Quality by Design (QbD) Approach for a Nanoparticulate Imiquimod Formulation as an Investigational Medicinal Product

Abstract

The present article exemplifies the application of the concept of quality by design (QbD) for the systematic development of a nanoparticulate imiquimod (IMQ) emulsion gel formulation as an investigational medicinal product (IMP) for evaluation in an academic phase-I/II clinical trial for the treatment of actinic keratosis (AK) against the comparator Aldara (EudraCT: 2015-002203-28). The design of the QbD elements of a quality target product profile (QTPP) enables the identification of the critical quality attributes (CQAs) of the drug product as the content of IMQ, the particle-size distribution, the pH, the rheological properties, the permeation rate and the chemical, physical and microbiological stability. Critical material attributes (CMAs) and critical process parameters (CPPs) are identified by using a risk-based approach in an Ishikawa diagram and in a risk-estimation matrix. In this study, the identified CPPs of the wet media ball-milling process’s milling time and milling speed are evaluated in a central composite design of experiments (DoEs) approach, revealing criticality for both factors for the resulting mean particle size, while only the milling time is significantly affecting the polydispersity. To achieve a mean particle size in the range of 300–400 nm with a minimal PdI, the optimal process conditions are found to be 650 rpm for 135 min. Validating the model reveals a good correlation between the predicted and observed values. Adequate control strategies were implemented for intermediate products as in-process controls (IPCs) and quality control (QC) tests of the identified CQAs. The IPC and QC data from 13 “IMI-Gel” batches manufactured in adherence to good manufacturing practice (GMP) reveal consistent quality with minimal batch-to-batch variability.

Enhancing Nose- To- Brain Delivery of Piribedil: Development of a Nanosuspension Dispersed in Nasal in-Situ Gelling System

“…A desirability function integrates responses into a single variable to predict optimal levels for independent variables. Desirability values span from 0 (representing an undesirable outcome) to 1 (reflecting the most favorable outcome) [37].…”