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Optimize, Scale, and Predict with Scientific Precision

Accelerating Pharma Manufacturing Innovation with Mechanistic Modeling

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3,000
Experiments
$100M
Cost
6 yrs.
Time

OUR GOAL

Transforming pharmaceutical development

Bringing a new drug to market isn’t just about discovery—it’s about figuring out how to manufacture it at scale. This process, known as process development, requires determining countless parameters such as temperature, mixing speed, and ingredient ratios. Traditional approaches are slow, costly, and time-consuming.

At Auxilart, we redefine process development with advanced mechanistic and hybrid modeling. By replacing physical experiments with digital simulations, we help pharmaceutical innovators.

With our solutions, you can:

Reduce Experiments

Improve Quality

Accelerate Launch

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3,000
Experiments
$100M
Cost
6 yrs.
Time

OUR CORE FEATURES

Transforming Pharma Process Development with Mechanistic Modeling

Mechanistic models use known physical, chemical, or biological laws to simulate how processes behave, providing insight beyond what data alone can offer. Unlike AI/ML models that require vast datasets, mechanistic models can operate accurately with minimal data if the system is well understood. This makes them especially valuable in regulated environments like pharmaceuticals.

INNOVATIVE SOLUTIONS

Optimize and scale your R&D processes

We use mechanistic models, powered by differential equations, to simulate real-world phenomena with precision. Unlike conventional AI, these models are firmly grounded in scientific principles. They may be complex to develop, but once built, they can replace hundreds of experiments with only a handful of time-series datasets. Because of this versatility, our technology can be applied across a wide range of use cases.

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Development Phase
Condition screening
Understand process behavior, reduce trial-and-error, and accelerate insights using historical data.
Characterization
Quantify the impact of small variations, define control strategies, and validate uncertainties with targeted experiments.
Stability & Quality Evaluation Phase
Conditions Optimization
Use iterative feedback between models and experiments to build adaptable design spaces and optimize conditions.
Process Performance Qualification (PPQ)
Ensure consistency, cut down trials, accelerate verification, and detect deviations early to mitigate risks.
Commercialization & Continuous Improvement Phase
Scale-Up
Assess and mitigate risks during scale-up, estimate optimal conditions, and ensure stable technology transfer.
Root-Cause Analysis (RCA)
Link deviations with model predictions, test hypotheses efficiently, and drive continuous improvement.