Monthly literature screening for adverse events, built for Indian drug makers
TL;DR Signalbrook screens newly published medical literature for possible adverse events involving a drug maker's products. It ranks articles, pulls out the patient, drug and reaction, and hands a short list to a trained safety reviewer, who decides. Indian pharma companies and the service firms that run their safety work pay.
India's drug regulator expects companies holding marketing authorisations to keep watching what is published about their products. The draft Version 2.0 guidance for these companies, from the Pharmacovigilance Programme of India, asks for a monthly literature review using databases such as PubMed and the big commercial journal databases, with any adverse event processed like a spontaneous report[1]. Serious events must reach CDSCO and PvPI within 15 calendar days of receipt from any source[1].
India has about 3,000 drug companies[2], and most mid-size ones sell dozens of molecules. Each month a safety associate runs searches, reads hundreds of abstracts and logs why each one was kept or dropped. It's tedious work, and a missed case has a nasty habit of turning up later in an inspection.
What it does, in regulatory terms - a screening and triage aid for trained pharmacovigilance staff. It does not assess causality, decide seriousness or submit reports. A qualified person makes and signs every decision, as PvPI guidance assumes.
The seed pitched AI literature synthesis for any researcher. That market is crowded with student tools, so I narrowed it to a buyer with a written monthly duty.
Each month Signalbrook runs saved searches per molecule, removes duplicates, and scores every article for whether it describes an identifiable patient, a suspect product and a reaction. It drafts the extracted fields, highlights the sentences behind them and keeps an audit trail of every screen-out with a reason. Indian journals not well indexed abroad get added as sources.
A 2026 review of 83 studies found language models useful for supervised extraction and triage but prone to errors and false positives[3]. So Signalbrook is built to help reviewers, not replace them.
The user is a drug safety associate. The payer is the head of pharmacovigilance at a mid-size Indian manufacturer, or a service firm running safety work for several clients. I'd price it per molecule per year. Today they spend on staff time or an outsourced contract, and I found no public price for either.
PvPI circulated draft Version 2.0 of its guidance for companies in January 2024 and has since listed the final version[4], which spells out the monthly literature duty. Language models became good enough at extraction to be useful under supervision[3]. Europe has long had a central literature service for common substances, run by EMA since September 2015[5]. India has no equivalent that I could find.
Nobody publishes a figure for what Indian drug makers spend on literature monitoring. The countable base is about 3,000 drug companies and 10,500 manufacturing units[2], and 130 companies that had reported adverse reactions to PvPI between 2011 and 2021[6]. The likely first buyers are that reporting group, plus exporters already serving European and US regulators.
One specialist vendor sells an AI platform built only for safety literature monitoring, covering global and local sources with machine translation[7]. EMA's service covers selected substances for European products[5]. Large service firms do the work by hand for multinational clients. The gap is a validated, India-priced tool tuned to PvPI's monthly cycle and to Indian journals, for companies too small for global platforms.
| Difficulty | Hard (52/100) |
| Starting capital | ₹1.2–2.2 Cr |
| Time to launch | 9–12 months |
Matching a skilled reviewer's sensitivity is the core technical job, and you must prove it with numbers. Buyers will ask for computer-system validation records and audit trails before any pilot. Selling into safety teams is slow, because a missed case is their worst nightmare and they're wary of anything new.
Signalbrook needs no drug licence, because it's a screening aid and not a marketing authorisation holder. But its clients' duties are strict, and the workflow must meet them.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
Don't let AI assess causality, decide seriousness or submit a report. Those calls belong to a qualified safety reviewer who signs every decision.
Want to work out the AI layer for Signalbrook? Talk to Yash →
Here's what can go south -
I like this as a focused business for a founder from pharmacovigilance who can pair with a strong NLP engineer. If you've never sat through a safety audit then I'd skip it. First prove sensitivity - on a year of past literature for ten molecules, Signalbrook must surface every case the client's team actually reported.
Plan for roughly ₹1.2–2.2 Cr, mostly on building the screening tool, trained safety reviewers and the validation drug makers will demand.
No drug licence, since you only screen and triage. Your clients carry the PvPI duties, so your workflow must give them the evidence they need.
Yes. Drug makers in most markets must monitor literature, and EMA has its own rules. Check your local regulator's requirements first.
The free Signalbrook validation kit has the line-by-line capital breakdown, the effort scoring, the PvPI, CDSCO and validation checklist, a 14-day validation sprint with pass/fail targets, a 90-day launch plan, 10 interview questions for safety teams, and 5 assumptions to test. Download the kit →
Thinking about building Signalbrook or something close to it? The make-or-break work is proving the screening misses nothing a trained reviewer would catch, and I can help you design that test. If you need a hand, you know where to find me. Talk to Yash →
For illustration only. Names are fictional and ideas hypothetical; sourced facts are real but may be out of date. Nothing here is legal, financial or professional advice, and Yash Khatri is not personally liable for any use of it. Read the full disclaimer.