MDDR-like rules

MDDR-like rules

MDDR-like rule is published by Tudor I. Oprea. The rule of five test produced similar results when applied to the ACDF and MDDRF subset, which 80% of ACDF and MDDRF pass the rule of five test. ACD database is non-drug database and MDDR database is drug database. ACDF and MDDRF are databases removed the reactive functional groups such as acyl-halides, sulfonyl-halides, Michael acceptors, etc.. In this study, therefore, he has concluded that the rule of five test cannot be used to discriminate between drugs and non-drugs. Descriptors used to MDDR-like rule are the number of rings, the number of rigid bonds and the number of rotatable bonds.

The probability of finding a ‘druglike’ compound is higher in its ranges (e.g., No.Rings ≥ 3, No. Rigid bonds ≥ 18, No. Rotatable bonds ≥ 6), while the probability of finding a ‘nondrug-like’ compound is higher in the ranges (e.g., No.Rings ≤ 2, No. Rigid bonds ≤ 17, No. Rotatable bonds ≤ 5). There is more information in the table below. In PreADMET, The result from MDDR-like rule is represented as classification of the following table and this rule can be customized by user. (Figure 5.5)

[ Oprea,T.I. J. Comput. Aid. Mol. Des. 2000, 14, 251. ]

  Condition
drug-like No. Rings ≥ 3

No. Rigid bonds ≥ 18

No. Rotatable bonds ≥ 6
nondrug-like No. Rings ≤ 2

No. Rigid bonds ≤ 17

No. Rotatable bonds ≤ 5
mid-structure structures of the other ranges
Druglikeness, Quick Help

CMC-like rule

CMC-like rule is similar to rule of five. This is published by Arup K. Ghose et al.. They defined druglike character for the CMC database, which is removed several classes of compounds such as diagnostic imaging agents, solvents, and pharmaceutical aids. In this study, the qualifying range (covering more than 80% of the compounds) of the calculated log P is between -0.4 and 5.6, with an average value of 2.52. For molecular weight, the qualifying range is between 160 and 480, with an average value of 357. For molar refractivity, the qualifying range is between 40 and 130, with an average value of 97. For the total number of atoms, the qualifying range is between 20 and 70, with an average value of 48. The following table is about the qualifying range.

[ Ghose,A.K. et al. J. Comb. Chem. 1999, 1, 55. ]

drug class   Qualifying Range in CMC Database
  AlogP

(80%)

 AMR

(80%)

 Molecular Weight

(80%)

 Number of Atoms

(80%)
CMC clean -0.4 ~ 5.6 40 ~ 130 160 ~ 480 20 ~ 70
inflammatory 1.4 ~4.5 59 ~ 119 212 ~ 447 24 ~ 59
depressant 1.4 ~ 4.9 62 ~ 114 210 ~ 380 32 ~ 56
psychotic 2.3 ~ 5.2 85 ~ 131 274 ~ 464 40 ~ 63
hypertensive -0.5 ~ 4.5 54 ~ 128 206 ~ 506 28 ~ 66
hypnotic 0.5 ~ 3.9 43 ~ 97 162 ~ 360 20 ~ 45
neoplastic -1.5 ~ 4.7 43 ~ 128 180 ~ 475 21 ~ 63
infective -0.3 ~ 5.1 44 ~ 144 145 ~ 455 12 ~ 64
Druglikeness, Quick Help

Leadlike rule

Identification and optimization of lead compounds as chemical staring points are very important in combinatorial chemistry. Lead-like rule is published by Simon J. Teague et al. and defined to consider designing libraries with druglike physicochemical properties. They have divided the common sources of lead compounds for drug discovery into three types like the following figure. [ Teague,S.J.et al., Angew. Chem. Int. Ed. 1999, 38, 3743. ]

leadlike

 Figure. Group of Leads of Drug

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Lipinski's rule

Lipinski’s Rule, so called “Rule of Five”, is published by Christopher A. Lipinski et al. in Pfizer Central Research (Groton, NJ,USA). They selected a subset of 2245 compounds from WDI (World Drug Index) database and defined drug-like character through this subset. The results are the followings: [ Lipinski,C.A. et al. Adv. Drug Deliv. Rev. 1997, 23, 3. ]

  • No. hydrogen bond donors ≤ 5 ( The sum of OHs and NHs )
  • No. hydrogen bond acceptor ≤ 10 ( The sum of Os and Ns )
  • Molecular weight ≤ 500
  • CLogP ≤ 5 ( MlogP ≤ 4.5 )

About 90 % of the above subset is included in defined rage with better solubility and permeability.

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Plasma Protein Binding

Generally, only the unbound drug is available for diffusion or transport across cell membranes, and also for interaction with a pharmacological target. As a result, a degree of plasma protein binding of a drug influences not only on the drug’s action but also its disposition and efficacy. In PreADMET can predict percent drug bound in plasma protein as in vitro data on human.

