Traditional pharmaceutical manufacturing is based on a complex supply chain that is vulnerable to spikes in demand and interruptions. Continuous pharmaceutical production in compact modules is a potential solution that allows for drug manufacturing when and where it is needed with significantly shorter lead times. As part of the Pharmacy on Demand (PoD) initiative, we demonstrate the potential for end-to-end manufacturing of multiple drug substances in reconfigurable devices, under common industrial constraints, and within a challenging manufacturing time frame. A new set of refrigerator-sized modules was constructed for the synthesis, isolation, and formulation of several drugs, with focus on achieving high manufacturing throughputs, and allowing for the production of pharmaceutical tablets. Their operation is demonstrated with the synthesis and formulation of USP-compliant tablets of diazepam, diphenhydramine hydrochloride, and ciprofloxacin hydrochloride, as well as liquid formulations of lidocaine hydrochloride and atropine sulfate.
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Combined cooling and antisolvent crystallization enables crystallization of many pharmaceutical products, but its process design typically neglects solvent composition influences on crystallization kinetics. This paper evaluates the influence of solvent-dependent nucleation and growth kinetics on the design of optimal, multistage mixed-suspension, mixed-product removal (MSMPR) crystallization cascades. The ability to independently select temperature and solvent compositions in each stage of the cascade serves to greatly expand the attainable region for a two-stage cascade, with diminishing returns for additional stages. Failure to include solvent-dependent kinetics can result in simulating incorrect attainable regions, active pharmaceutical ingredient (API) yields, and crystal size distributions. This work also demonstrates that commonly employed crystallization process design heuristics, such as equal antisolvent addition and decreasing temperature in successive stages, can result in suboptimal process design if kinetics are strongly solvent dependent.
Despite the known effects of foreign species on crystallization and the frequently large number of impurities present in multistep pharmaceutical syntheses, the early characterization of continuous crystallization operations may be limited by the availability of representative crude material. As part of the development of an end-to-end process for ciprofloxacin HCl monohydrate, this work evaluates the impact of upstream impurities on mixed-suspension, mixed-product removal (MSMPR) crystallization kinetics. The kinetic parameters for nucleation and crystal growth in MSMPR crystallization have been estimated for the commercial, purified active pharmaceutical ingredient (API), as well as crude API containing approximately 60 unknown impurities. Results show that, while the upstream impurities did not have a significant impact on the nucleation rate, both the temperature-dependent growth rate coefficient and the growth activation energy decreased in crude API crystallization. This behavior implies that the effects of impurities cannot simply be lumped into the temperature-independent rate coefficients. When evaluating the prediction capabilities of purified API data for crude crystallization, it was observed that a reasonable prediction of crystallization yield and crystal purity demands accurate knowledge on the thermodynamics of crude crystallization. Moreover, kinetics of crude API crystallization must be known to confidently predict the steady state particle size.
This
article describes the process characterization and development
of models to inform a process control strategy to prepare (R,R)-epoxy ketone 2, an intermediate
in the manufacture of carfilzomib. Model calibration for relevant
unit operations and the development of a dynamic integrated flowsheet-level
model in gPROMS FormulatedProducts software enabled investigation
of the impact of process disturbances and model uncertainties on the
critical quality attributes (CQAs) and identification of critical
process disturbances and failure modes to guide a process control
strategy. The model development was similar to that described in the
previous parts of this series, but with the added complexity of comparing
two distinct kinetic formulations for the epoxidation reaction. The
main CQAs for this process were (1) the conversion of enone 1 (target ≥99.0 mol % conversion) and (2) the purity
target for solids prior to cake wash (target ≥97.5% purity
by weight). Conversion of enone was not always achieved with the expected
disturbances: whereas 99.5% conversion was expected for normal operating
conditions, 97.2% conversion was predicted for the worst-case combination
of disturbances. The chiral purity of crystalline (R,R)-epoxy ketone 2 was not always achieved
with the expected disturbances: 98.2% purity was expected for normal
operating conditions, and 96.7% purity was expected for the worst-case
combination of disturbances. These analyses allowed for rank ordering
of critical process parameters that impact conversion and suitable
manipulated variables to develop a robust process control strategy
for the manufacturing scheme.
Traditional
pharmaceutical manufacturing operates around a supply
chain that is subject to complex logistics and is vulnerable to spikes
in demand and interruptions. In this context, continuous pharmaceutical
manufacturing in portable, refrigerator-sized factories is a promising
solution with applications in battlefield medicine, pandemic response,
and mitigation of local medical emergencies. A new iteration of the
pharmacy on demand initiative is hereby presented, involving the development
of equipment and processes for the manufacture of ciprofloxacin HCl
with commercialization in mind. This article covers the implementation
and the feedback control strategies for downstream manufacturing,
as well as the results of the first end-to-end continuous manufacturing
campaign. The results involve a significant leap from prior iterations,
consistently attaining drug substance specifications in a fully automated
process and with a 4-fold increase in the process throughput over
the most recent iteration.
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