GLP-1 data, programmatic access

The GLP-1 Data API

Continuously-updated database of GLP-1 medication safety, efficacy, pricing, patents, supply, and demand. 3,519+ data points across 15 side effects, 9 drugs, 11 countries. Built for researchers, pharma intelligence teams, and journalists — and already being read by the AI agents people ask about GLP-1 medications.

What you get

Side-effect database

3,519+ data points covering 15 side effects across 9 GLP-1 medications. Two-track methodology (clinical-trial vs real-world reports). Updated daily from PubMed, ClinicalTrials.gov, and Google News coverage of regulatory actions worldwide (incl. CDSCO India, ANVISA Brazil, EMA/MHRA/WHO). Three sources are not updating: patient-community collection from Reddit has been blocked since 2026-05-28, Drugs.com patient reviews have added no new point since 2026-08-12, and the FDA FAERS report-share rows are a dated snapshot — the collector queries openFDA every day, but the store’s identity for a FAERS row is the drug’s query URL plus the effect, so a term already stored is never re-read; the newest stored row is 2026-08-13. See /en/data for live per-source status, which shows each source’s last new point.

Economics data

Country-by-country pricing (list, typical-paid, generic vs branded). Patent timelines per jurisdiction. Manufacturing capacity (Novo Nordisk, Eli Lilly capex by site). Demand-driver trial readouts. Updated weekly via cloud agent + daily news pipeline.

Supply-demand forecast

Magistra's structured forecast through 2030 with bull/base/bear scenarios, manufacturing investments by site, generic capacity by country, and policy-lever analysis. Available as a citable JSON dataset.

India generics brand tracker

Brand-level tracking of India's post-patent semaglutide market: manufacturer, formulation, monthly INR price bands, DCGI status, and a per-row source-verification level. The current version is free to download as CSV or JSON; continuous dated updates, price-move history, and batch/recall alerts are available as custom work. See the tracker.

Patient-journey predictor

Programmatic access to the side-effect risk model (log-odds with random-effects CIs), weight-trajectory predictor, muscle-loss risk score, and discontinuation outlook. Suitable for embedding in clinical or research applications.

One-time purchase — no subscription

GLP-1 Real-World Evidence Brief — August 2026

A PDF brief plus the full dataset CSV — a dated corpus snapshot, currently 2026-09-15 with 2,109 rows, each carrying its source, date and extraction confidence, the drug where the source names one (552 rows name none) and, where a rate is a registry serious-adverse-event figure, a rateKind flag. 150 of those rows are April-2026 seed points, flagged in a "provenance" column and excluded from every published rate. Covers 15 side effects across 9 drugs, plus the 13 community rows whose own effect falls outside those 15 (kept in so the brief’s community reporting-frequency denominator is reproducible from the file — see the 17 September correction in the brief), drawn from PubMed, FDA FAERS, ClinicalTrials.gov, news coverage of EMA/MHRA/WHO safety actions, and public community reports, plus the full methodology annex. The CSV is a point-in-time snapshot, not a live feed; the free API stays current.

What this is not:incidence rates. Only a minority of sources state a numeric rate, so our per-effect estimates rest on small samples — as of 14 September 2026 no effect rests on a single rate-bearing source any more (hair loss did until that morning, and has since gained a third distinct source), one (emotional blunting) has no citable corpus rate at all and publishes no clinical number, and two rest partly or mostly on registry SERIOUS adverse-event rates (pancreatitis, 2 of its 3 sources; gallstones, 3 of its 10, as of 3 October 2026 — corrected 4 October 2026 from “gallstones, pancreatitis rest wholly or mostly”, which overstated gallstones: three of its seven sources on 21 September was already a minority), which are a floor on all-cause incidence and are labelled as such on every row (rateKind) — these memberships are recomputed daily, so read them from the API rather than from this sentence — and every estimate is published with its source count and a confidence grade, not as prevalence. The clinical and community tracks are kept separate and never blended; community reports carry self-selection bias. Treat the dataset as a sourced corpus and a discordance signal, not as an epidemiological estimate.

Known limitation of this snapshot: until 10 September 2026, our ClinicalTrials.gov registry collector kept only the five highest-percentage arm rows per trial — a selection on the very figure it measures. The collector was fixed that day and every affected trial is being re-collected in full. This 2026-09-15 snapshot postdates the fix and contains the re-collection landed to date, but the re-collection was still in progress when it was taken, so registry-derived figures in the file lag the live API. Current status: /data, and every /api/data response carries a samplingNote field until it is resolved. The previous 2026-09-02 file predated the fix and also carried 379 duplicate rows and 132 rows outside its stated 15-effect scope; it was superseded on 15 September and its defects are listed in the export errata shipped with every order.

Look before you buy: download a 31-row sample — every 70th row of the exact file, unmodified, with the same 14 columns. It is a systematic slice rather than a curated one, so 20 of its 31 rows carry a numeric rate, close to the 1,214 of 2,109 in the full file.

£49
one-time, delivered by email
Buy the brief

Pricing

Free / Public
£0
Open public API
  • Side-effect overview endpoint
  • Per-effect detail endpoint
  • Forecast JSON dataset
  • No hard rate limit — be reasonable
Try it now
Competitor tolerability benchmark
£990 · one-off
For obesity-drug development teams
  • Per-drug side-effect rates for the approved incretins, pooled from ClinicalTrials.gov results
  • Every source arm listed, with its n and interval
  • Free sample before you buy
See the benchmark
Custom data work
Quoted
One-off, scoped to your question
  • Bulk or dated exports of the corpus
  • Custom queries and data slices
  • We say up front what the data can and cannot support
Email Saurabh

Public endpoints (try them)

https://magistra.health/api/data?q=overview

Top-level summary: total data points, drugs tracked, sources

https://magistra.health/api/data?q=effects

List of all side effects with clinical and real-world rates

https://magistra.health/api/data?q=effect&id=nausea

Detailed view of a specific side effect including modifier ORs

https://magistra.health/api/data/review

Live model health-check — calibration, bias signals, proposed changes

https://magistra.health/data/glp1-supply-demand-forecast.json

Structured forecast dataset (schema.org Dataset, CC BY 4.0)

https://magistra.health/data/india-semaglutide-tracker.json

India semaglutide brand tracker — prices, formulations, DCGI status, per-row sources (CC BY 4.0; CSV at the same path)

https://magistra.health/data/glp1-aggregate-rates.csv

Aggregate pooled-rates table — per-effect pooled clinical estimate, 95% CI, stated-rates count, distinct sources (CC BY 4.0, redistributable with attribution; dated snapshot, JSON at the same path)

Full database, methodology, and citation guidance: /data · /methodology · /economics. Which effects have enough independent sources to trust: evidence strength by effect · the corpus-derived rates themselves.

Running an obesity-drug trial? Per-drug tolerability rates for the approved incretin drugs, with a free sample: competitor tolerability benchmark.

Who uses it

Research labs and academics

Citable dataset for systematic reviews, meta-analyses, comparative-effectiveness research, integrated with the published Magistra methodology (magistra.health/en/methodology).

Pharma intelligence and consulting

Continuous signal on competitor pricing, generic-launch readiness, supply-demand trajectory. Monthly written briefings help you contextualise our forecast against your internal models.

Health journalists and analysts

Reliable, sourced data for stories on GLP-1 economics, generics, gray market, regulatory shifts. Citable URLs, machine-readable forecast dataset.

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