A computer aided optimal inventory selection system for continuous quality improvement in drug product manufacture

• The interaction of the raw materials’ physical properties can be critical to the quality of the final drug product.
• Multivariate specifications become unfeasible to implement with materials sourced by different vendors.
• In legacy pharmaceutical operations, the only degree of freedom available to be used as a MV for control is the lot-to-lot variability in the raw materials.
• Optimizing the material selection for a full campaign, rather than just the next lot, greatly reduces quality trends and variabilty.

The multivariate interaction of the raw materials’ physical properties can be critical to the quality of the final drug product. Although an elegant solution to this problem is the establishment of multivariate specifications this becomes difficult (if not impossible) to implement when the interactions take place across materials that are sourced by different vendors. As an alternate solution, this work presents a feed-forward corollary approach to model predictive control (MPC) to improve the product quality from a lot-driven-operation; where there are no available manipulated variables (MV) in the process. In these special cases the only degree of freedom available to be used as a MV for control is the lot-to-lot variability in the raw materials. This work presents an extension to our earlier work (Ind. Eng. Chem. Res. 2013, 52 (17), pp. 5934–5942) to consider a horizon of n lots to be manufactured. By considering this horizon of future lots (rather than just the next one) our method allows the discretionary use of all materials to ensure that the quality of all the future n lots is within specification. This paper presents a detailed discussion of the objective function used and also reports the results of implementing this method to the manufacture of a pharmaceutical drug product in a commercial manufacturing setting.