Although there are some differences in the experimental values by compounds or their metabolisms, we can put into general categories like below.

Classification Plasma Protein Binding(%PPB)
Chemicals strongly bound more than 90%
Chemicals weakly bound less than 90%
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Blood Brain Barrier Penetration

Blood-Brain Barrier (BBB) penetration is represented as BB = [Brain]/[Blood], where [Brain] and [Blood] are the steady-state concentration of radiolabeled compounds in brain and peripheral blood. Predicting BBB penetration means predicting whether compounds pass across the blood-brain barrier. This is crucial in pharmaceutical sphere because CNS-active compounds must pass across it and CNS-inactive compounds mustn’t pass across it in order to avoid of CNS side effects. In PreADMET can predict in vivo data on rates for BBB penetration.

 

We can put into general categories like below, although there are some differences in the experimental values by compounds or their metabolisms.

[ Ajay,G.W. et al. J. Med. Chem. 1999, 42, 4942. ]

Classification BB (Cbrain/Cblood) logBB
CNS – Active compounds(+)  more than 1.0 more than 0
CNS – Inactive compounds(- )  less than 1.0 less than 0

 

There is another report for the classification. [ Lobell, M et al. J. Pharma. Sci. 2003, 92, 360. ]

Classification BB (Cbrain/Cblood) logBB
CNS – Active compounds(+) more than 0.40 more than -0.4
CNS – Inactive compounds(- ) less than 0.40 less than -0.4

 

The following is the actual classification that PreADMET uses. [ Ma X. et al. Acta Pharmacologica Sinica. 2005, 26, 500. ]

Classification BB (Cbrain/Cblood) logBB
High absorption to CNS more than 2.0 more than 0.3
Middle absorption to CNS 2.0 ~ 0.1 0.3 ~-1.0
Low absorption to CNS less than 0.1 less than -1.0
ADME Help

Skin Permeability

In the pharmaceutical, cosmetic and agrochemical fields, it is important to predict the skin permeability rate for a crucial parameter for the transdermal delivery of drugs and for the risk assessment of all chemicals that come into contact with the skin either accidentally or by design. In PreADMET can predict in vitro data on human for skin permeability.

PreADMET predicts in vitro skin permeability and the result value is given as logKp. Kp (cm/hour) is defined as:

Kp= Km*D/h

 

where Km is distribution coefficient between stratum corneum and vehicle, and D is average diffusion coefficient (cm2/h), and h is thickness of skin (cm).

[ Singh,S. et al. Med. Res. Rev. 1993, 13, 569. ]

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Human Intestinal Absorption

Predicting human intestinal absorption of drugs is very important for identify potential drug candidate. In PreADMET can predict percent human intestinal absorption (%HIA). Human intestinal absorption data are the sum of bioavailability and absorption evaluated from ratio of excretion or cumulative excretion in urine , bile and feces.

[ Zhao,Y.H. et al. J. Pharm. Sci. 2001, 90, 749. ]

 

For prediction of HIA in PreADMET, chemical structures at pH 7.4 are applied, because HIA is measured by in vivo test. This is one of options of PreAMDET and user can choose for this option.

 

Although there are some differences in the experimental values by compounds or their metabolisms, we can put into general categories like below.

[ Yee,S. Pharm. Res. 1997, 14, 763. ]

 

Classification HIA (Human Intestinal Absorption)
Poorly absorbed compounds 0 ~ 20 %
Moderately absorbed compounds 20 ~ 70 %
Well absorbed compounds 70 ~ 100 %
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Caco2 cell permeability

Numerous in vitro methods have been used in the drug selection process for assessing the intestinal absorption of drug candidates. Among them, Caco-2 cell model and MDCK cell model has been recommended as a reliable in vitro model for the prediction of oral drug absorption.

[ Yamashita,S. et al. Eur. J. Pharm. 2000, 10, 195. ]

Caco-2 cells are derived from human colon adenocarcinoma and possess multiple drug transport pathways through the intestinal epithelium.

For prediction of Caco-2 cell permeability in PreADMET, chemical structures at pH 7.4 are applied, because Caco-2 cell permeability and MDCK cell permeability are measured at about pH 7.4. This is one of options of PreAMDET and user can choose for this option.

Although there are some differences in the experimental values by compounds or their metabolisms, we can put into general categories like below.

[ Yazdanian,M. Pharm. Res. 1998, 15(9), 1490. ]

Classification PCaco-2 (nm/sec)
Low permeability less than 4
Middle permeability 4 ~ 70
High permeability more than 70

[ (1) Yamashita,S. et al. Eur. J. Pharm. Sci. 2000, 10, 195., (2) Irvine,J.D. et al. J. Pharm. Sci. 1999, 88, 28. ]

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