I’ve recently worked with clinical studies from ClinicalTrials.gov and other international registries. This post is a review on how to access data, a proposal for how it can be modeled using the Ontology for Biomedical Investigations (OBI), a proof-of-concept ontologization of ClinicalTrials.gov, and some insights into how this data can be integrated with other resources to address classical problems in drug discovery from a knowledge graph perspective.

This is a long read, so here’s a table of contents:

  1. Automated download of ClinicalTrails.gov with clinicaltrials-downloader
  2. Summarizing ClincialTrials.gov study types, allocations, and phases
  3. Example clinical studies
  4. Proposing an ontology meta-model
  5. Proof-of-concept ontology export of ClinicalTrials.gov using pyobo
  6. Reflections and what’s missing
  7. What’s this all useful for, anyway?

Automated Download

Similar to ChEMBL, DrugBank, and UMLS, I authored a Python package, that automates download and caching clinical studies from ClinicalTrials.gov. Its source code is available under the MIT license at https://github.com/cthoyt/clinicaltrials-downloader, and it can be installed with pip install clinicaltrials-downloader. The package exposes two main functions for getting the raw, unprocessed data:

from clinicaltrials_downloader import get_studies, get_studies_slim

# contains all fields, (~2GB, gzipped)
studies = get_studies()

# contains a useful subset of the fields, much smaller (~70MB, gzipped)
studies_slim = get_studies_slim()

Keep in mind that the data is updated daily, so the caches become out of date quickly. Pass force=True to either of the downloader functions to update the local cache of the database.

Summarization

I generated a few summary tables over the slim subset of ClinicalTrials.gov using this script. The tables can be regenerated with the following command, adding --force if you want to refresh the cache:

$ uv run --script https://gist.githubusercontent.com/cthoyt/12a3cb3c63ad68d73fe5a2f0d506526f/raw/98bd3795ec02ebd2c8bb8746e3a3a5d23aeffd75/clinicaltrials-summary.py

As an aside, if you’re not yet familiar with PEP-723, you can now put the requirements and other metadata inside a script. This also allows me to tell uv to use the development version of PyOBO and my fork of Gilda that doesn’t include all its heavy requirements.

Study Type and Allocation

ClinicalTrials.gov contains three main study types:

  1. interventional study (i.e., clinical trial) - a study in which participants are assigned zero or more diagnostic, therapeutic, or other types of interventions depending on the arm into which they are allocated
  2. observational study - a study in which participants are assessed for biomedical or health outcomes. They may receive interventions, but they are not assigned like in interventional studies
  3. expanded access (i.e., compassionate use) - a mechanism through which patients who are not participants in a clinical trial to receive access to non-approved/experimental medicine.

Interventional studies can be divided into two categories based on their allocation - the used to assign participants to an arm of a clinical study. They are randomized and non-randomized.

The table below adjusts the internal labels for legibility, aggregates missing values and NA entries, and sorts by most common.

Study Type Allocation Count
Interventional Randomized 261,643
Observational   120,775
Interventional   95,249
Interventional Non-Randomized 42,759
Expanded Access   966
    902

Clinical Trial Phases

The phase primarily communicates the objective of a clinical trial (i.e., interventional study). Observational trials and expanded access studies therefore do not have phases.

Image above from the Hepatitis B Foundation.

There are six common phases appearing on ClinicalTrials.gov:

  • Early Phase 1 (formerly, Phase 0) - Assess oral bioavailability, pharmacokinetics (very small group; almost always left out of diagrams like the one above). This is not the same thing as pre-clinical trials, which are often done with biochemical assays, cellular assays, and work with model organisms.
  • Phase 1 - Assess safety in healthy volunteers (small group)
  • Phase 2 - Assess efficacy and side effects (medium group)
  • Phase 3 - Assess efficacy, effectiveness, and safety ( large group)
  • Phase 4 - Post-approval surveillance
  • Phase Not Applicable (N/A) - Applied to trials without phases, such as trials with devices or behavioral interventions

The table below adjusts the internal clinical trial phases’ labels for legibility, aggregates missing values and NA entries, and sorts by progression.

