Intelligencia GraphQL API Reference

Welcome to the Intelligencia API. This API provides data regarding clinical programs which are in active development (otherwise called ongoing programs) in Oncology and parts of CNS and I&I (Immunology/Inflammation). Specifically, Intelligencia’s data is structured around the following main entities:

Clinical Program: A program (also known as clinical pipeline or drug pipeline) is the clinical development of a drug (or a set of drugs in case of combination therapies) by a pharmaceutical company (alone or in collaboration with other partners) for an indication. A program consists of a set of clinical trials with the ultimate goal of approval for marketing. Intelligencia’s database includes programs that are industry-led, FDA-tracked and based on interventional clinical trials.
Program Characteristics: Program characteristics are all the defining characteristics of a clinical program beyond its drug(s)/intervention(s), indication, primary sponsor and adjuvant value. It includes information that describes the program’s patient population, for example line of treatment, stage of disease, molecular characterization, age, sex, smoking status and previous treatments.
Clinical Trial: A clinical trial is a research study conducted in humans which assesses a treatment or a medical intervention. Intelligencia’s database includes interventional, industry-led clinical trials which are FDA-tracked and part of ongoing clinical programs. A trial may be linked to more than one program for example when a) a trial tests the same drugs/interventions in multiple indications (Basket Trial), b) a trial tests different drugs/interventions in the same indication (Umbrella Trial), c) a trial that tests multiple subsets of the same indication (e.g subsets of breast cancer), or multiple patient groups under the same indication. Note that a trial can be both basket and umbrella, namely it can test multiple different drug therapies in multiple indications.
Clinical Trial Arm: A trial arm describes the different groupings of the trial’s patients based on the administered drug(s)/intervention(s). Any difference in drug(s)/intervention(s) administered to a patient, be it a different drug, a different combination of drugs, a different dose or even a different treatment schedule constitutes a separate arm.
Clinical Trial Cohort: A cohort separates patients in further subgroupings based on common patient traits e.g. common genetic makeup, age, smoking status etc. Cohorts are used by the investigators for the analysis of the trial’s outcomes. A trial’s cohort may belong to one or multiple trial arms.
Clinical Trial Endpoint: An endpoint measures the outcomes of the trial. Endpoints are usually part of the clinical trial design. When a trial starts the information that is usually available includes the number and type/subtype of endpoints. Later in the trial, when outcomes are published e.g. in papers, conferences, company announcements, then clinical endpoints are filled with the relevant results (aka outcomes).
Program Drivers: Drivers are the features that the ML algorithms take into account to predict the program’s PTRS. Intelligencia’s PTRS drivers belong to feature families such as Biology, Clinical Trial Outcomes, Clinical Trial Design, Regulatory, Company Characteristics.
Benchmarks: Intelligencia’s benchmarks consist of the historical approval rate and the number of ongoing and historical (approved, failed) programs (or program-trial pairs) for key Biology, Regulatory and Trial Design dimensions. Benchmarks are provided per indication and phase where applicable and are based on data spanning across the last 20 years of clinical development.
Drug: Intelligencia’s drug library contains expertly curated information about drugs that are currently in ongoing clinical programs including drug synonyms, mechanisms of action, targets, modalities, genes, biological pathways, protein classes.
Target: Intelligencia’s library of targets along with information about their synonyms, related organism and target type (eg. protein, gene). Drugs can have multiple targets.
Indication Hierarchy: Intelligencia’s hand-curated therapeutic area-indication ontology for all covered therapeutic areas and indications (Oncology, Immunology/Inflammation and CNS).
Modality Hierarchy: Modality hierarchy is an expertly curated multilevel ontology of medical modalities, such as cell therapies, antibodies etc. Each drug may be linked to more than one modality.
Phase Hierarchy: It describes the hierarchy of clinical trial phases.

Contact

API Support Team
[email protected]

API Endpoints

Production:
https://api.intelligencia.ai/v1/graphql

Intelligencia supported indications

Therapeutic Area (TA)	Indications
Oncology	All cancer indications
Immunology/Inflammation	Asthma, Lupus erythematosus, Psoriasis, Rheumatoid Arthritis
CNS	Aggression associated with alzheimer's disease, Agitation associated with alzheimer's disease, Depressive Disorder, Multiple Sclerosis, Spinal Muscular Atrophy, Multiple System Atrophy, Parkinson Disease, Psychosis associated with alzheimer's disease, Psychosis associated with parkinson's disease, Post-Traumatic Stress Disorder

Intelligencia data sources

Intelligencia Entities	Data Sources
Clinical Program Program Characteristics Clinical Trial Clinical Trial Arm Clinical Trial Cohort Clinical Trial Endpoint	Clinicaltrials.gov, publications, company websites, FDA announcements
Drug	Public databases such as ChEMBL, NCI Thesaurus, FDA, NIH DailyMed, NCATS Inxight Drugs, etc
Indication Hierarchy	Medical Subject Headings (MeSH) & Experimental Factor Ontology (EFO)
Genes, Targets	ChEMBL, NCBI, UniPROT

Intelligencia's Graph

Check graph explorer here

Intelligencia's API Quick Start Guide

Check quick start guide here

Intelligencia's Postman Collection

Check our Postman collection here

Intelligencia's API Explorer

Check our API Explorer here

API limits

Default limits per type:

api_arm: 100
api_benchmarks: 50
api_cohort: 100
api_drug: 50
api_endpoint: 100
api_program: 50
api_program_characteristics: 100
api_program_drivers: 200
api_program_ptrs: 50
api_trial: 50

Queries

`api_benchmarks`

Fetch benchmarks.

Returns [api_benchmarks!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_benchmarks($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_benchmarks(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    approved
    benchmark_category
    benchmark_type
    failed
    historical
    historical_approval_rate
    indication
    metric
    metric_value
    ongoing
    phase
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 123,
  "offset": 987,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_benchmarks": [
      {
        "approved": 10,
        "benchmark_category": "Biology",
        "benchmark_type": "trial",
        "failed": 146,
        "historical": 156,
        "historical_approval_rate": "6%",
        "indication": "Lymphoma",
        "metric": "therapy_type",
        "metric_value": "Combination",
        "ongoing": 187,
        "phase": "Phase 2"
      }
    ]
  }
}

`api_benchmarks_aggregate`

Fetch aggregated results for benchmarks.

count, min, max can be used here.

Returns [api_benchmarks_aggregate!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_benchmarks_aggregate($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_benchmarks_aggregate(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    aggregate {
      ...aggregate_fieldsFragment
    }
    nodes {
      ...api_benchmarksFragment
    }
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 123,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_benchmarks_aggregate": [
      {
        "aggregate": aggregate_fields,
        "nodes": [api_benchmarks]
      }
    ]
  }
}

`api_drug`

Fetch drugs and any of their related types (api_program_drugs, api_trial_drugs)

Returns [api_drug!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_drug($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_drug(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    action_types
    biological_pathways
    drug_id
    genes
    mechanism_of_action
    modalities
    preferred_name
    program_drugs {
      ...api_program_drugFragment
    }
    protein_class
    synonyms
    targets
    trial_drugs {
      ...api_trial_drugFragment
    }
    treatment_types
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 123,
  "offset": 123,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_drug": [
      {
        "action_types": [{"name": Antagonist}],
        "biological_pathways": [
          {"name": Adaptive Immune System},
          {"name": Costimulation by the CD28 family},
          {"name": Immune System},
          {"name": PD-1 signaling}
        ],
        "drug_id": 987,
        "genes": [{"name": PDCD1}],
        "mechanism_of_action": [
          {
            "name": Programmed cell death protein 1 Antagonist
          }
        ],
        "modalities": [{"name": Monoclonal antibody}],
        "preferred_name": "Nivolumab",
        "program_drugs": [api_program_drug],
        "protein_class": [{"name": Receptor}],
        "synonyms": [
          {"name": BMS 936558},
          {"name": BMS-936558},
          {"name": BMS-936558-01},
          {"name": BMS-986298},
          {"name": MDX 1106},
          {"name": MDX-1106},
          {"name": NIVO},
          {"name": Nivolumab},
          {"name": Nivolumab BMS},
          {"name": ONO-0123},
          {"name": ONO 4538},
          {"name": ONO-4538},
          {"name": Opdivo}
        ],
        "targets": [
          {"name": Programmed cell death protein 1}
        ],
        "trial_drugs": [api_trial_drug],
        "treatment_types": [{"name": Immunotherapy}]
      }
    ]
  }
}

`api_drug_aggregate`

Fetch aggregated results for drugs.

count, min, max can be used here.

