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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.

Iridium complex Cp*(PMe3)Ir(CH3)OTf (1) and alkylaminocyclopropanes react via a well-understood initial C−H bond activation with a subsequent C−C bond-breaking step to yield a variety of π-allyl complexes [Cp*(PMe3)Ir(η3-C3H4NRR)][OTf] (R1 = R2 = Bn; R1 = Bn, R2 = Ph) and methane. However, diphenylamino, alkoxy, or siloxycyclopropanes, when treated with complex 1, yield a new π-allyl complex [Cp*(PMe3)Ir(η-C3H4CH3)][OTf] (8) and the corresponding amine or alcohol. The methyl group initially bound to iridium is no longer extruded as methane, but instead is incorporated into the allyl moiety to give a new carbon−carbon bond. A detailed mechanistic study provides evidence in support of an initial C−C bond activation mechanism as opposed to the initial C−H bond activation observed with other known substrates.

Process analytical technology (PAT; Raman spectroscopy and FBRM) was used to monitor the salt formation step of a development compound in real time. The data obtained were used to understand the crystallization kinetics and behavior. A mechanistic model was proposed on the basis of the sequence of events, and the mechanism was verified by the Raman profiles. Several operational parameters were studied in a matrix setting in order to identify the factors that affect the quality attributes of the final product such as particle size and morphology. The kinetic characterization through PAT revealed that the process is primarily influenced by temperature and solvent composition and that the acid addition rate and agitation speed were found to have a marginal effect. A regression model on API particle size was extracted from the data set, and its accuracy was verified at various set points of the parameter range covered in the investigation. The workflow adopted by this study illustrates how process knowledge generated by a combination of PAT and modeling, as well as a multistaged experimental plan executed on the basis of learning from technical insight, can efficiently lead to the process control as required under the current regulatory frameworks.