Cited by 199 · see citing works
Rodríguez-Rodríguez, C., Telpoukhovskaia, M., & Orvig, C. (2012) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2012.03.008 · Cited by 151 · see citing works
Targeting intrinsically disordered proteins at the edge of chaos
Ruan, H., Sun, Q., Zhang, W., Liu, Y., & Lai, L. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.09.017 · Cited by 133 · see citing works
Zhao, Z.-W., del Cueto, M., Geng, Y., & Troisi, A. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c02325 · Cited by 102 · see citing works
Arya, A., Chahal, R., Rao, R., Rahman, M. H., Kaushik, D., Akhtar, M. F., Saleem, A., Khalifa, S. M. A., El-Seedi, H. R., Kamel, M., et al. (2021) · Biomolecules · doi:10.3390/biom11030350 · Cited by 111 · see citing works
McKim, J. M. (2014) · Critical Reviews in Toxicology · doi:10.3109/10408444.2013.861797 · Cited by 108 · see citing works
Labbé, C. M., Laconde, G., Kuenemann, M. A., Villoutreix, B. O., & Sperandio, O. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.05.003 · Cited by 104 · see citing works
Issahaku, A. R., Mukelabai, N., Agoni, C., Rudrapal, M., Aldosari, S. M., Almalki, S. G., & Khan, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-22668-1 · Cited by 93 · see citing works
Ho, T. N. T., Abraham, N., & Lewis, R. J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.609005 · Cited by 87 · see citing works
O’ Donovan, D. H., De Fusco, C., Kuhnke, L., & Reichel, A. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c01577 · Cited by 83 · see citing works
The EU’s Per- and Polyfluoroalkyl Substances (PFAS) Ban: A Case of Policy over Science
Spyrakis, F., & Dragani, T. A. (2023) · Toxics · doi:10.3390/toxics11090721 · Cited by 78 · see citing works
Stabilizers of the Max Homodimer Identified in Virtual Ligand Screening Inhibit Myc Function
Jiang, H., Bower, K. E., Beuscher, A. E., Zhou, B., Bobkov, A. A., Olson, A. J., & Vogt, P. K. (2009) · Molecular Pharmacology · doi:10.1124/mol.109.054858 · Cited by 62 · see citing works
Increasing the efficiency of ligands for FK506-binding protein 51 by conformational control
Wang, Y., Kirschner, A., Fabian, A.-K., Gopalakrishnan, R., Kress, C., Hoogeland, B., Koch, U., Kozany, C., Bracher, A., & Hausch, F. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm400087k · Cited by 60 · see citing works
Artificial intelligence for dementia drug discovery and trials optimization
Doherty, T., Yao, Z., Khleifat, A. A. L., Tantiangco, H., Tamburin, S., Albertyn, C., Thakur, L., Llewellyn, D. J., Oxtoby, N. P., Lourida, I., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13428 · Cited by 48 · see citing works
How do metabolites differ from their parent molecules and how are they excreted?
Kirchmair, J., Howlett, A., Peironcely, J. E., Murrell, D. S., Williamson, M. J., Adams, S. E., Hankemeier, T., van Buren, L., Duchateau, G., Klaffke, W., et al. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300487z · Cited by 43 · see citing works
IFITM proteins assist cellular uptake of diverse linked chemotypes
Lou, K., Wassarman, D. R., Yang, T., Paung, Y., Zhang, Z., O’Loughlin, T. A., Moore, M. K., Egan, R. K., Greninger, P., Benes, C. H., et al. (2022) · Science · doi:10.1126/science.abl5829 · Cited by 42 · see citing works
Hwang, D., Ramsey, J. D., Makita, N., Sachse, C., Jordan, R., Sokolsky-Papkov, M., & Kabanov, A. V. (2019) · Journal of Controlled Release · doi:10.1016/j.jconrel.2019.06.037 · Cited by 40 · see citing works
Khalil, M. N. A., Choucry, M. A., El Senousy, A. S., Hassan, A., El-Marasy, S. A., El Awdan, S. A., & Omar, F. A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0219378 · Cited by 25 · see citing works
A Homogeneous HTRF Assay for the Identification of Inhibitors of the TWEAK-Fn14 Protein Interaction
Benicchi, T., Iozzi, S., Svahn, A., Axelsson, H., Mori, E., Bernocco, S., Cappelli, F., Caramelli, C., Fanti, P., Genesio, E., et al. (2012) · SLAS Discovery · doi:10.1177/1087057112447873 · Cited by 12 · see citing works
Anticancer Drug Conjugates Incorporating Estrogen Receptor Ligands
Zlotos, D. P., Kronenberger, T., & Laufer, S. A. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics15010067 · Cited by 9 · see citing works
Protein-Protein Interactions as Drug Targets
Wendt, M. D. (2012) · Topics in Medicinal Chemistry · doi:10.1007/978-3-642-28965-1_1 · Cited by 9 · see citing works
- Authors
- Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D.
- Affiliations
- GlaxoSmithKline (United States) (US)
- Corresponding author
- Kopple, Kenneth D.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-05-11
- Volume
- 45
- Issue
- 12
- Pages
- 2615-2623
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2105649494
- Cited by
- 8,420
- References
- 30
- Citations by year
- 2026: 858 · 2025: 912 · 2024: 891 · 2023: 846 · 2022: 843 · 2021: 716 · 2020: 553 · 2019: 406 · 2018: 356 · 2017: 265 · 2016: 236 · 2015: 181 · 2014: 196 · 2013: 170 · 2012: 150
- FWCI
- 77.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Drug Solubulity and Delivery Systems
- Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
- Keywords
- oral bioavailability, drug candidates, molecular properties, rotatable bonds, polar surface area, molecular weight, molecular weight cut-off, lipophilicity, in vivo clearance, rat pharmacokinetics, structure-activity relationship
- MeSH terms
- Administration, Oral, Animals, Biological Availability, Humans, Hydrogen Bonding, Lipid Bilayers, Male, Microsomes, Liver, Molecular Weight, Permeability, Pharmacokinetics, Structure-Activity Relationship, Molecular Structure, Databases, Factual, Rats, Sprague-Dawley, Cross-Over Studies, Rats, In Vitro Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review