Returns [api_drug_aggregate!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_drug_aggregate($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_drug_aggregate(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    aggregate {
      ...aggregate_fieldsFragment
    }
    nodes {
      ...api_drugFragment
    }
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 987,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_drug_aggregate": [
      {
        "aggregate": aggregate_fields,
        "nodes": [api_drug]
      }
    ]
  }
}

`api_indication_hierarchy`

Returns [api_indication_hierarchy!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_indication_hierarchy($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_indication_hierarchy(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    indication_child
    indication_parent
    distance
    parent_level
    child_level
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 123,
  "offset": 987,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_indication_hierarchy": [
      {
        "indication_child": "Breast carcinoma",
        "indication_parent": "Breast cancer",
        "distance": 123,
        "parent_level": 987,
        "child_level": 123
      }
    ]
  }
}

`api_modality_hierarchy`

Returns [api_modality_hierarchy!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_modality_hierarchy($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_modality_hierarchy(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    modality_child
    modality_parent
    distance
    parent_level
    child_level
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 123,
  "offset": 987,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_modality_hierarchy": [
      {
        "modality_child": "Trastuzumab",
        "modality_parent": "abc123",
        "distance": 123,
        "parent_level": 987,
        "child_level": 987
      }
    ]
  }
}

`api_phase_hierarchy`

Returns [api_phase_hierarchy!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_phase_hierarchy($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_phase_hierarchy(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    phase_child
    phase_parent
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 987,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_phase_hierarchy": [
      {
        "phase_child": "Phase 1b",
        "phase_parent": "Phase 1"
      }
    ]
  }
}

`api_program`

Fetch programs and any of their related types (api_program_drugs, api_program_latest_trial, etc.)

Returns [api_program!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_program($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_program(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    adjuvant
    administration_mode
    end_date
    indication
    intervention
    latest_trial
    latest_trial_with_outcomes
    link_to_platform
    primary_sponsor
    program_characteristics {
      ...api_program_characteristicsFragment
    }
    program_drivers {
      ...api_program_driversFragment
    }
    program_drugs {
      ...api_program_drugFragment
    }
    program_id
    program_latest_trial {
      ...api_trialFragment
    }
    program_latest_trial_with_outcomes {
      ...api_trialFragment
    }
    program_trial_arm_cohorts {
      ...api_program_trial_arm_cohortFragment
    }
    regulatory_pathways
    therapeutic_area
    therapy_type
    status
    program_ptrs {
      ...api_program_ptrsFragment
    }
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 987,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_program": [
      {
        "adjuvant": [{"name": Neoadjuvant}],
        "administration_mode": Injectable/Intravenous,
        "end_date": 2023-12-08,
        "indication": Leukemia, myeloid, acute,
        "intervention": [{"name": Radio}],
        "latest_trial": 789,
        "latest_trial_with_outcomes": 789,
        "link_to_platform": "https://insight.intelligencia.ai/insight/portfolio-optimizer/program-assessment/deep-dive#programId=986",
        "primary_sponsor": Jasper Therapeutics,
        "program_characteristics": [
          api_program_characteristics
        ],
        "program_drivers": [api_program_drivers],
        "program_drugs": [api_program_drug],
        "program_id": 986,
        "program_latest_trial": api_trial,
        "program_latest_trial_with_outcomes": api_trial,
        "program_trial_arm_cohorts": [
          api_program_trial_arm_cohort
        ],
        "regulatory_pathways": [{"name": Orphan drug}],
        "therapeutic_area": [{"name": Oncology}],
        "therapy_type": Combination,
        "status": "Ongoing",
        "program_ptrs": api_program_ptrs
      }
    ]
  }
}

`api_program_aggregate`

Fetch aggregated results for programs.

count, min, max can be used here.

Returns [api_program_aggregate!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_program_aggregate($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_program_aggregate(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    aggregate {
      ...aggregate_fieldsFragment
    }
    nodes {
      ...api_programFragment
    }
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 123,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_program_aggregate": [
      {
        "aggregate": aggregate_fields,
        "nodes": [api_program]
      }
    ]
  }
}

`api_trial`

Fetch trials and any of their related types (api_arm, api_trial_drugs, etc.)

Returns [api_trial!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_trial($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_trial(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    additional_sponsors
    adverse_events_criteria
    allocation
    arms {
      ...api_armFragment
    }
    basket
    biomarker_subgroup_analysis
    end_date
    enrollment
    indications
    intervention_model
    masking
    phase
    primary_sponsors
    program_trial_arm_cohorts {
      ...api_program_trial_arm_cohortFragment
    }
    safety_acceptable
    source_id
    source_link
    start_date
    termination_date
    title
    trial_drugs {
      ...api_trial_drugFragment
    }
    trial_id
    trial_status
    umbrella
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 123,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_trial": [
      {
        "additional_sponsors": jsonb,
        "adverse_events_criteria": "xyz789",
        "allocation": "xyz789",
        "arms": [api_arm],
        "basket": true,
        "biomarker_subgroup_analysis": true,
        "end_date": date,
        "enrollment": 987,
        "indications": jsonb,
        "intervention_model": "xyz789",
        "masking": "xyz789",
        "phase": "xyz789",
        "primary_sponsors": jsonb,
        "program_trial_arm_cohorts": [
          api_program_trial_arm_cohort
        ],
        "safety_acceptable": true,
        "source_id": "abc123",
        "source_link": "xyz789",
        "start_date": date,
        "termination_date": date,
        "title": "abc123",
        "trial_drugs": [api_trial_drug],
        "trial_id": 123,
        "trial_status": "xyz789",
        "umbrella": false
      }
    ]
  }
}

`api_trial_aggregate`

Fetch aggregated results for trials.

count, min, max can be used here.

Returns [api_trial_aggregate!]!

Name	Description
`distinct_on` - `[String!]`	distinct select on columns
`limit` - `Int`	limit the number of rows returned
`offset` - `Int`	skip the first n rows. Use only with order_by
`order_by` - `[String!]`	sort the rows by one or more columns
`where` - `bool_comparison_exp`	filter the rows returned

Example

Query

query api_trial_aggregate($distinct_on: [String!], $limit: Int, $offset: Int, $order_by: [String!], $where: bool_comparison_exp) {
  api_trial_aggregate(distinct_on: $distinct_on, limit: $limit, offset: $offset, order_by: $order_by, where: $where) {
    aggregate {
      ...aggregate_fieldsFragment
    }
    nodes {
      ...api_trialFragment
    }
  }
}

Variables

{
  "distinct_on": [
    "column_name"
  ],
  "limit": 987,
  "offset": 123,
  "order_by": [
    "column_name"
  ],
  "where": bool_comparison_exp
}

Response

{
  "data": {
    "api_trial_aggregate": [
      {
        "aggregate": aggregate_fields,
        "nodes": [api_trial]
      }
    ]
  }
}

Types

`Boolean`

The Boolean scalar type represents true or false.

Example

false

Types

`Boolean_comparison_exp`

Boolean expression to compare columns of type "Boolean". All fields are combined with logical 'AND'.