Phase Count
0 5,434
1 44,195
1, 2 15,219
2 59,412
2, 3 6,982
3 39,160
4 33,129
N/A or missing 318,763

Some trials are annotated with multiple phases, either 1/2 or 2/3. These could also be combined in a different way for “maximum clinical phase” aggregation operations.

Unsurprisingly, there is an attrition through the progression of phases, but it is not as stark as I would have expected. It might also be interesting to stratify this by year to see if trials are more likely to succeed as time goes on.

Example Clinical Studies

Having examples and doing spot-checks is always helpful when exploring new data, so I generated a table containing example clinical trials for each study type, allocation, and phase. While there are many studies with more than one intervention and/or condition, this table only shows trials with a single one of each to reduce complexity.

Study/Phase(s) NCT ID Title Condition Intervention Structure
Expanded Access NCT01317953 Oral Green Tea Extract for Small Cell Lung Cancer Small Cell Lung Carcinoma (-)-epigallocatechin 3-gallate
Observational NCT03418987 The Vertebral Vector in a Horizontal Plane. A Simple Way to See in 3D. Scoliosis    
Non-Randomized (Phase 0) NCT01209533 Inhaled Iloprost in Mild Asthma Asthma iloprost
Non-Randomized (Phase 1) NCT01682187 A Dose-Escalation Study in Participants With Recurrent Malignant Glioma Glioma lomustine
Non-Randomized (Phase 1/2) NCT00379587 Rituximab for Prevention of Chronic GVHD Hematologic Neoplasms rituximab
Non-Randomized (Phase 2) NCT00176787 Radiation Therapy With Capecitabine in Rectal Cancer Rectal Neoplasms capecitabine
Non-Randomized (Phase 2/3) NCT04431453 Study of Remdesivir in Participants Below 18 Years Old With COVID-19 COVID-19 remdesivir
Non-Randomized (Phase 3) NCT03184987 A Long-term Safety Study of Fixed Dose Combination Therapy Fluticasone […] Asthma albuterol
Non-Randomized (Phase 4) NCT03282487 Optimising Steroid Replacement in Patients With Adrenal Insufficiency Adrenal Insufficiency cortisol
Randomized (Phase 0) NCT04293887 Efficacy and Safety of IFN-α2β in the Treatment of Novel Coronavirus Patients Coronavirus Infections interferon
Randomized (Phase 1) NCT01166087 Bioequivalence Study of Fluoxetine Hydrochloride Delayed-Release Capsules […] Malnutrition fluoxetine
Randomized (Phase 1/2) NCT00106587 Treatment of In-Stent Restenosis by Paclitaxel Coated PTCA Balloons […] Coronary Restenosis paclitaxel
Randomized (Phase 2) NCT00094887 Nitric Oxide Inhalation to Treat Sickle Cell Pain Crises Anemia, Sickle Cell nitric oxide
Randomized (Phase 2/3) NCT00136487 Celecoxib (Celebrex) Versus Placebo in Men With Recurrent Prostate Cancer Prostatic Neoplasms celecoxib
Randomized (Phase 3) NCT00843687 A Comparison of the Pharmacokinetics and Safety of Long-acting Injectable […] Schizophrenia risperidone
Randomized (Phase 4) NCT03586687 Osteoarthritis Shoulder Injection Study Osteoarthritis triamcinolone

Proposing an Ontology Meta-model

Given my goal to create an ontology export of ClinicalTrials.gov, I had to start by making some modeling decisions. The first was that each clinical study in the resource is an instance. This meant that I had to start by finding the right class for each, corresponding to the study types and allocations that I explored above.

