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Multi-agent AI for biologic formulation

A multi-agent AI system for high-concentration biologics.Many specialists. One answer.

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.

Contact usMeet the agents

A sequence and a set of targets is enough to begin the conversation.

Biologic formulation high-concentration 3D structure

Formulation Analysis

4 specialists in progress. · Specialist reports 4 of 10

Wave 4 of 64 running in parallel40%
4 in progress
  1. Sequence analysisDone
  2. Regulatory precedentDone
    Literature evidenceDone
  3. Excipient selectionDone
  4. Compounding recipeIn progress
    High-concentration assessmentIn progress
    Study design (DoE)In progress
    Stability interpretationIn progress
  5. Regulatory validationQueued
  6. Integrated formulation reportQueued
QueuedIn progressDone
Chemistry computed in deterministic codeModel-agnostic — Claude, GPT, Gemini or your own
Input
A primary sequence and a set of product targets. No prior screening campaign.
Output
Rank-ordered, fully specified candidates with confidence, plus bench-level SOPs. 10 specialist reports.
Turnaround
Typically within two weeks — not the two to three development quarters it usually takes.
Security
Confidentiality terms and a secure transfer route agreed before any sequence changes hands.
The service

What a partner actually receives.

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.

The usual path

An open-ended empirical search

  • Two to three development quarters, or longer
  • Grams of precious drug substance consumed
  • A broad, unguided screening matrix
  • A series of disconnected screening reports
With Uplizd

A focused confirmatory exercise

  • A ranked strategy typically within two weeks
  • Material spent confirming, not searching
  • A prioritized set of conditions
  • One structured, statistically coherent dataset

For Uplizd’s partners, this converts formulation development from an open-ended empirical search into a focused confirmatory exercise.

Engagement models

One engine. Two ways to use it.

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.

Engagement one

We run the design

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.

  • The full agent system runs on your molecule, operated by our scientists
  • A primary sequence and product targets are the whole intake
  • A scored candidate space, ranked into a priority, contingency and exploratory set
  • A statistically designed DoE plan and the SOPs to execute it
  • Typically two weeks from complete intake to the package
  • Every outcome vests in you; your materials are destroyed at close-out

Best when the question is one molecule, and the answer is needed this quarter.

Contact us about a design
Engagement two

We build your platform

Custom 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.

  • The same agent system, customised to your molecules, rules and SOPs
  • Your SOPs, formulation rules and prior decisions held as persistent institutional memory
  • A web portal where every agent output is reviewable and traceable
  • Runs on any leading model, in your cloud or on your own hardware
  • Complete source-code transfer — you run it with or without us

Best when formulation is a capability you intend to own, not a task you outsource.

Contact us about a build

Not 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.

Multi-agent system

Not one model guessing. A team of specialists.

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.

Coordination

One agent runs the others and turns their findings into a formulation hypothesis.

Orchestrator

Formulation Manager

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.

Understand the molecule

Everything downstream depends on reading the sequence correctly.

Molecular profile

Sequence Analysis

Converts a primary sequence and annotated architecture into a formulation-relevant profile: biochemical descriptors, segment maps, pH guidance, hydrophobicity flags and liability reports.

Structure–function evidence

Literature Data

Retrieves structure–function insight from published literature, patents and your own knowledge base, and feeds it back into the descriptor analysis.

Ground it in precedent

What has already been approved for this modality and this route.

Precedent retrieval

Regulatory Information

Retrieves precedent from inactive-ingredient databases, drug labels and published formulations, returning typical compositions and concentration ranges with confidence scoring and an evidence chain.

Pre- and post-design gates

Regulatory Validation

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.

Design the formulation

Risk in, a complete and calculable excipient system out.

Excipient system

Excipient Selection

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.

Exact recipe

Compounding Calculator

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.

The hard regime

High-Concentration Specialist

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.

Plan and read the experiments

The bench plan, and what the returned data means.

Design space

DoE Planning

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.

Data interpretation

Stability Specialist

Interprets returned stability data, cross-correlates it against the sequence liabilities found upstream, and ranks candidates with a failure mechanism attached to each.

Write it down

Documentation that carries its own provenance.

Reports with provenance

Documentation

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.

Business impact

The benefit shows up in three places.

Speed, cost, and the quality of the knowledge your programme carries forward.

Speed

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.

  • Tox lots scheduled earlier
  • Stability starts brought forward
  • First-in-human enabling material unblocked
  • CMC sections written against a defined design space

Cost

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.

  • Less FTE time at the bench
  • Lower analytical load
  • Less purified drug substance consumed
  • Effort spent confirming, not searching

Efficiency and knowledge quality

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.

  • Confidence attached to every candidate
  • One structured dataset, not scattered reports
  • A deliberately defined design space
  • Stronger comparability, scale-up and lifecycle changes
Why it matters most here

At high concentration, a molecule stops behaving like a dilute solution.

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

Self-association and phase behavior

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 particle burden

Aggregation and subvisible particle burden increase, with interfacial and agitation stress becoming the controlling degradation route.

Chemical degradation hotspots

Deamidation, isomerization, oxidation and fragmentation become concentration- and excipient-sensitive.

Viscosity and deliverability

Viscosity rises steeply and non-linearly with concentration, threatening syringeability, injection force and autoinjector compatibility.

Manufacturability

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 design space

Six axes that combine into hundreds of conditions.

Each condition must be made and analyzed at high concentration, using material that may not yet exist in quantity.

Buffer species and pH

The window that governs charge state, self-association and chemical stability.

Sugars and polyols

Stabilizers whose benefit at high concentration is not a simple extrapolation from dilute solution.

Amino acid excipients

Arginine, histidine, glutamate, proline and glycine — often the lever that decouples viscosity from stability.

