DruMAP consists of a database for DMPK parameters and programs that can predict many DMPK parameters from the chemical structure of a compound.

Database

The current version contains the following chemicals and activity data:

	Number of records
All registered compounds	30,971
Freebase compounds	27,500
Freebase compounds ignoring stereo structure	25,622

Physicochemical parameters
Parameter name	Parameter type	Species	Our experimental data	Curated public data	Predicted data
Solubility (pH 7.4)	Sol7.4	None	163	367	27,505
Solubility (pH 1.2)	Sol1.2	None	163
Distribution coefficient (pH 7.4)	logD7.4	None	120

In vitro parameters
Parameter name	Parameter type	Species	Our experimental data	Curated public data	Predicted data
Fraction unbound in plasma	fu,p	Human	441	2,319	55,010
		Rat	422	120	55,010
Fraction unbound in brain homogenate	fu,brain	Rat	443
		Mammal		253	27,505
Blood-to-plasma concentration ratio	Rb	Human		213
		Rat	163
Hepatic intrinsic clearance in liver microsome	CLint	Human	166	5,230	54,977
		Rat	167
Probability metabolized by CYP1A2	CYP	Human			27,505
Probability metabolized by CYP2C9	CYP	Human			27,505
Probability metabolized by CYP2D6	CYP	Human			27,505
Probability metabolized by CYP3A4	CYP	Human			27,505
Site metabolized by CYP1A2	CYP	Human			10,018
Site metabolized by CYP3A4	CYP	Human			10,644
Permeability coefficient (Caco-2)	Papp	Human		4,408	27,505
Permeability coefficient (LLC-PK1)	Papp	Human	925		27,503
		Rat	115
		Pig	1,042
P-gp efflux ratio (LLC-PK1)	ER	Human	463
		Rat	58
		Pig	521
P-gp net efflux ratio (LLC-PK1)	NER	Human	446		27,505
		Rat	57

In vivo parameters
Parameter name	Parameter type	Species	In-house data	Curated public data	Common Thechnical Document	Interview Form	Predicted data
Drug concentration in plasma	C	Rat	1,335		16
Drug concentration in tissues	C	Rat	1,535		1,141
Initial drug concentration in plasma	C0	Rat	49
Peak drug concentration	Cmax	Rat	366
Elimination half-life of a drug	T1/2	Rat	415		65
Time to reach peak drug concentration	Tmax	Rat	366
Area under the drug concentration-time curve	AUC	Rat	830		70
Mean residence time of a drug in plasma	MRT	Rat	135
Brain-to-plasma concentration ratio	Kp,brain	Rat	100				27,505
Unbound brain-to-plasma concentration ratio	Kp,uu,brain	Rat					27,505
Volume of distribution	Vd	Human				102
		Rat	39			5
		Others				12
Apparent volume of distribution at oral administration	Vd/F	Human				197
		Rat	96
Clearance	CL	Human				78
		Rat	39			3
		Others				4
Apparent clearance at oral administration	CL/F	Human				133
		Rat	96
Renal clearance	CLr	Human		401		35	27,505
Bioavailability	F	Human				243
		Rat	35			32
		Others				33
Fraction absorbed	Fa	Human		945			27,505
Fraction excreted in urine	fe	Human		343			27,505
Excretion type in urine	CR type	Human					27,505

Toxicity data
Parameter name	Parameter type	Species	Data provided by accompanying projects
IC50 for hERG channel	IC50	Human	9,114
IC50 for Cav1.2 channel	IC50	Human	204
IC50 for Kv1.5 channel	IC50	Human	686
IC50 for Nav1.5 channel	IC50	Human	1,321
Link to Hepatotoxicity database		Human and rat	620

Predictive models

DruMAP provides predictive models for many PK parameters. For the current list of available models, see New Prediction page.

For in-house (commercial or academic) use of those programs, please consider the commercial version.

More Background

To address pharmacokinetic and toxicological issues in drug development, once the main source of late attrition of drug candidates, many pharmaceutical companies have now implemented early DMPK (Drug Metabolism and Pharmacokinetics) or early toxicological studies. However, such approaches are difficult to emulate in the academic drug discovery environment. Therefore, we began an initiative “Development of a Drug Discovery Informatics System” in collaboration with several other research groups. The main aim of this initiative is to develop more accurate prediction systems for DMPK and toxicological properties primarily targeting academic scientists. Our group’s focus is to develop a pharmacokinetics database and prediction models.

Any good prediction system depends on high-volume, high-quality training datasets. We collected pharmacokinetic and physicochemical parameters from the public bioactivity database, ChEMBL. However, since ChEMBL compiles data obtained in different experimental conditions, we developed a curation workflow to select the data measured in compatible conditions and to reformat the results as appropriate for our prediction system.

In addition to the public data, we have acquired both in vitro and in vivo experimental data under unified protocols. The in vitro experiments include physicochemical parameters such as solubility and distribution coefficient, and pharmacokinetic parameters such as metabolic stability, protein binding in plasma, protein binding in brain homogenate, and blood-to-plasma concentration ratio. In addition, we collected efflux ratio of P-glycoprotein (P-gp), which is the major transporter in gut and brain. The in vivo data include the drug concentrations in plasma and tissues after oral or intravenous administration of the drug and pharmacokinetic parameters calculated therefrom.

We are currently developing several new models and we plan to release them in due course. Send any questions or comments to drumap[at]nibiohn.go.jp (please replace [at] with @).