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Formulation Scientist

Run dissolution testing on prototype tablets

Enhances◐ 1–3 years

What You Do Today

Set up USP apparatus, run dissolution profiles at multiple pH values, compare to target release profile, adjust formulation if needed

AI That Applies

AI predicts dissolution profiles from formulation composition, reducing physical testing iterations; digital twins model tablet behavior

Technologies

Simulations Plus GastroPlusBIOVIA Pipeline Pilot

How It Works

The system ingests formulation composition as its primary data source. The simulation engine runs thousands of scenarios by varying each uncertain input across its probability range, building a distribution of outcomes that quantifies the risk. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Fewer physical dissolution runs needed — AI predicts which formulations will meet spec before you make them

What Stays

You still run confirmatory dissolution testing for regulatory submissions; hands-on understanding of dissolution behavior is essential

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for run dissolution testing on prototype tablets, understand your current state.

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Map your current process: Document how run dissolution testing on prototype tablets works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: You still run confirmatory dissolution testing for regulatory submissions; hands-on understanding of dissolution behavior is essential. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Simulations Plus GastroPlus tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long run dissolution testing on prototype tablets takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

“What data do we already have that could improve how we handle run dissolution testing on prototype tablets?”

They're prioritizing which operational processes to automate

your process improvement or lean lead

“Who on our team has the deepest experience with run dissolution testing on prototype tablets, and what tools are they already using?”

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

“If we brought in AI tools for run dissolution testing on prototype tablets, what would we measure before and after to know it actually helped?”

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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