We present the first known example of single crystal X-ray diffraction of small molecule crystals in a flow crystallisation environment via serial crystallography. Towards radiation damage mitigation, high-throughput screening and kinetic studies.

One of the most important challenges in the pharmaceutical industry is to produce crystals with desired size and shape distributions, to enhance the critical quality attributes of the drug product, such as efficacy, and to improve manufacturability during downstream processing, such as filtration, drying and granulation. The paper provides a framework for effective crystal shape and size tuning, based on a systematic exploration of standard techniques, such as the linear cooling and supersaturation control (SSC), and novel methods based on the systematic combination of several techniques, namely direct nucleation control (DNC), wet milling, SSC and shape modification additives. The crystallization of lovastatin, which is notorious for its challenging needle-shaped crystals, with an extremely high aspect ratio, was used as a case study, and polypropylene glycol (PPG-4000), at different concentrations, was used as an effective shape modifier from small-scale tests studied previously. The proposed techniques were implemented in the case of seeded and unseeded systems. It was demonstrated that the combination of temperature cycling and polymer additive enhances greatly the control over the aspect ratio and crystal size distribution, compared to conventional linear cooling and SSC strategies. The implementation of wet milling at the beginning of the process, or the introduction of seeds, enhances even further the control of the critical quality attributes of the crystalline product.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Data availability is a major obstacle to the successful application of artificial intelligence. In chemistry, pharmaceutical and materials science, generating labelled data on scale relies on expensive screening approaches that require major human and/or economic investments. Although industry uses sophisticated autonomous systems for such tasks, tight intellectual property concerns and strict confidentiality agreements limit data accessibility, thus restricting innovation and hindering the concept of "open science". Without automation, data collection is laborious and sample characterization often suffers from subjectivity introduced by the operator. This study tackles the challenge of pharmaceutical particle characterization by modifying a 3D printer to enable rapid, autonomous sample characterization using light microscopy. The system uses low-cost hardware and open-source software in a platform that is accessible and reproducible for the scientific community. As well as increasing throughput, this system uses deep learning to assign sample labels in real-time overcoming bias from subjective labelling, which hinders model performance. Different neural architectures were applied, including convolution and attention blocks, to maximize performance and demonstrate the transferability of the system. This autonomous system effectively characterized pharmaceutical crystal morphology, an area where subjective labelling and limited data have hindered applying machine learning models. By releasing this platform, researchers without access to sophisticated automation platforms can carry out larger in-house screening efforts to generate datasets for developing data-driven, intelligent models.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

The visualization of chemical processes that occur in the solid-state is key to the design of new functional materials. One of the challenges in these studies is to monitor the processes across a range of timescales in real-time. Here, we present a pump-multiprobe single-crystal X-ray diffraction (SCXRD) technique for studying photoexcited solid-state species with millisecond-to-minute lifetimes. We excite using pulsed LEDs and synchronise to a gated X-ray detector to collect 3D structures with sub-second time resolution while maximising photo-conversion and minimising beam damage. Our implementation provides complete control of the pump-multiprobe sequencing and can access a range of timescales using the same setup. Using LEDs allows variation of the intensity and pulse width and ensures uniform illumination of the crystal, spreading the energy load in time and space. We demonstrate our method by studying the variable-temperature kinetics of photo-activated linkage isomerism in [Pd(Bu 4 dien)(NO 2 )][BPh 4 ] single-crystals. We further show that our method extends to following indicative Bragg reflections with a continuous readout Timepix3 detector chip. Our approach is applicable to a range of physical and biological processes that occur on millisecond and slower timescales, which cannot be studied using existing techniques.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Salbutamol is an active pharmaceutical ingredient commonly used to treat respiratory distress and is listed by the World Health Organization as an essential medicine. Here, we establish the crystal structure of its oxalate form, salbutamol oxalate, and explore the nature of its crystallographic disorder by combined X-ray crystallography and 13C cross-polarization (CP) magic-angle spinning (MAS) solid-state NMR. The *C–OH chiral center of salbutamol (note that the crystal structures are a racemic mixture of the two enantiomers of salbutamol) is disordered over two positions, and the tert-butyl group is rotating rapidly, as revealed by 13C solid-state NMR. The impact of crystallization conditions on the disorder was investigated, finding variations in the occupancy ratio of the *C–OH chiral center between single crystals and a consistency across samples in the bulk powder. Overall, this work highlights the contrast between investigating crystallographic disorder by X-ray diffraction and solid-state NMR experiment, and gauge-including projector-augmented-wave (GIPAW) density functional theory (DFT) calculations, with their combined use, yielding an improved understanding of the nature of the crystallographic disorder between the local (i.e., as viewed by NMR) and longer-range periodic (i.e., as viewed by diffraction) scale.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Abstract We present a pump-multiprobe single-crystal X-ray diffraction (SCXRD) technique for studying photoexcited solid-state species with millisecond-to-minute lifetimes. We excite using pulsed LEDs and synchronise to a gated X-ray detector to collect 3D structures with sub-second time resolution while maximising photo-conversion and minimising beam damage. Our implementation provides complete control of the pump-multiprobe sequencing and can access a range of timescales using the same setup. Using LEDs allows variation of the intensity and pulse width and ensures uniform illumination of the crystal, spreading the energy load in time and space. We demonstrate our method by studying the variable-temperature kinetics of photo-activated linkage isomerism in [Pd(Bu4dien)(NO2)][BPh4] single-crystals. We further show that our method extends to following indicative Bragg reflections with a continuous readout Timepix3 detector chip. Our approach is applicable to a range of physical and biological processes that occur on millisecond and slower timescales, which cannot be studied using existing techniques.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

We present the use of artificial intelligence to predict the morphology of crystallizing active pharmaceutical ingredients, first using publicly available data, and then using our own screening efforts to address the limitations we identified.

Implementing Artificial Intelligence for chemical applications provides a wealth of opportunity for materials discovery, healthcare and smart manufacturing. For such applications to be successful, it is necessary to translate the properties of molecules into a digital format so they can be passed to the algorithms used for smart modelling. The literature has shown a wealth of different strategies for this task, yet there remains a host of limitations. To overcome these challenges, we present two-dimensional images of chemical structures as molecular representations. This methodology was evaluated against other techniques in both classification and regression tasks. Images unlocked (1) superior augmentation strategies, (2) application of specialist network architectures and (3) transfer learning, all contributing to superior performance and without prior specialised knowledge on cheminformatics required. This work takes advantage of image feature maps which do not rely on chemical properties and so can represent multi-component systems without further property calculations.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.