Our research

Framework for rapid determination of manufacturability of bulk product and final dosage form 

Key contact

Paul Dalby 

p.dalby@ucl.ac.uk
This research flagship aims to create indices that rank macromolecular products in terms of their manufacturability as a) the bulk active drug substance and b) the final drug product in a patient dosage form.

These indices will act as early predictors of bioprocess performance. Such ranking would allow decision makers to weigh up manufacturability and product dosage form options along with their knowledge of potential clinical efficacy, to select the most promising proteins from panels of candidates, and eliminate those most likely to fail due to difficulties in bulk product manufacture and final formulation.

To achieve this the research will generate new experimental tools as well as modelling capabilities for rapid, microscale assessment and ranking of protein properties.

The research will also establish more detailed cause-and-effect models to estimate manufacturing performance (e.g. yield, purity) as functions of critical process parameters (e.g. pH) and protein properties (e.g. aggregation propensity).

This will facilitate greater process understanding and pinpoint potential processing issues.

Decision-support tool for biomanufacturing lifecycle analysis under uncertainly

Key contact

Suzanne Farid 

s.farid@ucl.ac.uk

As the biopharmaceutical sector matures, increasing emphasis is being placed on early identification of failures to reduce costs and promote innovation. Production costs and capacity utilisation have become critical success factors. To maintain competitive while continuing to innovate, the sector needs to design flexible and cost-effective multi-product facilities that can cope with diverse drug candidate characteristics and process variations. 

This flagship research aims to:

• Generate a biomanufacturing lifecycle evaluation tool to enable the costs of manufacture, as well as the costs of treatment, to be predicted as a consequence of the decisions made during manufacture and product design. 
• Establish a decision-support optimisation tool  to locate the most cost-effective combination of process parameters,  process sequence, formulation method and facility design whilst assessing manufacturing robustness under uncertainty. 
• To develop advanced multivariate analysis tools to predict the degree of facility fit and root causes of product loss when optimal processes are transferred into       existing facilities. 

Developing these tools is a major undertaking and significant leverage can be       exerted by integrating these approaches to provide a novel linkage between       product design, process route selection, manufacturing approach and product delivery to the patient.