Subscription & licensing questions
Access to DIDB is provided via an annual subscription.
We can offer a free trial period to your organization in order to help you and your colleagues make the decision to subscribe to DIDB. Please contact us for more information.
Not at this time. Access to DIDB is granted to organizations via an annual subscription. The subscription allows for an unlimited number of users within an organization (or within a site for large organizations) to access DIDB.
DIDB is a research tool that was designed specifically for academic, pharmaceutical and regulatory scientists. DIDB is not intended as a primary clinical decision tool.
Firstly, your organization should hold a DIDB license for its employees to register to access the database, and you need to get your organization’s DIDB credentials.
To register, follow the link: https://www.druginteractionsolutions.org/register/. Enter your organization’s DIDB credentials and then provide your name and a work email address to create your own user account.
It is worth emphasizing that a user account is personal and cannot be shared with anyone within your company nor outside. Anyone within your organization interested in accessing the DIDB (even infrequently) should create their own user account.
Questions about the data in DIDB
No, regulatory documents covered in DIDB are limited to NDAs and BLAs from the FDA.
DIDB includes both qualitative and quantitative human in vitro and clinical information related to various extrinsic and intrinsic factors. These include interacting co-medications, excipients, food products, herbals, tobacco, organ impairment, and genetics, that can affect drug exposure in humans.
The data come from the scientific literature and the NDA reviews of the FDA-approved products. The majority of articles are indexed by NCBI PubMed; however, some articles are obtained from Embase.
Most scientific publications are in English; therefore, the majority of data available in DIDB is from publications in English. However, some articles in DIDB are in other languages when published in native language of one of the team members, or when the (English) abstract provides sufficient information to include.
DIDB has detailed processes that have been developed over the last 20 years to ensure the most relevant data is captured in an unbiased manner by our curation process. The DIDB editorial team manually reviews the most relevant scientific journals, performs queries of NCBI PubMed, and verifies that relevant cited data are entered as well.
When information from an article or an NDA review is unclear, we check additional sources (e.g. clinicaltrials.gov for clinical DDI studies) or reach out to the authors of the article for clarification. Most of the time, we add comments within DIDB entries to explain possible discrepancies.
Daily. As soon as the DIDB entries have been reviewed and validated, they become available to subscribers immediately.
All of the DIDB editorial work is conducted in-house, within the Department of Pharmaceutics at the University of Washington School of Pharmacy. Our team is comprised of pharmaceutical scientists, pharmacists, and physicians with expertise in drug metabolism, transport, pharmacokinetics, drug interactions, and clinical pharmacology.
DIDB includes mainly metabolism- and transporter-based pharmacokinetic interactions. However, other mechanisms (e.g. absorption-based DDIs, pharmacogenetics, food effects, hepatic and renal impairment,…) are also covered. Pharmacodynamic interactions which cannot be explained by changes in drug or metabolite exposure are not included.
A citation is either a publication or an NDA/BLA review and often contains multiple in vitro and/or clinical studies. An in vitro study refers to an experiment, which may contain one or multiple entries, whereas a clinical study may have one of multiple experiments and each experiment entered in DIDB is referred to as an entry.
Yes, modeling and simulation data that are performed in lieu of clinical studies are included.
Yes, DIDB contains in vitro and clinical data on biologics as objects (victims) and as precipitants (perpetrators).
Yes, DIDB covers drug-drug, food-drug, and natural product-drug interactions.
Yes, DIDB contains in vitro and clinical data on approximately 40 endogenous compounds.
Yes, DIDB contains in vitro and clinical DDI data mediated by non-CYP enzymes including, but not limited to, AOs, ADHs, AKRs, CESs, FMOs, MAOs, SULTs, and UGTs.
Yes, DIDB contains in vitro and clinical DDI data mediated by non-P-gp transporters including, but not limited to, BCRP, BSEP, CNTs, ENTs, MRPs, MATEs, OATs, OATPs, OCTs, OCTNs, and PEPTs.
Yes, DIDB contains clinical drug interaction data with acid-reducing agents such as antacids, histamine H2-receptor antagonists, and proton pump inhibitors (PPIs). This type of data can be easily identified based on its unique drug interaction meachanism categorized in DIDB.
Yes, DIDB contains 25 PK and compound parameters to support PBPK modeling and simulations.
Here is the list: absolute bioavailability, accumulation ratio, B/P, Cmax (mass/volume), Cmax (molar), Cmax dose/duration, clearance, clinically recommended dosage, dose proportionality, elimination pathway, Fa, Fe, Fg, Fm in vitro, Fm in vivo, Fu in vitro, Ka, LogP, permeability, pKa, plasma protein binding, solubility (at different pH), T1/2, Tmax, Vd.
As of November 2023, DIDB contains PMRs and PMCs for drugs approved by the FDA in 2014, 2015, and 2016. The DDI, hepatic impairment, and renal impairment results can be found under the drug’s original NDA or BLA numbers. Updated label recommendations are also presented in the drug DDI Summary.
Having access to this additional information is key to fully understand the DDI profile of NMEs, and the DIDB Editorial Team is now working on PMR/PMC data for drugs approved in 2017 and 2018.
DIDB application specific questions
The majority of biologics curated in DIDB belong to two therapeutic classes: immunomodulators biologics and neoplastic biologics. To retrieve data involving biologics belonging to these two therapeutic classes use the following query: Basic Queries > Therapeutic Class Queries > One Therapeutic Class and type in one of the therapeutic classes, select between objects or precipitants and “in vitro” and/or “in vivo”. Perform the same search for the other therapeutic class.