Searching for Existing Ontology Classes

The Ontology for Biomedical Investigations (OBI) is a high quality ontology that contains terms for assays, devices, objectives, and other aspects of biomedical investigations. Therefore, I would ideally be able to find terms corresponding to the study types and allocations that I explored above already inside it.

I used the Ontology Lookup Service (OLS) to search for classes corresponding to the study types I explored above, but didn’t find anything specific enough in OBI. However, I did note that if there would be classes for clinical studies, then they would appear under its high-level class for investigation (OBI:0000066)

While searching in the OLS, I did find the following relevant classes, each with their own issues:

  1. The Semantic Science Integrated Ontology (SIO) has a term for clinical trial (SIO:001000), but SIO doesn’t follow best practices from Open Biological and Biomedical Ontology (OBO) Foundry, meaning that it is difficult to reuse and less trustworthy.
  2. The Ontology of Precision Medicine and Investigation (OPMI) has a term for clinical trial (OPMI:0004507). I’m not comfortable reusing terms from this ontology for two main reasons:
    1. It’s use-case specific, and curated based on project-based needs, which means that it’s not a reliable resource.
    2. It’s not curated using modern ontology infrastructure, so I’m not sure that I can trust it will be maintained.
  3. The Informed Consent Ontology (ICO) also has a term for clinical trial (ICO:0000065) but I have the same reservations as for OPMI. There’s an overlap of the same authors with OPMI, so I have reservations to invest in reusing terms they haven’t been able to deduplicate themselves.
  4. Eagle-I Resource Ontology (ERO) is an OBO Foundry ontology that has a term clinical trial (ERO:0000016) which nicely subclasses OBI’s investigation class, but ERO has been abandoned and marked as deprecated in the OBO Foundry.
  5. The Ontology for MicroRNA Target (OMIT) haphazardly imported all of MeSH at some point and has a term Clinical Trial (OMIT:0016936).
  6. The Clinical Trials Ontology (CTO) is an OBO Foundry ontology that nominally has the correct scope and has a term clinical trial (CTO:0000220), but there are potential issues with its design choices, and it was produced by a group that has historically had difficulty maintaining its resources and actively participating in the OBO community. Despite these issues making it less suitable for reuse, the associated workshop proceeding CTO: a Community-Based Clinical Trial Ontology and its Applications in PubChemRDF and SCAIView contains some interesting ideas.

Honorable mentions from non-ontology resources, which are a not ideal from a modeling perspective to use as a parent class in an ontology:

  1. BioLink model has a term clinical trial (biolink:ClinicalTrial).
  2. SNOMED has a term Clinical trial (procedure) (SNOMED:110465008), but is not an ontology and is notorious for being a closed resource, hampering reuse.
  3. NCIT has a term for Clinical Trial (NCIT:C71104), but it is not curated an ontology (despite OBO Foundry having an OWL conversion of it).
  4. MeSH has a term Clinical Trials as Topic (mesh:D002986).

Despite all of what I could find, none of these terms were part of an ontology that I can trust. Further, most of them conflated interventional clinical studies, i.e., clinical trials, with all other clinical studies.

Therefore, the next step was to get in touch with OBI and ask them to mint an authoritative term, that also can capture the nuance in clinical studies that is lost in the other resources. I did that in their issue tracker obi-ontology/obi#1831. They were very receptive, we had a nice conversation that brought up several points, and they challenged me to go even further than just proposing the parent terms and begin to develop a standardized model.

A draft proposal

The following diagram represents a draft proposal that includes several terms that OBI could mint as well as the kinds of relations between them. This is not a perfect proposal - its goal is to be a discussion piece for an upcoming OBI community call. There are still several parts missing and open questions.

