Data analytics on raw material properties to accelerate pharmaceutical drug development

Autor: Antonio Benedetti, Massimiliano Barolo, Jiyi Khoo, Simeone Zomer, Sandeep Sharma, Pierantonio Facco
Rok vydání: 2019
Předmět:
Pharmaceutical drug
Support Vector Machine
Databases
Factual
Computer science
Process (engineering)
Surface Properties
medicine.medical_treatment
media_common.quotation_subject
Pharmaceutical Science
02 engineering and technology
Raw material
030226 pharmacology & pharmacy
Excipients
03 medical and health sciences
0302 clinical medicine
Data analytics
Machine learning
Material clustering
Multivariate data analysis
Pharmaceutical drug product development
Raw materials database
Drug Development
medicine
Quality (business)
Particle Size
media_common
business.industry
Models
Theoretical
021001 nanoscience & nanotechnology
Design for manufacturability
Pharmaceutical Preparations
New product development
Key (cryptography)
Data analysis
Biochemical engineering
0210 nano-technology
business
Rheology
Zdroj: International journal of pharmaceutics. 563
ISSN: 1873-3476
Popis: Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.
Databáze: OpenAIRE
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