The standard advice given to early-stage biotechs pursuing pharma partnerships is to get to the right data package. That advice is correct but incomplete, because what constitutes "the right data package" has shifted in the last few years, and there is now a gap between what preclinical companies think pharma wants and what the people doing the actual evaluation are prioritizing. This post is our attempt to close that gap based on direct conversations and public statements from business development teams, not on the received wisdom about what licensing deals look like.
We are a bootstrapped, preclinical mRNA company. We do not have a pharma partnership announced. We are building toward that eventuality with a realistic picture of what it takes to get there, and this post is a reflection of that thinking — not a victory lap.
What Pharma Actually Reviews at the Preclinical Stage
The first filter is almost never the data. It is the target rationale. Before any pharma business development team will spend meaningful time on a preclinical asset, they need to believe that the target is real — that there is a coherent mechanistic story connecting the target to the disease, that the patient population is addressable, and that the intervention logic is not built on shaky foundations. This sounds obvious, but a surprising number of early-stage licensing conversations fail here, not because the data is bad, but because the target argument was never formalized.
For mRNA therapeutics specifically, target rationale has an additional layer: why mRNA, rather than a small molecule, an antibody, or gene therapy? The modality choice needs to be justified on mechanistic grounds. If your target is an intracellular protein that can be upregulated by mRNA delivery but not effectively addressed by a small molecule or antibody, that is a real argument. If the target could be addressed by a simple generic, the mRNA approach is going to face skepticism about whether the complexity is worth it.
The Data Package That Moves Conversations Forward
In 2025, the minimum credible in vitro dataset for an mRNA asset in the longevity or neurodegeneration space includes:
- Expression confirmation: Western blot or quantitative ELISA in at least two relevant cell types — not just HEK293T, which tells you the construct works but says nothing about disease-relevant biology. For neurological targets, iPSC-derived neurons or primary culture from rodent models are expected.
- Functional readout: Some evidence that protein expression produces the expected biological effect. For a longevity-associated protein, this might be pathway activation (phospho-FOXO3 localization, downstream transcriptional response), resistance to an in vitro stress assay (oxidative stress, mitochondrial challenge), or a measurable effect on a disease-relevant readout. Expression without function is not enough.
- Selectivity data: Some evidence that the construct does not produce off-target effects at relevant doses. For mRNA, this typically means a cytokine profiling panel (IFN-alpha, IFN-beta, TNF-alpha, IL-6 at minimum) to confirm that modified nucleotide incorporation and formulation have reduced innate immune activation to acceptable levels.
- Formulation characterization: Basic LNP physicochemical data — particle size, PDI, encapsulation efficiency — and at least one in vitro potency comparison between formulation batches. Batch-to-batch variability in LNP formulation is a known failure mode, and pharma partners want to see that you understand this.
What has changed from five years ago is that pharma is more specific about what "functional readout" means. In 2019-2020, during peak mRNA enthusiasm post-COVID, partners were more willing to move on expression data alone, on the bet that the modality would deliver. In 2025, with a more realistic picture of what mRNA can and cannot do in non-vaccine contexts, partners want to see that the protein actually does something in a disease-relevant system. The bar has moved upward.
IP Position: The Conversation Nobody Wants to Have Early
The IP position question comes up early in serious pharma conversations, and many early-stage companies are not prepared for how specifically it is probed. Having a granted patent is not the same as having a defensible IP position. Pharma business development teams have seen too many preclinical deals fall apart in diligence because the IP analysis was superficial. The questions that come up are:
- What exactly is claimed — the target, the mRNA sequence, the formulation, the method of use?
- What is the freedom-to-operate analysis for the LNP formulation? (This is an area of significant crowding from Moderna, Alnylam, BioNTech, and associated patent estates.)
- Is there prior art that could invalidate the key claims?
- Is there a clear story for what IP position will look like at IND, not just at molecule filing?