Here are some missing parts to this model that could be added incrementally:

  1. A more detailed categorization of expanded access studies based on the expanded access types and expanded access status
  2. A more detailed categorization of observational studies based on the 1) assembly of groups and cohorts and 2) observational study models such as case-control, case-only, case-cross-over, ecologic or community studies, and family-based.
  3. A model for eligibility criteria and enrollment
  4. A model for outcomes, linked to OBI’s assay terms
  5. A model for investigators, funder types, and sponsors to support bibliometric investigation
  6. A model for geolocation data associated with clinical study sites
  7. A model for capturing adverse events and reasons for trial cancellation/end. In downstream applications, this could be used in tandem with the FDA’s Adverse Event Reporting System (FAERS) and the Vaccine Adverse Event Reporting System (VAERS).

There is high potential for applying natural language processing methods to extract more detailed information from the unstructured parts of clinical study records. I’ve been focusing on ClincialTrials.gov as an example in this post, but other clinical trial registries comprise almost exclusively unstructured text.

Proof-of-concept Ontology Export of ClinicalTrials.gov

PyOBO is Python software package that implements an in-memory data structure for OBO/OWL ontologies as well as I/O operations. On top of this, it implements workflows for converting databases like HGNC, MeSH, and ChEMBL into OBO/OWL ontolgies. These workflows are careful to make good design decisions, reusing classes and relations from other OBO ontologies when possible. This is crucial for them to be readily integratable with other resources.

The obo-db-ingest repository is responsible for automatically downloading new versions of the resources covered by PyOBO, converting them to OBO/OWL, archiving them to Zenodo, and assigning persistent URLs (PURLs) so the files can be accessed in a sustainable way. It’s also careful to include licensing information such that anyone can download these resources in a ready-to-use format, whereas the underlying resources are often less easy to use directly.

As a proof-of-concept, I implemented a converter for ClinicalTrials.gov in PyOBO at https://github.com/biopragmatics/pyobo/blob/main/src/pyobo/sources/clinicaltrials.py. The draft converter uses temporary classes to represent the study types and allocations I’m proposing to OBI. It also mints some of its own relationships, which would ideally be encoded in either OBI or the Relation Ontology (RO) for maximum reusability.

The initial export contains more than 500K clinical studies; nearly one million literature references, and near two million relationships between trials, interventions, and conditions (there are still several places for expansion and improvement discussed below). The ClinicalTrials.gov data is licensed under an equivalent to a public domain dedication, so there are few restrictions on remixing and redistributing the data this way.

A summary page can be found in the obo-db-ingest repository here and the exported artifacts are listed here:

Artifact Download PURL
OBO https://w3id.org/biopragmatics/resources/clinicaltrials/clinicaltrials.obo.gz
OFN https://w3id.org/biopragmatics/resources/clinicaltrials/clinicaltrials.ofn.gz
OWL https://w3id.org/biopragmatics/resources/clinicaltrials/clinicaltrials.owl.gz
OBO Graph JSON https://w3id.org/biopragmatics/resources/clinicaltrials/clinicaltrials.json.gz
Nodes https://w3id.org/biopragmatics/resources/clinicaltrials/clinicaltrials.tsv

Here’s what some OBO instances for clinical studies look like for each clinical study type:

[Instance]
id: clinicaltrials:NCT00000102
name: Congenital Adrenal Hyperplasia\: Calcium Channels as Therapeutic Targets
property_value: clinicaltrials:has_intervention mesh:D009543 ! has intervention Nifedipine
property_value: clinicaltrials:investigates_condition mesh:D000308 ! investigates condition Adrenocortical Hyperfunction
property_value: clinicaltrials:investigates_condition mesh:D000312 ! investigates condition Adrenal Hyperplasia, Congenital
property_value: clinicaltrials:investigates_condition mesh:D006965 ! investigates condition Hyperplasia
property_value: clinicaltrials:investigates_condition mesh:D047808 ! investigates condition Adrenogenital Syndrome
instance_of: interventional-clinical-trial

