Developing Cell Therapies: Enabling cost prediction by value systems modeling to manage developmental risk.

Abstract

This work quantifies the highest risk activities and interdependencies in cell therapy new product development (NPD).  A simulation model based upon an activates based and information driven  approach of the Design Structure Matrix (DSM), using Latin Hypercube sampling methods with discrete event simulation evaluated the interdependencies between critical development tasks.  Input data was collected from quarterly financial reports of cell therapy developers and developmental milestones as reported in company press releases and publications.  .

Successfully planning and managing development processes is problematic in an emerging industry lacking precedents and standardised technology platforms.   Methods of understanding and reducing developmental uncertainty and risk are needed to aid resourcing decisions.  A particular requirement is to understand the impact of process and clinical development, in this highly regulated sector.

Results from the model quantify the probability and impact of process iterations and failures that impact cost and duration of cell therapy NPD.  High impact areas quantified are the interdependence of Phase 1 clinical trials and investment, the scaling of the manufacturing process from Phase 1 to Phase 2 and Phase 2 to Phase 3.  The model also allows for the calculation of the probability of NPD success for given resource levels, time constraints and market conditions.  An application comparing alternative regulatory approaches indicates that the current favoured strategy of targeting an orphan indication gives little benefit for the tested clinic al indication because of reduced clinical trial recruitment rate.  While specifically developed for cell therapy NPD this modelling approach has potential application across the wider biotechnology industry.