TCMSP parameter information and criteria

TCMSP parameter information and criteria

Bohui Li*

Lab of Systems Pharmacology, Center of Bioinformatics, College of Life Science, Northwest A&F University, China.

* Email:

Suggested drug screening criteria

  • OB: 30%; DL 0.18;

  • BBB: -0.3 is non-penetrating (BBB-), from -0.3 to +0.3, moderate penetrating (BBB±),and0.3 strong penetrating (BBB+);

  • HL: Drug half-life 4 hours: fast-elimination group, between 4-8 hours are mid-elimination group and 8 hours are slow-elimination group;

  • TPSA: less than 60 angstroms squared is cell membrane permeable;

  • RBN: meets only the criteria of 10 or fewer rotatable bonds for good oral bioavailability.


Parameter information

1) OBOral bioavailability (Xu et al., 2012)

OB represents the percentage of an orally administered dose of unchanged drug that reaches the systemic circulation, which reveals the convergence of the ADME process. High oral bioavailability is often a key indicator to determine the drug-like property of bioactive molecules as therapeutic agents.


2DLDrug-likeness (Tao et al., 2013)

DL is a qualitative concept used in drug design for an estimate on how “drug-like” a prospective compound is, which helps to optimize pharmacokinetic and pharmaceutical properties, such as solubility and chemical stability. The ‘drug-like’ level of the compounds is 0.18, which is used as a selection criterion for the ‘drug-like’ compounds in the traditional Chinese herbs.


3FASA- (Shen et al., 2012)

Fractional water accessible surface area of all atoms with negative partial charge (FASA-) can be used asdrug-likeness evaluation for drug-like molecules as described in Mingyun Shen’s work (Shen et al., 2012).

4Caco-2 permeability

Since the human intestinal cell line Caco-2 is generally used as an efficient in vitro model to study the passive diffusion of drugs across intestinal epithelium, we use the ingredients’ transport rates (nm/s) in Caco-2 monolayers to represent the intestinal epithelial permeability in TCMSP.


5) BBB: blood-brain barrier

The blood-brain barrieris anatomically characterized by the presence of intercellular tight junctions between continuous non-fenestrated endothelial cells, which normally function to limit the passage of protein and potentially diagnostic and the rapeuticagents into the brain parenchyma (Tattersall et al., 1975). Understanding and evaluating the capacity of compounds of entering into the central nervous system is critical, and the compounds with BBB -0.3 were considered as non-penetrating (BBB-), from -0.3 to +0.3 moderate penetrating (BBB±), and 0.3 strong penetrating (BBB+).


6) HL: Drug half-life (Yao et al., 2014)

Drug half-life (t1/2), which defined as “the time taken for the amount of compound in the body to fall by half”, is arguably the most important property as it dictates for the timescale over which the compound may elicit therapeutic (Madden, 2010). The drug half-life model details were well-described in “A novel Systems Pharmacology model for herbal medicine injection: acase using Reduning Injection”.


7) AlogP

AlogP value represents the partition coefficient between octanol and water, which is critical for measuring hydrophobicity of molecule. It is based on the Ghose-Crippen method, which calculated from a regression equation based on the hydrophobicity contribution of 120 atom types, including common bonding of H,C, N, O, S and the Halogens (Viswanadhan et al., 1989).


8) Hdon and Hacc

The Hdon and Hacc are measures of the hydrogen-bonding ability of a molecule expressed in terms of number of possible hydrogen-bond donors and acceptors, respectively. These two parameters could influence compounds and targets interaction by function together with the other ADME parameters.


9) RBN

RBN is the number of bonds which allow free rotation around themselves. These are defined as any single bond, not in a ring, bound to a nonterminal heavy atom. Excluded from the count are amide C-N bonds because of their high rotational energy barrier (Veber et al., 2002)The number of rotatable bonds is a well-known descriptor for molecular flexibility, and roughly proportional to molecular size for many “drug-like” compounds. In particular, compounds which meet only the criteria of 10 or fewer rotatable bonds are predicted to have good oral bioavailability (Veber et al., 2002).


10) TPSA

TPSA is a physico chemical property describing the polarity of molecules. The polar surface area (PSA) of a molecule is defined as the surface sum over all polaratoms, primarily oxygen and nitrogen, also including their attached hydrogens (Ertl et al., 2000). PSA is a commonly used medicinal chemistry metric for the optimization of adrug's ability to permeate cells. Molecules with a polar surface area of greater than 140 angstroms squared tend to be poor at permeating cell membranes, while, a PSA less than 60 angstroms squared usually be good at permeating cell membranes. 

11) MW

MW (molecular weight) refers to the mass of a molecule. It is one of the parameters of Lipinski's “rule of five”, which 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 compounds with Molecular weight from 180 to 500 Dalton are perceived as more druggable.



Ertl, P. et al. (2000) Fast calculation of molecular polar surface area as a sumof fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem., 43, 3714–3717.

Madden, J. C. (2010) In silico approaches for predicting ADME properties. In, Recent advances in QSAR studies.Springer, pp. 283–304.

Shen,M. et al. (2012) Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. J. Cheminformatics, 4, 1–13.

Tao,W. et al. (2013) Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J. Ethnopharmacol., 145, 1–10.

Tattersall, M. H. et al. (1975) Pharmacokinetics of actinoymcin D in patients with malignant melanoma. Clin.Pharmacol. Ther., 17, 701–708.

Veber, D. F. et al. (2002) Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem.,45, 2615–2623.

Viswanadhan, V. N. et al. (1989) Atomic physico chemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J.Chem. Inf. Comput. Sci., 29, 163–172.

Xu,X. et al. (2012) A Novel Chemometric Method for the Prediction of Human Oral Bioavailability. Int. J. Mol. Sci.,13, 6964–6982.

Yao,Y. et al. (2014) A novel Systems Pharmacology model for herbal medicine injection: a case using Reduning Injection. BMC Complement. Altern. Med.

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