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Related Experiment Video

Updated: Mar 3, 2026

Author Spotlight: Process Development for the Spray-Drying of Probiotic Bacteria and Evaluation of the Product Quality

Author Spotlight: Process Development for the Spray-Drying of Probiotic Bacteria and Evaluation of the Product Quality

Published on: April 7, 2023

Enhanced formulation of precision probiotics through active machine learning.

Anweshit Panda¹, Manaswani Adhikari², Sourya Subhra Nasker²

¹School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University (KIIT-DU), Bhubaneswar, 751024, India.

Biology Methods & Protocols

|March 2, 2026

Summary

This summary is machine-generated.

Active machine learning (ML) accurately predicts how excipients affect probiotic growth, optimizing formulations. This approach enhances probiotic viability through the gastrointestinal tract for improved gut health.

Keywords:

Lactobacillus plantarum active machine learning gradient boosting growth prediction pharmaceutical excipients precision probiotics support vector machines

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Last Updated: Mar 3, 2026

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Area of Science:

Microbiology
Bioinformatics
Pharmacology

Background:

The human gut microbiome plays a vital role in overall health.
Microbial imbalances (dysbiosis) are linked to various diseases.
Probiotic interventions aim to restore microbial balance, but ensuring survival during gastrointestinal transit is difficult.

Purpose of the Study:

To employ an advanced active machine learning (ML) approach to predict the impact of excipients on the growth of *Lactobacillus plantarum*.
To enhance the selection of excipients for improved probiotic formulation stability and efficacy.

Main Methods:

Utilized an active ML strategy over three iterative rounds to predict excipient effects on *Lactobacillus plantarum* growth.
Trained and evaluated five ML models: neural networks, gradient boosting, logistic regression, random forest, and support vector machines.
Compared continuous model optimization against retraining new models per iteration.

Main Results:

Successfully predicted the effects of 116 excipients on probiotic growth, achieving model certainty levels near 90%.
The active ML approach demonstrated enhanced prediction stability and reduced uncertainty spread compared to traditional methods.
Validated ML predictions with complementary state-of-the-art experimental data.

Conclusions:

Active ML is a powerful tool for predicting excipient-probiotic interactions.
This method can significantly improve the accuracy and efficiency of selecting excipients for probiotic formulations.
The findings support the development of more effective precision probiotic therapies.