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Reports from ClinicalTrials.gov, PubMed and FDA data, guides to clinical research and Good Clinical Practice, and real questions answered by ClinLit AI, with every claim cited to its source.
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- How to design a phase 2b MASH trial: lessons from 219 trials and their protocols
Placebo response from 2% to 33%, screen failure up to 87%: what 219 phase 2 MASH trials and six protocols show about endpoints, sample size and biopsies.
- Eli Lilly R&D profile, November 2026: pipeline, trials and peer comparison
Eli Lilly's clinical pipeline as of 7 November 2026: 240 active trials, its late-stage drugs, readouts due, FDA approvals, and how it compares with four peers.
- Cleaning openFDA data: how we linked FAERS, drug labels and approvals to trials and PubMed
What it took to turn openFDA's FAERS reports, drug labels and approvals into linked, searchable data: the problems we hit, the fixes, and how we checked them.
- GLP-1 drug side effects in FAERS: which adverse events are reported disproportionately
Ten years of serious FDA adverse event reports for semaglutide, tirzepatide and four other GLP-1 drugs: what stands out, from pancreatitis to NAION, and what it can't tell you.
- Which clinical trials share their protocols? What 100,000 completed trials show
Only 27% of recently completed trials have posted a protocol, SAP or consent form on ClinicalTrials.gov, and 46% have left no public trace. Who shares, who doesn't, and why.
- Beyond diabetes and obesity: where GLP-1 drugs are being tested in 2026
340 trials test GLP-1 drugs in diseases other than diabetes and obesity, from heart failure and MASH to alcohol use and cancer. The data, the trials and their design.
All posts
- How to benchmark a clinical trial design against earlier trials and their protocols: a repeatable method
A step-by-step method for checking a trial design's assumptions against earlier trials, their protocols and results, with questions to reuse and the traps to avoid.
- How to design a phase 2b MASH trial: lessons from 219 trials and their protocols
Placebo response from 2% to 33%, screen failure up to 87%: what 219 phase 2 MASH trials and six protocols show about endpoints, sample size and biopsies.
- How accurate is ClinLit AI? 50 test questions, checked against the sources
We tested ClinLit AI on 50 new questions and checked each answer against ClinicalTrials.gov, PubMed and FDA sources. The results, with every error listed.
- Eli Lilly R&D profile, November 2026: pipeline, trials and peer comparison
Eli Lilly's clinical pipeline as of 7 November 2026: 240 active trials, its late-stage drugs, readouts due, FDA approvals, and how it compares with four peers.
- How to build a pharma company R&D profile from public data: a repeatable method
A step-by-step method for profiling a drug company's pipeline, trials, FDA record and peers from public data, with questions to reuse and the traps to check.
- Cleaning openFDA data: how we linked FAERS, drug labels and approvals to trials and PubMed
What it took to turn openFDA's FAERS reports, drug labels and approvals into linked, searchable data: the problems we hit, the fixes, and how we checked them.
- GLP-1 drug side effects in FAERS: which adverse events are reported disproportionately
Ten years of serious FDA adverse event reports for semaglutide, tirzepatide and four other GLP-1 drugs: what stands out, from pancreatitis to NAION, and what it can't tell you.
- Which clinical trials share their protocols? What 100,000 completed trials show
Only 27% of recently completed trials have posted a protocol, SAP or consent form on ClinicalTrials.gov, and 46% have left no public trace. Who shares, who doesn't, and why.
- Beyond diabetes and obesity: where GLP-1 drugs are being tested in 2026
340 trials test GLP-1 drugs in diseases other than diabetes and obesity, from heart failure and MASH to alcohol use and cancer. The data, the trials and their design.
- How we researched GLP-1 trials with ClinLit AI: questions, queries and checks
A step-by-step walkthrough of using ClinLit AI to analyse ClinicalTrials.gov, trial protocols and PubMed: the questions we asked, the SQL it ran and how we checked it.