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Properties

Introduction

CHEESE Search UI incorporates property predictions for several ADMET properties. In this section we provide a detailed explanation of the color coding used for the visualisation of the predicted properties for a molecule. Further, we provide concise details for each of the predicted property and refer to the datasets used in development of the predictive models.

Color Annotations

Color annotations helps to quickly evaluate the properties of molecules. CHEESE uses ADMETlab 2.01 as reference for color annotation of properties except for "LD50 in Rat" which is decided on the basis of observations from the Digital Drug Property Database.2

🟢 Perfect 🟡 Good 🔴 Poor

Absorption

Bioavailability

Oral bioavailability is defined as the rate and extent to which the active ingredient or active moiety is absorbed from a drug product and becomes available at the site of action.3

🟢 0.7 - 1.0
🟡 0.3 - 0.7
🔴 0.0 - 0.3

Prediction type: binary / probability

Caco2 Permeability (Log cm/s)

The human colon epithelial cancer cell line, Caco-2, is used as an in vitro model to simulate the human intestinal tissue. The experimental result on the rate of drug passing through the Caco-2 cells can approximate the rate at which the drug permeates through the human intestinal tissue.4

🟢 > -5.15
🔴 ≤ -5.15

Prediction type: regression

Human Intestinal Absorption

When a drug is orally administered, it needs to be absorbed from the human gastrointestinal system into the bloodstream of the humanbody. This ability of absorption is called human intestinal absorption (HIA) and it is crucial for a drug to be delivered to the target.5

🟢 0.7 - 1.0
🟡 0.3 - 0.7
🔴 0.0 - 0.3

Prediction type: binary / probability

Lipophilicity (LogD)

Lipophilicity measures the ability of a drug to dissolve in a lipid (e.g.fats, oils) environment. High lipophilicity often leads to high rate of metabolism, poor solubility, high turn-over, and low absorption. LogD is the logarithm of distribution coefficient and it is more appropriate measure of lipophilicity of ionizable molecules. LogD depends upon the pH of the solution.6

🟢 0.0 - 3.0
🔴 < 0.0 or > 3.0

Prediction type: regression

P-glycoprotein Inhibition

P-glycoprotein (Pgp) is an ABC transporter protein involved in intestinal absorption, drug metabolism, and brain penetration, and its inhibition can seriously alter a drug’s bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multi-drug resistance.7

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

Solubility (LogS)

Aqeuous solubility measures a drug’s ability to dissolve in water. Poor water solubility could lead to slow drug absorptions, inadequate bioavailablity and even induce toxicity. More than 40% of new chemical entities are not soluble. LogS is the logarithm of molar solubility and it is directly related to the water solubility of a drug molecule.8

🟢 -4.0 - 0.5
🔴 < -4.0 or > 0.5

Prediction type: regression

Lipophilicity (LogP)

The most commonly used measure of lipophilicity is LogP, this is the logarithm of partition coefficient of a molecule between an aqueous and lipophilic phases, usually octanol and water. Also, we should be aware that LogP is pH-independent.6

🟢 0.0 - 3.0
🔴 < 0.0 or > 3.0

Prediction type: regression

Distribution

Plasma Protein Binding Rate (%)

The human plasma protein binding rate (PPBR) is expressed as the percentage of a drug bound to plasma proteins in the blood. This rate strongly affect a drug’s efficiency of delivery. The less bound a drug is, the more efficiently it can traverse and diffuse to the site of actions.6

🟢 ≤ 90
🔴 > 90

Prediction type: regression

Volume of Distribution

The volume of distribution at steady state (VDss) measures the degree of a drug’s concentration in body tissue compared to concentration in blood. Higher VD indicates a higher distribution in the tissue and usually indicates the drug with high lipid solubility, low plasma protein binding rate.9

🟢 0.0 - 20.0
🔴 < 0.0 or > 20.0

Prediction type: regression

Blood-Brain Barrier Penetration

As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier (BBB) is the protection layer that blocks most foreign drugs. Thus the ability of a drug to penetrate the barrier to deliver to the site of action forms a crucial challenge in development of drugs for central nervous system.10

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

Metabolism

CYP2C9 Inhibition

The CYP P450 genes are involved in the formation and breakdown (metabolism) of various molecules and chemicals within cells. Specifically, the CYP P450 2C9 plays a major role in the oxidation of both xenobiotic and endogenous compounds.11

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

CYP2D6 Inhibition

The CYP P450 genes are involved in the formation and breakdown (metabolism) of various molecules and chemicals within cells. Specifically, CYP2D6 is primarily expressed in the liver. It is also highly expressed in areas of the central nervous system, including the substantia nigra.11

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

CYP3A4 Inhibition

The CYP P450 genes are involved in the formation and breakdown (metabolism) of various molecules and chemicals within cells. Specifically, CYP3A4 is an important enzyme in the body, mainly found in the liver and in the intestine. It oxidizes small foreign organic molecules (xenobiotics), such as toxins or drugs, so that they can be removed from the body.11

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

Excretion

Clearance Hepatocyte (mL/min/g)

Hepatocytes, the major parenchymal cells in the liver, play pivotal roles in drug metabolism, detoxification and excretion. They also activate innate immunity against invading microorganisms by secreting innate immunity proteins that directly kills bacteria and block iron uptake by bacteria. They determine the in vitro intrinsic clearance of any drug compound.6

🟢 ≥ 5
🔴 < 5

Prediction type: regression

Clearance Microsome (mL/min/g)