For a bootstrapped company that has not yet filed a patent, the honest answer is that you have trade secrets and a first-mover advantage in a specific research direction. That is not nothing — if your target selection rationale is sufficiently differentiated, the trade secret around the computational pipeline and unpublished target data may be genuinely valuable. But you need to be prepared to explain the IP strategy, not just assert that patents are forthcoming.
Manufacturing Path: Credibility Before Scale
Pharma does not expect a bootstrapped preclinical company to have GMP manufacturing capacity. That is not what they are asking about. What they are asking is whether the company understands the manufacturing challenges for the asset and has a credible plan to address them through CMO relationships.
For mRNA therapeutics, the known manufacturing challenges include: mRNA synthesis purity and yield at scale, LNP formulation batch consistency, cold chain requirements, and for CNS-targeted formulations, any specialized delivery vehicle components that may have additional CDMO complexity. A partner wants to see that you have thought through which of these will be solved by existing platforms (IVT enzymes, standard ionizable lipids) and which require custom development.
The worst answer is "we'll figure out manufacturing when we get there." The best answer is a specific description of which CMO relationships are in place or planned, with a realistic timeline and an honest assessment of where the bottlenecks are. You do not need to have solved the manufacturing problem to be credible — you need to have thought rigorously about it.
What Bootstrapped Preclinical Companies Should Not Overclaim
Several common overclaims reliably damage credibility in partnership conversations:
"Our platform can generate drug candidates for any longevity target." This is almost never true in a useful sense. Platforms have real scope limits, and a pharma partner trying to do diligence on the platform will find the limits quickly. Better to define exactly what the platform does well and where its current boundaries are.
"We have validated our approach in multiple disease areas." If you are preclinical and bootstrapped, you almost certainly have not validated your approach in multiple disease areas. You have explored hypotheses in a limited number of models. Framing preliminary data as validation overstates the evidence and creates expectations that diligence will not support.
Projecting clinical timelines without a funded IND-enabling study plan. Saying "we expect to file an IND in 2027" when you have no GLP toxicology budget and no CRO agreements is not a projection, it is a wish. Pharma partners who have managed IND-enabling programs know what they cost and how long they take. Ungrounded timelines are spotted immediately and signal that the team has not thought seriously about the path from preclinical to clinical.
How the Conversation Changes When You Position Honestly
The alternative to overclaiming is to position clearly and specifically: here is what we have built, here is the specific target rationale, here is what the data shows and what it does not show, here is the IP status, here is the manufacturing plan. This approach takes more confidence than throwing optimistic projections at a business development team, but it produces a qualitatively different kind of conversation.
The deals that move forward quickly from a preclinical stage tend to involve companies that are unusually clear about what they have and what they need. Pharma partners are not looking for companies that have solved every problem — they are looking for companies that have identified the right problems and are solving them systematically. A target-plus-rationale-plus-preliminary-data package presented with clear-eyed honesty about the remaining work is more compelling than an inflated claim set that falls apart in diligence.
For caVos specifically, our position is straightforward: we have a computational pipeline for target identification based on cross-species conserved variants, preliminary in vitro data on a small number of mRNA constructs targeting longevity-associated proteins, and a clear view of what preclinical work remains before we would consider ourselves ready for partnership-level conversations. We are not there yet. Building toward it with a realistic picture of what is required is, in our view, the correct approach — and this post is part of that building process.
The Tactical Question: When to Start the Conversation
A practical question for any preclinical company is when to initiate business development conversations. Starting too early — before you have the data package described above — risks being categorized and dismissed before you have the evidence to make the case. Starting too late means you may have burned money on studies that a partner would have co-funded, or missed the opportunity to shape a program around what a partner actually needs.
The rough answer we have heard from people on both sides of this table: start informal scientific conversations when you have compelling target rationale and at least some functional in vitro data. Do not start formal business development conversations until you have the full package. The scientific conversation is low-stakes intelligence gathering; the business development conversation has reputational consequences if you go in underprepared. Keep them in separate boxes until you are ready for the latter.
