Given certain formulation constraints for your new API, one could be interested in assessing the success probability of achieving a quality solid product through different processing routes.

For tablets, direct compression (DC) is generally preferred due to its lower inherent production complexity. Yet, in some cases, the powder properties of the active pharmaceutical ingredient (API) render it very difficult to ensure consistent tabletting performance, even in combination with excipients trying to abate these suboptimal property values. Most commonly, bad powder flow characteristics can form an insurmountable barrier here.

With formulation screening within the available excipient-combination candidates, a fast and resource-efficient assessment can be made of the plausibility of this route, even supported by Elegent in-house models. In the case that no feasible formulation is found for this route, the dry granulation (DG) route could be a working alternative. If the advantages of this pathway can combat any poor compressibility, superfluous dust generation, segregation or any other failures of the practically available formulation space, dry granulation can be the alternative to go for. Again, models allow for an assessment of this route's feasibility for successfully rendering a quality product in various formulation scenarios. 

For the sake of illustration, we imagine that a certain set of processible formulations can be found for roller compaction and subsequent tableting.  Yet, the margin of error could be low for these formulations, any feeding or raw material variability could pose risks to maintaining this target quality. If the API is not moisture-sensitive, a similar assessment can be run, again with limited data needed, for wet granulation (WG). Given that this method involves more complex process development (which can vary based on vendor and equipment), the application of predictive models can be particularly useful in defining the limits of a feasible formulation space, providing a quantitative and educated estimation to minimize the risk of development failures. Furthermore, the risks associated with processing the API through this method can be compared to those of other viable routes, facilitating informed decision-making. The ability to quickly scan a wide array of scenarios is what makes predictive modeling so advantageous for mitigating risks in processing pathway selection.

Elegent has models for prediction of critical quality attributes in direct compression, and can apply its experience and toolset to develop models for granulation routes. In case you would like more info on this aspect specifically, please contact us directly, and we will be happy to enlighten this specific toolbox.