Active Machine learning for formulation of precision probiotics.
Int J Pharm
; 616: 121568, 2022 Mar 25.
Article
em En
| MEDLINE
| ID: mdl-35150845
ABSTRACT
It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy microbiome balance. To reach the distal gut alive where the density of microbiota is highest, oral probiotics should be protected from harsh conditions during transit through the stomach and small intestines. At present, few probiotic formulations are designed with this delivery strategy in mind. This study employs an emerging machine learning (ML) technique, known as active ML, to predict how excipients at pharmaceutically relevant concentrations affect the intestinal proliferation of a common probiotic, Lactobacillus paracasei. Starting with a labelled dataset of just 6 bacteria-excipient interactions, active ML was able to predict the effects of a further 111 excipients using uncertainty sampling. The average certainty of the final model was 67.70% and experimental validation demonstrated that 3/4 excipient-probiotic interactions could be correctly predicted. The model can be used to enable superior probiotic delivery to maximise proliferation in vivo and marks the first use of active ML in microbiome science.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
3_ND
Problema de saúde:
3_zoonosis
Assunto principal:
Probióticos
/
Microbiota
/
Microbioma Gastrointestinal
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Int J Pharm
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Reino Unido