Delving into the Latest Updates on Ortho-aspirin with Synapse

Overview

Basic Info

Drug Type

Small molecule drug

Synonyms-

Target

EGFR

Mechanism

EGFR antagonists(Epidermal growth factor receptor erbB1 antagonists)

Therapeutic Areas

NeoplasmsDigestive System Disorders

Active Indication

Colorectal Cancer

Inactive Indication-

Originator Organization

University of Wolverhampton

Active Organization

University of Wolverhampton

Inactive Organization-

Drug Highest PhasePreclinical

First Approval Date-

Regulation-

AbstractIn the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

100 Deals associated with Ortho-aspirin

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