Statistical design of experiments (DoE) is used to aid in the development and execution of preparative liquid chromatography (LC) for large-scale purification of active pharmaceutical ingredients (API) and pharmaceutical intermediates. Four purification case studies were undertaken. In case study 1, a normal phase preparative silica method is developed and modeled. After initial method screening, DoE results were used to set mobile phase composition, flowrate, and sample diluent. Of the three particle sizes studied (10 µm, 20 µm, 50 µm) only 10 µm silica resin was able to produce purified API at the yield (>96%) and productivity (> 1 kg/kg-resin/day) necessitated by the project. The second case study uses DoE studies to identify critical process parameters of column load, mobile phase solvent ratio and basic modifier level for a low-resolution, preparative, chiral separation. Trade-offs between purity, yield and productivity are quantified in a tight separation which made compromising on process outcomes a necessity. The third case study troubleshoots a loss of yield experienced during operation of a process-scale reverse-phase LC purification. DoE is used to identify a critical interaction between levels of acetonitrile and phosphoric acid in the mobile phase. An operating region which increased yield from around 85% to 97% was defined and implemented. The fourth case study was initially designed as a preparative chromatography purification of API. DoE was used to screen mobile phase solubility. These experiments uncovered conditions where API is soluble, and impurities are not. The solubility model in acetonitrile/water mixtures is further defined via a response surface DoE. The resulting targeted solvent mixture allows bulk purification via dissolution of API while three less-polar impurities remain in the solid phase and are removed by filtration. These four case studies demonstrate the efficiency of DoE and response surface modeling as tools for process development and optimization.

Theophylline has been used as an active pharmaceutical ingredient (API) in the treatment of pulmonary diseases, but due to its low water solubility reveals very poor bioavailability. Based on its different hydrogen-bond donor and acceptor groups, theophylline is an ideal candidate for the formation of cocrystals. The crystal structure of the 1:1 benzamide cocrystal of theophylline, C7H8N4O2·C7H7NO, was determined from synchrotron X-ray powder diffraction data. The compound crystallizes in the tetragonal space group P41 with four independent molecules in the asymmetric unit. The molecules form a hunter\'s fence packing. The crystal structure was confirmed by dispersion-corrected DFT calculations. The possibility of salt formation was excluded by the results of Raman and (1)H solid-state NMR spectroscopic analyses.

As the demand for new drugs is rising, the pharmaceutical industry faces the quest of shortening development time, and thus, reducing the time to market. Environmental aspects typically still play a minor role within the early phase of process development. Nevertheless, it is highly promising to rethink, redesign, and optimize process strategies as early as possible in active pharmaceutical ingredient (API) process development, rather than later at the stage of already established processes. The study presented herein deals with a holistic life-cycle-based process optimization and intensification of a pharmaceutical production process targeting a low-volume, high-value API. Striving for process intensification by transfer from batch to continuous processing, as well as an alternative catalytic system, different process options are evaluated with regard to their environmental impact to identify bottlenecks and improvement potentials for further process development activities.

Assessing ambient exposure to chemical stressors often begins with time-consuming and costly monitoring studies to establish environmental occurrence. Both human and ecological toxicology are currently challenged by the unknowns surrounding low-dose exposure/effects, compounded by the reality that exposure undoubtedly involves mixtures of multiple stressors whose identities and levels can vary over time. Long absent from the assessment process, however, is whether the full scope of the identities of the stressors is sufficiently known. The Matthew Effect (a psychosocial phenomenon sometimes informally called the \"bandwagon effect\" or \"iceberg effect,\" among others) may adversely bias or corrupt the exposure assessment process. The Matthew Effect is evidenced by decisions that base the selection of stressors to target in environmental monitoring surveys on whether they have been identified in prior studies, rather than considering the possibility that additional, but previously unreported, stressors might also play important roles in an exposure scenario. The possibility that the Matthew Effect might influence the scope of environmental stressor research is explored for the first time in a comprehensive case study that examines the preponderance of \"absence of data\" (in contrast to positive data and \"data of absence\") for the environmental occurrence of a very large class of potential chemical stressors associated with ubiquitous consumer use - active pharmaceutical ingredients (APIs). Comprehensive examination of the published data for an array of several hundred of the most frequently used drugs for whether their APIs are environmental contaminants provides a prototype example to catalyze discussion among the many disciplines involved with assessing risk. The findings could help guide the selection of those APIs that might merit targeting for environmental monitoring (based on the absence of data for environmental occurrence) as well as the prescribing of those medications that might have minimal environmental impact (based on data of absence for environmental occurrence).