Input Field	Description
`_eq` - `Boolean`
`_gt` - `Boolean`
`_gte` - `Boolean`
`_in` - `[Boolean!]`
`_is_null` - `Boolean`
`_lt` - `Boolean`
`_lte` - `Boolean`
`_neq` - `Boolean`
`_nin` - `[Boolean!]`

Example

{
  "_eq": true,
  "_gt": false,
  "_gte": false,
  "_in": [true],
  "_is_null": false,
  "_lt": false,
  "_lte": false,
  "_neq": false,
  "_nin": [false]
}

Types

`Int`

The Int scalar type represents non-fractional signed whole numeric values. Int can represent values between -(2^31) and 2^31 - 1.

Example

Types

`Int_comparison_exp`

Boolean expression to compare columns of type "Int". All fields are combined with logical 'AND'.

Input Field	Description
`_eq` - `Int`
`_gt` - `Int`
`_gte` - `Int`
`_in` - `[Int!]`
`_is_null` - `Boolean`
`_lt` - `Int`
`_lte` - `Int`
`_neq` - `Int`
`_nin` - `[Int!]`

Example

{
  "_eq": 42,
  "_gt": 42,
  "_gte": 42,
  "_in": [42],
  "_is_null": false,
  "_lt": 42,
  "_lte": 42,
  "_neq": 42,
  "_nin": [42]
}

Types

`String`

The String scalar type represents textual data, represented as UTF-8 character sequences. The String type is most often used by GraphQL to represent free-form human-readable text.

Types

`String_comparison_exp`

expression to compare columns of type String. All fields are combined with logical 'AND'.

Input Field	Description
`_eq` - `String`
`_gt` - `String`
`_gte` - `String`
`_ilike` - `String`
`_in` - `[String!]`
`_is_null` - `Boolean`
`_like` - `String`
`_lt` - `String`
`_lte` - `String`
`_neq` - `String`
`_nilike` - `String`
`_nin` - `[String!]`
`_nlike` - `String`
`_nsimilar` - `String`
`_similar` - `String`

Example

{
  "_eq": "xyz789",
  "_gt": "xyz789",
  "_gte": "xyz789",
  "_ilike": "xyz789",
  "_in": ["abc123"],
  "_is_null": false,
  "_like": "xyz789",
  "_lt": "abc123",
  "_lte": "abc123",
  "_neq": "abc123",
  "_nilike": "xyz789",
  "_nin": ["xyz789"],
  "_nlike": "abc123",
  "_nsimilar": "abc123",
  "_similar": "xyz789"
}

Types

`aggregate_fields`

aggregate fields of "api.drug"

Field Name	Description
`avg` - `String`
`count` - `Int!`
Arguments `columns` - `[String!]` `distinct` - `Boolean`
`max` - `String`
`min` - `String`

Example

{"avg": "xyz789", "count": 987, "max": "xyz789", "min": "xyz789"}

Types

`api_arm`

A trial arm describes the different groupings of the trial’s patients based on the administered drug(s)/intervention(s). Any difference in drug(s)/intervention(s) administered to a patient, be it a different drug, a different combination of drugs, a different dose or even a different treatment schedule constitutes a separate arm.

One arm can be linked to one or multiple programs and can contain one or more cohorts

One arm is linked to multiple programs for example when the drug(s)/intervention(s) administered in this arm is tested against multiple indications (Basket trial)
An arm may have more than one cohort. For example: Patient cohorts that receive the same drug(s)/intervention(s) will relate to the same arm.

Unique Identifier: arm_id

Field Name	Description
`arm_cohorts` - `[api_cohort_arm!]!`	An array relationship
`arm_id` - `Int!`	Intelligencia’s unique arm identifier
`arm_name` - `String`	Arm name
`arm_type` - `String`	The arm type describes the role of the drug(s)/intervention(s) that participants receive
`cohort_arms` - `[api_cohort_arm!]!`	An array relationship
`discontinued` - `Boolean`	If the trial’s arm has been discontinued
`program_trial_arm_cohorts` - `[api_program_trial_arm_cohort!]!`	An array relationship
`trial_id` - `Int`	Intelligencia’s unique trial identifier. Each arm belongs to one and only trial_id, but a trial_id may have more than one arm

Example

{
  "arm_cohorts": [api_cohort_arm],
  "arm_id": 123,
  "arm_name": "Trastuzumab",
  "arm_type": "Comparator",
  "cohort_arms": [api_cohort_arm],
  "discontinued": true,
  "program_trial_arm_cohorts": [
    api_program_trial_arm_cohort
  ],
  "trial_id": 123
}

Types

`api_benchmarks`

Intelligencia’s benchmarks consist of the historical approval rate and the number of ongoing and historical (approved, failed) programs (or program-trial pairs) for key clinical development categories. Benchmarks are provided per indication and phase where applicable and are based on data spanning across the last 20 years of clinical development.

Some benchmarks refer to programs, while others refer to trials attached to relevant programs (else called program-trial pairs). For example:

Biology benchmarks refer to program biological characteristics (program level benchmarks)
Regulatory benchmarks refer to the regulatory designations that a program might have received (program level benchmarks)
Trial Design benchmarks refer to the design of clinical trials attached to programs (program-trial pair benchmarks)

Benchmarks have the following dimensions: indication, phase, type, metric, metric value.

Unique Index: indication, phase, metric, metric_value

Field Name	Description
`approved` - `Int`	Number of approved programs or program-trial pairs
`benchmark_category` - `String`	Indicates the benchmark’s category. For example: Biology, Trial Design or Regulatory
`benchmark_type` - `String`	Indicates if the benchmark counts programs (benchmark_type:program) or program-trial pairs (benchmark_type:trial)
`failed` - `Int`	Number of failed programs or program-trial pairs
`historical` - `Int`	Number of historical (approved+failed) programs or program-trial pairs
`historical_approval_rate` - `String`	% of historical programs or program-trial pairs approved by FDA
`indication` - `String`	Benchmarks are indication specific and include program/program-trial pairs of descendant indications (see indication hierarchy). Example: benchmarks for pancreatic cancer include all pancreatic cancer subindications, such as pancreatic carcinoma, in the program/program-trial counts
`metric` - `String`	Metric is a textual representation of the benchmark metric in question; example: modality, primary endpoints, etc. Empty (NULL) metric values are used for overall benchmarks in the indication and phase dimensions
`metric_value` - `String`	The benchmark subcategory. For instance for the benchmark “modality”, some potential benchmark subcategories are “vaccine”, “antibody” etc
`ongoing` - `Int`	Number of ongoing programs or program-trial pairs
`phase` - `String`	Indicates the clinical phase that the benchmark refers to. For example: The historical approval rate in pancreatic cancer is 18% for the metric=modality and metric_value=small molecule in phase 3. This means that 18% of programs with small molecule drugs that have passed through phase 3 in pancreatic cancer have been approved. For benchmarks across phases, phase is empty (NULL). Phase 1 includes phase 1b program/program-trial pairs. Phase 2 includes phase 1/2 program/program-trial pairs. Phase 3 includes phase 2/3 program/program-trial pairs.

Example

{
  "approved": 10,
  "benchmark_category": "Biology",
  "benchmark_type": "trial",
  "failed": 146,
  "historical": 156,
  "historical_approval_rate": "6%",
  "indication": "Lymphoma",
  "metric": "therapy_type",
  "metric_value": "Combination",
  "ongoing": 187,
  "phase": "Phase 2"
}

Types

`api_benchmarks_aggregate`

aggregated selection of "api.benchmark"

Field Name	Description
`aggregate` - `aggregate_fields`
`nodes` - `[api_benchmarks!]!`

Example

{
  "aggregate": aggregate_fields,
  "nodes": [api_benchmarks]
}

Types

`api_cohort`

A cohort separates patients in further subgroupings based on common patient traits e.g. common genetic makeup, age, smoking status etc. Cohorts are used by the investigators for the analysis of the trial’s outcomes.

