Lipophilic efficiency

Lipophilic efficiency^[1] (LiPE), sometimes referred to as ligand-lipophilicity efficiency (LLE) is a parameter used in drug design and drug discovery to evaluate the quality of research compounds, linking potency and lipophilicity in an attempt to estimate druglikeness.^[2][3] For a given compound LiPE is defined as the pIC₅₀ (or pEC₅₀) of interest minus the LogP of the compound.

A plot of LogP vs pIC₅₀ for 2 series of compounds (series 1: green dots, series 2: blue dots). Diagonal lines represents areas of equal LiPE. Analysis of this LiPE plot shows that series 1 includes many compounds with a high LiPE, and thus may represent a better lead series for further optimization.

$\ LiPE = pIC_{50} - LogP$

In practice, calculated values such as cLogP or calculated LogD are often used instead of the measured LogP or LogD. LiPE is used to compare compounds of different potencies (pIC₅₀s) and lipophilicities (LogP). High potency (high value of pIC₅₀) is a desirable attribute in drug candidates, as it reduces the risk of non-specific, off-target pharmacology at a given concentration. When associated with low clearance, high potency also allows for low total dose, which lowers the risk of idiosyncratic drug reaction.^[4][5]

On the other hand, LogP is an estimate of a compound's overall lipophilicity, a value that influence its behavior in a range of biological processes relevant to a drug discovery, such as solubility, permeability through biological membranes, hepatic clearance, lack of selectivity and non-specific toxicity.^[6] For oral drugs, a LogP value comprised between 2 and 3 is often considered optimal to achieve a compromise between permeability and first-pass clearance.

LiPE allows to capture both values in a single parameter, and empirical evidence suggest that quality drug candidates have a high LiPE (>6); this value corresponds to a compound with a pIC₅₀ of 8 and a LogP of 2. Plotting LogP against pIC₅₀ for a range of compounds allows to rank series and individual compounds.

An alternative equation uses the logarithm of the ratio of potency (measured as binding energy) and the partition coefficient to compute a lipophilic ligand efficiency index (LE) with a different scale.^[7]

$\ LElipo = log\bigg(\frac{\ -\ \Delta G}{P}\bigg)$

The following review discusses LipE in the context of other compound efficiency metrics.^[8]

References

1. Ryckmans T, Edwards MP, Horne VA, Correia AM, Owen DR, Thompson LR, Tran I, Tutt MF, Young T (August 2009). "Rapid assessment of a novel series of selective CB(2) agonists using parallel synthesis protocols: A Lipophilic Efficiency (LipE) analysis". Bioorganic & Medicinal Chemistry Letters 19 (15): 4406–9. doi:10.1016/j.bmcl.2009.05.062. PMID 19500981.
2. Edwards MP, Price DA (2010). "Role of Physicochemical Properties and Ligand Lipophilicity Efficiency in Addressing Drug Safety Risks". Annual Reports in Medicinal Chemistry 45: 381–391. doi:10.1016/S0065-7743(10)45023-X.
3. Leeson PD, Springthorpe B (November 2007). "The influence of drug-like concepts on decision-making in medicinal chemistry". Nature Reviews. Drug Discovery 6 (11): 881–90. doi:10.1038/nrd2445. PMID 17971784.
4. Uetrecht J (January 2001). "Prediction of a new drug's potential to cause idiosyncratic reactions". Current Opinion in Drug Discovery & Development 4 (1): 55–9. PMID 11727323.
5. Uetrecht J (January 2008). "Idiosyncratic drug reactions: past, present, and future". Chemical Research in Toxicology 21 (1): 84–92. doi:10.1021/tx700186p. PMID 18052104.
6. Hughes JD, Blagg J, Price DA, Bailey S, Decrescenzo GA, Devraj RV, Ellsworth E, Fobian YM, Gibbs ME, Gilles RW, Greene N, Huang E, Krieger-Burke T, Loesel J, Wager T, Whiteley L, Zhang Y (September 2008). "Physiochemical drug properties associated with in vivo toxicological outcomes". Bioorganic & Medicinal Chemistry Letters 18 (17): 4872–5. doi:10.1016/j.bmcl.2008.07.071. PMID 18691886.
7. García-Sosa AT, Hetényi C, Maran U (January 2010). "Drug efficiency indices for improvement of molecular docking scoring functions". Journal of Computational Chemistry 31 (1): 174–84. doi:10.1002/jcc.21306. PMID 19422000.
8. Shultz MD (November 2013). "Setting expectations in molecular optimizations: Strengths and limitations of commonly used composite parameters". Bioorganic & Medicinal Chemistry Letters 23 (21): 5980–91. doi:10.1016/j.bmcl.2013.08.029. PMID 24018190.