Buy-and-Bill vs Pharmacy Benefit: How Biotech Drugs Get Paid

Every biotech launch model starts the same way: take the list price, multiply by the number of patients, and call the result revenue. That misses the entire middle of the transaction. A drug does not flow straight from a factory to a patient. It moves through one of two channels, and the channel, not the price, decides how fast revenue books, how deep the discount is, and whether a launch ramps or quietly stalls.

The one-sentence answer: for every newly approved drug, the first question is not “what does it cost” but “is this buy-and-bill or pharmacy benefit?” Everything else in your model follows from that answer.

Step 1: Know the two channels

Buy-and-bill is how most physician-administered drugs reach patients. The physician, hospital, or integrated delivery network buys the drug from a specialty distributor, stores it, administers it in the office or clinic, and then bills the payer for both the drug and the administration. The practice is carrying the inventory and the reimbursement risk.

Pharmacy benefit is how pills and self-injected specialty drugs reach patients. The drug is dispensed at a retail or specialty pharmacy, the claim runs through a pharmacy benefit manager (PBM) against the patient’s Part D or commercial formulary, and the manufacturer pays rebates to hold its tier position on that formulary, the list of covered drugs. The drug pricing guide covers the PBM rebate layer in detail.

Step 2: Understand the ASP+6% formula

Under Medicare Part B, buy-and-bill reimbursement is roughly ASP plus 6%. ASP, or average sales price, is the volume-weighted average price the manufacturer actually nets for the drug. The 6% is an add-on payment the practice keeps to cover handling, storage, and administration costs. Since January 2005, Medicare has paid 106% of ASP for most Part B drugs, per the CMS average sales price files.

The important number is the spread: the difference between what the practice pays the distributor and what it gets reimbursed at ASP+6%. Because the practice buys at one price and bills at another, the practice’s own economics, not just the patient’s, drive adoption. A drug that is profitable for the practice to administer gets prescribed. A drug where the practice breaks even or loses money stalls, no matter how good the clinical data is.

Step 3: Map drug types to channels

The rule of thumb: if a doctor has to buy it and hang it, it is buy-and-bill.

Infused and injected biologics are buy-and-bill. That includes oncology antibodies like Keytruda (pembrolizumab), CAR-T cell therapies, gene therapies like Casgevy (exagamglogene autotemcel), ophthalmology injectables like Eylea (aflibercept), vaccines, and most hospital-administered rare-disease therapies. The gene therapy pricing guide and the CAR-T manufacturing piece both assume this channel.

Oral small molecules, the everyday pills, and self-injected specialty drugs dispensed through a specialty pharmacy go through the pharmacy benefit channel.

Step 4: Why the channel matters for your model

Three reasons, and each one shows up as a wrong number if you ignore it.

First, revenue timing. A buy-and-bill launch is gated by J-code assignment, the billing code a practice needs to actually get reimbursed, and by payer medical-benefit review. That gate is often slower than a pharmacy script ramp. A drug can be “approved” and “launched” for months before it books meaningful revenue. Newly approved drugs do not get a J-code automatically. CMS assigns permanent J-codes on a quarterly cycle, so a drug approved right after a cycle can wait months before practices can bill it cleanly. During that gap the practice either uses a miscellaneous code that payers scrutinize, or it holds off prescribing altogether. That is why a drug can win approval, print a launch press release, and still book nearly nothing for a quarter or two. The first 90 days after approval guide walks through that launch sequence.

Second, gross-to-net differs by channel. Buy-and-bill has no PBM rebate layer, but it does carry the 340B discount, which forces steep discounts of 25% to 50% on drugs sold to safety-net hospitals, plus ASP-based discounts. The discount you model for an infused oncology drug is not the same as the one you model for an oral pill.

Third, the spread. In buy-and-bill, the practice’s margin is on the table. In pharmacy benefit, it is the PBM’s margin. Different gatekeepers, different adoption incentives.

Step 5: The two risks most investors miss

Brown bagging is when the patient picks up the drug at a specialty pharmacy and carries it into the clinic for administration. White bagging is when the payer routes the drug through its own specialty pharmacy and ships it to the clinic. Both strip the practice’s buy-and-bill margin, which means the practice has less incentive to prescribe, which can slow or stall adoption of a drug that already “launched.”

White bagging is the bigger threat because it is a payer policy, not a patient choice. If a major commercial payer white-bags your drug, the launch trajectory flattens even when scripts look fine on paper.

Common mistakes

Modeling buy-and-bill revenue on a pharmacy script clock. You assumed a fast oral-drug ramp, but the drug needs a J-code and medical-benefit review first.

Ignoring white-bagging payer policies. The label is fine, the science is fine, but the practice earns nothing to administer it, so adoption stalls.

Reading the list price as revenue. The list-versus-net guide explains why the headline number is marketing, not revenue.

Treating every drug the same way. An infused oncology biologic and an oral pill have completely different launch clocks and discount structures.

Final checklist

• Is this drug buy-and-bill or pharmacy benefit?
• Does it have a J-code yet, or is it still waiting for one?
• What is the ASP disclosure versus the list price?
• Are there white-bagging or brown-bagging payer policies in force?
• Does the practice earn a spread on it, or lose money administering it?

The price tag tells you what a drug costs. The channel tells you whether anyone gets paid to move it. Model the channel first, and the revenue forecast stops being a guess.