of its ability to purge the impurity; thus, the higher the score, the greater the likelihood that the impurity would be purged from the process (Table 3.3).
Table 3.3 Purge factor calculation scoring system.
| Physicochemical parameter | Scale of purge factor |
|---|---|
| Reactivity | Highly reactive = 100 |
| Moderately reactive = 10 | |
| Low reactivity/unreactive = 1 | |
| Solubility | Freely soluble = 10 |
| Moderately soluble = 3 | |
| Sparingly soluble = 1 | |
| Volatility | Boiling point >20 °C below that of the reaction/process solvent = 10 |
| Boiling point within ±20 °C of that of the reaction/process solvent = 3 | |
| Boiling point >20 °C above that of the reaction/ process solvent = 1 | |
| pK a/pK b | Ionization potential of PMI significantly different from that desired product (3–10) |
| Physical processes: chromatography | Chromatography: 10–100 based on extent of separation |
| Physical processes: e.g. other scavenger resins | Evaluated on an individual basis (3−100) |
Hence, if a material is identified three steps from API, the characteristics of the material concerned should be critiqued with the nature of the three downstream processing stages to understand likelihood of purging. An overall purge factor can be assigned by multiplying the purge factors arising from each separate stage, and based on this value, a decision can be made as to what, if any, further action may be required.
The relationship between predicted purge and required purge was examined by Barber et al. [19]. ICH M7 outlines a series of control options, but it does not provide guidance in terms of how to systematically decide which is an appropriate control option. To address this, Barber et al. developed a regulatory decision tree (Figure 3.2) with detailed description of action limits (Table 3.4) depending on the ratio. The decision tree, and description of action limits, links purge predictions and their relationship with required purge (i.e. purge ratio) with recommendations for control strategy development. It also defines the level of detail and content in terms of supporting data presentation.
Figure 3.2 (P)MI purge factor decision tree for use under ICH M7.
Table 3.4 Relationship between purge factor ratios and regulatory reporting action limits and potential supplementary reporting requirements.
| If PR ≥ 1000× | If 1000 > PR ≥ 100× | If PR < 100× |
|---|---|---|
| Data collection recommendations | ||
| Collection of additional experimental data not recommended for noncommercial or commercial API routes to support scientific rationale. | Collection of additional non‐trace experimental data (solubility, reactivity, and volatility) recommended for both noncommercial and commercial API routes to support scientific rationale. Collection of additional trace PMI analysis not necessary for noncommercial or commercial API routes to support scientific rationale. | For noncommercial API routes, experimentally measure PMI purging, including trace PMI analyses as appropriate, to support scientific rationale. Note: additional data are expected to support an Option 4 control strategy when PMI purge ratio is <<100×. For commercial API routes, detailed experimental fate and purge studies are expected for all PMI to support a commercial Option 4 control strategy. |
| Regulatory reporting recommendations | ||
| Report “unlikely to persist” or cumulative predicted purge factor and purge ratio for noncommercial API routes in regulatory submissions. Replace with summary of key elements of predicted purge factor calculations and purge ratio for commercial API routes in regulatory submissions. | Report the cumulative predicted purge factor and purge ratio for noncommercial API routes in regulatory submissions. Replace with summary of key elements of predicted purge factor calculations, = purge ratio, and supporting non‐trace data on purge properties for commercial API routes in regulatory submissions. | Report summary of key elements of predicted purge factor calculations, purge ratio, and supporting non‐trace or trace data for noncommercial API routes in regulatory submissions. Replace with complete summary of predicted purge factor calculations, purge ratio, and supporting trace and non‐trace fate and purge data for commercial API routes in regulatory submissions. |
Table 3.5 Tests to investigate in vivo relevance of in vitro mutagens.
Source: Reproduced from ICH M7.
| Note 3 Tests to Investigate the in vivo Relevance of in vitro Mutagens (Positive Bacterial Mutagenicity) | |
| in vivo test | Factors to justify choice of test as fit‐for‐purpose |
| Transgenic mutation assays | For any bacterial mutagenicity positive. Justify selection of assay tissue/organ |
| Pig‐a assay (blood) | For directly acting mutagens (bacterial mutagenicity positive without S9)a |
| Micronucleus test (blood or bone marrow) | For directly acting mutagens (bacterial mutagenicity positive without S9) and compounds known to be clastogenica |
| Rat liver Unscheduled DNA Synthesis (UDS) test | In particular for bacterial mutagenicity positive with S9 only Responsible liver metabolite known to be generated in test species used to induce bulky adducts |
| Comet assay | Justification needed (chemical class specific mode of action to form alkaline labile sites or single‐strand breaks as preceding DNA |
a For indirect acting mutagens (requiring metabolic activation), adequate