A trial’s cohort may belong to one or multiple trial arms and it is usually connected to multiple endpoints. For example:

One patient cohort is connected with multiple arms when the trial’s endpoints are measured for the overall trial population
A cohort is usually connected to multiple endpoints per the design of the trial and it often includes primary, secondary and other endpoints.

For each cohort we provide quality of life information. Potential values include:

“QoL Mentioned” : quality of life is evaluated in the cohort but the result is not known.
“No Change”: there was no change in the cohort’s participants' quality of life.
“Negative QoL”: the cohort’s participants’ quality of life has decreased
“Positive QoL”:the cohort’s participants’ quality of life has improved

Unique Identifier: cohort_id

Field Name	Description
`cohort_arms` - `[api_cohort_arm!]!`	An array relationship
`cohort_id` - `Int!`	Intelligencia’s unique cohort identifier
`cohort_indication` - `jsonb`	The cohort’s indications. Along with the cohort’s indications we provide information for the evaluation criteria of outcomes relevant to each indication (if provided) and the therapeutic area that the indication belongs to. Cohort’s indications are always one or more of the relevant trial’s indications.
`cohort_name` - `String`	Cohort’s name
`cohort_patients` - `numeric`	Cohort’s name
`cohort_qol` - `String`	Cohort’s quality of life information. When the value is “QoL Mentioned” means that quality of life is evaluated in the cohort but the result is not known. When the value is “No Change” means that there was no change in the cohort’s participants quality of life. When the value is “Negative QoL” means that the cohort’s participants’ quality of life has decreased. When the value is “Positive QoL” means that the cohort’s participants’ quality of life has improved.
`cohort_safety_acceptable` - `Boolean`	If the cohort’s overall safety profile is acceptable or not
`endpoints` - `[api_endpoint!]!`	An array relationship
`trial_id` - `Int`	Intelligencia’s unique trial identifier

Example

{
  "cohort_arms": [api_cohort_arm],
  "cohort_id": 987,
  "cohort_indication": [
    {
      "indication": Kaposis sarcoma,
      "therapeutic_area": Oncology,
      "evaluation_criteria": null
    }
  ],
  "cohort_name": "Experimental: 10^6 IV  autologous ova-Tregs",
  "cohort_patients": 155,
  "cohort_qol": "Negative QoL",
  "cohort_safety_acceptable": true,
  "endpoints": [api_endpoint],
  "trial_id": 123
}

Types

`api_cohort_arm`

Provides the relationship between arms and cohorts.

A cohort may belong to more than one arm. For example multiple arms are connected with one patient cohort when the trial’s endpoints are measured for the overall trial population .

An arm may have more than one cohort. For example: Patient cohorts that receive the same drug(s)/intervention(s) will relate to the same arm.

Unique Identifier: arm_id, cohort_id

Field Name	Description
`arm` - `api_arm!`	An object relationship
`arm_id` - `Int!`	Intelligencia’s unique arm identifier
`cohort` - `api_cohort!`	An object relationship
`cohort_id` - `Int!`	Intelligencia’s unique cohort identifier

Example

{
  "arm": api_arm,
  "arm_id": 987,
  "cohort": api_cohort,
  "cohort_id": 987
}

Types

`api_drug`

Drugs in ongoing clinical programs which are industry-led, FDA-tracked and based on interventional trials, along with all relevant information regarding these drugs, specifically:

All names of this drug with preferred name indicated
Biology of this drug: modality, mechanism of action (also broken into action type & target), genes, biological_pathways,protein_class

Unique Identifier: drug_id

Field Name	Description
`action_types` - `jsonb`	The drug’s action type. Action type is part of each drug’s mechanism and indicates the effect of the drug on the drug target (eg. agonist, inhibitor etc)
`biological_pathways` - `jsonb`	The biological pathways that the drug’s targets are involved
`drug_id` - `Int!`	Intelligencia’s unique drug identifier
`genes` - `jsonb`	The drug’s target genes. This is relevant for drug targets that are protein-based
`mechanism_of_action` - `jsonb`	The drug’s mechanism(s) of action
`modalities` - `jsonb`	The drug’s modalities. For example: antibody, vaccine, etc.
`preferred_name` - `String`	The drug’s preferred name
`program_drugs` - `[api_program_drug!]!`	An array relationship
`protein_class` - `jsonb`	The drug’s target(s) protein class(es). Examples: enzyme, receptor, etc.
`synonyms` - `jsonb`	The drug’s synonyms which include drug development names and trade names (if the drug is marketed)
`targets` - `jsonb`	The drug’s targets
`trial_drugs` - `[api_trial_drug!]!`	An array relationship
`treatment_types` - `jsonb`	The drug’s treatment type. This is an optional field. Example values: chemotherapy, immunotherapy

Example

{
  "action_types": [{"name": Antagonist}],
  "biological_pathways": [
    {"name": Adaptive Immune System},
    {"name": Costimulation by the CD28 family},
    {"name": Immune System},
    {"name": PD-1 signaling}
  ],
  "drug_id": 987,
  "genes": [{"name": PDCD1}],
  "mechanism_of_action": [
    {"name": Programmed cell death protein 1 Antagonist}
  ],
  "modalities": [{"name": Monoclonal antibody}],
  "preferred_name": "Nivolumab",
  "program_drugs": [api_program_drug],
  "protein_class": [{"name": Receptor}],
  "synonyms": [
    {"name": BMS 936558},
    {"name": BMS-936558},
    {"name": BMS-936558-01},
    {"name": BMS-986298},
    {"name": MDX 1106},
    {"name": MDX-1106},
    {"name": NIVO},
    {"name": Nivolumab},
    {"name": Nivolumab BMS},
    {"name": ONO-0123},
    {"name": ONO 4538},
    {"name": ONO-4538},
    {"name": Opdivo}
  ],
  "targets": [{"name": Programmed cell death protein 1}],
  "trial_drugs": [api_trial_drug],
  "treatment_types": [{"name": Immunotherapy}]
}

Types

`api_drug_aggregate`

aggregated selection of "api.drug"

Field Name	Description
`aggregate` - `aggregate_fields`
`nodes` - `[api_drug!]!`

Example

{
  "aggregate": aggregate_fields,
  "nodes": [api_drug]
}

Types

`api_endpoint`

The endpoints of a cohort. A cohort typically contains more than one endpoint (either primary, secondary or other). One endpoint is always part of one cohort and cannot be attached to more than one cohort.

An endpoint measures the outcomes of the trial. Endpoints are usually part of the clinical trial design. When a trial starts the information that is usually available includes the number and type/subtype of endpoints. Later in the trial, when outcomes are published e.g. in papers, conferences, company announcements, then clinical endpoints are filled with the relevant results (aka outcomes). For pharmacokinetics endpoints value specifications (e.g. value, timeframe etc) are not available (n/a).

An endpoint is defined by a type and a subtype (when applicable). For example: for endpoint type “safety” there are several subtypes showing the type of adverse event assessment such as “Grade 2 AEs”, Grade 3 AEs” etc There are cases where the endpoint subtype is not provided such as:

When this endpoint has not applicable subtypes
When this endpoint has subtypes but it can also be measured as a standalone endpoint type e.g. PASI

Difference is relevant only to some endpoints. Possible values are “baseline”, “placebo”, “comparator”. Specifically:

When value is “Baseline”: difference between the endpoint measurement at the time of capturing the endpoint vs the measurement of the same endpoint at an earlier time, usually the initial measurement before treatment.
When value is either “placebo” or “comparator”: in the outcomes announcement, baseline difference of the experimental drug(s)/intervention(s) and the baseline difference for the placebo/comparator is mentioned, and then the difference of the two differences is measured providing the final endpoint value.