[Instance]
id: clinicaltrials:NCT00000104
name: Does Lead Burden Alter Neuropsychological Development?
property_value: clinicaltrials:investigates_condition mesh:D007855 ! investigates condition Lead Poisoning
property_value: clinicaltrials:investigates_condition mesh:D011041 ! investigates condition Poisoning
instance_of: observational-clinical-trial

[Instance]
id: clinicaltrials:NCT00000106
name: 41.8 Degree Centigrade Whole Body Hyperthermia for the Treatment of Rheumatoid Diseases
property_value: clinicaltrials:investigates_condition mesh:D003095 ! investigates condition Collagen Diseases
property_value: clinicaltrials:investigates_condition mesh:D012216 ! investigates condition Rheumatic Diseases
instance_of: randomized-interventional-clinical-trial

[Instance]
id: clinicaltrials:NCT00000250
name: Cold Water Immersion Modulates Reinforcing Effects of Nitrous Oxide - 2
property_value: clinicaltrials:has_intervention mesh:D009609 ! has intervention Nitrous Oxide
property_value: clinicaltrials:investigates_condition mesh:D009293 ! investigates condition Opioid-Related Disorders
property_value: clinicaltrials:investigates_condition mesh:D019966 ! investigates condition Substance-Related Disorders
instance_of: non-randomized-interventional-clinical-trial

[Instance]
id: clinicaltrials:NCT00040625
name: ALIMTA \(Pemetrexed\) Alone or in Combination With Cisplatin for Patients With Malignant Mesothelioma.
property_value: clinicaltrials:has_intervention mesh:D000068437 ! has intervention Pemetrexed
property_value: clinicaltrials:investigates_condition mesh:D000086002 ! investigates condition Mesothelioma, Malignant
property_value: clinicaltrials:investigates_condition mesh:D008654 ! investigates condition Mesothelioma
instance_of: expanded-access-study

Reflections, and, what’s missing?

I first became familiar with ClinicalTrials.gov and other clinical study registries while working on the RAPTER project, funded by the America Defense Threat Reduction Agency (DTRA) with the goal to integrate vaccine information and build computational tools to quicken the development of vaccines in response to future pandemics.

One of the key issues to overcome was the accuracy of the data within these resources. For example, ClinicalTrials.gov contains both a free-text and processed field for its conditions and interventions. In many cases, re-processing was required to ensure complete and accurate information. In my draft export of ClincalTrials.gov, I exclusively used the processed data fields, but a more careful conversion would require additional data science techniques.

Similarly, for data from the World Health Organization and other clinical trial registries, full NER and relation extraction is required to identify interventions, conditions, outcomes, and other fields.

Even when processed data is available, it’s using MeSH identifiers, which are not readily integrable with other resources. That’s why ontologies often curate MeSH cross-references themselves. I created the Biomappings project as a way to quickly predict and curate MeSH mappings to other chemical and disease vocabularies, such as Chemical Entities of Biological Interest (ChEBI) and the Disease Ontology (DOID). I also built SeMRA, a workflow for assembling and inferring mappings to best reuse existing mappings available from a wide variety of sources. These two approaches are crucial for making clinical study and trial information actionable in a data integration scenario.

I didn’t even mention the PICO (patient/population, intervention, comparison and outcomes) data model - this is a whole other can of worms for a different discussion. I will try to come back to that if I get the chance to write up some of my work with Jeremy Zucker’s team at PNNL on the y0 Causal Reasoning Engine and Judea Pearl-style causal inference applications in clinical study statistical analysis.

There’s a lot of work to do in this space, but this is a nice first step. For me, exporting lots of resources in a standard ontology format makes it easy to load up a complete set of nodes when building knowledge graphs. As a coda, I will say a little bit about what I like to do with this kind of data once I have got it structured and integrated with other sources.

What’s this all useful for, anyway?

My work in the past decade has focused on constructing and applying knowledge graphs to tasks in drug discovery. While a large part of this has incorporated machine learning, artificial intelligence, and causal inference, many problems can be formulated as queries over a graph-like data structure.

