ndc_drugs — FDA National Drug Code directory (2017)

Description

Bipartite networks of drug products and their pharmaceutical classes or active substances, from the National Drug Code (NDC) Directory. Under the Drug Listing Act of 1972, firms must list with the US Food and Drug Administration (FDA) the drugs they manufacture or distribute commercially, and the FDA publishes this information as the NDC Directory, where each product is identified by an NDC product code (labeler code and product code). The data correspond to a version of the directory obtained around 2017 (the latest marketing date is 2017-09-21). Each drug node is one NDC product code, so the same drug substance can appear in several nodes (e.g. different dosages, forms or manufacturers), and is annotated with the date when the product started being marketed. All products in the directory are included (no size restriction). Names are lower-cased.

classes: each drug is connected to the pharmaceutical classes the FDA assigned to it.
substances: each drug is connected to the substances (ingredients) it is made of.

The networks have the following properties:

is_drug: whether the node is a drug (NDC product code), otherwise a class (classes) or a substance (substances).
Drug nodes: name, the name of the drug (empty for a few products whose name is missing in the source data); ndc_code, the NDC product code, as "labeler-product"; time, the date when the drug started being marketed, in days since 1970-01-01 (negative before 1970; the earliest date is 1900-01-01; only meaningful for drug nodes).
Class and substance nodes: name, the name of the class or substance.
classes only: class_type, the kind of pharmaceutical class, as given in the source: epc (established pharmacologic class), moa (mechanism of action), pe (physiologic effect), or chemical/ingredient (chemical structure). A few class names that contain a comma in the directory were split into two nodes in the source data (e.g. "antibodies" and "monoclonal [chemical/ingredient]"); for these the first part has an empty class_type.

Tags

Public Health Chemical Unweighted Timestamps Metadata

Citation

A. R. Benson, R. Abebe, M. T. Schaub, A. Jadbabaie, J. Kleinberg, "Simplicial closure and higher-order link prediction." Proceedings of the National Academy of Sciences 115(48), E11221-E11230 (2018) 10.1073/pnas.1800683115 Sci-Hub

Upstream URL OK

https://www.cs.cornell.edu/~arb/data/NDC-classes/
https://www.cs.cornell.edu/~arb/data/NDC-substances/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
classes	50,887	156,251	6.14	63.99	136.70	1393.70	-0.26	0.00	19	0.85	Undirected	Bipartite	is_drug name class_type ndc_code time		373 KiB	813 KiB	792 KiB	911 KiB
substances	118,475	227,494	3.84	43.93	90.31	5904.75	-0.17	0.00	24	0.67	Undirected	Bipartite	is_drug name ndc_code time		1.0 MiB	1.7 MiB	1.7 MiB	2.0 MiB