Unique Identifier: endpoint_id

Field Name	Description
`cohort_id` - `Int`	Intelligencia’s unique cohort identifier
`endpoint_id` - `Int!`	Intelligencia’s unique endpoint identifier
`endpoint_subtype` - `String`	The endpoint subcategorization (when applicable)
`endpoint_type` - `String`	The endpoint name. Example: ORR, OS, PFS etc
`endpoint_value` - `String`	The endpoint outcome value
`endpoint_value_unit` - `String`	The endpoint’s value unit
`on_off` - `String`	This is an option only relevant to Parkinson’s disease. On/Off phenomenon in Parkinson's disease happens when the common treatment levodopa wears off and motor symptoms return, before it's time for the next dose. On/off episodes, also known as “off time,” typically happen more often as Parkinson's disease progresses, and levodopa becomes less effective. In a lot of cases, results are provided separately for the ON period and the OFF period.
`primary_choice` - `String`	If the endpoint is primary, secondary or other in this cohort
`difference` - `String`	“Difference” is relevant only to some endpoints. Possible values are “baseline”, “placebo”, “comparator”.
`timeframe` - `String`	The endpoint’s monitoring time period
`timeframe_name` - `String`	The endpoints timeframe unit (example: months, weeks, etc). When the value is “overall”, it means that the endpoint is monitored throughout the trial’s duration

Example

{
  "cohort_id": 123,
  "endpoint_id": 123,
  "endpoint_subtype": "Period",
  "endpoint_type": "PFS",
  "endpoint_value": "36",
  "endpoint_value_unit": "Percentage",
  "on_off": "Off",
  "primary_choice": "Primary",
  "difference": "Baseline",
  "timeframe": "12",
  "timeframe_name": "months"
}

Types

`api_indication_hierarchy`

Describes Intelligencia’s expertly curated indication ontology for the therapeutic areas that the supported indications belong to (Oncology, Immunology/Inflammation and CNS). The indication ontology is a multilevel hierarchy tree based on parent-child relationships. Ancestor-descendant indication relationships must be derived. For example:

The indication “Triple negative breast neoplasms” is a child of “breast carcinoma”, “breast carcinoma” is a child of “breast cancer”, therefore “Triple negative breast neoplasms” is a descendant of “breast cancer”.

Unique Index: indication_parent, indication_child

Field Name	Description
`indication_child` - `String`	Indication first-level child name. Example: breast carcinoma
`indication_parent` - `String`	Indication parent name. Example: breast cancer
`distance` - `Int`	Distance between parent and child in the indication tree. Example: 2
`parent_level` - `Int`	Parent's distance from the indication hierarchy root. Example: indication_parent: Neurodegenerative diseases parent_level: 1
`child_level` - `Int`	Child’s distance from the indication hierarchy root. Example: indication_child: Multiple sclerosis child_level: 2

Example

{
  "indication_child": "Breast carcinoma",
  "indication_parent": "Breast cancer",
  "distance": 987,
  "parent_level": 123,
  "child_level": 987
}

Types

`api_modality_hierarchy`

Intelligencia’s expertly curated drug modality ontology. The modality ontology is a multilevel hierarchy tree based on parent-child relationships.

Example: The modality “CAR T cells” is a child modality of “Cell Therapy”.

Unique Index: modality_parent, modality_child

Field Name	Description
`modality_child` - `String`	Modality first-level child name. Example: Bispecific antibody
`modality_parent` - `String`	Modality parent name. Example: Antibody
`distance` - `Int`	Distance between parent and child in the indication tree. Example: 2
`parent_level` - `Int`	Parent's distance from the modality hierarchy root. Example: indication_parent: Gene therapy parent_level: 1
`child_level` - `Int`	Child’s distance from the modality hierarchy root. Example: indication_child: Nonviral-vector gene therapy child_level: 2

Example

{
  "modality_child": "Trastuzumab",
  "modality_parent": "xyz789",
  "distance": 987,
  "parent_level": 987,
  "child_level": 123
}

Types

`api_phase_hierarchy`

Describes the hierarchy of clinical trial phases which is based on parent-child relationships.

Example: Phase 1b is a child of phase 1

Unique Index: phase_parent, phase_child

Field Name	Description
`phase_child` - `String`	Clinical phase first-level child name. Example: Phase 1b
`phase_parent` - `String`	Clinical phase parent name. Example: Phase 1

Example

{
  "phase_child": "Phase 1b",
  "phase_parent": "Phase 1"
}

Types

`api_program`

A program (also known as clinical pipeline or drug pipeline) is the clinical development of a drug (or a set of drugs in case of combination therapies) by a pharmaceutical company (alone or in collaboration with other partners) for an indication. A program consists of a set of clinical trials with the ultimate goal of approval for marketing. Intelligencia’s database includes programs that are industry-led, FDA-tracked and based on interventional clinical trials.

Programs are defined based on their drug(s)/interventions, indication, primary sponsor, adjuvant value and program characteristics. A program’s latest phase is derived from the program’s latest trial clinical phase.

Unique Identifier: program_id

Field Name	Description
`adjuvant` - `jsonb`	Indicates if the program’s therapy is either adjuvant and/or neoadjuvant when applicable
`administration_mode` - `citext`	The program’s primary drug route of administration in the human body. Example: Oral, Injectable
`end_date` - `date`	This is the end date of the program’s trial at the most advanced clinical phase with the latest completion date
`indication` - `citext`	The program’s indication (example: breast carcinoma)
`intervention` - `jsonb`	Indicates if the program includes other non-drug related interventions such as radiotherapy, surgery, photodynamic therapy etc
`latest_trial` - `Int`	The unique clinical trial identifier of the program’s latest clinical phase trial. This identifier can be used in conjunction with the clinical trial entity to find further information about this Program’s latest trial. Latest is defined as the trial that has the most advanced clinical phase and the latest end date.
`latest_trial_with_outcomes` - `Int`	The unique clinical trial identifier of the program’s latest clinical trial with published outcomes. This identifier can be used in conjunction with the clinical trial entity to find further information about this Program’s latest trial with outcomes. Latest with outcomes is defined as the trial that has the most advanced clinical phase and latest end date with outcomes published.
`link_to_platform` - `String`	The program’s link to Intelligencia’s Insight platform and specifically at the program’s deep dive section
`primary_sponsor` - `citext`	The program’s primary sponsor company name (example: Pfizer)
`program_characteristics` - `[api_program_characteristics!]!`	An array relationship
`program_drivers` - `[api_program_drivers!]!`	An array relationship
`program_drugs` - `[api_program_drug!]!`	An array relationship
`program_id` - `Int!`	Intelligencia’s unique program identifier
`program_latest_trial` - `api_trial`	An object relationship
`program_latest_trial_with_outcomes` - `api_trial`	An object relationship
`program_trial_arm_cohorts` - `[api_program_trial_arm_cohort!]!`	An array relationship
`regulatory_pathways` - `jsonb`	Indicates if the program has received a facilitated regulatory designation from FDA such as orphan drug, breakthrough therapy, fast track, priority review
`therapeutic_area` - `jsonb`	The generalized group of diseases that the program’s indication belongs to. Note that some indications might belong to more than 1 TAs. However, every program belongs to a single indication and a single TA.
`therapy_type` - `citext`	Indicates if the program’s therapy is monotherapy or combination. In the case of combination therapies one drug is the primary drug per program and the rest are the additional drugs
`status` - `String`	The program's status (example: Ongoing)
`program_ptrs` - `api_program_ptrs`	An object relationship

Example

{
  "adjuvant": [{"name": Neoadjuvant}],
  "administration_mode": Injectable/Intravenous,
  "end_date": 2023-12-08,
  "indication": Leukemia, myeloid, acute,
  "intervention": [{"name": Radio}],
  "latest_trial": 789,
  "latest_trial_with_outcomes": 789,
  "link_to_platform": "https://insight.intelligencia.ai/insight/portfolio-optimizer/program-assessment/deep-dive#programId=986",
  "primary_sponsor": Jasper Therapeutics,
  "program_characteristics": [
    api_program_characteristics
  ],
  "program_drivers": [api_program_drivers],
  "program_drugs": [api_program_drug],
  "program_id": 986,
  "program_latest_trial": api_trial,
  "program_latest_trial_with_outcomes": api_trial,
  "program_trial_arm_cohorts": [
    api_program_trial_arm_cohort
  ],
  "regulatory_pathways": [{"name": Orphan drug}],
  "therapeutic_area": [{"name": Oncology}],
  "therapy_type": Combination,
  "status": "Ongoing",
  "program_ptrs": api_program_ptrs
}

Types

`api_program_aggregate`

aggregated selection of "api.program"

Field Name	Description
`aggregate` - `aggregate_fields`
`nodes` - `[api_program!]!`

Example

{
  "aggregate": aggregate_fields,
  "nodes": [api_program]
}

Types

`api_program_characteristics`

Program characteristics are all the defining characteristics of a clinical program beyond its drug(s)/intervention(s), indication, primary sponsor and adjuvant value. It includes information that describes the program’s patient population, for example line of treatment, stage of disease, molecular characterization, age, sex, smoking status and previous treatments.