Ionic strength

Narrow windows where opalescence and liquid–liquid phase separation appear or disappear.

Surfactant type and level

The control on interfacial and agitation stress, the dominant degradation route at concentration.

Chelators and antioxidants

Direct control on oxidation and fragmentation hotspots.

The problem

Solving this empirically is expensive. Those axes combine into hundreds of plausible conditions, each of which must be made and analyzed at high concentration using material that may not yet exist in quantity. Practical screens therefore sample the space sparsely, and often miss the viscosity–stability trade-off until late — when a change is disruptive.

The approach

Uplizd’s service addresses exactly that gap: the space is explored computationally first, and the bench is used to confirm a short, well-chosen list.

How it works

From sequence to a short list worth making.

The design space is explored computationally first. The bench is used to confirm.

Inputs

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.

Computational exploration

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.

Ranked candidates

A rank-ordered, fully specified set of formulation candidates, with confidence attached to each, delivered as one structured and statistically coherent dataset.

Bench confirmation

Bench-level standard operating procedures for making each candidate, so the laboratory runs a focused confirmatory exercise instead of an open-ended search.

Deliverables

What lands on your bench, and in your filing.

One structured, statistically coherent dataset — not a series of disconnected screening reports.

Rank-ordered formulation candidates

Fully specified — every excipient, concentration and pH stated — and ordered so the bench knows what to make first.

Confidence for every candidate

Each candidate carries its own confidence, so prioritisation is a stated judgement rather than an implicit one.

Bench-level SOPs

The standard operating procedures needed to make the candidates, written for the people who will run them.

A deliberately defined design space

One structured, statistically coherent dataset instead of a series of disconnected screening reports — which is what later comparability, scale-up and lifecycle changes rest on.

Custom platform build

Your formulation platform, owned outright.

Built on a foundation we have already developed rather than from scratch, customised to your molecules and your rules, and handed over as source code your team can run without us.

  1. Work package one

    The foundation, and the first specialists

    The shared groundwork every agent depends on — how they coordinate, how they retrieve scientific and regulatory evidence, how their outputs are checked, and how the system runs securely end to end. Designed once, so the second package and everything after it can be added without a rebuild. Delivered alongside the first specialist agents and the portal that surfaces them.

  2. Work package two

    The full system, and the handover

    The remaining specialists, upgrades to the ones already running, and the rest of the portal — per-agent review, audit and export, report generation, and a gateway for your own internal data. Joint validation against your reference cases, training for your scientists and administrators, and a stabilisation period after acceptance.

  3. Handover

    You own it, and you can leave

    Source code, build and deployment artefacts, infrastructure-as-code, the documentation suite, and recorded knowledge-transfer sessions. Enough for your IT team to compile, deploy and reproduce the platform on any major cloud or on premises, without re-implementation and without buying a licence from us. Our role at completion becomes optional.

General models are non-deterministic

Even with careful prompting and fixed settings, the same prompt can return meaningfully different outputs across runs. That is acceptable for brainstorming. It is not acceptable for a buffer recommendation or a formulation recipe.

Regulated work has to be reproducible

Expectations for electronic records, computerised systems and AI-supported decisions require controls a hosted chat tool does not provide by default. These determine whether an output can be trusted, reviewed, repeated and defended.

No tool is compliant in a box

Compliance is a property of the system, its deployment and your own validation programme — not a badge a vendor can hand over. A platform you own and can audit is what makes that programme possible.

Build versus assemble

Why not just assemble it on a chat subscription?

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.

Accuracy
On a subscription: The model returns its best answer, and a confident wrong answer can look indistinguishable from a correct one.
With Uplizd: Critical formulation logic — buffers, pH, ionic strength — is computed through deterministic, error-checked code. Model judgement is advisory and checked against known-good results.
Repeatability
On a subscription: The same question can produce different answers across runs, models, or prompt variations.
With Uplizd: Core outputs are deterministic: the same validated inputs produce the same validated results.
Traceability
On a subscription: You receive an answer, but not a record of how it was reached, what evidence was used, or which rule was applied.
With Uplizd: Every result links back to its inputs, the rule applied, the source evidence and the decision path — an audit trail you can review and defend.
Memory
On a subscription: A chat session has limited and inconsistent memory of your molecules, rules, historical decisions and project context.
With Uplizd: Your SOPs, formulation rules, prior decisions and approved data are built into a persistent knowledge base every agent uses consistently.
Regulatory grounding
On a subscription: Answers are free-form and may lack citations, compliance logic, or any connection to current regulatory expectations.
With Uplizd: Recommendations are grounded in relevant FDA and EMA precedent, with citations and built-in compliance checks.
Validation
On a subscription: Output is not checked against your accepted cases unless you build that validation layer yourself.
With Uplizd: The system is tested against your gold-standard cases and accepted only when it meets agreed tolerances.
Deployment
On a subscription: Tied to one provider's platform, model, pricing and hosting. What you build may not move cleanly when requirements change.
With Uplizd: Runs on any leading model and deploys to your cloud or on premises. Delivered as source code you can change, redeploy and extend.
Ownership
On a subscription: Stop paying and the workflow disappears, or becomes difficult to operate independently.
With Uplizd: Full source-code handover. You own, run, audit and extend the platform — the logic built for you belongs to you.
Frequently asked

Questions partners ask first.

What the service needs, what it returns, and where it fits in a programme.

What do you need from us to start?

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.

How long does it take?

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.

What exactly is delivered?

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.

Does this replace laboratory work?

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.

Why is high concentration the hardest case?

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.

How does this affect our regulatory filing?

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.

Put the specialists on your molecule.

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.

Contact usSee how it works