Towards an autonomous DataFactory for the small-batch cooling crystallisation of active pharmaceutical ingredients

As a method of forming and purifying the pharmaceutically relevant polymorph[1], crystallisation of an active pharmaceutical ingredient (API) is a key step in pharmaceutical manufacturing. Determining an industrial-relevant approach for API crystallisation can be resource-intensive as a candidate crystallisation process is constrained by and assessed against industrial relevant solubilities, downstream processing practicalities, and regulatordetermined Critical Quality Attributes (CQA) of the API[2-4]. The DataFactory at the CMAC aims to use high throughput smallbatch cooling crystallisation experiments coupled with machine learning to reduce the time and material costs associated with this process. Alongside the development of automated data collection, we are incorporating an autonomous decision-making system to optimize the small-batch cooling crystallisation of APIs and calculate relevant kinetic parameters to inform larger-scale experiments. Here we present the steps we’re taking to integrate and automate different platforms via a cobot and a central control PC, in addition to the beginnings of the database that will be the foundation of a crystallisation classification system

Developing an autonomous DataFactory workflow for smallscale batch cooling crystallisation with the antiviral lamivudine

Lamivudine: Lamivudine is an antiviral medication used to treat and prevent human immunodeficiency virus (HIV)1. Past studies have well characterised the two polymorphs, form I as needles and form II as bipyramidal but the literature is sparse for solubility and kinetic parameter estimations2. DataFactory: The DataFactory project will be an autonomous data collection platform focusing on active pharmaceutical ingredient (API) solubility and kinetic parameters. Therefore, this work aims to design a consistent method that can be adapted by robotics to be carried out without supervision. Aims and Objectives: - Establish a workflow that guides decision making for the automated data collection of the DataFactory - Establish a crystallisation parameter database to be used towards a crystallisation classification system (CCS) - Integration of a solid/ solvent dosing station with the Crystalline (Technobis) platfor

Comparative study on adaptive Bayesian optimization for batch cooling crystallization for slow and fast kinetic regimes

Crystallization kinetic parameter estimation is important for the classification, design, and scale-up of pharmaceutical manufacturing processes. This study investigates the impact of supersaturation and temperature on the induction time, nucleation rate, and growth rate for the compounds lamivudine (slow kinetics) and aspirin (fast kinetics). Adaptive Bayesian optimization (AdBO) has been used to predict experimental conditions that achieve target crystallization kinetic values for each of these parameters of interest. The use of AdBO to guide the choice of the experimental conditions reduced material usage up to 5-fold when compared to a more traditional statistical design of experiments (DoE) approach. The reduction in material usage demonstrates the potential of AdBO to accelerate process development as well as contribute to Net-Zero and green chemistry strategies. Implementation of AdBO can lead to reduced experimental effort and increase efficiency in pharmaceutical crystallization process development. The integration of AdBO into the experimental development workflows for crystallization development and kinetic experiments offers a promising avenue for advancing the field of autonomous data collection exploiting digital technologies and the development of sustainable chemical processes

Prediction of mefenamic acid crystal shape by random forest classification

Purpose: This study describes the development and application of machine-learning models to the prediction of the crystal shape of mefenamic acid recrystallized from organic solvents. Method: Mefenamic acid crystals were grown in 30 different solvents and categorized according to crystal shape as either polyhedral or needle. A total of 87 random forest classification models were trained on this data. Initially, 3 models were built to assess the efficacy of this method. These models were trained on datasets containing Molecular Operating Environment (MOE) descriptors for the solvents and crystal shapes labels obtained by visual inspection of microscope images. The subsequent 84 models tested prediction accuracy for individual solvents that were sequentially excluded from the model training sets. In total, three different sets of MOE descriptors (one set that contained all available 2D descriptors, a second set that focused on molecular structure and a third set that focused on physical properties) were investigated to determine which of these three sets of descriptors resulted in the highest overall prediction accuracy across the different solvents. Results: For the initial three models, the highest prediction accuracy of crystal shape observed was 93.5% as assessed by 4-fold cross-validation. When solvents were sequentially excluded from training data, 32 out of 84 models predicted the shape of mefenamic acid crystals for the excluded solvent with 100% accuracy and a further 21 models had prediction accuracies from 50-100%. Reducing the feature set to only solvent physical property descriptors and supersaturations resulted in higher overall prediction accuracies than the models using atom count, bond count, and pharmacophore descriptors and the models using all solvent molecular descriptors. For the 8 solvents on which the models performed poorly (<50% accuracy), further characterisation of crystals grown in these solvents resulted in the discovery of a new mefenamic acid solvate. However, all other crystals were the previously known form I. Conclusion: Random forest classification models using solvent physical property descriptors can reliably predict crystal morphologies for mefenamic acid crystals grown in 20 out of the 28 solvents included in this work. Poor prediction accuracies for the remaining 7 solvents may be an indication that the factors not adequately covered by the training data result in these solvents being outliers

Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties

Understanding powder flow in the pharmaceutical industry facilitates the development of robust production routes and effective manufacturing processes. In pharmaceutical manufacturing, machine learning (ML) models have the potential to enable rapid decision-making and minimise the time and material required to develop robust processes. This work focused on using ML models to predict the powder flow behaviour for routine, widely available pharmaceutical materials. A library of 112 pharmaceutical powders comprising a range of particle size and shape distributions, bulk densities, and flow function coefficients was developed. ML models to predict flow properties were trained on the physical properties of the pharmaceutical powders (size, shape, and bulk density) and assessed. The data were sampled using 10-fold cross-validation to evaluate the performance of the models with additional experimental data used to validate the model performance with the best performing models achieving a performance of over 80%. Important variables were analysed using SHAP values and found to include particle size distribution D10, D50, and aspect ratio D10. The very promising results presented here could pave the way toward a rapid digital screening tool that can reduce pharmaceutical manufacturing costs

Developing a model-driven workflow for the digital design of small-scale batch cooling crystallisation with the antiviral lamivudine

We present a workflow that uses digital tools to optimise the experimental approach and maximise the efficiency in achieving the required process parameters for a desired set of crystallisation responses, kinetics and objectives. Model-driven small-scale experiments can contribute to reducing time and material waste in the development of pharmaceutical crystallisation processes. The workflow presented here guides the development of a small-scale batch cooling crystallisation process via solubility measurements, particle shape and size determination, form identification and preliminary kinetic parameter estimation to make crystals that satisfy quality target parameters (for shape, size and solubility) for a given active pharmaceutical ingredient (API). The case study herein follows the development of a crystallisation process for lamivudine, an API used in the preventative treatment of human immunodeficiency virus (HIV). This work identifies ethanol as a suitable solvent, meeting the acceptable solubility parameters for industrially relevant processes and yielded the biorelevant form, form I. The target kinetic parameters that were measured included induction time, growth rate and nucleation rate for lamivudine in ethanol under a range of conditions as guided by experimental planning models. Data was collected as part of the development of a DataFactory platform in which experimental optimisation can be autonomously implemented and all measurements stored in a crystallisation parameter database. This database will have further value in informing model development and continuous crystallisation process design and optimisation. The model objective-driven development workflow identified the following conditions, 19.9 °C, 600 RPM and supersaturation of 1.70, as achieving the desired objective successfully in 80 polythermal and 28 isothermal experiments. Integration of the workflow alongside the optimisation algorithm within the automated DataFactory system will enable fully autonomous, rapid data collection for small-scale API crystallisation. Such autonomous systems could play vital roles in pharmaceutical development and manufacturing driving towards more efficient and sustainable practices via digital transformation

Spectroscopic studies of halogen bonding in model systems : from one end of the electromagnetic spectrum to the other

At its simplest, chemical bonding involves a combination of two dominant contributions: direct electrostatics (ionic) and electron sharing (covalent). The relative importance of these contributors has been the subject of signif- icant study in primary (intramolecular) chemical interactions. For example, the relevance and importance of covalent contributions has been a primary focus of transition metal chemistry for decades. For weaker secondary chem- ical interactions such as hydrogen bonding (HB) and halogen bonding (XB), the prevailing view in the literature is that electrostatic interactions are so dominant that covalent contributions are negligible. A notable exception is that of so-called symmetric hydrogen bonds, which exhibit large covalent contributions. With X-ray Absorption Spectroscopy (XAS), we have provided the first direct experimental evidence of covalency in XB. From such studies, we ob- serve that XB exhibit a significantly higher degree of covalency compared with HB counterparts of similar bond strength. Notably, the degree of co- valency in certain XBs is equivalent to that observed in transition metal halides. Our studies provide information of the electronic changes that oc- cur in both the charge donor and charge acceptor in model systems, affording us a unique experimental view of these weak interactions. We also demon- strate the importance of covalent contributions in XBs by showing the effect of covalency in the electron transfer properties in XB-modified dye sensi- tised solar cells. These results lead us to conclude that XBs should more generally be classified as coordinate bonds (and thus identified using an ar- row) to distinguish them from significantly less covalent HBs and other weak interactions.Science, Faculty ofChemistry, Department ofGraduat

Autonomous DataFactory : high-throughput screening for large-scale data collection to inform medicine manufacture

Using small-scale crystallisation to inform downstream processes, we can reduce time and material costs in medicine manufacturing. This work introduces a preliminary workflow for information-rich data collection of crystallisation parameters including solubility, induction time, growth rate, secondary nucleation rate, particle shape and size. Large-scale data collection was achieved for 6 active pharmaceutical ingredients (APIs) in 31 solvents in less than 9 months with the results for aspirin presented here. Highlights include the identification of 24 potential alternative crystallisation solvents for manufacturing aspirin, all of which yield the biorelevant polymorph. Automation of this workflow will enable the use of robotics to further reduce time and material usage when conducting crystallisation experiments for future APIs