Lipinski's rule of five

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 make it a likely 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]

"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).

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.

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:[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:[7]

  • 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 in the rat.[8] In particular, compounds which meet only the two criteria of:

  • 10 or fewer rotatable bonds and
  • Polar surface area no greater than 140 Å2

are predicted to have good oral bioavailability.[8]

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 lipophility 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.[9]

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

See also

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". Adv. Drug Deliv. Rev. 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". J Chem Inf Comput Sci. 41 (5): 1308–15. 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". Nat Rev Drug Discov. 6 (11): 881–90. doi:10.1038/nrd2445. PMID 17971784.
  5. 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.
  6. Leo A, Hansch C, Elkins D (1971). "Partition coefficients and their uses". Chem Rev. 71 (6): 525–616. doi:10.1021/cr60274a001.
  7. 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". J Comb Chem. 1 (1): 55–68. doi:10.1021/cc9800071. PMID 10746014.
  8. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (June 2002). "Molecular properties that influence the oral bioavailability of drug candidates". J. Med. Chem. 45 (12): 2615–23. CiteSeerX 10.1.1.606.5270. doi:10.1021/jm020017n. PMID 12036371.
  9. Congreve M, Carr R, Murray C, Jhoti H (October 2003). "A 'rule of three' for fragment-based lead discovery?". Drug Discov. Today. 8 (19): 876–7. doi:10.1016/S1359-6446(03)02831-9. PMID 14554012.
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