In this last section, I’m going to give a few examples of graph queries that can enable experts to explore and generate interesting, explainable, testable hypotheses. In practice, I have been using Neo4j and RDF as graph models with their respective Cypher and SPARQL query languages, but the case studies here will stay at a high level.

Maximum Phase for a Drug

   graph LR
     drug[Drug] -- intervention in --> trial[Clinical Trial] -- has phase --> phase[Phase]
     drug -. has maximum phase (aggregated) .-> phase

Because the same intervention may appear in multiple clinical trials, it’s useful to know the maximum phase reached over all trials. If the maximum phase is four, then it can be concluded that the drug was approved for use. If the maximum phase is three (and they are all completed), then it can be concluded that the drug ultimately failed to be effective. Such drugs are can be good candidates for repositioning since the expensive safety studies have already been run.

Maximum Phase for a Scaffold or Substructure

   graph LR
     substructure[Substructure] -- substructure of --> drug[Drug] -- intervention in --> trial[Clinical Trial] -- has phase --> phase
     substructure -. has maximum phase (aggregated) .-> phase

For small molecule drugs, the principle of structure-activity relationship (SAR) states that there is often a high correlation between the chemical structure and its functional activity. Many similar drugs work because they share a privileged substructure. For example, the beta-hydroxy lactone appearing in statins enable their HMC-CoA reducatase inhibitor activity, which confers their ability to reduce LDL and risk of cardiovascular disease. Similarly, sulfonamides were a classic substructure used in first-generation antibacterials due to their competitive inhibition of dihydropteroate synthase, a key part of folate biosynthesis in most non-human, non-eukaryote organisms.

Ontologies like ChEBI contain relationships between these substructures (and scaffolds) and chemicals. By combining ChEBI and ClinicalTrials.gov in a single graph, the previous query that aggregated maximum phase over a specific chemical can be generalized to substructures. Further, the “substructure of” relationship is transitive, meaning that multiple substructure relations can be chained together to ask the same question at many levels of granularity.

Ultimately, you can get results like beta-hydroxy lactone have appeared in clinical trials of a maximum phase of 4 and ask even more granular questions, like what is the maximum clinical phase for drugs with an arsenic atom as part of their structure (surprisingly, the answer is more than 0!). On the flip side, this can either identify opportunities for structures that haven’t been in clinical trials, or beg the question of why they haven’t so far.

Note that the aggregation over maximum phase is just an example - being able to query over a combination of one resource (here, ClinicalTrials.gov) and the hierarchy of another (here, ChEBI) is a more general and powerful concept.

Maximum Phase for Pairs of Substructures and Diseases

   graph LR
     substructure[Substructure] -- substructure of --> drug[Drug] -- intervention in --> trial[Clinical Trial] -- studies --> disease[Disease]
     trial -- has phase --> phase[Phase]
     substructure -. has maximum phase (aggregated) .-> phase

The previous query becomes even more powerful when extending the path from the clinical trial to a disease. Then, the aggregation operation can be done over the pair of the substructure and the disease to answer questions like what’s the maximum phase that arsenic-containing drugs have been used in for each disease?, or, more specifically, what’s the maximum phase that arsenic-containing drugs have been used in for malaria?

Maximum Phase for Pairs of Substructures and Disease Classes

   graph LR
     substructure[Substructure] -- substructure of --> drug[Drug] -- intervention in --> trial[Clinical Trial] -- studies --> disease[Disease] -- subclass of --> diseaseclass[Disease Class]
     trial -- has maximum phase --> phase[Phase]
     substructure -. has maximum phase .-> phase

The second case study extended the way we look at drugs by adding the hieararchical substructure relationships from ChEBI. If we want to aggregate diseases at a different granularity, we can do so by incorporating the subclass relationships from a resource like the Disease Ontology. Then, we can ask the previous question at a chosen level of granularity, like what’s the maximum phase that arsenic-containing drugs have been used in for each parasitic infectious disease?

