Most pharma teams treat modular content and a pre-MLR check as two competing line items on the same operations roadmap. Pick one, fund it, move on. That framing is wrong, and it costs you the part of the benefit that is hardest to get. The two are not alternatives. They compound, and the compounding is where the 50-60 day review cycle actually breaks.
Start with where the time goes. Indegene reports that MLR review is the biggest bottleneck before a campaign can launch, with cycles often stretching 50 to 60 days per content piece, and that modular content can cut MLR review time by up to 60 percent. That is why modular content has become the default operations answer. But "up to 60 percent" is a ceiling teams rarely hit, because they implement half the system. They build the modules and then send the assembled asset into review as if it were a fresh page. The pre-check is the missing half, and without it the modular investment underdelivers.
What modular content actually is, and why it shortens MLR
Modular content is the practice of breaking an asset into pre-approved, reusable blocks: a claim, the visual that supports it, the reference that backs it, the disclaimer or fair-balance text that legally must travel with it. Each module is reviewed and approved once, then reassembled into emails, rep-triggered messages, banners, and congress decks without re-reviewing the parts that never changed.
The mechanism that shortens MLR is narrow. When an asset is fully modular, the reviewer is not re-reading approved language. They are confirming that approved modules were assembled correctly and that any genuinely new block is sound. Shaman estimates modular content can cut MLR cycle time by 57 percent, and that hybrid teams combining human reviewers with AI have reached 40 to 60 percent content cycle-time reductions. The savings come from shrinking the surface the reviewer has to judge, not from making the reviewer faster.
This is not a fringe bet anymore. Veeva reports that by 2025 roughly half of biopharma commercial content is modular, eight of the top 10 biopharmas use Vault PromoMats for modular content, and EU content now takes about 20 days on average to approve across 1.3 review cycles. Half the industry has crossed over, and those teams set the benchmark: 20 days, 1.3 cycles.
The claims library is the asset, not the campaign
The thing that makes modular content work is not the assembly tool. It is the library underneath it: a governed set of claims, each with its approved wording, its tied reference, its required fair-balance and safety text, and its provenance. The campaign is disposable. The library is the asset that compounds.
Building one is a structural exercise, not a content exercise.
- Atomize down to the claim. A claim is the smallest unit that can be true or false against the label. "Reduces flare frequency in the approved population" is a claim. A paragraph that bundles three claims and a disclaimer is not a module, it is an asset pretending to be one.
- Bind every claim to its evidence and its mandatory companions. A claim module that can be assembled without its reference, or without the fair-balance text that legally accompanies it, is a defect waiting to ship. The binding is what makes reuse safe.
- Govern provenance and version. Every module needs to know which label version it was approved against and when. The day the label changes, you need to find every module that touched the changed language, not guess.
This is also the structure AI authoring depends on. An AI assistant that drafts from a governed claims library is recombining approved language. An AI assistant pointed at a folder of finished PDFs is paraphrasing, and a paraphrase is a new claim. pharmaphorum puts it bluntly: a large language model paraphrasing a claim optimizes for fluency, not fidelity to approved language. Without the library, AI does not accelerate MLR, it floods MLR with fluent, slightly-off new claims.
Why a pre-check is the other half, not a nice-to-have
Modular content controls the blocks you reuse. It does nothing for the blocks that are new. And new blocks are exactly where MLR spends its scarce, accountable attention, because new is where the risk lives.
Here is the failure mode that eats the modular savings. A team assembles an asset that is 80 percent approved modules and 20 percent new copy. It goes into MLR as one object. The reviewer cannot fully trust that the modules were assembled without drift, so they re-read more than they should, and they read the new 20 percent cold, with no signal about where the genuine novelty is or whether it is even mechanically sound. The 57 percent ceiling collapses toward 30 because the review still treats the whole asset as suspect.
