(141a) Prediction of Risk in Drug Substance Starting Material Selection

Regulatory approval for drug substance (DS) starting materials (SMs) represents a significant milestone in the progression towards approval of the proposed DS manufacturing process. Development of a robust data package in support of a proposed SM designation is a significant undertaking, and the requisite route optimization, control strategy development, method development, supplier qualification, and documentation for each proposed SM can translate to substantial resource utilization and financial investment.

To efficiently characterize the risk associated with a potential SM designation, Eli Lilly has developed a prototype tool that objectively predicts viability of a proposed SM, informed by an assessment of molecule complexity, genotoxic impurity control, and propinquity to the DS. To develop the tool, the team applied multivariate data analysis (MVDA) to identify molecular and route attributes that have previously correlated to acceptance or rejection of the SM by global regulatory agencies. As an outcome of the MVDA approach, two simplified metrics were empirically derived that applied broadly to the Lilly portfolio for which non-binding scientific advice or NME approval was available. These metrics classify high-risk SMs into at least one of two unacceptable classes: high molecular complexity (number of atoms and ring structures contributed to the DS) or a high number of genotoxic or stereospecific structures. The utility of the approach was tested and verified using both internal-to-Lilly and publicly-available external case studies.