Lipinski's rule of five

"Rule of five" redirects here. For the rule of thumb as it applies to the C++11 programming language, see Rule of five (C++ programming).

Lipinski's rule of five, also known as Pfizer's rule of five or simply the rule of five (RO5), is a rule of thumb to evaluate druglikeness or determine if a chemical compound with a certain pharmacological or biological activity has chemical properties and physical properties that would likely make it an orally active drug in humans. The rule was formulated by Christopher A. Lipinski in 1997, based on the observation that most orally administered drugs are relatively small and moderately lipophilic molecules.^[1][2]

The rule describes molecular properties important for a drug's pharmacokinetics in the human body, including their absorption, distribution, metabolism, and excretion ("ADME"). However, the rule does not predict if a compound is pharmacologically active.

The rule is important to keep in mind during drug discovery when a pharmacologically active lead structure is optimized step-wise to increase the activity and selectivity of the compound as well as to ensure drug-like physicochemical properties are maintained as described by Lipinski's rule.^[3] Candidate drugs that conform to the RO5 tend to have lower attrition rates during clinical trials and hence have an increased chance of reaching the market.^[2][4]

Omeprazole is a popular drug that conforms to Lipinski's rule of five.

Some authors have criticized the rule of five for the implicit assumption that passive diffusion is the only important mechanism for the entry of drugs into cells, ignoring the role of transporters. For example, O'Hagan and co-authors wrote as follows:^[5]

This famous "rule of 5" has been highly influential in this regard, but only about 50 % of orally administered new chemical entities actually obey it.

Studies have also demonstrated that some natural products break the chemical rules used in Lipinski filters such as macrolides and peptides.^[6][7][8]

Components of the rule

Lipinski's rule states that, in general, an orally active drug has no more than one violation of the following criteria:^[9]

• No more than 5 hydrogen bond donors (the total number of nitrogen–hydrogen and oxygen–hydrogen bonds)
• No more than 10 hydrogen bond acceptors (all nitrogen or oxygen atoms)
• A molecular mass less than 500 daltons
• A calculated octanol-water partition coefficient (Clog P) that does not exceed 5

Note that all numbers are multiples of five, which is the origin of the rule's name. As with many other rules of thumb, such as Baldwin's rules for ring closure, there are many exceptions.

Variants

In an attempt to improve the predictions of druglikeness, the rules have spawned many extensions, for example the Ghose filter:^[10]

• Partition coefficient log P in −0.4 to +5.6 range
• Molar refractivity from 40 to 130
• Molecular weight from 180 to 480
• Number of atoms from 20 to 70 (includes H-bond donors [e.g. OHs and NHs] and H-bond acceptors [e.g. Ns and Os])

Veber's Rule further questions a 500 molecular weight cutoff. The polar surface area and the number of rotatable bonds has been found to better discriminate between compounds that are orally active and those that are not for a large data set of compounds.^[11] In particular, compounds which meet only the two criteria of:

• 10 or fewer rotatable bonds and
• Polar surface area no greater than 140 Ų

are predicted to have good oral bioavailability.^[11]

Lead-like

During drug discovery, lipophilicity and molecular weight are often increased in order to improve the affinity and selectivity of the drug candidate. Hence it is often difficult to maintain drug-likeness (i.e., RO5 compliance) during hit and lead optimization. Hence it has been proposed that members of screening libraries from which hits are discovered should be biased toward lower molecular weight and lipophilicity so that medicinal chemists will have an easier time in delivering optimized drug development candidates that are also drug-like. Hence the rule of five has been extended to the rule of three (RO3) for defining lead-like compounds.^[12]

A rule of three compliant compound is defined as one that has:

• octanol-water partition coefficient log P not greater than 3
• molecular mass less than 300 daltons
• not more than 3 hydrogen bond donors
• not more than 3 hydrogen bond acceptors
• not more than 3 rotatable bonds

See also

• Biopharmaceutics Classification System
• Chemical structure
• Chemicalize.org § List of the predicted structure based properties
• Fragment-based lead discovery
• QSAR

References

1. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (March 2001). "Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings". Advanced Drug Delivery Reviews. 46 (1–3): 3–26. doi:10.1016/S0169-409X(00)00129-0. PMID 11259830.
2. Lipinski CA (December 2004). "Lead- and drug-like compounds: the rule-of-five revolution". Drug Discovery Today: Technologies. 1 (4): 337–341. doi:10.1016/j.ddtec.2004.11.007. PMID 24981612.
3. Oprea TI, Davis AM, Teague SJ, Leeson PD (2001). "Is there a difference between leads and drugs? A historical perspective". Journal of Chemical Information and Computer Sciences. 41 (5): 1308–1315. doi:10.1021/ci010366a. PMID 11604031.
4. Leeson PD, Springthorpe B (November 2007). "The influence of drug-like concepts on decision-making in medicinal chemistry". Nature Reviews. Drug Discovery. 6 (11): 881–890. doi:10.1038/nrd2445. PMID 17971784. S2CID 205476574.
5. O Hagan S, Swainston N, Handl J, Kell DB (2015). "A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs". Metabolomics. 11 (2): 323–339. doi:10.1007/s11306-014-0733-z. PMC 4342520. PMID 25750602.
6. Doak BC, Over B, Giordanetto F, Kihlberg J (September 2014). "Oral druggable space beyond the rule of 5: insights from drugs and clinical candidates". Chemistry & Biology. 21 (9): 1115–1142. doi:10.1016/j.chembiol.2014.08.013. PMID 25237858.
7. de Oliveira EC, Santana K, Josino L, Lima E, Lima AH, de Souza de Sales Júnior C (April 2021). "Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space". Scientific Reports. 11 (1): 7628. Bibcode:2021NatSR..11.7628D. doi:10.1038/s41598-021-87134-w. PMC 8027643. PMID 33828175.
8. Doak BC, Kihlberg J (February 2017). "Drug discovery beyond the rule of 5 - Opportunities and challenges". Expert Opinion on Drug Discovery. 12 (2): 115–119. doi:10.1080/17460441.2017.1264385. PMID 27883294.
9. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (March 2001). "Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings". Advanced Drug Delivery Reviews. 46 (1–3): 3–26. doi:10.1016/S0169-409X(00)00129-0. PMID 11259830.
10. Ghose AK, Viswanadhan VN, Wendoloski JJ (January 1999). "A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases". Journal of Combinatorial Chemistry. 1 (1): 55–68. doi:10.1021/cc9800071. PMID 10746014.
11. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (June 2002). "Molecular properties that influence the oral bioavailability of drug candidates". Journal of Medicinal Chemistry. 45 (12): 2615–2623. CiteSeerX 10.1.1.606.5270. doi:10.1021/jm020017n. PMID 12036371.
12. Congreve M, Carr R, Murray C, Jhoti H (October 2003). "A 'rule of three' for fragment-based lead discovery?". Drug Discovery Today. 8 (19): 876–877. doi:10.1016/S1359-6446(03)02831-9. PMID 14554012.

External links

• using ChemAxon's Marvin and Calculator Plugins – requires Java
• Calculation of Druglikeness – requires Java