Program characteristics are organized in criteria groups describing patient groups with homogenous characteristics. For example:

group 1: patients with stage 3 disease and EGFR+ gene expression
group 2: patients with stage 2 disease and MET+ gene expression

Each unique program characteristic is defined by a criterion category (eg. line of treatment), a criterion type (eg. Line I) and a value.

Unique Identifier: criteria_id

Field Name	Description
`category` - `String`	Criterion categories are: line of treatment, stage of disease, healthy (enrollment of healthy participants), smoking status, sex, indication, previous treated with, patient aging group, molecular, other
`criterion_type` - `String`	Criterion types are subcategories that further define each criterion category. For example, criterion types of the criterion category “previously treated with” are: untreated, drug, other, surgery, radio
`criteria_group_id` - `Int`	Intelligencia’s unique criterion group identifier
`value` - `String`	Value corresponding to the characteristic of the program. Expected values depend on the category, and criterion type. For example: Values for criterion category “previously treated with” and criterion_type “drug”, indicate the drug name. Values for criterion category “molecular” and a criterion_type referring to a protein or gene, include options such as mutation, high, medium, low, amplification etc to further characterize the molecular signature of the particular patient criteria group. Values for criterion category “Line of Treatment” and criterion_type “Line II”, are “Yes”/”No” indicating the inclusion of Line II patients in the program’s clinical trials.
`program` - `api_program!`	An object relationship
`criteria_id` - `Int`	Intelligencia’s unique criterion identifier

Example

{
  "category": "Molecular",
  "criterion_type": "Receptor tyrosine-protein kinase erbB-2",
  "criteria_group_id": 123,
  "value": "Positive",
  "program": api_program,
  "criteria_id": 123
}

Types

`api_program_drivers`

Program’s PTRS drivers and relevant historical approval rates. Drivers are the features that the ML algorithms take into account to predict the program’s PTRS. Intelligencia’s PTRS drivers belong to feature families such as Biology, Clinical Trial Outcomes, Clinical Trial Design, Regulatory, Company Characteristics.

Each PTRS driver has a boolean or numeric type (see “feature_type” field) and a value for the related program. For instance,

“Randomized trial” indicates if the trial uses randomization, and the program values are 0/1 (boolean feature).
“number of sites” indicates the number of trial sites, and the program values are numeric (numeric feature). Certain drivers refer to program level data (otherwise called program level drivers), and other drivers refer to trial specific data (otherwise called program-trial pair drivers). Program-trial pair drivers appear multiple times if the program selected has more than one trial. Specifically:
Biology drivers relate to the biological characteristics of the program (program level drivers)
Clinical trial outcomes relate to the outcomes of every trial attached to this program (program-trial pair drivers)
Clinical Trial Design drivers relate to the design of every trial attached to this program (program-trial pair drivers)
Regulatory drivers relate to the regulatory designations that a program might have received (program level drivers)
Company characteristics relate to the company sponsor of each trials attached to this program (program-trial pair drivers)

PTRS driver historical approval rate is the % of historical programs/program-trial pairs having similar program values in this indication approved by FDA. Specifically:

For program level drivers: the historical approval rate refers to programs in the same indication with values similar to the selected program. For example: for the PTRS driver “drug’s action type: antagonist” and program value 1 (TRUE) and indication breast cancer, historical approval rate is calculated as the approved breast cancer programs with action type antagonist over all historical breast cancer programs with the same action type.
For program-trial pair drivers: the historical approval rate refers to program-trial pairs with values similar to the relevant program-trial pairs at this indication and phase. For example: for the PTRS driver “Drug administration: Oral” and program-trial value 1 (TRUE) and indication breast cancer and phase 2, the historical approval rate is calculated as the approved breast cancer phase 2 program-trial pairs with oral drug administration over all historical breast cancer program-trial pairs with oral drug administration at phase Similar program or trial values are defined as follows:
For features with boolean type, these are the exact programs (or program-trial pairs) values
For features with numeric values, similar program (or program-trial pair) values are identified by a classification algorithm that determines ranges of values per feature that exhibit similar historical approval behavior.

In cases where there is a limited number of programs (or program-trial pairs) with similar values, the approval rate is not available.

Unique Index: program_id, trial_id, ptrs_feature, indication

Field Name	Description
`feature_type` - `String`	If the feature is boolean or numeric
`indication` - `String`	The indication for which each PTRS driver’s historical approval rate refers to
`program_id` - `Int`	Intelligencia’s unique program identifier
`program_value` - `numeric`	The program’s value for the PTRS drive
`ptrs_feature_description` - `String`	PTRS driver name
`ptrs_feature_family` - `String`	Intelligencia’s PTRS drivers are categorized in feature families: Biology, Clinical Trial Design, Regulatory, Clinical Trial Outcomes, Company Characteristics
`historical_approval_rate` - `numeric`	% of historical programs/program-trial pairs having similar program values in this indication approved by FDA. In cases where there is a limited number of programs (or trials) with similar value, the approval rate is not available
`trial_id` - `Int`	Intelligencia’s unique trial identifier for the program’s trial that the specific driver refers to. This is relevant only for program-trial pair drivers: Clinical Trial design, Clinical Trial Outcomes and Company Characteristics

Example

{
  "feature_type": "boolean",
  "indication": "Stomach cancer",
  "program_id": 123,
  "program_value": 1,
  "ptrs_feature_description": "Drug's action type group: Blocker",
  "ptrs_feature_family": "Biology",
  "historical_approval_rate": numeric,
  "trial_id": 123
}

Types

`api_program_drug`

Links a program with its related drugs. Specifically this entity links the program’s program_id with the program’s primary drug ID and additional drug IDs (in case of combination therapies). A program’s primary drug is defined as the program’s main experimental drug.

Unique Identifier: program_id, drug_id

Field Name	Description
`drug` - `api_drug!`	An object relationship
`drug_id` - `Int!`	Intelligencia’s unique drug identifier
`is_primary` - `Boolean`	`true`=the drug is the program’s primary drug, `false`=the drug is part of the combination therapy of this program but it is not the primary drug
`program` - `api_program!`	An object relationship
`program_id` - `Int!`	Intelligencia’s unique program identifier

Example

{
  "drug": api_drug,
  "drug_id": 123,
  "is_primary": true,
  "program": api_program,
  "program_id": 987
}

Types

`api_program_ptrs`

Contains Intelligencia’s Probability of Phase Transition and Probability of Technical and Regulatory Success (PTRS) of ongoing, interventional, industry-sponsored, clinical development programs.

Probability of phase transition is the probability of a program to move to the next phase and it is available for Phases I to II and II to III Programs with latest phase trial at phase 3 do not have Probability of Phase transition.
There are cases where PTRS or Probability of phase transition are not provided due to insufficient data. In those cases, the program will not appear in this table

Program’s PTRS is compared to other similar ongoing programs’ PTRS and categorized in a quarter i.e.PTRS of the fourth quarter means that the program PTRS is better than the PTRS of the 75% of ongoing programs with similar characteristics.