Located in the liver,Microsomes are subcellular fractions, that contains membrane bound drug metabolishing enzymes, can determine the in-vitro intrinsic clearance of a drug molecule. Microsomes are pooled from multiple donors to minimise the effect of inter-individual variability. Liver microsomes are more predictive of in vivo clearance than hepatocytes, when in vitro intrinsic clearance in microsomes is faster than hepatocytes.6

🟢 ≥ 5
🔴 < 5

Prediction type: regression

Half-life in Human (hour)

Half life of a drug is the duration for the concentration of the drug in the body to be reduced by half. It measures the duration of actions of a drug

🟢 ≥ 3
🔴 < 3

Prediction type: regression

Toxicity

AMES Mutagenicity

Mutagenicity means the ability of a drug to induce genetic alterations. Drugs that can cause damage to the DNA can result in cell death or other severe adverse effects. Nowadays, the most widely used assay for testing the mutagenicity of compounds is the Ames experiment which was invented by a professor named Ames. The Ames test is a short-term bacterial reverse mutation assay detecting a large number of compounds which can induce genetic damage and frameshift mutations.13

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

Drug Induced Liver Injury

Drug-induced liver injury (DILI) is fatal liver disease caused by drugs and it has been the single most frequent cause of safety-related drug marketing withdrawals for the past 50 years (e.g. iproniazid, ticrynafen, benoxaprofen).14

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

hERG Inhibition

Human ether-à-go-go related gene (hERG) is crucial for the coordination of the heart’s beating. Thus, if a drug blocks the hERG, it could lead to severe adverse effects. Therefore, reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages.15

🟢 0.0 - 0.3
🟡 0.3 - 0.7
🔴 0.7 - 1.0

Prediction type: binary / probability

LD50 in Rat

Acute toxicity LD50 measures the most conservative dose that can lead to lethal adverse effects. The higher the dose, the more lethal of a drug.16

🟢 ≥ -2.4
🔴 < -2.4

Prediction type: regression

References

  1. Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T, Cao D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49(W1):W5–W14. https://doi.org/10.1093/nar/gkab255 ↩

  2. Li Q, Cheng T, Wang Y, Bryant SH. DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development. Database (Oxford). 2022;baab083. https://doi.org/10.1093/database/baab083 ↩

  3. Ma C, Yang SY, Zhang H, Sun H. Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA–CG–SVM method. J Pharm Biomed Anal. 2008;47(4–5):677–682. https://doi.org/10.1016/j.jpba.2008.03.023 ↩

  4. Wang NN, Dong J, Deng YH, Zhu MF, Wen M, Yao ZJ, et al. ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting. J Chem Inf Model. 2016;56(4):763–773. https://doi.org/10.1021/acs.jcim.5b00642 ↩

  5. Hou TJ, Wang JM, Zhang W, Xu XJ. ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. J Chem Inf Model. 2007;47(1):208–218. https://doi.org/10.1021/ci600343x ↩

  6. AstraZeneca. Experimental in vitro DMPK and physicochemical data on a set of publicly disclosed compounds. 2016. https://tdcommons.ai/single_pred_tasks/adme/ ↩↩↩↩↩

  7. Broccatelli F, Carosati E, Neri A, Frosini M, Goracci L, Oprea TI, Cruciani G. A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields. J Med Chem. 2011;54(6):1740–1751. https://doi.org/10.1021/jm101421d ↩

  8. Sorkun MC, Khetan A, Er S. AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds. Sci Data. 2019;6:143. https://doi.org/10.1038/s41597-019-0151-1 ↩

  9. Lombardo F, Jing Y. In silico prediction of volume of distribution in humans. Extensive data set and the exploration of linear and nonlinear methods coupled with molecular interaction fields descriptors. J Chem Inf Model. 2016;56(10):2042–2052. https://doi.org/10.1021/acs.jcim.6b00201 ↩

  10. Martins IF, Teixeira AL, Pinheiro L, Falcão AO. A Bayesian approach to in silico blood–brain barrier penetration modeling. J Chem Inf Model. 2012;52(6):1686–1697. https://doi.org/10.1021/ci300124c ↩

  11. Veith H, Southall N, Huang R, James T, Fayne D, Inglese J, et al. Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat Biotechnol. 2009;27(11):1050–1055. https://doi.org/10.1038/nbt.1586 ↩↩↩

  12. Obach RS, Lombardo F, Waters NJ. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos. 2008;36(7):1385–1405. https://doi.org/10.1124/dmd.108.020479 ↩

  13. Xu C, Cheng F, Chen L, Du Z, Li W, Liu G, et al. In silico prediction of cytochrome P450-mediated drug metabolism. J Chem Inf Model. 2012;52(11):2840–2847. https://doi.org/10.1021/ci300312w ↩

  14. Xu Y, Dai Z, Chen F, Gao S, Pei J, Lai L. Deep learning for drug-induced liver injury. J Chem Inf Model. 2015;55(10):2085–2093. https://doi.org/10.1021/acs.jcim.5b00238 ↩

  15. Wang S, Sun H, Liu H, Li D, Li Y, Hou T. ADMET evaluation in drug discovery. 16. Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches. Mol Pharm. 2016;13(8):2855–2866. https://doi.org/10.1021/acs.molpharmaceut.6b00360 ↩

  16. Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, et al. Combinatorial QSAR modeling of rat acute toxicity by oral exposure. Chem Res Toxicol. 2009;22(12):1913–1921. https://doi.org/10.1021/tx900189p ↩