It’s a bit tricky to ask the open-ended question what’s the maximum phase that arsenic-containing drugs have been used in for each disease class? because it depends on the construction of the disease hierarchy. Other common aggregations here are for rare diseases, cancers, neurodegenerative diseases, etc. In practice, I usualy have an additional way of tagging the terms in the hierarchy that I want to aggregate on, either by labeling the node in a property graph, or using yet another relationship to a node representing a grouping of my desired query terms.

Maximum Phase for Pairs of Vaccine Platforms and Disease Classes

   graph LR
     vaccineplatform[Vaccine Platform] -- platform for --> vaccine[Vaccine] -- intervention in --> trial[Clinical Trial] -- studies --> disease[Disease] -- subclass of --> diseaseclass[Disease Class]
     trial -- has phase --> phase[Phase]
     vaccineplatform -. has maximum phase .-> phase

The original use case for a query like the one before was for the RAPTER project, where we were asked to summarize for each vaccine platform (e.g., RNA vaccines, DNA vaccines, viral vector vaccines) what was the maximum clinical trial phase for trials over COVID-19 and a few other classes of parasitic and bacterial infections. The diagram above has the same shape and flavor as the previous one with substructures, chemicals, and diseases except the type of relation and direction between a vaccine and a vaccine platform is different. Despite this, the query is effectively shaped the same.

Phenotypic Drug Discovery Scenarios

When there exists a good cellular model of disease or good model organisms, it’s possible to eschew the typical target focus of a drug discovery campaign. However, during or after a phenotypic drug discovery, it’s useful to uncover a deeper mechanistic context through a combination of target identification and mechanism of action deconvolution.

Target Identification

graph LR
  disease[Disease] -- studied in --> trial[Clinical Trial] -- uses intervention --> drug[Drug] -- regulates --> protein[Protein]
  disease -. has putative target .-> protein

Target identification is typically the process of identifying a protein whose modulation in a given disease context can result in a therapeutic effect.

If we have a successful clinical trial performed following a phenotypic drug discovery campaign, it might not be known what targets it modulates. Therefore, we can combine the clinical trial data with a chemical activity database like ChEMBL, so we can get all the targets for the chemical, then hypothesize that one or more of them are responsible for the drug’s therapeutic effect.

This is even more powerful when combining other databases like OpenTargets, which aggregates a wide variety of orthogonal evidence and provide workflows and resources for further triage and DisGeNet, which aggregates text mining co-occurrence evidence as a proxy for association, which can be useful when done at scale with the appropriate statistical adjustments.

Finally, target identification hypotheses are testable, especially following a phenotypic drug discovery campaign with a good cellular model or model organism. Tool compounds that are specific modulators for the target or genomic experiments like knockdowns, knockouts, or over-expression can provide more confident confirmation of the target’s viability.

Mechanism of Action Deconvolution

graph LR
  drug[Drug] -- intervention in --> trial[Clinical Trial] -- studies --> disease[Disease] -- has target --> protein[Protein]
  drug -. has putative mechanism of action .-> protein

The dual problem to target identification is mechanism of action (MoA) deconvolution. After a successful clinical trial following a phenotypic drug discovery campaign, but not know the target that your drug modulates. You can get the target information by integrating parts of a database like OpenTargets or DisGeNet, then you will be able to use the path query to propose putative targets.

MoA hypotheses are often easier to test than target identification hypotheses because they can be done in biochemical assays. However, there are lots of tricky targets (including ones that aren’t proteins) where this isn’t so straightforward.

For both target identification and MoA, you might start imagining ways to take into account chemical similarity, protein similarity, and other hierarchical structures for more sophisticated queries. Thinking about this is why I’ve loved transitioning from a medicinal chemist in my early career to a chem/bioinformatician in my mid-career. I’ll leave the last part as an exercise to the reader, or for someone who wants to pay for consultation ;)