Sequence analysis, regulatory precedent, excipient selection, compounding, high-concentration behaviour, DoE and validation — each owned by a specialist agent, coordinated by an orchestrator, running in parallel where the science allows. The design space is explored computationally first, so the bench only has to confirm a short, well-chosen list. Run it as a service, or have us build and hand over your own instance.
Each stage is owned by a specialist agent, and the orchestrator runs them in waves — in parallel where the science allows, in sequence where one stage depends on another.
Formulation Analysis
Specialists in progress.
Formulation is not one question, so it is not answered by one model. A coordinated set of specialists each owns a part of the problem, hands its findings to the next, and runs in parallel where the work allows — with an orchestrator routing between them and a validation gate governing what they are allowed to conclude.
One agent runs the others and turns their findings into a formulation hypothesis.
Routes information between the specialists, aggregates their outputs to the portal, and synthesises the results into formulation hypotheses. Reads modality — IgG, Fab, VHH, ADC, fusion — and directs the run accordingly.
Everything downstream depends on reading the sequence correctly.
Converts a primary sequence and annotated architecture into a formulation-relevant profile: biochemical descriptors, segment maps, pH guidance, hydrophobicity flags and liability reports.
Retrieves structure–function insight from published literature, patents and your own knowledge base, and feeds it back into the descriptor analysis.
What has already been approved for this modality and this route.
Retrieves precedent from inactive-ingredient databases, drug labels and published formulations, returning typical compositions and concentration ranges with confidence scoring and an evidence chain.
Governs what the other agents are allowed to conclude, at two gates. Flags excipients outside precedent for the intended route and documents every gating decision and override.
Risk in, a complete and calculable excipient system out.
Translates molecular and regulatory risk into a complete excipient system — aggregation risk to stabilising sugar and surfactant, chemical instability to antioxidants and pH, high viscosity to arginine.
Converts the excipient design into an exact aqueous recipe: buffer pH, acid–base ratio, ionic strength and protein contribution. Deterministic and error-checked for out-of-range conditions and known incompatibilities.
Activates at elevated target concentration and addresses self-association, opalescence and phase separation through arginine optimisation, ionic-strength tuning, pH adjustment and co-solute selection.
The bench plan, and what the returned data means.
Maps the design space with screening and optimisation designs — factorial, Plackett–Burman, Box–Behnken, central composite, D-optimal — and exports a screening matrix, sample requirements and assay panels.
Interprets returned stability data, cross-correlates it against the sequence liabilities found upstream, and ranks candidates with a failure mechanism attached to each.
Documentation that carries its own provenance.
Generates formulation rationale, excipient justification, risk-based justification and stability reports from the agents’ own outputs, each section carrying provenance metadata and hyperlinked citations.
Agents run in waves rather than a queue: independent work happens in parallel, and each wave begins only once the evidence it depends on exists. The same system runs behind both engagements — we operate it for your molecule, or we build and hand over your own instance of it.
Speed, cost, and the quality of the knowledge your programme carries forward.
Weeks rather than quarters.
A defensible, ranked formulation strategy is available in weeks rather than quarters, which pulls in the timing of tox lots, stability starts, first-in-human enabling material, and eventually CMC sections of regulatory filings.
A prioritized set, not a broad matrix.
Screening effort is concentrated on a prioritized set of conditions instead of a broad, unguided matrix. This reduces FTE time, analytical load, and the quantity of purified drug substance consumed — usually the scarcest and most expensive input at early stage.
One coherent dataset, not scattered reports.
The partner receives a single, structured, statistically coherent dataset with confidence for every candidate, rather than a series of disconnected screening reports. The design space is defined deliberately, which strengthens later comparability, scale-up and lifecycle changes.
The value of front-loaded design is greatest in high-concentration formulation. Subcutaneous and autoinjector presentations increasingly require >50 mg/mL — and mostly 100–250 mg/mL — of protein in 1–2 mL volume. In that regime the behavior of a molecule stops being a linear extrapolation of its dilute-solution behavior: protein–protein interactions dominate, and several problems appear at once.
>50 mg/mL
Where high-concentration behaviour begins
100–250 mg/mL
Typical subcutaneous and autoinjector range
1–2 mL
Delivered volume the formulation must fit
Reversible self-association, opalescence and liquid–liquid phase separation emerge in narrow pH and ionic-strength windows that are easy to miss in a coarse screen.
Aggregation and subvisible particle burden increase, with interfacial and agitation stress becoming the controlling degradation route.
Deamidation, isomerization, oxidation and fragmentation become concentration- and excipient-sensitive.