To find all biologics included in DIDB, go to Monographs > PK profiles. In the table review results select “yes” in the column of “Biologics”. Then you can use the DIDB queries to find data for any biologics included in this table but not belonging to the above therapeutic classes.
Additionally, you can use the full text search function on the DIDB homepage by typing in “biologics” to do a broad search from citations, monographs, and resources.
If you are interested in the newly approved biologics, you can access all data extracted from BLA reviews
A quick way to find all the relevant in vitro and/or in vivo data involving food products is to use the following queries: Basic Queries > Therapeutic Class Queries > One Therapeutic Class, type or select “Food Products” from the drop-down list in the Therapeutic Class area, and select between Objects or Precipitants and in vitro and/or in vivo. In specific, the “Therapeutic Class (Advanced)” query on the same page can be used to search data with a focus on a specific mechanism.
In vitro, different extracts (e.g., aqueous, ethanol, methanol, etc.) of a natural product (NP) may be studied. To be sure to retrieve all in vitro results related to a given NP, users need to include these extracts in the query in addition to the NP itself and the various ingredients.
Natural products (NP) have often multiple ingredients. When a NP is considered as precipitant in vivo, its ingredients are only found when they have been administered as such. For example, most of the grapefruit juice studies will be found under precipitant = grapefruit Juice, while its main ingredient bergamottin will only appear as a precipitant when specifically formulated and administered as bergamottin only. So, the best way to retrieve all results related to grapefruit juice is to include grapefruit, grapefruit juice, and bergamottin together in the search. On the other hand, to view in vivo data related to a NP as object, users need to query using the names of its ingredients since they are the entities measured in the studies (and not the parent NP).
To retrieve all data involving cannabinoids use the following queries: Basic Queries > Therapeutic Class Queries > One Therapeutic Class, and type in “Cannabinoids”, select between object or precipitant and “in vitro” and/or “in vivo”.
If you are interested in clinical data, you type the compound name in your query starting by “endogenous”, then you will have the whole list of biomarkers included in DIDB. If you wish to retrieve in vitro data, you can perform the query using the compound name directly without typing “endogenous”.
Yes, in vitro metabolism data can be searched based on a specific in vitro experimental system using the following query: Queries > Additional Queries > Other Queries. In the first subquery “System”, type or select the specific system of interest.
Some prodrugs may be detected in plasma along with their active moiety while others may not. Therefore, to search data involving prodrugs you need perform the search using both the prodrug and the active moiety as objects and precipitants.
Currently in vitro transporter induction data is not included, but we are working on adding this module in the future when experimental protocols are well standardized and guidance from regulatory agencies is available. However, clinical transporter DDIs involving transporter induction are included in DIDB.
You can watch our tutorial documents and/or videos for tips (link). If you still have questions, feel free to contact us for assistance.
To retrieve data about a specific pharmacokinetic parameter use the following query: Monographs (located at the top panel of DIDB homepage) > PK profiles (link). All compounds with different pharmacokinetic parameters will be presented in a table. You can use the table function, e.g., filter or re-order associated with each column, to generate the dataset of interest.
Yes. Fm in vitro and Fm in vivo are parameters presented in the compound PK profile. You can use the following query to find fm values of a specific compound if they are available: Monographs (located at the top panel of DIDB homepage) > PK profiles (link).
You can use the “Advanced Table Search” function associated with the result table to easily exclude simulation data, if there is such data in the result. First, you need to check if simulation data exists in your result. On the table result page, you can either type “PBPK” or “population PK” in the first filter row or review all the options in the second filter row in the column of “Study Design”. If such data exists, then click “Advanced Table Search” above the table and “Add Condition” will show. Click “Add Condition”, and select “Study design” Column, “Not” Condition, and any option containing “PBPK Modeling” Value. Of note, there may be multiple options containing “PBPK Modeling”, thus you need to click “Add Condition” to exclude all the other study design options including “PBPK Modeling” one by one. Similarly, you can remove population PK data by selecting “Population PK Analysis”.
Yes. These parameters are presented in the compound PK profile under the name of “Fu in vitro“. The specific in vitro system used to obtain the fu value is presented after the value, when available. You can use the following query to find such values of a specific compound: Monographs (located at the top panel of DIDB homepage) > PK profiles (link). The Fu in vitro values presented in the PK profile is usually available for newly marketed drugs obtained from the NDA reviews. Additionally, fu in vitro values are curated together with specific in vitro metabolism data if they are provided within the same citation. Thus, you may find the fu in vitro values of the drug of interest using an in vitro metabolism query, e.g., Km/Vmax/CLint. In the result, such values are presented in the column of “Fu in vitro“, when available.
A quick way to find all the relevant data regarding QT interval prolongation (from both the literature and recent NDA/BLA reviews) is to use the following query:
Additional Queries > QT interval Queries > QT summaries in a searchable table
Other QT interval queries include:
– A list of all drugs with clinical evidence of QT interval prolongation potential,
– In vivo studies reporting Torsades de Pointes/Ventricular Fibrillation as a side effect,
– In vivo studies reporting QT Interval Prolongation as a side effect, and
– In vivo drug interaction studies in which QT interval measurements were performed.
To reference DIDB in publications, you may write:
“Copyright Certara Drug Interaction Database (DIDB), accessed on: … [Fill in with date of extraction of the data/information]”.