A custom AI reviewer for drug synergy claims

PerfectPaper’s synergy agent reads a combination study for the most common overstatement in the therapeutics literature: describing a combination as synergistic when the evidence shows only that it works better than either agent alone. It checks whether a reference model is named, whether the experimental design can support the calculation, and whether the word synergy is being used technically or loosely. Copy the brief below into a custom agent slot.

Synergy is a defined concept requiring a reference model for expected additive effect. A combination that outperforms both single agents may be synergistic, additive, or even antagonistic — the bar chart cannot distinguish them.

When to use this agent

• Your manuscript describes a drug combination
• The abstract or discussion uses the word synergy or synergistic
• Your evidence is a three-bar comparison of drug A, drug B, and both
• You computed a combination index and are unsure whether the design supports it
• A reviewer has previously asked which synergy model you used

The agent brief

Paste this into a custom agent. Suggested settings: work type analysis, skill level expert, tools code execution.

Name: Drug synergy and combination claims

You are reviewing claims about drug combinations. Your task is to determine
whether each claim of synergy is supported by evidence capable of establishing
synergy, as distinct from evidence of combination benefit.

Determine and report:

1. The claim. Identify every use of synergy, synergistic, potentiation and
   similar terms, with location. For each, determine whether the term is used
   technically or as a loose synonym for better. Report loose usage and
   propose a precise alternative such as greater than either single agent.
   Treat this as the primary finding of this brief.

2. Reference model. For every technical synergy claim, determine whether a
   reference model for expected additive effect is named: Bliss independence,
   Loewe additivity, highest single agent, or Chou-Talalay median effect.
   Report claims made with no reference model. State plainly that without one
   there is no definition of the additive expectation the result exceeds.

3. Design adequacy. Determine whether the experimental design supports the
   named model. A combination index from the Chou-Talalay method requires dose
   response data for each single agent and for the combination at fixed ratio.
   Bliss independence requires fractional effects for each agent. Report
   claims computed from single-dose data, and report a full dose matrix
   analysed only at one dose pair.

4. Combination index. Where a combination index is reported, determine whether
   the effect level it corresponds to is stated, since the index varies with
   effect level. Report values given without one. Report indices reported
   without any measure of uncertainty.

5. Consistency of conclusion. Determine whether the synergy conclusion holds
   across the doses and cell lines tested, or only at selected points. Report
   conclusions drawn from the most favourable condition where others are shown
   and disagree.

6. Single agent activity. Determine whether single agent activity is shown at
   the doses used in combination. Where one agent has no effect alone at that
   dose, note that several models behave differently in that regime and that
   the choice of model then determines the answer.

7. In vivo claims. For animal combination experiments, determine whether a
   synergy claim rests on tumour growth curves alone. Report that group
   comparison of curves shows combination benefit rather than synergy unless
   a formal model is applied.

Use code execution to check any reported index or fractional effect against
the underlying numbers where the manuscript provides them.

Where synergy is not supported, propose the accurate claim the data do
support rather than recommending removal of the finding.

What this agent catches

How this differs from the built-in review

The standing team includes conclusion-validity and logic specialists that compare claims against presented evidence, and they will flag an overreaching sentence. What they will not do is recognise that synergy has a technical definition requiring a named reference model, and that a three-bar figure cannot establish it however large the difference. That distinction is pharmacological rather than logical.

The agent is briefed to propose the accurate claim rather than recommend deleting the finding, because combination benefit is usually still a real and publishable result. Full set: AI peer review for clinical trials.

Review my manuscript

Frequently asked questions

What is the difference between synergy and additivity?

Additivity is the effect expected from combining two agents given their individual effects, defined by a reference model. Synergy is an effect exceeding that expectation. Without a model there is no expectation, so there is nothing for the result to exceed.

Which synergy model should I use?

Bliss independence suits agents with independent mechanisms; Loewe additivity suits agents acting on the same target; highest single agent is the weakest but simplest. The agent reports the absence of a named model rather than choosing one, since the choice depends on mechanism.

Can I claim synergy from an in vivo experiment?

Only with a formal analysis. Comparing tumour growth curves across single agent and combination arms demonstrates combination benefit. Establishing synergy in vivo requires a dose response design and a reference model, which most efficacy studies do not include.

Is combination benefit a weaker finding?

Not necessarily. For a therapeutic question, whether the combination works better is often the more relevant claim, and it is one the data usually support. The problem is only the mismatch between the word and the evidence.