A pre-check closes that gap. It runs the moment the asset is assembled, before review, and does two things modular content alone cannot. It confirms the approved modules survived assembly intact, that fair balance is present and in proportion, that the safety block matches the canonical version, that references still resolve. And it isolates the new blocks, checks them against the label, and tells the reviewer precisely which sentences are net-new and whether they already read clean. The reviewer stops re-reading approved language and stops reading new language blind.
The interaction is multiplicative. Modular content reduces how much reaches review. The pre-check ensures what does reach review arrives already clean and already flagged for where the human judgment is actually needed. The pharmaphorum analysis notes that when claims are managed as governed, structured assets, platform vendors report up to 50 percent fewer content reviews and 86 percent fewer submission errors. Fewer reviews is the modular half. Fewer errors is the pre-check half. You want both, and you only get both when the structure and the gate are joined.
What this looks like on Varigel
Take a fictional brand, Varigel, approved for one narrow indication with a known contraindication and a fixed safety block. The team needs forty assets for a congress push and has a mature claims library: the indication claim, the efficacy claim, the contraindication, and the fair-balance text are all governed modules.
With modular content alone, the team assembles forty assets fast, most 70 to 90 percent approved modules with a new headline or transition per asset. All forty go into MLR. The reviewer trusts the modules in principle but re-reads to confirm assembly, then reads every new headline cold. Cycle time beats the old fully-bespoke process, but the new copy hides a scatter of small risks: two headlines that phrase the benefit a hair past the label, one transition that orphaned the fair balance from its claim. Finding those is slow, careful reading across forty assets.
Add the pre-check and the same forty arrive at review pre-sorted. The gate confirms the modules assembled intact, catches the orphaned fair balance and bounces it back before review, and flags the two off-label headlines by name. The thirty-seven clean assets carry a note: 85 percent approved modules, new copy checked and on-label. The reviewer reads the genuinely new sentences in the few assets that have them and skims the rest, knowing the mechanical layer is vouched for. Same reviewer, same standard. The combined cycle is a fraction of either fix alone.
The lever is the supply, not the reviewer
The instinct when MLR is slow is to ask reviewers to go faster. That is the one move that does not work, because the reviewer is the control and the legal backstop, and you cannot ask the control to be less careful. Volume makes it worse: US promotional material production rose 29 percent year over year while 77 percent of approved content is rarely or never used. More is made, most is wasted, and all of it still queues for the same reviewers.
The lever is upstream, in the content supply itself. Modular content governs what you reuse so less reaches review. A pre-check ensures what reaches review arrives clean so the review lands only on real judgment. Together they let a small approved core feed a large, fast output without asking any reviewer to drop their standard. It is the operations version of moving the mechanical work upstream of review, applied to the structure of the content, and why the content supply chain, not the review step, is the thing worth rebuilding.
Juncture is built for the join. Its Pre-check runs against the same approved label your modules were cleared against: it confirms the modules survived assembly, flags fair-balance, safety, on-label, and reference breaks, isolates the new blocks, and hands the reviewer a checked asset plus a content-reuse score instead of a raw pile. Because that approved label is also what the Answer Monitor measures AI engines against on the outside, the modular core you assemble fast inside is the same line defended when a machine paraphrases it later. Modular content gives you the structure. The pre-check protects the fidelity of that structure during assembly. Run one without the other and you leave most of the cycle-time benefit on the table.
The 50-60 day cycle does not break because reviewers are slow. It breaks because the supply that feeds them is unstructured and unchecked. Fix the structure with modules, fix the cleanliness with a pre-check, and the two savings stop adding and start to multiply. Bring one brand, its label, and its claims library, and we will run the pre-check on your next assembled batch live, show which assets would never have reached a reviewer, and put a reuse score on the rest. See it on your brand, then decide.
Sources
- Indegene, "MLR bottlenecks in pharma," 2025. indegene.com
- Shaman, "Modular content in pharma," 2025. getshaman.com
- Veeva Systems, "Using benchmarks to speed and scale life sciences," 2025. veeva.com
- pharmaphorum, "AI: the missing link fixing MLR, or the reason it breaks," 2026. pharmaphorum.com