Viscosity rises steeply and non-linearly with concentration, threatening syringeability, injection force and autoinjector compatibility.
Ultrafiltration/diafiltration behavior, filterability, hold-time stability and fill-finish performance all degrade if the excipient system is chosen without regard to process.
These do not appear one at a time.
They appear together, in narrow pH and ionic-strength windows that a coarse screen is likely to step straight over.
The same multi-agent system sits behind both. Bring us a molecule and we run it for you, returning a ranked formulation strategy. Or have us build your own instance of it — customised, deployed in your environment, and handed over as source code you keep.
Accelerated Formulation Design
For a programme that needs a defensible formulation strategy now. You send a primary sequence and a set of product targets; we return the ranked candidates and the bench plan to confirm them.
Best when the question is one molecule, and the answer is needed this quarter.
Contact us about a designCustom multi-agent build
For a team that wants the capability in-house and across the pipeline. We customise our formulation platform to your molecules, rules and SOPs, deploy it in your environment, and hand over the source code.
Best when formulation is a capability you intend to own, not a task you outsource.
Contact us about a buildNot sure which fits? Tell us about the molecule and we will say plainly which of the two we would recommend. Engagements begin with a signed agreement; access follows.
Starting from nothing more than a primary sequence and a set of product targets, Uplizd delivers a rank-ordered, fully specified set of formulation candidates, and the bench-level standard operating procedures needed to make them — typically within two weeks.
For Uplizd’s partners, this converts formulation development from an open-ended empirical search into a focused confirmatory exercise.
The design space is explored computationally first. The bench is used to confirm.
A primary sequence and a set of product targets — concentration, presentation, device, and the constraints the program already carries. Nothing more is required to start.
The design space — buffer and pH, sugars and polyols, amino acid excipients, ionic strength, surfactant, chelators and antioxidants — is explored computationally rather than sampled sparsely at the bench.
A rank-ordered, fully specified set of formulation candidates, with confidence attached to each, delivered as one structured and statistically coherent dataset.
Bench-level standard operating procedures for making each candidate, so the laboratory runs a focused confirmatory exercise instead of an open-ended search.
They are the reason the deliverable has the shape it does.
The design space is explored computationally, and the laboratory is used to confirm a short, well-chosen list. That ordering is the whole design of the service.
Purified drug substance is usually the most expensive thing an early programme owns. Screening effort should be concentrated on a prioritized set of conditions, not spent on a broad, unguided matrix.
A candidate list without confidence is a preference. Every candidate carries its own confidence, so prioritisation is a stated judgement rather than an implicit one.
A single, structured, statistically coherent dataset is what later comparability, scale-up and lifecycle changes actually rest on.
A design space that was chosen — rather than whatever a sparse screen happened to touch — is defensible when it appears in a CMC section years later.
Every candidate states its excipients, concentrations and pH, and ships with bench-level SOPs. If the laboratory has to interpret it, the work is not finished.
Foundation models are remarkable, and they will assemble a working prototype from a plain-language brief in minutes. The distance between that prototype and a system a regulated scientific decision can rest on is the whole of the work.
Uplizd builds infrastructure for applied AI. The Biologic Formulation Platform is where that work meets biologics development.
What the service needs, what it returns, and where it fits in a programme.
A primary sequence and a set of product targets. The service is designed to start from nothing more than that — it does not require a prior screening campaign, and it does not require drug substance in hand.
Typically within two weeks. The comparison point is the two to three development quarters — or longer — that an open-ended empirical search usually takes to arrive at a defensible excipient system.
A rank-ordered, fully specified set of formulation candidates, each with its own confidence, delivered as one structured and statistically coherent dataset, together with the bench-level standard operating procedures needed to make them.
No. It changes what the laboratory is for. The design space is explored computationally first, and the bench is used to confirm a short, well-chosen list — a focused confirmatory exercise rather than an open-ended empirical search.
Because above roughly 50 mg/mL — and typically at 100–250 mg/mL in 1–2 mL — a molecule stops behaving like a linear extrapolation of its dilute-solution behavior. Protein–protein interactions dominate, and self-association, aggregation, chemical degradation, viscosity and manufacturability all become sensitive at once. Sparse screens tend to miss the viscosity–stability trade-off until a change is disruptive.
A deliberately defined design space strengthens later comparability, scale-up and lifecycle changes, and an earlier ranked strategy pulls in the timing of tox lots, stability starts, first-in-human enabling material, and eventually the CMC sections of regulatory filings.
Engagements begin with a conversation and a signed agreement; access credentials follow. Send a primary sequence and your product targets, or tell us what you want built, and we will reply from a person.
