Lipinski's Rule of Five

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Lipinski's Rule of Five[1] is a rule of thumb to evaluate druglikeness, or determine if a chemical compound with a certain pharmacological or biological activity has 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 medication drugs are relatively small and lipophilic molecules.[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 for drug development where a pharmacologically active lead structure is optimized step-wise for increased activity and selectivity, as well as drug-like properties as described by Lipinski's rule. The modification of the molecular structure often leads to drugs with higher molecular weight, more rings, more rotatable bonds, and a higher lipophilicity.[3]

Contents

[edit] The rule

Lipinski's Rule of Five states that, in general, an orally active drug has no more than one violation of the following criteria:

Note that all numbers are multiples of five, which is the origin of the rule's name.

[edit] Improvements

To evaluate druglikeness better, the rules have spawned many extensions, for example one from a 1999 paper by Ghose et al.:[4]

  • Partition coefficient log P in -0.4 to +5.6 range
  • Molar refractivity from 40 to 130
  • Molecular weight from 160 to 480
  • Number of heavy atoms from 20 to 70

Over the past decade Lipinski's profiling tool for druglikeness has led to further investigations by scientists to extend profiling tools to lead-like properties of compounds in the hope that a better starting point in early discovery can save time and cost.

[edit] See also

[edit] References

  1. ^ Lipinski CA,Lombardo F,Dominy BW and Feeney PJ.Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.Adv Drug Del Rev,1997,23:3-25.
  2. ^ C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Del. Rev., 2001, 46, 3-26. (doi:10.1016/S0169-409X(00)00129-0)
  3. ^ T. I. Oprea, A. M. Davis, S. J. Teague, P. D. Leeson, Is There a Difference between Leads and Drugs? A Historical Perspective, J. Chem. Inf. Comput. Sci., 2001, 41, 1308-1315.
  4. ^ Arup K. Ghose, Vellarkad N. Viswanadhan, and John J. Wendoloski, A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery, J. Combin. Chem., 1999, 1, 55-68. (doi:10.1021/cc9800071)

[edit] External links