Program similarity is defined based on the indication family and development stage. If there is not a sufficient number of programs, there will be no PTRS quarter or quartiles showing up but similar programs will still be available

Unique Identifier: program_id

Field Name	Description
`program_id` - `Int!`	Intelligencia’s unique program identifier
`ptrs` - `String`	Program’s Probability of Technical and Regulatory Success
`phase_transition_probability` - `String`	Program’s probability to transition to the next clinical phase. For programs with max clinical phase= phase 3, the value is not applicable
`ptrs_quarter` - `Int`	Program’s PTRS quarter compared to other similar ongoing programs. Values: 1, 2, 3, 4
`ptrs_indication` - `String`	Program's accounted indication family for the purpose of comparing it with other similar programs
`development_stage` - `String`	Program’s latest development stage for the purpose of comparing it with other similar programs Value: Phase X started i.e. Phase 1 started Description: These programs are at the start of the latest phase trial. Value: Phase X; public outcomes not available Description: Public outcomes for the major endpoints are unavailable for the latest phase trial of these programs. Value: Phase X with public outcomes Description: The latest phase trial of these programs has available outcomes for the major endpoints
`ptrs_quartiles` - `jsonb`	Quartiles are calculated based on the PTRS of all similar programs. The value describes the upper end of the quartile. Example. {"1": 20, "2": 35, "3": 43}. 20 is the PTRS value under which 25% of programs are found when arranged in increasing order.
`program_related_programs` - `[api_program_related_programs]`	An array relationship

Example

{
  "program_id": 123,
  "ptrs": "20%",
  "phase_transition_probability": "79%",
  "ptrs_quarter": 1,
  "ptrs_indication": "Non-Small cell lung carcinoma",
  "development_stage": "Phase 2; public outcomes not available",
  "ptrs_quartiles": {"1": 2, "2": 6, "3": 10},
  "program_related_programs": [
    api_program_related_programs
  ]
}

Types

`api_program_related_programs`

Links a program with its similar programs for the purpose of comparing their PTRS.

Unique Identifier: program_id, related_program_id

Field Name	Description
`program_ptrs` - `api_program_ptrs!`	An object relationship
`program_id` - `Int!`	Intelligencia’s unique program identifier
`related_program` - `api_program!`	An object relationship
`related_program_id` - `Int!`	Intelligencia’s unique program identifier

Example

{
  "program_ptrs": api_program_ptrs,
  "program_id": 987,
  "related_program": api_program,
  "related_program_id": 987
}

Types

`api_program_trial_arm_cohort`

Links a program with its trials, arms and cohorts.

A program may have more than one trial.
A trial may be linked to more than one program. A trial may be linked to more than one program for example when a) a trial tests the same drug(s)/intervention(s) in multiple indications (Basket Trial), b) a trial tests different drug(s)/intervention(s) in the same indication (Umbrella Trial), c) a trial that tests multiple subsets of the same indication (e.g subsets of breast cancer), or multiple patient groups under the same indication. Note that a trial can be both basket and umbrella, namely it can test multiple different drug therapies in multiple indications.
A trial may have more than one arm. Specifically, any difference in drug(s)/intervention(s) administered to a patient, be it a different drug, a different combination of drugs, a different dose or even a different treatment schedule constitutes a separate arm.
An arm may have more than one cohort. A cohort is a patient subgrouping where patients share a common trait. Patients could be grouped by genetic makeup, age, smoking status, etc. Patient subgroups that receive the same drug(s) / intervention(s) will relate to the same arm.
A cohort may be linked to more than one arm. For example, multiple arms are connected with one patient cohort when the trial’s endpoints are measured for the overall trial population

Unique Index: program_id, trial_id, arm_id, cohort_id

Field Name	Description
`arm` - `api_arm`	An object relationship
`arm_id` - `Int`	Intelligencia’s unique arm identifier
`cohort` - `api_cohort`	An object relationship
`cohort_id` - `Int`	Intelligencia’s unique cohort identifier
`program` - `api_program`	An object relationship
`program_id` - `Int`	Intelligencia’s unique program identifier
`trial` - `api_trial`	An object relationship
`trial_id` - `Int`	Intelligencia’s unique trial identifier

Example

{
  "arm": api_arm,
  "arm_id": 123,
  "cohort": api_cohort,
  "cohort_id": 987,
  "program": api_program,
  "program_id": 987,
  "trial": api_trial,
  "trial_id": 987
}

Types

`api_target`

Contains targets along with information about their synonyms, related organism and target type (eg. protein, gene). A drug target is a molecule in the body, usually a protein, that is intrinsically associated with a particular disease process and that could be addressed by a drug to produce a desired therapeutic effect. Drugs can have multiple targets.

Field Name	Description
`target_id` - `Int!`	Intelligencia’s unique target identifier
`name` - `String`	Intelligencia’s unique program identifier
`organism` - `String`	Target’s organism i.e. Human, House Mouse
`type` - `String`	Target’s type i.e. Protein, Gene
`synonyms` - `jsonb`	Target’s synonyms

Example

{
  "target_id": 234,
  "name": "Homeobox protein DLX-5",
  "organism": "human",
  "type": "Protein",
  "synonyms": [{"name": Homeobox protein DLX-5}]
}

Types

`api_trial`

Clinical trials that relate to currently ongoing programs. A clinical trial is a research study conducted in humans which assesses a treatment or a medical intervention. Intelligencia’s database includes interventional, industry-led clinical trials which are FDA-tracked and part of ongoing clinical programs.

A trial may be related to more than one program and each trial may have more than one arm. Specifically,
A trial may be linked to more than one program for example when a trial tests the same drug(s)/intervention(s) in multiple indications (Basket Trial) During a trial, any difference in drug(s)/intervention(s) administered to a patient - be it a different drug, a different combination of drugs, a different dose or even a different treatment schedule - constitutes a separate arm.

Unique Identifier: trial_id

Field Name	Description
`additional_sponsors` - `jsonb`	The trial’s additional sponsors
`adverse_events_criteria` - `String`	The type of adverse events criteria the trial uses to report adverse events
`allocation` - `String`	Indicates if the trial is randomized or not
`arms` - `[api_arm!]!`	An array relationship
`basket` - `Boolean`	A basket trial is a clinical trial that tests a specific drug or drug combination/intervention(s) in multiple separate indications. `true`= the trial is basket, `false`= the trial is not basket
`biomarker_subgroup_analysis` - `Boolean`	True when any of the trial’s arms’ contains cohorts that are defined based on a biomarker (most common cases of biomarkers can be molecular, previous treatments or age)
`end_date` - `date`	The trial’s completion date as in clinicaltrials.gov. If the trial’s completion date is missing then the field has the trial’s primary completion date as in clinicaltrials.gov
`enrollment` - `Int`	The trial’s participant population
`indications` - `jsonb`	The trial’s indications. Along with the trial’s indications we provide information for the evaluation criteria of outcomes relevant to each indication (if provided) and the therapeutic area that the indication belongs to
`intervention_model` - `String`	This describes the general design for assigning therapies and interventions to the trial’s participants. Example: Single Group Assignment, Parallel Assignment etc
`masking` - `String`	The type of masking that is implemented in the trial
`phase` - `String`	The trial’s clinical phase
`primary_sponsors` - `jsonb`	The trial’s primary sponsors
`program_trial_arm_cohorts` - `[api_program_trial_arm_cohort!]!`	An array relationship
`safety_acceptable` - `Boolean`	If the trial’s overall safety profile is acceptable or not
`source_id` - `String`	Unique trial’s identifier as in clinicaltrials.gov. For trials that are not registered in clinicaltrials.gov because they are very old, the source_id starts with the IAI- prefix
`source_link` - `String`	The link to clinicaltrials.gov for each tria
`start_date` - `date`	The trial’s start date
`termination_date` - `date`	The termination date of trials with status “terminated”
`title` - `String`	The trial’s title as it appears in clinicaltrials.gov. For trials with the IAI- prefix this is absent
`trial_drugs` - `[api_trial_drug!]!`
`trial_id` - `Int!`	Intelligencia’s unique trial identifier
`trial_status` - `String`	The current trial’s status. Example: Active, non recruiting
`umbrella` - `Boolean`	An umbrella trial is a clinical trial that tests multiple different drug(s)/intervention(s) in one specific indication. `true`= the trial is umbrella, `false`= the trial is not umbrella

Example

{
  "additional_sponsors": jsonb,
  "adverse_events_criteria": "abc123",
  "allocation": "abc123",
  "arms": [api_arm],
  "basket": false,
  "biomarker_subgroup_analysis": false,
  "end_date": date,
  "enrollment": 987,
  "indications": jsonb,
  "intervention_model": "xyz789",
  "masking": "abc123",
  "phase": "abc123",
  "primary_sponsors": jsonb,
  "program_trial_arm_cohorts": [
    api_program_trial_arm_cohort
  ],
  "safety_acceptable": false,
  "source_id": "abc123",
  "source_link": "xyz789",
  "start_date": date,
  "termination_date": date,
  "title": "abc123",
  "trial_drugs": [api_trial_drug],
  "trial_id": 123,
  "trial_status": "xyz789",
  "umbrella": false
}

Types

`api_trial_aggregate`

aggregated selection of "api.trial"

Field Name	Description
`aggregate` - `aggregate_fields`
`nodes` - `[api_trial!]!`

Example

{
  "aggregate": aggregate_fields,
  "nodes": [api_trial]
}

Types

`api_trial_drug`

Provides the relationship between trials and drugs. One trial can have one drug (monotherapy) or multiple drugs administered (combination therapy).

Unique Identifier: trial_id, drug_id

Field Name	Description
`drug` - `api_drug!`	An object relationship
`drug_id` - `Int!`	Intelligencia’s unique drug identifier
`is_primary` - `Boolean!`	True: when the drug is one of the main experimental drugs in the trial, False: the drug is not one of the main experimental drug of the trial
`trial` - `api_trial!`	An object relationship
`trial_id` - `Int!`	Intelligencia’s unique trial identifier

Example

{
  "drug": api_drug,
  "drug_id": 123,
  "is_primary": false,
  "trial": api_trial,
  "trial_id": 123
}

Types

`bool_comparison_exp`

Boolean expression to filter rows from any api type . All fields are combined with a logical 'AND'.

Input Field	Description
`_and` - `[bool_comparison_exp!]`
`_not` - `bool_comparison_exp`
`_or` - `[bool_comparison_exp!]`
`boolean_field` - `Boolean_comparison_exp`
`jsonb_column` - `jsonb_comparison_exp`
`citext_column` - `citext_comparison_exp`
`int_column` - `Int_comparison_exp`
`numeric_column` - `numeric_comparison_exp`
`relation` - `bool_comparison_exp`

Example

{
  "_and": [bool_comparison_exp],
  "_not": bool_comparison_exp,
  "_or": [bool_comparison_exp],
  "boolean_field": Boolean_comparison_exp,
  "jsonb_column": jsonb_comparison_exp,
  "citext_column": citext_comparison_exp,
  "int_column": Int_comparison_exp,
  "numeric_column": numeric_comparison_exp,
  "relation": bool_comparison_exp
}

Types

`citext`

Example

object

Types

`citext_comparison_exp`

Boolean expression to compare columns of type "citext". All fields are combined with logical 'AND'.

Input Field	Description
`_eq` - `citext`
`_gt` - `citext`
`_gte` - `citext`
`_ilike` - `citext`	does the column match the given case-insensitive pattern
`_in` - `[citext!]`
`_iregex` - `citext`	does the column match the given POSIX regular expression, case insensitive
`_is_null` - `Boolean`
`_like` - `citext`	does the column match the given pattern
`_lt` - `citext`
`_lte` - `citext`
`_neq` - `citext`
`_nilike` - `citext`	does the column NOT match the given case-insensitive pattern
`_nin` - `[citext!]`
`_niregex` - `citext`	does the column NOT match the given POSIX regular expression, case insensitive
`_nlike` - `citext`	does the column NOT match the given pattern
`_nregex` - `citext`	does the column NOT match the given POSIX regular expression, case sensitive
`_nsimilar` - `citext`	does the column NOT match the given SQL regular expression
`_regex` - `citext`	does the column match the given POSIX regular expression, case sensitive
`_similar` - `citext`	does the column match the given SQL regular expression

Example

{
  "_eq": citext,
  "_gt": citext,
  "_gte": citext,
  "_ilike": citext,
  "_in": [citext],
  "_iregex": citext,
  "_is_null": true,
  "_like": citext,
  "_lt": citext,
  "_lte": citext,
  "_neq": citext,
  "_nilike": citext,
  "_nin": [citext],
  "_niregex": citext,
  "_nlike": citext,
  "_nregex": citext,
  "_nsimilar": citext,
  "_regex": citext,
  "_similar": citext
}

Types

`date`

Example

object

Types

`date_comparison_exp`

Boolean expression to compare columns of type "date". All fields are combined with logical 'AND'.

Input Field	Description
`_eq` - `date`
`_gt` - `date`
`_gte` - `date`
`_in` - `[date!]`
`_is_null` - `Boolean`
`_lt` - `date`
`_lte` - `date`
`_neq` - `date`
`_nin` - `[date!]`

Example

{
  "_eq": date,
  "_gt": date,
  "_gte": date,
  "_in": [date],
  "_is_null": false,
  "_lt": date,
  "_lte": date,
  "_neq": date,
  "_nin": [date]
}

Types

`jsonb`

Example

object

Types

`jsonb_comparison_exp`

Boolean expression to compare columns of type "jsonb". All fields are combined with logical 'AND'.

Input Field	Description
`_contained_in` - `jsonb`	is the column contained in the given json value
`_contains` - `jsonb`	does the column contain the given json value at the top level
`_eq` - `jsonb`
`_gt` - `jsonb`
`_gte` - `jsonb`
`_has_key` - `String`	does the string exist as a top-level key in the column
`_has_keys_all` - `[String!]`	do all of these strings exist as top-level keys in the column
`_has_keys_any` - `[String!]`	do any of these strings exist as top-level keys in the column
`_in` - `[jsonb!]`
`_is_null` - `Boolean`
`_lt` - `jsonb`
`_lte` - `jsonb`
`_neq` - `jsonb`
`_nin` - `[jsonb!]`

Example

{
  "_contained_in": jsonb,
  "_contains": jsonb,
  "_eq": jsonb,
  "_gt": jsonb,
  "_gte": jsonb,
  "_has_key": "xyz789",
  "_has_keys_all": ["abc123"],
  "_has_keys_any": ["xyz789"],
  "_in": [jsonb],
  "_is_null": true,
  "_lt": jsonb,
  "_lte": jsonb,
  "_neq": jsonb,
  "_nin": [jsonb]
}

Types

`numeric`

Example

object

Types

`numeric_comparison_exp`

Boolean expression to compare columns of type "numeric". All fields are combined with logical 'AND'.

Input Field	Description
`_eq` - `numeric`
`_gt` - `numeric`
`_gte` - `numeric`
`_in` - `[numeric!]`
`_is_null` - `Boolean`
`_lt` - `numeric`
`_lte` - `numeric`
`_neq` - `numeric`
`_nin` - `[numeric!]`

Example

{
  "_eq": numeric,
  "_gt": numeric,
  "_gte": numeric,
  "_in": [numeric],
  "_is_null": true,
  "_lt": numeric,
  "_lte": numeric,
  "_neq": numeric,
  "_nin": [numeric]
}